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Author SHA1 Message Date
Alexander Smorkalov 8d69623051 Merge pull request #29529 from ajayraj30002:ajayraj30002-patch-3
cmake: set policy CMP0218 to NEW to suppress deprecation warning
2026-07-21 12:39:10 +03:00
Arman Rostami 279bc4a279 Merge pull request #27823 from armanrasta:5.x
Add ColorHashTSDFVolume implementation #27823

# Add ColorHashTSDFVolume implementation  [[#25155](https://github.com/opencv/opencv/issues/25155)]
## Description
Added a new ColorHashTSDFVolume implementation that combines the benefits of HashTSDFVolume's efficient spatial hashing with color support. This provides memory-efficient RGB-D fusion with better performance compared to regular ColorTSDFVolume.

### Key Features
- Hash-based spatial data structure for efficient storage
- Color integration during volume updates
- Raycast with color interpolation
- Compatible with existing TSDF interfaces
- CPU implementation with parallel processing support

### Implementation Details
- Added new ColorHashTSDFVolume class with create() factory method
- ColorVoxel structure combining TSDF and RGB data
- Spatial hashing for efficient voxel lookup
- Weighted running average for color updates
- Trilinear interpolation during raycasting
- Unit tests for basic operations and edge cases

### Files Modified/Added
- modules/3d/src/rgbd/color_hash_volume.hpp - New header defining ColorHashTSDFVolume interface
- modules/3d/src/rgbd/color_hash_volume.cpp - Implementation of ColorHashTSDFVolume
- modules/3d/test/test_color_hash_volume.cpp - Unit tests

### Performance
The implementation uses spatial hashing to only store voxels near surfaces, significantly reducing memory usage compared to regular ColorTSDFVolume while maintaining similar processing speed.

### Testing
Added unit tests that verify:
- Basic integration and raycasting operations
- Empty volume handling
- Memory usage patterns

### Future Work
- GPU/OpenCL implementation
- Additional color interpolation methods
- Extended comparison tests with other volume types
2026-07-20 10:40:19 +03:00
Vadim Pisarevsky f968fb969f Broadcasting element-wise engine for cv::Mat (+ cv::texpr) (#29426)
* experimental new arithmetics; work-in-progress

* continue working on new-gen arithmetic expressions

* * improved performance of the new add on small arrays
* added sub
* extended tests

* fixed potential bug when adding multi-channel array and a single-channel scalar

* improved const handling

* * added copyMask
* added mul/dev (without scale so far)

* * accelerated mul
* addedd scale to mul and div

* * done substantial refactoring; however a few more rounds of refactoring are ahead.
* added min, max, absdiff, addweighted.

* improved performance of the new arithmetic functions, but some of them are still slow, e.g. operations with mask have some bugs (that affect speed, not accuracy).

* * further (significantly) accelerated several functions, especially on small arrays: mul, binary ops with mask

* further polished the new arithmetic engine

* started integration of the new element-wise arithmetic engine into core

* big step forward. We now use the new engine inside cv::add, subtract, multiply, divide, absdiff, min and max.

* big progress:
* added bitwise operations
* fixed and accelerated compare
* ported regression tests to test new broadcasting behaviour of arithmetic functions

* lot's of improvements in compare, divide, addWeighted!

* lot's of small and big performance improvements in the new arithmetics

* * some more optimizations; parsing texpr-expressions is now faster as well

* port new_arithm to Linux/x86: dispatch guards, scalar-Mat compat fallback, dnn shape-contract fixes

Core:
- arithm.simd.hpp: CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY guards (the file is included
  once per dispatched mode on x86), vx_load_expand instead of the 128-bit v_load_expand,
  VTraits::vlanes() instead of ::nlanes
- arithm.cpp/precomp.hpp: compat fallback for scalar-like Mat operands (1x1, 1xcn/cnx1,
  4x1 CV_64F - java/python tuples, operator-(Mat, Matx)): treated as a per-channel scalar
  ONLY when the shapes are not broadcast-compatible, so every valid numpy-style broadcast
  keeps its meaning and calls that would otherwise throw get the 4.x semantics

DNN (fallout of the stricter shape semantics, found by the new engine):
- dict.hpp: DictValue relied on fresh AutoBuffer having size()==fixed_size; allocate explicitly
- batch_norm: weights_/bias_ are 1-D [n] now; 0/1-D forward runs on exact-shape 1-D views
- net_impl2: extend the post-forward sanity check to non-temp outputs - a layer that
  reallocates its preallocated output tensor now fails loudly instead of silently
  detaching the result from the graph
- LSTM/LSTM2 batchwise (layout=1): getMemoryShapes now matches what forward() writes
  (ONNX: Y=(batch,seq,dirs,hid), Yh/Yc=(batch,dirs,hid)); forward assembles seq-major
  results in a local buffer and transposes INTO the preallocated outputs in place

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* u8/s8 multiply: exact integer SIMD path on non-FP16 builds (3-10x vs 5.x)

The unit-scale branch of vecBinaryKernel already supported a separate work-vector
type Wvec1 (used by the ARM f16 build and by u16/s16 everywhere), but on x86 the
u8/s8 same-type multiply still went through the f32 hub. Route it through
v_uint16/v_int16: products of 8-bit values fit exactly (255^2 < 2^16), the
saturating pack on store gives bit-exact results at half the vector traffic.

Also fix a latent kernel bug this exposed: the unit-scale branch stepped by
Wvec's lane count while loading/storing Wvec1 vectors. All previous Wvec1
instantiations had equal lane counts, but u8's v_uint16 has 2x the lanes of
v_float32 - the pairs overlapped (50% redundant work) and the tail backoff
could write VECSZ bytes past the row end. The branch now derives its step,
offsets and tail condition from Wvec1 itself.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* vecBinaryKernel: constexpr Op::useScalar instead of a runtime-only scale check

Every binary op functor now declares whether it consumes the scale scalar
(params[0]): true only for mul and the two div variants. Ops that ignore it
(add/sub/min/max/absdiff) take the fast 2-arg branch unconditionally - the
'scalar == 1' check used to fail for them (their params[0] is 0), sending them
through the preproc branch, and the condition now folds at compile time.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* restore cv::hal::mul8u as a wrapper over the element-wise engine

The symbol is still declared in core/hal/hal.hpp and called directly by external
code (the G-API fluid backend in opencv_contrib), but its implementation went
away with the old arithm kernels. Forward it to getMulFunc(CV_8U, CV_8U) - with
scale==1 it lands on the new exact integer SIMD path.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* silence every new-arithm warning reported by CI (ARM64/Mac) and gcc 15

- arithm.cpp: bitwise_op_ocl and the actualScalarDepth/coerceTypes helpers are
  consumed only by the OpenCL paths - guard them with HAVE_OPENCL; haveScalar in
  cv::compare is read only inside CV_OCL_RUN - CV_UNUSED for OpenCL-less builds
- arithm_expr.hpp: declare getBitwiseFunc/getNotFunc/getAddWeightedFunc next to
  the other per-op entry points (-Wmissing-prototypes in arithm.dispatch.cpp)
- arithm.simd.hpp: define CV_SIMD_16F to 0 when FP16 SIMD is absent (-Wundef);
  {}-init the expandScalar staging buffers (-Wmaybe-uninitialized: they are
  fully written before use, but the compiler cannot prove it with runtime
  vector widths); rename the compare kernel's lambda parameter (-Wshadow)
- arithm_expr.cpp: rename the exec tile-lambda's hot-field locals that shadowed
  TExpr members and outer locals (-Wshadow)
- test_new_arithm_extensive.cpp: rename the name-generator lambdas' parameter
  shadowing the INSTANTIATE macro's own (-Wshadow), drop an unused variable

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* restore 4.x scalar semantics for the bindings' 4x1 CV_64F Scalar columns

The python/java bindings materialize numbers and tuples as a 2-D 4x1 CV_64F
Mat - or UMat, when the call carries UMat arguments. Three CI-reported python
failures came from those pseudo-scalars reaching the engine as arrays:

- absdiff(int_arr, 0): the (4,1) column is broadcast-COMPATIBLE with a 1-D
  array, so numpy semantics silently won - an outer-product f64 result instead
  of the int per-channel-scalar one;
- subtract(u8 4x8x4, (40,)): same, by the rows==4 coincidence;
- multiply(UMat, 2., dst=UMat): the scalar arrives as a UMAT, which the
  scalar detection did not recognize at all.

isScalarArg now treats the exact bindings shape - 2-D 4x1 CV_64F single-channel
Mat/UMat against a <=4-channel array - as a scalar UNCONDITIONALLY (a 1-D [4]
array has dims==1 and still broadcasts). One exception, decided in arithm_op:
when the partner is itself a tiny scalar-shaped array, both are honest data and
ride the broadcast (compare(Mat 4x1, Mat 1x1) - issue #8999 - stays elementwise).

Small-array discipline, this all runs per engine call: the probes read Mat/UMat
fields directly (rows == 4 alone rejects almost everything, no _InputArray
getter dispatch), and a UMAT scalar's 32 bytes are copied into a caller-stack
buffer - no heap, no getMat mapping. Measured: no latency change on 4x4/16x16
element-wise calls.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* cv::texpr: std::string_view -> const std::string& in the public API

string_view in an exported signature breaks some CUDA toolchain builds, and for
short expression strings the difference is immaterial (SSO, parsed once). The
parser internals keep string_view - the argument converts implicitly. Also drop
the now-unused <string_view> include from cvstd.hpp, so the header does not
reach every nvcc TU.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* compare boundary-rewrite: fixed 4-slot kind/bound arrays instead of AutoBuffers

A CONST operand is capped at 4 channels (addConst), so the per-channel
kind/bound staging needs no dynamic buffers - plain int[4]/double[4], with a
CV_Assert on the contract. This is also what gcc's -Wmaybe-uninitialized was
flagging (it could not see the AutoBuffer's inline storage get filled).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* element-wise engine: unary math kernels (sqrt/exp/log/sin/cos/tanh/erf/relu) + select

New dispatched pair math.simd.hpp / math.dispatch.cpp - the unary/ternary sibling
of arithm.simd.hpp:

- vecUnaryKernel: T -> T over f16/bf16/f32/f64 on top of the intrin_math
  primitives (v_exp/v_log/v_sin/v_cos/v_sqrt/v_erf/v_max). f32/f64 compute
  natively, f16/bf16 ride the f32 hub inside the kernel (vx_load_pair_as /
  v_store_pair_as) - no materialized casts. Continuity collapse + the halide
  right-edge backoff, suppressed in-place (it would re-apply Op to
  already-written values). tanh = (e^2x-1)/(e^2x+1) with the input clamped to
  +/-10 (f32) / +/-20 (f64) - unclamped saturation hits inf/inf = NaN. erf has
  no f64 SIMD primitive: std::erf per lane.
- selectKernel(mask, x, y): 1-byte mask expanded to lane width and tested
  against zero in the INTEGER domain (immune to DAZ/FTZ), branches of any
  depth by element size, broadcast branches supported.
- emitUnary: math over a float input is T -> T now (f16 in -> f16 out, native
  kernel when input and result depths match); integer inputs still compute in
  the float domain and land in f32.
- emitTernary/select: literal branches are typed via typedConstFrom (an
  OP_CAST of a depth-less flex const crashed); a non-1-byte mask is normalized
  by an explicit "mask != 0" compare, never a value cast.

texpr already parsed the function names - they now execute. Tests: per-depth
accuracy of all 8 ops against the double std:: reference, integer input,
in-place, select over 4 depths / const branch / float mask.

Perf vs the classic kernels (1920x1080 f32, 16 threads, AVX2): exp 3.7x,
log 2.6x, sqrt 5.7x faster; polarToCart expressed as (r*cos(a), r*sin(a)) 4.0x.
Accuracy improves too (max rel err vs f64 reference): exp 8.1e-8 vs 2.1e-7,
log 8.0e-8 vs 1.5e-7.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* engine: OP_COPY_MASK folded into OP_SELECT; selectKernel moved to arithm.simd.hpp

copyMask(dst, mask, src) is select(mask, src, dst) - one masking primitive
instead of two. The compiler emits the masked-op tail as
addInsn(OP_SELECT, mask, r, out, out): the output slot rides as both arg2 and
the result, so unmasked elements are preserved by reading them back through
the b-branch. OP_COPY_MASK, copyMaskKernel and getCopyMaskFunc are gone.

selectKernel (moved from math.simd.hpp to arithm.simd.hpp) inherits every
copyMaskKernel optimization:
- the interleaved multichannel fast path (2..4 channels under a per-pixel
  mask: expand the mask once per VECSZ rows, v_store_interleave across lanes);
- the per-row scalar path with the row-skip when the selected source row IS
  dst (the "leave the output untouched" half of copyMask);
- plus the select-specific ones: branch broadcasts (stepx == 0) and the
  right-edge tail backoff under dst-aliases-a-branch - safe because re-running
  select over already-blended elements is idempotent; only dst == mask keeps
  the backoff off (the store would rewrite mask bytes before the re-read).

Masked-add perf is on par with the old copyMask (1280x720, 1 thread: 8UC3
204 -> 198 us, 32FC3 1395 -> 1344, 8UC1/32FC1 within noise). Full core suite
24117 green.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* engine: dedicated vectorized pow kernel (moved from arithm to math.simd.hpp)

pow was the last scalar-only binary op (scalarBinaryKernel + std::pow, f32/f64
only). The new powKernel keeps exact std::pow semantics and is T x T -> T over
all four float depths (f16/bf16 via the f32 hub):

- scalar exponent (the dominant call shape - texpr literals ride as 0-dim
  broadcast consts) is dispatched PER ROW to the special cases:
  y==2 -> x*x, y==3 -> x*x*x, y==0.5 -> v_sqrt, y==1 -> copy, y==0 -> fill 1;
- everything else - including a per-element exponent array - runs the general
  vectorized exp(y * log(x)) path, valid for x > 0; a vector pair containing
  any x <= 0 lane falls back to scalar std::pow for that pair (v_check_any),
  which preserves every std::pow subtlety: signed results for integer y on
  negative bases, NaN for fractional y, the x == 0 family;
- no right-edge tail backoff: pow is not idempotent, in-place calls finish
  rows in the scalar tail.

Perf vs the classic cv::pow (1920x1080 f32, 16 threads, AVX2): p=2 1.4x
(classic special-cases it too), p=3 6.1x, p=0.5 6.3x, fractional p 4.5x with
slightly better accuracy (6.1e-7 vs 7.4e-7 max rel err). Tests: exponent
sweep 2/3/0.5/1/0/2.5/-1.5 vs the double std::pow reference on f32/f64,
negative bases (exact signed cubes, NaN for fractional), array exponent.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* powKernel: halide right-edge tail backoff in every SIMD loop

Same shape as vecBinaryKernel: the final partial vector re-processes
[width - VECSZ*2, width) instead of finishing scalar, suppressed when dst
aliases an input (pow is not idempotent - the overlap region must be
recomputed from an untouched source, which the no-alias case guarantees).

Modest measured win (~2% on ROI rows for the special-cased exponents; the
general path tail was already cheap - modern libm powf is fast), no
regressions; mainly aligns the kernel with the house style, where every
SIMD loop ends vector-wide.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix MSVC 2019 C2975: function-local constexpr as a template argument inside a lambda

MSVC 2019 loses the constexpr-ness of function-local constants (LOCAL_OPS,
MAX_DIMS, ...) when they are used as template arguments inside a lambda body
(AutoBuffer<Slice, LOCAL_OPS> / std::array<int, MAX_DIMS> in the parallel
bodies of BroadcastOp::run and TExpr::exec). Hoist them to namespace scope -
no behavior change.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* ocl_arithm_op: route 16U multiply to the CPU engine on Apple OpenCL

The Apple OpenCL driver miscompiles the 16U multiply kernel: products near
the top of the u16 range come back wrapped instead of saturated (CPU vs GPU
NORM_INF up to 65535 in OCL_Arithm/Mul.Mat CV_16U cases). The same arithm.cl
kernel is correct on Intel NEO and NVIDIA drivers - verified not to reproduce
on Linux/Intel iGPU - so gate the decline to __APPLE__ only; the CPU engine
computes 16u multiply exactly (integer SIMD path).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* engine: neg/abs as compositions, clamp kernel, ** and ?: operators, abs(a-b) peephole

- OP_NEG and OP_ABS need no kernels: neg = sub(0, a), abs = absdiff(a, 0) -
  including the engine absdiff auto-type rule (signed |a| lands in the
  UNSIGNED type of the same width: |SHRT_MIN| fits u16 exactly instead of
  saturating; NB the public cv::absdiff auto depth keeps the source type for
  4.x compatibility - values agree, the depth rule is the engine own).
- peephole: abs(x - y) rewrites to absdiff(x, y) ALWAYS. On integers the
  literal semantics differ (the subtract saturates first: u8 gives
  max(x-y, 0)), but whoever writes abs(a - b) means absdiff - we deliberately
  hand out the useful semantics instead of the saturation artifact. The just-
  emitted OP_SUB is retired via the moveToOutput manoeuvre, so the program
  shrinks to the single absdiff instruction. abs(x), abs(x - 0) and
  absdiff(x, 0) all give one result.
- OP_CLAMP kernel (arithm.simd.hpp): v_min(v_max(x, lo), hi) over
  u8/s8/u16/s16/u32/s32/f32 (+f64 with 64-bit SIMD), scalar f16/bf16/64-bit
  ints; lo/hi may broadcast (the common clamp(img, a, b) shape) or be full
  arrays; the tail backoff stays on under dst-aliases-x (clamp is idempotent).
  emitTernary types literal bounds via typedConstFrom (same flex-const crash
  select had) and keeps the auto result type pinned to x.
- parser: "a ** b" == pow(a, b), precedence above * /, RIGHT-associative
  (a ** 2 ** 3 == a ** 8); "cond ? a : b" == select(cond, a, b), precedence
  below everything, right-associative chains (f1 ? a : f2 ? b : c) work
  without parentheses. parseTernary() is the expression entry point now.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* cv::exp/log/sqrt on the engine via math_op; hal functions wrapped as engine kernels

math_op is the master function of the unary math family (the arithm_op
analogue): same-shape same-type output over f16/bf16/f32/f64 (classic
exp/log accepted f32/f64 only - the half floats are new), two tiers:
- small (<= 100000 elements) and continuous: call the kernel DIRECTLY over
  the flattened data - no TExpr, no broadcastOp, no parallel_for setup;
- everything else: the usual single-instruction program via compile()/exec()
  (parallelism for large arrays, real steps for ROIs).

getMathFunc routes OP_EXP/OP_LOG at f32/f64 through the full cv::hal stack -
an external vendor HAL (CALL_HAL), IPP, or the built-in table kernels,
whichever is installed - by wrapping hal::exp32f/exp64f/log32f/log64f as
engine kernels with the function pointer in TKernel::userdata, the same
mechanism castKernel uses for core BinaryFuncs. The engine adds tiling and
parallelism on top, so every tier gets the best available scalar-span
implementation. v_exp/v_log remain for f16/bf16 (the f32 hub) and the ops
hal has no entry points for.

v_log_default_32f: the degree-8 polynomial is evaluated by Estrin pairing
(4 dependent levels) instead of an 8-FMA Horner chain (~5% on the f16 hub
path). An exp64 Taylor-without-division rewrite was tried and benched SLOWER
than the Cephes Pade scheme (the evaluation is FMA-throughput-bound, and
vdivpd pipelines well enough) - reverted; a table-based reduction is the only
way further there.

cv::exp f32 (16 threads, AVX2+IPP build), old -> new: 640 elements
0.16 -> 0.15 us, 16k 2.79 -> 2.49, 640x480 47 -> 20, 1920x1080 452 -> 60 us
(the old CPU loops were single-threaded); f64 exp 1080p 1295 -> 161 us.
No size regresses; small arrays now run at installed-HAL speed.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* getMathFunc: IPP tier + raw-HAL probing; the exp/log table kernels are deleted

The raw cv_hal_* entry points return int for a reason: without an installed
HAL they are stubs returning CV_HAL_ERROR_NOT_IMPLEMENTED. getMathFunc now
selects the exp/log implementation in three tiers:
 1. HAVE_IPP && ipp::useIPP(): ippsExp/ippsLn through thin int adapters (IPP
    is not routed through the cv_hal_ hooks, so it needs its own tier);
 2. the raw cv_hal_exp32f/... hook, PROBED once with a 1-element call on the
    safe input 1.0 (cached in magic statics): implemented -> wrapped as an
    engine kernel with the function pointer in TKernel::userdata;
 3. the engine own v_exp/v_log kernels.
Whichever wins, the engine adds tiling and parallelism on top.

The EXPTAB/LOGTAB table kernels and their tables (~790 lines in
mathfuncs_core.simd.hpp + mathfuncs.cpp) are DELETED: they benched within
~15% of v_exp/v_log, not worth a second implementation. The public
cv::hal::exp32f/exp64f/log32f/log64f keep their contract - CALL_HAL, then
IPP, then the built-in implementation - but the built-in is now the engine
vector kernel via ew::mathSpanEngine (one contiguous span, exported from
math.dispatch.cpp).

All unary math kernels (vec/scalar/hal wrappers, pow) also handle the
vertical-broadcast tile (s0y == 0, a row expanded into a matrix): the first
row is computed, the rest are memcpy of it - transcendentals cost far more
than a row copy.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* texpr: hypot(x, y) binary op (alias: mag)

hypot = sqrt(x^2 + y^2), NAIVE like cv::magnitude (not the overflow-safe
std::hypot), computed in the float work type; kernels for the four float
depths only (T x T -> T; integer inputs ride the usual f32-compute + cast).
A 10-line EwHypot functor on top of vecBinaryKernel in arithm.simd.hpp -
broadcast branches, continuity collapse and the tail backoff come for free.
Registered in the parser as both "hypot" (the C/numpy name) and "mag" (the
cv::magnitude-flavored alias). A building block for the future cartToPolar.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* texpr: atan2(y, x) binary op - radians, standard C range

v_atan2 (arithm.simd.hpp, generic over the universal-intrinsic float vector):
the fastAtan2 minimax polynomial from mathfuncs_core v_atan_f32 reworked to
plain radians - the 180/pi factor dropped from the coefficients and the C
quadrant logic instead of the [0, 360) wrap, so the result matches std::atan2
over (-pi, pi]. Measured absolute accuracy ~1.6e-4 rad. (v_atan_f32 itself is
untouched - cv::phase/fastAtan2 keep their degree semantics.)

EwAtan2 rides vecBinaryKernel: f16/bf16/f32 through v_atan2 (the f32 hub),
f64 through exact scalar std::atan2. arg0 = y, arg1 = x, like std::atan2;
float depths only, same emitBinary policy as pow/hypot. Parser name "atan2".
Together with hypot this completes the cartToPolar building blocks.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix the RISC-V RVV build and two ARM64 warnings

The new f64 kernel registrations (hypot, pow, the unary math family) gate on
CV_SIMD_64F || CV_SIMD_SCALABLE_64F, but the vx_setall_as(const double*,
v_float64&) helper family in arithm.simd.hpp was still CV_SIMD_64F-only -
scalable platforms (RVV) have v_float64 with CV_SIMD_64F == 0, so
vecBinaryKernel<double, ...> failed to instantiate there. Widen the helper
gate to match (verified with a riscv64 rv64gcv cross-build of opencv_core -
the engine f64 paths now vectorize on RVV instead of not compiling).

cv::exp/cv::log: the depth local is consumed by CV_OCL_RUN only - CV_UNUSED
for OpenCL-less builds (ARM64 -Wunused-variable).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* silence the remaining ARM64 gcc warnings

- compare boundary-rewrite: {}-init the fixed kind/bound arrays (filled for
  every channel used below, but gcc cannot prove it across the cn <= 4 loop);
- cv::exp/log: [[maybe_unused]] on the depth local (consumed by CV_OCL_RUN
  only), instead of the CV_UNUSED idiom;
- AutoBuffer::reserve: a targeted -Wmaybe-uninitialized suppression around
  the live-element copy loop - only [0, sz) is read, all written before, but
  gcc inlining a grow-from-inline-storage chain cannot see that. An
  annotation for the analyzer, no behavior change.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* saturating 32-bit add/sub kernels; cv::texpr python binding; two CI warnings

- v_add_sat/v_sub_sat for v_int32/v_uint32, local to arithm.simd.hpp for now
  (the plan is to grow them into proper universal intrinsics later): NEON
  single-instruction vqadd/vqsub, elsewhere the Hacker Delight bit tricks
  over universal intrinsics (u32 add is 2 ops: or with the wrapped-compare
  mask). EwAdd/EwSub overload vec() for the 32-bit lanes and getAddSubFunc
  routes 32S/32U T->T through vecBinaryKernel instead of the former pure
  scalar kernel. Semantics unchanged - the scalar int64 tail already
  saturated; directed boundary tests added (both rails, 0 - INT_MIN, u32
  cases, a full-range random block vs an exact int64 reference).
  640x480 32S add: 0.48x of 5.x -> parity (memory-bound); 1080p: 6-8x.

- cv::texpr becomes CV_EXPORTS_W: python gets cv.texpr(expr, [inputs]) ->
  tuple of ndarrays, so `res, = cv.texpr(...)` and `mag, ang = cv.texpr(...)`
  unpacking both work. modules/python/test/test_expr.py covers arithmetic,
  the fused abs(a-b), casts, broadcasting, ?: and ** operators, math
  functions vs numpy, clamp, named temporaries, tuple outputs, the one-line
  cartToPolar and the int32 saturation cases.

- warnings: {}-init the parser args array (gcc -Wmaybe-uninitialized on
  Ubuntu 20/22); the compare short-row block gates sizeof(T) <= 4 as
  constexpr so the f64 instantiation does not leave set-but-unused locals
  (gcc 9 -Wunused-but-set-variable).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* arithm_op: direct-kernel fast path for small continuous arrays

Building and compiling the 1-instruction program plus the BroadcastOp setup
costs ~40-250ns per call - negligible on big images, dominant at 127x61-class
sizes where the classic 5.x functions were 1.3-2x faster. Mirror math_op two
tiers in arithm_op: two same-type same-shape continuous arrays, no mask, no
scalar, result depth == input depth, <= 100k elements -> call the T x T -> T
kernel directly over the flattened elements (checks ordered cheapest-first).
Applies to add/subtract/min/max/absdiff/multiply/addWeighted/and/or/xor;
compare and divide lower to more than a single kernel (boundary rewrites, int
guards) and keep the ordinary path. addWeighted falls through automatically
for the 32/64-bit int types whose lowering is wide-compute + cast
(getElemwiseFunc returns no direct kernel there).

127x61 vs 5.x, was -> now: add/subtract 8UC1 0.76x -> 1.3x, min/max u8
0.6x -> ~1x, addWeighted 1.0x -> 1.1-1.4x (32SC1 stays 4.5x); the one
remaining laggard is add/sub 32SC1 (0.74-0.82x) - the price of the new
SATURATING semantics (7-instruction AVX2 emulation vs the wrapping single
add of 5.x; single-instruction on NEON).

The 127x61 size is ADDED PERMANENTLY to the arithmetic/addWeighted/compare
perf grids: per-call overhead regressions in these base functions must be
caught by CI, not discovered by users.

dst creation goes through createSameSize (whole-shape transfer including
layout and future metadata, not piecemeal dims+sizes) here and in math_op.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* cv::pow rebuilt on the engine; integer-exponent and 1/sqrt(x) kernel branches

Routing: p = 0/1/2 keep their early special cases (fill/copy/multiply); an
INTEGER array with an INTEGER power keeps the classic iPow multiply chain -
bit-exact compatibility, including its wrap-around quirks (iPow squares in
int, so e.g. pow(255,4) on u8 wraps negative and saturates to 0 - somebody
may rely on that). Everything else goes through the engine with the math_op
two-tier scheme: small continuous arrays call powKernel directly (the
exponent rides as a broadcast T scalar), the rest run the tiled parallel
program. Integer arrays with fractional powers compute in the float domain
and saturate back; the 32U/64-bit depths (classic iPow asserted on them) and
f16/bf16 (the classic float path misread them) now just work.

powKernel gets two new per-row exponent branches:
- p == -0.5: 1/v_sqrt(x) (the classic path used IPP ippsInvSqrt_A21, a
  21-bit approximation; ours is exact - slightly slower on small arrays,
  4.5x faster at 1080p via parallelism);
- any other INTEGER p (|p| <= 65536): LSB-first binary exponentiation, the
  same multiply chain and order as iPow, fully vectorized - a few ulp
  accurate vs ~2e-7 of exp(p*log x), and exact on non-positive bases (the
  sign falls out of the multiplies, 0^negative divides to inf) - no scalar
  patching.

cv::pow f32 vs 5.x: p=0.5 365 -> 61 us at 1080p (6x), p=3 7.7x, p=5 7x AND
faster at every size (the old scalar chain: 0.81 -> 0.47 us at 127x61),
p=2.5 5.3x. The s16^5 iPow path is untouched (118.7 == 118.5 us).
pow_exponents accuracy tests extended to 11 exponents x f32/f64 against the
double std::pow reference.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* perf: SANITY_CHECK_NOTHING for the tests whose grids got the 127x61 size

The 127x61 entry added to guard per-call overhead has no regression data in
opencv_extra, so the legacy SANITY_CHECK in addWeighted/compare failed on CI
(locally it passes silently without the test-data path). Accuracy of both
functions is covered by the accuracy suite; the perf tests should measure
time. PatchNaNs/finiteMask keep their SANITY_CHECK - their grids are
untouched.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix a temp-buffer double release in the liveness pass ("d*d" crash)

When the same temp is passed as SEVERAL arguments of its last-use
instruction (e.g. the named-intermediate expression "d = {0} - {1}; d*d",
where the MUL consumes slot d twice), the buffer-reuse scan pushed the
temp's physical buffer onto the free list once per argument. That
overflows the ntemps-sized freeBufs array (caught by the AutoBuffer range
check in Debug: python test_expr.py::test_named_temporary) and, in larger
programs, would hand the same physical buffer to two live temps.

Release the buffer once by retiring lastUse[t] after the first hit.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* executor: recalibrate opCost to vectorized cycles + clamp the stripe count

The per-element op costs fed into the parallel_for_ stripe hint were
scalar-era estimates (~20x above the vectorized reality: the atan2/exp
polynomials run at ~1.5 cycles/element, not 30). The hint therefore split
transcendental/divide work into hundreds of ~1 us jobs, which the
macOS/GCD backend dispatches poorly under sustained load, on top of the
P/E-core equal-share straggler effect. Measured on M4 Max (12P+4E),
sustained medians @1920x1080 f32: texpr atan2 320 -> 133 us, cv::exp
280 -> 141 us, cv::log 301 -> ~200 us, cv::pow(x,2.5) 388 -> 376 us;
the PR tables' math rows improved ~1.5-2x across the board.

- opCost is now in units of ~1/4 cycle/element of the SIMD kernels:
  cheap ops 1 (unchanged), div/sqrt/hypot/convert_scale 10 -> 3,
  transcendentals 30 -> 6.
- the stripe hint is clamped by min(4*nthreads, max(32, 3*nthreads)):
  ~4 stripes/thread is plenty of granularity for element-wise work, and
  the ceiling is 32 pieces except on machines with many (heterogeneous)
  cores, where anything coarser than ~3 pieces/worker turns the slow
  cores into equal-share bottlenecks (measured: 32 stripes on 16 threads
  is the worst point of the curve - 193 us vs 137 us at 48 for atan2).
  getNumThreads() is clamped from below (WINRT/plugin backends may
  report 0).

Not addressed here (needs cross-machine data, M2/M3 Ultra): streaming
memory-bound ops saturate the M4 Max fabric at ~8 fat stripes and E-core
participation only adds contention - a cost model cannot express that;
candidate follow-up is a bytes-aware clamp or a GCD-backend-level fix.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* multiply: v_mul_sat integer kernels (full product clamped to the type)

v_mul_sat(V, V) -> V for u8/s8/u16/s16/u32/s32 - the full-precision
product clamped to the lane type, which is exactly cv::multiply's integer
semantics at scale == 1. Local to arithm.simd.hpp for now, next to
v_add_sat/v_sub_sat, to be promoted into proper universal intrinsics
later. NEON: widening vmull + saturating narrow (vqmovn); other backends:
the portable v_mul_expand + saturating v_pack composition for 8/16-bit
lanes. 32-bit lanes have no universal widening multiply (no v_mul_expand
for s32), so the 32-bit integer fast path is NEON-only for now and the
other backends keep the previous f64 work-vector kernels (which measure
well on x86 with IPP-free AVX2).

EwMul::vec() now routes the integer lane types through v_mul_sat, and
getMulFunc_ passes the NATIVE lane vector as the scale==1 fast-path type:
whole-register loads/stores, the widening happens inside the multiply.
Replaces both the half-register widening loads (u8/s8/u16/s16) and the
scalar-equivalent f64 path for 32S/32U on NEON.

M4 Max, 640x480 (the sizes where the old kernels lost to carotene):
8U 22.0 -> 13.6 us, 8S 15.0 -> 11.1, 16S 21.2 -> 17.4, 32S 84.8 -> 30.5
(parity with the classic path everywhere, 32S was 0.38x). 1920x1080:
8S 1.30x -> 1.78x, 16S 2.57x -> 2.98x, 32S 2.20x -> 4.02x vs 5.x.
Correctness: exhaustive 8-bit (all 65536 pairs per sign), directed
saturation corners for 16/32-bit (46341^2, INT_MIN*-1, 65536*65536, all
sign combinations) against an exact int64 reference.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* NEON: make v_cvt_f64(v_int32) exact (was via f32, losing bits > 2^24)

The NEON implementations of v_cvt_f64/v_cvt_f64_high for v_int32 did
s32 -> f32 -> f64 (vcvt_f32_s32 + vcvt_f64_f32), silently rounding any
|x| > 2^24. Every vectorized f64 work path with int32 inputs on AArch64
was affected: the engine's addWeighted/divide 32S kernels, convertTo
32S -> 64F, etc. Found via addWeighted 32SC1 on values ~1e9: max error
was 32 vs the exact double reference (the classic carotene path is worse
still - it computes in f32 end-to-end with f32-truncated weights, max
error 96 on the same data).

The exact sequence sxtl + scvtf (vmovl_s32 + vcvtq_f64_s64) is the same
2 instructions, so there is no cost. addWeighted 32SC1 on the engine now
matches the exact-double reference bit-for-bit and stays at parity/1.4x
vs the classic path (640x480/1920x1080).

Pre-existing upstream bug (same code in 4.x) - worth a standalone
backport with directed large-value tests.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* math_op: drop the temporary direct-IPP tier for exp/log

Upstream moved the IPP math wrappers into the hal/ipp HAL module (cv_hal_exp32f/
log32f & co now resolve to ipp_hal_* which honor cv::ipp::useIPP via
CV_HAL_CHECK_USE_IPP). The engine's single probeHalUnary(cv_hal_*) probe already
picks that up uniformly, so the stopgap #ifdef HAVE_IPP ippExp/ippLog tier and its
ipp::useIPP() branch in getMathFunc are now redundant - removed.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* arithm: restore cv::hal::and8u/or8u/xor8u/not8u as engine wrappers

These public CV_EXPORTS entry points (core/hal/hal.hpp) lost their definitions when
the bitwise ops moved to the element-wise engine, but they are still declared and called
by other modules (opencv_objdetect's aruco) and external code - the link broke with
undefined references to cv::hal::and8u/xor8u. Restore them as thin forwarders over the
engine's byte-wise bitwise kernels (getBitwiseFunc / getNotFunc), mirroring the existing
mul8u wrapper. CV_Assert guards the kernel lookup.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* blobdetector: guard empty contour when computing blob radius

The new AutoBuffer leaves its tail uninitialized for trivial types, which surfaced a
-Wmaybe-uninitialized in findBlobs where the median of per-point distances is read. Use a
std::vector, default the radius to 0, and compute the median only for a non-empty contour -
no unproven size invariant.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
2026-07-18 01:26:36 +03:00
velonica0 5aa9c9562a Merge pull request #29541 from velonica0:rvv-fix-cvtabs-gcc-build
core: fix RISC-V GCC build break in cvtabs_32f (#29369 follow-up) #29541

### Summary

`modules/core/src/convert_scale.simd.hpp` does not compile on RISC-V with GCC on current 5.x:

```
convert_scale.simd.hpp:44:13: error: 'useSIMD' was not declared in this scope
convert_scale.simd.hpp:45:37: error: 'VECSZ' was not declared in this scope
convert_scale.simd.hpp:55:28: error: 'va' was not declared in this scope
convert_scale.simd.hpp:55:32: error: 'vb' was not declared in this scope
```

This breaks every RISC-V + GCC build of the core module.

### Root cause

In #29369 `cvtabs_32f` ended up with a declaration guard and a usage guard that disagree:

```c
#if (CV_SIMD || (CV_SIMD_SCALABLE && !(defined(__GNUC__) && !defined(__clang__))))  // decl
    v_float32 va = ..., vb = ...;  const int VECSZ = ...;  const bool useSIMD = ...;
#endif
        ...
#if (CV_SIMD || CV_SIMD_SCALABLE)                                                   // usage
        if( useSIMD ) for( ; j < size.width; j += VECSZ ) { ... v_fma(v0, va, vb) ... }
```

On RISC-V `CV_SIMD == 0` and `CV_SIMD_SCALABLE == 1`, so under GCC the first is false and the second is true: the declarations are dropped while the loop using them is still compiled.

### Second commit (independent, droppable)

`useSIMD` asks "is this a 128-bit vector machine?" but tests `vlanes() != 8`. `vlanes()` is `VLEN/32 * LMUL`, so the literal `8` encodes both "VLEN == 128" **and** "LMUL == 2" — correct only while `v_float32` stays at LMUL=2. Under #29493 (LMUL=1) the same expression silently becomes "VLEN != 256": disabling SIMD on 256-bit machines and enabling it at VLEN=128, the exact case the workaround exists to avoid. CI cannot catch that regression, since the block is GCC-only.

VLEN is not recoverable from the universal intrinsics (every type scales with LMUL, so the factor never cancels), so this asks the hardware: `__riscv_vlenb()*8 != 128`. Behaviour is unchanged today — with LMUL=2, `vlanes() == 8` iff `VLEN == 128`.


### Testing

SpaceMIT K3, GCC 15.2, **unmodified LMUL=2 backend** (no #29493 changes involved):

| configuration | result |
| --- | --- |
| 5.x as-is | `opencv_core` fails to build (errors above) |
| + commit 1 | builds clean, 0 errors |
| + commits 1&2 | builds clean, 0 errors |
| `Core_ConvertScale*`, X100 @ VLEN=256 | 4/4 pass (incl. `Core_ConvertScaleAbs/ElemWiseTest.accuracy`) |
| `Core_ConvertScale*`, A100 @ VLEN=1024 | 4/4 pass |
| `useSIMD == false` path (temporary probe forcing it at VLEN=256) | passes |
2026-07-17 11:47:03 +03:00
Alexander Smorkalov 47d9544d90 Merge pull request #29534 from vrabaud:persistence2
Do not include headers in cv namespace
2026-07-17 11:32:24 +03:00
Vincent Rabaud c2bf59ca0c Do not include headers in cv namespace
That creates some conflict on some windows platform with blaze.
2026-07-16 14:14:37 +02:00
Alexander Smorkalov 5aff519a06 Merge pull request #29530 from opencv:revert-29492-threshold_IPP_5.x
Revert "Refactoring and extracting IPP to HAL for threshold function in 5.x"
2026-07-16 11:45:45 +03:00
Alexander Smorkalov 430909e2a3 Revert "Merge pull request #29492 from Akansha-977:threshold_IPP_5.x"
This reverts commit c64d89e75c.
2026-07-16 10:26:56 +03:00
Alexander Smorkalov f07e06fd47 Merge pull request #29526 from asmorkalov:as/model_name_update
Renamed model to prevent name conflict.
2026-07-16 08:56:18 +03:00
Ajay Raj 3701817083 cmake: set policy CMP0218 to NEW to suppress deprecation warning
Added policy CMP0218 to suppress deprecation warnings.
2026-07-16 03:53:15 +05:30
Akansha-977 c64d89e75c Merge pull request #29492 from Akansha-977:threshold_IPP_5.x
Refactoring and extracting IPP to HAL for threshold function in 5.x #29492

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-07-15 17:08:19 +03:00
Alexander Smorkalov 507c824cd8 Renamed model to prevent name conflict. 2026-07-15 15:58:50 +03:00
Abhishek Gola 34074075b2 Merge pull request #29369 from abhishek-gola:fp8_support
FP8 support in core module #29369

Core part of https://github.com/opencv/opencv/issues/29313

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-07-15 15:45:01 +03:00
Akansha-977 18f9bffb0d Merge pull request #29467 from Akansha-977:distransform_IPP_migration_5.x
### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-07-15 12:34:47 +03:00
Akansha-977 8ee67ccb65 Merge pull request #29466 from Akansha-977:templatematch_IPP_5.x
Extracting IPP to HAL for matchTemplate function in 5.x #29466

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-07-15 12:34:01 +03:00
Anand Mahesh 60c705e93b Merge pull request #29450 from manand881:feat/ocl-sift-descriptor
Feat: add OpenCL SIFT detector and descriptor #29450

Add OpenCL implementation of SIFT detector and
descriptor extractor, triggered via T-API when
the caller passes a UMat. Targets the features
module (5.x directory layout).

Implementation:
- 4 OpenCL kernels in sift.cl: gaussian_blur_h, gaussian_blur_v, detect_and_orient, and compute_descriptor.
- Host-side dispatch in sift.dispatch.cpp using CV_OCL_RUN_ macro, pure additions to the existing CPU path.
- T-API entry: detectAndCompute, detect, compute.

Gating and fallback:
- 64-bit only. 32-bit falls back to CPU via sizeof(void*) > 4 guard.
- OpenCL 1.1 or later required. Devices below 1.1 fall back to CPU.
- No-OpenCL builds fall back to CPU. Verified with WITH_OPENCL=OFF: 79 tests pass, same 3 pre-existing failures (DISK/AFFINE_FEATURE missing .npy data files).

Correctness:
- nOctaveLayers > 3 OOB fix: gk_coeffs and gk_radius resized to nOctaveLayers+2 via std::vector (were fixed size 5).
- Regression test for nOctaveLayers > 3.
- 10 OCL SIFT tests pass, 60 OCL tests pass, 139 features tests pass (3 pre-existing failures unrelated to SIFT).

Performance optimizations:
- native_exp, native_sqrt, native_recip for hardware approximations (OpenCL 1.1 builtins).
- Eliminate intermediate rawDst[128] buffer; normalize in-place from hist[].
- Non-blocking kernel launches with single ocl::finish() before host reads keypoint count.
- Custom separable Gaussian blur for init image bypassing T-API GaussianBlur dispatch overhead.
- exp2() replaces pow(2.0f, x); scl_octv reused.
- Hoist UMat tmp allocation out of pyramid loop.
- Interleaved keypoint output buffer (6 arrays to 1) for coalesced writes and fewer copies.
- Consolidated descriptor keypoint copies (2N to 2 host-to-device transfers).
- std::map replaced with flat vector indexed by pyramid level (O(1) vs O(log n)).
- DoG computed on-the-fly in detect kernel via READ_DOG macro, eliminating 45 subtract() calls and dog_pack allocation.
- Shared gauss_packs between detect and descriptor, eliminating duplicate copyTo.
- Cross-level packing per octave: one descriptor kernel launch per octave instead of per level. Keypoint buffer expanded to 5 floats (added layer index). Levels packed into single UMat. Reduces kernel launch overhead and improves GPU utilization for octaves with few keypoints.
- __local memory tiling for Gaussian blur kernels with 16x16 workgroups and cooperative halo loading. Reduces global memory traffic.
- Direct uchar descriptor output for CV_8U: kernel writes convert_uchar_sat_rte directly instead of float buffer + host convertTo pass. Eliminates temp allocation and extra kernel launch.

Performance result (stitching/s2.jpg resized):
- DetectAndCompute: OCL wins at >=960x540 (1.32x at 960x540, 1.80x at 1280x720, 1.87x at 1920x1080).
- Compute-only: OCL wins at >=1280x720 (1.60x at 1280x720, 1.78x at 1920x1080).

Tests:
- modules/features/test/ocl/test_feature2d.cpp with OCL SIFT correctness tests on real images (leuven img1.png, a3.png, s2.jpg).
- DescriptorType, Regression_26139, and Batch tests mirror CPU coverage (CV_8U descriptor type, single-keypoint edge case, 6-image detect+compute).
- modules/features/perf/opencl/perf_sift.cpp with real image and 5-size sweep.
- CPU SIFT_Scaled fixture added to perf_feature2d.cpp for aligned OCL vs CPU comparison.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-07-15 12:29:43 +03:00
Alexander Smorkalov 5c7b438295 Merge pull request #29518 from Teddy-Yangjiale:rvv-lk
video: add RVV intrinsics path for pyramidal LK optical flow iteration loop
2026-07-15 10:35:45 +03:00
Alexander Smorkalov 647b32ac7c Merge pull request #29517 from Teddy-Yangjiale:rvv-k1-05-conv-mr1
dnn: add RVV fp32 microkernel for MR=1 (depthwise-remain) convolution
2026-07-14 16:06:10 +03:00
Alexander Smorkalov 2d2611d5bd Merge pull request #29519 from alessio-antochi:fix-missing-geometry-include
Add missing geometry module include to opencv.hpp
2026-07-14 15:18:08 +03:00
Teddy-Yangjiale 3803eb828e Merge pull request #29440 from Teddy-Yangjiale:rvv-k1-06-filter-cov
imgproc: extend boxFilter / gaussianBlurBinomial coverage in RISC-V RVV HAL #29440

### Summary

This PR extends RVV HAL coverage of the two most frequently used smoothing filters. Previously, `cv_hal_boxFilter` was only implemented for single-channel types (plus 32FC3) at 3×3/5×5, and `cv_hal_gaussianBlurBinomial` for 8UC1/16UC1/8UC4. The most common cases in practice — interleaved **8UC3** images and **7×7** box kernels — fell back to the core `FilterEngine` path, which runs single-threaded, costing 6–19× on multi-core RISC-V hardware.

New coverage:

- `boxFilter`: **8UC3 / 8UC4** (3×3, 5×5, 7×7); **7×7** for 8UC1, 16SC1, 32FC1, 32FC3 and the 8U→16U variant
- `gaussianBlurBinomial`: **8UC3** (3×3, 5×5)

Combinations already covered upstream keep their existing kernels byte-for-byte; net diff is +274/−162 across two files.

### Implementation

1. **Channel-blind "flat" kernels** (`boxFilterFlat8U<ksize, cn>`, `gaussianBlurFlat8U<ksize, cn>`). An interleaved row is processed as `width*cn` flat u8 elements whose horizontal taps sit at strides of `cn` elements: element *i* sums exactly its own channel's taps at `i, i+cn, …, i+(ksize−1)·cn`. One template therefore serves all channel counts, and:
   - the horizontal pass becomes `ksize` independent unaligned `vle8` loads combined with widening adds — no `vlseg`/`vsseg` and no serial `vslide1down` chains, which are microcoded/serialized on current RVV hardware;
   - the vertical pass is a single contiguous u16 stream processed at LMUL=8;
   - the output is a plain `vse8` after saturating narrowing.
2. **Incremental vertical sliding window** for box with `ksize > 3`: the column-sum ring buffer keeps `ksize+1` rows so the window slides in O(1) per output row (add the entering row, subtract the leaving row) instead of re-summing `ksize` rows. u16 modular arithmetic stays exact because window sums never exceed 49·255 = 12495.
3. **Division-free normalization**: normalized box output uses multiply-high + shift (`m = ceil(2^18 / k²)`, Granlund–Montgomery; exact for divisors 9/25/49 over the full value range, verified per divisor) instead of long-latency vector integer division. Rounding is bit-identical to the previous `(sum + k²/2) / k²`.
4. **Existing kernels are extended, not rewritten**: `boxFilterC1` and the float `boxFilterC3` gain a 7×7 step in their existing unrolled style, so the 3×3/5×5 instantiations compile to the same code as before. The previous per-channel gaussian 8UC4 segment kernel is replaced by the flat kernel at measured parity, which is why the diff removes more gaussian lines than it adds.
5. Constraints respected: no hard-coded VLEN, RVV 1.0 intrinsics only, no scalable vector types in arrays.

### Performance

SpacemiT K1 (X60, rv64gcv, VLEN=256, 8×1.6 GHz), 1280×720, against a clean build of current 5.x HEAD.

**Measurement protocol:** each case is timed for **30 iterations** (after 2 warm-up runs) and the minimum is taken; the whole benchmark is executed in **2 interleaved rounds per library** (baseline/candidate alternated to cancel frequency/thermal drift, measured at up to 2× per run on this board), reporting the per-case minimum across rounds.

| Case | Generic | HAL | Speedup |
|------|--------:|----:|--------:|
| boxFilter 8UC1 7×7 | 6.539 ms | 0.330 ms | 19.8× |
| boxFilter 8UC3 3×3 | 7.325 ms | 0.994 ms | 7.4× |
| boxFilter 8UC3 5×5 | 8.118 ms | 1.142 ms | 7.1× |
| boxFilter 8UC3 7×7 | 15.930 ms | 1.328 ms | 12.0× |
| boxFilter 8UC4 3×3 | 9.552 ms | 1.466 ms | 6.5× |
| boxFilter 8UC4 5×5 | 10.775 ms | 1.841 ms | 5.9× |
| boxFilter 8UC4 7×7 | 14.268 ms | 2.112 ms | 6.8× |
| gaussianBlur 8UC3 3×3 | 2.382 ms | 1.023 ms | 2.3× |
| gaussianBlur 8UC3 5×5 | 2.746 ms | 1.684 ms | 1.6× |

**Geometric mean: ~6.1×** across the newly covered cases. Most of the box gain is the HAL's row-parallel execution (core `FilterEngine` has no internal parallelism) multiplied by the flat-layout kernels; the incremental window gives 7×7 a further ~1.9× over the plain flat version.

Regression check: the full `opencv_perf_imgproc` sweep (5610 timed cases, `--perf_min_samples=10`, one round per library) shows a geomean of 1.015× with no regressions beyond run-to-run noise — every case initially below 0.90× was re-verified with 2×2 interleaved rounds and per-case minima, and none persisted. The blur fixtures independently confirm 4.7–8.6× on 8UC4 across sizes and border types.

### Accuracy

- **Bit-exact against the core fallback**: for every newly covered combination (all 15 type/ksize pairs exercised), the HAL output was checksummed against the generic implementation on identical random inputs — all checksums match exactly. This includes the multiply-high normalization (bit-identical rounding to the previous division) and the incremental vertical window (u16 modular add/subtract cancels exactly).
- **Full accuracy suites**: `opencv_test_imgproc` filter/blur/smooth tests (`*BoxFilter*:*GaussianBlur*:*Blur*:*blur*:*Smooth*:*smooth*`) were run on K1 against a clean upstream HEAD build: **819 tests pass on both libraries, and the failure sets (pre-existing upstream failures, unrelated to this PR) are line-for-line identical**. These suites cover random anchors, all border modes, ROI offsets, and in-place operation.
- Combinations already covered upstream are unchanged by construction (same kernels, same codegen), so their accuracy behavior is inherited.

Note for reviewers: the large baseline gap also reflects that core `FilterEngine` runs single-threaded; that affects all non-HAL targets and may deserve a separate issue.


### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
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      Patch to opencv_extra has the same branch name.
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2026-07-14 12:16:46 +03:00
Alexander Smorkalov a88eac3223 Merge pull request #29515 from Prasadayus:yolov3_replace
Replace Qualcomm yolov3.onnx with darknet-converted yolov3 for better results
2026-07-14 12:12:26 +03:00
Alexander Smorkalov 4c936e6c18 Merge pull request #29468 from aoguntayo:fix-mlas-s390x-missing-headers
mlas: add missing s390x ZVECTOR kernel headers to fix build
2026-07-14 10:45:51 +03:00
Abhishek Gola 42f41f749a Merge pull request #29516 from abhishek-gola:add_missing_mlas_support
3rdparty(mlas): add missing power (ppc64le) kernel headers #29516

Closes: https://github.com/opencv/opencv/issues/29465

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
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2026-07-14 10:45:06 +03:00
Alexander Smorkalov 526e42f355 Merge branch 4.x 2026-07-14 09:19:26 +03:00
Alexander Smorkalov d94b6d0df3 Merge pull request #29506 from manand881:fix/bfmatcher-crosscheck-mask
Fix: BFMatcher isMaskSupported false on crossCheck
2026-07-14 08:46:06 +03:00
Alessio Antochi bb90bb0d64 core: add missing geometry module include to opencv.hpp 2026-07-13 18:51:59 +03:00
Teddy-Yangjiale eec2c6fd3d video: add RVV intrinsics path for pyramidal LK optical flow iteration loop 2026-07-13 23:51:22 +08:00
Teddy-Yangjiale 60825757e4 dnn: add RVV fp32 microkernel for MR=1 (depthwise-remain) convolution 2026-07-13 22:47:39 +08:00
Prasadayus 56ae1e603b replace Qualcomm yolov3.onnx with darknet-converted yolov3 2026-07-13 16:20:24 +05:30
Anand Mahesh 0fb620f0f0 Fix: BFMatcher isMaskSupported false on crossCheck
isMaskSupported now returns false when the matcher is
created with crossCheck enabled, because the mask
path is unsupported in that mode. knnMatchImpl also
drops the mask so it cannot reach batchDistance and
trigger its mask.empty() assertion.

https://github.com/opencv/opencv/issues/22093
2026-07-13 08:56:07 +05:30
Alexander Smorkalov 7f9f6acb83 Merge pull request #29502 from asmorkalov:as/msmf_audio
videoio(MSMF): fix audio sample-rate validation and dropped last frame
2026-07-12 17:11:55 +03:00
Varun Jaiswal 4533beab85 videoio(MSMF): fix audio sample-rate validation and dropped last frame (#27438) 2026-07-12 12:14:59 +03:00
Alexander Smorkalov 8fdf15e26e Merge pull request #29499 from varun-jaiswal17:msmf-audio
videoio MSMF: audio sample-rate validation and last frame
2026-07-12 12:12:33 +03:00
Madan mohan Manokar 498e4692d6 Merge pull request #29479 from amd:5_fast_norm_simd
core: SIMD optimizations for norm, distance and Hamming APIs (5.x) #29479

- Cache normHamming SIMD impl via a function pointer to cut per-call dispatch cost (improves ORB & BRISK)
- Add masked norm and norm-diff SIMD kernels (incl. cn==4 paths) and AVX-512 dispatch for norm/stat
- Add brute-force matcher perf test
- Alternative port of PR #29335 adapted to the 5.x norm.simd.hpp structure

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
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      Patch to opencv_extra has the same branch name.
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2026-07-10 18:31:01 +03:00
Akansha-977 87af90b847 Merge pull request #29463 from Akansha-977:distransform_IPP_migration_4.x
Distransform function IPP migration to HAL in 4.x #29463

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
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2026-07-10 18:28:10 +03:00
Varun Jaiswal 97c2c31d9a videoio(MSMF): fix audio sample-rate validation and dropped last frame (#27438) 2026-07-10 19:29:04 +05:30
FAN YUCHEN d7e6e58652 Merge pull request #29487 from Functionhx:fix/docs-combined
Fix calibration tutorial docs, decomposeProjectionMatrix, and convertMaps performance claims #29487

Fixes #25655, #26791, #27277.

Three doc fixes:
1. Calibration tutorial: rows/cols swapped, fixed np.mgrid consistency
2. decomposeProjectionMatrix: clarified transVect is camera center in homogeneous coordinates
3. convertMaps: replaced overstated 2x speed claim

### Pull Request Readiness Checklist
- [x] I agree to contribute under Apache 2 License
- [x] Not based on GPL/incompatible license
- [x] PR proposed to proper branch (4.x)
- [x] Reference to original bug report and related work
- [ ] Accuracy test, performance test, test data: N/A (doc-only)
- [x] Feature well documented and sample code buildable
2026-07-10 15:59:29 +03:00
LiuChenjiayi b21eae2b8b Merge pull request #29149 from EuropaRanger:gapi_doc_update
Gapi doc update #29149

This PR improves G-API documentation by clarifying the introduction and kernel API pages
- fixing some spelling errors
- operator|() can now link to the overloaded version that is actually called.
- supplemented and revised the doc based on the author's annotations.

The change is documentation-only and does not affect runtime behavior. I tested the doc reconstruction of this module. 

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
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      Patch to opencv_extra has the same branch name.
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2026-07-10 15:44:14 +03:00
Alexander Smorkalov b41dd0fdff Merge pull request #29491 from Akansha-977:threshold_IPP_4.x
Extracted IPP to HAL for threshold function in 4.x
2026-07-10 15:06:40 +03:00
Alexander Smorkalov be4ef205b1 Merge pull request #29490 from asmorkalov:as/disable_ipp_warpPerspective_F32C4_nearest
Disabled IPP implementation for warpPerspective F32C4 as it triggers exception on Windows
2026-07-10 14:19:41 +03:00
Akansha Mallick 062a00bab3 Extracted IPP to HAL for threshold function 2026-07-10 14:00:33 +03:00
Prasad Ayush Kumar b022a9b321 Merge pull request #29434 from Prasadayus:canny_ipp_extract
Extracting IPP integaration as HAL for Canny #29434

Backport of : https://github.com/opencv/opencv/pull/29433

**Performance Numbers on Intel(R) Core(TM) i9-11900K:** https://docs.google.com/spreadsheets/d/1RMvUZP1tSQtdjuNQY9JasYmlx1L5iN9XWGhXtG5u1uI/edit?usp=sharing 

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-07-10 13:59:24 +03:00
Akansha-977 7230c1ce08 Merge pull request #29464 from Akansha-977:templatematch_IPP_4.x
Extracting IPP to HAL for matchTemplate function in 4.x #29464

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
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2026-07-10 12:16:45 +03:00
Alexander Smorkalov bc647fe3ec Merge pull request #29484 from velonica0:dnn-rvv-winograd-vlen-fix
dnn: fix out-of-bounds access in RVV Winograd kernels when VLEN > 256
2026-07-10 11:02:30 +03:00
Vincent Rabaud cbd227741f Merge pull request #29453 from vrabaud:persistence
Replace "static inline" by "inline" in headers #29453

This fixes #29436

I also replaced CV_INLINE which can be removed in a later PR.

I can make a similar PR for 4.x if you want.

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
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2026-07-10 10:52:42 +03:00
Alexander Smorkalov 818145f41d Disabled IPP implementation for warpPerspective F32C4 as it does not pass tests. 2026-07-10 09:13:56 +03:00
Andrei Fedorov 93cd614472 Merge pull request #29477 from intel-staging:ipp/sort_enabling
Enable ipp calls for sort, sortIdx#29477

Follow up on https://github.com/opencv/opencv/pull/29184
Also introduced parallelization to sort similar to #29192
Performance is up to ~17x faster per our measurements
+ ~127KB to binary size.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-07-10 08:39:21 +03:00
Alexander Smorkalov 7559555599 Merge pull request #29480 from amd:5_fast_countNonZero
core: speed up countNonZero with AVX-512 signmask path (5.x)
2026-07-09 18:40:40 +03:00
Alexander Smorkalov 7507accf2e Merge pull request #29486 from asmorkalov:as/drop_yuv_gray_hal
Drop HAL for cv_hal_cvtColorYUV2Gray as it's just copy.
2026-07-09 18:06:28 +03:00
Alexander Smorkalov 4b38c5562b Merge pull request #29430 from Teddy-Yangjiale:rvv-k1-04-resize-nn
imgproc: re-enable RVV HAL resize NEAREST/NEAREST_EXACT
2026-07-09 18:05:56 +03:00
Abhishek Gola aa1b8a2a21 Merge pull request #29288 from abhishek-gola:doc_v3
Documentation fixes, Added How to use pre-built opencv doc #29288

closes: https://github.com/opencv/opencv/issues/29263

co-authored by: @kirtijindal14 @Akansha-977 
### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
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      Patch to opencv_extra has the same branch name.
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2026-07-09 17:31:43 +03:00
Alexander Smorkalov 8eb2c8998c Drop HAL for cv_hal_cvtColorYUV2Gray as it's just copy. 2026-07-09 16:51:20 +03:00
Alexander Smorkalov 112959651a Merge pull request #29478 from vrabaud:persistence2
Fix case for intrin.h
2026-07-09 16:24:36 +03:00
Alexander Smorkalov 738739d7c2 Merge pull request #29483 from asmorkalov:update_version_4.14.0-pre
pre: OpenCV 4.14.0 (version++)
2026-07-09 15:35:03 +03:00
velonica0 c86d642d19 dnn: fix out-of-bounds access in RVV Winograd kernels when VLEN > 256 2026-07-09 02:37:34 -07:00
Alexander Smorkalov e978c193cf pre: OpenCV 4.14.0 (version++) 2026-07-09 12:35:42 +03:00
Alexander Smorkalov abb0115648 Merge branch 4.x 2026-07-09 12:17:24 +03:00
yyyisyyy11 b887e2fd3e Merge pull request #29018 from yyyisyyy11:project4_Lunhan_Yan
dnn: add DynamicQuantizeLinear ONNX layer support #29018

### Pull Request Readiness Checklist

- [x] I agree to contribute to the project under Apache 2 License.
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- [x] There is a reference to the original bug report and target platform(s)
- [x] There is an accuracy test
- [ ] There is a performance test

### Description

Implements the ONNX `DynamicQuantizeLinear` operator (opset 11) for the OpenCV DNN module.

**What it does:**

- Adds `QuantizeDynamicLayer` and `DequantizeDynamicLayer` layer classes
- Registers layers and adds ONNX importer dispatch for `DynamicQuantizeLinear`
- Computes scale and zero-point at runtime from activation min/max
- Quantizes FP32 input to int8 (stored as uint8 - 128, matching OpenCV convention)

**Framework limitation & workaround:**
Due to the single-dtype-per-layer constraint in `LayerData::dtype` (see #29017), the float32 scale output cannot be passed through the CV_8S blob pipeline directly. As a workaround, the scale is encoded as 4 raw bytes in a CV_8S `{1,4}` blob using `memcpy`, and decoded by the downstream `DequantizeDynamic` layer.

**Testing:**

- Custom accuracy tests reproduce all 3 ONNX conformance test cases: `test_dynamicquantizelinear`, `test_dynamicquantizelinear_max_adjusted`, `test_dynamicquantizelinear_min_adjusted`
- Each test verifies: quantized values, scale, zero point, and round-trip dequantize accuracy
- All existing quantization regression tests pass (42/42)

**Conformance tests:**
The 6 conformance tests for `DynamicQuantizeLinear` remain in the parser denylist (they were already denylisted before this PR) because the framework cannot produce mixed-type outputs. The custom tests provide equivalent coverage.

### Files changed

- `modules/dnn/include/opencv2/dnn/all_layers.hpp` — layer class declarations
- `modules/dnn/src/init.cpp` — layer registration
- `modules/dnn/src/onnx/onnx_importer.cpp` — ONNX import dispatch
- `modules/dnn/src/int8layers/quantization_utils.cpp` — layer implementations
- `modules/dnn/test/test_onnx_importer.cpp` — custom accuracy tests
2026-07-09 11:07:12 +03:00
Vincent Rabaud 71d900dc36 Fix case for intrin.h
This is similar to: https://github.com/opencv/opencv/pull/28754
2026-07-09 01:17:15 +02:00
Alexander Smorkalov 0eb3778a1b Merge pull request #29473 from velonica0:28870
imgproc: make RVV HAL resize bit-exact for 8U INTER_LINEAR (fixes #28870)
2026-07-08 19:19:35 +03:00
Madan mohan Manokar 9ce06a5675 core: speed up countNonZero with AVX-512 signmask path (5.x)
- Count zero lanes via v_signmask()+popcount in AVX-512 dispatch units
    - Alternative port of PR #29390 adapted to the 5.x macro-based kernels, including a SIMD path for 64F.
2026-07-08 12:53:12 +00:00
velonica0 9121ebe28b imgproc: make RVV HAL resize bit-exact for 8U INTER_LINEAR 2026-07-08 05:24:31 -07:00
Anu Oguntayo eafce2f0ce mlas: add missing s390x ZVECTOR kernel headers to fix build
The previous commit bdf348c13a introduced
MLAS support including the s390x backend, but accidentally omitted the
required internal header files. This causes a compilation failure on
s390x:

  fatal error: SgemmKernelZVECTOR.h: No such file or directory

This PR restores the build by adding the following missing headers from
upstream (microsoft/onnxruntime):

- FgemmKernelZVECTOR.h
- SgemmKernelZVECTOR.h

This is the same class of bug as #29422 (loongarch64), which was fixed
by PR #29423 for that architecture only.
2026-07-07 14:02:27 +01:00
Anushka d48bf69f65 Merge pull request #29455 from anushkagupta200615-jpg:fix-issue-29452
Fix #29452: Remove <complex.h> to prevent _Complex macro conflicts #29455

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

---

**Description:**
Resolves https://github.com/opencv/opencv/issues/29452

**Reason for the issue:**
The C99 `<complex.h>` header defines the macro `complex` on some platforms (like NetBSD with GCC 14). Because it was included before C++ `<complex>`, this caused conflicts where `std::complex<T>` was being expanded into `std::_Complex<T>`, resulting in the reported syntax errors.

**Changes made:**
- Removed the unnecessary C-style `#include <complex.h>`.
- Added a safety guard to `#undef complex` in case any transitive lapack headers attempt to define it, ensuring `std::complex` works cleanly without C-preprocessor interference.
2026-07-07 15:20:09 +03:00
Alexander Smorkalov aa70c82ebe Merge pull request #29418 from uwezkhan:einsum-subscript-bound
bound einsum subscript index before reading input shape
2026-07-07 14:42:49 +03:00
Alexander Smorkalov c85397c3bb Merge pull request #29462 from intel-staging:use_ipp_check_derive
Added missing IPP check in IPP HAL
2026-07-07 13:20:45 +03:00
Alexander Smorkalov 4ce2389a62 Merge pull request #29425 from fkenmar:dnn_remove_caffe_leftovers
dnn: remove Caffe protobuf leftovers, fix builds with external protobuf
2026-07-07 12:52:37 +03:00
Andrei Fedorov 0488f6e942 Update deriv_ipp.cpp 2026-07-07 11:39:39 +02:00
Akansha-977 35bf0b4ac6 Merge pull request #29428 from Akansha-977:filter2D_IPP_migration
Filter2D IPP extraction to HAL for 5.x #29428

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
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2026-07-07 12:36:03 +03:00
Alexander Smorkalov 51495995d2 Merge pull request #29431 from asmorkalov:as/no_exact_ipp
Enable non-exact IPP optimizations if build-time algorithm hint allows it
2026-07-07 11:45:59 +03:00
Scott d1264cc6ec Merge pull request #29457 from Scott-Nx:fix/objdetect-new-dnn-target
dnn: silence false-positive CPU target warning for New graph engine #29457

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

* [x] I agree to contribute to the project under Apache 2 License.
* [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
* [x] The PR is proposed to the proper branch (`5.x`)
* [ ] There is a reference to the original bug report and related work
  No existing OpenCV issue or pull request directly covers this warning. This PR is the original report and fix.
* [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
  Not applicable. This patch changes only diagnostic behavior for an existing CPU no-op path.
* [ ] The feature is well documented and sample code can be built with the project CMake
  Not applicable. This patch adds no feature or public API.

## Summary

Suppress a misleading warning emitted when OpenCV 5 New DNN graph engine receives its default CPU target request.

`FaceDetectorYN` and `FaceRecognizerSF` call:

```cpp
net.setPreferableTarget(DNN_TARGET_CPU);
```

after loading their networks.

When New graph engine is active, target switching is not supported. OpenCV therefore currently prints:

```text
Targets are not supported by the new graph engine for now
```

However, New graph engine already executes on CPU. The CPU request does not require a target change, is ignored, and inference continues through New graph engine.

The warning therefore suggests a failed configuration or fallback to Classic DNN even though neither occurs.

## Change

Treat `DNN_TARGET_CPU` as a silent no-op when generic New graph engine is active.

```text
New graph engine + CPU target
→ no target change needed
→ return unchanged
→ no warning

New graph engine + non-CPU target
→ target remains unsupported
→ preserve existing warning
→ return unchanged
```

## Behavior before

```text
New graph engine active
→ caller requests CPU
→ request is ignored
→ warning emitted
→ inference continues on New graph engine
```

## Behavior after

```text
New graph engine active
→ caller requests CPU
→ request is ignored
→ no warning
→ inference continues on New graph engine
```

## Unchanged behavior

* New graph engine selection is unchanged.
* Inference execution is unchanged.
* CPU remains the effective target for generic New graph engine.
* Classic DNN behavior is unchanged.
* ONNX Runtime handling is unchanged.
* Non-CPU targets continue to emit the existing warning.

## Motivation

The current diagnostic is a false positive for the default CPU request. It reports unsupported target selection even though CPU is already the active execution target and inference succeeds through New graph engine.

## Testing

* Built OpenCV locally.
* Ran relevant DNN tests.
* Verified New graph engine still loads and executes affected models.
* Verified CPU target requests no longer emit the misleading warning.
* Verified non-CPU target requests retain the existing unsupported-target warning.
2026-07-07 11:32:25 +03:00
Alexander Smorkalov 0b5ff7b3a2 Merge pull request #29449 from asmorkalov:as/skip_dnn_tests_32bit
Skip some BERT tests on 32-bit platforms as they do not fit into 2gb ram
2026-07-07 11:29:52 +03:00
Alexander Smorkalov c344b7dcef Merge pull request #29461 from rmsalinas:update-truco-ref
doc: update TRUCO publication status
2026-07-07 10:21:31 +03:00
rmsalinas 0e6433327a doc: update TRUCO publication status 2026-07-07 08:08:43 +02:00
Kenmar Francisco f7ad23157f dnn: remove Caffe protobuf leftovers, fix external protobuf builds
The Caffe importer removal (#28678) left behind the protoc-generated
opencv-caffe.pb.{cc,h} and caffe_io.{cpp,hpp}. The generated files can
only be compiled with an old libprotobuf and can no longer be
regenerated since opencv-caffe.proto was deleted, so any build with
PROTOBUF_UPDATE_FILES=ON or an external protobuf failed (#29291, #29419).

- Move the generic ReadProto* helpers, the only remaining consumers of
  caffe_io (used by the TensorFlow importer), to src/protobuf_io.{hpp,cpp};
  delete src/caffe and misc/caffe, and move glog_emulator.hpp to src/.
- Guard the protobuf version check so it also works with newer protobuf
  where GOOGLE_PROTOBUF_VERSION is not defined.
- Drop the unconditional opencv-onnx.pb.h include from cast2_layer.cpp,
  using the spec-fixed ONNX data type values instead, and update stale
  readNetFromONNX/readNetFromTensorflow stubs to the current signatures
  so WITH_PROTOBUF=OFF builds link again.
2026-07-06 14:38:46 -04:00
Alexander Smorkalov 77dff6dc75 Merge pull request #29448 from asmorkalov:as/skip_deep_features_tests_32bit
Skip DNN features test on 32-bit platforms as they time-to-time do not fit 2GB RAM
2026-07-06 17:46:26 +03:00
Alexander Smorkalov f6be96989e Merge pull request #29442 from orbisai0security:fix-filter-buffer-size-overflow
fix: in filter in filter.cpp
2026-07-06 17:19:05 +03:00
MAAZIZ Adel Ayoub e9289fc7c4 Merge pull request #29447 from Adel-Ayoub:fix/matexpr-mul-scalar-lifetime
core: fix use-after-scope when Mat::mul() is given a scalar #29447

### Summary

`cv::Mat::mul()` called with a scalar returns a `MatExpr` that reads a dead stack slot when it is evaluated. In a normal (non-instrumented) build this produces silently wrong values as soon as the slot is reused:

```cpp
static cv::MatExpr makeExpr(const cv::Mat& m)
{
    return m.mul(7);               // 7.0 is a temporary double in THIS frame
}

cv::Mat matrix(2, 3, CV_32FC1, cv::Scalar(3.0f));
cv::MatExpr expr = makeExpr(matrix);
// ... any further calls reuse the dead frame ...
cv::Mat result = expr;             // observed: all 0, expected: all 21
```

Under AddressSanitizer this is the `stack-use-after-scope` reported in #23577, with the same stack trace (`cvt64s` -> `convertAndUnrollScalar` -> `arithm_op` -> `multiply` -> `MatOp_Bin::assign`).

Storing the expression is the documented lazy-evaluation usage of `MatExpr`; the argument is ordinary supported API usage (`mat.hpp` itself shows `Mat C = A.mul(5/B);`).

### Root cause

A scalar argument binds to `_InputArray(const double& val)`, which records the **address** of the temporary with kind `MATX`:

```cpp
inline _InputArray::_InputArray(const double& val)
{ init(FIXED_TYPE + FIXED_SIZE + MATX + CV_64F + ACCESS_READ, &val, Size(1,1)); }
```

`Mat::mul()` then parks `m.getMat()` inside the returned `MatExpr`. For `MATX` kind, `getMat_()` returns a non-owning, non-refcounted header over that stack memory (`return Mat(sz, flags, obj);`). The temporary dies at the end of the full expression, but the `MatExpr` keeps the header, and `MatOp_Bin::assign()` later feeds it to `cv::multiply()`. `Matx`/`Vec` arguments take the same path.

`Mat::mul()` is the only `MatExpr` factory in `matrix_expressions.cpp` that takes an `InputArray`; every other scalar operand there is stored by value in the `Scalar` member (`e.s`), so no other expression path can capture a stack pointer this way.

### Fix

Snapshot the operand with `clone()` unless it is a `Mat`/`UMat`, which keep the current zero-copy behaviour: their headers are refcounted and already safe to defer. Any other `InputArray` kind (a scalar, `Matx`, `Vec`, `std::vector`, an evaluated expression) is a potentially non-owning view, so it is copied once at expression construction, off any hot path.

### Test

Adds `Core_MatExpr.mul_scalar_use_after_scope_23577` to `modules/core/test/test_operations.cpp`. It builds the expression in a helper frame and overwrites the stack before evaluating; the helpers are called through volatile function pointers so they cannot be inlined, which makes the stale read deterministic. The test fails before the fix (result is all 0 instead of all 21) and passes after. It is self-contained: no opencv_extra data is needed.

Verified locally on macOS/AArch64 (Apple clang 17, Release): full `opencv_test_core` passes, and the AddressSanitizer reproducer from the issue is clean after the fix.

Fixes #23577.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Self-contained accuracy regression test in `modules/core/test/test_operations.cpp`; no opencv_extra data required. No performance test: no existing perf test covers `Mat::mul` expression construction, and the copy happens once at expression construction, only for non-`Mat`/`UMat` operands.
- [ ] The feature is well documented and sample code can be built with the project CMake
      N/A - bug fix, no new API.
2026-07-06 17:11:51 +03:00
Alexander Smorkalov 50beb24c6b Enable non-exact IPP optimizations if build-time algorithm hint allows it. 2026-07-06 11:50:53 +03:00
Alexander Smorkalov fcc3418cd7 Merge pull request #29446 from anushkagupta200615-jpg:fix-apple-conversions-cpp26
macOS: Fix CGBitmapInfo C++26 enum compilation error
2026-07-06 10:15:38 +03:00
Alexander Smorkalov 3d0ae71ac1 Merge pull request #29429 from vrabaud:persistence
Fix CALIB_DISABLE_SCHUR_COMPLEMENT and CALIB_TILTED_MODEL collision
2026-07-06 10:11:57 +03:00
Alexander Smorkalov 80da12e55a Merge pull request #29432 from asmorkalov:as/python_testing
Replace np.testing.assert_allclose due to Numpy bug
2026-07-06 10:10:23 +03:00
Alexander Smorkalov bb96382942 Skip some BERT tests on 32-bit platforms as they do not fit into 2gb ram. 2026-07-06 10:08:21 +03:00
Alexander Smorkalov 854cf4225d Skip DNN features test on 32-bit platforms as they time-to-time do not feet 2GB RAM. 2026-07-06 10:03:24 +03:00
Akansha-977 80fda1da8c Merge pull request #29427 from Akansha-977:filter2D_IPP_migration_4.x
Filter2D IPP migration to HAL for 4.x #29427

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-07-06 09:52:20 +03:00
anushkagupta200615-jpg cddfd62a18 macOS: Fix CGBitmapInfo C++26 enum compilation error 2026-07-06 02:58:34 +05:30
orbisai0security a9573e5ac0 fix: V-002 security vulnerability
Automated security fix generated by OrbisAI Security
2026-07-05 12:15:53 +00:00
Alexander Smorkalov 100496b371 Replace np.testing.assert_allclose due to Numpy bug. 2026-07-03 16:25:51 +03:00
Alexander Smorkalov 24030ff3a2 Merge pull request #29390 from amd:fast_countNonZero
core: Optimized countNonZero with AVX-512 signmask path
2026-07-03 15:44:43 +03:00
Vincent Rabaud e770c269b0 Fix CALIB_DISABLE_SCHUR_COMPLEMENT and CALIB_TILTED_MODEL collision 2026-07-03 13:19:11 +02:00
Prasad Ayush Kumar bbfe2eb0de Merge pull request #29414 from Prasadayus:box_filter_refactor
Merge pull request #29414 from Prasadayus:box_filter_refactor

Moving IPP functions to HAL for box_filter in Imgproc #29414

**Performance Numbers on Intel(R) Core(TM) i9-11900K:** https://docs.google.com/spreadsheets/d/1puWmOSTtAFwWjPu8J1SpuccPQvxUJU8NlFQs7mAZwL8/edit?usp=sharing

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-07-03 13:45:20 +03:00
Prasad Ayush Kumar ae93a81ec0 Merge pull request #29389 from Prasadayus:cvtcolor_refactor
Refactoring and optimizing cvtcolor in Imgproc #29389

**Key Changes:**

1. Unified three divergent SIMD swap implementations into a single shared `v_swap` helper.
2. Consolidated the repeated `CV_8U/CV_16U/CV_32F` depth-dispatch chains into a single `CvtColorLoopDepth` template.
3. Extracted the duplicated coefficient-selection loops in the YCrCb/YUV constructors into `selectYuvCoeffs`.
4. Collapsed the shared YUV420 store logic duplicated across both decode invokers into `storeYUV420block`.
5. Generalized the inline blue-channel coefficient swaps in `color_lab.cpp` into `swapBlueCoeffsCols`/`swapBlueCoeffsRows`.
6. Added a `v_dotprod`-based SIMD path (`v_RGB2Y`/`v_RGB2UV`) for the previously scalar `RGB8toYUV422Invoker`.
7. Migrated all inline `CV_IPP_CHECK` cvtColor paths out of the dispatch files and into the IPP HAL plugin (`hal/ipp/src/color_ipp.cpp`), replacing each call site with `CALL_HAL`.

**Performance Numbers on Intel(R) Core(TM) i9-11900K**: https://docs.google.com/spreadsheets/d/1pz0aHlTeG4Ao8RT7LipgdNSjhCJz92QfZG3GmNa63hU/edit?usp=sharing

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-07-03 10:53:13 +03:00
Alexander Smorkalov db37d38eb4 Merge pull request #29424 from Teddy-Yangjiale:rvv-k1-03-conv2-int8-kernel
dnn: add RVV kernel for new-engine int8 convolution
2026-07-03 10:48:59 +03:00
Teddy-Yangjiale 3aadaa8385 imgproc: re-enable RVV HAL resize NEAREST/NEAREST_EXACT 2026-07-03 14:29:07 +08:00
Prasad Ayush Kumar c77286749a Merge pull request #29420 from Prasadayus:box_filter_ipp_extract
Extract IPP integration as HAL function for box_filter #29420

Backport of https://github.com/opencv/opencv/pull/29414

**Performance Numbers on Intel(R) Core(TM) i9-11900K:** https://docs.google.com/spreadsheets/d/1kMKiZWh--pH30hqsQo6j1suNw1lfQiKzSankMCMa2FI/edit?usp=sharing

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-07-02 13:10:52 +03:00
Alexander Smorkalov d82fbe3530 Merge pull request #29421 from Teddy-Yangjiale:rvv-k1-01-02-fastgemm-packb
dnn: add RISC-V RVV FP32 fastGemm micro-kernel and Pack-B support
2026-07-02 13:06:31 +03:00
Madan mohan Manokar e6d0c0340b Merge pull request #29413 from amd:fast_basic_op
core: Fix mul32f and addWeighted32f to use native f32 SIMD paths #29413

OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1388

- add 32FC1 coverage to addWeighted benchmark
- avoid intermediate double for f32 variants of scaled multiply and addWeighted.
- Relax AddWeighted 32F test tolerance to match f32 FMA semantics.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-07-02 12:58:39 +03:00
Alexander Smorkalov 9460e30793 Merge pull request #29400 from amd:fast_sum
core: enable AVX-512 dispatch for sum
2026-07-02 12:56:27 +03:00
Alexander Smorkalov 1439136ef8 Merge pull request #29423 from wojiushixiaobai:fix_loongarch64_5.x
mlas: add missing LoongArch64 kernel headers to fix build
2026-07-02 11:28:51 +03:00
吴小白 ee9c019556 mlas: add missing LoongArch64 kernel headers to fix build
Signed-off-by: 吴小白 <296015668@qq.com>
2026-07-02 11:50:07 +08:00
Teddy-Yangjiale e39c6c6bcc dnn: add RVV kernel for new-engine int8 convolution 2026-07-02 07:16:59 +08:00
Teddy-Yangjiale dcae1f1dc1 dnn: add RISC-V RVV FP32 fastGemm micro-kernel and Pack-B support 2026-07-02 05:46:43 +08:00
Uwez Khan b4164a4bc4 bound einsum subscript index before reading input shape 2026-07-01 18:19:00 +05:30
Madan mohan Manokar 579a505734 Merge pull request #29335 from amd:fast_norm_simd
core: SIMD optimizations for norm, distance and Hamming APIs. (Improves ORB & BRISK) #29335

Generic universal-intrinsic kernels (benefit all SIMD backends: NEON, AVX2, AVX-512, etc.), plus enabling wider dispatch for the norm module.

- hal::normHamming: cached-pointer dispatch (resolve once, no per-call dispatch chain or trace region) + vector popcount path. cv::norm(NORM_HAMMING) and binary-descriptor matching (BFMatcher ORB/BRISK/FREAK via cv::batchDistance).
- hal::normL2Sqr_ / normL1_: direct inlinable kernels with single-vector tail (cv::batchDistance / BFMatcher float L2/L1, cv::kmeans).
- cv::norm masked NORM_INF: deinterleave SIMD for multichannel + back-step tail.
- cv::norm(src1, src2, type, mask): SIMD masked norm-diff; INF is one templated kernel for all element types, plus uchar L1/L2 and int L1 kernels.
- Enable AVX512_SKX/AVX512_ICL dispatch for the norm module.
- features2d: add BFMatcher knnMatch perf tests (float L2/L1, binary Hamming).
- ORB and BRISK performance improved
### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-07-01 15:48:47 +03:00
Abhishek Gola c7b8fb28b6 Merge pull request #29333 from abhishek-gola:attention_layer_extension
Extended Attention layer support #29333

Implemented present/past KV support.

Merge with: https://github.com/opencv/opencv_extra/pull/1381

Co-authored by: @Akansha-977 

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-07-01 15:41:44 +03:00
Teddy-Yangjiale 5866bbd9ff Merge pull request #29411 from Teddy-Yangjiale:rvv-dnn-int8-conv
dnn: add Winograd F(6,3) RVV implementation for RISC-V (VLEN≥256) #29411

### Summary

Adds a RISC-V Vector (RVV) backend for the Winograd F(6,3) convolution path
in the DNN module, targeting VLEN≥256 processors (tested on SpacemiT K1,
rv64gcv).

### Background

The Winograd F(6,3) path was already gated on `useSIMD128 || useAVX ||
useAVX2 || useNEON` at runtime, and protected by the same set of compile-time
macros. RVV was simply missing from both guards, so it always fell through to
generic convolution even when the hardware supports it.

A second issue: `conv_winograd_f63` was registered with
`ocv_add_dispatched_file`, which skips ISA variants not present in the host
toolchain during cross-compilation. Changing to `ocv_add_dispatched_file_force_all`
forces the `.rvv.cpp` translation unit to be generated unconditionally, matching
how every other RVV-enabled kernel in the DNN module is registered.

### Implementation notes

**Atom width.** `vsetvlmax_e32m1()` returns 8 on VLEN=256, so `winoAtomF32=8`
is chosen, matching the AVX2 atom width. The `impl_accum_F32` and transform
functions are structured identically to the AVX2 path (4 output channels ×
6 input tiles per atom).

**Input/output transform.** AVX2 uses `_mm256_unpacklo/hi_ps` + `permute2f128`
for an in-register 8×8 transpose. RVV has no equivalent cross-lane shuffle at
this width without `vrgather`, which adds index-vector overhead for a
non-bottleneck step. Instead the 8×8 intermediate matrix is transposed
scalar-in-memory between the two `wino_bt8x8_rvv` / `wino_at8x6_rvv` passes.
This keeps the transform code simple and correct; the GEMM in `impl_accum_F32`
dominates runtime.

**VLEN guard.** `getWinofunc_F32` checks `vsetvlmax_e32m1() >= 8` at runtime
and returns an empty functor on narrower implementations, so the code is safe
on VLEN=128 targets without a separate code path.

### Testing

**Correctness:** `ConvolutionWinograd.Accuracy` passes on the K1 board (9 ms).

**Performance:** `opencv_perf_dnn`, SpacemiT K1 (rv64gcv, VLEN=256),
GCC 13, `-O2 -march=rv64gcv`. Reported times are per-iteration medians from
`[ PERFSTAT ]` output (`--perf_min_samples=5`). "Generic" is the same build
with Winograd disabled via `OPENCV_DNN_DISABLE_WINOGRAD=1`.

| Input → Output C         | Winograd (ms) | Generic (ms) | Speedup |
|--------------------------|--------------|-------------|---------|
| {1,128,52,52} → 256     | 35.97        | 62.86       | 1.75×   |
| {1,512,13,13} → 1024    | 65.18        | 112.50      | 1.73×   |
| {1,256,75,75} → 256     | 159.75       | 271.65      | 1.70×   |
| {1,64,300,300} → 64     | 174.21       | 295.32      | 1.69×   |
| {1,1152,16,16} → 1152   | 176.12       | 245.33      | 1.39×   |

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-07-01 14:33:46 +03:00
Alexander Smorkalov 83ee55fcda Merge pull request #29417 from intel-staging:deriv_hal
Moved IPP-based Sobel and Scharr to HAL
2026-07-01 14:20:57 +03:00
Madan mohan Manokar 28a1d0dddb Merge pull request #29242 from amd:fast_gemm_simd
Optimized gemm implementation with Universal SIMD #29242

- vectorized GEMMSingleMul and GEMMBlockMul

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-07-01 13:47:43 +03:00
Alexander Smorkalov 80f48cc952 Merge pull request #29416 from vrabaud:persistence
Remove useless logger include from the public API
2026-07-01 13:41:50 +03:00
Saravana Balaji Mohan Balaji e2f0876777 Merge pull request #29217 from MBSaravanaBalaji:feat/onnx-lppool
dnn: implement LpPool ONNX operator #29217

## Summary

Implements the `LpPool` ONNX operator (opset 1–18), which was previously unregistered and caused a parse failure. `LpPool` computes the Lp-norm pooling: `(sum(|x|^p))^(1/p)` over a sliding window.

## Changes

- New `LpPoolLayer` in `modules/dnn/src/layers/lppool_layer.cpp`
  - Supports `kernel_shape`, `strides`, `dilations`, `pads`, `auto_pad` (NOTSET/SAME_UPPER), `ceil_mode`, and `p` (default 2)
  - SIMD fast paths for p=1 (abs + accumulate) and p=2 (square + accumulate + sqrt); scalar fallback for other values of p
- Registered `LpPool` dispatch entry in both `onnx_importer.cpp` (classic engine) and `onnx_importer2.cpp` (new graph engine)
- Added `LpPoolLayer` declaration to `modules/dnn/include/opencv2/dnn/all_layers.hpp`
- Registered layer class in `modules/dnn/src/init.cpp`
- Re-enabled 8 lppool conformance tests in `test_onnx_conformance.cpp` (previously in parser denylist)
- `test_lppool_2d_same_lower` added to the global conformance denylist — same known SAME_LOWER padding bug that affects `averagepool` and `maxpool`

## Testing

All applicable ONNX conformance tests pass:

| Test | Result |
|------|--------|
| test_lppool_1d_default | PASSED |
| test_lppool_2d_default | PASSED |
| test_lppool_2d_dilations | PASSED |
| test_lppool_2d_pads | PASSED |
| test_lppool_2d_same_lower | SKIPPED (known SAME_LOWER padding bug, consistent with avgpool/maxpool) |
| test_lppool_2d_same_upper | PASSED |
| test_lppool_2d_strides | PASSED |
| test_lppool_3d_default | PASSED |

Tested on: macOS (x86_64/SSE4, Rosetta 2) and Linux x86_64 (AVX2/AVX-512, GCC 13.3.0), Release build
OpenCV version: 5.0.0-pre

## Related Issues

None

---

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-07-01 13:27:55 +03:00
Fedorov, Andrey 2f779c5c36 moved ipp deriv to hal 2026-07-01 02:13:48 -07:00
Vincent Rabaud 68fb8aeb30 Remove useless logger include from the public API
This creates a conflict with some internal Google lib defining
the same symbols.
2026-07-01 11:09:57 +02:00
Alexander Smorkalov d36133b325 Merge pull request #29403 from Teddy-Yangjiale:rvv-dnn-conv-rvv
dnn: add RISC-V RVV fp32 microkernel for classic ConvolutionLayer
2026-06-30 14:19:32 +03:00
Alexander Smorkalov 5b83b0c440 Merge pull request #29405 from Teddy-Yangjiale:rvv-dnn-conv-4x
dnn: add RVV LMUL=1 kernel for convBlock_F32 (ConvolutionLayer, rv64gcv)
2026-06-29 18:08:55 +03:00
ZC a16c4a9fe2 Merge pull request #29359 from zcinclude:fix/avfoundation-videowriter-destructor-sigsegv
videoio(avfoundation): fix SIGSEGV crash when releasing VideoWriter on iOS #29359

Replace deprecated synchronous finishWriting with
finishWritingWithCompletionHandler + dispatch_semaphore
to properly wait for the async completion handler.

The synchronous finishWriting method is deprecated since iOS 6
and, despite blocking until writing status completes, does NOT
wait for internal NSOperation KVO observer blocks that are
dispatched asynchronously to GCD worker threads. When the
destructor returns and drains the autorelease pool, these
blocks may access already-released objects, causing SIGSEGV.

This fix uses a semaphore to block until the
finishWritingWithCompletionHandler callback has fully completed,
eliminating the race condition between the async block and
autorelease pool drain.

Fixes #28165
2026-06-29 15:22:16 +03:00
Alexander Smorkalov 97f62825a2 Merge pull request #29402 from tonuonu:perf-dnn-act-5x
dnn: SIMD for 13 transcendental activations in the 5.x engine
2026-06-29 14:31:21 +03:00
Anshu 2a47e2c1a5 Merge pull request #29396 from 0AnshuAditya0:fix-macos-framework-rpath-29028
cmake: fix #29028 macOS dynamic framework RPATH for relocatable install #29396

### Problem
When building OpenCV as a dynamic framework on macOS (`-DAPPLE_FRAMEWORK=ON -DBUILD_SHARED_LIBS=ON`), installed binaries contain an absolute `LC_RPATH` pointing to the build tree, making the install non-relocatable. Moving the install directory or deploying to another Mac causes binaries to crash on launch.

### Root cause
`cmake/OpenCVInstallLayout.cmake` unconditionally sets `CMAKE_INSTALL_RPATH` to `${CMAKE_INSTALL_PREFIX}/${OPENCV_LIB_INSTALL_PATH}` for all platforms. Additionally, `cmake/platforms/OpenCV-Darwin.cmake` was empty and missing the `@executable_path`/`@loader_path` linker flags that the iOS toolchain already sets for the same `APPLE_FRAMEWORK AND BUILD_SHARED_LIBS` case.

### Fix
- Guard `CMAKE_INSTALL_RPATH` in `OpenCVInstallLayout.cmake` to use relocatable `@executable_path` tokens for Apple framework builds
- Add equivalent linker flags to `OpenCV-Darwin.cmake`, mirroring `platforms/ios/cmake/Modules/Platform/iOS.cmake`

Fixes #29028

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-29 14:27:21 +03:00
Jonas Perolini 37f3c7539b Merge pull request #29252 from JonasPerolini:pr-aruco-bit-threshold-in-refine
Use validBitIdThreshold for Aruco refineDetectedMarkers #29252

The goal of this PR is to solve the issue raised by @vrabaud in https://github.com/opencv/opencv/pull/28289 (comment: https://github.com/opencv/opencv/pull/28289#discussion_r3355812646). 

**Issue:** 

`refineDetectedMarkers()` converted the extracted cell ratios with `convertTo(CV_8UC1)` (an implicit 0.5 threshold) before computing the code distance, ignoring `detectorParams.validBitIdThreshold`. 

**Solution:** 

Make the refine path consistent with the main detection path `Dictionary::identify`.

**Changes:**

- Add a `Dictionary::getDistanceToId()` overload that takes the float cell pixel ratio matrix and `validBitIdThreshold` (similar to how it's done for the `identify()` overload.
- Move the per cell distance computation into a private `getDistanceToIdImpl` helper used by both `identify()` and the new overload of `getDistanceToId()` to avoid repetitions.
- `refineDetectedMarkers()` now calls the new overload.

**Tests:**

- `CV_ArucoRefine.validBitIdThreshold`: a marker with one degraded cell is recovered at threshold 0.7 but not at 0.49.
- `CV_ArucoDictionary.getDistanceToIdCellPixelRatio`: unit-tests both `getDistanceToId` overloads.

All passed
2026-06-29 13:37:43 +03:00
Alexander Smorkalov 3c10324630 Merge pull request #29404 from tonuonu:perf-svm-kernels
ml: SIMD for SVM kernel reductions 🧑‍💻🤖
2026-06-29 11:40:14 +03:00
Teddy-Yangjiale c831290769 dnn: add RVV LMUL=1 kernel for convBlock_F32 (ConvolutionLayer, rv64gcv) 2026-06-29 00:47:44 +08:00
Tonu Samuel a015333f04 ml: SIMD for SVM kernel reductions
SVM::predict is dominated by the per-feature kernel reduction over the
support vectors. Vectorize the four reduction kernels in svm.cpp with
universal intrinsics (two independent accumulators + scalar tail):
calc_non_rbf_base (dot product), calc_rbf (squared distance),
calc_intersec (min-sum) and calc_chi2. Same approach as the KNN
findNearest reduction in #29380. Adds modules/ml/perf/perf_svm.cpp
covering the RBF/POLY/INTER/CHI2 kernels.
2026-06-28 18:09:05 +03:00
Mulham Fetna 5dbfbcdcac Merge pull request #28935 from molhamfetnah:fix/21960-unicode-temp-path
core: fix Unicode temp path handling on Windows (fix #21960) #28935

## Summary

Fixes getCacheDirectoryForDownloads and related temp file functions failing when the user home directory contains Unicode characters like C:\\Users\\テスト\\AppData\\Local\\Temp.

## Root Cause

On Windows, OpenCV was using ANSI versions of GetTempPath and GetTempFileName which fail with Unicode paths.

## Fix

Replace with wide-character GetTempPathW plus UTF-8 conversion:

- modules/core/src/utils/filesystem.cpp - getCacheDirectory for cache paths
- modules/core/src/system.cpp - temporary file creation  
- modules/ts/src/ts_gtest.cpp - test stream capture

## Testing

The fix has been verified compiles. Manual testing on Windows with Unicode username required.

## Risk

Low: Uses same output format, just different encoding path. Existing ASCII functionality preserved.

---

Fixes opencv/opencv#21960
2026-06-28 14:20:45 +03:00
Alexander Smorkalov edd89cfca5 Merge pull request #29371 from uwezkhan:tflite-pad-resize-bounds
check parameter tensor length in tflite parsePadding and parseResize
2026-06-28 14:19:17 +03:00
uwezkhan 15b5d28325 Merge pull request #29378 from uwezkhan:qr-alpha-map-bound
bound alphanumeric values in qr decodeAlpha before map lookup #29378

decodeAlpha in the no-quirc QR backend reads alphanumeric symbols from the post-ECC bitstream and indexes a fixed 45-entry map[] with the raw values. The 11-bit pair from next(11) goes up to 2047, so tuple/45 lands on 45 once the pair passes 2024, and the 6-bit trailing char from next(6) goes up to 63, so map[value] runs out to map[63]. A QR whose data codewords carry an alphanumeric segment with one of those out-of-range values reads past the static array, and the stray byte lands in the string returned by detectAndDecode. WITH_QUIRC defaults off, so this is the decoder a stock build runs.

Before, the only guard was on the encode side, which never emits those values, so the decoder trusted the stream and indexed map[] directly. New version checks value range and return empty string for malformed qr codes.

### Pull Request Readiness Checklist

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-28 14:18:20 +03:00
Teddy-Yangjiale cb0b3d820b dnn: add RISC-V RVV fp32 microkernel for classic ConvolutionLayer 2026-06-28 05:44:31 +08:00
Tonu Samuel 4d839a16f0 dnn: SIMD for 13 transcendental activations in the 5.x engine
Port of #29377 to 5.x. Wires Log, Erf, Exp, Sin, Cos, Sinh, Cosh, Tan,
Softplus, BNLL, Asinh, Acosh, Atanh into the dispatched activation_kernels
registry, plus a Layer_Activation perf test. 1.4-12.3x across
M4/A76/Threadripper/Xeon, correct to <=5e-7 vs scalar.
2026-06-27 09:11:09 +03:00
Alexander Smorkalov dd541965e9 Merge pull request #29397 from Prasadayus:cvtcolor_ipp_extract
Extract IPP integration as HAL function for cvtcolor
2026-06-27 08:32:00 +03:00
Alexander Smorkalov da4a417b2a Merge pull request #29346 from Ijtihed:fix/flann-heap-pool-tls
flann: use per-thread storage for heap pool to remove lock contention
2026-06-26 16:35:27 +03:00
Alexander Smorkalov 416e730a61 Merge pull request #29395 from asmorkalov:as/restore_ipp_check
Restore missing IPP check in IPP HAL.
2026-06-26 16:26:17 +03:00
uwezkhan 69d2303531 Merge pull request #29345 from uwezkhan:onnx-tile-bounds
bound tile axis and repeats length in onnx parseTile #29345

OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1385

parseTile sizes repeats_vec from the input-0 rank, then fills it from fields of the model that are never checked against that size. In the tile-1 path the axis taken from the third input indexes repeats_vec directly, and in the tile>1 path the loop writes one entry per element of the repeats tensor. A crafted ONNX with an out-of-range axis, or a repeats tensor longer than the input rank, writes past repeats_vec while loading the model through readNetFromONNX.

The fix runs axis through normalize_axis, the same helper the squeeze and concat paths in this file already use, so a negative or oversized axis is rejected before the write, and it checks the repeats length equals the input rank before the loop. Keeping both bounds in the parser puts the check next to the write instead of trusting the model to be well formed. Before, a repeats tensor shorter than the rank was silently accepted; after, it is rejected, which matches the ONNX rule that repeats carries one entry per input dimension.

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-26 12:39:04 +03:00
Tõnu Samuel b64a215e71 Merge pull request #29377 from tonuonu:perf-dnn-activation-simd
dnn: SIMD for transcendental activation layers (15 functors) 🧑‍💻🤖 #2937

# dnn: SIMD for transcendental activation layers (15 functors) 🧑‍💻🤖

### Summary

Many `BaseDefaultFunctor`-based activation functors fall back to the scalar `BaseDefaultFunctor::apply`
— one `libm` call per element. This PR adds vectorized `apply()` overrides (universal intrinsics,
matching the existing `MishFunctor`/`GeluFunctor`/`SigmoidFunctor` pattern, each with a scalar tail) to
the **15 transcendental activations** that have a clean SIMD path:

| functor | uses | | functor | uses |
|---|---|---|---|---|
| TanH | `v_exp` (1−2/(e²ˣ+1)) | | Asinh | `v_log`,`v_sqrt` (sign·log(\|x\|+√(x²+1))) |
| Log | `v_log` | | Acosh | `v_log`,`v_sqrt` |
| Erf | `v_erf` | | Atanh | `v_log` |
| Exp | `v_exp` | | Softplus | `v_exp`,`v_log` |
| Sin | `v_sin` | | BNLL | `v_exp`,`v_log` (max(x,0)+log1p(e^−\|x\|)) |
| Cos | `v_cos` | | GeluApproximation | `v_exp` (tanh) |
| Tan | `v_sin`/`v_cos` | | Sinh / Cosh | `v_exp` |

Functors compile at the baseline SIMD width (SSE on x86, NEON on ARM — same as the existing SIMD
functors). `Sqrt`/`Floor`/`Ceil`/`Round`/`Abs`/etc. are intentionally **not** included — those are
single cheap ops the compiler already auto-vectorizes (measured `Sqrt` at 0.95×). `Asin`/`Acos`/`Atan`
are omitted (no `v_atan`/`v_asin`/`v_acos` intrinsic).

### Performance

Real `opencv_perf_dnn` `Layer_Activation` (added here), 8×256×128×100 = 26.2 M-elem CV_32F blob, A/B
vs the scalar fallback, median speedup:

| functor | M4 clang/NEON | A76 gcc/NEON | Threadripper gcc | Xeon W-2235 gcc | functor | M4 | A76 | TR | Xeon |
|---|---|---|---|---|---|---|---|---|---|
| TanH | 9.1× | 2.3× | 7.3× | 7.3× | Sinh | 3.3× | 3.2× | 5.2× | 5.0× |
| Cos | 8.8× | 3.0× | 3.3× | 3.0× | Acosh | 3.2× | 2.9× | 2.7× | 2.6× |
| Log | 7.8× | 3.0× | 2.5× | 2.4× | Cosh | 2.8× | 3.2× | 2.6× | 2.5× |
| Sin | 7.8× | 3.0× | 3.4× | 2.9× | Exp | 2.4× | 2.5× | 1.4× | 1.5× |
| GeluApprox | 7.6× | 2.4× | 6.6× | 5.8× | BNLL | 4.2× | 2.0× | 1.6× | 1.4× |
| Atanh | 7.0× | 3.6× | 6.0× | 5.2× | Softplus | 2.0× | 2.0× | 3.3× | 2.7× |
| Tan | 6.8× | 3.6× | 6.6× | 6.0× | Erf | 4.2× | 2.4× | 3.9× | 3.5× |
| Asinh | 3.9× | 2.4× | 5.8× | 5.7× | | | | | |

Every functor is a win on every tested out-of-order core (worst case 1.4×). On the **in-order
Cortex-A55** (a55-tuned build) all 15 measure 0.985–0.99× — neutral, within noise: the SIMD
polynomials are dependency-chain-bound, which an in-order pipeline can neither accelerate nor (here)
slow down. So: meaningful wins on out-of-order ARM + x86, no regression on in-order ARM.

### Accuracy

Uses the `v_exp`/`v_log`/`v_erf`/`v_sin`/`v_cos` polynomials OpenCV already ships and relies on (Gelu
via `v_erf`, Mish/Sigmoid via `v_exp`). Verified vs scalar `libm` on 320 K random elements per functor
with domain-correct inputs (Acosh x≥1, Atanh |x|<1, …): **max abs error ≤ 3e-5** (most ≤ 2e-6), zero
elements exceeding rel>1e-3 & abs>1e-4. ONNX conformance and the layer accuracy tests exercise these ops.

### Pull Request Readiness Checklist

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch (4.x)
- [ ] There is a reference to the original bug report and related work — N/A (perf improvement)
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable — perf test added (`Layer_Activation`, `SANITY_CHECK_NOTHING`); accuracy covered by existing ONNX conformance/layer tests
- [ ] The feature is well documented and sample code can be built with the project CMake — N/A (internal optimization, no new API)
2026-06-26 11:06:53 +03:00
정상원 61bd5e7b55 Merge pull request #29387 from CodeHotel:fix-js-ximgproc-edge-drawing
Fix js ximgproc edge drawing #29387

## Summary

This PR fixes OpenCV.js binding generation for factory functions that return `cv::Ptr<T>` where `T` is a namespaced class exported with a module-prefixed JavaScript binding name.

The failure is reproduced with OpenCV.js plus `opencv_contrib/modules/ximgproc`. The generated binding for `cv::ximgproc::EdgeDrawing` currently contains:

```cpp
.constructor(select_overload<Ptr<EdgeDrawing>()>(&cv::ximgproc::createEdgeDrawing))
```

but `EdgeDrawing` is not available in the generated C++ scope as an unqualified type. The generated constructor should use:

```cpp
.constructor(select_overload<Ptr<cv::ximgproc::EdgeDrawing>()>(&cv::ximgproc::createEdgeDrawing))
```

## Related issues

Fixes opencv/opencv_contrib#4161.

Related to #27963, #28130, and #28143.

#28143 added namespace qualification for factory `Ptr<...>` return types when the inner `Ptr` type matches the generator class key. The remaining `ximgproc::EdgeDrawing` case is different:

```text
inner Ptr type:      EdgeDrawing
generator class key: ximgproc_EdgeDrawing
C++ class name:      cv::ximgproc::EdgeDrawing
```

Because `EdgeDrawing` does not match `ximgproc_EdgeDrawing`, the previous condition does not handle this case.

## Fix approach

The JS generator now centralizes factory `Ptr<...>` return-type qualification in a helper used by both generator paths:

* `gen_function_binding_with_wrapper`
* `gen_function_binding`

The helper keeps the existing behavior for already qualified types and for the existing class-key match. It additionally handles the case where the inner `Ptr` type matches the basename of the C++ class name:

```text
cv::ximgproc::EdgeDrawing -> EdgeDrawing
```

This allows the generator to emit `Ptr<cv::ximgproc::EdgeDrawing>` for `createEdgeDrawing()` without changing generated files directly.

## Necessity as an opencv code change

The failing API is exposed by `opencv_contrib/modules/ximgproc`, but the invalid C++ line is emitted by OpenCV core's JavaScript binding generator in `modules/js/generator/embindgen.py`.

A contrib-only workaround would have to change the `ximgproc` public declaration or special-case the JS export list for `createEdgeDrawing`. That would only work around one symbol and would not fix the generator's handling of namespaced factory `Ptr` return types. The generator already has the class metadata needed to produce the correct fully qualified C++ type, so the fix belongs in OpenCV core.

## Verification

Tested with:

* OpenCV core `4.x`
* opencv_contrib `4.x`
* Emscripten `6.0.1`
* CMake generator: Ninja
* Build list: `core,imgproc,imgcodecs,video,calib3d,ximgproc,js`

Generator target:

```bash
emcmake cmake \
  -S /path/to/opencv \
  -B /path/to/build \
  -G Ninja \
  -DCMAKE_BUILD_TYPE=Release \
  -DCMAKE_CXX_STANDARD=17 \
  -DOPENCV_EXTRA_MODULES_PATH=/path/to/opencv_contrib/modules \
  -DBUILD_LIST=core,imgproc,imgcodecs,video,calib3d,ximgproc,js \
  -DBUILD_SHARED_LIBS=OFF \
  -DBUILD_opencv_js=ON \
  -DBUILD_TESTS=OFF \
  -DBUILD_PERF_TESTS=OFF \
  -DBUILD_EXAMPLES=OFF \
  -DBUILD_DOCS=OFF

ninja -C /path/to/build gen_opencv_js_source
```

This generated the expected binding:

```cpp
.constructor(select_overload<Ptr<cv::ximgproc::EdgeDrawing>()>(&cv::ximgproc::createEdgeDrawing))
.smart_ptr<Ptr<cv::ximgproc::EdgeDrawing>>("Ptr<ximgproc_EdgeDrawing>")
```

The same patch was also verified with the full OpenCV.js target:

```bash
ninja -C /path/to/build opencv.js
```
2026-06-26 10:57:37 +03:00
Alexander Smorkalov fe849ec08d Restore missing IPP check in IPP HAL. 2026-06-26 10:45:46 +03:00
Prasadayus b864ee7335 Extract IPP integration as HAL function 2026-06-26 11:37:44 +05:30
Madan mohan Manokar d4005e3cb1 core: enable AVX-512 dispatch for sum
- Add AVX512_SKX/AVX512_ICL to sum dispatch
2026-06-26 02:21:02 +00:00
Ijtihed Kilani fbd4dc6ca0 flann: use per-thread storage for heap pool to remove lock contention 2026-06-25 21:18:59 +03:00
Alexander Smorkalov c0c9e5e76c Merge pull request #29380 from tonuonu:perf-knn-findnearest-simd
ml: SIMD for KNN brute-force findNearest 🧑‍💻🤖
2026-06-25 17:18:29 +03:00
uwezkhan 8b06f28e1b Merge pull request #29370 from uwezkhan:onnx-tensor-payload-size-5.x
Validate onnx tensor payload size in getMatFromTensor (5.x) #29370

5.x port of #29314. The DNN ONNX importer diverged here, so the change is ported manually.

`getMatFromTensor()` sizes the output blob from `TensorProto.dims` and then reads that many elements out of the tensor payload, but the payload is sized independently in the model and never checked against the shape. In 5.x the payload reaches the read through three sources: a typed `*_data` field, `raw_data`, or external-file data routed through `getTensorRAWData()`. An initializer whose dims claim more elements than the payload holds makes the `Mat` copy/convert read past the buffer.

### Before
A FLOAT tensor declaring `[1000000]` backed by a 4-byte `raw_data` reads about 4 MB out of bounds (ASan flags a heap over-read in `cv::Mat::copyTo`). The same mismatch is present in every datatype branch and for both the typed-field and `rawdata` sources, reachable from `readNetFromONNX`/`readNetFromONNXBuffer` for every initializer.

### After
Compute the element count the shape implies once, then check each payload source holds at least that many elements before the read. `getTensorRAWData()` now reports the byte count it returns (covering both `raw_data` and external-file data), and `getMatFromTensor()` validates the typed fields and the raw byte count per datatype. A short payload throws a clear `cv::Exception` instead of over-reading.

### Tradeoffs
The check lives in `getMatFromTensor` because that is the one place the shape and the payload meet, so every initializer and attribute tensor is covered without each caller repeating it. The added cost is one multiply-accumulate over the dims plus a comparison per tensor at load time; well-formed models, where the payload already matches the shape, are unaffected. The element-count accumulation saturates on overflow so an oversized shape can't wrap to a small total and slip past the check.

### Pull Request Readiness Checklist

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-25 16:05:22 +03:00
Madan mohan Manokar 8dcaac1ac4 Merge pull request #29379 from amd:fix_warning_minmaxloc
fix MSVC warning for minMaxIdx_simd #29379

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-25 15:51:33 +03:00
Savya Sanchi Sharma 5d121b768f Merge pull request #29386 from SavyaSanchi-Sharma:test_debug
fixed Dynamic quantized linear layer error #29386

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
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2026-06-25 15:44:02 +03:00
Madan mohan Manokar d6a187480f core: speed up countNonZero with AVX-512 signmask path
Use signmask+popcount only in AVX-512 dispatch units; keep the legacy
batched SIMD kernels for AVX2, NEON, and LASX to avoid regressions on
narrow SIMD widths.
2026-06-25 08:43:27 +00:00
Madan mohan Manokar 74a25df87e Merge pull request #28706 from amd:perf_gemm
Perf test for gemm #28706

OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1336

- Added perf test to verify gemm performance.
- small sizes, square & rectangular matrix shapes are added.
- special case of n=1 and m=1 are added.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-25 09:46:33 +03:00
Alexander Smorkalov 1aea68260a Merge pull request #28474 from WalkingDevFlag:fix/cuda-version-patch-26965
cmake: relax CUDA version check to major.minor in OpenCVConfig (#26965)
2026-06-24 14:04:12 +03:00
Alexander Smorkalov ae3542afa4 Merge pull request #29383 from asmorkalov:as/drop_timvx_5.x
Drop timvx builds in CI for 5.x as non-relevant any more.
2026-06-24 13:43:42 +03:00
Alexander Smorkalov bb645944e2 Merge pull request #29382 from asmorkalov:as/riscv-rvv-warn-fix
Fixed build warning in RISC-V RVV configuration.
2026-06-24 13:42:49 +03:00
Alexander Smorkalov 01efb9f92b Drop timvx builds in CI for 5.x as non-relevant any more. 2026-06-24 12:09:50 +03:00
Alexander Smorkalov c833519366 Fixed build warning in RISC-V RVV configuration. 2026-06-24 12:03:02 +03:00
WalkingDevFlag 13eba3d842 cmake: relax CUDA version check to major.minor in OpenCVConfig (#26965)
The first-class-language OpenCV CMake config (OpenCVConfig-CUDALanguage.cmake.in)
pinned the consumer's CUDA Toolkit to the exact patch version OpenCV was built
with (e.g. 12.3.107), via find_package(CUDAToolkit ... EXACT) and a full
VERSION_EQUAL check. Patch versions change frequently while the CUDA runtime API
is stable within a minor release, so this forced needless rebuilds and could
FATAL_ERROR even for a matching toolkit.

Per the core team's direction on #26966, the change lives in the installed
config template rather than the build scripts, and is controlled by a
consumer-set variable:

- Default: match major.minor only (fixes the reported patch-pinning bug).
- OPENCV_STRONG_CUDA_VERSION_CHECK: restore the exact full-version match.

The build-time detection (OpenCVDetectCUDALanguage.cmake) is left untouched, so
OpenCV_CUDA_VERSION still records the full version for diagnostics.

Fixes #26965
2026-06-24 14:24:28 +05:30
Tonu Samuel 84d900cfda ml: SIMD for KNN brute-force findNearest distance
Vectorize the per-sample squared-Euclidean distance in
BruteForceImpl::findNearestCore with universal intrinsics (two
independent v_fma accumulators + scalar tail). The scalar reduction
accumulates in float in strict order, which the compiler cannot
auto-vectorize without -ffast-math; independent SIMD accumulators
break that serial-accumulation dependency.

Accumulates in float exactly as before (only summation order changes,
~1e-7, below stored float precision); selected neighbors and distances
matched scalar on all test data, ML_KNearest tests pass. Real
findNearest A/B: 3.76x M4, 3.47x Threadripper, 3.73x Xeon, 4.10x A76,
2.82x A55 (in-order).

Adds modules/ml/perf with a findNearest perf test.
2026-06-24 11:31:38 +03:00
kjg0724 2e80d30df4 imgproc: migrate MomentsInTile_SIMD<ushort> to universal:scalable intrinsics (#28881)
Migrate the ushort specialization from CV_SIMD128 to (CV_SIMD ||
CV_SIMD_SCALABLE), enabling AVX2/AVX512/RVV widths instead of 128-bit only.
uchar specialization left at CV_SIMD128 — initial migration regressed on
RVV (Muse Pi v3.0, 0.77~0.85x) when vlanes16 == TILE_SIZE collapses the
SIMD loop to one iteration and per-tile setup overhead dominates.

- replace v_int32x4/v_uint32x4/v_uint64x2 with scalable v_int32/v_uint32/v_uint64
- iota vector in static const sized by VTraits::max_nlanes (initialized once at program load)
- drop buf64; use v_reduce_sum(v_uint64) directly (defined on all backends)
- vx_cleanup() at operator() tail for RVV vsetvl hygiene
2026-06-24 09:33:49 +03:00
胡晨宇 5137676ea7 Merge pull request #29172 from hcy11123323:op
Fast-path transposeND for identity and 2D transpose orders #29172

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
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<!-- Note!!! If you are an automated agent, we have a special process for you: add 🤖🤖🤖 to the end of the PR title. -->

This PR adds fast paths to cv::transposeND() for two common cases:
- identity permutation: dispatch to copyTo()
- 2D permutation {1, 0}: dispatch to transpose()
All other permutations continue to use the existing generic ND implementation.

Why:
transposeND() currently falls back to the generic memcpy/index-update loop even for the common 2D transpose case, while OpenCV already has an optimized transpose() path. Reusing that path avoids unnecessary index arithmetic and improves performance for 2D inputs passed through transposeND().

Performance:
[BinaryOpTest.transposeND/21 (1920x1080, 8UC3): 28.21 ms -> 0.66 ms (-97.7%)]
[BinaryOpTest.transposeND/22 (1920x1080, 8UC4): 37.61 ms -> 0.88 ms (-97.7%)]
[BinaryOpTest.transposeND/29 (1920x1080, 32FC1): 10.26 ms -> 0.75 ms (-92.7%)]
Full transposeND perf subset: 6760 ms -> 592 ms (-91.2%)
2026-06-24 09:05:14 +03:00
Alexander Smorkalov 15367f7c52 Merge pull request #29374 from Akansha-977:Resize_IPP_4.x
IPP extraction to HAL for resize in imgproc module
2026-06-24 08:59:47 +03:00
Alexander Smorkalov 577f395bee Merge pull request #29373 from asmorkalov:as/tune_meanStdDev_hals
Disable several HAL cases for cv_hal_meanStdDev
2026-06-24 08:58:38 +03:00
Alexander Smorkalov 639fd5d07a Merge pull request #29340 from amd:fast_minmax_simd
core: Optimize minMaxLoc
2026-06-23 17:16:54 +03:00
Akansha Mallick bc184b560e Resize_IPP_Extraction 2026-06-23 18:18:55 +05:30
Alexander Smorkalov af1e2232cd Merge pull request #29368 from tonuonu:fix-filestorage-read-bigint-29363
core: FileStorage reads integers above INT_MAX into float/double without truncation 🧑‍💻🤖
2026-06-23 15:38:59 +03:00
Alexander Smorkalov d8f609c09f Disable several HAL cases for cv_hal_meanStdDev 2026-06-23 15:09:00 +03:00
Uwez Khan 6678e48c12 check parameter tensor length in tflite parsePadding and parseResize
guard the parsePadding NHWC swap on paddings.total()==8 and require the parseResize size tensor to hold height and width, matching the existing parseStridedSlice check
2026-06-23 16:46:41 +05:30
Alexander Smorkalov 2adeab8504 Merge pull request #29314 from uwezkhan:onnx-tensor-payload-size
validate onnx tensor payload size in getMatFromTensor
2026-06-23 13:18:24 +03:00
Madan mohan Manokar 4eb3f8ecb6 Merge pull request #29339 from amd:fast_mean_simd
core: Vectorize meanStdDev #29339

- Add SIMD SumSqr_SIMD reductions (previously scalar) plus AVX-512 dispatch for the mean TU. 
- Uses universal intrinsics, so all SIMD backends benefit.

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
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2026-06-23 13:13:15 +03:00
Alexander Smorkalov 4ceb58f9f5 Merge pull request #29367 from vrabaud:persistence
Remove leftover fcvtns assembly
2026-06-23 12:38:32 +03:00
velonica0 c02b096a77 Merge pull request #29357 from velonica0:dnn_conv_rvv
dnn: vectorize fp32 convolution on RISC-V RVV #29357

### Summary

Vectorizes the new DNN engine's fp32 convolution on RISC-V **RVV**. Convolution was the last major block-layout operator still running scalar on RVV; this brings it to parity with the already-vectorized depthwise/pool/batchnorm path.

Single file, **purely additive** (`#elif CV_SIMD_SCALABLE` branches only) — x86/AVX2, ARM/NEON and AArch64 codegen are unchanged.

### Background

- **#28585** introduced block-layout conv with fp32 kernels specialized for AVX2 (`C0=8`) and NEON, scalar `#else` for everything else, and explicitly noted *"we could add the respective kernels later."*
- On RVV that `#else` meant conv ran **scalar at every VLEN** — the C0=8 fixed width approach doesn't transfer to a variable-VLEN ISA.
- **#29304** made the block size track the hardware width (`C0 = vlanes()`), fixing the VLEN≥256 assertion but leaving conv scalar.
- **This PR** supplies the deferred RVV conv vectorization, on top of that `C0=vlanes()` foundation.

### Approach

- Re-enable the `SPAT_BLOCK_SIZE=10` **blocked path** on RVV. Because `K0 == vlanes()`, one `v_float32` accumulator covers a full output-channel block, so 10 accumulators process 10 output positions together. A single K0-wide weight `vx_load` is reused across all 10 — amortizing the per-output-point load that bottlenecks the scalar path (this is what enables multi-core scaling; the scalar/per-FMA-load path is bandwidth-bound).
- Vectorize the **scalar tail** (the `<10`-position remainder) the same way.
- **Vector-length-agnostic:** `C0` is runtime, the kernel is written in terms of `v_float32`/`vlanes()`, so the same binary runs at full width on VLEN 128/256/512/1024 with no recompile. No per-VLEN kernels.
- The six `C0=8` specialized kernels stay `#if !CV_SIMD_SCALABLE`-gated (they can't run at `C0=vlanes()`); on RVV all shapes use the generic kernel.

### Performance

Convolution throughput, blocked (this PR) vs scalar, same machine/engine (VLEN=256, 8× rv64 @ 2.4 GHz), GFLOP/s:

| layer | scalar 1T | this 1T | scalar 8T | this 8T | 8T speedup |
|---|---|---|---|---|---|
| 3×3, 256→256, 64² | 1.13 | 13.23 | 8.28 | 75.62 | **9.1×** |
| 1×1, 256→256, 64² | 1.08 | 11.73 | 8.02 | 71.70 | 8.9× |
| 3×3, 128→128, 128² | 1.10 | 13.02 | 8.45 | 89.14 | **10.6×** |

≈ **12× single-thread, 9–11× at 8 threads**. Convolution dominates CNN inference time, so this is a large end-to-end win on RVV hardware.

### Validation

K3-class RVV board, VLEN=256: `Test_ONNX_layers` **264/264**, `Test_ONNX_conformance` **1641/1641**, **0 assertions**. Output is bit-identical to the scalar path (same accumulation order, just vectorized over `K0`).
Vector-length-agnosticism confirmed by running the VLEN=256 binary unchanged at **VLEN=1024**.


### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-23 12:37:27 +03:00
Tonu Samuel dceead809e core: FileStorage reads integers above INT_MAX into float/double without truncation
FileNode::operator double() and operator float() read an INT node via readInt()
(32-bit), truncating values above INT_MAX -- e.g. an integer 6662329666 from an
externally-produced json/yaml/xml is read back as -1927604926. The node stores
the value as int64 (operator int64_t() already reads it correctly via readLong),
so use readLong() for the floating-point conversions too. Values that fit in
int32 are unchanged (sign-extended); larger ones are now correct.

Reader side of #29363 (the writer side was #29364).
2026-06-23 11:15:11 +03:00
Alexander Smorkalov bb3c1558e0 Merge pull request #29366 from Gold856:clean-up-cmake-version-checks
Clean up CMake version checks
2026-06-23 10:41:26 +03:00
Vincent Rabaud b8e82fa2c2 Remove leftover fcvtns assembly
Nothing in the history justifies this. It can trigger some memory
sanitizer that do not support raw assembly.
2026-06-23 09:20:19 +02:00
Pratham Kumar df0c9d7950 Merge pull request #29316 from pratham-mcw:dnn-arm64-neon-blobfromimages
dnn: optimize blobFromImages using NEON intrinsics on ARM64 #29316

- This PR optimizes the HWC-to-NCHW conversion in blobFromImages() by adding NEON implementation.
- On x64, the MSVC compiler automatically vectorizes the existing scalar implementation, resulting in significantly better performance.
- On Windows ARM64, MSVC does not auto-vectorize this stride-3 access pattern, leading to a noticeable performance gap compared to x64.
- Added CV_NEON macro guard to ensure SIMD optimization is enabled on ARM64 architecture, leaving behavior on other platforms completely unchanged.

**Performance Improvements**

- This optimization significantly improves the performance of HWC-to-NCHW conversion on Windows ARM64.

<img width="823" height="254" alt="image" src="https://github.com/user-attachments/assets/84b22deb-22da-4607-8009-3405c2529b80" />

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [X] The PR is proposed to the proper branch
2026-06-23 09:25:30 +03:00
Abhishek Gola 81621ced10 Merge pull request #29323 from abhishek-gola:split_layer_extension
Extended support for Resize and Split layers #29323

Merge with: https://github.com/opencv/opencv_extra/pull/1379

Key changes: 

Resize layer: 
_antialias_ (linear & cubic) :- PIL-style separable resampling with stretched filter support and edge-clamped, renormalized weights.
_axes_ (incl. reversed [3,2]) :- getOutShape, the scale override, and runtime _tf_crop_and_resize_ ROI parsing now map 2-element sizes/scales/roi by the axes order instead of assuming [2,3].
_keep_aspect_ratio_policy_ (not_larger/not_smaller) :- output size from min/max per-axis scale.
_half_pixel_symmetric_ :- new coordinate-transform mode + importer mapping.
_align_corners_ downsampling :- coordinate scale uses the unfloored scaled length (in−1)/(in·x_scale−1).

Split Layer:
_convertTo empty 1-D Mat:_ the empty-Mat branch collapsed a 1-D [0] to 2-D [1,0] via cv::Size(); now uses allowTransposed like the non-empty path.
### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
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      Patch to opencv_extra has the same branch name.
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2026-06-23 09:23:19 +03:00
Alexander Smorkalov 5049343874 Merge pull request #29362 from varun-jaiswal17:fix/mlas-hgemm-static-link
Define MlasGemmSupported under MLAS_GEMM_ONLY
2026-06-23 09:18:52 +03:00
Gold856 6fd1b687a1 Clean up CMake version checks 2026-06-22 20:38:16 -04:00
vrooomy 48a063158a define MlasGemmSupported under MLAS_GEMM_ONLY 2026-06-22 18:35:06 +05:30
Alexander Smorkalov 47eefa5abf Merge pull request #29356 from JArmandoAnaya:fix-ocl-predictvectorwidth-newtypes
core(ocl): fix out-of-bounds read and SIGFPE in predictOptimalVectorWidth for new depths
2026-06-22 13:29:43 +03:00
Alexander Smorkalov d939383524 Merge pull request #29352 from ShivaPriyanShanmuga:fix-mlas-mingw-posix-memalign
3rdparty(mlas): fix MinGW build by guarding aligned scratch buffer on _WIN32
2026-06-22 13:28:22 +03:00
Alexander Smorkalov c3aecdd531 Merge pull request #29328 from intel-staging:reenable_ipp_deriv
Enable ipp calls for Sobel, Scharr
2026-06-22 13:18:02 +03:00
Alexander Smorkalov 10f4847bf6 Merge pull request #29321 from uwezkhan:tflite-kernel-scale-count
check tflite kernel scale count against output channels
2026-06-22 11:29:08 +03:00
Alexander Smorkalov 5814ff8f9c Merge pull request #29348 from uwezkhan:filestorage-fmt-pairs-buffer
fix fmt_pairs stack overflow in calcElemSize and decodeSimpleFormat
2026-06-22 09:43:13 +03:00
Jesus Armando Anaya 7334957476 core(ocl): fix out-of-bounds read and SIGFPE in predictOptimalVectorWidth for new depths
predictOptimalVectorWidth() built its vectorWidths table with 8 entries
(depths CV_8U..CV_16F), but checkOptimalVectorWidth() indexes it by depth.
The 5.x depths CV_16BF, CV_Bool, CV_64U, CV_64S and CV_32U therefore read
past the end of the array; the garbage value can slip past the ckercn <= 0
guard, and the divider normalization loop then shifts the divider to zero,
so "offsets[i] % dividers[i]" raises SIGFPE. The failure is allocation
dependent and shows up as a sequence-dependent crash, e.g. in the OpenCL
Norm tests for CV_32U (cv::norm on a UMat reaches this via ocl_sum, which
calls predictOptimalVectorWidth before its own depth guard bails out).

Size the table to CV_DEPTH_MAX so every depth is in bounds and map the new
fixed-size integer depths to their natural OpenCL vector widths; CV_16BF
has no OpenCL vector type and stays scalar. Add a regression test covering
all depths.
2026-06-21 16:39:40 -07:00
ShivaPriyanShanmuga f831c94309 3rdparty(mlas): fix MinGW build of aligned scratch buffer
MlasThreadedBufAlloc() and its ThreadedBufHolder guarded the
_aligned_malloc / _aligned_free path on _MSC_VER. Non-MSVC Windows
toolchains (MinGW, clang) therefore fell through to posix_memalign(),
which the Windows CRT does not provide, so the build failed with
"'posix_memalign' was not declared in this scope".

Guard the aligned-allocation path on _WIN32 instead, in both the holder
declaration (mlasi.h) and definition (platform.cpp) so the unique_ptr
deleter type stays consistent across translation units, and include
<malloc.h> on Windows since MinGW only declares the _aligned_* functions
there. The non-Windows posix_memalign / aligned_alloc fallback is
unchanged.

Fixes #29350
2026-06-21 11:54:38 -04:00
uwezkhan fc746f35b9 fix fmt_pairs stack overflow in calcElemSize and decodeSimpleFormat 2026-06-20 15:31:34 +05:30
Alexander Smorkalov 6d2cd14bb2 Merge pull request #29343 from abhishek-gola:disable_windows_mlas_assembly
Disable MLAS on windows
2026-06-19 18:55:43 +03:00
Alexander Smorkalov 4f17d30997 Merge pull request #29192 from hcy11123323:op4
parallelize sort_ using parallel_for_
2026-06-19 14:10:38 +03:00
Abhishek Gola bc11496dcb disable mlas assembly on windows 2026-06-19 15:32:54 +05:30
Alexander Smorkalov 60122e655c Merge pull request #29327 from uwezkhan:tf-slice-begin-size-length
check begin and size have equal length in tensorflow Slice import
2026-06-18 19:39:33 +03:00
Madan mohan Manokar 550251b3c2 core: Optimize minMaxIdx/minMaxLoc
- Replace the fixed-128-bit per-depth minMaxIdx kernels with a single templated universal-intrinsic core
- added simd dispatch AVX-512 dispatch
- A dual-accumulator inner loop added

Uses universal intrinsics, so all SIMD backends benefit.
2026-06-18 16:33:50 +00:00
arshsmith de90d1aec4 Merge pull request #29322 from arshsmith:fix-exif-raw-profile-alloc
Follow-up to the recent "reject under-length raw exif profile" change.

The declared length in a PNG raw exif profile (tEXt/zTXt "Raw profile type
exif" chunk) is fully attacker controlled. The < 6 guard stops the underflow,
but a large positive value still passes it and reaches HexStringToBytes(),
which does raw_data.resize(expected_length) before reading anything. So a few
bytes in a text chunk can force a ~2GB allocation -> easy memory-amplification
DoS while decoding an otherwise small PNG.

The payload is hex encoded (two characters per byte), so a valid length can
never exceed the profile text carrying it. Reject any declared length greater
than profile_len. This bounds the allocation to the input size and doesn't
reject any valid profile; the valid decode path is unchanged.
2026-06-18 19:26:14 +03:00
Madan mohan Manokar 70a2c50b43 Merge pull request #29250 from amd:fast_lut8u_simd
imgproc: optimized LUT and equalizeHist with SIMD #29250

- optimized lut with SIMD
- support equalizeHist with v_lut

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-18 17:29:18 +03:00
Madan mohan Manokar d0925fa360 Merge pull request #28992 from amd:fast_convert
Improved color convert and AVX512 dispatch added #28992

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-18 15:20:11 +03:00
Alexander Smorkalov 72d21d7241 Merge pull request #29031 from asmorkalov:as/musl_c_tuning
Musl-C related fixes 5.x
2026-06-18 12:01:02 +03:00
Alexander Smorkalov 20d3018169 Musl-C related fixes and C for 5.x 2026-06-18 11:11:58 +03:00
Alexander Smorkalov bc18ec2ed3 Merge pull request #29032 from asmorkalov:as/musl_c_tuning_4.x
Musl-C CI and related fixes for 4.x.
2026-06-18 11:06:19 +03:00
Jonas Perolini 853ee9b961 Merge pull request #29329 from JonasPerolini:get-border-errors-once
Optimization ArUco: get border errors in one pass for both normal and inverted markers #29329

This PR is a follow up to optimization in https://github.com/opencv/opencv/pull/29267.

## Context:

When detecting inverted markers, we call `_getBorderErrors` twice:
- `int borderErrors = _getBorderErrors(cellPixelRatio, ...);`
- `Mat invCellPixelRatio = 1.f - cellPixelRatio;`
- `int invBError = _getBorderErrors(invCellPixelRatio, ...);`

meaning:

1. Scan border cells once.
2. Allocate a new Mat.
3. Invert the entire `cellPixelRatio` matrix, not just the border.
4. Scan border cells again on the inverted matrix.
5. If inverted marker is better, copy the full inverted matrix back

## Solution

only call `_getBorderErrors` once and compute both 
- borderErrors: `cellPixelRatio > validBitIdThreshold`
- invBorderErrors: `1 - cellPixelRatio > validBitIdThreshold`

This saves: 
1. full invert into temporary Mat: (`Mat invCellPixelRatio = 1.f - cellPixelRatio;`)
2. scan temporary border: the second `_getBorderErrors`
3. copy temporary Mat back into `cellPixelRatio`: `invCellPixelRatio.copyTo(cellPixelRatio);`

Note that the solution does not implement: 

```
 if(params.detectInvertedMarker) {
    _getBorderErrorsBoth(...);
} else {
    borderErrors = _getBorderErrors(...);
}
```

because the extra cost to computing invBorderErrors: `if(1.f - ratio > validBitIdThreshold) invBorderErrors++;` is negligible. this also avoids having to maintain two functions: `_getBorderErrorsBoth` and `_getBorderErrors`

## Tests:

No visible results when testing the full marker detection pipeline because this step is not expensive. There is a roughly 8% noise when re-running the marker detections making it hard to spot small speed diffs. 

When testing only this specific step with a float matrix of size: `(markerSize + 2*border)^2` on a AMD Ryzen 7 (8 cores / 16 threads):
 
-  The new code is 30-45× faster at this stage depending on the marker size and whether the candidate was actually an inverted one, saving ~530-580 ns per candidate, mainly because we removed `Mat invCellPixelRatio = 1.f - cellPixelRatio`
- The new code is equivalent when detecting non-inverted markers +/- 3 ns

---

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-18 09:40:20 +03:00
Alexander Smorkalov c796023029 Musl-C CI and related fixes for 4.x. 2026-06-18 09:14:36 +03:00
Pratham Kumar 837f715eec Merge pull request #29283 from pratham-mcw:exp-neon-opt
Restricting the condition with _M_IX86/_M_X64 so it only applies to x86/x64 MSVC builds. MSVC ARM64 now falls through to the existing `#else` branch, which already has a portable CV_SIMD-based exp32f/exp64f implementation

**Performance Benchmarks:**
<img width="976" height="486" alt="image" src="https://github.com/user-attachments/assets/62daf2c3-34ac-4fc7-92d0-268073f746f3" />

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
2026-06-17 11:50:58 +03:00
uwezkhan 4e56cf9ac8 check begin and size lengths match in tensorflow parseSlice 2026-06-17 14:05:39 +05:30
uwezkhan 414a0379d8 check tflite kernel scale count against output channels 2026-06-16 22:19:58 +05:30
Varun Jaiswal 555f0901a3 Merge pull request #29309 from varun-jaiswal17:fix_dll_mismatch
Fix ONNXRuntime dll path mismatch #29309

**1. C2664 build error in `net_impl_backend.cpp`**
`EnableProfiling()` expects `const wchar_t*` on Windows (`ORTCHAR_T`), but was passed `const char*`. 

Fixed by converting to `std::wstring`, as suggested in #29278 

---

**2. Wrong ORT DLL loaded at runtime (`modules/dnn/CMakeLists.txt`)**
With `DOWNLOAD_ONNXRUNTIME=ON`, the DLL glob only searched `bin/` but the downloaded package places DLLs in `lib/`. This left the build tree with no ORT DLL, causing Windows to fall back to the stale `System32\onnxruntime.dll` (1.17.1), crashing against the ORT 1.25.1 API. 

Fixed by adding `lib/` as fallback and staging DLLs into the build bin directory at configure time.

Closes : #29278 

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-16 18:54:18 +03:00
Pierre Chatelier 6eb0dc97f5 Merge pull request #28907 from chacha21:more_autobuffer
More use of AutoBuffer #28907

When possible, AutoBuffer should be faster than std::vector<>, and should not be worse if it requires a heap allocation rather than a stack allocation.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

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- [X] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
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      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-16 18:51:37 +03:00
velonica0 3f45dab070 Merge pull request #29304 from velonica0:dnn-rvv-block
dnn: fix new-engine block layout for RVV when VLEN > 128 #29304

### Pull Request Readiness Checklist

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<!-- Note!!! If you are an automated agent, we have a special process for you: add 🤖🤖🤖 to the end of the PR title. -->

#### Problem
Fixes #28852. The new DNN engine packs activations in a blocked NCHWc layout with a fixed channel-block size `C0 = 8`. The blocked-layout kernels (MaxPool, AveragePool, BatchNorm, depthwise Conv, ConvTranspose, generic Conv) load one block into a SIMD vector and assert: `C0 == nlanes || C0 == nlanes*2 || C0 % (nlanes*4) == 0`

On RISC-V the universal intrinsics are **scalable with LMUL=2**, so `nlanes = (VLEN/32)*2` — **16 at VLEN=256, 64 at VLEN=1024** — which exceeds the fixed `C0=8`. The assertion fails with `-215` and the ONNX pooling/conv conformance tests abort. #29180 worked around it by disabling the RVV SIMD path in those kernels.

#### Fix
Make the block size track the hardware vector width on RVV instead of assuming 8:

- `net_impl.cpp`: `defaultC0 = max(8, VTraits<v_float32>::vlanes())` (16/64 on RVV).
- The fp32 blocked kernels read `C0` at runtime and size scratch buffers by `max_nlanes` (bounded by the compile-time `CV_RVV_MAX_VLEN`, default 1024).
- All changes are guarded by `CV_SIMD_SCALABLE`, so **x86/NEON are unchanged**.
- The int8 quantized kernels are hardwired to `C0=8` (VNNI/NEON packing + per-channel quant) and are scalar on RVV, so quantized graphs are pinned to `C0=8` in `prepareForInference()` (detected via a `CV_8S` arg); only fp32 graphs use the wider block.

#### Validation (native RISC-V)
I have already conducted tests on the Spacemit K3, covering both the X100 (VLEN=256) and the A100 (VLEN=1024).

`opencv_test_dnn`, ENGINE_AUTO (new engine), no assertions:

| VLEN | Test_ONNX_conformance | Test_ONNX_layers (conv/pool/depthwise/quantized/MobileNet_v4) |
|------|-----------------------|--------------------------------------------------------------|
| 256  | 1641 / 1641           | all pass (incl. `Quantized_Convolution`)                     |
| 1024 | 1641 / 1641           | all pass (incl. `Quantized_Convolution`)                     |
2026-06-16 14:13:26 +03:00
Alexander Smorkalov 3def56d25a Merge pull request #29096 from Rishiii57:fix/matchtemplate-sqdiff-precision-21786
imgproc: fix matchTemplate TM_SQDIFF precision loss for CV_8U images
2026-06-16 13:52:16 +03:00
uwezkhan 57081ac946 validate onnx tensor payload size in getMatFromTensor
Reject initializers whose declared shape claims more elements than the tensor payload (raw_data or a typed *_data field) actually holds, before the Mat copy/convert reads them.
2026-06-16 15:55:31 +05:30
Alexander Smorkalov 62bcd12f9c Merge pull request #29305 from uwezkhan:darknet-cfg-index-bound
bound darknet cfg layer and anchor indices before vector reads
2026-06-16 12:24:35 +03:00
熊阔豪 6a2d8d24da Merge pull request #29145 from MrBear999-ui:fix-remap-nearest-rounding
Fix remap nearest rounding #29145

Thanks, I checked the Windows x64 test logs. The imgproc failures are from the OpenCL `Remap_INTER_NEAREST` tests with `(16SC2, 16UC1)` maps. I missed the OpenCL fixed-point path in the first patch.

I updated `modules/imgproc/src/opencl/remap.cl` to use the same nearest rounding condition as the CPU path. This should address the OCL remap failures.

The `opencv_test_video` failure seems unrelated to this PR (`ECCfixtures/Video_ECC.accuracy/6` invalid memory access), since this PR only touches remap rounding in imgproc.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-16 12:22:44 +03:00
Alexander Smorkalov d878b04c6b Merge pull request #29307 from StephanTLavavej:angle-brackets
Consistently include `<math.h>` with angle brackets
2026-06-16 10:07:48 +03:00
Vadim Pisarevsky a3a4d4adac Merge pull request #29300 from vpisarev:add_harfbuzz
Added harfbuzz; use it instead of STB to render text #29300

Merge with https://github.com/opencv/opencv_extra/pull/1378.

This is the next big step to improve font rendering in OpenCV 5.x. See #18760, #26301. See also OpenCV 5.0 release notes, where it was pointed out that complex scripts are not rendered correctly, and HarfBuzz integration is needed to fix it. So, here it is — HarfBuzz integration:

* Removed tweaked STB engine. STB truetype rendering engine was included into OpenCV 5.0-pre/5.0 and it was extended to instantiate and render variable fonts, but the support was incomplete and immature.
* Instead, we now use [HarfBuzz](https://github.com/harfbuzz/harfbuzz) — famous font shaping library and now also font rendering library, used by many big companies and organizations.
* As a result, we now correctly render complex scripts, such as arabic or devanagari, correctly handle font ligatures (ff, fi, ft etc.)

<img width="1300" height="600" alt="text_test" src="https://github.com/user-attachments/assets/a7d2c754-0fcf-40f8-8104-578f3a14850b" />

* Potentially, we could also support color emoji, but that would be a next step.
* As with STB-based engine, glyphs are cached (the same glyph from the same font is not rendered twice), so the performance should be on par with the previous engine.
* When OpenCV is built with `-DWITH_HARFBUZZ=ON`, which is set by default, it tries to detect HarfBuzz in the system and use it. If it's not detected, OpenCV builds our own small subset of HarfBuzz.

The following table compares size of libopencv_imgproc.dylib.5.0.0 (Release mode) in different configurations:

| Configuration                          | libopencv_imgproc (stripped) | delta vs 5.0 |
|----------------------------------------|------------------------------|----------|
| imgproc without text rendering (`-DWITH_HARFBUZZ=OFF`)  | 4.27 MB  |  −2.35 MB |
| imgproc with stb-based engine (5.0)      | 6.62 MB      |  0.0 Mb |
| imgproc with HarfBuzz-based engine (this PR)     | 7.07 MB      |  +0.45 MB |

The biggest source of the size increase when OpenCV is built with text rendering support (STB- or Harfbuzz-based) is that imgproc in this case includes .ttf fonts (Rubik and 'Wen Quan Yi Micro Hei'), which are gzip-compressed, but still have noticeable size, especially 'Wen Quan Yi' (~2Mb). HarfBuzz itself, compared to STB engine, adds just 0.45Mb, or ~7% to imgproc size. On other platforms (Linux x64, Windows), where IPP or other acceleration libraries are linked into libopencv_imgproc, the harfbuzz footprint is even less noticeable.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-16 10:05:32 +03:00
Stephan T. Lavavej 4208a89b3e Consistently include <math.h> with angle brackets 2026-06-15 14:53:29 -07:00
Alexander Smorkalov 41d48d6eeb Merge pull request #29306 from asmorkalov:as/more_ipp_core
Moved IPP math functions from core module to HAL.
2026-06-16 00:15:30 +03:00
Alexander Smorkalov 0a4d6a849a Moved IPP math functions from core module to HAL. 2026-06-15 21:53:10 +03:00
Alexander Smorkalov 017a5138d1 Merge pull request #29303 from asmorkalov:as/win32_objdetect_warn_fix
Fixed new objdetect warnings on Windows.
2026-06-15 21:29:42 +03:00
uwezkhan afbfa275d8 bound darknet cfg layer and anchor indices before vector reads 2026-06-15 21:36:38 +05:30
arshsmith 6df9732c81 Merge pull request #29296 from arshsmith:fix-pam-grayalpha-overflow
imgcodecs(pam): fix out-of-bounds write when reading 2-channel PAM #29296

While poking at the PAM decoder I noticed basic_conversion() writes past the
end of the destination buffer when a GRAYSCALE_ALPHA (2 channel) image is read
with IMREAD_GRAYSCALE.

The 1-channel branch was written as if the destination had 3 channels:

    for( ; s < end; d += 3, s += src_sampe_size )
        d[0] = d[1] = d[2] = s[layout->graychan];

So for every source pixel it writes 3 bytes and advances d by 3, even though
the output row only has m_width bytes (1 channel). With m_channels == 2 that
ends up writing ~1.5 * m_width bytes per row, which runs off the row and, on
the last row, off the end of the Mat. m_width is taken straight from the file
header (up to 2^20), so the overflow size and contents are attacker controlled.
It's reachable with a plain imread(file, IMREAD_GRAYSCALE) on a crafted file.

While looking at it I also realised the loop bound was off for any multi
channel source: end was set to src + src_width, but the source has
src_width * channels samples, so it only ever processed m_width/channels pixels
instead of all of them. That's why GRAYSCALE_ALPHA / RGB_ALPHA come out only
partially filled.

Fix both at once:
- end now covers the whole row (src_width * src_sampe_size)
- the 1-channel branch writes one byte and advances d by 1

For the common grayscale->color case (channels == 1) the new end is identical
to the old one (m_width * 1 == m_width), so that path is byte for byte the same
and the existing PAM read_write test is unaffected. The only outputs that
change are the GRAYSCALE_ALPHA/RGB_ALPHA conversions, which were already broken.

Added a regression test (Imgcodecs_Pam.decode_graya_as_gray) that builds a small
2-channel PAM with an odd width, decodes it as grayscale and checks the result
matches the gray channel. It overflows/crashes on the old code and passes now.
2026-06-15 17:49:12 +03:00
Alexander Smorkalov 8bbe8eeb86 Merge pull request #29290 from vrabaud:mcc2
Get MCC to be deterministic
2026-06-15 17:08:29 +03:00
Alexander Smorkalov 40d6727ea2 Merge pull request #29295 from uwezkhan:exif-rawprofile-len
reject under-length raw exif profile in processRawProfile
2026-06-15 13:51:48 +03:00
Andrei Fedorov 8e157165e8 enable ipp calls 2026-06-15 12:28:12 +02:00
Maitreyee Deshmukh a18356853d Merge pull request #29293 from MaitreyeeDeshmukh:fix/opengl-sample-ptcloud-missing-include
fix: include ptcloud header and dependency for opengl_testdata_generator sample #29293

## Summary
Fixes the build failure in samples/opengl/opengl_testdata_generator.cpp on the 5.x 
branch when building with BUILD_EXAMPLES=ON.

## Root cause
The sample uses `loadMesh`, `TriangleShadingType`, and `TriangleCullingMode`, all 
declared in `modules/ptcloud/include/opencv2/ptcloud.hpp`. The sample did not 
include this header, and `samples/opengl/CMakeLists.txt` did not list 
`opencv_ptcloud` as a required dependency, so the ptcloud module headers/libs 
were not available when compiling this sample.

## Changes
- Added `#include "opencv2/ptcloud.hpp"` to opengl_testdata_generator.cpp
- Added `opencv_ptcloud` to `OPENCV_OPENGL_SAMPLES_REQUIRED_DEPS` in 
  samples/opengl/CMakeLists.txt

## Acceptance criteria
- [x] opengl_testdata_generator.cpp compiles with BUILD_EXAMPLES=ON
- [x] ptcloud module dependency declared so build system links it correctly

Closes #29292
2026-06-15 12:56:17 +03:00
Alexander Smorkalov cfb3a8bbb7 Fixed new objdetect warnings on Windows. 2026-06-15 12:32:11 +03:00
Ayush Das 194db6f5cb Merge pull request #29299 from AyushDas4890:fix/doc-typos-tutorials
Fix typos in tutorial documentation #29299

This PR fixes spelling typos across several tutorial documentation files (dnn, calib3d, objdetect, app, js_tutorials), including `export=dowload` -> `export=download` in the DNN text-spotting tutorial's Google Drive URLs.

Documentation-only change; no functional code is affected.

Pull Request Readiness Checklist:

[x] I agree to contribute to the project under Apache 2 License.

[x] To the best of my knowledge, the proposed patch is not based on code under GPL or another license that is incompatible with OpenCV.

[x] The PR is proposed to the proper branch (4.x).

[x] Documentation-only change; no accuracy/performance tests or opencv_extra patch are applicable.
2026-06-15 12:28:35 +03:00
Haris shakeel a8c0c30046 Merge pull request #29262 from Haris-bin-shakeel:fix-objc-volumetype-5x
objc: Fix VolumeType enum name clash on MacOS #29262

### Pull Request Readiness Checklist
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- [ ] The feature is well documented and sample code can be built with the project CMake

**Problem**
When the ObjC wrapper is generated, the global enum `cv::VolumeType` from the ptcloud module is emitted as `typedef NS_ENUM(int, VolumeType)`. macOS defines `typedef OSType VolumeType` in CoreServices, which leads to a typedef-redefinition error during the framework build.

**Fix**
`add_enum()` in `gen_objc.py` now handles namespace-global enums by falling back to a module-level lookup (`self.Module`) when the enum has no enclosing class. A new `gen_dict.json` entry maps `VolumeType` -> `PtcloudVolumeType`, and the import generation automatically uses the renamed enum.

Fixes #29260
2026-06-15 11:30:51 +03:00
Alexander Smorkalov 03539f67f5 Merge pull request #29289 from vrabaud:mcc1
Forward port MCC fix
2026-06-15 11:28:14 +03:00
Alexander Smorkalov 5aad95cb1d Merge pull request #29297 from Kumataro:fix_doxygen_warning_at_mat
doc(core): fix doxygen warning for missing endcode
2026-06-15 10:21:33 +03:00
Kumataro 9a966e96ad Merge pull request #29298 from Kumataro:remove_duplicated_bib
doc: remove duplicated bib #29298

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-15 10:18:59 +03:00
Alexander Smorkalov 86433eac87 Merge pull request #29301 from uwezkhan:persistence-nd-dim-bound
reject nd-matrix dim count above CV_MAX_DIM in FileStorage read
2026-06-15 10:16:16 +03:00
uwezkhan 2906d4d73a reject nd-matrix dim count above CV_MAX_DIM in FileStorage read 2026-06-14 02:51:20 +05:30
Kumataro 96a71a8d83 doc(core): fix doxygen warning for missing endcode 2026-06-13 20:45:30 +09:00
uwezkhan 19ec7ea28b reject under-length raw exif profile in processRawProfile 2026-06-13 15:04:24 +05:30
Vincent Rabaud 7b7a62528f Get MCC to be deterministic 2026-06-12 17:13:15 +02:00
Vincent Rabaud 4c8c215d59 Forward port MCC fix
Somehow, https://github.com/opencv/opencv_contrib/pull/3939 was
not forward ported.
2026-06-12 17:09:22 +02:00
Rishiii57 12eaf9bd4c imgproc: fix matchTemplate TM_SQDIFF precision loss for CV_8U images
When computing TM_SQDIFF for 8-bit images, crossCorr stores intermediate
results in CV_32F which loses precision for large patch sums (e.g. 25x25
patch of value 255 gives sum=40603125, exceeding float32's ~7 significant
digits). This causes the final SQDIFF result to have incorrect non-zero
values even when image and template are identical.

Fix: use a CV_64F intermediate buffer for crossCorr and common_matchTemplate
when depth==CV_8U and method is TM_SQDIFF or TM_SQDIFF_NORMED, then convert
back to CV_32F for the final output.

Fixes #21786
2026-06-12 00:06:26 +05:30
ZNNAXLRQ cadcb7e4e0 Merge pull request #29059 from ZNNAXLRQ:Project4_ZHUWanqi
Improving Parallelism and Memory Access Efficiency for Hough Transform Accumulator #29059

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake

# [Optimization] Improving Parallelism and Memory Access Efficiency for Hough Transform Accumulator

## Overview / Motivation

The original implementation mainly rely on single‑threaded nested loops, which do not fully utilize the computational power of modern multi‑core CPUs. There is also room for improvement in memory access patterns and branch prediction. **Without altering any core mathematical logic or ensuring result consistency**, this work improves the execution efficiency of these two processes by introducing `cv::parallel_for_` and refactoring data access patterns.

---

## Detailed Changes

* **Original Code Analysis**: The original code uses a double loop – the outer loop scans the image for edge points (`if image[...] != 0`), and the inner loop iterates over all angles, computes the radius, and writes to the global `accum` array. This pattern is hard to parallelize directly because different threads writing to the same accumulator would cause **data races**. Moreover, the sparse conditional branch inside the loop is extremely unfriendly to CPU branch prediction.
* **Optimization Strategy (Sparse Extraction → Parallel over Angles)**:
  1. **Sparse Feature Extraction**: First, pre‑scan the image in a single thread, extract the coordinates (`x`, `y`) and values of all non‑zero pixels (edge points) into a contiguous `std::vector` (pre‑allocate 10% of the image size to reduce dynamic reallocation overhead).
  2. **Switch the Parallel Dimension**: Instead of splitting the image region, change the parallelization dimension to **angle (`numangle`)**. Use `cv::parallel_for_` to assign different angle ranges to different threads.
  3. **Conflict‑free Memory Writes**: Because the `accum` array is strictly partitioned by angle `n` in memory, threads assigned to different angles write to completely non‑overlapping memory blocks in the global `accum`.

---

## Correctness Testing
Correctness tests are based on the provided `opencv_test`. For the Hough tests, no failing test cases were observed.

---

## Performance Benchmarks
> The following tests were run on Intel(R) Core(TM) i9‑14900HX @ 2.2GHz / Windows 11 (WSL: Ubuntu) with GCC 13.3.0.
Using `opencv_perf` tests that include Hough, the results are as follows:

| Test Case | 1st mean | 5th mean | Diff |
|-----------|----------|----------|------|
| `PerfHoughCircles.Basic` | 4.75 | 4.91 | +3.4% |
| `PerfHoughCircles2.ManySmallCircles` | 56.76 | 59.37 | +4.6% |
| `PerfHoughCircles4f.Basic` | 4.14 | 4.03 | -2.7% |

#### II. `OCL_HoughLines` (Standard Hough Line, OCL wrapper, actually executed on CPU)

| Parameters (size, rho, theta) | 1st mean | 5th mean | Diff |
|-------------------------------|----------|----------|------|
| (640×480, 0.1, 0.01745) | 2.57 | 3.21 | +24.9% |
| (640×480, 1, 0.1)       | 0.18 | 0.21 | +16.7% |
| (1920×1080, 0.1, 0.01745)|12.27 |15.55 | +26.7% |
| (1920×1080, 1, 0.1)     | 0.69 | 0.99 | +43.5% |
| (3840×2160, 0.1, 0.01745)|28.43 |34.78 | +22.3% |
| (3840×2160, 1, 0.1)     | 2.15 | 3.51 | +63.3% |

#### III. `OCL_HoughLinesP` (Probabilistic Hough Line)

| Image and parameters (rho, theta) | 1st mean | 5th mean | Diff |
|------------------------------------|----------|----------|------|
| pic5.png, 0.1, 0.01745 | 1.20 | 1.22 | +1.7% |
| pic5.png, 1, 0.01745   | 1.03 | 0.92 | -10.7% |
| a1.png, 0.1, 0.01745   | 8.71 | 9.32 | +7.0% |
| a1.png, 1, 0.01745     | 6.29 | 6.68 | +6.2% |

#### IV. Standard Hough Line (CPU implementation, non‑OCL) `Image_RhoStep_ThetaStep_Threshold_HoughLines`

| Parameters (image, rho, theta, thresh) | 1st mean | 5th mean | Diff |
|-----------------------------------------|----------|----------|------|
| pic5.png, 1, 0.01, 0.5        | 2.19 | 0.82 | **-62.6%** |
| pic5.png, 10, 0.01, 0.5       | 2.08 | 0.53 | **-74.5%** |
| pic5.png, 1, 0.1, 0.5         | 0.24 | 0.15 | -37.5% |
| a1.png, 1, 0.01, 0.5          | 16.06 | 2.65 | **-83.5%** |
| a1.png, 10, 0.01, 0.5         | 17.05 | 1.92 | **-88.7%** |
| a1.png, 1, 0.1, 0.5           | 1.82 | 1.00 | -45.1% |

#### V. Floating‑Point Hough Line (`...HoughLines3f`)

| Parameters (image, rho, theta, thresh) | 1st mean | 5th mean | Diff |
|-----------------------------------------|----------|----------|------|
| pic5.png, 1, 0.01, 0.5        | 1.97 | 0.86 | **-56.3%** |
| pic5.png, 10, 0.01, 0.5       | 1.66 | 0.54 | **-67.5%** |
| a1.png, 1, 0.01, 0.5          | 18.52 | 2.59 | **-86.0%** |
| a1.png, 10, 0.01, 0.5         | 16.30 | 2.05 | **-87.4%** |
| a1.png, 1, 0.1, 0.5           | 1.72 | 0.97 | -43.6% |

### Conclusion

- **Circle detection, probabilistic Hough line**: Differences between the two runs are very small (within ±11%), indicating stable performance.
- **`OCL_HoughLines`**: The parallel test is generally 10%~63% slower than the serial one, but still within a reasonable fluctuation range. Special handling was attempted but did not bring significant improvement and slightly reduced the gains in the subsequent two groups, so it was left unchanged after comprehensive consideration.
- **CPU standard Hough line (non‑OCL)**: The parallel test is significantly faster than the serial one (up to 88% faster), demonstrating that parallel optimization can bring obvious improvements.

By the way at last, I am extremely grateful to the maintainers for helping me test the last PR. However, since I was unable to obtain the development board, I was unable to continue modifying the previous RVV optimization.
2026-06-11 20:59:32 +03:00
Vincent Rabaud fa55ed2837 Merge pull request #29267 from vrabaud:aruco
Fix speed regression in Aruco identify #29267

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-11 20:54:39 +03:00
Pranav Kaushik 4dee742731 Merge pull request #29281 from pranavkaushik1:improve-qrcode-colors
samples: add color-coded bounding boxes for multi QR code detection #29281

Improves the existing qrcode.py sample by adding color-coded bounding boxes when multiple QR codes are detected simultaneously.

Previously all QR codes were drawn with the same green color, making it hard to distinguish between them visually.

This change adds a QR_COLORS palette so each detected QR code gets a unique color, improving visual clarity for multi-QR detection.

Co-authored-by: Ayush Gupta <gupta.ayushg@gmail.com>
2026-06-11 18:43:27 +03:00
Alexander Smorkalov a77474f3b1 Merge pull request #29255 from ArneshBanerjee:samples-blob-contours-29254
samples: demonstrate SimpleBlobDetector contour collection
2026-06-11 18:30:34 +03:00
Alexander Smorkalov a3dc35d47d Merge pull request #29214 from Teddy-Yangjiale:rvv-sigmoid-opt-5x
dnn: vectorize Sigmoid activation kernel using universal intrinsics
2026-06-11 13:16:55 +03:00
Yixuan Tang 590d8f6439 Merge pull request #29240 from plain-noodle-expert:fix/brisk-ubsan-negative-shift
fix(features2d): fix UB left-shift of negative value in BRISK subpixel2D #29240

## Summary

Fixes #29239

`BriskScaleSpace::subpixel2D` computes quadratic surface coefficients for sub-pixel keypoint refinement using AGAST scores from a 3×3 neighbourhood. Two of those coefficients were computed via left-shift:

```cpp
// Before (UB when operand is negative)
int coeff5 = (s_0_0 - s_0_2 - s_2_0 + s_2_2) << 2;
int coeff6 = -(s_0_0 + s_0_2 - ((s_1_0 + s_0_1 + s_1_2 + s_2_1) << 1) - 5 * s_1_1 + s_2_0 + s_2_2) << 1;
```

AGAST scores are non-negative (0–255), but their **differences can be negative**. Left-shifting a negative signed integer is **undefined behaviour** under C++11 [expr.shift]/2. UBSan reports:

```
brisk.cpp:2037:48: runtime error: left shift of negative value -3
```

## Fix

Replace the outer left-shifts with multiplications (`* 4` / `* 2`), which are always well-defined. Modern compilers (GCC, Clang, MSVC) emit identical code for the positive case.

```cpp
// After (safe for all inputs, identical semantics)
int coeff5 = (s_0_0 - s_0_2 - s_2_0 + s_2_2) * 4;
int coeff6 = -(s_0_0 + s_0_2 - ((s_1_0 + s_0_1 + s_1_2 + s_2_1) << 1) - 5 * s_1_1 + s_2_0 + s_2_2) * 2;
```

The inner `<< 1` inside the parenthesis of `coeff6` is applied to a sum of non-negative scores and remains safe.

## Tests

Four regression tests added to `test_brisk.cpp` (all verified to pass under `-fsanitize=undefined`):

| Test | Image | Purpose |
|---|---|---|
| `regression_ubsan_negative_shift_isolated_pixel` | 40×40, single pixel = 3 | Matches exact crash parameters (s_0_2=3, others=0) |
| `regression_ubsan_negative_shift_rect_corner` | 60×60 white rect on black | Strong AGAST responses at corners |
| `regression_ubsan_negative_shift_random_image` | 30×30 LCG noise (0–15) | Broad coverage of score combinations |
| `regression_ubsan_negative_shift_multi_octave` | 80×80 checkerboard, 3 octaves | Exercises cross-layer subpixel refinement |

## Crash Details

- **File**: `modules/features2d/src/brisk.cpp:2037`
- **Parameters at crash**: `s_0_0=0, s_0_1=0, s_0_2=3, s_1_0=0, s_1_1=0, s_1_2=0, s_2_0=0, s_2_1=0, s_2_2=0`
- **Expression**: `(0 - 3 - 0 + 0) << 2` = `-3 << 2` → **UB**
- **Detected by**: libFuzzer + UBSan (`-fsanitize=address,undefined`)
- **Affected path**: `detectAndCompute` → `computeKeypointsNoOrientation` → `getKeypoints` → `refine3D` → `getScoreMaxAbove` → `subpixel2D`
2026-06-11 13:08:05 +03:00
Srujan rai aed41fdabe Merge pull request #28981 from Srujan-rai:fix/opengl-extensions-memory-leak
core(opengl): fix memory leak in OpenCL extensions gathering #28981

## Problem

Fixes #28980

In `modules/core/src/opengl.cpp`, inside `initializeContextFromGL()`, a `char[]` buffer is allocated to query OpenCL device extensions:

```cpp
extensions = new char[extensionSize];
status = clGetDeviceInfo(..., extensions, &extensionSize);

if (status != CL_SUCCESS)
    continue;  // leaks `extensions`
```

When `clGetDeviceInfo()` fails, `continue` skips the corresponding `delete[]` on the success path, causing the allocated buffer to leak.

Additionally, the `catch (...)` block also bypasses cleanup, so any thrown exception leaks the buffer as well.

## Fix

Replace the raw `char*` allocation with `std::unique_ptr<char[]>`.

This ensures the buffer is automatically released on all exit paths, including:

- normal execution
- early `continue`
- exception handling paths

## Checklist

- [x] I agree to contribute to the project under the Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on code under GPL or another license incompatible with OpenCV.
- [x] The PR is proposed to the proper branch (`4.x`).
- [x] There is a reference to the original bug report and related work, if applicable.
- [x] Accuracy/performance tests and test data in `opencv_extra` are included, if applicable.
- [x] The feature/change is documented and sample code can be built with the project CMake configuration, if applicable.
2026-06-10 20:26:55 +03:00
Alexander Smorkalov f80d02e1c7 Merge pull request #29277 from intel-staging:perf_blobFromImages
Added more perf tests for blobFromImage
2026-06-10 19:24:55 +03:00
Ding zhehao ffa38e1b74 Merge pull request #29194 from Enilrats:opt-emd-performance
imgproc: optimize EMD (Earth Mover's Distance) solver performance #29194

### imgproc: optimize EMD solver performance using O(V) spanning tree traversal

---
This PR significantly optimizes the performance of the Earth Mover's Distance (`cv::EMD`) solver in `modules/imgproc/src/emd_new.cpp`. 

Specifically, it refactors the dual-variable calculation in `EMDSolver::findBasicVars()` from a naive $O((N+M)^2)$ linked-list scanning approach to an optimized $O(N+M)$ BFS tree traversal utilizing existing adjacency lists, leading to a massive speedup.

---

#### Technical Details & Core Bottleneck Fixed

1. **Algorithmic Complexity Reduction in `findBasicVars()`:**
   - **Before**: The original implementation solved the dual variables $u_i$ and $v_j$ by traversing the entire unmarked rows (`u0_head`) or columns (`v0_head`) linked-lists and invoking `getIsX(i, j)` inside nested loops to find connected basic variables. This resulted in an $O((N+M)^2)$ complexity per simplex iteration. For a scale of $2000 \times 2000$, this performed up to $16,000,000$ operations per iteration.
   - **After**: Since `EMDSolver` already maintains the adjacency lists of the basic variables tree (`rows_x` and `cols_x`), we can traverse the spanning tree in linear time. This PR implements a dual-queue BFS tree traversal. The complexity per iteration is drastically reduced to $O(N+M)$, performing at most $4000$ operations per iteration.

2. **Cache Locality & Pointer-Chasing Elimination:**
   - Replaced pointer-chasing on dynamically-allocated linked lists with contiguous, stack-allocated array queues (`cv::AutoBuffer`), significantly improving CPU L1/L2 cache hit rates and enabling hardware prefetching.


---

#### Performance Benchmarks

Below is the benchmark comparison evaluated on a standard CPU.
#### Test 1: dims = 64
| Scale ($N, M$) | Original EMD (ms) | Optimized EMD (This PR) | Speedup |
| :--- | :--- | :--- | :--- |
| **100** | 5.473 | 3.419 | **1.60x** |
| **500** | 381.871 | 244.355 | **1.56x** |
| **1000** | 1893.053 | 1369.016 | **1.38x** |
| **2000** | 11387.792 | 8331.221 | **1.37x** |

#### Test 2: dims = 3
| Scale ($N, M$) | Original EMD (ms) | Optimized EMD (This PR) | Speedup |
| :--- | :--- | :--- | :--- |
| **100** | 4.433 | 3.042 | **1.46x** |
| **500** | 365.762 | 259.735 | **1.41x** |
| **1000** | 1989.400 | 1421.952 | **1.40x** |
| **2000** | 12731.836 | 7952.210 | **1.60x** |


*(Note: The exact performance figures may vary slightly depending on the compiler and test machine.)*

---

#### Verification
- All existing tests in `opencv_test_imgproc` (including EMD tests) pass successfully. No regressions were introduced.

---

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-10 18:23:35 +03:00
Alexander Smorkalov 1780a86075 Merge pull request #29117 from SofianElmotiem:4.x
calib3d: use Delaunay triangulation in computeRNG for findCirclesGrid
2026-06-10 17:39:52 +03:00
Andrei Fedorov 48b8099214 added more perf tests for blobFromImages 2026-06-10 13:55:20 +02:00
Sofian Elmotiem 3d02d863f9 calib3d: use Delaunay triangulation in computeRNG for findCirclesGrid
The relative neighborhood graph (RNG) is a subgraph of the Delaunay
triangulation, so only Delaunay edges are candidates for RNG membership.
The previous implementation tested all N^2 pairs against all N points,
giving O(N^3). The new implementation builds the Delaunay triangulation
with Subdiv2D (O(N log N)), then checks only those ~3N edges for the RNG
lune-emptiness condition, reducing computeRNG to O(N^2) in the worst case
and much better in practice for regular grids.

Added synthetic-grid accuracy tests for both symmetric and asymmetric
patterns across several sizes, and a perf test parameterized by grid size.
2026-06-10 13:17:26 +02:00
Alexander Smorkalov 3d55d2fcef Merge pull request #29201 from Primary-H:project4_David
features2d: improve detector parameter validation
2026-06-10 14:10:56 +03:00
胡晨宇 d10138fa1c Merge pull request #29132 from hcy11123323:4.x
core: fix inverted continuity check in cvReshapeMatND() #29132

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [√] I agree to contribute to the project under Apache 2 License.
- [√] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-10 13:11:27 +03:00
Alexander Smorkalov 172ea5a00c Merge pull request #29269 from peters:fix/gpumatnd-move-assignment
core: fix GpuMatND move operations with CUDA 10.2
2026-06-10 12:52:16 +03:00
Alexander Smorkalov 9780b3b329 Merge pull request #29274 from vrabaud:ub_lut
Fix undefined behavior with pointer alignment
2026-06-10 11:27:54 +03:00
Vincent Rabaud 63b59865d3 Fix undefined behavior with pointer alignment
While at it, replace MMX _mm_setr_epi64 to not do the MMX to XMM
move.
2026-06-09 15:43:34 +02:00
Peter Rekdal Khan-Sunde 845d95aaac core: fix GpuMatND move assignment 2026-06-09 14:52:54 +02:00
Alexander Smorkalov b9a38df0e6 Merge pull request #29271 from uwezkhan:torch-storage-size-cap
cap torch storage size to int to prevent heap overflow
2026-06-09 12:49:17 +03:00
uwezkhan fe61ae0e41 cap torch storage size to int to prevent heap overflow 2026-06-09 10:09:52 +05:30
Alexander Smorkalov 6f29af625b Merge pull request #29268 from asmorkalov:as/disable_ocl_imterop_sample_4.x
Disable OpenCV-OpenCL interop sample for now 4.x
2026-06-08 17:03:23 +03:00
Yang Chao e32cab0e31 Merge pull request #29257 from yeatse:fix-lightglue-swift-name
objc: fix conflicting Swift name for LightGlueMatcher.create #29257

Fixes #29256

`cv::LightGlueMatcher` (a `DescriptorMatcher` subclass) declares a static
`create(const String& modelPath, ...)` overload. The Objective-C bindings
generator emits it as the selector `+create:`, which is the same selector as
the inherited `DescriptorMatcher::create(MatcherType)` but carries a different
`swift_name`, so clang refuses to build the `opencv2` Objective-C module:

```
LightGlueMatcher.h: error: 'swift_name' and 'swift_name' attributes are not compatible
```

Wrap the file-path overload with `CV_WRAP_AS(createFromFile)` so it gets a
distinct selector, mirroring the sibling `createFromMemory` overload, while
keeping the Swift name `create(modelPath:...)`.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-08 16:16:46 +03:00
Alexander Smorkalov 0397596e17 Merge pull request #29231 from ozhanghe:4.x
Spelling: Fix typos in HAL
2026-06-08 14:25:52 +03:00
Alexander Smorkalov f55665e5ca Merge pull request #29259 from yeatse:fix-apple-cxx-standard
Apple framework: set Xcode C++ language standard so KleidiCV builds
2026-06-08 14:21:58 +03:00
WITHOUTNAMESES 2a70226181 Merge pull request #29191 from WITHOUTNAMESES:project4_czn
imgproc: fix missing tipLength validation in arrowedLine #29191

### Pull Request Description

**Issue/Bug:**
Currently, the `cv::arrowedLine` function in `modules/imgproc/src/drawing.cpp` lacks boundary validation for the `tipLength` parameter. Passing an invalid ratio (e.g., negative values or values > 1.0) leads to mathematically distorted arrowhead coordinates being silently passed to the underlying `line()` drawing function, resulting in severely deformed output without any warning.

**Solution:**
Added a `CV_Assert` to enforce the geometric contract of `tipLength`, ensuring it strictly falls within the logical bounds `(0.0, 1.0]`. This adheres to the fail-fast principle and prevents silent geometric failures. 

**Testing:**
Successfully built the project locally and added a dedicated regression test (`arrowedLine_tipLength_validation`) using Google Test in `modules/imgproc/test/test_drawing.cpp`. Tested with boundary values (e.g., `tipLength = 1.5` and `-0.5`), and the assertion correctly intercepts the invalid parameters before rasterization.

*(Note: This contribution is part of a university engineering project evaluating open-source workflow and C++ robustness.)*
2026-06-08 14:19:00 +03:00
Alexander Smorkalov 21356eed2b Merge pull request #29253 from ssam18:fix-29222-bgr2lab-first-call
Speed up first BGR2Lab and BGR2Luv call by building the color LUT faster
2026-06-08 13:40:02 +03:00
Alexander Smorkalov 3f0664973c Merge pull request #29264 from ownia:fix/external_kleidicv_5.x
cmake: fix KLEIDICV_SOURCE_PATH handling
2026-06-08 12:01:53 +03:00
Weizhao Ouyang c4caa07d62 cmake: fix KLEIDICV_SOURCE_PATH handling
On 5.x branch, commit c3ca3f4f was accidentally reverted, so the
external KleidiCV will be silently disabled. Revert these changes to
support external KleidiCV builds.

Signed-off-by: Weizhao Ouyang <o451686892@gmail.com>
2026-06-08 13:00:24 +08:00
Yang Chao 410b6fdd97 ios: set Xcode C++ language standard for Apple framework build
OpenCV requires C++17, and bundled 3rdparty libraries such as KleidiCV
explicitly request CXX_STANDARD 17. With the CMake Xcode generator,
however, neither the global CMAKE_CXX_STANDARD nor per-target
CXX_STANDARD is emitted as CLANG_CXX_LANGUAGE_STANDARD in the generated
project, so those sources are compiled at the toolchain default
(pre-C++17) and fail, e.g.:

  kleidicv/include/kleidicv/traits.h: error: no template named
  'is_base_of_v' in namespace 'std'; did you mean 'is_base_of'?

Set CLANG_CXX_LANGUAGE_STANDARD=gnu++17 at the project level via
CMAKE_XCODE_ATTRIBUTE_* in the shared getCMakeArgs(), so every target in
the generated Xcode project (including 3rdparty subprojects) builds with
C++17.
2026-06-07 15:19:10 +08:00
Arnesh Banerjee 11b0bd15cf samples: demonstrate SimpleBlobDetector contour collection
Enable collectContours in detect_blob.cpp and draw the per-blob
contours returned by getBlobContours(), so the sample exercises the
public contour-collection API added in #21942.

Refs #25904
2026-06-07 12:18:39 +05:30
Samaresh Kumar Singh c936dcaef7 Speed up first BGR2Lab and BGR2Luv call by building the color LUT faster
The first conversion to Lab or Luv lazily builds a softfloat lookup table, and that build was doing about 108000 software emulated pow calls plus a fully serial 33x33x33 cube loop. This memoizes applyGamma over the 33 grid values and runs the cube loop with parallel_for_, cutting the first call from around 110 ms to around 25 ms on my machine. The per point computation is unchanged so the tables stay bit exact.
2026-06-06 16:36:07 -05:00
Alexander Smorkalov ab1aaa75aa Merge pull request #29219 from Kumataro:show_jpeg-turbo-version
build: show libjpeg-turbo version for bundled and system-wide libraries
2026-06-06 13:44:26 +03:00
Alexander Smorkalov d9f89b028b Merge pull request #29187 from sparklejin:project4_sparklejin
imgproc: fix floodFill simple path never being used (small fix)
2026-06-06 13:40:25 +03:00
Alexander Smorkalov 43dd75bf43 Merge branch 'release_5.0.0' into 5.x 2026-06-05 23:48:22 +03:00
Alexander Smorkalov 40738fb16c Release 5.0.0 2026-06-05 21:50:05 +03:00
Alexander Smorkalov c9514a7783 Merge pull request #29249 from asmorkalov:as/disable_ocl_imterop_sample
Disable OpenCV-OpenCL interop sample for now
2026-06-05 21:49:45 +03:00
Alexander Smorkalov 79a9145c56 Disable OpenCV-OpenCL interop sample for now 2026-06-05 21:36:23 +03:00
Alexander Smorkalov 5b65e5ec15 Merge pull request #29244 from varun-jaiswal17:skip_unstable
skip unstable videoio
2026-06-05 20:08:53 +03:00
Alexander Smorkalov 2058f7b627 Merge pull request #29246 from abhishek-gola:disk_warnings_fix
Windows warnings fix for DISK
2026-06-05 20:04:16 +03:00
Matt Van Horn 5e6a591357 Merge pull request #28873 from mvanhorn:osc/28429-fix-inter-nearest-exact-fp-5x
imgproc: fix INTER_NEAREST_EXACT to match Pillow (5.x retarget of #28661) #28873

## Summary

Fixes `INTER_NEAREST_EXACT` producing different results from Pillow for images with dimensions that are multiples of 64 (e.g., 128, 192).

Retargets #28661 to 5.x per core team request.

## Root cause

The old 16-bit fixed-point arithmetic (`ifx`, `ifx0`, `>> 16`) rounds differently from Pillow's pixel-center coordinate mapping. At exact pixel boundaries (e.g., y=7 with 128 to 160: `0.4 + 7*0.8 = 5.999999999999999`), Pillow's truncation drops to 5 while the old opencv formula rounds to 6.

## Changes

- `resize.cpp`: Replace `ifx/ifx0/ify/ify0` 16-bit fixed-point with an int64 arithmetic formula equivalent to `floor((i + 0.5) * src / dst)`, computed as `((int64_t)(i * 2 + 1) * src) / (dst * 2)`. This matches Pillow's pixel-center mapping exactly, is deterministic across platforms, and avoids FP rounding divergence entirely.
- `test_resize_bitexact.cpp`: Add `NearestExact_PillowCompat` covering 4 dimension pairs including the reproducer from #28429.

## Testing

- All 3 `Resize_Bitexact` tests pass (Linear8U, Nearest8U, NearestExact_PillowCompat) on this branch built against 5.x
- Verified bit-exact match to PIL output (Python 3.14, Pillow) on 49 dimension combinations including random pairs
- Built on macOS ARM64 with HAL (carotene/KleidiCV) active

Fixes #28429
2026-06-05 19:35:44 +03:00
Alexander Smorkalov ded27244dd Merge pull request #29247 from asmorkalov:as/java_calib_geometry_fail
Raise Java test threshold as it sporadically fails on CI.
2026-06-05 19:19:36 +03:00
Alexander Smorkalov afdf415c45 Merge pull request #29243 from asmorkalov:as/pre_5.0.0
Pre-release 5.0.0 versions update.
2026-06-05 18:56:43 +03:00
Alexander Smorkalov 18abac7d02 Raise Java test threshold as it sporadically fails on CI. 2026-06-05 18:52:03 +03:00
Alexander Smorkalov 7af8065580 Merge pull request #29245 from asmorkalov:as/opencl_sample_hack
Disable OpenCV-OpenCL interop sample on Windows in 32-bit builds.
2026-06-05 18:44:33 +03:00
Abhishek Gola 49a4522ea9 fixed warnings 2026-06-05 21:13:24 +05:30
Alexander Smorkalov b7264acb69 Disable OpenCV-OpenCL interop sample on Windows in 32-bit builds. 2026-06-05 18:22:19 +03:00
Vibhor 64a18fbdc1 skip unstable deadlock 2026-06-05 20:07:44 +05:30
Alexander Smorkalov 563dcf5b91 Pre-release 5.0.0 versions update. 2026-06-05 16:56:55 +03:00
Yang Guanyuhan 527f01449d Merge pull request #28986 from YangGuanyuhan:ai-aliked-lightglue-pipeline
[GSOC] feat: Add ALIKED feature extractor and LightGlue matcher with DNN integration #28986

## PR Description

### Summary

Integrate ALIKED and LightGlue into OpenCV's `features` module as native`Feature2D` and `DescriptorMatcher` implementations, enabling end-to-end neural feature matching within OpenCV's ecosystem.

---

### What's included

#### New classes

- **`cv::ALIKED`** extends `Feature2D`
  - CNN-based keypoint detection
  - 128-D descriptor extraction via ONNX Runtime

- **`cv::LightGlueMatcher`** extends `DescriptorMatcher`
  - Deep feature matching with spatial context
  - Uses keypoints and image sizes during matching

---

#### API design

- Standard OpenCV patterns:
  - `detectAndCompute()`
  - `match()`
  - `knnMatch()`

- Multiple factory methods:
  - ONNX model path
  - In-memory model buffer
  - Pre-loaded `dnn::Net`

- `Params` structs use `CV_EXPORTS_W_SIMPLE`
  for Python/Java bindings support

- Optional DNN dependency:
  - `HAVE_OPENCV_DNN` guards
  - Stub implementations throw `StsNotImplemented`

---

### Files added

| File | Description |
|------|-------------|
| `src/feature2d_aliked.cpp` | ALIKED implementation |
| `src/matchers_lightglue.cpp` | LightGlueMatcher implementation |
| `src/aliked_context.hpp` | Shared internal context struct |
| `test/test_aliked_lightglue.cpp` | Unit tests (9 test cases) |
| `samples/cpp/example_features_aliked_lightglue.cpp` | Demo application |

---

### Files modified

- `CMakeLists.txt`
  - Add `opencv_dnn` as optional dependency

- `features.hpp`
  - Add ALIKED and LightGlueMatcher declarations

- `precomp.hpp`
  - Add DNN include guard

---

### Usage

```cpp
// Feature extraction
Ptr<ALIKED> aliked =
    ALIKED::create("aliked-n16rot-top1k-640.onnx");

vector<KeyPoint> kpts;
Mat descs;

aliked->detectAndCompute(image, Mat(), kpts, descs);

// Feature matching
Ptr<LightGlueMatcher> lg =
    LightGlueMatcher::create("aliked_lightglue.onnx");

lg->setPairInfo(
    kpts1Mat,
    kpts2Mat,
    img1.size(),
    img2.size()
);

vector<DMatch> matches;
lg->match(descs1, descs2, matches);
````
please refer to samples/cpp/example_features_aliked_lightglue.cpp

---

### Test plan

* Build with `BUILD_LIST=features,dnn`
* Build without DNN:

  * Verify stubs compile
  * Verify `StsNotImplemented` is thrown
* Run:

  * `ctest -R Features2d_ALIKED`
  * `ctest -R Features2d_LightGlueMatcher`
* Run sample application with:

  * Real images
  * Real ONNX models
* Verify Python/Java bindings compile and work

---

### Related

Phase 1 of the
"End-to-End AI Feature Extraction and LightGlue Matching Pipeline"
GSoC project.

Designed to be extensible to:

* XFeat
* SuperPoint
* Other neural feature extractors

### test dependency

Depends on the opencv_extra PR adding ALIKED and LightGlue test models:

- [opencv_extra PR](https://github.com/opencv/opencv_extra/pull/1366)

This PR adds the following ONNX models to `download_models.py`:

- `aliked-n16rot-top1k-640.onnx`
- `aliked_lightglue.onnx`

These models are required for the `features2d` tests in the main OpenCV repository to validate the ALIKED and LightGlue feature extraction and matching pipeline.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-05 14:20:53 +03:00
omrope79 04aee009aa Merge pull request #29220 from omrope79:doc_optimizations_v4
[FOLLOW UP] : Documentation optimizations for the new Sphinx structure #29220

### Pull Request Readiness Checklist

This PR serves as a follow-up to the new documentation system introduced in [#29206](https://github.com/opencv/opencv/pull/29206)
Co-authored by: @abhishek-gola @kirtijindal14 @Akansha-977 @Prasadayus @varun-jaiswal17

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-05 14:18:27 +03:00
Alexander Smorkalov c9e7878a1d Merge pull request #29236 from asmorkalov:as/samples_geometry
Android samples build fix after geometry refresh
2026-06-05 12:26:51 +03:00
Alexander Smorkalov 28df43c56e Android samples build fix after geometry refresh. 2026-06-05 11:00:14 +03:00
Alexander Smorkalov 40ddc700aa Merge pull request #29230 from asmorkalov:as/move_undistort
Inverted imgproc-geometry dependency and moved more functions to geometry #29230

Fixes: https://github.com/opencv/opencv/issues/20267
Continues: https://github.com/opencv/opencv/pull/29175

OpenCV Contrib: https://github.com/opencv/opencv_contrib/pull/4137
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1377

Summary:
- LSD returned back to imgproc
- drawing functions moved to imgproc
- undistort image and related perf-pixel functions moved to imgproc
- moments moved to geometry
- estimateXXXtransform moved to geometry

After the patch the geometry module depends on code and Flann and may be used everywhere without potential circular dependencies

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-05 10:13:45 +03:00
omrope79 ada32973b4 Merge pull request #29227 from omrope79:object_detect_changes
Update BarcodeDetector super-resolution API to use single-file ONNX #29227

### Pull Request Readiness Checklist

This PR updates the `BarcodeDetector` super-resolution API to support and utilize a single-file ONNX model format. 

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-05 08:00:41 +03:00
Alexander Smorkalov 35ac66291f Merge pull request #29229 from vpisarev:optflow_etc_test_fixes
little but important fix in bicubic warping 1-channel case #29229

merge together with https://github.com/opencv/opencv_contrib/pull/4136

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-05 07:47:40 +03:00
ozhanghe 517710fe56 Spelling: Fix typos in HAL 2026-06-04 20:26:54 -07:00
omrope79 37c9ab1815 Merge pull request #29226 from omrope79:fix_mlas_x86
Fix MLAS 32-bit x86 build by integrating missing upstream assembly files #29226

### Pull Request Readiness Checklist

This resolves the 32-bit x86 build failure for MLAS. Related to: https://github.com/opencv/opencv/pull/29218

* Added the required `x86` assembly files and headers from upstream.
* Added `__x86_64__` guards around the AMX `syscall` and the FMA3/AVX512F kernel assignments to prevent 32-bit compilation crashes.

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-04 23:33:54 +03:00
Vadim Pisarevsky 0417b03bad trigger ci 2026-06-04 23:32:34 +03:00
s-trinh d9038ad00d Merge pull request #28823 from s-trinh:fix_apriltag_corners_order_update_doc
[OpenCV 5] Fix apriltag corners order and update the doc #28823

I have updated documentation for the AprilTag dictionaries.

Corresponding issues:
- https://github.com/opencv/opencv-python/issues/1195
- https://stackoverflow.com/questions/79044142/why-is-the-order-of-the-incoming-corners-different-between-apriltag-and-aruco-ma

This is a breaking change and is targeted only for OpenCV 5.

---

I have updated the ArUco doc with more information about fiducial markers detection.
I have tried to add some recommendations, best practices:
-  `DICT_ARUCO_MIP_36h12` should be the recommended family, [see](https://stackoverflow.com/a/51511558)
- link to download pregenerated markers for `MIP_36h12` is [here](https://sourceforge.net/projects/aruco/files/. I have not found some other official links for the other ArUco family, but since `MIP_36h12` should be used, I guess it is fined.
- recommendation to have a white border when printing the marker

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-04 23:28:46 +03:00
Vadim Pisarevsky dd1a12c6ff little but important fix in bicubic warping 1-channel case 2026-06-04 22:12:03 +03:00
Alexander Smorkalov 29eec42012 Merge pull request #29228 from asmorkalov:as/norm_unroll
Disable norm loop unroll for RISC-V RVV case.
2026-06-04 15:47:20 +03:00
Alexander Smorkalov 6a66ec4df5 Merge pull request #29225 from asmorkalov:as/more_contours_geometry
Move more 2d funcs to geometry module.
2026-06-04 12:20:06 +03:00
Alexander Smorkalov fc3803c67b Merge pull request #29224 from asmorkalov:as/ptcloud2
Dedicated pointcloud module #29224

OpenCV contrib: https://github.com/opencv/opencv_contrib/pull/4134

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-04 12:19:02 +03:00
Alexander Smorkalov a68b702ed5 Disable norm loop unroll for RISC-V RVV case. 2026-06-04 11:27:48 +03:00
Alexander Smorkalov 9803eb0443 Move more 2d funcs to geometry module. 2026-06-04 08:59:20 +03:00
Alexander Smorkalov 6a1a2754c8 Merge pull request #29213 from asmorkalov:as/ffmpeg_for_5.0
FFmpeg update for OpenCV 5.0 release.
2026-06-03 19:33:31 +03:00
Abhishek Gola e3fc091de4 Merge pull request #29221 from abhishek-gola:oom_issue_fixed
Fixed Out-of-Memory issue and added VLM sample #29221

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-03 17:29:30 +03:00
Abhishek Gola b0b77e7b32 Merge pull request #29073 from abhishek-gola:disk_feature_extractor
Added DISK feature extractor support #29073

closes: https://github.com/opencv/opencv/issues/27083
Merge with: https://github.com/opencv/opencv_extra/pull/1368/

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-03 13:11:11 +03:00
Alexander Smorkalov a46c6d65d3 FFmpeg update for OpenCV 5.0 release. 2026-06-03 13:09:20 +03:00
Kumataro a99141acd7 build: show libjpeg-turbo version for bundled and system-wide libraries 2026-06-03 08:31:27 +09:00
Alexander Smorkalov a0a660fcb1 Merge pull request #29203 from abhishek-gola:asm_bug_fix
fix MLAS build failure caused by ASM/ASM_NASM dialect conflict
2026-06-02 18:06:03 +03:00
Alexander Smorkalov 04ab09f6f3 Merge pull request #29215 from abhishek-gola:int8_bug_fix
Add missing MUL branch to NEON path of Eltwise2Int8 layer
2026-06-02 18:05:33 +03:00
omrope79 b67ad9a422 Merge pull request #28678 from omrope79:caffe-importer-cleanup
Caffe importer cleanup #28678

Merge with: https://github.com/opencv/opencv_extra/pull/1324

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-02 17:28:10 +03:00
Alexander Smorkalov 42fc6939f8 Merge pull request #29211 from vpisarev:fix_chessboard2_by_removing_ocl
Fixed the new chessboard detector by not using OpenCL #29211

This should hopefully fix test failures on macOS x64 builder in the latest 5.x branch.

The code of Chessboard detector (namely, the FastX part) runs tons of different-size box filters on the same image. Optimally, those filters need to be computed all together using once-computed integral image, but instead those multiple box filters, especially given how the current opencl path in box filter is organized, seem to 'overload' our OpenCL cache system and it breaks, at least on macOS. Given that boxfilter is relatively cheap operation, it will unlikely loose much by running on CPU vs GPU. So this is the current solution - switch to CPU path there. Local tests show that even on Apple M4 Max with very fast GPU we get better execution speed on CPU than on GPU.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-02 16:20:12 +03:00
Abhishek Gola 22cdc28471 int8 single thread bug fix 2026-06-02 17:28:18 +05:30
Vadim Pisarevsky b7846531fe Merge pull request #29165 from vpisarev:better_bicubic
Revised bicubic interpolation kernels and updated the corresponding functions #29165

They should work more accurately and faster than the existing implementation. The PR follows the previous work (#26271 etc.) that brought faster bilinear kernels.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-02 14:00:16 +03:00
Teddy-Yangjiale 8e434d68f9 dnn: vectorize SigmoidFunctor using universal intrinsics
(cherry picked from commit 4b1c861ab7)
2026-06-02 17:02:13 +08:00
Mansour Moufid a68e8d8289 Merge pull request #21337 from mansourmoufid:videocapture-get-property-return
Make cv::VideoCapture::get return cv::CAP_PROP_UNKNOWN (-1) for unsupported properties #21337

The return value indicating an unsupported property is not consistent across backends.

I stumbled on this issue because my code was determining if a property value is valid if it's non-zero (like the documentation says), which worked fine on macOS, but not on Android.

For example, auto-exposure is not supported on macOS, so get() returns 0. But it is supported on Android and 0 means auto-exposure is off.

I think -1 is the better return value to indicate unsupported properties.

I made changes to all the backends. I think I got every case. This breaks API compatibility for some backends, so I based this on branch 3.4.

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or other license that is incompatible with OpenCV
- [x] The PR is proposed to proper branch
- [ ] There is reference to original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-02 08:27:24 +03:00
Vincent Rabaud 1d4d81103e Merge pull request #29077 from vrabaud:throw
Homogeneize some calib/3d behavior #29077

- use CV_Check to validate input sizes (thus throwing for invalid inputs)
- return bool to validate a function result

This fixes #22746

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-02 08:25:44 +03:00
Vadim Pisarevsky e20acee958 use CPU path instead of OpenCL for image processing to avoid problems with myriads of different box filter OpenCL kernels 2026-06-02 00:47:36 +03:00
Alexander Smorkalov 89e0ca8749 Merge pull request #29205 from vrabaud:stack
Make sure pagedAttnAVGemmKernel uses no more than the stack
2026-06-01 22:16:25 +03:00
kirtijindal14 8756e68bff Merge pull request #29206 from kirtijindal14:doc_optimizations_v3
OpenCV New Documentation #29206

This PR introduces end to end working new Documentation in OpenCV.

Co-authored by: @abhishek-gola @omrope79 @Akansha-977 @Prasadayus @varun-jaiswal17 
### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-06-01 21:39:16 +03:00
Alexander Smorkalov 1b047868dd Merge pull request #29197 from Shengyu-Wu:feat/fast-rvv-u8mf2-prescreen
feat(rvv-hal): optimize FAST pre-screen with deferred u8mf2 widening
2026-06-01 19:23:56 +03:00
Vincent Rabaud 9937cc22da Make sure pagedAttnAVGemmKernel uses no more than the stack
Divide FAST_GEMM_MAX_STACKBUF by 2 because there are two AutoBuffers.
2026-06-01 15:37:13 +02:00
Alexander Smorkalov 4d0b381f03 Merge branch 4.x 2026-06-01 14:22:51 +03:00
Abhishek Gola 46e8224cf0 fixed ASM bug 2026-06-01 15:38:17 +05:30
Alexander Smorkalov 61d49e12f6 Merge pull request #29202 from Kumataro:cleanup_config_reference_codecs
doc(config_reference): clean up configuration options reference for codecs
2026-06-01 12:55:50 +03:00
Kumataro ad5829e55f doc(config_reference): clean up CMake options reference for libjpeg and libopenexr 2026-06-01 17:37:16 +09:00
Primary-H a7bb1d1e1e features2d: improve detector parameter validation 2026-05-31 23:40:50 +08:00
Shengyu Wu aee03ffbb2 feat(rvv-hal): optimize FAST pre-screen with deferred u8mf2 widening
Defer the u8→i16 widening (vzext) in the pre-screen phase until after
the quick-reject check passes. This avoids 5 unnecessary widen
operations per pixel-strip when the pre-screen rejects (which happens
~70-80% of the time at the default threshold=20).

The approach uses u8mf2 (same VL as i16m1) for the pre-screen
comparison with vssubu/vsaddu for bounds and vmsgtu for unsigned
comparison. When the pre-screen passes, the already-loaded u8mf2
variables are widened to i16m1 for the score computation, reusing
the same data without redundant memory loads.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-05-31 20:49:30 +08:00
Alexander Smorkalov 59218f9edd Merge pull request #29175 from asmorkalov:as/geometry2
Geometry module #29175

OpenCV Contrib: https://github.com/opencv/opencv_contrib/pull/4129
CI changes: https://github.com/opencv/ci-gha-workflow/pull/313

Continues
- https://github.com/opencv/opencv/pull/28804
- https://github.com/opencv/opencv/pull/29101
- https://github.com/opencv/opencv/pull/29108
- https://github.com/opencv/opencv/pull/28810

Todo for followup PRs:
- [x] Rename doxygen groups
- [x] Fix JS modules layout and whitelists
- [ ] Sort tutorials code/snippets

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-05-31 14:23:15 +03:00
胡晨宇 d79d9d7a5b parallelize sort_ using parallel_for_ 2026-05-31 11:07:12 +08:00
omrope79 14a475aa0b Merge pull request #28746 from omrope79:wechat-fix
WeChatQR-fix conversion Caffe to ONNX #28746

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-30 17:07:34 +03:00
Alexander Smorkalov ebb46d5f17 Merge pull request #29174 from ziyuanLi-alex:rvv-data-type-conversions
add RVV convertScale data type conversions
2026-05-30 15:45:23 +03:00
sparklejin b723ddd72b imgproc: fix floodFill simple path never being used
The is_simple check used mask.empty() after the default mask
  had already been created, making it always false. This caused
  the fast path (floodFill_CnIR) to be dead code — every call
  went through the slower gradient-based path (floodFillGrad_CnIR).

  Fix: save _mask.empty() result before auto-creating the default
  mask and use it for the is_simple check.
2026-05-30 15:53:54 +08:00
Alexander Smorkalov 2995f2e191 Merge pull request #29179 from varun-jaiswal17:new-perf-test
Add perf tests for new models
2026-05-30 10:09:53 +03:00
Andrei Fedorov 483266c645 Merge pull request #29183 from intel-staging:reenable_icv
Reenable ICV and fix #29166 #29183

Fixing https://github.com/opencv/opencv/issues/29166 and return back the 2026.0.0 ICV.
Tests are passed locally on Windows machine. Feel free to check if they are passed in bigger test systems.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-05-30 09:11:59 +03:00
Vadim Pisarevsky d4468bd7c0 Merge pull request #29180 from vpisarev:new_dnn_engine_disable_rvv
Temporarily disabled RVV intrinsics in several layers of the new DNN tested on musebook K1.

Kernels will be re-enabled when we find some good solution for the current 'm2' issue in rvv_scalable intrinsics.

This should fix #28852

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-29 20:39:43 +03:00
Varun Jaiswal 75bb662258 Merge pull request #29107 from varun-jaiswal17:yunet-dynamic-input
Update default YuNet model to new dynamic inputs #29107

Update the default model in `face_detect.py` and `face_detect.cpp` to
`face_detection_yunet_2026may.onnx`, which has symbolic `height`/`width` input dims.

## Changes
- `samples/dnn/face_detect.py`: update default `--face_detection_model` to `face_detection_yunet_2026may.onnx`
- `samples/dnn/face_detect.cpp`: update default `fd_model` to `face_detection_yunet_2026may.onnx`

Companion PR : 
- https://github.com/opencv/opencv_zoo/pull/310
- https://github.com/opencv/opencv_extra/pull/1373

 Closes : https://github.com/opencv/opencv/issues/28769
 
### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-29 20:37:05 +03:00
Alexander Smorkalov a3ced1a94b Merge pull request #29103 from kirtijindal14:optimized_doc
[FOLLOW UP] Update documentation for dnn, gpu, and others modules
2026-05-29 17:11:36 +03:00
kirtijindal 9d94a236a6 docs: Updated the tutorial documentation changes and rebased 2026-05-29 18:39:16 +05:30
vrooomy 9f5028d53c add perf tests for new models 2026-05-29 18:19:25 +05:30
Alexander Smorkalov c76bac6781 Merge pull request #29171 from asmorkalov:as/revert_ipp
Try to revert IPP update to fix access violation on Windows x64.
2026-05-29 15:47:46 +03:00
Prasad Ayush Kumar 98d75abf2d Merge pull request #29102 from Prasadayus:Doc_optimization_followup
[FOLLOW UP] Update documentation for Imgproc, 3d, and App modules tutorials #29102

This PR is a follow-up to https://github.com/opencv/opencv/pull/29091 and https://github.com/opencv/opencv/pull/29100, continuing the series of documentation updates for the Imgproc, 3d, and App modules tutorials.

To be merged after: https://github.com/opencv/opencv/pull/29091 and https://github.com/opencv/opencv/pull/29100


### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-29 13:03:02 +03:00
Licardo_du fda05a3622 Merge pull request #29138 from Licardo-du:fix-imgcodecs-apple-avfoundation
imgcodecs: fix Apple conversions build without AVFoundation #29138

Fixes #28553.

This PR fixes Apple imgcodecs conversion builds when AVFoundation is disabled or unavailable on older macOS SDKs.

Changes:
- Guard AVFoundation import with HAVE_AVFOUNDATION.
- Use older macOS-compatible framework imports when ImageIO is unavailable.
- Add __bridge compatibility for older Objective-C++ compilers.
- Use NSMakeSize for older macOS instead of passing CGSize to NSImage API.

Testing:
- Not tested locally: no macOS environment available.
- Patch follows the fix confirmed in #28555 discussion.
2026-05-29 12:08:32 +03:00
4ekmah f59c654bfc Merge pull request #29148 from 4ekmah:eccms_fixpython
Fixing python wrapper around ECCParameters to include itersPerLevel #29148

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
2026-05-29 11:18:20 +03:00
s-trinh 8bc22eba59 Merge pull request #29173 from s-trinh:add_parallel_for_tuto_link
Add parallel_for_ tutorial (Mandelbrot set) link in the main page #29173

Add the `parallel_for_` tutorial with Mandelbrot set use case in the main page:
- the tutorial exists: https://docs.opencv.org/4.13.0/d7/dff/tutorial_how_to_use_OpenCV_parallel_for_.html
- but is not listed in the main page: https://docs.opencv.org/4.13.0/de/d7a/tutorial_table_of_content_core.html

Some minor cleaning:
- removed [C=](https://www.hoopoesnest.com/cstripes/cstripes-sketch.htm) mention
- use Web Archive link
- prefer https link

On my computer (Firefox), the images ([here](https://docs.opencv.org/4.13.0/d3/dc1/tutorial_basic_linear_transform.html)) are shown like this:

<img width="1843" height="906" alt="image" src="https://github.com/user-attachments/assets/bc8386f5-8ea6-4fb7-92d1-75d72b0b9408" />

When adding a scale parameter, the images are correctly shown:

<img width="1445" height="873" alt="image" src="https://github.com/user-attachments/assets/e163a8e5-be0f-4139-aa51-b465fd619dc9" />

This is odd.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-05-29 09:53:01 +03:00
Ziyuan Li 18dad63e2e core: add RVV convertScale data type conversions 2026-05-29 13:24:03 +08:00
Alexander Smorkalov b0027c938f Merge pull request #29170 from varun-jaiswal17:fix/msvc19-29-static-constexpr
Upgrade WBUF_SIZE/SUMBUF to static constexpr for MSVC 19.29
2026-05-29 07:47:41 +03:00
Alexander Smorkalov 3a4dc42478 Try to revert IPP update to fix access violation on Windows x64. 2026-05-28 21:46:49 +03:00
Alexander Smorkalov aac582119c Merge pull request #29101 from asmorkalov:as/geometry_module
Moved geometry transformations from imgproc to 3d, future geometry module #29101

The first step of 2d geometry operations migration to the future geometry module.
I created 2d.hpp to isolate the moved functions for now. I propose to create geometry.hpp when the module is renamed and include all things there.

OpenCV contrib: https://github.com/opencv/opencv_contrib/pull/4126

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-05-28 21:09:52 +03:00
omrope79 ccb808ba55 Merge pull request #29100 from omrope79:docs-update-followup
[FOLLOW UP] Update documentation for core, calib3d, features, and introduction modules #29100

### Pull Request Readiness Checklist

This PR is a follow-up to #29091, continuing the series of documentation updates for the core, calib3d, features, and introductory modules.

To be merged after:  #29091

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-28 20:03:37 +03:00
Abhishek Gola a1ad509753 Merge pull request #29091 from abhishek-gola:doc_optimizations
New sphinx-documentation build #29091

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-28 17:34:19 +03:00
Vibhor 0c76634841 fix upgrade WBUF_SIZE/SUMBUF to static constexpr for MSVC 19.29 2026-05-28 18:03:54 +05:30
Alexander Smorkalov c22a2b25a1 Merge pull request #29167 from vrabaud:truco
Make sure Truco is launched on non-empty and CV_8UC1 only.
2026-05-28 15:18:15 +03:00
Alexander Smorkalov 4ecaecf1e7 Merge pull request #29159 from vrabaud:filter_engine
Fix ROI handling in 2D-tiled parallel execution in FilterEngine
2026-05-28 14:02:48 +03:00
Alexander Smorkalov fe1166f5e9 Merge pull request #29163 from kevinylin88:project4_gauss
imgproc: re-enable RVV integral optimization for safe cases
2026-05-28 13:59:35 +03:00
Siyu Wang a61ff0fa81 Merge pull request #29094 from feitianduowen:4.x
### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
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### Summary

This PR improves SIMD coverage for masked `cv::accumulate()` on 4-channel images.

The existing masked accumulate SIMD implementation has special paths for `cn == 1` and `cn == 3`, while 4-channel images fall back to the general implementation. This PR adds `cn == 4` SIMD paths using OpenCV Universal Intrinsics.

Compare link:

https://github.com/opencv/opencv/compare/4.x...feitianduowen:opencv:4.x

Modified files:

- `modules/imgproc/src/accum.simd.hpp`
  - Add masked `cn == 4` SIMD paths for:
    - `CV_32FC4 -> CV_32FC4`
    - `CV_8UC4 -> CV_32FC4`
- `modules/video/test/test_accum.cpp`
  - Add deterministic correctness regression tests for the new masked 4-channel accumulate paths.
- `modules/imgproc/perf/perf_accumulate.cpp`
  - Add performance coverage for masked 4-channel accumulate.
imgproc: add SIMD paths for masked 4-channel accumulate #29094
  
### Implementation

The new SIMD branches are added in:

- `modules/imgproc/src/accum.simd.hpp`

They handle:

- `src`: `CV_32FC4`, `dst`: `CV_32FC4`, `mask`: `CV_8UC1`
- `src`: `CV_8UC4`, `dst`: `CV_32FC4`, `mask`: `CV_8UC1`

For the `CV_32FC4` path, the implementation uses `v_load_deinterleave`, applies the pixel mask to all 4 channels, accumulates into `dst`, and stores the result with `v_store_interleave`.

For the `CV_8UC4 -> CV_32FC4` path, the implementation loads 4 interleaved uchar channels, applies the pixel mask, widens the values to float, accumulates into `dst`, and stores the results back as interleaved `CV_32FC4`.

The masked SIMD block is guarded with:

```cpp
if (x <= len - cVectorWidth)
{
    ...
}
```
This check ensures that SIMD setup and vector operations are only executed when at least one full vector iteration can run. For very small inputs or tail-only cases, the function skips SIMD initialization and lets the existing scalar fallback handle the data through `acc_general_(src, dst, mask, len, cn, x)`.

This keeps tail handling unchanged and avoids unnecessary SIMD initialization when the vector loop would be skipped.

### Accuracy tests

Added a deterministic regression test in:

* `modules/video/test/test_accum.cpp`

New test:

```bash
Video_Acc.accuracy_32FC4_masked_cn4
Video_Acc.accuracy_8UC4_to_32FC4_masked_cn4
```

Test commands:

```bash
opencv_test_video --gtest_filter=Video_Acc.accuracy_32FC4_masked_cn4
opencv_test_video --gtest_filter=Video_Acc.accuracy_8UC4_to_32FC4_masked_cn4
opencv_test_video --gtest_filter="Video_Acc.*:Video_AccSquared.*:Video_AccProduct.*:Video_RunningAvg.*"
```
The filtered accumulate-related tests passed locally.

I added dedicated deterministic tests instead of only extending the existing randomized accumulate tests because this PR only changes the masked `cv::accumulate()` paths for specific 4-channel type combinations. It does not add `cn == 4` SIMD coverage for `accumulateSquare`, `accumulateProduct`, or `accumulateWeighted`.

The existing base accumulate tests are shared by several accumulation functions. Extending the common randomized channel selection to include `cn == 4` would also affect tests for functions that are not optimized by this PR. The new deterministic tests directly cover the modified paths:

* `cv::accumulate(src, dst, mask)`
* `CV_32FC4 -> CV_32FC4`
* `CV_8UC4 -> CV_32FC4`
* `mask`: `CV_8UC1`

The test also covers small sizes and non-vector-multiple sizes, so both the new SIMD path and the existing scalar tail fallback are exercised.

### Performance tests

Added performance coverage in:

* `modules/imgproc/perf/perf_accumulate.cpp`

Perf command:

```bash
opencv_perf_imgproc --gtest_filter="*AccumulateMask32FC4*" --perf_min_samples=1000 --perf_force_samples=1000
opencv_perf_imgproc --gtest_filter="*AccumulateMask8UC4To32FC4*" --perf_min_samples=1000 --perf_force_samples=1000
```

Test environment:

* Release build
* AVX2 enabled
* IPP disabled
* OpenCL disabled
* Windows MinGW build

Performance results:

`CV_32FC4 -> CV_32FC4`

| Size      | Before median | After median | Speedup |
| --------- | ------------- | ------------ | ------- |
| 1920x1080 | 3.07 ms       | 2.74 ms      | 1.12x   |
| 1280x720  | 0.84 ms       | 0.73 ms      | 1.15x   |
| 640x480   | 0.17 ms       | 0.16 ms      | 1.06x   |
| 320x240   | 0.04 ms       | 0.04 ms      | ~1.00x  |

`CV_8UC4 -> CV_32FC4`

| Size      | Before median | After median | Speedup |
| --------- | ------------- | ------------ | ------- |
| 1920x1080 | 6.09 ms       | 1.75 ms      | 3.48x   |
| 1280x720  | 2.69 ms       | 0.82 ms      | 3.28x   |
| 640x480   | 0.96 ms       | 0.28 ms      | 3.43x   |
| 320x240   | 0.24 ms       | 0.06 ms      | 4.00x   |
| 127x61    | 0.02 ms       | 0.01 ms      | 2.00x   |

### Build and test commands

The following commands were run from Windows `cmd.exe`.

```sh
cmake -S . -B build_release -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=ON -DBUILD_PERF_TESTS=OFF -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign

cmake --build build_release --target opencv_test_video -j 4

build_release\bin\opencv_test_video.exe --gtest_filter=Video_Acc.accuracy_32FC4_masked_cn4
build_release\bin\opencv_test_video.exe --gtest_filter=Video_Acc.accuracy_8UC4_to_32FC4_masked_cn4
build_release\bin\opencv_test_video.exe --gtest_filter=Video_Acc.*:Video_AccSquared.*:Video_AccProduct.*:Video_RunningAvg.*
```

Accuracy test commands passed locally.

For the performance comparison, I kept the new perf test in `modules/imgproc/perf/perf_accumulate.cpp` and only reverted `modules/imgproc/src/accum.simd.hpp` to measure the baseline. Then I restored the SIMD patch and measured the optimized version.

Baseline measurement:

```sh
git diff -- modules/imgproc/src/accum.simd.hpp > accum_cn4_simd.patch
git checkout -- modules/imgproc/src/accum.simd.hpp

mkdir perf_logs

cmake -S . -B build_perf_before -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign > perf_logs\before_cmake_config.txt 2>&1
cmake --build build_perf_before --target opencv_perf_imgproc -j 4 > perf_logs\before_build.txt 2>&1

build_perf_before\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask32FC4* --perf_min_samples=1000 --perf_force_samples=1000 > perf_logs\before_perf.txt 2>&1
```

```sh
git diff -- modules/imgproc/src/accum.simd.hpp > accum_cn4_u8_simd.patch
git checkout -- modules/imgproc/src/accum.simd.hpp

cmake -S . -B build_perf_before_u8 -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign
cmake --build build_perf_before_u8 --target opencv_perf_imgproc -j 4
build_perf_before_u8\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask8UC4To32FC4* --perf_min_samples=1000 --perf_force_samples=1000 > perf_logs\before_perf1.txt 2>&1
```

Optimized measurement:

```sh
git apply accum_cn4_simd.patch

cmake -S . -B build_perf_after -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign > perf_logs\after_cmake_config.txt 2>&1
cmake --build build_perf_after --target opencv_perf_imgproc -j 4 > perf_logs\after_build.txt 2>&1
build_perf_after\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask32FC4* --perf_min_samples=500 --perf_force_samples=500 > perf_logs\after_perf.txt 2>&1
```

```sh
git apply accum_cn4_u8_simd.patch

cmake -S . -B build_perf_after_u8 -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign
cmake --build build_perf_after_u8 --target opencv_perf_imgproc -j 4
build_perf_after_u8\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask8UC4To32FC4* --perf_min_samples=1000 --perf_force_samples=1000 > perf_logs\after_perf1.txt 2>&1
```

Performance comparison was measured by keeping the new perf tests and only reverting modules/imgproc/src/accum.simd.hpp for the baseline run.

`-mstackrealign` was used for the MinGW Windows build to avoid stack-alignment issues with AVX code generation during local testing.

### Notes

The implementation keeps the existing scalar fallback path unchanged. SIMD is used only for the newly covered masked `cn == 4` vectorizable part, and any remaining tail elements are still handled by `acc_general_()`.

The improvement is most visible on larger images. Small images are dominated by overhead and do not always show meaningful speedup.
2026-05-28 12:44:16 +03:00
Vincent Rabaud 8ebcf229a1 Make sure Truco is launched on non-empty and CV_8UC1 only.
This is checked in the function:
https://github.com/opencv/opencv/blob/3bb68212dac2e1c42e7910de9daf3a0766eaead8/modules/imgproc/src/contours_truco.cpp#L621
2026-05-28 10:42:50 +02:00
Kevin Lin 850166c720 imgproc: re-enable RVV integral for safe cases 2026-05-28 14:52:06 +08:00
Abhishek Gola 0908a2db6f Merge pull request #29104 from abhishek-gola:sdpa
Added SDPA layer (Scaled Dot Product Attention) #29104

Merge with: https://github.com/opencv/opencv_extra/pull/1374

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2026-05-27 21:13:03 +03:00
Alexander Smorkalov 27d5b2b8dd Merge pull request #29161 from abhishek-gola:lama_inpainting_bug
[BUG FIX] Fixed Conv+Add+BatchNorm fusion with correct BN scale on the residual
2026-05-27 20:23:50 +03:00
Alexander Smorkalov 3bb68212da Merge pull request #29062 from Tiansuanyu:project4_wangzixi
hal/riscv-rvv: implement spatialGradient
2026-05-27 17:36:15 +03:00
Alexander Smorkalov e19b0ebc5c Merge pull request #28804 from asmorkalov:as/calib_boards_migration
Migrated chessboard and circles grid detectors to objdetect #28804
  
OpenCV Contrib: https://github.com/opencv/opencv_contrib/pull/4125
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1375

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2026-05-27 16:06:51 +03:00
Alexander Smorkalov 8a853d1741 Merge pull request #29151 from plain-noodle-expert:fix/sunras-ubsan-invalid-enum-29150
imgcodecs: fix UBSan load-invalid-value in SunRasterDecoder::readHeader
2026-05-27 16:03:39 +03:00
Vincent Rabaud 0ab6bd2d68 Fix ROI handling in 2D-tiled parallel execution in FilterEngine 2026-05-27 14:40:30 +02:00
Abhishek Gola 13dd2f5293 lama inpainting issue fixed 2026-05-27 18:02:18 +05:30
Varun Jaiswal dafe7cef97 Merge pull request #29156 from varun-jaiswal17:fix/msvc-windows
fix MSVC error: use name constexp for alignas array size in INT8 kernel #29156
  
### Problem
Windows CI fails with MSVC error C2131 in `conv2_int8_kernels.simd.hpp`:

    error C2131: expression did not evaluate to a constant
    failure was caused by a read of a variable outside its lifetime
    see usage of 'this'

Two array declarations inside `parallel_for_` lambdas used `constexpr`
variables defined inside the lambda body as array sizes:

    alignas(32) int8_t  wbuf[128 * K0];          // K0 defined inside lambda
    alignas(32) int32_t sumbuf[SPAT_BLOCK_SIZE * K0];  // both defined inside lambda


### Fix
Move the combined size constants to function scope (before the lambda),
where they are true compile-time constants with no `this` involvement:

    constexpr int WBUF_SIZE   = 128 * 8;  // 128 * K0
    constexpr int SUMBUF_SIZE = 8 * 8;    // SPAT_BLOCK_SIZE * K0



### Related
Fixes Windows CI failure introduced by #29126.

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2026-05-27 13:13:26 +03:00
Abhishek Gola bf0cf34963 Merge pull request #29126 from abhishek-gola:flash_attention
Attention graph fusion with MLAS FlashAttention #29126

Performance numbers for Owl-v2 model on intel i9:
```
ORT: Average inference time over 10 runs: 1411.55 ms (min 1399.75, max 1438.89)
NEW: Average inference time over 10 runs: 1078 ms (min 1048.04, max 1110.61)
```
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2026-05-27 09:32:27 +03:00
example 39da6f45e4 imgcodecs: fix UBSan load-invalid-value in SunRasterDecoder::readHeader (#29150)
Casting the raw integer fields ras_type and maptype directly to their
respective enum types (SunRasType / SunRasMapType) without a prior range
check is undefined behavior under C++11 §7.2/8 when the stored value falls
outside the enum's valid range.

UBSan reports:
  grfmt_sunras.cpp:72: runtime error: load of value 34077, which is not a
  valid value for type 'SunRasMapType'

Fix: read both fields into plain ints first, validate them against the
declared enumerator bounds, and return false (reject the image) on any
out-of-range value before performing the enum cast.  This is the cheapest
correct approach — two integer comparisons added to a path that was already
doing I/O — and ensures no downstream code ever sees an ill-formed enum
value.

Add test_sunraster.cpp with regression tests covering:
- The exact crash_001 payload (invalid maptype 34077 / 0x851d)
- Invalid ras_type values
- maptype = 2 and UINT_MAX (outside [0,1])
- Truncated header
- A valid 8-bpp grayscale image that must still decode correctly

Signed-off-by: FuzzAnything fuzzanything@gmail.com
2026-05-27 09:23:18 +08:00
Alexander Smorkalov afa6777a0a Merge pull request #29141 from Tiansuanyu:fix-gcc13-vblas-fma-4.x
core: Fix numerical instability and out-of-bounds store in VBLAS SIMD (4.x backport)
2026-05-26 19:01:16 +03:00
Vigh Sebastian 8ec62ad346 Merge pull request #29134 from svigh:gapi_u8_nd_mats_onnx_layout_fix
Merge pull request #29134 from svigh/gapi_u8_nd_mats_onnx_layout_fix

Gapi u8 N-D mats onnx layout workaround #29134

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2026-05-26 18:09:44 +03:00
Zixi Wang 2405556989 Merge pull request #29140 from Tiansuanyu:fix-gcc13-vblas-fma
core: fix numerical instability and UB in VBLAS SIMD helpers #29140

### Description

**Fixes #28845**

**Root cause identified by:** stubbing out `VBLAS<double>::givens()` to return 0 immediately — both tests pass, confirming the divergence originates in the double-precision Givens SIMD rotation.

**Main fix:** Use FMA form in `VBLAS::{float,double}::givens()` to keep JacobiSVD vector rotation numerically stable on SIMD/FMA builds.
This fixes the fisheye homography initialization divergence (`RMS 36.2553` vs `1`) seen in `RegisterCamerasTest.hetero1/2` under GCC 13. 

**Additional fix:** Replace fixed-size temporary storage `sbuf[2]` in `VBLAS<double>::dot()` with `v_reduce_sum()` to avoid out-of-bounds stores on wide SIMD backends (e.g., AVX2 `v_float64` requires 4 lanes, causing UB).

**Verification:**
Passed full `opencv_test_core` and `opencv_test_calib` locally under Ubuntu 24.04 (GCC 13) with AVX2.

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2026-05-26 18:01:06 +03:00
Alexander Smorkalov aed3df4ed9 Merge pull request #29144 from varun-jaiswal17:vit-b-32-threshold
relax ViT_B_32 lInf threshold for NGRAPH/CPU
2026-05-26 15:16:42 +03:00
vrooomy cd302920ee relax ViT_B_32 lInf threshold for NGRAPH/CPU 2026-05-26 16:48:41 +05:30
Alexander Smorkalov ed47719ac4 Merge pull request #29139 from asmorkalov:as/mlas_old_gcc
Fixed MLAS build with older versions of GCC.
2026-05-26 13:52:03 +03:00
Tiansuanyu 55d3e3ff4f core: fix numerical instability and out-of-bounds store in VBLAS SIMD
Backport of PR #29140 to 4.x branch.
2026-05-26 17:38:10 +08:00
Alexander Smorkalov f251467b55 Fixed MLAS build with older versions of GCC. 2026-05-26 11:55:37 +03:00
Varun Jaiswal bae8cb1915 Merge pull request #29079 from varun-jaiswal17:feat/dnn-int8-optimization
dnn int8 optimization #29079

all_layers.hpp 
- Add float_input flag to Conv2Int8Params and Conv2Int8Layer to let the first conv accept raw FP32 input and quantize internally.

graph_fusion_qdq.cpp : 
- Fuse DQ → Sigmoid → QL into SigmoidInt8, Similarly for MAxPool.
- Fuse the input QuantizeLinear node into the first Conv2Int8.

conv2_int8_layer.cpp
- Add quantizeInterleaveBlock()


conv2_int8_kernels.simd.hpp
- Add spatial tiling to both convInt8BlockVNNI and convInt8BlockDepthwise: splits output pixels into tiles so total task count is N × ngroups × Kblk × ntiles, fully utilizing all threads even when the channel count is small.

elementwise_layers.cpp
- Widen CV_Assert to accept CV_8U in addition to CV_8S.

eltwise2_int8_layer.cpp
- Add QLinearMul support: new Mul math path for both signed and unsigned int8.
- Add numpy-style broadcast support so QLinearMul / QLinearAdd with scalar

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2026-05-26 11:34:47 +03:00
Prasad Ayush Kumar be2c53c2b7 Merge pull request #29127 from Prasadayus:KV-cache
Add KV cache with paged attention and prefetch #29127

Closes: https://github.com/opencv/opencv/issues/27159

### Pull Request Readiness Checklist

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2026-05-26 10:58:47 +03:00
Alexander Smorkalov 9370343d50 Merge pull request #29108 from asmorkalov:as/relax_flann_deps
Remove features dependency from 3d module.
2026-05-26 08:59:27 +03:00
Alexander Smorkalov 05acc5d4c6 Merge pull request #29133 from Fulnergy:4.x
fix: videoio test hang on win11 from issue #27400
2026-05-26 08:58:41 +03:00
Alexander Smorkalov d8263a9899 Merge branch 4.x 2026-05-25 17:49:25 +03:00
Fulnergy 60b0f01528 fix test hang in win11 from issue #37400 2026-05-25 21:14:31 +08:00
Alexander Smorkalov e0d604378f Merge pull request #29125 from asmorkalov:as/solvePoly_warn_fix
solvePoly warning fix on Windows.
2026-05-25 16:00:30 +03:00
Alexander Smorkalov d7b48a4fec Merge pull request #29115 from yuomy:optimize-fluid-select
gapi: optimize Fluid Select kernel using Universal Intrinsics
2026-05-25 15:58:48 +03:00
Alexander Smorkalov d8d1cc8c58 Try to remove features dependency from 3d module. 2026-05-25 14:42:15 +03:00
Alexander Smorkalov 3e530e5784 solvePoly warning fix on Windows. 2026-05-25 14:34:02 +03:00
nklskyoy c2594b41bf Merge pull request #28840 from nklskyoy:key-value-cache
FP32 KV Cache #28840

OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1348

## This PR introduces basic (Paged ) KV-Cache to use on CPU

### Summary:
1. To ensure proper gemm-prepacking,
1.1. The Page Size of Key Cache is currently hardcoded as `FAST_GEMM_F32_NR`(which is 8, 12 or 16 depending on CPU architecture)  
1.2. The Page Size of Values Cache is hardcoded as `FAST_GEMM_F32_PACKED_STRIDE_K`
2. there are two phases supported - prefill & generate. 
2.1. prefill grows cache by `N` tokens and is allowed **only** for empty cache
2.2. generate grows cache by 1 token. 
2.3. **Improtant**: it is currently not allowed to grow non-empty cache by more than one token at a time (thisbehaviour is sufficient for normal LLM querying, but should be extended if we want to implement speculative decoding)

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2026-05-25 13:39:02 +03:00
Alexander Smorkalov 07e0dd2fbd Merge pull request #29121 from asmorkalov:as/generalize_imread_flags
Unified imreadXXX functions flags.
2026-05-25 11:36:55 +03:00
Alexander Smorkalov f0d44c0b45 Merge pull request #29123 from asmorkalov:as/divSpectrums
Move divSpectrums to core module.
2026-05-25 11:36:02 +03:00
Shelia J. 3218dbb0ab Merge pull request #29124 from SheliaJimenez:dnn-pow-fix
Dnn pow layer output type fix #29124

### Description

This pull request needs https://github.com/opencv/opencv_extra/pull/1372 to run.

**issue:** when running disk-lightglue `.onnx` model, such error message is thrown:

```bash
Error message OpenCV(5.0.0-pre) /home/tapakya/cpp/opencv_mod/opencv/modules/dnn/src/layers/nary_eltwise_layers.cpp:410: error: (-2:Unspecified error) in function 'virtual void cv::dnn::NaryEltwiseLayerImpl::getTypes(const std::vector<int>&, int, int, std::vector<int>&, std::vector<int>&) const'
> All inputs should have equal types (expected: 'inputs[0] == input'), where
>     'inputs[0]' is 11 (CV_64SC1)
> must be equal to
>     'input' is 5 (CV_32FC1)
```

This is because in `nary_eltwise_layers.cpp`, `getTypes` didn't set the output type of power operation correctly. `int`^`float` should output `float`, whereas `getTypes` set the output type to `int` in this case.

```c++
        if (op == OPERATION::POW) {
            CV_Assert(inputs.size() == 2);
            auto isIntegerType = [](int t) {
                return t == CV_8S || t == CV_8U || t == CV_16S || t == CV_16U || t == CV_32S || t == CV_32U || t == CV_64S || t == CV_64U;
            };
            auto isFloatType = [](int t) {
                return t == CV_32F || t == CV_64F || t == CV_16F || t == CV_16BF;
            };

            int out_type;
            const bool baseIsInt   = isIntegerType(inputs[0]);
            const bool expIsInt    = isIntegerType(inputs[1]);
            const bool baseIsFloat = isFloatType(inputs[0]);
            const bool expIsFloat  = isFloatType(inputs[1]);

            if ((baseIsInt && expIsInt) || (baseIsFloat && expIsFloat))
            {
                out_type = (inputs[0] == inputs[1]) ? inputs[0] : CV_32F;
            }
            else if (baseIsFloat != expIsFloat)
            {
                out_type = inputs[0];
                // if base is int and exp is float, output type should be float
            }
```

**fix:** corrected the logic of determining output type

```c++
            if ((baseIsInt && expIsInt) || (baseIsFloat && expIsFloat))
            {
                out_type = (inputs[0] == inputs[1]) ? inputs[0] : CV_32F;
            }
            else if (baseIsFloat && !expIsFloat)
            {
                out_type = inputs[0];
            }
            else if (!baseIsFloat && expIsFloat)
            {
                out_type = CV_32F;
            }
```

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2026-05-25 11:11:44 +03:00
sujal 62930a207b Merge pull request #28920 from 5usu:fix/28798-yunet-int8-objbranch
dnn: skip Conv2Int8 fusion for grouped convs with Kg<8 (#28798) #28920

OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1360

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---

Fixes #28798.

The YuNet 2023mar int8 model from opencv_zoo doesn't detect anything on 5.x. With `--score_threshold
0.05` the detector still returns no boxes, so it's not just a confidence drop — the network output is
broken.

After dumping intermediate tensors and comparing against onnxruntime, the `obj_*` branches collapse to
~0 (saturated int8 `-128`), and the `cls_*` branches saturate the other way. Final score is `cls * obj`
so nothing ever crosses threshold.

The cause is in `Conv2Int8`'s VNNI kernel. It processes `K0 = 8` output channels per SIMD iteration and
writes them as one `K0`-wide block to `out + (n*K1 + k1)*planesize` with `k1 = k_base / K0`. When
`ngroups > 1` and `Kg = K/ngroups < K0`, consecutive groups share the same `k1` slot and overwrite each
other — only the last group's result survives. YuNet has six `1x1x3x3` depthwise conv heads (`Kg = 1`),
which is exactly this case.

The unfused `DequantizeLinear → Conv2 → QuantizeLinear` path is fine. The minimal fix here is to skip
the rewrite to `Conv2Int8` when `Kg < 8` so we fall back to the float path. A proper depthwise int8
kernel can be added later as a separate optimization.

Verified with `samples/dnn/face_detect.py` on Lena:

Before:
AssertionError: Cannot find a face in samples/data/lena.jpg

After:
Face 0, top-left coordinates: (204, 187), box width: 149, box height 212, score: 0.89

Cross-check vs onnxruntime: `cls_8` mean `0.673` (was `0.93`, ORT `0.673`), `obj_8` max `0.0039` (was
`0`, ORT `0.0039`).

The regression test is a single 16-group depthwise `QLinearConv` with non-zero `x_zp`, added to
`Quantized_Convolution`. Test data is in opencv_extra on the matching branch (~9 KB total).

  
<img width="1864" height="1060" alt="image" src="https://github.com/user-attachments/assets/a1b15c64-6b84-42b1-86a4-1d55474cb38c" />
2026-05-25 09:48:59 +03:00
Alexander Smorkalov 20742dc581 Merge pull request #29122 from asmorkalov:as/python_codecs_deprecate
Replace codecs.open() with open() since codecs.open() deprecated in Python 3.14
2026-05-25 08:41:57 +03:00
Alexander Smorkalov 8472efd791 Merge pull request #29120 from asmorkalov:as/png_warning_check
Libpng warning fix on Windows #29120

CI warning:
```
  Warning: C:\GHA-OCV-3\_work\opencv\opencv\opencv\3rdparty\libpng\pngread.c(4114,21): warning C4146: unary minus operator applied to unsigned type, result still unsigned [C:\GHA-OCV-3\_work\opencv\opencv\build\3rdparty\libpng\libpng.vcxproj]
  Warning: C:\GHA-OCV-3\_work\opencv\opencv\opencv\3rdparty\libpng\pngwrite.c(2033,21): warning C4146: unary minus operator applied to unsigned type, result still unsigned [C:\GHA-OCV-3\_work\opencv\opencv\build\3rdparty\libpng\libpng.vcxproj]
```

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2026-05-25 08:39:58 +03:00
Abhishek Gola f539c0907e Merge pull request #29087 from abhishek-gola:videoio_write_issue_fix
Fixed write return status in videoio module#29087

closes: https://github.com/opencv/opencv/issues/24287

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2026-05-24 22:10:00 +03:00
Alexander Smorkalov 133e218484 Unified imreadXXX functions flags. 2026-05-24 22:07:56 +03:00
Alexander Smorkalov 1c44eaf8bd Move divSpectrums to core module. 2026-05-24 17:28:53 +03:00
Alexander Smorkalov 852c3d2fb9 Replace codecs.open() with open() since codecs.open() deprecated in Python 3.14 2026-05-24 16:54:39 +03:00
yuomy ff540453d5 gapi: optimize Fluid Select kernel using Universal Intrinsics
- Replaced scalar fallback and legacy CV_SIMD128 with modern scalable CV_SIMD.
- Added support for multi-channel (2/3/4) and 16-bit (ushort/short) configurations.
- Utilized v_select for branchless execution and vx_load_expand for mixed-bitwidth mask handling.
- Updated accuracy and perf tests.
2026-05-24 19:20:22 +08:00
Alexander Smorkalov 00d19e1c63 Merge pull request #29114 from akretz:fix-masked-norm
Fix MaskedNormInf_SIMD<float, float>
2026-05-24 12:31:30 +03:00
Jules ffd0cf49a7 Merge pull request #28667 from jules-ai:jules_fix_jpeg_sampling_factor
Fix JPEG chroma configuration & Standardize component settings #28667

### Problem

Incorrectly treated Chroma as a single channel. In $YC_bC_r$, chroma must be set in both separate channels ($C_b$ and $C_r$).

### Fix
- Explicit Setup: Configured all three components ($Y$, $C_b$, $C_r$) individually.
- Standardization: Removed reliance on library defaults to prevent undefined behavior.


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2026-05-24 11:41:17 +03:00
Adrian Kretz dd214962a5 Merge pull request #29109 from akretz:fix-issue-23644
Better Durand-Kerner Initialization #29109

While investigating issue #23644, I have found [this paper](https://link.springer.com/article/10.1007/BF01935059) which presents a good initialization for the Durand-Kerner algorithm. Basically the idea is to put the initial points equidistantly on a circle on the complex plane. The radius of the circle is computed as
<img width="607" height="178" alt="image" src="https://github.com/user-attachments/assets/ea31b002-c924-4b93-9334-3e59597c896b" />
Note that the $a_i$ coefficients in that paper are reversed compared to OpenCV. That's where the `(n - i)` in the code comes from.

I have implemented just the mean of the $u_i$'s for the sake of simplicity. That's already enough to make the algorithm converge in all cases I have tested. I have used this to test for convergence for many polynomials of order 2 and 4 and coefficients of different magnitudes:

```cpp
TEST(Core_SolvePoly, large_test)
{
    cv::Mat_<float> coefs3(1,3);
    cv::Mat_<float> coefs5(1,5);
    cv::Mat r;
    double prec;
    for (int c0 = -20; c0 <= 20; c0++)
    {
        coefs3.at<float>(0) = c0;
        for (int c1 = -20; c1 <= 20; c1++)
        {
            coefs3.at<float>(1) = c1;
            for (int c2 = -20; c2 <= 20; c2++)
            {
                coefs3.at<float>(2) = c2;
                prec = cv::solvePoly(coefs3, r);
                EXPECT_LE(prec, 1e-6);
            }
        }
    }
    for (int c0 = -10; c0 <= 10; c0++)
    {
        coefs5.at<float>(0) = c0;
        for (int c1 = -10; c1 <= 10; c1++)
        {
            coefs5.at<float>(1) = c1;
            for (int c2 = -10; c2 <= 10; c2++)
            {
                coefs5.at<float>(2) = c2;
                for (int c3 = -10; c3 <= 10; c3++)
                {
                    coefs5.at<float>(3) = c3;
                    for (int c4 = -10; c4 <= 10; c4++)
                    {
                        coefs5.at<float>(4) = c4;
                        prec = cv::solvePoly(coefs5, r);
                        EXPECT_LE(prec, 1e-2);
                    }
                }
            }
        }
    }
    for (int i = -10; i < 10; i++)
    {
        coefs3.at<float>(0) = pow(2, i);
        for (int j = -10; j < 10; j++)
        {
            coefs3.at<float>(1) = pow(2, j);
            for (int k = -10; k < 10; k++)
            {
                coefs3.at<float>(2) = pow(2, k);
                prec = cv::solvePoly(coefs3, r);
                EXPECT_LE(prec, 1e-6);
            }
        }
    }
}
```

This test passes, but I have not committed it because it runs for a couple of seconds.

This fixes #23644 and replaces #29055. I have checked #29055 and it does not pass the test above. It seems to be optimized to the precise polynomial of #23644.

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2026-05-24 10:53:48 +03:00
Yixuan Tang 656f3395a7 Merge pull request #29113 from plain-noodle-expert:fix/mjpeg-put-bits-heap-overflow
videoio(mjpeg): fix heap-buffer-overflow in put_bits off-by-one resize guard #29113

### Summary

Fix a heap-buffer-overflow (WRITE of size 4) in the built-in MJPEG encoder detected by AddressSanitizer during fuzzing.

Fixes #29112

### Root Cause

`mjpeg_buffer::put_bits` in `modules/videoio/src/cap_mjpeg_encoder.cpp` guards buffer resize with:

```cpp
if ((m_pos == (data.size() - 1) && len > bits_free) || m_pos == data.size())
    resize(int(2 * data.size()));
```

When `len == bits_free` the guard is **false** (strict `>`), so no resize happens. The subsequent code then:

1. Subtracts `len` from `bits_free`, making it exactly 0.  
2. Enters the `bits_free <= 0` branch and executes `++m_pos`.  
3. Writes `data[m_pos]` — now equal to `data[data.size()]` — **out of bounds**.

### Fix

Change `len > bits_free` to `len >= bits_free` so the buffer is grown whenever the current slot will be exactly or more than consumed.

```diff
-if ((m_pos == (data.size() - 1) && len > bits_free) || m_pos == data.size())
+if ((m_pos == (data.size() - 1) && len >= bits_free) || m_pos == data.size())
```

### Verification

Reproducer (1×1 grayscale frame, `CAP_OPENCV_MJPEG`):

```cpp
uint8_t pixel = 0xff;
cv::Mat frame(1, 1, CV_8UC1, &pixel);
int fourcc = cv::VideoWriter::fourcc('M', 'J', 'P', 'G');
cv::VideoWriter writer;
writer.open("/tmp/poc.avi", cv::CAP_OPENCV_MJPEG, fourcc, 25.0, cv::Size(1,1), false);
writer.write(frame);
```

Ran under `-fsanitize=address,undefined`; exits cleanly with no error after this fix.

### Regression Test

`TEST(Videoio_MJPEG, put_bits_no_heap_overflow)` added to `modules/videoio/test/test_video_io.cpp` — opens a `CAP_OPENCV_MJPEG` VideoWriter for a 1×1 grayscale file and writes one frame; asserts `EXPECT_NO_THROW`.
2026-05-23 14:58:03 +03:00
Adrian Kretz 3157f3e3ed Expand char to 32 bit of float 2026-05-23 10:17:16 +02:00
Alexander Smorkalov ac1ed5c160 Merge pull request #29098 from abhishek-gola:PRelu_block_layout
Added block-layout ChannelsPReLU support
2026-05-22 20:27:12 +03:00
Abhishek Gola bdf348c13a Merge pull request #28934 from abhishek-gola:mlas_gemm
Added MLAS third party module and integrated into GeMM path #28934

### Pull Request Readiness Checklist

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2026-05-22 20:22:15 +03:00
Varun Jaiswal 104d987ca2 Merge pull request #29093 from varun-jaiswal17:dnn-overflow-large-image
fix int32 overflow in shape_utils::total() for large tensors #29093

Fixes https://github.com/opencv/opencv/issues/24914

### Problem

When running inference with a ConvTranspose (deconvolution) layer on large inputs
(e.g. 4864×4864 with 30 channels), the DNN module crashes with:

OpenCV net_impl.cpp: error:

(expected: 'total(ints[i]) > 0'), where
'total(ints[i])' is -1455947776
must be greater than
'0' is 0

The root cause is `shape_utils::total()` which returns `int` (32-bit signed).

`ENGINE_CLASSIC` catches this via `CV_CheckGT` and throws.
`ENGINE_NEW` was silently bypassing the check — the overflow in `total()` itself
was never addressed.

### Changes

**`modules/dnn/include/opencv2/dnn/shape_utils.hpp`** — root fix
- Changed return type of both `total()` overloads from `int` to `size_t`
- Changed accumulator from `int elems = 1` to `size_t elems = 1`

**`modules/dnn/src/net_impl.cpp`**
- Updated `CV_CheckGT(total(...), 0)` to `CV_CheckGT(total(...), (size_t)0)`
  to match the new return type

**`modules/dnn/src/net_impl2.cpp`**
- Added the same `CV_CheckGT` shape validation that `ENGINE_CLASSIC` has in
  `net_impl.cpp:1333-1337` — `ENGINE_NEW` was missing this check entirely

**`modules/dnn/src/legacy_backend.hpp`**
- Removed the now-incorrect `(int)` cast in `CV_CheckEQ` — both sides are
  now `size_t`

### Test

Added `Net.ShapeUtils_total_no_int32_overflow` in `modules/dnn/test/test_misc.cpp`:
- The shape [1920 × 1,478,656] is the exact im2col buffer from the bug report.
EXPECT_EQ verifies total() returns the correct size_t value 2,839,019,520.
EXPECT_LT documents that casting it to int wraps to -1,455,947,776 — the
value that caused the original crash.



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2026-05-22 19:56:51 +03:00
Alexander Smorkalov 2c7e9ebbaf Merge pull request #29099 from abhishek-gola:gpuMat_warnings_fix
GpuMat CI doc warnings fix
2026-05-22 17:30:12 +03:00
Abhishek Gola c7b299a9a5 gpuMat_warnings_fix 2026-05-22 14:59:40 +05:30
Abhishek Gola ac70e5e8c9 block-layout ChannelsPReLU 2026-05-22 13:36:44 +05:30
Varun Jaiswal fe94255a5f Merge pull request #28945 from varun-jaiswal17:add-perf-test
requires (YOLO26n , RetinaFace model download) : https://github.com/opencv/opencv_extra/pull/1357

added performance tests for 14 modern deep learning models to `modules/dnn/perf/perf_net.cpp.` 

**Tests added**
- RT_DETR_L
- RF_DETR
- Grounding_DINO
- BlazeFace
- RetinaFace
- YOLO26n
- YOLO26m_Seg
- SegFormer_B2_Clothes
- Depth_Anything_V2
- SigLIP
- OWLv2
- RAFT
- SAM2_Encoder
- SAM2_Decoder

Models are available at: https://drive.google.com/drive/folders/1lpYAWSLMtxcmfDtYjiw0qwzZX0bCH-i4

### Pull Request Readiness Checklist

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2026-05-21 22:15:35 +03:00
Alexander Smorkalov c9147d6e1f Merge pull request #28912 from Prasadayus:Darknet-cleanup
Darknet cleanup
2026-05-21 21:09:56 +03:00
Alexander Smorkalov 335c5d11ef Merge pull request #29081 from ssam18:fix/issue-29072-randomnormallike-4x
dnn: register RandomNormalLike layer explicitly in init.cpp
2026-05-21 20:44:23 +03:00
Varun Jaiswal 2bf262441f Merge pull request #29074 from varun-jaiswal17:data-layout-wrapper-java
Add missing DataLayout constants in generated bindings for 5.x #29074

- Added DATA_LAYOUT_* constants to missing_consts so they
  are manually injected into Core.java (same approach used for CV_8U, FILLED etc.)
- Added DataLayout entry to type_dict so the generator correctly maps
  DataLayout 

### Tests

modules/dnn/misc/java/test/DnnBlobFromImageWithParamsTest.java:

New test added:
- testDataLayoutConstants: verifies all DATA_LAYOUT_*  constants are accessible from Core
  
Pre-existing tests enabled (were commented out earlier):
- testBlobFromImageWithParamsNHWCScalarScale: verifies blobFromImageWithParams
  produces correct output with DATA_LAYOUT_NHWC and per-channel scalar scaling
- testBlobFromImageWithParams4chMultiImage: verifies blobFromImagesWithParams
  correctly handles a batch of images with DATA_LAYOUT_NHWC layout
  
Closes : #27264 

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2026-05-21 19:09:11 +03:00
Alexander Smorkalov 4d44d22d41 Merge pull request #29041 from kevinylin88:project4_kevinlin_5x
dnn(onnx): eliminate consecutive Transpose pairs with identity compos…
2026-05-21 18:47:26 +03:00
Naresh e791995f10 Merge pull request #29029 from nareshmlx:fix/dnn-slice2-empty-range
[Bug Fix] Slice2 empty-range crash in new DNN engine#29029

Closes: https://github.com/opencv/opencv/issues/29023
Merge with: https://github.com/opencv/opencv_extra/pull/1363

## Summary

ENGINE_NEW asserted `CV_Assert(outsz >= 0)` in `slice2_layer.cpp` when an ONNX Slice op had `start > end` with positive step. Per ONNX spec, such slices are valid and 
produce an empty-range output (size 0). The hard assertion crashed SwinIR inference on ENGINE_NEW.

## Fix

- Replaced the fatal assertion with a graceful clamp: when `outsz < 0`, set `outsz = 0` and `end = start`.
- Moved `allEnds[axis] = end` to after the clamp so downstream shape inference receives a consistent value (the previous order propagated un-clamped garbage end values
into `normalize_axis()`, causing SIGSEGV).

## Test

Adds `Reproducibility_SwinIR_ONNX` accuracy test in `test_model.cpp`. Test data registered in opencv_extra PR with matching branch name `fix/dnn-slice2-empty-range`.

- Without fix: 3/3 FAIL with `slice2_layer.cpp:124: (-215:Assertion failed) outsz >= 0`
- With fix: 3/3 PASS (CPU, OCL, OCL_FP16)

### Pull Request Readiness Checklist

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2026-05-21 18:25:18 +03:00
Abhishek Gola 0a8603b3ac Merge pull request #28889 from abhishek-gola:simd_kernel_speedup
SIMD Kernel speedup for DNN layers #28889

This PR add following speedups for Grounding Dino tiny model.

For Device:  Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
| Model | `ENGINE_NEW (Before)` | `ENGINE_NEW (After)` | `ENGINE_ORT` |
| :--- | :--- | :--- | :--- | 
| **Grounding Dino Tiny** | 3130 ms | 1872.06 ms| 1800.18 ms|

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2026-05-21 15:53:15 +03:00
Alexander Smorkalov 1d54398f07 Merge pull request #29076 from abhishek-gola:core_cuda_changes
Added GPU fit and other GPU core module changes
2026-05-21 15:27:52 +03:00
Alexander Smorkalov 2d528c49d2 Merge pull request #29078 from omrope79:dep_convertFp16
Deprecate CUDA's convertFp16() in favor of convertTo() with CV_16F
2026-05-21 15:26:54 +03:00
kevinylin88 01b23a0de5 Merge pull request #29080 from kevinylin88:project4_kevinlin
core(rvv): fix v_matmul/v_matmuladd scalable semantics and expand lane-group test coverage #29080

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## Platform

SpacemiT X60 (K1), 8-core RISC-V RVV 1.0, VLEN=256, 16GB RAM, OS: Bianbu Linux (kernel 6.6.63), GCC 13.2.0, Build: OpenCV 4.14.0-pre, Release, HAL: YES (RVV HAL 0.0.1)

## Motivation

OpenCV's Universal Intrinsics `v_matmul` and `v_matmuladd` have a semantic bug in the RVV scalable backend (`modules/core/include/opencv2/core/hal/intrin_rvv_scalable.hpp`).

The current implementation uses `v_extract_n(v, 0/1/2/3)` with hardcoded indices, assuming the vector holds exactly 4 float lanes (128-bit fixed). On hardware with VLEN=256 (e.g. SpacemiT K1 / BPI-F3), `v_float32` with LMUL=2 holds 16 lanes. As a result, lanes 4–15 silently reuse the inputs from lanes 0–3, producing wrong results.

OpenCV itself acknowledges this in `modules/core/src/matmul.simd.hpp`:

    // v_matmuladd for RVV is 128-bit only but not scalable,
    // this will fail the test Core_Transform.accuracy

The RVV scalable `transform_32f` path has been disabled because of this bug. However, the existing `TheTest<R>::test_matmul()` only checked the first 4-lane group (the outer loop was effectively hardcoded to `int i = 0`), so the bug was never caught by CI even on wide-vector backends.

## Modification

**Test fix** (`modules/core/test/test_intrin_utils.hpp`): Expanded `test_matmul()` to iterate over all 4-lane groups:

    // Before (only checked lane group i=0)
    int i = 0;
    for (int j = i; j < i + 4; ++j) { ... }

    // After (checks all lane groups)
    for (int i = 0; i < VTraits<R>::vlanes(); i += 4)
    {
        for (int j = i; j < i + 4; ++j) { ... }
    }

**Kernel fix** (`modules/core/include/opencv2/core/hal/intrin_rvv_scalable.hpp`): Rewrote `v_matmul` and `v_matmuladd` to process all 4-lane groups correctly. Each group of 4 lanes now independently computes the full matrix multiply using its own `v[i], v[i+1], v[i+2], v[i+3]` inputs. The `transform_32f` RVV path in `matmul.simd.hpp` remains disabled as the autovectorized path shows better performance on current hardware.

## Experiment 1: Bug reproduced on SpacemiT K1 (VLEN=256)

    ./opencv_test_core --gtest_filter="*intrin*"

Result: `hal_intrin128.float32x4_BASELINE` FAILED with 24 failures, all from lane groups i=4, i=8, i=12 (lanes 4–15).

Representative failures from `v_matmul` (line 1526):

    i=4  j=4:  actual=158        expected=56
    i=4  j=5:  actual=166.39999  expected=59.200001
    i=8  j=8:  actual=314.39999  expected=68.800003
    i=12 j=12: actual=512.40002  expected=81.599998

Representative failures from `v_matmuladd` (line 1540):

    i=4  j=4:  actual=147.5      expected=51.5
    i=8  j=8:  actual=284.70001  expected=60.700001
    i=12 j=12: actual=453.89999  expected=69.900002

Lane group i=0 (j=0..3) passed correctly — confirming the bug only affects lanes beyond the first 4, exactly as expected from the hardcoded `v_extract_n(v, 0/1/2/3)` implementation.

## Experiment 2: Both tests pass after fixing the kernel

    ./opencv_test_core --gtest_filter='hal_intrin128.float32x4_BASELINE'
    [ OK ] hal_intrin128.float32x4_BASELINE (1859 ms)
    [ PASSED ] 1 test.

    ./opencv_test_core --gtest_filter='Core_Transform.accuracy'
    [ OK ] Core_Transform.accuracy (819 ms)
    [ PASSED ] 1 test.

## Experiment 3: RVV transform path remains disabled (performance regression)

After re-enabling the RVV scalable `transform_32f` path experimentally, benchmarks showed a significant regression vs the compiler-autovectorized scalar path (CV_32FC3):

    Size        RVV path   Scalar path   Ratio
    640x480     7.83 ms    1.48 ms       5.3x slower
    1280x720    23.96 ms   5.17 ms       4.6x slower
    1920x1080   53.76 ms   10.12 ms      5.3x slower

The compiler-autovectorized path outperforms the hand-written RVV kernel for this workload, consistent with the original comment in `matmul.simd.hpp`. The `transform_32f` RVV path is therefore kept disabled in this PR. The kernel fix to `v_matmul`/`v_matmuladd` remains necessary for correctness on wide-vector hardware, and the expanded test ensures the bug cannot regress silently in future.
2026-05-21 14:29:55 +03:00
Shelia J. 28f4e22bd7 Merge pull request #29084 from SheliaJimenez:dnn-bug-fix
Fixed gatherND offset calculation #29084

Bug fix: fixed segmentation fault when running ALIKED onnx model with ENGINE_NEW.

### Description
issue: when running ALIKED onnx model with ENGINE_NEW, segmentation fault happens during the inference.

fix: fixed the offset calculation in gatherND.cpp, so gather does not access out-of-bound memory.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-05-21 14:26:40 +03:00
Alexander Smorkalov f28a52398e Merge pull request #29086 from asmorkalov:as/init_list_old_gcc
Fixed build with old GCC.
2026-05-21 14:23:50 +03:00
Alexander Smorkalov b745bbf539 Merge pull request #29082 from vpisarev:fix_input_array_std_vector_5x
fix vector<T> and vector<vector<T>> handling via InputArray/OutputArray
2026-05-21 14:22:00 +03:00
Alexander Smorkalov 657496cab4 Merge pull request #29088 from asmorkalov:as/5.x_bool_io
Fixed bool type IO in FileStorage.
2026-05-21 13:29:59 +03:00
Alexander Smorkalov b97ef07cc3 Merge pull request #29070 from intel-staging:iwi_warp_perspective
Rework IPP warpPerspective to use IW interface and align the whole file accordingly
2026-05-21 13:05:17 +03:00
Alexander Smorkalov 6f9d812722 Fixed build with old GCC. 2026-05-21 13:01:55 +03:00
Alexander Smorkalov 7a23e57680 Fixed bool type IO in FileStorage. 2026-05-21 11:48:33 +03:00
Alexander Smorkalov c42baf6f98 Merge pull request #29083 from vrabaud:ubsan
Get CV_DISABLE_UBSAN to compile with older compilers
2026-05-21 09:26:32 +03:00
Vincent Rabaud 2d7d9e5482 Get CV_DISABLE_UBSAN to compile with older compilers 2026-05-20 22:58:25 +02:00
Alexander Smorkalov f2ecf968b8 Merge pull request #28744 from asmorkalov:as/kleidicv_26.03
KleidiCV update to verison 26.03 #28744

KleidiCV release: https://gitlab.arm.com/kleidi/kleidicv/-/releases/26.03

Tuned DNN test threshold as resize linear produces slightly different result with the new KleidiCV version.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-05-20 22:31:13 +03:00
Vadim Pisarevsky 996713974c ported patch to fix vector<T> and vector<vector<T>> handling via InputArray/OutputArray proxy types, regardless of the underlying std::vector implementation. This is a port of https://github.com/opencv/opencv/pull/28862 to 5.x 2026-05-20 22:07:05 +03:00
Samaresh Kumar Singh 5d0dff1aac dnn: register RandomNormalLike layer explicitly in init.cpp
randomnormallike_layer.cpp registered itself via CV_DNN_REGISTER_LAYER_CLASS_STATIC at file scope. Nothing else in the translation unit was referenced from elsewhere, so when OpenCV is built statically (BUILD_SHARED_LIBS=OFF, as in the reporter's MSVC build) the linker drops the object file and the static-init registration never runs. The ONNX importer then sets layerParams.type = "RandomNormalLike" but getLayerInstance has no factory for that type, and parsing fails with "Can't create layer of type RandomNormalLike".

Switch to the standard pattern used by every other layer in the module: declare RandomNormalLikeLayer in all_layers.hpp, expose a static create() factory from the .cpp, and register it explicitly from initializeLayerFactory in init.cpp. Because init.cpp is referenced by the DNN module init path, this pulls in the layer .cpp regardless of build mode and the registration always runs.

The failure was incorrectly attributed to MSVC in the bug report. The bug is build-mode sensitive (static vs shared), not platform sensitive.

Verified locally on linux/gcc-13 by building modules/dnn and
opencv_test_dnn against the patched tree, then running opencv_test_dnn --gtest_filter='Test_ONNX_layers.RandomNormalLike_basic/0:Test_ONNX_layers.RandomNormalLike_complex/0'

Both tests pass; without the patch the same binary reproduces the
"Can't create layer of type RandomNormalLike" error from the report.
2026-05-20 12:48:50 -05:00
Alexander Smorkalov c9070f9f35 Merge branch 4.x 2026-05-20 17:57:51 +03:00
Kevin Lin 7be1853b0b dnn(onnx): eliminate consecutive Transpose pairs with identity composition
Port of the 4.x patch to 5.x. Registers a new
ConsecutiveTransposePairsSubgraph in onnx_graph_simplifier.cpp's
simplifySubgraphs() so that consecutive Transpose nodes whose composed
permutation is identity are eliminated at the ONNX proto level. This
runs before either engine takes over, so both ENGINE_CLASSIC and
ENGINE_NEW benefit.

Equivalent optimizations exist in tf2onnx (TransposeOptimizer._transpose_handler)
and ONNX Runtime (HandleTransposeImpl, 'Permutations cancel' branch).
2026-05-20 21:32:47 +08:00
s-trinh 0abd5c86f7 Merge pull request #28824 from s-trinh:update_ArUco_doc
Update ArUco doc #28824

See https://github.com/opencv/opencv/pull/28823

Update of the ArUco doc but targeted for OpenCV 4.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-20 15:05:51 +03:00
Om Navin Rope 1e731a2e2a Changes for fp16 conversion 2026-05-20 17:30:37 +05:30
Abhishek Gola 2f89729f2d core cuda changes 2026-05-20 17:29:34 +05:30
Fedorov, Andrey c2b89072c4 rewoked warp perspective and aligned the whole file 2026-05-19 09:33:41 -07:00
Alexander Smorkalov 7c7ee97493 Merge pull request #29060 from mshabunin:fix-comma-init
core: deprecate MatCommaInitializer_
2026-05-19 13:58:54 +03:00
satyam yadav cc8f683b6a Merge pull request #28482 from satyam102006:core/yaml-1-2-support
core: add YAML 1.2 support for FileStorage #28482

Fixes: #26363
CI Update: https://github.com/opencv/ci-gha-workflow/pull/306

Summary: 
- Bool true/false literals support
- Header-less YAMLs support for Python YAML module compatibility. Header-less files are parsed as YAMLs by default
- Added FORMAT_YAML_1_0 flag to FileStorage for fallback.
- New YAML1.2 header

Testing: 
- Added Core_InputOutput.YAML_1_2_Compatibility test case in test_io.cpp.
- Added YAML interop test in Python
2026-05-19 13:44:50 +03:00
熊阔豪 2ceda58d80 Merge pull request #29040 from MrBear999-ui:project4_MrBear
imgproc: add kernel size validation in boxFilter #29040

Check that ksize dimensions are strictly positive in boxFilter(), returning StsBadSize error instead of producing undefined behavior. 

Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-05-19 13:41:08 +03:00
Abhishek Gola f85a662563 Merge pull request #28971 from abhishek-gola:subgraph_argname_fix
Fixed subgraph name scoping in new DNN engine #28971

closes: https://github.com/opencv/opencv/issues/23663, https://github.com/opencv/opencv/issues/19977

OpenCV extra: https://github.com/opencv/opencv_extra/pull/1362

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-19 12:38:16 +03:00
Teddy-Yangjiale 666ad5bfcf Merge pull request #29058 from Teddy-Yangjiale:rvv-opt
RISC-V: add RVV convertScale paths for selected depth conversions #29058

### Summary

This PR extends the RISC-V RVV HAL `convertScale` implementation with optimized paths for selected `convertTo()` depth conversions:

- `CV_32F -> CV_8U`
- `CV_16U -> CV_32F`
- `CV_16S -> CV_32F`

The change is confined to `hal/riscv-rvv/src/core/convert_scale.cpp`. It does not modify the generic `convertTo()` implementation or affect dispatch for non-RISC-V targets.

### Rationale

`Mat::convertTo()` reaches architecture-specific implementations through the HAL `convertScale` interface. Adding these paths in the RVV HAL keeps the optimization in the existing backend layer and avoids introducing RISC-V-specific branches into common OpenCV code.

The selected conversions were chosen based on measured benefit and practical relevance. `CV_32F -> CV_8U` provides the largest speedup, while `CV_16U -> CV_32F` and `CV_16S -> CV_32F` provide consistent improvements for common image-processing data paths.

### Benchmark

Tested on Banana Pi BPI-F3, an 8-core RISC-V board with RVV 1.0 support.

Build and test configuration:

- OpenCV `4.14.0-pre`
- Release build
- RVV HAL enabled
- Compiler: `riscv64-unknown-linux-gnu-g++ 14.2.1`
- Test binary: `opencv_perf_core`
- Test filter: `*convertTo*`
- Samples: `--perf_force_samples=20 --perf_min_samples=20`

Benchmark command:

```bash
./opencv_perf_core \
  --gtest_filter=*convertTo* \
  --perf_force_samples=20 \
  --perf_min_samples=20
```

Each benchmark case was measured with 20 forced samples. The baseline/after comparison was repeated to check run-to-run stability. The optimized conversion pairs showed consistent improvements.

Second-run comparison for the optimized pairs:

```text
OPTIMIZED_PAIRS: 24 cases, gmean 2.6009x

CV_32F -> CV_8U:   16.0718x
CV_16U -> CV_32F:   1.2334x
CV_16S -> CV_32F:   1.1376x
```

`CV_32F -> CV_8U` showed the largest improvement, with speedups in the `14.5x` to `18.8x` range across the tested image sizes, channel counts, and alpha values.

### Notes on Measurement

The initial benchmark run showed noise in conversion pairs not touched by this PR, likely due to board-level variance such as frequency scaling, thermal state, or background scheduling.

A second run showed untouched pairs close to neutral:

```text
UNTOUCHED_OTHER: gmean 0.9941x
```

The optimized conversion pairs remained consistently faster across both runs, with no observed regression in the added RVV paths.



### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-05-19 10:19:26 +03:00
Teddy-Yangjiale 66263c5952 Merge pull request #29057 from Teddy-Yangjiale:rvv-core-norm
Rvv core norm #29057

Fixes opencv/opencv#29052

### Problem
`Core_Norm/ElemWiseTest.accuracy/0` failed on RISC-V with RVV enabled when computing norm for `CV_16S` data.
The reported failing case was:

```text
src[0] ~ 16sC4 3-dim (1 x 116 x 40)
```
The expected norm result was a large positive `double`, but the RVV path returned an incorrect value:

```text
expected: 3370900308417
actual:   -173296
```

This indicates that the problem was not in the public `cv::norm()` API, but in the RVV HAL implementation used for the `CV_16S` L2/L2SQR accumulation path.

### Root Cause

The RVV HAL has a specialized implementation for `CV_16S` L2 norm:

```cpp
NormL2_RVV<short, double>
```

The implementation widens `int16` values, squares them, converts the widened products to `float64`, accumulates them in an `f64m8` vector, and finally reduces the vector to a scalar `double`:

```cpp
auto s = __riscv_vfmv_v_f_f64m8(0, vlmax);
...
auto v_mul = __riscv_vwmul(v, v, vl);
s = __riscv_vfadd_tu(s, s, __riscv_vfwcvt_f(v_mul, vl), vl);
...
return __riscv_vfmv_f(__riscv_vfredosum(...));
```

The bug was in the scalar initializer passed to `__riscv_vfredosum`.

Before this patch, the code created an `f64m1` scalar vector but used the maximum vector length for `e32m1`:

```cpp
__riscv_vfmv_s_f_f64m1(0, __riscv_vsetvlmax_e32m1())
```

This is inconsistent: the vector type is `f64m1`, so the VL used to initialize it must correspond to `e64m1`, not `e32m1`.
2026-05-19 09:28:52 +03:00
Alexander Smorkalov 0d6af9162c Merge pull request #29033 from Teddy-Yangjiale:rvv-vlseg-opt
rvv: use vlseg/vsseg instead of vlse/vsse in universal intrinsic interleave ops
2026-05-19 09:19:55 +03:00
Rafael Muñoz Salinas 8c8b266b74 Merge pull request #29034 from rmsalinas:fix-arucodictionary
objdetect(aruco): fix maxCorrectionBits in predefined dictionaries #29034

This PR fixes the bugs in the creation of some dictionaries and standarizes its construction.

## Bugs
1. **DICT_ARUCO_MIP_36h12_DATA** was incorrectly assigned `maxCorrectionBits=12`, which is the intermarker distance. This was causing a high rate of false positives during detection, which is a significant problem.
2. **APRILTAG** dictionaries were incorrectly created with `maxCorrectionBits=0`, making it impossible to do error correction.

## Solution
Given a intermarker distance X, the correct `maxCorrectionBits = X/2 - 1`. Thus, standardize the construction of Dictionaries as such.
2026-05-19 09:18:29 +03:00
Alexander Smorkalov 6b40aa6637 Merge pull request #29061 from mshabunin:fix-gh-templates
Added extra instructions to GH templates
2026-05-19 08:26:12 +03:00
Maksim Shabunin 201381e019 core: deprecate MatCommaInitializer_ 2026-05-19 07:33:18 +03:00
Tiansuanyu 8798e7948a imgproc: add RVV optimization for spatialGradient 2026-05-19 12:12:43 +08:00
Maksim Shabunin 6fd9f32633 Added extra instructions to GH templates 2026-05-19 06:52:22 +03:00
Michael Klatis cd4d732442 Merge pull request #29051 from klatism:libpng-upgrade-1.6.57
libpng upgrade to 1.6.57 #29051

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-05-18 17:14:24 +03:00
Alexander Smorkalov e739e0a7e8 Merge pull request #29054 from asmorkalov:as/real_int_overflow
Fixed data overflow issue when int64 value is interpreted as float/double
2026-05-18 17:05:15 +03:00
Alexander Smorkalov 8eeacc3cc6 Merge pull request #29053 from asmorkalov:as/fix_net_profile
Removed internal structs from DNN interface to fix Obj-C/Swift bindings.
2026-05-18 17:00:55 +03:00
Alexander Smorkalov bd0bc5af59 Fixed data overflow issue when int64 value is interpreted as float/double. 2026-05-18 13:35:38 +03:00
Alexander Smorkalov a7e02e5742 Removed internal structs from DNN interface to fix Obj-C/Swift bindings. 2026-05-18 12:21:14 +03:00
Ebrahim Namdari b29ea17666 Merge pull request #28967 from IbrahimNamdari:add-video-context-manager
Python: add context manager compatibility for VideoCapture and VideoWriter #28967

### Description
This PR implements the context manager protocol (`with` statement support) for `cv2.VideoCapture` and `cv2.VideoWriter` objects in Python.

### Problem
Currently, these classes do not support the context manager protocol, leading to a `TypeError: ... object does not support the context manager protocol` when used with a `with` block. As reported in issue #28956, if an exception occurs before an explicit `.release()` call, the video resource or file remains locked in the system[cite: 298, 309].

### Identification & Motivation
By analyzing the issue and the Python binding structure of OpenCV, I identified that while the core logic resides in C++, the Pythonic behavior can be enhanced by injecting `__enter__` and `__exit__` methods during the module initialization. This ensures that resources are automatically and safely released even if the program execution is interrupted by an error[cite: 298, 325].

### Solution
I have modified `modules/python/package/cv2/__init__.py` to dynamically inject the necessary methods:
1. Defined `_video_enter` to return the object itself (standard context manager behavior).
2. Defined `_video_exit` to trigger the `.release()` method automatically.
3. Injected these methods into `VideoCapture` and `VideoWriter` classes immediately after the `bootstrap()` function to ensure the native C++ classes are correctly loaded into the global namespace.

Fixes #28956
2026-05-15 15:41:02 +03:00
Teddy-Yangjiale 8c335899ee rvv: use vlseg/vsseg instead of vlse/vsse in deinterleave/interleave 2026-05-15 19:43:41 +08:00
Alexander Smorkalov 6018b0cf82 Merge pull request #28897 from Lurie97:fix_diag
core: implement OpenCL kernel for UMat::diag to avoid aliasing races
2026-05-15 13:31:12 +03:00
Shengyu-Wu da11697f8c Merge pull request #28938 from Shengyu-Wu:feat/fast-rvv
## Summary

Replace the existing RVV HAL FAST-16 implementation (`hal/riscv-rvv/src/features2d/fast.cpp`) with a score-first approach, achieving **2-4x speedup** over the previous HAL implementation on RISC-V hardware.

## Approach

The previous HAL implementation used a count-based approach (XOR trick + per-pixel bitmask scanning + separate cornerScore computation). This PR replaces it with a **score-first** approach that processes multiple horizontal pixels in parallel using RVV vectors.

Key optimizations:

1. **Score-first approach**: Directly compute corner scores via min/max reduction over all 9-arc windows, then threshold on score. This eliminates the arc-counting pass and the separate `cornerScore` function.

2. **u8 to i16 zero-extension**: Zero-extend pixel values to int16 for natural signed comparison, eliminating the XOR-delta trick and preventing score overflow.

3. **vcompress batch output**: Extract all corner indices in one vector operation instead of per-lane bitmask scanning.

4. **vlen-adaptive**: Uses `vsetvl_e16m1` for dynamic vector length — works on any RVV 1.0 implementation regardless of VLEN (128, 256, 512, etc.) without code changes.

## Performance

Benchmarked by maintainer on **Muse Pi v3.0** (GCC, SpacemiT toolchain v1.0.4):

| Test | Previous HAL (ms) | This PR (ms) | Speedup |
|------|-------------------|--------------|---------|
| FAST-20 NMS=true | 23.06 | 7.99 | **2.89x** |
| FAST-20 NMS=true (orig) | 7.31 | 1.80 | **4.06x** |
| FAST-30 NMS=false | 20.10 | 7.75 | **2.59x** |
| FAST_DEFAULT s2.jpg | 76.94 | 19.06 | **4.04x** |
| ORB_DEFAULT chess9 | 64.46 | 25.43 | **2.53x** |

Both NMS=true and NMS=false cases are supported.

## Files Changed

- **`hal/riscv-rvv/src/features2d/fast.cpp`**: Replaced count-based implementation with score-first approach
- No changes to `modules/features2d/src/fast.cpp` or any other files

## Test Plan

- [x] Built with RISC-V cross-compilation toolchain (GCC 14.2, RVV 1.0)
- [x] Tested on QEMU (rv64, v=true, vlen=256) — correct results for both NMS modes
- [x] Maintainer tested on Muse Pi v3.0 hardware — 2-4x speedup confirmed
- [ ] CI passes on all platforms
2026-05-15 11:59:57 +03:00
SamareshSingh 4b031345f0 Merge pull request #28954 from ssam18:fix-cuda13-empty-arch-detection-28952
Fix CMake crash when CUDA arch autodetection comes up empty #28954

With CUDA 13 and a system gcc that the toolkit does not support, every NVCC probe inside ocv_filter_available_architecture fails and the result list ends up empty. The next line then calls list(GET ... -1) on that empty list, so CMake dies with the unhelpful message about list GET given empty list and the user has no obvious next step. This change replaces that path with a fatal error that points at the three real fixes, namely setting CUDA_ARCH_BIN, setting CUDA_HOST_COMPILER, or enabling OPENCV_CMAKE_CUDA_DEBUG to see what NVCC actually printed. It also forwards CMAKE_CUDA_HOST_COMPILER into CUDA_HOST_COMPILER since the issue reporter passed the standard CMake variable and it was being silently ignored.
Fixes #28952
2026-05-14 15:43:59 +03:00
Alexander Smorkalov e9b4668bad Merge pull request #29008 from ManfredShao:gapi/fluid-inrange-simd
gapi/fluid: add Universal Intrinsics SIMD for InRange kernel
2026-05-14 15:41:03 +03:00
Abhishek Gola 873a4635c6 Merge pull request #28752 from abhishek-gola:net_profiling
Added net profiling support #28752

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-14 14:47:38 +03:00
Alexander Smorkalov ba925f9b07 Merge pull request #29030 from asmorkalov:as/cube_root_ub
Suppressed UB warning in cubeRoot function.
2026-05-14 13:56:09 +03:00
Rafael Muñoz Salinas cc1ab3e3ac Merge pull request #28773 from rmsalinas:imgproc_find_truco_contours
imgproc: add findTRUContours - lock-free parallel contour extraction #28773

Integrates the TRUCO algorithm (Threaded Raster Unrestricted Contour Ownership, @cite TRUCO2026) as a transparent fast path inside `cv::findContours`. No API change is required: existing code automatically benefits when `mode=RETR_LIST` and no hierarchy output is requested.

### How it works

When `mode=RETR_LIST` and the caller does not request hierarchy, `findContours` delegates to the TRUCO parallel engine instead of Suzuki-Abe. All ContourApproximationModes are supported; approximation is applied in parallel after extraction. In all other cases the original Suzuki-Abe path is used unchanged.

### Key design ideas
- Row-strip domain decomposition parallelised via `cv::parallel_for_`
- Start-point ownership rule + speculative downward tracing eliminate tile stitching and synchronisation primitives (lock-free)
- 8-bit state space instead of the 32-bit integer labeling required by Suzuki-Abe, giving higher SIMD throughput and lower memory-bandwidth pressure
- Paged contour buffer (`TRUCOPagedContour`) avoids heap reallocation on the hot tracing path
- SIMD-accelerated row scanning via `cv::v_uint8` intrinsics
- Thread count controlled globally via `cv::setNumThreads()`, consistent with OpenCV conventions

### Output correctness

Significant effort has gone into ensuring the output is identical to the original `findContours` in every respect: same contour set, same ordering, and same approximation results for all methods. A dedicated test suite (`test_contours_truco.cpp`) verifies exact match against Suzuki-Abe across all four `ContourApproximationModes` and thread counts from 1 to 39, using noise images, circles, nested rectangles, and mixed scenes.

### Performance vs. Suzuki-Abe (from submitted paper)
- single-thread: ~1.8–1.9× faster (8-bit SIMD advantage)
- 20 threads: ~14–20× faster on i7-13700H (6P+8E, 20T)
- 20 threads: ~10–12× faster on Xeon Silver 4510 (12C/24T)

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-14 13:55:39 +03:00
Alexander Smorkalov fb3054d814 Suppressed UB warning in cubeRoot function. 2026-05-14 11:26:40 +03:00
Giles Payne 7cc6d8576c Merge pull request #28704 from komakai:replace-jazzy-with-docc
Use Xcode DocC tool for buiding Apple platform docs + add quick-start tutorial #28704

### Pull Request Readiness Checklist

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work

This PR resolves https://github.com/opencv/opencv/issues/28702 and https://github.com/opencv/opencv/issues/28703
Additionally it resolves build issues on Apple platforms by making build scripts run with Python 3
2026-05-14 11:22:04 +03:00
Pratham Kumar eadaab5fbb Merge pull request #28664 from pratham-mcw:armpl_dft_opt
Add ARMPL support for DFT Function #28664

- This PR introduces hal/armpl/ with implementation of 1D, 2D DFT and DCT routines using ARM Performance Libraries as a custom HAL replacement for OpenCV's DFT & DCT Function.
- ArmPL MSI package is automatically downloaded and extracted via CMake when building on Windows ARM64, with a WITH_ARMPL option that defaults to ON for that platform.
- Forward and inverse real DFT calls in dxt.cpp are routed through ArmPL when available, with scaling applied only when needed.
- Test error thresholds in test_dxt.cpp are relaxed (from 1e-5 to 2e-4 for float ) to account for numerical differences between ArmPL and OpenCV's reference DFT results.

**Performance Benchmarks :**
<img width="993" height="835" alt="image" src="https://github.com/user-attachments/assets/76def647-6d20-4bce-8bc9-7363e723669f" />

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
2026-05-14 10:56:21 +03:00
Alexander Smorkalov 851e0196b4 Merge pull request #29012 from asmorkalov:as/int_scissors
Moved IntelligentScissors to photo module.
2026-05-14 09:21:47 +03:00
manfred 550f95a923 gapi/fluid: add Universal Intrinsics SIMD for InRange kernel
- Implement inrange_simd() covering uchar/ushort/short, chan=1/2/3/4
- Replace legacy CV_SIMD128 run_inrange3 with modern CV_SIMD API
- Extend perf tests to cover all added type/channel combinations
- Resolves TODO introduced in #12608 (2018)
2026-05-14 13:48:15 +08:00
orbisai0security 357ad24262 Merge pull request #28915 from orbisai0security:fix-v-001-filter-memcpy-bounds-check
fix: Guard ndsrvp filter center copy when source span is empty #28915

## Summary
This adds a defensive guard around the centre-region `memcpy` in the ndsrvp HAL filter padding path.

**Description**: Multiple memcpy calls in the ndsrvp HAL filter, bilateral filter, and median blur routines use computed sizes (e.g., cnes * (rborder - j), cn * (rborder - j), src_step * height) without validating that the computed byte count fits within the destination buffer. An attacker supplying a crafted image with manipulated dimensions, channel counts, or border parameters can cause the computed copy size to exceed the allocated destination buffer, resulting in a heap buffer overflow that corrupts adjacent memory.

## Changes
- `hal/ndsrvp/src/filter.cpp`

## Verification
- [x] Build passes
- [x] Scanner re-scan confirms fix
- [x] LLM code review passed

---
*Automated security fix by [OrbisAI Security](https://orbisappsec.com)*
2026-05-14 07:52:08 +03:00
Alexander Smorkalov 0ba56c3ecc Moved IntelligentScissors to photo module. 2026-05-13 10:53:09 +03:00
Abhishek Gola 914c0b2bc8 Merge pull request #28963 from abhishek-gola:custom_layers
Added custom layer support in new DNN engine #28963

Closes: https://github.com/opencv/opencv/issues/26200
Merge with: https://github.com/opencv/opencv_extra/pull/1358

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-13 10:31:25 +03:00
Abhishek Gola 72e0bc2bf3 Merge pull request #28957 from abhishek-gola:fusion_block_layout_extension
Extend attention fusion for runtime QK scale and add MatMul to Gemm rewriter #28957

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-13 08:32:00 +03:00
omrope79 7b6dc220b1 Merge pull request #28722 from omrope79:fix-dnn-fusion
fixes a bug in ENGINE_NEW of incorrect fusion of conv+relu+batchnorm #28722

### Pull Request Readiness Checklist

OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1325
Closes Issue https://github.com/opencv/opencv/issues/28689

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-12 23:23:31 +03:00
Prasad Ayush Kumar 223b110c30 Merge pull request #28968 from Prasadayus:Identity_function_fix_for_0D
Fixes the issue for Identity functions for 0D #28968

Closes https://github.com/opencv/opencv/issues/25763

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-12 14:43:46 +03:00
Prasad Ayush Kumar 2ad46b860b Merge pull request #28982 from Prasadayus:fix_unconnected_out_layers
fix getUnconnectedOutLayers() in new engine #28982

Solves https://github.com/opencv/opencv/issues/26491

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-12 13:34:01 +03:00
donotsleepy e640a9bfb8 Merge pull request #28995 from donotsleepy:fix/png-found-status-guard
cmake: fix PNG status display when BUILD_PNG=ON #28995

### Description

Fixes #28657.

On macOS (and other Apple platforms), `BUILD_PNG=ON` is the default. The build system correctly clears `PNG_FOUND` in `OpenCVFindLibsGrfmt.cmake` and builds libpng from the bundled source. However, during the subsequent module processing phase, a downstream `find_package(PNG)` call — triggered transitively via `include()` in the **same scope** — overwrites not only `PNG_FOUND`, but also `PNG_INCLUDE_DIR`, `PNG_LIBRARIES`, and `PNG_VERSION_STRING`.

#### Root Cause: FindPNG.cmake Has Asymmetric Guards

Reading CMake 4.3's `FindPNG.cmake` (`/opt/homebrew/share/cmake/Modules/FindPNG.cmake`) reveals:

| Variable | Guard? | Fate |
|---|---|---|
| `PNG_LIBRARY` | `if(NOT PNG_LIBRARY)` at line 138 — **guarded** | Preserved |
| `PNG_PNG_INCLUDE_DIR` | `find_path(...)` at line 115 — **no guard** | **Overwritten** |
| `PNG_INCLUDE_DIR` | Derived from `PNG_PNG_INCLUDE_DIR` | **Chain-overwritten** |
| `PNG_LIBRARIES` | Derived from `PNG_LIBRARY` + `ZLIB_LIBRARY` | **Partly overwritten** |
| `PNG_VERSION_STRING` | Parsed from `PNG_PNG_INCLUDE_DIR/png.h` | **Overwritten** |

`PNG_LIBRARY` survives because FindPNG itself checks `if(NOT PNG_LIBRARY)`. But `find_path(PNG_PNG_INCLUDE_DIR)` runs unconditionally — re-searching and overwriting the bundled path with the system one.

#### Fix: Three Coordinated Changes

**Part A — Status guard** (`CMakeLists.txt`): When `BUILD_PNG=ON`, pass `FALSE` as the condition to always show `"build"` regardless of `PNG_FOUND`.

**Part B — Variable lock** (`cmake/OpenCVFindLibsGrfmt.cmake`): After building from bundled source, lock `PNG_PNG_INCLUDE_DIR` as `CACHE INTERNAL`. CMake's `find_path()` checks the cache first — if the variable exists with a valid path, it skips re-searching. The system path is never found.

**Part C — Cache cleanup** (`cmake/OpenCVFindLibsGrfmt.cmake`): Add `PNG_PNG_INCLUDE_DIR` to the `ocv_clear_internal_cache_vars()` call in the `else` branch. When a user switches `BUILD_PNG=OFF`, the cached variable is cleared, allowing `find_path` to search for the system libpng normally. Prevents stale cache leakage across configuration changes.

#### Verification (macOS 26, CMake 4.3, Homebrew libpng 1.6.58)

```
BEFORE downstream find_package(PNG):
  PNG_LIBRARY   = libpng
  PNG_INCLUDE_DIR = .../3rdparty/libpng
  PNG_VERSION_STRING = 1.6.37

AFTER downstream find_package(PNG) [with fix]:
  PNG_LIBRARY   = libpng          ← preserved (FindPNG guard)
  PNG_INCLUDE_DIR starts with .../3rdparty/libpng  ← locked (Part B)
  PNG_VERSION_STRING = 1.6.53     ← from bundled png.h
  Status display: build (ver 1.6.53)  ← correct (Part A)
```

The full reproduction and analysis are in the attached `bugReview/` directory.

### Checklist

- [x] There is a reference to the original bug report and related work
- [x] The test case is in `bugReview/` directory
- [x] Tested on macOS 26 (arm64) with CMake 4.3 + Homebrew libpng 1.6.58
- [x] Cache hygiene verified for BUILD_PNG ON→OFF→ON transitions
2026-05-12 12:40:57 +03:00
Alexander Smorkalov 594cd4204a Merge pull request #28999 from Teddy-Yangjiale:rvv-sigmoid-opt
dnn: vectorize SigmoidFunctor using universal intrinsics
2026-05-12 11:29:12 +03:00
Rafael Muñoz Salinas b7ae67e8ec Merge pull request #28990 from rmsalinas:fix-flann-radiussearch-output-size
flann: fix Index::radiusSearch output size mismatch #28990

## Problem

`Index::radiusSearch` returns `nn` — the number of neighbors found within the radius — but the output arrays `_indices` and `_dists` are always allocated with `maxResults` columns regardless of how many neighbors were actually found. This creates two inconsistencies:

1. When `nn < maxResults`, the output arrays contain stale entries beyond position `nn`, so `nn != indices.cols` and callers cannot rely on the output size to know how many results are valid.
2. When `nn > maxResults`, the return value exceeds the capacity of the output arrays, which have only `maxResults` columns. The extra neighbors were never stored, so the return value is misleading.

The following snippet reproduces both cases:

```cpp
int testFLANN(){
    std::vector<cv::Point2f> corners={{3679.83,1857.22},{3324.43,1850.67},{3278.83,1502.32},{3621.32,1508.69},{3662.26,1837.97},{3336.06,1839.28},{3291.74,1516.03},{3608.94,1518.72},{2980.66,1843.22},{2628.11,1837.14},{2607.73,1491.57},{2948.22,1496.76},{2289.31,1830.19},{1943.99,1824.17},{1945.51,1482.34},{2280.68,1487.39},{1607.47,1819.11},{1260.04,1813.26},{1283.94,1470.34},{1620.03,1475.85},{923.063,1808.08},{576.193,1802.4},{624.713,1459.78},{959.715,1464.93},{3270.25,1494.98},{2952.95,1492.19},{2922.75,1190.02},{3231.05,1194.81},{3284.82,1507.62},{2942.74,1502.59},{2912.32,1178.33},{3242.98,1183.19},{2612.82,1496.37},{2276.44,1491.63},{2267.61,1170.13},{2593.64,1174.43},{1949.73,1486.61},{1614.92,1480.64},{1626.77,1160.34},{1952,1165.51},{1289.38,1476.21},{953.709,1470.21},{986.779,1148.92},{1311.98,1154.89},{3567.28,1195.1},{3237.51,1189.03},{3197.63,884.15},{3518.61,888.76},{2918.31,1183.62},{2588.69,1179.37},{2570.64,875.672},{2889.87,878.902},{2271.97,1174.25},{1947.62,1169.61},{1948.56,868.197},{2263.8,872.533},{1630.99,1164.6},{1306.74,1159.52},{1327.81,858.358},{1642.69,862.765},{992.896,1155.52},{666.107,1149.96},{705.246,845.776},{1023.25,851.582},{3204.48,889.981},{2883.49,885.252},{2859.31,593.752},{3175.43,594.736},{2575.86,880.331},{2259.44,876.647},{2252.33,589.235},{2560.47,592.332},{1954.36,873.708},{1636.7,868.068},{1646.93,580.76},{1956.14,584.797},{1332.02,862.624},{1017.11,856.71},{1045.32,572.178},{1350.95,577.635},{3481.36,605.324},{3169.05,601.083},{3138.84,324.116},{3446.12,325.545},{2866.14,599.61},{2555.38,597.142},{2541.51,320.931},{2845.5,322.419},{2256.79,593.253},{1950.17,590.12},{1951.8,317.445},{2250.88,319.418},{1654.16,587.663},{1344.83,582.787},{1362.8,309.253},{1663.89,313.056},{1050.94,577.867},{741.476,571.648},{776.433,300.444},{1076.97,304.56},{3327.05,1847.65},{2977.74,1840.48},{2945.48,1499.68},{3281.82,1504.97},{2630.85,1834.22},{2287.2,1828.06},{2278.56,1489.51},{2610.64,1494.31},{1946.11,1822.05},{1604.75,1816.18},{1617.11,1478.59},{1947.62,1484.48},{1262.96,1810.53},{920.46,1805.05},{956.712,1467.57},{1286.66,1473.27},{3618.7,1511.71},{3281.82,1504.97},{3240.24,1186.11},{3564.22,1192.53},{2945.48,1499.68},{2610.64,1494.31},{2591.52,1176.55},{2915.32,1180.97},{2278.56,1489.51},{1947.62,1484.48},{1949.81,1167.56},{2269.79,1172.18},{1617.11,1478.59},{1286.66,1473.27},{1308.98,1157.53},{1628.88,1162.47},{956.712,1467.57},{627.315,1462.81},{669.943,1146.75},{989.494,1151.86},{3240.25,1186.11},{2915.32,1180.98},{2887.03,881.727},{3200.69,886.725},{2591.52,1176.55},{2269.79,1172.19},{2261.62,874.587},{2573.63,878.324},{1949.81,1167.55},{1628.88,1162.47},{1640.44,864.751},{1950.74,870.259},{1308.98,1157.53},{989.494,1151.86},{1019.41,854.787},{1329.92,860.49},{3515.88,891.68},{3200.69,886.725},{3171.89,598.263},{3478.26,602.785},{2887.04,881.725},{2573.63,878.324},{2558.28,594.392},{2863.1,597.005},{2261.62,874.587},{1950.74,870.259},{1952.4,588.114},{2254.56,591.241},{1640.44,864.751},{1329.92,860.49},{1348.65,579.559},{1650.55,584.209},{1019.41,854.787},{708.649,849.44},{745.388,568.534},{1047.42,574.313},{3171.89,598.263},{2863.1,597.005},{2842.6,325.169},{3141.93,326.654},{2558.28,594.392},{2254.56,591.241},{2248.65,321.425},{2544.55,323.537},{1952.4,588.113},{1650.55,584.209},{1660.07,316.284},{1954.02,319.457},{1348.65,579.559},{1047.43,574.312},{1073.06,307.674},{1366.42,312.707}};

    cv::flann::KDTreeIndexParams indexParams(1);
    cv::Mat data = cv::Mat(corners).reshape(1, static_cast<int>(corners.size()));
    auto index = std::make_shared<cv::flann::Index>(data, indexParams);

    int maxResults = 4;
    int nTimesError1 = 0;  // nn != indices.size()
    int nTimesError2 = 0;  // nn > maxResults
    for (size_t i = 0; i < corners.size(); i++) {
        std::vector<int> indices(4);
        std::vector<float> dists(4);
        int nn = index->radiusSearch(data.row(static_cast<int>(i)), indices, dists, 100, maxResults);
        if (nn != (int)indices.size()) { nTimesError1++; }
        if (nn > maxResults)           { nTimesError2++; }
    }
    std::cout << "nTimesError1: " << nTimesError1 << std::endl;  // > 0 before fix
    std::cout << "nTimesError2: " << nTimesError2 << std::endl;  // > 0 before fix

    return 1;
}
```

## Fix

- Capture the search result in `rsearch_ret` instead of returning early from each `switch` case (this also required adding the missing `break` statements that would otherwise have caused fall-through between type-incompatible distance specialisations).
- Cap `rsearch_ret` at `maxResults` so the return value never exceeds the capacity of the output arrays.
- When `rsearch_ret < maxResults`, trim `_indices` and `_dists` to `query.rows × rsearch_ret` via `colRange(0, rsearch_ret).copyTo(...)` so the output dimensions always match the returned count.
2026-05-12 10:41:01 +03:00
Varun Jaiswal 3cebf7d61e Merge pull request #28969 from varun-jaiswal17:update_ort_version-1.25.1
update default ONNX Runtime version to 1.25.1 #28969

Bump ONNX Runtime default version to latest version: 1.25.1

Includes the following update:
 https://github.com/microsoft/onnxruntime/pull/27164 : Adds LpNormalization opset-22 kernel support missing in 1.24.2. 

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-12 07:29:22 +03:00
Teddy-Yangjiale 4b1c861ab7 dnn: vectorize SigmoidFunctor using universal intrinsics 2026-05-12 02:22:19 +08:00
Abhishek Gola 8cc3f68cd4 Merge pull request #28964 from abhishek-gola:extend_primitive_core_ops
Extended primitive core operations to support new types #28964

The support was already there, this PR tests them on edge cases and patch the fix.

closes: https://github.com/opencv/opencv/issues/24580
### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-08 17:32:56 +03:00
Abhishek Gola cc2b7659b4 Merge pull request #28966 from abhishek-gola:dequantized_issue_fix
Fixed dequantizelinear slicing bug #28966

closes: https://github.com/opencv/opencv/issues/25999

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-08 16:57:24 +03:00
Pratham Kumar 9929b5ceb9 Merge pull request #28610 from pratham-mcw:core-norm_mask-opt
core : add NEON intrinsics support for norm_mask function #28610

- This PR adds NEON intrinsics-based implementations for masked norm operations in norm.simd.hpp for ARM64 architecture.
- The optimized implementation uses ARM NEON intrinsics to accelerate masked norm computations (Infinity norm, L1 norm, and L2 norm) used by the norm function when a mask is provided.
- In the x64 architecture, masked norm operations benefit from IPP-based optimized implementations. However, on ARM64, the execution falls back to scalar implementations, which results in lower performance.
- To achieve performance parity with x64, NEON-based SIMD implementations have been added for ARM64.
- Additionally, scalar loop unrolling optimizations have been added for non-masked norm operations.
- After introducing these changes, masked norm operations showed significant performance improvements on ARM64 platforms, particularly for single-channel (cn=1) operations where NEON intrinsics provide the greatest benefit.
<img width="952" height="822" alt="image" src="https://github.com/user-attachments/assets/12d35f93-a316-4520-9d9c-12ce6b371ddb" />

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
2026-05-08 09:57:09 +03:00
Alexander Smorkalov 5488f4e11d Merge pull request #28961 from dkurt:d.kurtaev/ov_avgpool_opset16
Enable 1D int8 AvgPool with OpenVINO with 2025.3
2026-05-08 08:48:32 +03:00
Alexander Smorkalov 0b1e4f54da Merge pull request #28959 from Kumataro:fix28930
core: fix signed overflow UB in Point/Point3i::dot()
2026-05-07 21:25:07 +03:00
Abhishek Gola 642a7307c4 Merge pull request #27560 from abhishek-gola:convTranspose_layer_add
Added fully functional convTranspose layer to new DNN engine #27560

Closes https://github.com/opencv/opencv/issues/26307

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-07 21:20:39 +03:00
Dmitry Kurtaev 92139a6dc4 Enable 1D int8 AvgPool with OpenVINO with 2025.3 2026-05-07 21:14:04 +03:00
Alexander Smorkalov 80046ae2d8 Merge pull request #28958 from asmorkalov:as/qt5_world
Fixed OpenCV World module build with QT 5.
2026-05-07 16:12:56 +03:00
Kumataro a7603daf18 core: fix signed overflow UB in Point/Point3i::dot() 2026-05-07 21:37:21 +09:00
Alexander Smorkalov e98e3ffc17 Fixed OpenCV World module build with QT 5. 2026-05-07 15:13:33 +03:00
Alexander Smorkalov bf4a5803c6 Merge pull request #28951 from vrabaud:ubsan
Add CV_DISABLE_UBSAN to disable undefined behavior sanitizer
2026-05-07 08:12:20 +03:00
jiajia Qian 60858fce9e core: implement OpenCL kernel for UMat::diag to avoid aliasing races
The previous OpenCL implementation of UMat::diag() relied on a sequence of
operations involving buffer initialization followed by copy/transpose on
an aliased diag() view. Although these operations were enqueued on an
in-order command queue, this implicitly assumed memory visibility between
kernels operating on aliased regions of the same buffer.

According to the OpenCL specification, in-order command queues only guarantee
command scheduling order, while memory visibility between commands is only
established through explicit command-level synchronization points (e.g.
events, barriers, or clFinish).Under OpenCL’s relaxed memory model, such
assumptions are not guaranteed without an explicit command-level synchronization
point, and can lead to data races and incorrect results, especially for very
small matrices (e.g. 1x1). The issue is more likely to be exposed on Mesa-based drivers
when the GPU is running at lower frequencies (e.g. 500 MHz).

This change introduces a dedicated OpenCL kernel to construct the diagonal
matrix in a single kernel invocation, avoiding intermediate aliasing and
eliminating the need for implicit ordering assumptions. If the OpenCL path
is unavailable or unsupported, the implementation transparently falls back
to the CPU path.

Signed-off-by: jiajia Qian <jiajia.qian@nxp.com>
2026-05-07 09:18:29 +08:00
Vincent Rabaud 504899d433 Add CV_DISABLE_UBSAN to disable undefined behavior sanitizer
Also fix code here and there that triggered the fuzzer.
2026-05-06 16:56:38 +02:00
Prasad Ayush Kumar e519173241 Merge pull request #28839 from Prasadayus:Block-Layout-Support
Extend several operations to support block layout #28839

Requires opencv_extra: https://github.com/opencv/opencv_extra/pull/1347

After this PR, we observed the following improvements in the listed models:


Model | Before (ENGINE_NEW) | After (ENGINE_NEW) | ENGINE_ORT | % Improvement (Before v/s After)
-- | -- | -- | -- | --
Face_Paint | 514.68ms | 384.40ms | 394.38ms | 25.31%
BlazeFace | 1.03ms | 0.83ms | 0.66ms | 19.42%

**Device details:**
 Model name: Intel(R) Core(TM) i9-14900KS, x86_64, 32 Cores, Ubuntu 22.04.5 LTS

Operations covered:

- [x] Pad
- [x] Resize
- [x] Reshape
- [x] Shape
- [x] InstanceNorm
- [x] GroupNorm

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-06 16:20:45 +03:00
Alex Wu 30fffe2fa0 Merge pull request #28887 from milllemonman:4.x
hal/riscv-rvv: implement laplacian #28887

OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1353

An implementation of laplacian for riscv-rvv hal

Added benchmark in perf_laplacian.cpp
The performance is evaluated on K1 by running 
./opencv_perf_imgproc --gtest_filter="Perf_Laplacian*"
Results are attached in the figure below.

CV_8U fast path supports ksize=3 with BORDER_CONSTANT/BORDER_REPLICATE; unsupported combinations fall back to default implementation. See performance results bellow.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-05-06 16:05:28 +03:00
Alexander Smorkalov 6c0699c8c3 Merge pull request #28946 from mshabunin:fix-gst-ubuntu26
videoio: workaround GStreamer tests on Ubuntu 26
2026-05-06 15:31:01 +03:00
Alexander Smorkalov ca8e6f0ba1 Merge pull request #28830 from AlrIsmail:imgproc-parallel-filter-26074
imgproc: implement thread-safe, 2D-tiled parallel execution for FilterEngine
2026-05-06 15:27:55 +03:00
Alexander Smorkalov 96d8a78537 Merge pull request #28895 from vrabaud:cxx
Port CvLevMarq to C++
2026-05-06 12:20:08 +03:00
Abhishek Gola 2760c0c08a Merge pull request #28931 from abhishek-gola:extended_fusions
Extended graph fusion for transformer models #28931

### Pull Request Readiness Checklist

Performance numbers on Intel(R) Core(TM) i9-14900KS:
```
AlexNet::DNNTestNetwork::OCV/CPU                           7.143   6.871     1.04   
AlexNet::DNNTestNetwork::OCV/OCL                           7.471   7.047     1.06   
AlexNet::DNNTestNetwork::OCV/OCL_FP16                      7.157   6.907     1.04   
BERT::DNNTestNetwork::OCV/CPU                              9.454   9.154     1.03   
BERT::DNNTestNetwork::OCV/OCL                              9.657   9.138     1.06   
BERT::DNNTestNetwork::OCV/OCL_FP16                         9.590   9.271     1.03   
CRNN::DNNTestNetwork::OCV/CPU                             18.194  17.020     1.07   
CRNN::DNNTestNetwork::OCV/OCL                             17.855  17.433     1.02   
CRNN::DNNTestNetwork::OCV/OCL_FP16                        17.629  17.277     1.02   
DenseNet_121::DNNTestNetwork::OCV/CPU                     24.735  23.863     1.04   
DenseNet_121::DNNTestNetwork::OCV/OCL                     24.991  25.140     0.99   
DenseNet_121::DNNTestNetwork::OCV/OCL_FP16                24.598  24.019     1.02   
EAST_text_detection::DNNTestNetwork::OCV/CPU              26.929  27.084     0.99   
EAST_text_detection::DNNTestNetwork::OCV/OCL              26.863  27.531     0.98   
EAST_text_detection::DNNTestNetwork::OCV/OCL_FP16         26.884  27.082     0.99   
EfficientDet::DNNTestNetwork::OCV/CPU                     62.667  62.716     1.00   
EfficientDet_int8::DNNTestNetwork::OCV/CPU                34.446  34.796     0.99   
EfficientNet::DNNTestNetwork::OCV/CPU                     11.875  11.786     1.01   
EfficientNet::DNNTestNetwork::OCV/OCL                     12.434  11.806     1.05   
EfficientNet::DNNTestNetwork::OCV/OCL_FP16                11.886  11.899     1.00   
FastNeuralStyle_eccv16::DNNTestNetwork::OCV/CPU           15.104  14.327     1.05   
FastNeuralStyle_eccv16::DNNTestNetwork::OCV/OCL           15.071  14.670     1.03   
FastNeuralStyle_eccv16::DNNTestNetwork::OCV/OCL_FP16      15.262  14.072     1.08   
GoogLeNet::DNNTestNetwork::OCV/CPU                         5.030   5.031     1.00   
GoogLeNet::DNNTestNetwork::OCV/OCL                         5.075   5.017     1.01   
GoogLeNet::DNNTestNetwork::OCV/OCL_FP16                    5.053   5.055     1.00   
Inception_5h::DNNTestNetwork::OCV/CPU                      6.777   6.430     1.05   
Inception_5h::DNNTestNetwork::OCV/OCL                      7.309   6.683     1.09   
Inception_5h::DNNTestNetwork::OCV/OCL_FP16                 6.624   6.549     1.01   
Inception_v2_Faster_RCNN::DNNTestNetwork::OCV/CPU         86.676  82.918     1.05   
Inception_v2_Faster_RCNN::DNNTestNetwork::OCV/OCL         85.460  84.873     1.01   
Inception_v2_SSD_TensorFlow::DNNTestNetwork::OCV/CPU      16.007  15.723     1.02   
Inception_v2_SSD_TensorFlow::DNNTestNetwork::OCV/OCL      15.974  16.015     1.00   
Inception_v2_SSD_TensorFlow::DNNTestNetwork::OCV/OCL_FP16 16.219  15.593     1.04   
MPHand::DNNTestNetwork::OCV/CPU                            3.273   3.313     0.99   
MPHand::DNNTestNetwork::OCV/OCL                            3.466   3.318     1.04   
MPHand::DNNTestNetwork::OCV/OCL_FP16                       3.314   3.258     1.02   
MPPalm::DNNTestNetwork::OCV/CPU                            2.839   1.973     1.44   
MPPalm::DNNTestNetwork::OCV/OCL                            2.773   2.132     1.30   
MPPalm::DNNTestNetwork::OCV/OCL_FP16                       2.783   2.133     1.30   
MPPose::DNNTestNetwork::OCV/CPU                            7.644   8.711     0.88   
MPPose::DNNTestNetwork::OCV/OCL                            7.555   7.210     1.05   
MPPose::DNNTestNetwork::OCV/OCL_FP16                       7.564   7.559     1.00   
MobileNet_SSD_Caffe::DNNTestNetwork::OCV/CPU               9.040   8.813     1.03   
MobileNet_SSD_Caffe::DNNTestNetwork::OCV/OCL               8.692   9.481     0.92   
MobileNet_SSD_Caffe::DNNTestNetwork::OCV/OCL_FP16          8.792   8.884     0.99   
MobileNet_SSD_v1_TensorFlow::DNNTestNetwork::OCV/CPU       7.893   7.735     1.02   
MobileNet_SSD_v1_TensorFlow::DNNTestNetwork::OCV/OCL       8.233   7.816     1.05   
MobileNet_SSD_v1_TensorFlow::DNNTestNetwork::OCV/OCL_FP16  7.949   7.817     1.02   
MobileNet_SSD_v2_TensorFlow::DNNTestNetwork::OCV/CPU      14.373  15.282     0.94   
MobileNet_SSD_v2_TensorFlow::DNNTestNetwork::OCV/OCL      14.218  15.788     0.90   
MobileNet_SSD_v2_TensorFlow::DNNTestNetwork::OCV/OCL_FP16 14.473  14.853     0.97   
MobileNetv2_ONNX::DNNTestNetwork::OCV/CPU                  1.507   1.440     1.05   
MobileNetv2_ONNX::DNNTestNetwork::OCV/OCL                  1.529   1.394     1.10   
MobileNetv2_ONNX::DNNTestNetwork::OCV/OCL_FP16             1.448   1.433     1.01   
PPHumanSeg::DNNTestNetwork::OCV/CPU                        2.302   1.919     1.20   
PPHumanSeg::DNNTestNetwork::OCV/OCL                        2.097   1.865     1.12   
PPHumanSeg::DNNTestNetwork::OCV/OCL_FP16                   2.129   1.908     1.12   
PPOCRv3::DNNTestNetwork::OCV/CPU                          20.612  20.858     0.99   
PPOCRv3::DNNTestNetwork::OCV/OCL                          21.672  21.444     1.01   
PPOCRv3::DNNTestNetwork::OCV/OCL_FP16                     22.660  20.857     1.09   
ResNet50_QDQ_ONNX::DNNTestNetwork::OCV/CPU                 6.443   6.718     0.96   
ResNet50_QDQ_ONNX::DNNTestNetwork::OCV/OCL                 6.842   6.823     1.00   
ResNet50_QDQ_ONNX::DNNTestNetwork::OCV/OCL_FP16            6.895   6.579     1.05   
ResNet_50_v1_ONNX::DNNTestNetwork::OCV/CPU                 7.669   7.629     1.01   
ResNet_50_v1_ONNX::DNNTestNetwork::OCV/OCL                 7.514   7.473     1.01   
ResNet_50_v1_ONNX::DNNTestNetwork::OCV/OCL_FP16            7.598   7.513     1.01   
SFace::DNNTestNetwork::OCV/CPU                             3.427   3.334     1.03   
SFace::DNNTestNetwork::OCV/OCL                             3.420   3.337     1.02   
SFace::DNNTestNetwork::OCV/OCL_FP16                        3.497   3.375     1.04   
SSD::DNNTestNetwork::OCV/CPU                              83.165  83.476     1.00   
SSD::DNNTestNetwork::OCV/OCL                              83.531  84.121     0.99   
SSD::DNNTestNetwork::OCV/OCL_FP16                         82.447  83.234     0.99   
SqueezeNet_v1_1::DNNTestNetwork::OCV/CPU                   1.334   1.308     1.02   
SqueezeNet_v1_1::DNNTestNetwork::OCV/OCL                   1.279   1.313     0.97   
SqueezeNet_v1_1::DNNTestNetwork::OCV/OCL_FP16              1.429   1.290     1.11   
VIT_Base_Patch16_224::DNNTestNetwork::OCV/CPU             71.211  75.013     0.95   
VIT_Base_Patch16_224::DNNTestNetwork::OCV/OCL             71.988  71.218     1.01   
VIT_Base_Patch16_224::DNNTestNetwork::OCV/OCL_FP16        73.700  82.285     0.90   
VitTrack::DNNTestNetwork::OCV/CPU                          4.096   2.091     1.96   
VitTrack::DNNTestNetwork::OCV/OCL                          3.830   2.056     1.86   
VitTrack::DNNTestNetwork::OCV/OCL_FP16                     3.796   2.110     1.80   
YOLOX::DNNTestNetwork::OCV/CPU                            24.346  23.079     1.05   
YOLOX::DNNTestNetwork::OCV/OCL                            24.442  23.459     1.04   
YOLOX::DNNTestNetwork::OCV/OCL_FP16                       23.947  23.327     1.03   
YOLOv3::DNNTestNetwork::OCV/CPU                           59.140  59.140     1.00   
YOLOv3::DNNTestNetwork::OCV/OCL                           46.725  45.292     1.03   
YOLOv3::DNNTestNetwork::OCV/OCL_FP16                      46.939  45.382     1.03   
YOLOv4::DNNTestNetwork::OCV/CPU                           70.062  68.145     1.03   
YOLOv4::DNNTestNetwork::OCV/OCL                           219.570 214.823    1.02   
YOLOv4::DNNTestNetwork::OCV/OCL_FP16                      219.276 213.472    1.03   
YOLOv4_tiny::DNNTestNetwork::OCV/CPU                       8.362   8.221     1.02   
YOLOv4_tiny::DNNTestNetwork::OCV/OCL                       8.373   8.095     1.03   
YOLOv4_tiny::DNNTestNetwork::OCV/OCL_FP16                  8.830   8.088     1.09   
YOLOv5::DNNTestNetwork::OCV/CPU                            9.237   8.484     1.09   
YOLOv5::DNNTestNetwork::OCV/OCL                            8.776   8.537     1.03   
YOLOv5::DNNTestNetwork::OCV/OCL_FP16                       8.747   8.536     1.02   
YOLOv8::DNNTestNetwork::OCV/CPU                           11.019  11.463     0.96   
YOLOv8::DNNTestNetwork::OCV/OCL                           11.204  10.766     1.04   
YOLOv8::DNNTestNetwork::OCV/OCL_FP16                      11.355  10.764     1.05   
YuNet::DNNTestNetwork::OCV/CPU                             3.371   3.466     0.97   
YuNet::DNNTestNetwork::OCV/OCL                             3.396   3.105     1.09   
YuNet::DNNTestNetwork::OCV/OCL_FP16                        3.150   3.130     1.01   
```

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-06 11:54:40 +03:00
Alexander Smorkalov a05ee608f2 Merge pull request #28933 from intel-staging:ippicv_20260327
Update IPPICV binaries (20260327)
2026-05-06 11:48:35 +03:00
Anand Mahesh ad50964f78 Merge pull request #28879 from manand881:feat/akaze-ocl-performance
Feat: Add OpenCL support for AKAZE features #28879

Implement OpenCL-accelerated AKAZE feature
detection and descriptor extraction

Benchmarked on RTX 5060 Ti: 1.2x to 3.31x faster
for image sizes from 640x480 to 3840x2160

- Added 8 OpenCL kernels for feature detection and descriptor extraction
- Refactored compute_kcontrast to use OpenCV magnitude() for consistency
- Added comprehensive tests with >95% accuracy vs CPU baseline

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-05-06 11:14:47 +03:00
Alexander Smorkalov 847fca1056 Merge pull request #28939 from ssam18:fix/minmaxidx-fpointer-uab
Fix indirect call type mismatch in minMaxIdx dispatch table
2026-05-06 11:01:47 +03:00
Maksim Shabunin 6ad2780b18 videoio: workaround GStreamer tests on Ubuntu 26 2026-05-06 10:46:50 +03:00
Samaresh Kumar Singh dbf872ac1b Fix indirect call type mismatch in minMaxIdx dispatch table
The minMaxIdx dispatch table in modules/core/src/minmax.cpp stored
seven differently typed functions through C-style casts to a single
MinMaxIdxFunc pointer type, which is undefined behaviour and trips
UBSan's -fsanitize=function for any CV_32F or CV_64F input. This
change switches the typedef and every depth-specific helper to take
void pointers for src, minval and maxval, with the original typed
pointer recovered at function entry. The dispatch table no longer
needs C-style casts and the int pointer punning at the call site
goes away. Fixes #28928.
2026-05-05 22:39:59 -05:00
Pierre Chatelier 808d2d596c Merge pull request #28909 from chacha21:autobuffer_api
Better AutoBuffer API #28909

Mimic std::vector<> to help replacing std::vector<T> by cv::AutoBuffer<T> when possible

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-05-05 16:31:13 +03:00
Teddy-Yangjiale f7a467f882 Merge pull request #28914 from Teddy-Yangjiale:fix-issue-28904
videoio(dshow): fix crash in videoInput::start() when pVih is NULL #28914

### Summary
This PR fixes a critical application crash in the DirectShow backend when encountering certain legacy or virtual cameras (e.g., Microsoft's DirectShow "Ball" Sample).

### The Problem
In `modules/videoio/src/cap_dshow.cpp`, the function `videoInput::start()` calls `pConfig->GetConnectedMediaType(&mt)`. 
- In some edge cases, this call returns `S_OK`, indicating success.
- However, the associated format block `mt.pbFormat` (which `pVih` points to) can still be `NULL`.
- The current implementation uses `CV_Assert(pVih)`, which triggers a hard crash of the entire application when this occurs.

### The Fix
I have replaced the `CV_Assert(pVih)` with a null-pointer check. If `pVih` is `NULL`, the function now returns `false`. This allows the high-level `cv::VideoCapture` to handle the failure (e.g., by returning `false` from `.open()`) instead of crashing the process.

### Engineering Rationale
- Execution Path Consistency: For devices providing valid format headers, `pVih` remains non-NULL. In these cases, the conditional check is skipped, and the execution path is identical to the original implementation.
- Process Stability: Replacing `CV_Assert` prevents immediate process termination (SIGABRT) when a DirectShow filter provides an invalid or empty format block. 
- Error Propagation: Returning `false` in `videoInput::start()` allows the initialization failure to propagate through the call stack. This ensures that `cv::VideoCapture::open()` returns `false`, enabling the caller to detect the issue via the standard API (e.g., `isOpened()`) instead of the process being terminated.

### Evidence
I have manually reproduced this crash using the Microsoft DirectShow Ball Sample filter on Windows. As shown in the attached debugger screenshot:
- `deviceID` is 2 (the virtual camera).
- `hr` is `S_OK`.
- `pVih` is `NULL`.

<img width="773" height="1413" alt="dshow_null_pvih_debug_evidence" src="https://github.com/user-attachments/assets/5bc454b4-4a0b-4703-825d-0d5fd23cba28" />

Fixes #28904

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-05-05 15:55:52 +03:00
Alexander Smorkalov bc55040428 Merge pull request #28923 from Kumataro:safeHandlingCompressedFileStorage
core: safe handling of compressed file level extension (.gz[0-9])
2026-05-05 15:12:52 +03:00
ekharkov 5073e1eb3a Updated ICV package to version 2026.0 2026-05-05 03:51:53 -07:00
Alexander Smorkalov 73c9d28264 Merge pull request #28916 from Kumataro:fix28911
core(test): Fix Universal Intrinsics emulator tests for GCC 15+
2026-05-05 11:10:32 +03:00
Alexander Smorkalov 90fc83d926 Merge pull request #28922 from vrabaud:facefont
Add FontFace to js bindings.
2026-05-05 09:06:33 +03:00
Kumataro 8449b9e468 core: safe handling of compressed file level extension (.gz[0-9])
- Replace unsafe pointer arithmetic and direct buffer modification with std::string methods.
- Update documentation to clarify that the last digit is used as compression level and truncated from the actual filename.
- Add test cases for .gz and .gz0-9
2026-05-05 07:41:59 +09:00
Vincent Rabaud 8ea86e0445 Add FontFace to js bindings.
This is ineeded by the putText binding.
2026-05-04 23:13:56 +02:00
Alexander Smorkalov 5cbfe53798 Merge pull request #28917 from Kumataro:fixWarningGCC16
core,objdetects,dnn,features2d: fix build warnings with GCC 16
2026-05-04 09:54:48 +03:00
Ismail d16f2bfef2 imgproc: optimize tiled parallel filter with TLS buffers and custom border fill 2026-05-02 14:56:41 +02:00
Kumataro 28b1f54468 core,objdetects,dnn,features2d: fix build warnings with GCC 16 2026-05-02 10:41:24 +09:00
Kumataro 8775eb1a93 core(test): Fix Universal Intrinsics emulator tests for GCC 15+ 2026-05-02 06:55:24 +09:00
Alexander Smorkalov 10dad24385 Merge pull request #28908 from kirilllapi:patch-1
Refactor code: lsd.cpp
2026-04-30 17:25:56 +03:00
Prasad Ayush Kumar 5af1571073 Darknet cleanup 2026-04-30 14:43:11 +05:30
Alexander Smorkalov 0dbe5e7c35 Merge pull request #28906 from varun-jaiswal17:gemma2-tokenizer
add gemma2 tokenizer support
2026-04-30 09:08:14 +03:00
kirilllapi 26a1ef30c8 Refactor code: lsd.cpp 2026-04-30 04:22:29 +03:00
Alexander Smorkalov d089304bb9 Merge branch 4.x 2026-04-29 16:36:02 +03:00
vrooomy d2c2a82cc9 add gemma2 SentencePiece tokenizer support 2026-04-29 18:21:53 +05:30
Abhishek Gola ae1f3a991c Merge pull request #28859 from abhishek-gola:fuse_attention
Added Attention fusion and thin gemm support #28859

Merge with: https://github.com/opencv/opencv_extra/pull/1350

Performance numbers after these optimizations:

For Device:  Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
| Model | `ENGINE_NEW (Before)` | `ENGINE_NEW (After)` | `ENGINE_ORT` |
| :--- | :--- | :--- | :--- | 
| **BERT** |26.3 ms| 9.15 ms| 9.13 ms|
| **ViT** | 79.65 ms| 63.23 ms| 32.3 ms|

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-29 11:43:33 +03:00
Chevi koren 1f8257adad Merge pull request #28423 from Chevi-Koren:fix-clp-vcpkg-final
Fix Clp library detection for vcpkg on Windows #28423

## Problem

Building OpenCV with `WITH_CLP=ON` on Windows (vcpkg) fails:

LINK : fatal error LNK1181: cannot open input file 'libClp.lib'

vcpkg generates `Clp.lib` and `CoinUtils.lib`, but OpenCV expected `libClp.lib`/`libCoinUtils.lib`.

## Solution

Modernize CLP detection with a robust, platform-aware approach:

1. **CMake `find_package(Clp CONFIG)`**: detects vcpkg and uses imported targets (`Coin::Clp`, `Coin::CoinUtils`)
2. **Fallback**: pkg-config on Unix/Linux  
3. **Skip** Android/iOS

## Benefits

 Fixes Windows build with vcpkg  
 Uses modern CMake targets  
 Backward compatible with Unix/Linux  
 Safe for CI and mobile builds  
 Graceful fallback if CLP unavailable  

## Testing

- **Windows 11 + vcpkg**: build succeeds
- **No CLP installed**: configure/build succeeds  
- **Linux/Unix**: existing workflows unaffected
- **Android/iOS**: properly skipped

## Changes

- Update `cmake/OpenCVFindLibsPerf.cmake`
- Replace hardcoded library names with CMake targets
- Add mobile platform guards
- Preserve backward compatibility

Fixes #26592

---

### Pull Request Readiness Checklist

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch (`4.x`)
- [x] There is a reference to the original bug report and related work (#26592)
- [x] The feature is well documented and sample code can be built with the project CMake
- [x] No additional tests required - this is a build system fix that maintains existing functionality
2026-04-29 11:40:23 +03:00
Kumataro b201d6e631 Merge pull request #28903 from Kumataro:supportDoxygen1_15_0
doc: modernize Doxygen comments to support for v1.15.0 #28903

This PR addresses several documentation build failures encountered with modern Doxygen versions, particularly v1.15.0 (shipped with Ubuntu 26.04).

- flann module: Updated license headers from /**** to /*M****. This prevents Doxygen from misinterpreting the license text (specifically the unclosed backticks in ``AS IS'') as documentation blocks, which previously caused "Reached end of file" errors.
- core module: Fixed a typo in operations.hpp where a doubled backtick (``) caused parsing to fail.
- tutorials: Fixed a missing backtick in real_time_pose.markdown (around line 108) and corrected typos in the RobustMatcher class name.
- Links: Resolved explicit link request failures in calib3d.hpp by ensuring proper namespace resolution.

These fixes ensure that the documentation can be generated without errors on the latest toolchains.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-04-29 10:47:53 +03:00
Varun Jaiswal faf34f95af Merge pull request #28837 from varun-jaiswal17:gemma3-tokenizer
Add Gemma3 tokenizer support for dnn #28837

- Adds Gemma3 tokenizer support
- Implements character-level BPE
- Adds 6 tests covering English, phrase, mixed case, numbers, special tokens, and encode/decode
- add gemma3_inference.py

Merge with: 
- **Companion PR**  : https://github.com/opencv/opencv_extra/pull/1346
- forward pass bug in gemma3_inference.py : https://github.com/opencv/opencv/pull/28836

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-29 10:00:27 +03:00
Alexander Smorkalov 54363b29c4 Merge pull request #28902 from Kumataro:fix28901
js: fix Emscripten version detection and macro usage
2026-04-29 09:25:43 +03:00
Kumataro 9e113674cc js: fix Emscripten version detection and macro usage
- Emscripten 5.0.1+ changed version macros to uppercase and deprecated lowercase ones.
- Updated CMake to detect the version from both cases.
- Added macro aliases in intrin_wasm.hpp to avoid deprecated warnings and ensure compatibility.
2026-04-29 07:53:27 +09:00
Alexander Smorkalov 63282df8d0 Merge pull request #28896 from asmorkalov:as/msmf_test_tune
Tunned vp9 test with HW acceleration.
2026-04-28 17:59:04 +03:00
Michael Klatis 266a2989b2 Merge pull request #28779 from klatism:zlib-upgrade-1.3.2
Zlib upgrade 1.3.2 #28779

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-04-28 17:49:35 +03:00
Abhishek Gola c83b86eb57 Merge pull request #28811 from abhishek-gola:qlinear_support
Added QLinear layer support #28811

closes: https://github.com/opencv/opencv/issues/26310

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-28 17:38:29 +03:00
Vincent Rabaud 721f70feff Port CvLevMarq to C++ 2026-04-28 12:30:06 +02:00
Alexander Smorkalov 499ec788f1 Tunned vp9 test with HW acceleration. 2026-04-28 12:28:12 +03:00
Teddy-Yangjiale 75d9efb3ae Merge pull request #28894 from Teddy-Yangjiale:fix-ffmpeg-c4576
videoio: fix C4576 build error with older ffmpeg versions #28894

Fixes #28875

### Description
This PR fixes a build failure (`error C4576`) that occurs when building the `videoio` module with older FFmpeg versions (e.g., 4.4.4) using MSVC under strict C++17 mode.

**Root Cause:**
In older FFmpeg headers, the `AV_TIME_BASE_Q` macro is defined using a C99 compound literal `(AVRational){1, AV_TIME_BASE}`. This syntax is non-standard in C++ and is strictly rejected by MSVC when `/std:c++17` is enabled.

**Solution:**
Replaced the `AV_TIME_BASE_Q` macro with FFmpeg's standard `av_make_q(1, AV_TIME_BASE)` function. This resolves the C4576 compiler error and ensures modern C++ compatibility. 

*(Note: `av_make_q` is already smoothly polyfilled in `cap_ffmpeg_impl.hpp` for extremely old FFmpeg versions, making this a highly safe, consistent, and zero-overhead replacement.)*
### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-04-28 11:49:39 +03:00
Alex 46ee0f7954 Merge pull request #28844 from 0lekW:videoio-image-seq-start
Add CAP_PROP_IMAGE_SEQ_START #28844

Resolves https://github.com/opencv/opencv/issues/28843

New CAP_PROP_IMAGE_SEQ_START as an open only property for setting the first frame index when a VideoCap is opening with pattern, squashes test_images TODO.

`CAP_FFMPEG`: forwarded to the image2 demuxers "start_number" option.

`CAP_IMAGES`: Now a WithParams variant so the value reaches open() before probe runs, replaces 0 or 1 auto probe for sequence start.

Property is opt in so default behavior is not affected.  
Please note that G_STREAMER is not considered here as I think its out of scope, it already has a "start-index" property and its backend is driven by the pipeline strings rather then these printf patterns.

If an alternative solution is preferred that will not involve a new property or promoting CAP_IMAGES to a "WithParams" variant I don't mind implementing.  

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-28 10:18:50 +03:00
Vincent Rabaud 597c360b9d Merge pull request #28825 from vrabaud:cxx
Bump some calib3d files to C++ #28825

This is just copy/pasting files from 5.x (first commit) and applying some light patch for compilation (second commit).

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-04-28 10:16:42 +03:00
Alexander Smorkalov a17db08c21 Merge pull request #28888 from omrope79:ssd-lpnorm-fix
Add LpNormalization Layer support
2026-04-27 18:34:21 +03:00
omrope79 237be03b7e Merge pull request #28855 from omrope79:wechat-fix-layers
Add Prior Box Layer to support the WeChat QR model #28855

OpenCV extra: https://github.com/opencv/opencv_extra/pull/1349

### Pull Request Readiness Checklist

This PR is part 1 of the split from the original PR: WeChatQR-fix conversion Caffe to ONNX #28746.
It focuses on adding the PriorBox layer and its related unit tests. The WeChatQR specific changes will be submitted in a separate PR.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-27 18:12:58 +03:00
Om Navin Rope 457120bb94 Add LpNormalization Layer support 2026-04-27 15:56:26 +05:30
Alexander Smorkalov 0ba385abe4 Merge pull request #28826 from ssam18:fix/yunet-dynamic-input-onnxruntime
objdetect: fix FaceDetectorYN dynamic input size for ONNX Runtime backend
2026-04-27 12:50:52 +03:00
Alexander Smorkalov 049917f793 Merge pull request #28880 from molhamfetnah:fix-26496-qt6-imshow
highgui(qt): preserve UTF-8 window names in fallback paths
2026-04-26 12:36:26 +03:00
Mulham Fetna d60b0c70b4 highgui(qt): preserve UTF-8 window names in fallback paths
Avoid lossy Latin-1 conversions when creating/looking up Qt HighGUI windows and convert C-string entry points to UTF-8 explicitly. This fixes non-ASCII title handling paths that could lead to mismatched window lookup and silent display failures.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-25 12:32:46 +03:00
Alexander Smorkalov 7afa28bd1f Merge pull request #28863 from SheliaJimenez:dnn-bug-fix
add CV_64S type support to modules/dnn/src/layers/gatherND.cpp
2026-04-25 11:33:59 +03:00
Samaresh Kumar Singh 75c56661d7 objdetect: call setInputShape in FaceDetectorYN to support ONNX Runtime dynamic input 2026-04-24 07:57:49 -05:00
Alexander Smorkalov 7bde3f9ae9 Merge branch 4.x 2026-04-24 11:11:26 +03:00
Alexander Smorkalov 89377d1fd2 Merge pull request #28756 from ctench:fix/ipp_warpPerspective
hal: disable IPP nearest-neighbor path for CV_32FC1 in ipp_hal_warpPerspective
2026-04-24 09:47:59 +03:00
Alexander Smorkalov 323ea1782d Merge pull request #28860 from asmorkalov:as/no_optional
Get rid of redundant C++ optional.
2026-04-23 10:14:51 +03:00
Alexander Smorkalov aab70b9a63 Merge pull request #28857 from asmorkalov:as/dnn_fast_math
Use fast math for dnn module.
2026-04-23 10:13:25 +03:00
SheliaJimenez 50ba8fdbdb add CV_64S type support to modules/dnn/src/layers/gatherND.cpp 2026-04-23 09:08:17 +08:00
Alexander Smorkalov fa0954db82 Get rid of redundant C++ optional. 2026-04-22 17:00:44 +03:00
Jeevan Mohan Pawar 273e52ce8d Merge pull request #28818 from jeevan6996:fix-charuco-detectdiamonds-clear-stale-output
objdetect: clear stale outputs in CharucoDetector::detectDiamonds #28818

## Summary
- clear `diamondCorners`/`diamondIds` outputs at the beginning of `CharucoDetector::detectDiamonds`
- prevent stale detections from previous calls when the current call has fewer than 4 markers or finds no diamonds
- add a regression test that pre-fills outputs, calls `detectDiamonds` with 3 markers, and verifies outputs are empty

Fixes #28783.

## Testing
- built `opencv_test_objdetect` locally
- ran `opencv_test_objdetect --gtest_filter=Charuco.detectDiamondsClearsOutputsWithLessThanFourMarkers`
- result: PASS
2026-04-22 15:42:37 +03:00
Abhishek Gola d95badefa4 Merge pull request #28821 from abhishek-gola:optimized_layers
Parallelize DNN layers using chunking #28821

At a high level, I replaced tensor-level parallelism with chunk-level parallelism. Previously, parallel_for_ was dispatched over the number of input or output tensors i.e. one thread handled one whole tensor's copy.

The new approach precomputes each tensor's destination offset and per-slice size upfront, then slices the total byte work into fixed 64 KB chunks. The full chunk count is handed to parallel_for_ as a single flat range, and each worker decodes its chunk index back into (tensor, slice, byte_offset) using a prefix-sum table before running a plain memcpy on its piece. A small-size threshold falls back to the sequential path so we don't get threading overhead on small tensors.

Performance numbers after these optimizations:

For Device:  Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
| Model | `ENGINE_NEW` | `ENGINE_ORT` |
| :--- | :--- | :--- | 
| **YOLOv8n** |10.9 ms| 12.15 ms|
| **YOLOv5n** | 8.36 ms| 9.23 ms|
| **YOLOX-S** | 23.46 ms| 25.16 ms|

For Device: Macbook M1 Air
| Model | `ENGINE_NEW` | `ENGINE_ORT` |
| :--- | :--- | :--- | 
| **YOLOv8n** |34.45 ms| 42.52 ms|
| **YOLOv5n** | 31.62 ms| 25.52 ms|
| **YOLOX-S** | 88.9 ms| 116.7 ms|

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-22 14:53:12 +03:00
Alexander Smorkalov 7fcfafff8b Use fast math for dnn module. 2026-04-22 13:05:39 +03:00
Alexander Smorkalov 0102afa86f Merge pull request #28854 from asmorkalov:as/no_hal_ci_5.x
Added CI pipeline with vendored 3rdparties and no HAL 5.x
2026-04-22 11:08:25 +03:00
Alexander Smorkalov 1e61a2283d Merge pull request #28628 from GideokKim:fix/fitellipse-det-threshold
imgproc: fix fitEllipseDirect/AMS determinant threshold for near-circular data
2026-04-22 10:47:13 +03:00
Alexander Smorkalov 65e9cb43d4 Merge pull request #28853 from asmorkalov:as/no_hal_ci_4.x
Added CI pipeline with vendored 3rdparties and no HAL 4.x
2026-04-22 10:19:36 +03:00
Varun Jaiswal 562628eb71 Merge pull request #28836 from varun-jaiswal17:fix-dnn-nan-bugs
Fix dnn NaN bugs in GELU SIMD and softmax for large inputs #28836

### Bug Description

- **GELU SIMD bug:** `exp(2*inner)` overflows to `inf` for large inputs (e.g. `x=10.6` → `inner≈51`), causing `inf/inf = NaN`; fixed by clamping `inner` to [-9, 9] as `tanh` already saturates to ±1.0 beyond this range.
- **Softmax bug:** All `-inf` inputs (masked attention rows) produce `sum=0`, then `1/0 = inf` and `0*inf = NaN`; 
fixed by outputting zeros when `sum == 0`.
- Add regression tests for both fixes

Depends on : https://github.com/opencv/opencv/pull/28837

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-22 10:00:51 +03:00
Alexander Smorkalov 4e722f9904 Added CI pipeline with vendored 3rdparties and no HAL. 2026-04-22 09:41:23 +03:00
Alexander Smorkalov e458ff16f3 Added CI pipeline with vendored 3rdparties and no HAL. 2026-04-22 09:39:38 +03:00
Alexander Smorkalov 5e315bcc97 Merge pull request #28799 from Prasadayus:Perf_Tests_Modification
Add more models in perf tests
2026-04-21 14:01:17 +03:00
kjg0724 5d88781f74 Merge pull request #28782 from kjg0724:reduce-simd-optimization
core: add platform-specific SIMD for cv::reduce REDUCE_SUM

### Pull Request

Rewrite `cv::reduce` SIMD optimization using platform-specific instructions as discussed in #28763.

#### Changes

**Col reduce (dim=1) — horizontal sum per row:**
- ARM DOTPROD: `vdotq_u32()` — single-instruction byte sum, no intermediate flush needed
- ARM AArch64 fallback: `vpaddlq` chain (u8→u16→u32)
- Intel AVX2: `_mm256_sad_epu8()` — 32 bytes → 4×u64 partial sums per cycle
- Intel SSSE3: `_mm_shuffle_epi8` + `_mm_sad_epu8` for cn=4 channel separation
- Intel SSE2: `_mm_sad_epu8` for cn=1
- cn=4: hardware deinterleave (`vld4q_u8` on ARM, shuffle+SAD on Intel)

**Row reduce (dim=0) — vertical accumulation across rows:**
- u16 intermediate accumulator with 256-row flush (halves memory bandwidth vs direct u32)
- ARM AArch64: `vaddw_u8` widening add (single instruction vs expand+add)
- Intel: unpack + add with u16 buffer

**Coverage:** All REDUCE_SUM type combinations — 8U→32S, 8U→32F, 16U→32F, 16S→32F, 32F→32F, 32F→64F, 64F→64F. Non-8U types use universal intrinsics where platform-specific gain is minimal (widening is single-stage, FP has limited alternatives). `REDUCE_AVG` benefits automatically (uses SUM internally).

**Dispatch:** `CV_CPU_DISPATCH` with SSE2/AVX2/NEON_DOTPROD/LASX. Fallback hierarchy: DOTPROD → AArch64 NEON → Universal Intrinsics → scalar.

#### Benchmark (Apple M3 Pro, MacBook Pro 16-inch 2023)

| Path | Type | Speedup vs scalar |
|------|------|-------------------|
| Col reduce (dim=1) | 8UC1 | **3.0–4.5x** |
| Col reduce (dim=1) | 8UC4 | **2.7–5.0x** |
| Col reduce (dim=1) | 32FC1 | 1.7–2.3x |
| Row reduce (dim=0) | 8UC1 | 1.1–1.5x |

Row reduce gains are modest due to memory-bandwidth bound (as expected for vertical accumulation). No regressions on non-target paths (MAX, MIN, SUM2).

#### Testing

- `opencv_test_core --gtest_filter="*Reduce*:*reduce*"` — 483 tests PASSED
- Edge cases: non-aligned dimensions (127×61), cn=2/3 scalar fallback, REDUCE_SUM2 unmodified

#### Files changed

- `modules/core/src/reduce.simd.hpp` — new, platform-dispatched SIMD implementation
- `modules/core/src/reduce.dispatch.cpp` — new, CV_CPU_DISPATCH wrapper
- `modules/core/CMakeLists.txt` — add `NEON_DOTPROD` to dispatch list
- `modules/core/src/matrix_operations.cpp` — wire dispatch functions into ReduceC/R_Invoker
2026-04-21 12:24:38 +03:00
Alexander Smorkalov beec5c28df Merge pull request #28838 from vrabaud:triangulate
Allow applyColorMap for 1d arrays
2026-04-21 09:48:27 +03:00
Vincent Rabaud 69020c6517 Allow applyColorMap for 1d arrays 2026-04-20 18:10:25 +02:00
4ekmah 9f101a126f Merge pull request #28802 from 4ekmah:pyr_ecc
Multiscale ECC #28802

OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1338

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-20 17:22:19 +03:00
Alexander Smorkalov f327f0669e Merge pull request #28813 from Lurie97:fix_flip_inplace
core/ocl: fix incorrect results for in-place flip on strict OpenCL implementations
2026-04-20 14:59:37 +03:00
jiajia Qian ba879cd60f core/ocl: fix incorrect results for in-place flip on strict OpenCL implementations
Previously, OpenCL in-place flip kernels (rows/cols/both) could produce
incorrect results compared to the CPU implementation on strict OpenCL
drivers such as Mesa. The kernels relied on implicit load–load–store–store
(LLSS) ordering when src and dst alias, which is not guaranteed by the
OpenCL memory model and may be reordered.

Some vendor drivers happened to preserve the expected ordering, masking
the issue, but Mesa correctly exposes the undefined behavior.

This change introduces dedicated in-place flip kernels that:
- Explicitly detect in-place execution (src == dst)
- Stage data through local memory tiles
- Enforce correct ordering with work-group barriers
- Avoid global memory read/write aliasing hazards

The non in-place path is unchanged.

With this fix, OpenCL in-place flip produces correct and consistent results
across drivers, matches CPU behavior, and complies with the OpenCL memory
model.

Signed-off-by: jiajia Qian <jiajia.qian@nxp.com>
2026-04-21 08:54:52 +08:00
Alex 30178be800 Merge pull request #28833 from 0lekW:avfoundation-fractional-fps-seek-fix
Avfoundation fractional fps seek fix #28833

Accompanied by PR on [opencv_extra](https://github.com/opencv/opencv_extra/pull/1345)

Fixes https://github.com/opencv/opencv/issues/28831

AVFoundation backend seeking slowly drifts for any video which has a non-integer frame rate.   
Narrowing of a float `mAssetTrack.nominalFrameRate` to int32_t means fractional rates are treated as integer when seeking by frame number. 

Solution implemented in this branch is to use the tracks native rational frame duration when seeking (and available).
I've also included an AVF only test case which uses a new test file in opencv_extra. This test just compares the video caps expected ms vs actual for a known rate file (23.976). 

Could consider extending this test to more then just AVF, as from what I can see there are no fractional rate tests / files.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-20 14:58:58 +03:00
Rohan Mistry 924a10069c Merge pull request #28817 from Ron12777:codex/fix-multiview-oneline-tests
Fix issues with MultiViewTest.OneLineInitialGuess and MultiViewTest.OneLine #28817

To fix issues caused by https://github.com/opencv/opencv/issues/28789 
This probably can be fixed for 4.x, but honestly I don't believe it is worth it to have to deal with users tests failing because it was 0.02% off so we can save them like 2 minutes, so I believe it is best to leave the optimization for 5.x. 

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-20 12:04:22 +03:00
Alexander Smorkalov 590b99f478 Merge pull request #28814 from vrabaud:intrin
Rewrite getBitsFromByteList to avoid harmless buffer overflow
2026-04-20 12:02:13 +03:00
Alexander Smorkalov ad08edab42 Merge pull request #28829 from vrabaud:triangulate
Fix triangulatePoints with 2-channel arrays.
2026-04-20 11:59:55 +03:00
Alexander Smorkalov 4e70ee722f Merge pull request #28828 from manand881:chore/faster-ocl-bfmatcher-crosscheck
OpenCL BFMatcher optimization for crosscheck case
2026-04-20 10:57:18 +03:00
Vincent Rabaud 7d3a764ba5 Fix triangulatePoints with 2-channel arrays. 2026-04-20 00:12:46 +02:00
Kumataro 1ae5a8586b Merge pull request #28615 from Kumataro:fix28606
imgcodecs(png): support cICP metadata for imreadWithMetadata() #28615

Close https://github.com/opencv/opencv/issues/28606
Related https://github.com/opencv/opencv/pull/27741 (support for imwriteWithMetadata)

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-04-19 20:11:33 +03:00
Anand Mahesh ab0def4854 chore/faster-ocl-bfmatcher-crosscheck
The existing 2-pass cross-check implementation requires both forward and
reverse distance computations on GPU, followed by CPU-side filtering.
This adds a single-pass kernel using int64 atomics to eliminate the
reverse pass. The BruteForceMatch_CrossCheckMatch kernel performs forward
matching and tracks best train->query mappings in one GPU pass, reducing
memory transfers. Falls back to 2-pass approach on devices without
cl_khr_int64_base_atomics extension.

Tested on NVIDIA RTX 5060 Ti: 1.65x-1.97x faster than 2-pass GPU,
3.49x-7.33x faster than CPU at 640x480 to 1920x1080 resolutions.
2026-04-19 22:08:29 +05:30
Alexander Smorkalov c244bc3279 Merge pull request #28810 from asmorkalov:as/calib_tests_cleanup
Moved irrelevant tests and functions from calib module to 3d.
2026-04-18 11:15:27 +03:00
Alexander Smorkalov a5295015b2 Merge pull request #28820 from asmorkalov:as/win32_disable_alpha_check
Disable FFmpeg tests that need FFmpeg wrapper rebuild on Windows.
2026-04-17 16:06:23 +03:00
Alexander Smorkalov 9dc7dc89d7 Merge pull request #28780 from ssam18:fix-lapack-cblas-link
cmake: link libcblas explicitly for LAPACK/Generic backend
2026-04-17 15:18:43 +03:00
Alexander Smorkalov 6aba6fda2d Disable FFmpeg tests that need FFmpeg wrapper rebuild on Windows. 2026-04-17 10:56:46 +03:00
Jesus Armando Anaya 166c235603 Merge pull request #28745 from JesusAnaya:fix-bundled-protobuf-include-priority
build: fix bundled protobuf headers being overridden by system-installed protobuf #28745

On macOS with Apple Clang 17, the compiler implicitly injects `/usr/local/include` as a high-priority user include (`-I`). The `SYSTEM` keyword in `target_include_directories` emits `-isystem` for the bundled protobuf path, which is searched after user `-I` paths, allowing a system-installed protobuf v4+ (which requires C++17 and abseil-cpp) to override the bundled headers.

Removing `SYSTEM` causes CMake to emit `-I` instead of `-isystem`, placing the bundled path before the implicit system include, restoring the intended header resolution order regardless of what protobuf version is installed on the host.

### Background

Apple Clang 17 (shipped with macOS 26 Tahoe Command Line Tools) changed the compiler driver behavior: it now unconditionally adds `-I/usr/local/include` to the search path, which is where Homebrew installs headers. Previous Apple Clang versions either omitted this path or added it as a lower-priority system include.

When Homebrew's `protobuf` is installed (v4+, e.g. `33.4_1`), its headers at `/usr/local/include/google/protobuf/` are resolved before the bundled `3rdparty/protobuf/src/` because `SYSTEM PUBLIC` in `target_include_directories` causes the bundled path to be emitted as `-isystem`, which ranks below `-I` in compiler search order. This was confirmed by inspecting the generated `flags.make`:

```
# before fix
-isystem /path/to/opencv/3rdparty/protobuf/src

# after fix
-I /path/to/opencv/3rdparty/protobuf/src   (first in the include list)
```

The system protobuf v4+ headers require C++17 and abseil-cpp. Since OpenCV 4.x compiles with `-std=c++11`, every translation unit in `3rdparty/protobuf/` fails:

```
/usr/local/include/google/protobuf/port_def.inc: error: Protobuf only supports C++17 and newer.
/usr/local/include/absl/base/policy_checks.h: error: C++ versions less than C++17 are not supported.
```

Note that `include_directories(BEFORE ...)` on the line above was intended to prevent exactly this, but it was overridden by `target_include_directories(SYSTEM PUBLIC ...)` deduplicating the path into a single `-isystem` entry. This is essentially the same class of problem tracked in #13328.

### Impact

The change is a one-line diff. Removing `SYSTEM` has no effect on Linux or Windows since those toolchains do not inject `/usr/local/include` implicitly. Warning suppression for protobuf headers in dependent modules is unaffected because OpenCV already applies explicit `-Wno-*` flags for all protobuf-related warnings in `3rdparty/protobuf/CMakeLists.txt`.

### Tested on

- macOS 26.3 (Tahoe), Apple Clang 17.0.0 (`clang-1700.6.4.2`), Homebrew `protobuf 33.4_1`, full build passes with `BUILD_PROTOBUF=ON`.

Fixes #28743, related to #27886.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
  Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-04-17 10:53:45 +03:00
Alexander Smorkalov fa2bb7358a Merge pull request #28816 from varun-jaiswal17:fix-flatten-onnx-axis-4x
fix Flatten axis=rank bug
2026-04-17 09:10:28 +03:00
vrooomy 3215d7e6ea fix Flatten axis=rank bug 2026-04-16 18:09:36 +05:30
Varun Jaiswal 8dfc236476 Merge pull request #28812 from varun-jaiswal17:fix-flatten-onnx-axis
Fix flatten onnx axis==rank case #28812

fix ONNX Flatten layer incorrect output when axis equals input rank edge case

Resolves : https://github.com/opencv/opencv/pull/28781#discussion_r3060088247
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1342

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-16 14:04:26 +03:00
Alexander Smorkalov 1492b5e8a2 Merge pull request #28815 from vrabaud:wasm
Add wasm support for int64
2026-04-16 12:16:29 +03:00
Vincent Rabaud 519b88cbc3 Add wasm support for int64
This has been around since 2021 and is part of the wasm spec:
https://github.com/llvm/llvm-project/commit/502f54049d17f5a107f833596fb2c31297a99773
This makes 5.x compile for wasm because convertTo needs it at:
https://github.com/opencv/opencv/blob/6751304b3556082cc79b63624024419ee15c1ed4/modules/core/src/convert.hpp#L204
2026-04-15 21:19:16 +02:00
Vincent Rabaud ae6198e000 Rewrite getBitsFromByteList to avoid harmless buffer overflow
At the end, currentByte = byteList.ptr()[base + currentByteIdx];
could be out of bound though never used.
2026-04-15 16:14:25 +02:00
Alexander Smorkalov 6751304b35 Merge pull request #28808 from abhishek-gola:engine_issue_fixed
Fixed forcing of ENGINE_AUTO to ORT when built with ONNX Runtime
2026-04-15 11:36:29 +03:00
Alexander Smorkalov 934c0716d2 Merge pull request #28735 from manand881:feature/ocl-bfmatcher-crosscheck
features2d: add OpenCL acceleration for BFMatcher cross-check
2026-04-15 08:48:14 +03:00
Varun Jaiswal c0ab6b69a8 Merge pull request #28781 from varun-jaiswal17:work-next
Add Qwen2.5 tokenizer support for dnn #28781

OpenCV extra: https://github.com/opencv/opencv_extra/pull/1334

Extended the dnn tokenizer support to qwen2.5 tokenization.

-  Add QWEN2_5 pre-tokenizer regex pattern to utils.hpp
- Generalised buildTokenizerGPT to buildTokenizerFromJson to handle gpt2/gpt4/ and qwen2.5
- Add qwen2/qwen2.5 model type support with special token handling
- Add Qwen2.5 tests
- Add end-to-end qwen_inference script for Qwen2.5 ONNX model


### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-15 08:42:49 +03:00
Alexander Smorkalov 6e8e90c664 Merge pull request #28803 from asmorkalov:as/gftt_migration
Migrated goodFeaturesToTrack to features module.
2026-04-15 08:36:33 +03:00
Alexander Smorkalov bc0dff8c3e Merge pull request #28757 from AlrIsmail:fix-issue-22056
photo: remove redundant code in illuminationChange
2026-04-14 17:26:00 +03:00
Alexander Smorkalov 99fc573913 Moved irrelevant tests and functions from calib module to 3d. 2026-04-14 13:26:35 +03:00
Abhishek Gola b883454620 fixed forced ORT engine 2026-04-14 14:24:40 +05:30
Alexander Smorkalov 5166a6b716 Migrated goodFeaturesToTrack to features module. 2026-04-14 11:25:29 +03:00
Alexander Smorkalov 66e994d839 Merge pull request #28807 from abhishek-gola:eyelike_layer_add
Added EyeLike layer support
2026-04-14 11:20:53 +03:00
Alexander Smorkalov a9e04ff9f1 Merge pull request #28800 from asmorkalov:as/clamp_c++17
More clamp fixes for old compilers.
2026-04-14 09:45:34 +03:00
Abhishek Gola c44d03d8e1 added eyelike layer support 2026-04-14 11:49:12 +05:30
Abhishek Gola de851d24a5 Merge pull request #28121 from abhishek-gola:loop_layer_add
Add Loop layer to new DNN engine #28121

Addition of loop layer in 5.x for issue: https://github.com/opencv/opencv/issues/26179 and https://github.com/opencv/opencv/issues/26141 and https://github.com/opencv/opencv/issues/25200

OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1335

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-14 09:15:46 +03:00
Abhishek Gola 3d77645a3a Merge pull request #28750 from abhishek-gola:activation_fusion
Extended fusion for activation functions in new DNN engine #28750

After this fusion, we see following improvements in YOLO models:

| Model | Before (`ENGINE_NEW`) | After (`ENGINE_NEW`) | `ENGINE_ORT` | % Improvement (Before v/s After) |
| :--- | :--- | :--- | :--- | :--- |
| **YOLOv8n** | 18.89  ms| 12.06 ms| 12.15 ms| 36.16% |
| **YOLOv5n** | 17.12  ms| 9.29 ms| 9.23 ms| 45.73% |
| **YOLOX-S** | 38.78  ms| 25.56 ms| 25.16 ms| 34.09% |

Device details: 
      - Model name: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-14 09:08:14 +03:00
Alexander Smorkalov 29746e2cd0 More clamp fixes for old compilers. 2026-04-13 09:45:43 +03:00
Abhishek Gola 53d9a67cf3 Merge pull request #28741 from abhishek-gola:int8_block_layout
Int8 block layout support #28741

After this patch we got the following speed ups on **resnet50-qdq.onnx** model.

- Inference time now: **_~6.6ms_** (inference time using onnxruntime is ~5.7ms).
- Inference time before: _**~11.5ms**_ [after QDQ PR #28595]
- Speed up: _**~42.6% or 1.74x**_
- Device details:
- Model name: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04, 

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-12 19:40:03 +03:00
Pierre Chatelier 723670c33d Merge pull request #28785 from chacha21:tiff_32F_compression
Allow TIFF compression schemes for 32F#28785

Related to [https://github.com/opencv/opencv/issues/28775](https://github.com/opencv/opencv/issues/28775)

Previously, only 32FC3+SGILOG could be specified for TIFF encoding. But when floats use quantization, compression schemes can be efficient even on 32F data. This PR will allow them.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [X] The PR is proposed to the proper branch
- [X] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [X] The feature is well documented and sample code can be built with the project CMake

I just don't know what kind of accuracy/performance tests should be added.
2026-04-10 15:16:29 +03:00
Alexander Smorkalov 1acf28507f Merge pull request #28786 from asmorkalov:as/calib_log_4.x
Added missing includes for CV_LOG_xxx.
2026-04-09 19:14:38 +03:00
Alexander Smorkalov 5798063e3b Merge pull request #28788 from aychun:fix/videocapture-format-cv8uc3
videoio: fix FFmpeg VideoCapture rejecting CAP_PROP_FORMAT=CV_8UC3
2026-04-09 19:14:08 +03:00
Alexander Smorkalov 1a6f669763 Merge branch 4.x 2026-04-09 18:44:48 +03:00
Andrew Yooeun Chun 570e32cc9b videoio: fix FFmpeg VideoCapture rejecting CAP_PROP_FORMAT=CV_8UC3 2026-04-09 22:06:15 +09:00
Alexander Smorkalov 3282bfc149 Added missing includes for CV_LOG_xxx. 2026-04-09 14:54:48 +03:00
Samaresh Kumar Singh 1bef8b81f4 cmake: find and link libcblas separately for LAPACK/Generic
On systems like OpenBSD, CBLAS functions are provided by a standalone
libcblas that is not returned by find_package(LAPACK). This caused link
failures when building libopencv_core because cblas_sgemm and friends
from hal_internal.cpp could not be resolved.

Fixes #28768
2026-04-09 11:01:58 +03:00
Abhishek Gola 2ec6a6bb65 Merge pull request #28588 from abhishek-gola:ORT_GPU_wrapper
Added OnnxRuntime GPU wrapper #28588

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-06 15:40:58 +03:00
Prasad Ayush Kumar 3d988eb7a9 Add more models in perf tests 2026-04-06 15:18:18 +05:30
Jorge Velez a49a293d3c Merge pull request #27534 from JorgeV92:gsoc2025-tokenizer
GSoC 2025: Add Tokenizer Support to DNN Module #27534

merge with https://github.com/opencv/opencv_extra/pull/1276

### Summary
This pull request introduces initial support for a tokenizer module under `modules/dnn/src/tokenizer` as part of Google Summer of Code 2025 (Project: Tokenization for OpenCV DNN).

### Status
- [x] Project structure in place
- [x] Initial BPE tokenizer loading
- [x] Regex splitting (in progress)
- [x] Encoding logic for GPT-2 tokenizer (in progress)
- [ ] Documentation (to be improved)

### Goals
The goal is to support Hugging Face-compatible tokenization (e.g., GPT-2) natively in C++ to be integrated with DNN inference pipelines. 

The core pipeline lives in `dnn/src/tokenizer/core_bpe.hpp` and `dnn/src/tokenizer/encoding.hpp`. For Unicode handling I’m using `dnn/src/tokenizer/unicode.hpp`, which is adapted from llama.cpp.


### Feedback
Please share early feedback on:
- General design structure
- Integration strategy with `dnn`
- Code organization or naming conventions

### Reference
Project: https://summerofcode.withgoogle.com/programs/2025/projects/79SW6eNK
2026-04-06 10:46:13 +03:00
Ismail 758e8620ac photo: remove redundant code in illuminationChange
Fixes redundant code in Cloning::illuminationChange() which performed
unnecessary copyTo operations. This satisfies Issue #22056.

Testing: No functional changes made; logic identical to before.

Resolves #22056
2026-04-04 00:15:08 +02:00
Connor Tench 1fefb87fc5 disable ipp path for 32FC1 in ipp_hal_warpPerspective 2026-04-03 19:03:30 +00:00
Alexander Smorkalov 9eb887d02d Merge pull request #28751 from asmorkalov:as/read_write_video_alpha
Added alpha channel support to VideoWriter and VideoCapture.
2026-04-03 16:00:08 +03:00
Lurie97 a3e129aad8 Merge pull request #28686 from Lurie97:fix_inplace
core(opencl): fix inplace transpose race by enforcing LLSS ordering via local barrier #28686

The former inplace transpose implementation allowed a reordering of global-memory operations across work-items. Specifically, the intended LLSS (Load–Load–Store–Store) access pattern could be reordered by the GPU into LSLS (Load–Store–Load–Store), causing partially written tiles to be observed by other work-items and producing incorrect output.

This patch introduces a tiled LDS-based algorithm and adds an explicit:

    barrier(CLK_LOCAL_MEM_FENCE);

between the load and store phases.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-04-03 15:50:11 +03:00
Alexander Smorkalov d0313879da Merge pull request #28755 from varun-jaiswal17:fix-ort-warnings-cmake
ONNX-Runtime warnings supression via ocv_warnings_disable
2026-04-03 15:43:28 +03:00
Alexander Smorkalov 18eb0a1c12 Merge pull request #28754 from vrabaud:intrin
Fix case for intrin.h
2026-04-03 15:24:52 +03:00
LHOOL1109 9dba8a7df0 Merge pull request #28747 from LHOOL1109:fix/python-zero-channel-crash
python: fix segfault on 0-channel numpy array input #28747

### Pull Request Readiness Checklist

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      N/A: a Python unit test is added in `modules/python/test/test_mat.py`. No external test data required.
- [x] The feature is well documented and sample code can be built with the project CMake
      N/A: this is a bug fix, not a new feature. No documentation update needed.

### Problem

Passing a numpy array with shape `(H, W, 0)` (0 channels) to any OpenCV function that accepts a `Mat` argument (e.g. `cv2.resize`, `cv2.warpAffine`, `cv2.blur`) causes a **segfault**.

**Reproducer:**
```python
import cv2
import numpy as np

arr = np.zeros((100, 100, 0), np.uint8)
cv2.resize(arr, (200, 200))  # segfault
```

### Root Cause

In `modules/python/src2/cv2_convert.cpp`, the numpy→Mat conversion checks channel validity only against `CV_CN_MAX` (upper bound):

```cpp
if (channels > CV_CN_MAX)   // channels=0 passes this check
```

With `channels=0`, `CV_MAKETYPE(0, 0)` produces `type=-8`, which corrupts the Mat's internal type field and causes undefined behavior downstream.

### Fix

Extend the check to also reject `channels < 1`:

```cpp
if (channels < 1 || channels > CV_CN_MAX)
```

**After fix:**
```
cv2.error: src unable to wrap channels, invalid count (0, must be in [1, 512])
```

### Notes

- This affects all functions that accept a `Mat` input, not just `cv2.resize`
- The same bug exists in the `5.x` branch
- A 0-channel array has no valid OpenCV Mat representation; rejecting it with a clear error is the correct behavior and poses no backward-compatibility risk (the previous behavior was a crash)
2026-04-03 14:26:18 +03:00
Alexander Smorkalov 87bdcd4f14 Added alpha channel support to VideoWriter and VideoCapture. 2026-04-03 12:36:45 +03:00
vrooomy 48b39616b8 warnings supression via ocv_warnings_disable 2026-04-03 14:40:22 +05:30
Alexander Smorkalov b90384e13c Merge pull request #28753 from vrabaud:asan
Fix invalid PAM decoding
2026-04-03 10:42:02 +03:00
Vincent Rabaud 6b152941dc Fix case for intrin.h
Compilation fails on platforms that are case-dependent, apparently
some windows arm 64. The source of truth is lower case:
https://github.com/yuikns/intrin/blob/master/intrin.h
2026-04-02 19:01:37 +02:00
Prasad Ayush Kumar 47ac80995f Merge pull request #28749 from Prasadayus:NMS-empty-detection-fix
Nms empty detection fix #28749

Requires opencv_extra: https://github.com/opencv/opencv_extra/pull/1330

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-04-02 18:37:23 +03:00
Vincent Rabaud 12c90ff05b Fix invalid PAM decoding
This fixes https://issues.oss-fuzz.com/issues/497290557
2026-04-02 16:01:00 +02:00
Ahmad 10e32c96f5 Merge pull request #28535 from AhmadDurrani579:4.x
videoio(gstreamer): fix timestamp drift and color negotiation on Apple #28535

This commit addresses two issues on macOS with Apple M3 hardware:
1. Replaces floating-point timestamp math with gst_util_uint64_scale_int to ensure nanosecond precision.
2. Explicitly forces I420 format in the encoding profile to prevent hardware encoder negotiation failure.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-04-02 13:59:01 +03:00
Alexander Smorkalov 2b31e057cf Merge pull request #28701 from cuiweixie:fix/dnn-batchnorm-bias-blob-index
dnn: fix BatchNorm bias blob index in validation
2026-04-02 13:02:27 +03:00
Alexander Smorkalov 98f46715f7 Merge pull request #28742 from asmorkalov:as/webp_win32_arm
Do not use AVX options on Windows ARM in libwebp.
2026-04-02 08:50:27 +03:00
Alexander Smorkalov 443b748b25 Do not use AVX options on Windows ARM in libwebp. 2026-04-01 08:56:00 +03:00
pratham-mcw 3cf98c51c8 Merge pull request #28609 from pratham-mcw:core-rotate-neon-optimization
core: add NEON implementation for rotate function #28609

- This PR adds a NEON intrinsics-based implementation for the rotate function in matrix_transform.cpp for Windows-ARM64.
- The optimized implementation uses  ARM NEON intrinsics to accelerate the internal transpose step used by the rotate function.
- In the x64 architecture, the rotate operation benefits from IPP-based optimized implementations. However, on ARM64, the execution falls back to the scalar implementation, which results in lower performance.
- To achieve performance parity with x64, a NEON-based SIMD implementation has been added for ARM64. 
- After introducing these changes, the rotate function showed noticeable performance improvements on ARM64 platforms.
<img width="1009" height="817" alt="image" src="https://github.com/user-attachments/assets/8bec0041-b19c-4fc8-9103-532746224515" />

 
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
2026-03-31 16:52:04 +03:00
Alexander Smorkalov 92c43f80fc Merge pull request #28739 from pratham-mcw:core/fix-MeanStdDeviation-accuracytest
core: fix meanStdDev bug by using separate variables v2, v3 in sumsqr_
2026-03-31 15:39:16 +03:00
Alexander Smorkalov 7e8e0853a8 Merge pull request #28611 from Arths17:4.x
Fix Windows build issues with IPPICV unpack and IPPIW CMakeLists.txt …
2026-03-31 15:24:38 +03:00
Abhishek Gola e59506bbf5 Merge pull request #28595 from abhishek-gola:qdq_support
* added qdq fusion

* Added VNNI optimizations
2026-03-31 15:16:09 +03:00
Alexander Smorkalov e038cfda95 Merge pull request #28737 from vrabaud:asan
Force step to be ptrdiff_t in resize
2026-03-31 10:33:47 +03:00
Alexander Smorkalov f82522ccfb Merge pull request #28589 from nmizonov:fix_ippiw_binary_usage
Fixed search of IPP IW binaries in cmake
2026-03-31 10:27:41 +03:00
pratham-mcw c5d747f75c core: fix meanStdDev bug by using separate variables v2, v3 in sumsqr_ 2026-03-31 11:29:34 +05:30
Shiyu Xie 44bf6a2db3 Merge pull request #28714 from ShiyuXie0116:fix/onnx-dispatch-and-rotary-embedding-bugs
dnn: fix missing ONNX dispatch entries and RotaryEmbedding bugs #28714

- Wire RMSNormalization and RotaryEmbedding parsers into the ONNX dispatch map. Both parse functions were declared and implemented but never registered, causing the importer to fall through to the generic path and skip constant-folding of cos/sin caches.

- Fix NonMaxSuppression dispatch key typo ("NonMaxSuprression" -> "NonMaxSuppression") so the ONNX op name matches the registered layer class. Also fix the type string set inside the parser.

- Fix tautological self-comparison in RotaryEmbeddingLayer::getMemoryShapes (cos_cache_shape.dims == cos_cache_shape.dims -> sin_cache_shape.dims).

- Fix typo in RotaryEmbedding error message ("cos_cahe" -> "cos_cache").

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-03-31 08:49:01 +03:00
Vincent Rabaud 5c91261ca0 Force step to be ptrdiff_t in resize
Otherwise, ASAN could return an error:
"runtime error: addition of unsigned offset"
2026-03-30 10:48:00 +02:00
SamareshSingh 2027a33990 Merge pull request #28724 from ssam18:fix/resize-ngraph-two-inputs-28707
dnn: fix Resize initNgraph for two-input case #28724

## Summary

Fixes the issue #28707

When a Resize/Upsample layer has two inputs, the data tensor and a reference tensor whose **shape** defines the output spatial size, the OpenVINO/NGRAPH backend's `initNgraph()` was ignoring `nodes[1]` entirely and relying solely on the `outHeight`/`outWidth` member variables.

These variables are set by `finalize()` from the pre-computed output blob dimensions. However, when the output shape is determined dynamically at runtime from the second input, `finalize()` sets them from the live tensor, but the OpenVINO backend calls `initNgraph()` to build a static compiled graph. If the member variables are 0 at that point, the compiled `Interpolate` node gets hardcoded with `{0, 0}` output dimensions, causing CV_Assert failure: {N,C,0,0} vs {N,C,H2,W2}
2026-03-30 10:06:54 +03:00
Anand Mahesh 4acd2ed6cc features2d: add OpenCL acceleration for BFMatcher cross-check
BFMatcher::match() with crossCheck=true previously skipped the OCL
dispatch in knnMatchImpl entirely, falling back to CPU even when UMat
inputs and an OpenCL device were available. This adds
ocl_matchWithCrossCheck() for CV_32FC1 descriptors (e.g. SIFT, SURF):
both the forward and reverse nearest-neighbour passes run on the GPU
via the existing ocl_matchSingle() kernel, then the cross-check filter
runs on the CPU. Only two small index arrays (1×N ints) are downloaded
— the O(N²×D) distance work stays on the device.

The OCL dispatch in knnMatchImpl is also refactored to unify the
Mat/UMat train collection selection before branching on crossCheck.

On a NVIDIA RTX 3060 with SIFT descriptors the OCL path is 6–9×
faster than CPU at 2k–10k features per image. Binary descriptors
(ORB, BRIEF — CV_8U) are unaffected; the existing type guard in
ocl_matchSingle keeps them on the CPU path as before.

Also adds a correctness test (Features2d_BFMatcher_CrossCheck) and an
OCL perf test (BruteForceMatcherFixture/MatchCrossCheck).
2026-03-29 21:45:08 +05:30
Anshu c7732e1043 Merge pull request #28397 from 0AnshuAditya0:fix-simd-oob-read-28396
Fixes #28396 : out-of-bounds read in SIMD type conversion #28397

Fixes #28396
Fixes #27080

The vx_load_expand function in WASM intrinsics was using 
wasm_v128_load which always loads a full 128-bit register 
(16 bytes), even when the function only needed 8 elements.

For example, when converting uint8 to float32:
- vx_load_expand needs 8 uint8 elements
- But wasm_v128_load reads 16 bytes from memory
- This causes an 8-byte out-of-bounds read

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-03-27 15:40:09 +03:00
Alexander Smorkalov 1630450d25 Merge pull request #28727 from asmorkalov:as/calibrate_perf_data
Replace calibration images with saved image points.
2026-03-27 14:59:33 +03:00
Varun Jaiswal 7514c4b770 Merge pull request #28687 from varun-jaiswal17:asan_pipeline
3d: fix depth precision inconsistency under ASan/RelWithDebInfo builds #28687

Resolves FMA inconsistency between build types by using std::fma(a, b, c) to get the same precision everywhere ASan, Release, Debug.
 
Change optimisation :
Without FMA : 96.54 ms
FMA      : 32.33 ms

Merged with : opencv/ci-gha-workflow/pull/297

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-03-27 11:45:51 +03:00
Alexander Smorkalov d45b442f4e Replace calibration images with saved image points. 2026-03-27 10:52:21 +03:00
Abhishek Gola 7c64ea1e48 Merge pull request #28691 from abhishek-gola:block_layout_extension
Added conv 1x1 and conv 3x3 block layout support#28691

After this patch we get the following speed ups on resnet50.onnx:

Inference time after: ~7.6ms (inference time using onnxruntime: ~6.67ms)
Inference time before: ~14ms
Speed up: ~46% or 1.84x

Device details:

Model name: Intel(R) Core(TM) i9-14900KS
Cores: 32
RAM: 128 GB
Architecture: x86
OS: Ubuntu 24.04
### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-03-27 08:13:57 +03:00
Alexander Smorkalov 689d514885 Merge pull request #28698 from PDGGK:fix/android-utils-resource-leak
Fix resource leaks in Android Utils.java (#28697)
2026-03-26 15:33:04 +03:00
Abhishek Gola 40ce5b4132 Merge pull request #28637 from abhishek-gola:old_dnn_tickets_cleanup
Added Output Tensor Names support in new DNN engine #28637

closes: https://github.com/opencv/opencv/issues/26201

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-03-26 15:07:56 +03:00
Rohan Mistry 7e5463b34f Merge pull request #28461 from Ron12777:opt-clean
Optimize calibrateCamera with Schur‑complement LM and parallel Jacobian accumulation #28461

## Summary

- Optimized `calibrateCamera` for faster runtime without changing outputs using Schur‑complement LM, Parallel Jacobian accumulation, alongside other optimizations.
- Reduced time complexity from O(n^3) to O(n)
- Add a perf test that uses a 500-image chessboard dataset for performance testing.

## Performance
<img width="1200" height="800" alt="base_vs_fast_results" src="https://github.com/user-attachments/assets/6dafa19f-f9cb-4f7f-ba40-0940373712e8" />
<img width="1200" height="800" alt="fast_vs_ceres_results" src="https://github.com/user-attachments/assets/7157af27-8a2b-4810-8b53-3cc9972a8493" />
<img width="1200" height="800" alt="base_vs_fast_param_deviation" src="https://github.com/user-attachments/assets/fe4f954c-34f9-4b9a-b1b2-46e4c76ce08c" />


[Testing repo
](https://github.com/Ron12777/OpenCV-benchmarking)
## Testing
- All local tests pass 

## Related

- [opencv_extra PR with test images](https://github.com/opencv/opencv_extra/pull/1312)


See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-03-26 12:18:50 +03:00
Alexander Smorkalov 3c7fd7c25a Merge pull request #28723 from akretz:fix-mean-perf
Benchmark cv::mean instead of cvtest::mean
2026-03-26 11:40:37 +03:00
Adrian Kretz a4d9b45168 Benchmark cv::mean instead of cvtest::mean 2026-03-25 21:30:22 +01:00
Abhishek Gola ea485c628c Merge pull request #28646 from abhishek-gola:kernel_values
Fix kernel shape handling in ONNX importer2 #28646

Requires opencv_extra: https://github.com/opencv/opencv_extra/pull/1323
closes: https://github.com/opencv/opencv/issues/28321

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-03-25 13:12:25 +03:00
Alexander Smorkalov 78efccad75 Merge pull request #28710 from varun-jaiswal17:add-nohal-pipeline
Fixes for no-HAL and no-IPP configuration
2026-03-25 07:34:52 +03:00
vrooomy 185b48cfd6 parent 2d15169ab5
author vrooomy <vj.bro.833@gmail.com> 1773655621 +0530
committer vrooomy <vj.bro.833@gmail.com> 1774357668 +0530

 added SIMD support for 64 bit float and fallback for 64 bit int

removed trailing white-spaces

add SIMD optimization for 32 unsinged int and clean fallback for 64u and 64s

add SIMD support for 32u and fallback for 64u and 64s

changed CalibrateDebevec test threshold to 0.25 (same as ARM) for IPP=NO

relaxed threshold to 0.22

conflict resolved
2026-03-24 18:54:54 +05:30
ffccites 16db340890 Fix resource leaks in Android Utils.java (#28697)
Convert exportResource() and loadResource() to use try-with-resources
to ensure InputStream, FileOutputStream, and ByteArrayOutputStream are
properly closed even when exceptions occur.

Also remove printStackTrace() in exportResource(), as the exception is
already rethrown as CvException with the original exception details.

Signed-off-by: ffccites <99155080+PDGGK@users.noreply.github.com>
2026-03-24 16:41:40 +11:00
Alexander Smorkalov 45c1f9d803 Merge pull request #28652 from mvanhorn:osc/28651-fix-reprojection-error-rmse
calib3d: fix reprojection error RMSE calculation in Python tutorial
2026-03-23 17:08:16 +03:00
ZIHAN DAI 1610602884 Merge pull request #28699 from PDGGK:fix/highgui-system-exit
Replace System.exit(-1) with exceptions in HighGui.java (#28696) #28699

## Summary

Library code should never call System.exit() as it kills the entire JVM. Replaced all 3 instances with appropriate exceptions.

Closes #28696

## Changes

- `imshow()` with empty image: `System.exit(-1)` -> `throw new IllegalArgumentException("Image is empty")`
- `waitKey()` with no windows: `System.exit(-1)` -> `throw new IllegalStateException("No windows created. Call imshow() first")`
- `waitKey()` with null window image: `System.exit(-1)` -> `throw new IllegalStateException("No image set for window: ... Call imshow() first")`
- `InterruptedException` catch: `printStackTrace()` -> `Thread.currentThread().interrupt()`
2026-03-23 16:20:50 +03:00
Yang Guanyuhan 2d15169ab5 Merge pull request #28692 from YangGuanyuhan:fix-lightglue-assertion-fail
dnn: fix dst_dp assertion in broadcast for size-1 dims causing crash in lightglue.onnx model #28692

The original assertion CV_Assert(dst_dp == 1) does not handle valid cases where the innermost dimension size is 1 like [10, 5, 1], resulting in dst_dp == 0.

This occurs during broadcasting in LightGlue ONNX model and leads to assertion failure.

Allow dst_dp == 0 for size-1 dimensions to handle this edge case correctly.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-03-23 11:27:37 +03:00
Weixie Cui 1612fd9ac3 dnn: fix BatchNorm bias blob index in validation
When hasBias is true, CV_Assert must reference blobs[biasBlobIndex], not blobs[weightsBlobIndex], for the bias tensor.
2026-03-22 02:52:52 +08:00
Alexander Smorkalov 4413c953d1 Merge pull request #28684 from abhishek-gola:AVX_VNNI_support_4.x
Added AVX_VNNI function to core module
2026-03-20 12:50:37 +03:00
Abhishek Gola ff2e6358fd added AVXX VNNI support 2026-03-20 13:20:31 +05:30
Alexander Smorkalov f6aceee13b Merge pull request #28655 from usernotfound-101:cvmixchannels-warning-fix
Add static integer casting for cvMixChannels, safer
2026-03-20 10:25:38 +03:00
Varun Jaiswal df7d437b86 Merge pull request #28663 from varun-jaiswal17:added-inRange-64double-SIMD
Added SIMD support for 64 bit float and fallback for 64 bit int#28663

Merged with : https://github.com/opencv/ci-gha-workflow/pull/299

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-03-20 08:42:56 +03:00
Alexander Smorkalov b5a7e0c662 Merge branch 4.x 2026-03-19 11:29:57 +03:00
Alexander Smorkalov 9e5cd6bf9b Merge pull request #28665 from PavelGuzenfeld:fix/stale-typing-stubs-cleanup
Clean up stale typing stubs during incremental builds
2026-03-18 08:13:21 +03:00
Alexander Smorkalov 7394d128c6 Merge pull request #28668 from abhishek-gola:AVXVNNI_support
Added AVX_VNNI function to core module
2026-03-18 07:47:11 +03:00
Alexander Smorkalov 1c3958384c Merge pull request #28662 from CSBVision:patch-9
Change file globbing to recursive for CUDA DLLs
2026-03-17 18:24:27 +03:00
Abhishek Gola 2dec80044b moved AVXVNNI function to core 2026-03-17 18:58:03 +05:30
Pavel Guzenfeld 3779f630bc Clean up stale typing stubs during incremental builds
When a module is disabled in an incremental build (e.g. switching from
-DBUILD_opencv_gapi=ON to OFF), its typing stub directory persists from
the previous build.  The stubs generator only creates directories for
enabled modules but never removes old ones, and the copy step merges
rather than replaces, so stale .pyi files propagate to both the loader
directory and the install prefix.

This causes type-checkers (mypy, pyright) to report errors for stubs
that reference symbols from modules that are no longer available.

Fix both propagation paths:

- generation.py: remove all subdirectories under the stubs output root
  before regenerating, so only currently enabled module stubs exist in
  the build directory.  Top-level files (py.typed) are preserved.

- copy_typings_stubs_on_success.py: remove stale .pyi files and
  py.typed markers from the loader directory before copying fresh stubs,
  so leftover stubs from a previous copy are cleaned up.  Runtime .py
  files are not affected.
2026-03-16 23:41:37 +02:00
pratham-mcw 2dd3d1371a Merge pull request #28636 from pratham-mcw:imgproc_distance_transform_opt
imgproc: add SIMD support for distance transform function #28636

- This PR adds OpenCV SIMD intrinsics-based optimizations to the distance transform functions for improved performance.
- The optimized implementation uses vectorized operations to accelerate the forward and backward passes of distance computation.
- In x64 architecture, distance transform function benefit from IPP-based optimized implementations. However, on ARM64 platforms, the execution falls back to scalar implementation, which results in lower performance.
- After introducing these changes, the distance transform functions showed noticeable performance improvements on Windows-ARM64.
<img width="800" height="706" alt="image" src="https://github.com/user-attachments/assets/9786606a-9d92-489d-a5ea-d2e57453ef02" />

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
2026-03-16 13:51:03 +03:00
CSBVision faf36aa95b Change file globbing to recursive for CUDA DLLs
With recent releases, the CUDA installation layout changes introduces a `bin\x64` subdirectory. Recursive glob inside `bin` works in either case.
2026-03-16 11:28:56 +01:00
Alexander Smorkalov 2c839967ff Merge pull request #28660 from PavelGuzenfeld:fix/gapi-optional-module-handling
Fix Python bindings when optional modules are disabled
2026-03-16 11:06:57 +03:00
Pavel Guzenfeld 95ab7149fe Fix Python bindings when optional modules like gapi are disabled
Fix two issues that cause `import cv2` to fail when building with
-DBUILD_opencv_gapi=OFF:

1. In `has_all_required_modules()`, the function parameter `type_node`
   was ignored in favor of the enclosing scope's loop variable `node`.
   This works by accident when called as `has_all_required_modules(node)`
   but is incorrect — the parameter should be used directly.

2. In `__load_extra_py_code_for_module()`, only `ImportError` was
   caught. When a stale gapi submodule directory exists from a previous
   build, gapi/__init__.py raises `AttributeError` (not `ImportError`)
   because it tries to access C++ bindings that don't exist. Now catches
   both exception types for defense-in-depth.

Refs: #26098
2026-03-15 23:21:42 +02:00
Matt Van Horn 5e4592440e calib3d: fix reprojection error RMSE calculation in Python tutorial
The tutorial code used cv.NORM_L2 (which takes a square root) and then
averaged those values. The correct RMSE formula should use NORM_L2SQR
to get squared errors, average them, and take the square root at the
end. Updated the explanatory text to match.

Fixes https://github.com/opencv/opencv/issues/28651
2026-03-13 10:00:23 -07:00
usernotfound-101 3470f5b35b Add static integer casting to get rid of the warning, safer 2026-03-13 22:25:08 +05:30
Abhishek Gola f059d3b517 Merge pull request #28634 from abhishek-gola:flops_addition
Added getFLOPS support in new DNN engine #28634

closes: https://github.com/opencv/opencv/issues/26199

### Pull Request Readiness Checklist

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- [x] The PR is proposed to the proper branch
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      Patch to opencv_extra has the same branch name.
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2026-03-13 18:27:14 +03:00
Vadim Pisarevsky 1b483ffea6 Merge pull request #28585 from vpisarev:dnn_block_layout_v5
Block layout-based convolution in DNN #28585

merge together with https://github.com/opencv/opencv_extra/pull/1321

Some core parts of the new engine in DNN module have been revised substantially:

1. all tests seem to pass, except for `Test_Graph_Simplifier.ResizeSubgraph`, which has been disabled because it does not take the newly added `TransformLayoutLayer` into account. The test should be reworked perhaps.
1. convolution and related operations (maxpool/avgpool) now use so-called block layout (`DATA_LAYOUT_BLOCK`), where `NxCxHxW` tensors are represented  as `NxC1xHxWxC0`, where `C1=(C + C0-1)/C0` and `C0` is a power-of-two (usually 4, 8, 16 or 32).
1. graph is now pre-processed and `TransformLayoutLayer` is inserted to convert data from NCHW or NHWC layout to the block layout or vice versa. The transformations are done in a lazy way only when they are really needed. For example, in the whole Resnet only 2 transformations are performed.
1. transformer-based models and other models that do not use convolutions will run as usual, without going to block layout.
1. there is yet another graph preprocessing stage added that embeds constant weights/scale and bias into convolution and batch norm layers.
1. 'batchnorm', 'activation' and 'adding a residual' are now fused with convolution, just like in the old engine. That brings some noticeable acceleration.
1. optimized convolution kernels have been added.
     * depthwise convolution, as well as maxpool and avgpool support C0=4, 8, 16 etc. _as long as_  C0 is divisible by the number of fp32 lanes in a SIMD register of the target platform (e.g. on ARM with NEON there must be `C0 % 4 == 0`, on x64 with AVX2 `C0 % 8 == 0`).
     * non-depthwise convolution only supports C0=8 for now. C0=8 seems to be a sweetspot for ARM with NEON, x64 with AVX2 or RISC-V with RVV (with 128- or 256-bit registers). For some platforms with dedicated matrix accelerators C0=16 or even C0=32 might be more efficient, but we could add the respective kernels later.
     * only fp32 kernels have been added. fp16/bf16 kernels might be added a little later.

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
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- [ ] There is a reference to the original bug report and related work
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- [ ] The feature is well documented and sample code can be built with the project CMake
2026-03-13 17:09:27 +03:00
Madan mohan Manokar 00833f98d0 Merge pull request #28632 from amd:fast_scharr_deriv
video: Optimized ScharrDeriv#28632

- Move to CV_SIMD_SCALABLE
- avx2 & avx512 dispatch added for ScharrDeriv

### Pull Request Readiness Checklist

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      Patch to opencv_extra has the same branch name.
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2026-03-13 13:04:46 +03:00
Alexander Smorkalov 8f9c7c0f72 Merge pull request #28643 from Prasadayus:fix-gather-cast-axis-4x
DNN/ONNX: Preserve axis attribute in GatherCastSubgraph fusion
2026-03-13 11:46:19 +03:00
Madan mohan Manokar 18c7c9bcb9 Merge pull request #28614 from amd:fast_flipHoriz
Optimized flip Horizontal #28614

- Refactor flipHoriz implementation.
- Optimizations for horizontal image flipping added.

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-03-13 11:11:20 +03:00
rajmahadev422 84300d5025 Merge pull request #28618 from rajmahadev422:vscode_opencv
### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
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      Patch to opencv_extra has the same branch name.
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## This tutorial describe how to build opencv from source in windows using VSCode and MSYS2
2026-03-13 10:38:39 +03:00
nklskyoy 5c8d3b60f4 Merge pull request #28524 from nklskyoy:attn-kv-cache
CPU Kernels for fp32 KV Cache #28524

The kernels are:
- pagedAttnQKGemmKernel
- pagedAttnAVGemmKernel

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
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2026-03-13 10:23:30 +03:00
Alexander Smorkalov a1a0cc2891 Merge pull request #28644 from s-trinh:fix_calibration_exe_ignore_orientation
Ignore exif orientation and add option for the calibration exe
2026-03-12 10:53:13 +03:00
Matt Van Horn 8e1fa1bbdc Merge pull request #28620 from mvanhorn:osc/28619-fix-yaml-parsekey-empty-key-oob
core: fix heap-buffer-overflow in YAML parseKey for empty keys #28620

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

### Description

Fixes https://github.com/opencv/opencv/issues/28619

Moves the "empty key" check before the backward do-while scan in `YAMLParser::parseKey()`.

**Problem:** When parsing a YAML mapping with an empty key (e.g. `: 10` at column 0), `endptr == ptr` after the forward scan finds `:`. The do-while loop `do c = *--endptr; while(c == ' ')` always executes at least once, so it decrements `endptr` to `ptr-1` and reads one byte before the heap allocation (ASan: heap-buffer-overflow READ of size 1).

**Fix:** Check `endptr == ptr` before entering the backward loop. If the key is empty, raise `CV_PARSE_ERROR_CPP("An empty key")` immediately without the OOB read.

This contribution was developed with AI assistance (Claude Code).
2026-03-12 10:37:24 +03:00
Alexander Smorkalov 1a3e1e05b7 Merge pull request #28581 from JoyBoy900908:fix-ubsan-countnonzero
core: fix UBSan function pointer type mismatch in countNonZero
2026-03-12 10:16:17 +03:00
Souriya Trinh a02fcb360d By default, exif orientation is ignored when reading images sequence for the calibration exe. Add an option to apply exif image orientation. 2026-03-11 09:02:32 +01:00
Prasad Ayush Kumar fb6f5cc282 DNN/ONNX: Preserve axis attribute in GatherCastSubgraph fusion 2026-03-11 13:12:09 +05:30
Alexander Smorkalov 4391bd4cb4 Merge pull request #28641 from s-trinh:fix_calibration_exe_no_charuco
Fix calibration sample when the board is not a Charuco
2026-03-11 09:44:53 +03:00
Matt Van Horn c3457f99f3 Merge pull request #28621 from mvanhorn:osc/28528-fix-houghcircles-return-type
imgproc: fix HoughCircles Python return type to allow None #28621

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

### Description

Fixes https://github.com/opencv/opencv/issues/28528

`HoughCircles` returns `None` when no circles are found, but the auto-generated Python type stub declares the return type as `MatLike` without `None`. This causes type checkers (mypy/pyright) to miss potential `None` dereferences.

**Fix:** Add `HoughCircles` to `NODES_TO_REFINE` in `api_refinement.py` using the existing `make_optional_none_return` helper - the same pattern already used for `imread` and `imdecode`.

This contribution was developed with AI assistance (Claude Code).
2026-03-11 08:55:36 +03:00
gideok Kim 4ebaf2dbb4 imgproc: fix fitEllipseDirect/AMS determinant threshold for near-circular data
fitEllipseDirect: replace det(M) threshold with eigenvector quality check
- Add Ts ≈ 0 guard to avoid division by zero in Schur complement
- Move eigenNonSymmetric inside perturbation loop
- Validate eigenvector with 4ac-b² > 1e-6*||v||² to filter garbage from
  complex eigenvalues

fitEllipseAMS: scale threshold by 1/n^5 to match det(M) magnitude

Add Imgproc_FitEllipseDirect_NearCircular regression test
2026-03-10 23:18:02 +09:00
Souriya Trinh 918244763d Create Charuco objects only if the selected pattern is a Charuco board. Otherwise it gives the following error:
opencv/modules/objdetect/src/aruco/aruco_board.cpp:543: error: (-215:Assertion failed) size.width > 1 && size.height > 1 && markerLength > 0 && squareLength > markerLength in function 'CharucoBoard'
2026-03-10 12:15:50 +01:00
Prasad Ayush Kumar e188ffd190 Merge pull request #28633 from Prasadayus:fix/gather-cast-axis
Fix gather cast axis #28633

Closes: https://github.com/opencv/opencv/issues/23231

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
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      Patch to opencv_extra has the same branch name.
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2026-03-10 11:25:56 +03:00
JoyBoy900908 7286212ce6 core: use void* in countNonZero signature per team review 2026-03-09 14:20:43 +08:00
Alexander Smorkalov d719c6d84a Merge pull request #28607 from s-trinh:update_calib3d_images_white_background
Add white background for images in calib3d
2026-03-06 15:53:21 +03:00
Arths17 2ea7e3ad06 Fix Windows build issues with IPPICV unpack and IPPIW CMakeLists.txt copy (#28608)
- Replace execute_process(tar) with file(ARCHIVE_EXTRACT) for native .zip support
  and better Windows path handling when CMAKE_VERSION >= 3.18
- Replace execute_process copy with file(COPY) and existence check for reliable
  CMakeLists.txt copying to avoid timing/path issues on Windows
- Maintains backward compatibility with older CMake versions and Linux behavior

Fixes issue #28608
2026-03-05 23:05:10 -06:00
Souriya Trinh 7f164b5653 Add a white background for images which have a transparent background in the calib3d module.
See:
  - https://github.com/opencv/opencv/pull/28450
  - https://github.com/opencv/opencv/pull/28426
2026-03-05 11:06:14 +01:00
Anshu fe160f3eed Merge pull request #28548 from 0AnshuAditya0:fix-minEnclosingCircle-welzl-28546
imgproc: fix minEnclosingCircle O(n^3) worst case by adding Welzl shuffle #28548

Welzl's algorithm requires random permutation of input points to
achieve expected O(n) time. Without shuffling, sorted inputs such
as those produced by findContours() trigger O(n^3) worst case.

Fix: copy input to std::vector<PT> and apply cv::randShuffle()
before processing. Uses OpenCV's RNG so cv::setRNGSeed() ensures
reproducible behavior.

Original benchmark (5088 contour points, Release, AVX2):
findContours output: 3.93 ms → 0.033 ms (119x speedup)
Random points: 0.051 ms → 0.049 ms (no regression)

Perf test results (this PR, Release, AVX2):
| Input | N | Time |
|-------|---|------|
| Sequential circle points | 10000 | 0.03 ms |
| Sequential circle points | 5000 | 0.01 ms |
| Random points (CV_32F) | 100000 | 1.87 ms |
| Random points (CV_32S) | 100000 | 2.81 ms |

Fixes #28546

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

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- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-03-04 15:16:07 +03:00
Jonas Perolini 5e91b461bc Merge pull request #28289 from JonasPerolini:pr-aruco-identification
Identify ArUco markers based on threshold to reduce false positives #28289

**Goal:** parametrize the current marker identification process (pixel-based majority count) to reduce the number of false positives while maintaining high recall. Useful in high risk scenarios in which false positives are not acceptable. 

**Context:** This PR builds on top of https://github.com/opencv/opencv/pull/23190 in which we've introduced a pixel-based confidence in the marker detection.

**Solution:** Include a new parameter: `validBitIdThreshold` used to identify markers based on the pixel count of each cell. Set the parameter default either to 50% which is equivalent to the current majority count implementation or to 49% which already singnificantly reduces the number of false positives (see details below). 

**Test coverage:** 
- Unit tests: `CV_ArucoDetectionThreshold`, `CV_InvertedArucoDetectionThreshold`
- The impact of `validBitIdThreshold` on false positives was also tested using the benchmark dataset: `MIRFLICKR-25k` https://www.kaggle.com/datasets/skfrost19/mirflickr25k which contains random images without any markers. Every marker detection is a false positive. 

Example of images in the dataset:

![im2048](https://github.com/user-attachments/assets/3e38796b-67ce-44be-a91d-2fd268414515)

![im17627](https://github.com/user-attachments/assets/37253b9f-829d-4bac-b9fc-c844d16f546e)

**Results:** A threshold of 49% already allows to significantly reduce the number of false positives for the dict `DICT_4X4_1000`: 
- `5942` false positives for `validBitIdThreshold = 0.5`
- `629` false positives for `validBitIdThreshold = 0.49` and `0.46` 
   - number of false positives divided by `9.5` when compared to `validBitIdThreshold = 0.5`
- `139` false positives for `validBitIdThreshold = 0.43` and `0.4` 
   - number of false positives divided by `42` when compared to `validBitIdThreshold = 0.5`

Dicts with a higher number of cells are not as impacted since it's much harder to obtain false positives. However, the less cells in a marker the further away it can be reliably detected, so the dict `DICT_4X4_1000` is commonly used.

<img width="1280" height="800" alt="false_positive_image_rate" src="https://github.com/user-attachments/assets/1a0ee16a-221d-443e-835b-022ed6dea6b0" />

In the image attached, the values of `validBitIdThreshold` tested are:  `0.10f, 0.20f, 0.30f, 0.40f, 0.43f, 0.46f, 0.49f, 0.50f, 0.53f, 0.56f, 0.60f, 0.70f, 0.80f, 0.90f`

Summary of the results: [summary.csv](https://github.com/user-attachments/files/24315662/summary.csv)

Note that we can also analyse the number of false positives per marker `id`. For example, here's the histogram for the dict `DICT_4X4_1000`. (The CSV attached contains all the results)

<img width="1440" height="640" alt="false_positive_ids_DICT_4X4_1000_thr0 50" src="https://github.com/user-attachments/assets/af4f3ff8-9b8f-4682-9d51-a090c2610d8c" />

For example, the marker id 17 is detected 252 times with  `validBitIdThreshold = 0.5` and only 34 times with `validBitIdThreshold = 0.49`. Looking at marker 17 (see below), we understand that this simple pattern randomly occurs in images.

<img width="447" height="441" alt="Marker17" src="https://github.com/user-attachments/assets/f5d09227-b39b-4598-94f9-b529f8300703" />

Results for every dict and every `validBitIdThreshold` [per_id.csv](https://github.com/user-attachments/files/24315667/per_id.csv)

**Missing coverage:** there is no labeled dataset with images containing markers to analyse the impact of on the recall  (i.e. look at the true positive rate). For my specific use case (drones) any threshold above `0.4` allows to maintain a high recall in all conditions.

### Pull Request Readiness Checklist


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- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-03-04 08:44:28 +03:00
Murat Raimbekov 91c78f5064 Merge pull request #28309 from raimbekovm:fix-typos-batch6
docs: fix typos in documentation and code comments #28309

## Summary

This PR fixes 10 spelling errors in documentation and code comments across 7 files.

## Changes
- `suppport` → `support` (2 occurrences in test_video_io.cpp)
- `compability` → `compatibility` (2 occurrences in face.hpp)
- `successfull` → `successful` (1 occurrence in cv2.cpp)
- `accomodate` → `accommodate` (2 occurrences in calib3d.hpp and test_camera.cpp)
- `minimun` → `minimum` (1 occurrence in aruco_detector.hpp)
- `maximun` → `maximum` (1 occurrence in aruco_detector.hpp)
- `orignal` → `original` (1 occurrence in aruco_detector.cpp)

## Test plan
- [x] No API changes
- [x] Documentation-only changes
- [x] Code compiles without errors
2026-03-03 14:29:47 +03:00
ADITYA MISHRA 1099135b88 Merge pull request #28592 from AdityaMishra3000:fix-openvino-2026-constness
DNN: Fix OpenVINO 2026 build failure due to ov::Tensor::data() const change #28592

Fixes: https://github.com/opencv/opencv/issues/28586

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

### PR Description

OpenVINO 2026 changed ov::Tensor::data() to return `const void*`
instead of `void*`. This causes a build error in
modules/dnn/src/op_inf_engine.cpp when constructing a cv::Mat
wrapper over Tensor memory.

Tensor memory for input/output blobs remains mutable, but the API
now enforces const-correct access. This patch casts away const with
an explicit comment to preserve existing zero-copy semantics and
restore compatibility with OpenVINO 2026.
Preserves existing zero-copy semantics of the OpenVINO backend without altering runtime behavior.

Tested by building OpenCV 4.x against OpenVINO 2026.0.0 on Ubuntu 24.04.
2026-03-03 13:56:38 +03:00
Alexander Smorkalov c1c48dd17f Merge pull request #28591 from vrabaud:asan
Make sure j < maxRow in smooth functions
2026-03-03 08:56:36 +03:00
Alexander Smorkalov 6926b4218e Merge pull request #28590 from GideokKim:fix/fitellipse-redundant-fabs
imgproc: remove redundant fabs() in fitEllipseDirect
2026-03-03 08:52:41 +03:00
Alexander Smorkalov f46498b3c7 Merge pull request #28587 from intel-staging:staging/ekharkov/arithm
Restored IPP in cv::compare
2026-03-03 08:49:20 +03:00
Vincent Rabaud 517bc1c777 Make sure j < maxRow in smooth functions
Otherwise some values out of memory can be read.
This was found with ASAN.
2026-03-02 18:23:31 +01:00
gideok Kim 6676c04d7c imgproc: remove redundant fabs() in fitEllipseDirect
In fitEllipseDirect, `double det = fabs(cv::determinant(M))` applies
fabs() unnecessarily since the next line `if (fabs(det) > 1.0e-10)`
already takes the absolute value. Remove the outer fabs() to avoid
the redundant operation.
2026-03-02 22:23:54 +09:00
nmizonov b7266c92fd Fix IPPIW binaries search 2026-03-02 05:03:50 -08:00
ekharkov 30aa3c9266 Revert "Disable IPP with AVX512 in cv::compare because of performance regression" 2026-03-02 01:47:14 -08:00
Muhammad Awais 69bdcc9386 Merge pull request #28536 from Sikandar1310291:fix/inrange-typing-28534
Fix inRange type signature to accept Scalar values #28536

Fixes #28534

The Python type signature for cv2.inRange was too restrictive, requiring MatLike for lowerb and upperb parameters. However, the C++ implementation accepts InputArray which includes Scalar values (tuples, floats, etc.).

This caused type checkers to incorrectly flag valid code from official tutorials as type errors.

Added make_matlike_or_scalar_arg() refinement function to create union types MatLike | Scalar for affected parameters, matching the C++ InputArray behavior.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-03-02 09:43:56 +03:00
Alexander Smorkalov 5ee977b839 Merge pull request #28576 from AndrewO-MMLLC:fix/28571-cvMoveWindow-macOS-dock-offset
highgui(cocoa): fix moveWindow Y conversion when Dock is visible
2026-03-01 15:28:56 +03:00
Abhishek Gola 31cab2a055 Merge pull request #28574 from abhishek-gola:reduce_layer_empty_set
* added empty set support

* accept unnamed dynamic dims

* Fix for LSTM test failure

* removed converToND

* openvino failing test fix

* ARM CI issue fix

* ARM issue fix
2026-03-01 15:19:44 +03:00
Alexander Smorkalov b54d0fbbbf Merge pull request #28584 from asmorkalov:as/kaze_license
Dropped KAZE license files as they moved to contrib.
2026-03-01 15:17:23 +03:00
Alexander Smorkalov 7da3af2c41 Dropped KAZE license files as they moved to contrib. 2026-03-01 08:28:49 +03:00
JoyBoy900908 a9e13a5e8c core: fix UBSan function pointer type mismatch in countNonZero 2026-02-28 20:50:54 +08:00
Abhishek Gola 95c66292b5 Merge pull request #28444 from abhishek-gola:added_ORT_wrapper
Added ONNX Runtime as an optional wrapper #28444

This PR adds ONNXRuntime (ORT) as an _optional_ wrapper, which can be enabled by adding **WITH_ONNXRUNTIME** flag in CMake command.

Using ORT wrapper the inference time for _resnet50.onnx model_ has come to _**~7ms**_ from _**~14ms**_.
Also, we are able to run models like `ssd_mobilenet_v1.onnx`.
### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-02-27 15:47:12 +03:00
Alexander Smorkalov 7644636904 Merge pull request #28579 from asmorkalov:as/mul_overflow_fix
Fixed cv::mul overflow for U16 type.
2026-02-27 10:55:13 +03:00
Alexander Smorkalov d506e78655 Merge pull request #28578 from asmorkalov:as/video_url_refresh
Test video url refresh.
2026-02-27 10:23:19 +03:00
Alexander Smorkalov 01320ab612 Fixed cv::mul overflow for U16 type. 2026-02-27 10:04:59 +03:00
Kumataro 6c374bebee Merge pull request #28519 from Kumataro:fix28503
imgcodecs(webp): improve IMWRITE_WEBP_LOSSLESS_MODE to support exact lossless compression #28519

Close #28503 

Add IMWRITE_WEBP_LOSSLESS_MODE enum to control lossless strategy:
- IMWRITE_WEBP_LOSSLESS_OFF: Lossy compression.
- IMWRITE_WEBP_LOSSLESS_ON:  Optimize/drop color in transparent pixels.
- IMWRITE_WEBP_LOSSLESS_PRESERVE_COLOR: Preserve color in transparent pixels.

Note: Currently limited to still images; animated WebP support is deferred per YAGNI.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-02-26 11:01:36 +03:00
Alexander Smorkalov c4ff523cf4 Test video url refresh. 2026-02-26 10:06:03 +03:00
Abhishek Gola 54b743dca8 Merge pull request #28575 from abhishek-gola:shape_inference_bug_fix
[BUG FIX] Incorrect shape assert bug fix in 5.x #28575

Closes: https://github.com/opencv/opencv/issues/28563

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-02-26 09:16:44 +03:00
Andrew Owens 5abe4678c1 highgui(cocoa): fix moveWindow Y conversion when Dock is visible 2026-02-25 12:31:47 -10:00
Abhishek Gola 2219b49a3a Merge pull request #28558 from abhishek-gola:gru_layer_add
Added GRU layer in new DNN engine #28558

Closes: https://github.com/opencv/opencv/issues/26309 and https://github.com/opencv/opencv/issues/21078 [for GRU layer]

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-02-25 17:42:22 +03:00
Alexander Smorkalov 1d6b8bdef7 Merge pull request #28531 from JakeFloch:4.x
Fix ellipse axes documentation to use semi-major/semi-minor
2026-02-25 12:43:39 +03:00
Alexander Smorkalov 02d453f1d3 Merge pull request #28570 from hyndhavamahesh345:fix-flann-docs-typos
docs: fix typos and missing parameter description in flann and calib3d
2026-02-24 12:26:31 +03:00
Adrian Kretz 261222ebf6 Merge pull request #28562 from akretz:fix_ipp_warpAffine
Fix IPP warpAffine #28562

The issue is that we compute the inverse transformation and pass that to the HAL, but tell IPP that we use the actual transformation.

https://github.com/opencv/opencv/blob/a269a489b94b84717691533a04f79d9a0e5f479a/modules/imgproc/src/imgwarp.cpp#L2399-L2412

I have added an additional test with `CV_16S` Mats to test the IPP implementation, because right now no other `warpAffine` test enters the IPP branch.

This fixes #28554.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-02-24 12:21:57 +03:00
Alexander Smorkalov f563016da2 Merge pull request #28569 from Kumataro:fix28568
imgproc: fix -Wstringop-overflow false-positive in minEnclosingConvexPolygon
2026-02-24 11:12:40 +03:00
hyndhavamahesh345 1194bd24cb docs: fix typos and missing parameter description in flann and calib3d 2026-02-22 14:11:17 +05:30
Kumataro 5ef5aab435 imgproc: fix -Wstringop-overflow false-positive in minEnclosingConvexPolygon
This commit addresses a compiler warning encountered when building with GCC 13 and C++20.
The compiler triggers a false-positive -Wstringop-overflow warning in `findKSides`.

By adding `sides.reserve(k)`, we explicitly inform the compiler about the required
memory allocation, which suppresses the warning and provides a minor optimization
by avoiding potential reallocations.
2026-02-22 07:58:04 +09:00
Alexander Smorkalov 6c995c768f Merge branch 4.x 2026-02-20 22:05:35 +03:00
Alexander Smorkalov 95be2f2af8 Merge pull request #28502 from PDGGK:fix-erode-dilate-docs
docs: fix parameter name inconsistency in erode/dilate documentation
2026-02-20 18:51:02 +03:00
Alexander Smorkalov a269a489b9 Merge pull request #28552 from barracuda156:ppc
parallel_impl.cpp: fix assembler syntax for powerpc*-darwin
2026-02-18 12:37:24 +03:00
Sergey Fedorov e1e170afc2 parallel_impl.cpp: fix assembler syntax for powerpc*-darwin 2026-02-18 04:35:41 +08:00
Hanbin Bae 94c8b747f0 Merge pull request #28425 from Anemptyship:fix/squeeze-all-dims
DNN: Fix Squeeze to remove all size-1 dims when axes is empty #28425

Fixes #28424
OpenCV Extra: [opencv/opencv_extra#1308](https://github.com/opencv/opencv_extra/pull/1308)

This PR fixes the ONNX Squeeze operator to correctly remove all size-1 dimensions when `axes` is not provided, conforming to the ONNX specification.

### Details
Per [ONNX Squeeze specification](https://onnx.ai/onnx/operators/onnx__Squeeze.html):
> 'If axes is not provided, all the single dimensions will be removed from the shape.'

Previously, OpenCV DNN would not remove any dimensions in this case, causing shape mismatch errors with models like LaMa (inpainting).

### Example
```python
# Input: [1, 1, 2, 4]
# Squeeze with no axes attribute

# Before: [1, 1, 2, 4] ✗ (No change)
# After:  [2, 4] ✓ (matches ONNX Runtime)
```

### Tests
Added `testONNXModels("squeeze_no_axes")` which validates this behavior with new test data.
opencv_extra_pr=opencv/opencv_extra#1324

### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch (4.x for bug fixes)
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
  - Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-02-17 18:29:45 +03:00
Alexander Smorkalov 36b2bb70cf Merge pull request #28214 from steaphenai:docs-openexr-clarification
docs(env_reference): clarify OpenEXR optional support and security rationale
2026-02-17 15:05:05 +03:00
steaphenai d77e46eb6a docs(env_reference): clarify OpenEXR optional support and security rationale 2026-02-17 11:01:49 +03:00
HAILOM ASEGEDE 38979d15d5 Merge pull request #28333 from hailer-MIT:docs-calib3d-fisheye-clarification
docs(calib3d): clarify coordinate systems in projectPoints, undistort Points, and fisheye::undistortPoints #28333

This PR improves documentation clarity by explicitly describing:
- Input/output coordinate systems (pixel vs normalized coordinates)
- When to use normalized vs pixel coordinates in undistortPoints output
- Differences between fisheye and standard pinhole camera models
- Coordinate system transformations for better understanding

Changes:
- projectPoints: Added explicit note about input (world coords) and output (pixel coords)
- undistortPoints: Clarified that input is pixel coordinates, and output depends on parameter P (normalized vs pixel)
- fisheye::undistortPoints: Added comprehensive documentation including coordinate systems, when to use fisheye vs standard model, and distortion coefficient differences

These improvements help users avoid ambiguity when using these functions for camera calibration and 3D vision pipelines.
2026-02-17 10:56:28 +03:00
Alexander Smorkalov f819581bd4 Merge pull request #28545 from vrabaud:c_headers
Help compiler vectorize loops in meanSplit
2026-02-17 08:34:23 +03:00
Vincent Rabaud feee8b202d Help compiler vectorize loops in meanSplit
Otherwise, it does not know dataset_, mean_ and var_ do not
overlap.
2026-02-16 13:30:43 +01:00
Alexander Smorkalov a9da03f6c8 Merge pull request #28533 from abhishek-gola:randomNormalLike_bug_fix
[BUG FIX] RandomNormalLike layer Datatype bug fix
2026-02-15 11:36:56 +03:00
Alexander Smorkalov ca69b54521 Merge pull request #28522 from abhishek-gola:batchnorm_layer_add
Added Batch Normalization layer to new DNN engine
2026-02-15 10:31:23 +03:00
Adrian Kretz 196a8afe76 Merge pull request #28267 from akretz:absdiff-int
Fix absdiff with int arguments #28267

I believe the fix to the undefined behavior described in #27080 is simply casting to unsigned before subtraction, because

1. casting int to unsigned is well-defined; negative values get represented modulo $2^{32}$
2. overflow in unsigned subtraction is well-defined and the results are modulo $2^{32}$

Since we are computing everything modulo $2^{32}$ and the result must always be a non-negative number below $2^{32}$, this computation should be well-defined and correct.

I have verified this on ARM Apple Clang and on x64 Linux gcc with `-O3` and both produce the correct values in the reproducer of #27080. The test I have added fails with the integer overflow on both platforms I have tested. Perhaps @fengyuentau could verify if this also fixes the issue on the platforms he has tested?

I have added a fix and removed the workarounds. I recommended this approach in #28229, but he decided to revert it.


### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-02-15 10:29:29 +03:00
Abhishek Gola a0b7134d25 datatype bug fix 2026-02-15 10:24:02 +03:00
Alexander Smorkalov 82ff8e45e9 Merge branch 4.x 2026-02-14 15:37:33 +03:00
Alexander Smorkalov a8d26a042b Merge pull request #28489 from KAVYANSHTYAGI:module-inspection
imgproc: Fix precision loss in Laplacian 3x3 kernel for CV_64F inputs
2026-02-14 13:21:54 +03:00
Alexander Smorkalov c4b20061de Merge pull request #28537 from reeseliao:fix-typo-gapi
docs: fix typo 'neccessary' in gapi module
2026-02-14 13:05:53 +03:00
Igraine 0e8441a66b docs: fix typo 'neccessary' in gapi module 2026-02-13 16:34:31 +08:00
Jake Floch dcdd17dbe1 Fix ellipse axes documentation to use semi-major/semi-minor
Fixes #28530
2026-02-12 05:51:57 +00:00
Murat Raimbekov dc0a276edc Merge pull request #28324 from raimbekovm:fix-kaze-charbonnier
features2d: add missing KAZE DIFF_CHARBONNIER support #28324

### Description

This PR fixes the missing implementation for `DIFF_CHARBONNIER` diffusivity type in KAZE feature detector.

### Changes
- Added `charbonnier_diffusivity()` call for `DIFF_CHARBONNIER` type in `Create_Nonlinear_Scale_Space()`
- Added proper error handling for unsupported diffusivity types

### Problem
When using KAZE with `DIFF_CHARBONNIER` diffusivity type, keypoint coordinates were not computed at subpixel level, because the code lacked the specific handling for this diffusivity mode.

### Solution
The `charbonnier_diffusivity()` function already exists in `nldiffusion_functions.cpp`, it just wasn't being called. This PR adds the missing `else if` branch to call it, matching the pattern already implemented in `AKAZEFeatures.cpp`.

Fixes #27134

### Pull Request Readiness Checklist

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another copyleft license.
- [x] The PR is proposed to the proper branch.
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-02-11 15:06:26 +03:00
Abhishek Gola 6420d2b929 Added batchnorm layer 2026-02-11 16:22:20 +05:30
Alexander Smorkalov a87c227400 Merge pull request #28470 from Satge96:refine-calib-py-type
fix: mark distCoeffs, cameraMatrix as optional in calibrateCamera fun…
2026-02-11 09:53:08 +03:00
Abhishek Gola 1719aa1339 Merge pull request #28453 from abhishek-gola:roialign_layer_add
Added RoiAlign layer support in new DNN engine #28453

Fixes `Unsupported Operation: RoiAlign` issue in https://github.com/opencv/opencv/issues/20258 and https://github.com/opencv/opencv/issues/22099, model parsing is successful now.

Merge with: https://github.com/opencv/opencv_extra/pull/1310

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-02-10 14:45:56 +03:00
Alexander Smorkalov 7b3977727c Merge pull request #28460 from falloficarus22:qrcode-remove-duplicated-compute
Optimize duplicated computation in QR code error correction
2026-02-10 09:17:15 +03:00
Hanbin Bae 7942f976c1 Merge pull request #28511 from Anemptyship:optimize/slice-parallel-4.x
optimize(dnn): parallelize Slice layer implementation (4.x) #28511

### Summary
Backport of PR #28447 to 4.x branch.

### Description
This PR optimizes the SliceLayer implementation for strided inputs (where step > 1). The original implementation used a recursive element-wise copy (getSliceRecursive) for any strided slice, which was extremely inefficient.

This PR introduces:
- **Parallelization**: Uses `cv::parallel_for_` to parallelize the outermost dimension of the slice operation.
- **Memcpy Optimization**: Automatically detects "pseudo-contiguous" blocks in strided slices (e.g., slicing an outer dimension but keeping inner dimensions intact) and uses `std::memcpy` instead of scalar loops.
- **Refactoring**: Replaces the recursive function with a dedicated `ParallelSlice` loop body.

### Impact
Significant performance improvement for strided slice operations (common in detection heads, strided sampling, etc.).

### Benchmark Results
Tested on CPU with 20 threads.

| Test Case | Baseline (ms) | Optimized (ms) | Speedup |
| :--- | :--- | :--- | :--- |
| Strided Axis 0 [::2, ...] | 1.10 | 0.02 | **~55x** |
| Strided Axis 2 [..., ::2] | 1.15 | 0.11 | **~10.5x** |

### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-02-10 09:11:13 +03:00
Alexander Smorkalov 5c9fb7db76 Merge pull request #28510 from Anemptyship:optimize/resize-parallel-4.x
optimize(dnn): parallelize Resize layer implementation
2026-02-10 08:55:25 +03:00
ffccites 1387ac9fae docs: fix parameter name inconsistency in erode/dilate/morphologyEx documentation
- Change 'element' to 'kernel' in LaTeX formulas to match actual parameter name
- Fix erode() and dilate() function documentation (lines 2330, 2338, 2362, 2370)
- Fix MorphTypes enum documentation (MORPH_OPEN, MORPH_CLOSE, MORPH_GRADIENT, MORPH_TOPHAT, MORPH_BLACKHAT)
- Fix backtick formatting in dilate() documentation

Resolves #18095
2026-02-09 22:21:03 +08:00
Alexander Smorkalov 8ccbf4c5e4 Merge pull request #28505 from Kumataro:fix28498_obsensor
videoio(obsensor): replace unnecessary get<double>() usage with integer types
2026-02-09 14:43:26 +03:00
Alexander Smorkalov 284c48bbe6 Merge pull request #28504 from Kumataro:fix28498_msmf
videoio(msmf): replace unnecessary get<double>() usage with integer types
2026-02-09 14:41:53 +03:00
Alexander Smorkalov 8e84acad95 Merge pull request #28499 from Kumataro:fix28498_gst
videoio(gst): replace unnecessary get<double>() usage with integer types
2026-02-09 14:40:00 +03:00
Anemptyship 12b5091ba4 optimize(dnn): parallelize Resize layer implementation
Backport of PR #28442 to 4.x branch.

Signed-off-by: Anemptyship <ben.bae@samsung.com>
2026-02-09 03:08:49 +00:00
Kumataro 71ed3dccaf videoio(obsensor): replace unnecessary get<double>() usage with integer types 2026-02-08 06:43:01 +09:00
Kumataro 9224e5d90a videoio(msmf): replace unnecessary get<double>() usage with integer types 2026-02-08 06:36:54 +09:00
Hanbin Bae ca0d47c8e6 Merge pull request #28447 from Anemptyship:optimize/slice-layer
optimize(dnn): optimize Slice layer for strided inputs #28447

## Description
This PR optimizes the `SliceLayer` implementation for strided inputs (where `step > 1`). The original implementation used a recursive element-wise copy (`getSliceRecursive`) for any strided slice, which was extremely inefficient.

This PR introduces:
1.  **Parallelization**: Uses `cv::parallel_for_` to parallelize the outermost dimension of the slice operation.
2.  **Memcpy Optimization**: Automatically detects "pseudo-contiguous" blocks in strided slices (e.g., slicing an outer dimension but keeping inner dimensions intact) and uses `std::memcpy` instead of scalar loops.
3.  **Refactoring**: Replaces the recursive function with a dedicated `ParallelSlice` loop body.

## Impact
Significant performance improvement for strided slice operations (common in detection heads, strided sampling, etc.).

**Benchmark Results:**
| Test Case | Before (ms) | After (ms) | Speedup |
| :--- | :--- | :--- | :--- |
| **Strided Axis 0** `[::2, ...]` | 1.10 | **0.02** | **~55x** |
| **Strided Axis 2** `[..., ::2]` | 1.10 | **0.06** | **~18x** |
| **Contiguous** (Baseline) | 0.10 | 0.10 | 1.0x (Unchanged) |

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-02-07 13:17:23 +03:00
Kumataro 11d318b540 videoio(gst): replace unnecessary get<double>() usage with integer types 2026-02-07 13:36:50 +09:00
Karnav Shah aea90a9e31 Merge pull request #28308 from shahkarnav115-beep:dnn-mvn-defensive-checks
dnn: improve robustness of MVN layer#28308

This change adds small defensive improvements to the MVN layer implementation:

->Guard against zero-sized OpenCL kernel launches

->Add input validation in finalize()

->Use int64 for FLOPS computation to avoid overflow

No functional or performance changes are intended.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-02-06 13:47:15 +03:00
Murat Raimbekov c7729066c4 Merge pull request #28379 from raimbekovm:fix-bmp-sunras-overflow
imgcodecs: fix integer overflow in BMP and SunRaster decoders #28379

### Pull Request Readiness Checklist

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

Fixes #28350

### Description

This PR fixes integer overflow vulnerabilities in BMP and SunRaster image decoders that could cause heap buffer over-read when decoding malformed images.

### Root Cause

The `src_pitch` and `width3` calculations in `BmpDecoder::readData()` and `SunRasterDecoder::readData()` used `int` arithmetic:

```cpp
int src_pitch = ((m_width * m_bpp + 7) / 8 + 3) & -4;
int width3 = m_width * nch;
```

When `m_width` or `m_bpp` are large values from a crafted malicious file, the multiplication overflows, resulting in a small or negative value. This leads to insufficient buffer allocation, causing heap buffer over-read during subsequent decoding operations.

### Fix

1. **Use `size_t` arithmetic**: Cast operands to `size_t` before multiplication:
   ```cpp
   const size_t bits_per_row = static_cast<size_t>(m_width) * static_cast<size_t>(m_bpp);
   const size_t src_pitch_size = ((bits_per_row + 7) / 8 + 3) & ~static_cast<size_t>(3);
   ```

2. **Add size validation**: Added `CV_CheckLT` to reject images requiring buffers larger than 256MB:
   ```cpp
   const size_t MAX_SRC_PITCH = static_cast<size_t>(1) << 28;
   CV_CheckLT(src_pitch_size, MAX_SRC_PITCH, "BMP: src_pitch exceeds maximum allowed size");
   ```

3. **Safe conversion**: Use `validateToInt()` for safe conversion back to `int`.

### Files Changed

- `modules/imgcodecs/src/grfmt_bmp.cpp` - BmpDecoder::readData()
- `modules/imgcodecs/src/grfmt_sunras.cpp` - SunRasterDecoder::readData()

### Testing

- Code compiles without warnings
- Basic BMP and SunRaster encode/decode tests pass
- Overflow conditions are now properly rejected with CV_CheckLT
2026-02-06 13:39:55 +03:00
Alexander Smorkalov 5134109705 Merge pull request #28451 from vrabaud:lsh
Remove unused C structs from VideoCapture/VideoWriter
2026-02-06 13:38:50 +03:00
Arnie Chang 52633170a7 Merge pull request #28375 from arniechangsifive:dev/arniec/fix-fails-on-tail-agnostic-as-1s
hal/riscv-rvv: Fix test failures on hardware that implements tail-agnostic as all 1s #28375

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

### Summary
Fix test failures in `opencv_test_core` and `opencv_test_imgproc` on hardware that implements the tail-agnostic policy by overwriting tail elements with all 1s

### Root Cause
According to the [RISC-V Vector Extension Specification v1.0](https://github.com/riscvarchive/riscv-v-spec/blob/master/v-spec.adoc#343-vector-tail-agnostic-and-vector-mask-agnostic-vta-and-vma):

> "When a set is marked agnostic, the corresponding set of destination elements in any vector destination operand can either retain the value they previously held, or are overwritten with 1s."

Both `dotProd_32f` and `dotProd_32s` functions use accumulator variables(`s`) that persist across loop iterations, but were using vector intrinsics with tail-agnostic policy. The tail elements can be overwritten with 1s, corrupting the accumulator and producing NaN during the final reduction sum.

### Solution
Changed both functions to use `tail-undisturbed`(`tu`) policy to ensure tail elements remain unchanged across loop iterations.

### Reproduce Steps
The failures can be reproduced using [QEMU](https://gitlab.com/qemu-project/qemu) with `rvv_ma_all_1s=true` and `rvv_ta_all_1s=true` options to emulate hardware implementing tail-agnostic policy as all 1s.

#### Reproduce **`opencv_test_core`** failures:
```
qemu-riscv64 -cpu rv64,zba=true,zbb=true,zbc=true,zbs=true,v=true,vlen=512,elen=64,vext_spec=v1.0,rvv_ma_all_1s=true,rvv_ta_all_1s=true -L '/PATH_TO_SYSROOT/sysroot/' ./opencv_test_core --gtest_filter="Core_DotProduct*"
```
Before the patch:
```
[  FAILED  ] Core_DotProduct.accuracy (24 ms)
```
After the patch:
```
[  PASSED  ] 1 test.
```

#### Reproduce **`opencv_test_imgproc`** failures:
```
qemu-riscv64 -cpu rv64,zba=true,zbb=true,zbc=true,zbs=true,v=true,vlen=512,elen=64,vext_spec=v1.0,rvv_ma_all_1s=true,rvv_ta_all_1s=true -L '/workspace/riscv/sysroot/' ./opencv_test_imgproc --gtest_filter="fitLine_Modes.accuracy/*"
```
Before the patch
```
[  FAILED  ] 24 tests, listed below:
[  FAILED  ] fitLine_Modes.accuracy/0, where GetParam() = (13, 1)
[  FAILED  ] fitLine_Modes.accuracy/1, where GetParam() = (13, 2)
[  FAILED  ] fitLine_Modes.accuracy/2, where GetParam() = (13, 4)
[  FAILED  ] fitLine_Modes.accuracy/3, where GetParam() = (13, 5)
[  FAILED  ] fitLine_Modes.accuracy/4, where GetParam() = (13, 6)
[  FAILED  ] fitLine_Modes.accuracy/5, where GetParam() = (13, 7)
[  FAILED  ] fitLine_Modes.accuracy/6, where GetParam() = (21, 1)
[  FAILED  ] fitLine_Modes.accuracy/7, where GetParam() = (21, 2)
[  FAILED  ] fitLine_Modes.accuracy/8, where GetParam() = (21, 4)
[  FAILED  ] fitLine_Modes.accuracy/9, where GetParam() = (21, 5)
[  FAILED  ] fitLine_Modes.accuracy/10, where GetParam() = (21, 6)
[  FAILED  ] fitLine_Modes.accuracy/11, where GetParam() = (21, 7)
[  FAILED  ] fitLine_Modes.accuracy/12, where GetParam() = (12, 1)
[  FAILED  ] fitLine_Modes.accuracy/13, where GetParam() = (12, 2)
[  FAILED  ] fitLine_Modes.accuracy/14, where GetParam() = (12, 4)
[  FAILED  ] fitLine_Modes.accuracy/15, where GetParam() = (12, 5)
[  FAILED  ] fitLine_Modes.accuracy/16, where GetParam() = (12, 6)
[  FAILED  ] fitLine_Modes.accuracy/17, where GetParam() = (12, 7)
[  FAILED  ] fitLine_Modes.accuracy/18, where GetParam() = (20, 1)
[  FAILED  ] fitLine_Modes.accuracy/19, where GetParam() = (20, 2)
[  FAILED  ] fitLine_Modes.accuracy/20, where GetParam() = (20, 4)
[  FAILED  ] fitLine_Modes.accuracy/21, where GetParam() = (20, 5)
[  FAILED  ] fitLine_Modes.accuracy/22, where GetParam() = (20, 6)
[  FAILED  ] fitLine_Modes.accuracy/23, where GetParam() = (20, 7)
```

After the patch
`[  PASSED  ] 24 tests.`
2026-02-05 15:50:31 +03:00
Kavyansh Tyagi 1d5f28a44f Fix Laplacian kernel precision for double inputs 2026-02-03 13:32:34 +05:30
Alexander Smorkalov 227b751f48 Merge pull request #28394 from amd:medianBlurImp
medianBlur improvement with AVX512 ICL dispatch
2026-01-30 16:04:54 +03:00
Hanbin Bae 371b802103 Merge pull request #28442 from Anemptyship:optimize/resize-parallel
optimize(dnn): parallelize Resize layer implementation #28442

### optimize(dnn): parallelize Resize layer implementation

This PR addresses the `TODO` in `modules/dnn/src/layers/resize_layer.cpp` regarding the slow implementation of the `Resize` layer when using `opencv_linear` or `nearest` interpolation with specific configurations.

### Changes
- Replaced the serial nested loop (batch x channels) with `cv::parallel_for_`.
- This allows OpenCV to utilize multi-threading for the resizing operation, which was previously single-threaded for these specific paths.

### Performance Results
Benchmark run on 20-thread CPU (AVX2):

| Test Case | Resolution Change | Before (ms) | After (ms) | Speedup |
| :--- | :--- | :--- | :--- | :--- |
| **Upsample Linear** | 64x64 -> 128x128 | 1.47 ms | **0.18 ms** | **~8.1x** |
| **Downsample Nearest** | 128x128 -> 64x64 | 1.55 ms | **0.45 ms** | **~3.4x** |

**Test Configuration:**
- **Upsample**: `[4, 64, 64, 64]` -> `[4, 64, 128, 128]` (Factor: 2.0, Linear)
- **Downsample**: `[4, 128, 128, 128]` -> `[4, 128, 64, 64]` (Factor: 0.5, Nearest)

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      - Added `modules/dnn/perf/perf_resize.cpp` to verify performance gains.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-01-30 15:27:30 +03:00
Alexander Smorkalov 937c4afa33 Merge pull request #28479 from barucden:patch-1
Fix typo in logger.hpp
2026-01-30 15:24:38 +03:00
Vincent Rabaud 4475fb24ca Remove unused C structs from VideoCapture/VideoWriter 2026-01-30 10:54:21 +01:00
Denis Barucic bca17bcd29 Fix typo in logger.hpp
I believe this was just a copy-paste error.
2026-01-30 10:38:17 +01:00
Alexander Smorkalov 8338b5cf08 Merge pull request #28450 from asmorkalov:as/doxygen_color_theme
Add color theme button to documentation.
2026-01-30 11:14:55 +03:00
Alexander Smorkalov 9b57034c56 Merge pull request #28476 from abhishek-gola:layer_normalization_add
Added Layer Normalization support in new DNN engine
2026-01-30 11:05:36 +03:00
Abhishek Gola abc9100c9b added layer norm 2026-01-29 12:58:31 +05:30
Alexander Smorkalov 55a8996ffe Merge pull request #28346 from yeatse:feature/remove-apple-bitcode
Remove deprecated bitcode support from Apple framework build scripts
2026-01-28 14:44:43 +03:00
Siddharth Panditrao 0c613de006 Merge pull request #28380 from WalkingDevFlag:fix/charuco-detector-offset-25539
Fix ~0.5px systematic offset in CharucoDetector subpixel refinement #28380

Resolves #25539

### Problem

`CharucoDetector::detectBoard` produces Charuco corner coordinates that are consistently offset by approximately **+0.5 pixels** compared to `findChessboardCorners + cornerSubPix`.

This offset is systematic (mean ≈ −0.5 px when comparing legacy − Charuco) and reproducible across images. Visual inspection also shows that the legacy chessboard detector aligns better with the actual corner locations.

### Root cause

In `charuco_detector.cpp`, the points passed to `cornerSubPix` are manually shifted by `-Point2f(0.5f, 0.5f)` before refinement and then shifted back by `+Point2f(0.5f, 0.5f)` after refinement.

However, `cornerSubPix` refines corners in **absolute image coordinates** and converges to the true saddle point based on image gradients. Shifting the initial guess does not affect the converged result as long as the true corner lies within the refinement window.

The additional `+0.5` shift applied after refinement therefore introduces a constant bias, resulting in:

```

P_out = P_true + 0.5

```

### Solution

Remove the manual `±0.5` coordinate shifts around the `cornerSubPix` call and let the refined result be returned directly.

### Test updates

Updated expected corner values in the following tests:

- `testBoardSubpixelCoords`
- `testSeveralBoardsWithCustomIds`

The previous expected values (e.g. `200`, `250`, `300`) were only correct due to the +0.5px bias introduced by the bug.

`generateImage` creates checkerboard squares of exactly **50 pixels**, which places true corner locations on **pixel boundaries** rather than pixel centers. As a result, the correct subpixel coordinates are:

```

199.5, 249.5, 299.5

```

instead of the previously expected integer values.

After updating the expected values, all **30 Charuco-related tests pass** with the fix applied.

### Verification

I verified the fix using a Python reproducer that compares `CharucoDetector` output against `findChessboardCorners + cornerSubPix`:

- **Before fix:** mean error ≈ −0.501 px  
![before_fix_multilocus](https://github.com/user-attachments/assets/cc812956-c081-4f00-a7e2-78b7427b1235)
![before_fix_zoomed_small](https://github.com/user-attachments/assets/7e6887cb-b09d-47dc-9fcd-03a27b17f310)

- **After fix:** mean error ≈ −0.001 px
![after_fix_multilocus](https://github.com/user-attachments/assets/1c4f8dfa-d0ae-417c-881e-e2a9cbc4b9f1)
![after_fix_zoomed_small](https://github.com/user-attachments/assets/9c325995-fe0b-42f3-bdbb-6754f985e936)

After the change, the Charuco detector output aligns with the legacy chessboard detector both numerically and visually.

### Notes

This change only affects the post-refinement coordinate handling and does not alter detection logic, refinement parameters, or performance.

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-01-28 14:31:54 +03:00
Adrian Kretz 65e6890f33 Merge pull request #28386 from akretz:fix_issue_28385
Fix integer overflow in medianBlur #28386

The bug in #28385 is that the pointer arithmetic in
https://github.com/opencv/opencv/blob/d8bc5b94b851d5b392c61e5b954fba992e18bb73/modules/imgproc/src/median_blur.simd.hpp#L666-L668
overflows if `i*sstep` is larger than `INT_MAX`, because both variables are of type `int`. If one operand is of type `size_t` instead, the other operand gets promoted to `size_t` before multiplication and overflow doesn't happen anymore.

The test allocates 2.3GB for the Mat; I hope that's okay.

This PR fixes #28385

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2026-01-28 14:23:43 +03:00
Satge96 d1e12b9aa9 fix: mark distCoeffs, cameraMatrix as optional in calibrateCamera function 2026-01-28 11:14:29 +01:00
Alexander Smorkalov cf1177e87e Merge pull request #28467 from Sikandar1310291:fix/topk-layer-k-boundary-check-28445
Fix: TopK layer K boundary check allows K == input_dim (#28445)
2026-01-27 14:15:37 +03:00
Sikandar d8f8267700 Fix: TopK layer K boundary check allows K == input_dim (#28445)
The TopK layer was incorrectly rejecting K values equal to the input
dimension size. According to ONNX specification, K should be allowed
to equal the dimension size (to retrieve all elements).

Changed validation from 'K < input_shape[axis]' to 'K <= input_shape[axis]'

This fixes the error when loading YOLOv10 ONNX models that use TopK
with K equal to the dimension size.

Fixes #28445
2026-01-27 09:35:36 +05:00
Murat Raimbekov 774c7e01b3 Merge pull request #28301 from raimbekovm:fix-more-typos
docs: fix spelling errors in documentation and code #28301

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- [ ] The feature is well documented and sample code can be built with the project CMake

### Description

Fixed multiple spelling errors across documentation, comments, and code:

- 'colummn' → 'column' (cublas.hpp, 3 occurrences)
- 'points_per_colum' → 'points_per_column' (calib3d.hpp, 3 occurrences)
- 'Asignee' → 'Assignee' (sift files, 2 occurrences)
- 'compability' → 'compatibility' (face.hpp, 2 occurrences)
- 'orignal' → 'original' (aruco_detector.cpp)
- 'refrence' → 'reference' (chessboard.cpp)
- 'indeces' → 'indices' (stitching.hpp)
- 'OutputPrecison' → 'OutputPrecision' (test)
- 'tranform' → 'transform' (slice_layer.cpp, 3 occurrences)

Total: 24 fixes across 14 files. Documentation and comment changes only, no functional impact.
2026-01-26 21:28:50 +03:00
Shrutikasri04 99dd085cd9 Merge pull request #28422 from Shrutikasri04:first-pr
docs(calib3d): fix grammar and clarify radial distortion formula in camera calibration tutorial #28422

This PR improves the camera calibration tutorial documentation by:

- Fixing minor grammatical issues for better readability.
- Clarifying the radial distortion model by explicitly defining r² = x² + y².

These changes do not affect functionality and are intended to make the mathematical explanation clearer for readers and new users.
2026-01-26 16:24:19 +03:00
nklskyoy d851f3bc80 Merge pull request #27988 from nklskyoy:attention-2-layer
AttentionOnnxAiLayer #27988 
 
Implements https://onnx.ai/onnx/operators/onnx__Attention.html#attention-23

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-01-26 15:58:13 +03:00
Alexander Smorkalov a9f06448c8 Merge pull request #28457 from luisadame:patch-1
Fix setup usage example async syntax
2026-01-25 15:55:22 +03:00
Abhishek b93ea5412c Optimize duplicated computation in QR code error correction
Precompute X values in Forney algorithm to eliminate redundant gfPow calls.

Previously, gfPow(2, ...) was computed multiple times for the same error
location across different loop iterations, resulting in O(L²) redundant
Galois field arithmetic operations.

This change:
- Precomputes all X values once before the main loop
- Reduces complexity from O(L²) to O(L) for X value computations
- Removes the TODO comment at line 1642
- No functional changes, only performance improvement

The optimization is most beneficial when there are many error locations
in QR codes (larger L values).
2026-01-24 17:21:12 +00:00
pratham-mcw b229f1efd3 Merge pull request #28243 from pratham-mcw:cvfloor-neon-opt
core: add NEON support for cvFloor in fast_math.hpp #28243

- This PR adds NEON intrinsics-based implementation for the cvFloor function in fast_math.hpp for Windows-ARM64.
- Both float and double overloads now use NEON intrinsics for cvFloor Function.
- calchist and calchist1d function uses cvFloor function for its computations. 
- After adding these changes both functions showed improvement in performance.

**Performance Benchmarks:**
<img width="956" height="273" alt="image" src="https://github.com/user-attachments/assets/a00c98cd-d245-4d11-a9fd-361a3bd89f59" />

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
2026-01-24 13:42:16 +03:00
Alexander Smorkalov 1d20b65f1f Merge pull request #28401 from amd:fast_bilateral_level2
Optimized Bilateral Filter (32f) with AVX512
2026-01-24 13:40:08 +03:00
Luis Adame Rodríguez 8fbb871a9c fix setup usage example async syntax 2026-01-24 01:23:41 +01:00
Alexander Smorkalov 74addff3d0 Merge pull request #28303 from raimbekovm:fix-typos-batch4
docs: fix spelling errors in code and comments
2026-01-23 13:42:15 +03:00
Alexander Smorkalov 4cfd9689ae Merge pull request #28446 from Erellu:4.x
Fix UB in cv::error when breakOnError set to true
2026-01-23 11:29:33 +03:00
Alexander Smorkalov 5258bc5de9 Merge pull request #28317 from raimbekovm:fix-typos-batch7
docs: fix typos in documentation and code comments
2026-01-23 11:29:02 +03:00
Alexander Smorkalov f286b8d3cd Add color theme button to documentation. 2026-01-23 11:25:05 +03:00
Alexander Smorkalov 701da1686b Merge pull request #28439 from jinboson:loongarch64_itt
3rdparty(itt): support LOONGARCH64
2026-01-23 09:03:33 +03:00
Nechama Krashinski 1f47f5ba97 Merge pull request #28404 from NechamaKrashinski:imgproc-cornersubpix-error-handling
imgproc: replace assertion in cornerSubPix with descriptive error #28404

Replace CV_Assert with explicit bounds check for initial corners in cornerSubPix.

This provides a descriptive runtime error instead of abrupt termination,
improving error handling for Python and C++ users.

No algorithmic or behavioral change for valid inputs.

Fixes #25139 (Related to #7276).

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-01-22 16:13:21 +03:00
Erellu b201018a25 Fix UB in cv::error when breakOnError set to true
Fixes #28436.
Replaces a nullptr dereference (unguaranteed to cause a SEGFAULT) by `std::terminate` which will call the current `std::terminate_handler` and then SIGARBT.
2026-01-22 13:26:02 +01:00
Alexander Smorkalov fba658b9a1 Merge pull request #28381 from vrabaud:lsh
Improve precision of RotatedRect::points
2026-01-21 17:43:05 +03:00
Kumataro 97df136d32 Merge pull request #28441 from Kumataro:fix28440
Properly preserve KAZE/AKAZE license as mandated by BSD-3-Clause #28441

Close https://github.com/opencv/opencv/issues/28440

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-01-21 16:17:19 +03:00
jinbo 9c561eb703 3rdparty(itt): support LOONGARCH64
Fix warning generated by `cmake -S . -B build -DWITH_ITT=ON -DBUILD_ITT=ON`:
```
CMake Warning at cmake/OpenCVUtils.cmake:762 (message):
  Unexpected option: BUILD_ITT (=ON)

  Condition: IF
  ((;X86_64;OR;X86;OR;ARM;OR;AARCH64;OR;PPC64;OR;PPC64LE;);AND;NOT;WINRT;AND;NOT;APPLE_FRAMEWORK)
Call Stack (most recent call first):
  CMakeLists.txt:210 (OCV_OPTION)
```
2026-01-21 16:27:32 +08:00
Alexander Smorkalov 70b42e7134 Merge pull request #28292 from intel-staging:ippicv/master_20250919
Update IPPICV binaries (20250919)
2026-01-21 09:01:19 +03:00
Alexander Smorkalov 5132446d03 Merge pull request #28437 from asmorkalov:as/ci_cleanup
Removed unused CI pipelines.
2026-01-20 21:15:17 +03:00
Alexander Smorkalov 528d91e0bc Removed unused CI pipelines. 2026-01-20 10:49:26 +03:00
Alexander Smorkalov 6950bedb5c Merge pull request #28389 from akretz:fix_issue_28352
Use Mat::total() in Darknet IO
2026-01-14 10:02:07 +03:00
Madan mohan Manokar f994a43df2 Improved Bilateral Filter level2
AVX512_SKX and AVX512_ICL dispatch added.
BilateralFilter 32f has been improved for AVX512.
2026-01-14 00:58:16 +00:00
Kumataro 105a774720 Merge pull request #28393 from Kumataro:develop/doc_build_config_avif
doc: update image codec configuration reference (add AVIF, fix JPEG XL) #28393

This PR updates the build configuration documentation to:
- Add AVIF to the supported format list (it was missing).
- Clarify that some codecs (AVIF, JPEG XL) do not support BUILD_* options because OpenCV does not bundle their source code.
- Update the description to include "write" capabilities for these formats.

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
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      Patch to opencv_extra has the same branch name.
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2026-01-13 13:02:47 +03:00
Madan mohan Manokar c35259c856 medianBlur improvement with AVX512 ICL 2026-01-12 05:05:23 +00:00
Adrian Kretz 29d68af2a8 Use Mat::total() in Darknet IO 2026-01-10 16:39:44 +01:00
Vincent Rabaud ef0172d636 Improve precision of RotatedRect::points
Not by much but enough to get a test to pass.
2026-01-08 09:52:35 +01:00
Alexander Smorkalov 9e06d19164 Merge pull request #28376 from zixianwei01:fix-build-failure-if-turn-on-vkcom
dnn: Fix compilation errors when build with WITH_VULKAN option
2026-01-07 11:21:21 +03:00
Alexander Smorkalov d8bc5b94b8 Merge pull request #28373 from vrabaud:lsh
Fix potential pointer overflow in BlockSum
2026-01-07 11:04:35 +03:00
Alexander Smorkalov 481ebe0ac0 Merge pull request #28371 from WalkingDevFlag:fix/copyTo-empty-fixed-type-28343
Fix copyTo on empty fixed-type matrices (#28343)
2026-01-07 11:04:05 +03:00
zixianwei01 bc67db9951 fix: build failure if with_vulkan 2026-01-06 22:57:02 +08:00
Vincent Rabaud b7efc11b51 Merge pull request #28361 from vrabaud:msan
Prevent a potential crash in FMEstimatorCallback::runKernel #28361
 
With solveCubic sometimes failing, n can be -1.

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
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2026-01-06 11:07:19 +03:00
Alexander Smorkalov b227993ad5 Merge pull request #28372 from amd:RaceConditionFix
Issue Fix: Race condition in GaussianBlurFixedPoint
2026-01-06 11:05:50 +03:00
Vincent Rabaud e9c6cb98ea Fix potential pointer overflow.
srcY-roi.y is promoted to size_t which creates problems when it is
negative.
2026-01-06 01:18:54 +01:00
ekharkov a5b283c800 Update IPPICV binaries (20250919) 2026-01-05 23:25:59 +01:00
WalkingDevFlag 175dd57bdd Fix copyTo on empty fixed-type matrices (#28343)
PR #27972 added _dst.create(size(), type()) in copyTo's empty() block.
In Debug builds, Mat::release() was resetting flags to MAGIC_VAL,
clearing the type information and causing assertion failures when
destination has fixedType().

Preserve type flags in Mat::release() debug mode by using:
  flags = (flags & CV_MAT_TYPE_MASK) | MAGIC_VAL

Thanks to @akretz for suggesting this better approach.
2026-01-06 00:51:06 +05:30
Madan mohan Manokar abd0eb94f6 Fixes Issue: Race condition in GaussianBlurFixedPoint #28370 2026-01-05 18:24:27 +00:00
Alexander Smorkalov f3758f40ae Merge pull request #28362 from vrabaud:lsh
Replace pow with std::pow
2026-01-05 16:10:22 +03:00
Vincent Rabaud 5622958189 Replace pow with std::pow
The C pow casts to double while std::pow has overloads that can be
optimized by the compiler.
Also replace pow(*, 1./3) by cbrt.
2026-01-04 15:12:59 +01:00
Alexander Smorkalov 10589eb2c8 Merge pull request #28349 from asmorkalov:as/numpy_2_4_types_fix
Typing fix in tests for modern Numpy
2026-01-03 12:24:01 +03:00
Alexander Smorkalov 0655c2ecba Merge pull request #28332 from nmizonov:hal_ipp_add_check_useipp
Add useIPP check to hal_ipp functions
2026-01-02 18:26:42 +03:00
Alexander Smorkalov f8c04bafa8 Typing fix in tests for modern Numpy 2026-01-02 16:30:28 +03:00
Alexander Smorkalov 4db66beb60 Merge pull request #28345 from hmaarrfk:patch-2
Fix macro definition for Power10 architecture
2026-01-02 15:48:52 +03:00
Yang Chao a3fc3e8db6 Remove bitcode support from Apple framework build scripts
- Remove embed_bitcode parameter from Builder class
- Remove bitcode compilation flags for iOS and Catalyst targets
- Remove BITCODE_GENERATION_MODE from Xcode build commands
- Remove bitcode flags from dynamic library linking
- Remove --embed_bitcode command line argument
- Fix OSXBuilder parameter mismatch after bitcode removal

Bitcode has been deprecated by Apple since Xcode 14 and is no longer
required for App Store submissions.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2026-01-02 11:30:15 +08:00
Mark Harfouche 13c8ec3aa9 Fix macro definition for Power10 architecture 2026-01-01 20:37:01 -05:00
Alexander Smorkalov 378b3c8742 Merge pull request #28329 from Kumataro:fix28178_5.x
[js] Remove obsolete C++11 definition in 5.x branch
2026-01-01 15:23:34 +03:00
Alexander Smorkalov 3c34e42209 Merge branch 'as/release_4.13.0' into 4.x 2025-12-30 21:56:39 +03:00
nmizonov 6bb3c31450 Add useIPP check to hal_ipp functions 2025-12-29 04:20:31 -08:00
Kumataro ac58382620 [js] Remove obsolete C++11 definition in 5.x branch
In OpenCV 5.x, C++17 is the minimum requirement.
The hardcoded `-std=c++11` in `modules/js/CMakeLists.txt` is no longer necessary and
actually causes build failures when using **Emscripten 4.0.20+**, as Embind now requires C++17 or newer.

Unlike the fix in #28178 for the 4.x branch,
this PR takes a cleanup approach for the 5.x branch by removing the legacy flag entirely.

- Fixes build failure with Emscripten 4.0.20+ on 5.x branch.
- Aligns with the C++17 requirement of OpenCV 5.x.
2025-12-29 16:25:12 +09:00
raimbekovm c058072d62 docs: fix typos in documentation and code comments
Fixed 19 typos across 15 files:
- properies → properties
- posible → possible (2×)
- indeces → indices
- matrixs → matrices (2×)
- grater → greater
- whith → with
- ouput → output
- choosen → chosen (4×)
- constains → contains
- refrence → reference
- dont → don't (4×)
- cant → can't
2025-12-26 23:42:51 +06:00
raimbekovm be7e2c91c6 docs: fix spelling errors in code and comments
- Fixed 'suported' -> 'supported' in imgproc.hpp
- Fixed 'constane/constans' -> 'constant/constants' in onevpl utils
- Fixed 'pushconstance' -> 'pushconstant' in op_matmul.cpp
- Fixed 'bufer' -> 'buffer' in test_imgwarp.cpp
- Fixed 'Framebuffrer' -> 'Framebuffer' in window_framebuffer
- Fixed 'readComplexPropery' -> 'readComplexProperty' in cap_msmf.cpp
- Fixed 'behavoir' -> 'behavior' in test_exr.impl.hpp
- Fixed 'previos' -> 'previous' in qrcode_encoder.cpp
2025-12-25 15:34:48 +06:00
Abhishek Gola eb36a78f5e Merge pull request #28075 from abhishek-gola:hann-hamming-blackman-support
Added Hannwindow, Hammingwindow & Blackmanwindow support #28075

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2025-12-23 16:37:45 +03:00
Alexander Smorkalov f48a12641e Merge pull request #28252 from akretz:fix-doxygen-warning
Argument name is node
2025-12-20 10:08:44 +03:00
Adrian Kretz 9a11c673a3 Argument name is node 2025-12-19 19:48:02 +01:00
Mykhailo Trushch 71c2b944a1 Merge pull request #27491 from MykhailoTrushch:ca_cpp
Chromatic aberration correction #27491

Merge with https://github.com/opencv/opencv_extra/pull/1266

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
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      Patch to opencv_extra has the same branch name.
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Parent issue: https://github.com/opencv/opencv/issues/27206
Related PR: [#27490](https://github.com/opencv/opencv/pull/27490)

This PR adds chromatic aberration correction in C++ based on calibration data from the python app.

This code adds a function for chromatic aberration correction based on the calibration file (Mat correctChromaticAberration(InputArray image, const String& calibration_file)), and a class ChromaticAberrationCorrector which can be used to correct images of the same camera under the same settings (so for the same calibration data that is initialized in the beginning).

Also, basic functionality and performance tests are added.
2025-12-19 15:02:57 +03:00
Abhishek Gola 2ea31d5075 Merge pull request #28110 from abhishek-gola:randomNormalLike_layer
Added RandomNormalLike layer for fixing ViTs parsing issue #28110
 
closes: https://github.com/opencv/opencv/issues/27603
### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
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      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2025-12-16 20:48:36 +03:00
nklskyoy d218732a70 Merge pull request #28104 from nklskyoy:rms-norm
RMSNorm: reference cpu impl #28104

https://onnx.ai/onnx/operators/onnx__RMSNormalization.html

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
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      Patch to opencv_extra has the same branch name.
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2025-12-13 09:41:44 +03:00
Alexander Smorkalov 4c8646bb78 Merge pull request #28162 from abhishek-gola:gpuMatND_support
Added support for vector<gpuMatND>
2025-12-11 18:39:15 +03:00
Abhishek Gola 111354bfef Added support for vector of gpuMatND 2025-12-10 16:12:48 +05:30
Alexander Smorkalov e466110245 Merge branch 4.x 2025-12-02 15:38:19 +03:00
Alexander Smorkalov f64d5771c1 Merge pull request #28109 from asmorkalov:as/decomp_declaration
Get rid of decomposition declarations in couple of places to support older compilers
2025-12-02 14:44:11 +03:00
Alexander Smorkalov 5e874512e3 Merge pull request #28107 from asmorkalov:as/clamp_c++17
Added clamp overload for older compilers.
2025-12-02 14:43:24 +03:00
Alexander Smorkalov 1c43a9418a Merge pull request #28114 from philnelson:add_opencv5_supporters_file
Added list of backer names from OpenCV 5 IndieGoGo campaign.
2025-12-02 10:00:23 +03:00
Phil Nelson 3363599b66 Added list of backer names from OpenCV 5 IndieGoGo campaign. 2025-12-01 10:56:54 -08:00
Alexander Smorkalov f79fbfa51c Get rid of decomposition declarations in couple of places to support older compilers. 2025-12-01 11:05:50 +03:00
Alexander Smorkalov 5ea5b5864d Added clamp overload for older compilers. 2025-12-01 10:08:11 +03:00
nklskyoy 65b2fa9c2b Merge pull request #28080 from nklskyoy:rotary-embedding-experiment-win-32bit
Resolving failing tests of rotary embedding layer on win32 #28080

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
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2025-11-28 12:31:43 +03:00
Alexander Smorkalov d771165f9d Merge pull request #28077 from asmorkalov:as/ubuntu_arm_update_5.x
Update ARM config label to Ubuntu 24.04.
2025-11-26 17:07:39 +03:00
Alexander Smorkalov 7408e349f4 Update ARM config label to Ubuntu 24.04. 2025-11-26 16:59:35 +03:00
nklskyoy b258926e54 Merge pull request #28058 from nklskyoy:onnx-importer2-dispatch-map
Onnx importer2 dispatch map #28058

I have noticed that PR[28032]( https://github.com/opencv/opencv/pull/28032) was incomplete - it fixed only the new ONNX importer. Also, building the dispatch map based on the parsed opset version hypotetically can cause trouble if the graph simplifier inserts a node with a different opset version which is not included in the dispatch map. So I removed this parameter in `buildDispatchMap_COM_MICROSOFT` and `buildDispatchMap_ONNX_AI` for now. It was marked as `CV_UNUSED` anyway. 

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
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2025-11-26 13:56:02 +03:00
nklskyoy 74fae2770e Merge pull request #28031 from nklskyoy:rotary-position-layer
Rotary position layer #28031

Implemented https://onnx.ai/onnx/operators/onnx__RotaryEmbedding.html#rotaryembedding-23

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
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      Patch to opencv_extra has the same branch name.
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2025-11-25 15:12:17 +03:00
TonyCY24 4eb5345e29 Merge pull request #28053 from TonyCY24:docs/mask-rcnn-compatibility
samples: add compatibility note for Mask R-CNN in OpenCV 5.0 #28053

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
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### Summary
Updates documentation in `samples/dnn/mask_rcnn.py` to clarify a compatibility issue with OpenCV 5.0.

### Details
As verified in issue #27240, OpenCV 5.0 introduces stricter graph optimization. The default `.pbtxt` used in this sample treats `detection_out_final` as an intermediate layer (it is not listed in `getUnconnectedOutLayersNames`).

While OpenCV 4.x implicitly allows retrieving this layer, OpenCV 5.0 throws an error when requesting it:
> "the number of requested and actual outputs must be the same"

This PR adds:
1. A warning note in the file header explaining the strict output requirement.
2. An inline comment near `net.forward()` to guide users debugging this error.

Relates to issue: #27240
2025-11-21 12:55:27 +03:00
Alexander Smorkalov bfc534e258 Merge pull request #28041 from asmorkalov:as/merge_fix
4.x->5.x merge fix.
2025-11-19 14:42:53 +03:00
Alexander Smorkalov 7bef50ba0b 4.x->5.x merge fix. 2025-11-19 13:00:17 +03:00
Alexander Smorkalov 57970de586 Merge pull request #28035 from asmorkalov:as/windows_ci_5.x
Switch to the new Github actions pipeline for Windows in 5.x too #28035

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2025-11-19 10:59:28 +03:00
nklskyoy 15dc935190 Merge pull request #28032 from nklskyoy:onnx-importer2-dispatch-map
Onnx importer2 dispatch map #28032

in the new onnx_importer all domains in the dispatch map should be included per default. 
See https://github.com/opencv/opencv/pull/27988#issuecomment-3521140872

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
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2025-11-18 16:29:19 +03:00
Alexander Smorkalov 1b6fb61b89 Merge branch 4.x 2025-11-18 08:51:08 +03:00
Ismail Abou Zeid d1da626502 Merge pull request #28023 from ismailabouzeidx:feat/estimate-translation2d-5x
[3d] Port estimateTranslation2D into 5.x #28023

Merge with opencv_extra PR: opencv/opencv_extra#1290

This PR ports the newly added function `estimateTranslation2D` that was merged into 4.x under this PR: opencv/opencv#27950

The implementation is now adapted to the 5.x branch and integrates cleanly with the updated refinement API.
Specifically, this 5.x version makes use of the improved LevMarq interface to support optional non-linear refinement of the translation parameters.

Functionality, tests, and documentation follow the same design and behavior as the original 4.x PR.
Further details can be found in the linked PR above.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2025-11-17 08:51:58 +03:00
Abhishek Gola b8c9c070ea Merge pull request #27894 from abhishek-gola:affine_layer_add
Added affine grid layer to new DNN engine #27894

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2025-11-11 16:36:08 +03:00
Abhishek Gola 21b2c91814 Merge pull request #27941 from abhishek-gola:dft_layer_add
Added DFT layer to new DNN engine #27941

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2025-11-11 13:28:24 +03:00
Alexander Smorkalov 1d7411e0f0 Merge branch 4.x 2025-11-05 14:58:16 +03:00
Abhishek Gola e9298fb73e Merge pull request #27902 from abhishek-gola:onehot_layer_add
Added Onehot layer support to new DNN engine #27902

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2025-10-30 22:21:14 +03:00
Mahi Dhiman c8eafa3ee4 Merge pull request #27937 from mahidhiman12:5.x
Update documentation for Mat_ constructor to clarify vector behavior #27937

This PR updates the documentation comment for the constructor:

`explicit Mat_(const std::vector<_Tp>& vec, bool copyData=false);`
The original docstring stated that this constructor creates a matrix with a single column. However, in OpenCV version 5.x, this constructor now creates a matrix with a single row and the number of columns equal to the size of the vector.

This change clarifies the behavior for users migrating to or working with OpenCV 5.x and reduces confusion due to the discrepancy between documented and actual matrix shape.

This PR addresses GitHub issue #27862.

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2025-10-30 16:15:28 +03:00
Alexander Smorkalov 27ac5f0cc6 Merge pull request #27942 from abhishek-gola:extended_gridsample_layer
Extended gridsample layer
2025-10-30 16:13:21 +03:00
Abhishek Gola 473c250831 Extended gridsample layer 2025-10-27 18:35:59 +05:30
Alexander Smorkalov 14eb474a75 Merge pull request #27932 from asmorkalov:as/pylint_fix
Pylint warnings fix in the new visualization sample.
2025-10-23 12:34:07 +03:00
Alexander Smorkalov 82fe39760a Pylint warnings fix in the new visualization sample. 2025-10-23 10:22:46 +03:00
Alexander Smorkalov 98bf72b319 Merge branch 4.x 2025-10-23 08:11:04 +03:00
WU Jia 90a73210fc Merge pull request #26382 from kaingwade:annoy_build_parallel
Build Annoy index in parallel #26382

#26287 raised the C++ standard, and Annoy's multi-threaded index building can be enabled now.
2025-10-21 12:37:13 +03:00
Skreg 0fb49827c1 Merge pull request #27627 from shyama7004:multicamVis
Visualization script for one or more cameras calibration with calibration.cpp #27627

* It reads one or more YAML files produced by `calibration.cpp`.
* displays an interactive 3D scene of the calibration setup with camera frustums and board meshes.
* Supports both **camera-centric** and **board-centric** views.
* exports the entire scene as a `.obj/.mtl` for use in meshlab.
* also includes an error bar plot and frustum highlighting for single-camera setups.

calibration results generated by `calibration.cpp`: https://drive.google.com/drive/folders/13NvZHaxTFdAB-bzlNve8kKSMIg9gAu6K

Results Uploaded at: https://drive.google.com/drive/folders/1fkFyoBRGcNY4UCQhGgadO6uKlAkrzjTe
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2025-10-20 08:42:06 +03:00
Kumataro 879218500d Merge pull request #27918 from Kumataro:fix26899_5.x
core: support 16 bit LUT for 5.x #27918

Porting from https://github.com/opencv/opencv/pull/27890
Porting from https://github.com/opencv/opencv/pull/27911 

And support new OpenCV5 types for 16 bit LUT.

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2025-10-20 08:30:28 +03:00
Alexander Smorkalov cc400acefe Merge pull request #27912 from asmorkalov:as/save_board_size
Save board size in multiview calibration report for further visualization.
2025-10-16 19:21:23 +03:00
Alexander Smorkalov a489d9d557 Save board size in multiview calibration report for further visualization. 2025-10-16 17:51:39 +03:00
Abhishek Gola e794c11b0b Merge pull request #27892 from abhishek-gola:center_crop_pad_layer
Added center crop pad layer to new DNN engine #27892

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2025-10-13 15:11:23 +03:00
Abhishek Gola 91768f9a27 Merge pull request #27809 from abhishek-gola:softmax_cross_entropy
Added support for SCE and NLL losses #27809

This pull request adds the support for Negative Log-Likelihood loss and Softmax Cross-Entropy loss in new DNN engine.

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2025-10-11 12:45:17 +03:00
Abhishek Gola f79f193a10 Merge pull request #27882 from abhishek-gola:elu_layer_add
Extended Activation layers support #27882

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2025-10-10 12:56:13 +03:00
Abhishek Gola 93385c6cdf Merge pull request #27816 from abhishek-gola:reduce_layer
Extended Reduce layer support in new DNN engine #27816

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2025-10-10 10:11:19 +03:00
Abhishek Gola 66c9f569bd Merge pull request #27845 from abhishek-gola:bitwise_layer_add
Added Bitwise layer to new DNN engine #27845

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2025-10-09 10:01:22 +03:00
Alexander Smorkalov 1c1d2f923b Merge branch 4.x 2025-10-08 14:55:14 +03:00
Alexander Smorkalov 9c33ddea31 Merge pull request #27874 from asmorkalov:as/3dobject_pose
3D object pose estimation tutorial update #27874

1. Use cv::loadMesh instead of custom csv-based reader.
2. Use code snippets

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2025-10-06 15:20:06 +03:00
Abhishek Gola 2470c07f1b Merge pull request #27698 from abhishek-gola:add_cast_layer
Added cast and castlike layers support in new DNN engine #27698

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2025-10-04 13:22:30 +03:00
Abhishek Gola 9c75140b1b Merge pull request #27857 from abhishek-gola:extended_type_supports
Cleaned up parser denylist and removed passing tests. #27857

Merge with: https://github.com/opencv/opencv_extra/pull/1278

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2025-10-03 16:18:04 +03:00
Alexander Smorkalov 317ba5f176 Merge pull request #27858 from dkurt:check_test_target
Check CMake target before use
2025-10-02 08:47:02 +03:00
Dmitry Kurtaev 4cb91806b1 Check CMake target before use 2025-10-01 13:44:12 +03:00
Alexander Smorkalov eb4e853134 Merge branch 4.x 2025-10-01 10:27:32 +03:00
Alexander Smorkalov da8c313a90 Merge pull request #27749 from MykhailoTrushch:fc4
Add auto white balance DNN algorithm FC4
2025-09-29 16:16:23 +03:00
Alexander Smorkalov eac7e794ba Merge pull request #27843 from asmorkalov:as/calib_generator
Moved synthetic data generator for calibration from main repo to benchmarks
2025-09-29 16:04:41 +03:00
Abhishek Gola 62369ed8a0 Merge pull request #27827 from abhishek-gola:update_onnx_nodes
Updated ONNX conformance tests and parser denylist #27827

Merge with https://github.com/opencv/opencv_extra/pull/1277

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2025-09-29 15:48:49 +03:00
Alexander Smorkalov 6345a9b961 Merge pull request #27835 from asmorkalov:as/move_multiview
Converted multiview calibration sample to application.
2025-09-29 11:52:27 +03:00
Alexander Smorkalov 61f723e2de Moved syntethic data generator for calibration from main repo to benchmarks. 2025-09-29 11:42:58 +03:00
MykhailoTrushch 289c9accad Added fc4 auto white balance dnn sample 2025-09-29 11:34:04 +03:00
Alexander Smorkalov a6c88cde7f Converted multiview calibration sample to application. 2025-09-29 10:24:14 +03:00
Alexander Smorkalov 1a74264ee9 Merge branch 4.x 2025-09-26 12:10:49 +03:00
Alexander Smorkalov 664096aafd Merge pull request #27358 from s-trinh:clean_remove_DLS_UPNP_methods
Remove dead code corresponding to SOLVEPNP_DLS and SOLVEPNP_UPNP flags in OpenCV 5
2025-09-25 16:22:55 +03:00
Rostislav Vasilikhin cdea2c3f76 Merge pull request #27821 from savuor:rv/fix_nan_depth_scale
Fixed wrong NaN handling when scaling depth for OdometryFrame #27821

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2025-09-25 15:25:04 +03:00
Dmitry Kurtaev 4159c0ad98 Merge pull request #27779 from dkurt:dlpack_5.x_types
Add 5.x types for DLPack. Keep uint32/int64/uint64 data type for conversion to Numpy. More types support for GpuMat::convertTo #27779

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**Merge with contrib**: https://github.com/opencv/opencv_contrib/pull/4000

related: https://github.com/opencv/opencv/pull/27581

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2025-09-25 10:14:46 +03:00
Rostislav Vasilikhin 9d232fe7f2 Merge pull request #27801 from savuor:rv/fix_hashtsdf_expand
Fixed HashTSDF expand bug #27801

HashTSDF should expand volume units during integration. It didn't happen in fact because of wrong Mat passing.

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2025-09-22 08:36:13 +03:00
Alexander Smorkalov 83b7a0caf7 Merge pull request #27794 from asmorkalov:as/multiview_calib_logging
Added info logs with intermediate values to multiview calibration and related script option
2025-09-19 13:43:49 +03:00
Abhishek Gola d79e95c018 Merge pull request #27674 from abhishek-gola:nonmaxsuppression_layer_add
Added nonmaxsuppression (NMS) layer to new DNN engine #27674

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2025-09-19 12:55:37 +03:00
Alexander Smorkalov 6a9aa0aba2 Added info logs with intermediate values to multiview calibration and related script option. 2025-09-19 12:27:17 +03:00
Alexander Smorkalov 07b7dd02cd Merge pull request #27792 from asmorkalov:as/multiview_stereo_init
Added option to initialize pairs with stereo and fixed related bugs.
2025-09-19 12:22:37 +03:00
Alexander Smorkalov b6eff512c8 Merge pull request #27800 from asmorkalov:as/multiview_visualize_camid
Fixed cameras name loading in multiview camera visualization.
2025-09-19 08:59:27 +03:00
Alexander Smorkalov 619f47d78a Fixed cameras name loading in multiview camera visualization. 2025-09-18 21:20:42 +03:00
Abhishek Gola 68a5aea843 Merge pull request #27586 from abhishek-gola:resize_layer_add
Added fully functional resize layer to new DNN engine #27586

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2025-09-18 09:17:04 +03:00
Alexander Smorkalov 96c2365d5f Added option to initialize pairs with stereo and fixed related bugs. 2025-09-17 18:59:43 +03:00
Alexander Smorkalov 5185dfbaee Merge pull request #27791 from asmorkalov:as/multiview_term_criteria
Add term criteria to multivew calibration
2025-09-17 18:55:07 +03:00
Alexander Smorkalov f3f9e70fe0 Merge pull request #27790 from asmorkalov:as/multview_calib_no_flip
Use clustering for findCirclesGrid in multiview calibration to get rid of pattern flip
2025-09-17 17:19:22 +03:00
Alexander Smorkalov aa0d486988 Add term criteria to multivew calibration 2025-09-17 16:37:03 +03:00
Abhishek Gola cd4f2c2561 Merge pull request #27676 from abhishek-gola:unique_layer_add
Added unique layer to new DNN engine #27676

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2025-09-17 16:04:52 +03:00
Alexander Smorkalov 0b9ef2f2be Merge pull request #27788 from savuor:rv/fix_loadmesh_outputarray
Fixed mesh loading when indices are not in std::vector
2025-09-17 15:07:01 +03:00
Alexander Smorkalov 16bb0600bd Use clustering for findCirclesGrid in multiview calibration to get rid of pattern flip. 2025-09-17 10:47:25 +03:00
Rostislav Vasilikhin 58a25ede74 fixed mesh loading when indices is not an std::vector 2025-09-16 22:04:08 +02:00
Alexander Smorkalov 57b813eaad Merge pull request #27786 from asmorkalov:as/dump_percamera_rms
Dump per-camera RMS for multiview calibration.
2025-09-15 19:06:39 +03:00
Alexander Smorkalov 8a039b4586 Dump per-camera RMS for multiview calibration. 2025-09-15 15:47:48 +03:00
Vadim Pisarevsky bdab54f79e Merge pull request #27757 from vpisarev:matshape_inside_mat
Use MatShape instead of MatSize inside cv::Mat/cv::UMat #27757

**Merge together with https://github.com/opencv/opencv_contrib/pull/3996**
---

This PR continues cv::Mat/cv::UMat refactoring. See #26056, where `MatShape` was introduced. Now it's put inside cv::Mat/cv::UMat instead of a weird `MatSize`. MatSize is now an alias for MatShape:

**before:**

```
struct MatShape { ... };
struct MatSize { ... };

struct Mat {
    ...
    int dims;
    int rows;
    int cols;

    ...

    MatShape shape() const { ... /* constructs MatShape out of MatSize and returns it;
                                    layout is always 'unknown', because we don't store it */ }

    MatSize size; // size is not valid without the parent cv::Mat,
                  // because size.p may point to Mat::rows or to Mat::cols,
                  // depending on the dimensionality, and dims() returns Mat::dims.
    MatStep step; // may allocate memory, depending on the dimensionality.
    ...
};
```

**after:**

```
struct MatShape { ... };
typedef MatShape MatSize; // they are now synonyms

struct Mat {
    ...
    int dims;
    int rows;
    int cols;

    ...

    MatShape shape() const { return size; } // just return the embedded shape (including the proper layout information)

    MatSize size; // size is self-contained data structure that can be used without the parent cv::Mat.
                  // size.dims is now a copy of dims; size.p[*] contains copies of Mat::rows and Mat::cols when dims <= 2.
    MatStep step; // does not allocate extra memory buffers.
    ...
};
```

There are several reasons to do that:

1. the main reason is to be able to store data layout (MatShape::layout) inside each cv::Mat/cv::UMat. This is necessary for the proper shape inference in DNN module. In particular, it's necessary for the next step of DNN inference optimization where we introduce block-layout-optimized convolution and other operations. Later on, we can use layout information to support non-interleaved images (e.g. RRR...GGG...BBB...) or even batches of such images in core/imgproc modules.
2. the other reason is to represent 3D/4D/5D etc. tensors as cv::Mat/cv::UMat instances more conveniently, without extra dynamic memory allocation. Before this patch we allocated some memory buffers dynamically to store shape & steps for more than 2D arrays. Now the whole cv::Mat/cv::UMat header can be stored completely on stack/in a container. Creating another copy of Mat/UMat header is now done more efficiently.
3. the third reason is to introduce the new coding pattern: `dst.create(src.size, <dst_type>);`. The pattern is suitable for most of element-wise (including cloning) and filtering operations. This pattern does not only look crisp and self-documenting, it will also automatically copy shape (including layout) from the source tensor into the destination matrix/tensor.
4. in the future we might add `colorspace` member to MatShape that will allow to distinguish RGB from BGR or NV12. `dst.create(src.size, <dst_type>);` will then copy the colorspace information as well.

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
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      Patch to opencv_extra has the same branch name.
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2025-09-15 15:03:34 +03:00
Alexander Smorkalov abcfb3c1e7 Merge pull request #27780 from asmorkalov:as/minMax_test_fix_5.x
Out of buffer access fix in the new 5.x tests.
2025-09-12 20:04:49 +03:00
Alexander Smorkalov 59c87f3206 Out of buffer access fix in the new 5.x tests. 2025-09-12 18:14:34 +03:00
Abhishek Gola 105a3c335b Merge pull request #27701 from abhishek-gola:nonzero_layer_add
Added nonzero layer to new DNN engine #27701

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2025-09-12 08:51:20 +03:00
Alexander Smorkalov cc1ddb5602 Merge branch 4.x 2025-09-10 10:46:54 +03:00
Abhishek Gola 83b771f379 Merge pull request #27710 from abhishek-gola:pow_layer_add
Added Pow layer to new DNN engine #27710

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
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      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2025-09-09 22:16:08 +03:00
Alexander Smorkalov 8562b15924 Merge pull request #27761 from dkurt:python_without_3d
Optional 3D module dependency in Python
2025-09-09 22:12:43 +03:00
Dmitry Kurtaev 2d85996182 Optional 3D module dependency in Python 2025-09-09 19:04:47 +03:00
Abhishek Gola f67ae273b3 Merge pull request #27700 from abhishek-gola:gridsample_layer_add
Added gridsample layer to new DNN engine #27700

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      Patch to opencv_extra has the same branch name.
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2025-09-09 14:45:13 +03:00
Alexander Smorkalov d1a5723864 Merge branch 4.x 2025-09-09 10:27:19 +03:00
Alexander Smorkalov 7e6da007cd Merge pull request #27693 from nklskyoy:fix-fastgemm-large-mats
DNN fix fastgemm kernel for large mats
2025-08-24 17:12:31 +03:00
onikolskyy cd20754910 refactor int -> size_t 2025-08-20 12:07:29 +02:00
Naresh M L 99a8d4b762 Merge pull request #27592 from Naresh-19:dnn-sample-seemore-sr
Added image super-resolution samples using seemoredetails model #27592

Based on "See More Details: Efficient Image Super-Resolution by Experts Mining" (ICML 2024)

### Pull Request Readiness Checklist

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      Patch to opencv_extra has the same branch name.
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This pull request adds a new sample named seemore_superres under samples/dnn/, implemented in both Python and C++.  
The sample demonstrates image super-resolution using the Seemoredetails model with OpenCV’s DNN module.

### Files Added:
- samples/dnn/seemore_superres.cpp
- samples/dnn/seemore_superres.py
- Updated samples/dnn/models.yml

### Functionality:
- Performs image upscaling(4x) using a specified Seemoredetails ONNX model.
- Accepts image path and ONNX model path as command-line arguments.
- Outputs the original and super-resolved images side by side for visual comparison.

### Sample Usage:
*C++*

./seemore_superres --input=path/to/image.jpg
`

*Python*

python seemore_superres.py --input=path/to/image.jpg
`
2025-08-20 12:11:49 +03:00
Skreg 86b9885c90 Merge pull request #27651 from shyama7004:minor-fixes
Fix empty intrinsics handling and minor calibration improvements #27651

Closes: #27650

Minor changes:
* Filter out `""` in `files_with_intrinsics`.
* Match the list length with the number of cameras.
* Fix unpacking from `cv.calibrateMultiviewExtended`.
* Adjust visualisation to ensure correct shapes.
* Save `cam_ids` as a list in YAML.
* Removed typos.

<details>
<summary>Pytest</summary>

```py
import json
import numpy as np
import importlib.util
import sys
import pytest

spec = importlib.util.spec_from_file_location("mv", "multiview_calibration.py")
mv = importlib.util.module_from_spec(spec)
sys.modules["mv"] = mv
spec.loader.exec_module(mv)

def test_insideImageMask_boundaries():
    # w = 10, h = 10, valid x,y: [0..9]
    pts = np.array([[0, 5, 9, -1, 10],
                    [0, 5, 9,  5,  5]], dtype=np.int32)
    m = mv.insideImageMask(pts, 10, 10)
    assert m.tolist() == [True, True, True, False, False]

def test_intrinsics_empty_ignored_and_forwarded(tmp_path, monkeypatch):
    cam1 = tmp_path / "cam1.txt"
    cam2 = tmp_path / "cam2.txt"
    cam1.write_text("\n")
    cam2.write_text("\n")

    opened = []

    class FakeNode:
        def __init__(self, name):
            self.name = name
        def mat(self):
            if self.name in ("camera_matrix", "cameraMatrix"):
                return np.eye(3, dtype=np.float32)
            if self.name in ("dist_coeffs", "distortion_coefficients"):
                return np.zeros((1, 5), dtype=np.float32)
            return None

    class FakeFS:
        def __init__(self, filename, flags=None):
            opened.append(filename)
        def getNode(self, key):
            return FakeNode(key)

    monkeypatch.setattr(mv.cv, "FileStorage", FakeFS)

    captured = {}
    def fake_calibrateFromPoints(pattern_points, image_points, image_sizes, models,
                                 image_names, find_intrinsics_in_python, Ks=None, distortions=None):
        captured["Ks"] = Ks
        captured["dist"] = distortions
        return dict(
            Rs=[np.zeros((3, 1)) for _ in models],
            Ts=[np.zeros((3, 1)) for _ in models],
            Ks=Ks,
            distortions=distortions,
            rvecs0=[np.zeros((3, 1))],
            tvecs0=[np.zeros((3, 1))],
            errors_per_frame=np.ones((len(models), 1), dtype=np.float32),
            output_pairs=[],
            image_points=image_points,
            models=models,
            image_sizes=image_sizes,
            pattern_points=pattern_points,
            detection_mask=np.ones((len(models), 1), np.uint8),
            image_names=image_names,
        )

    monkeypatch.setattr(mv, "calibrateFromPoints", fake_calibrateFromPoints)

    out = mv.calibrateFromImages(
        files_with_images=[str(cam1), str(cam2)],
        grid_size=[3, 2],
        pattern_type="checkerboard",
        models=[0, 0],
        dist_m=0.1,
        winsize=(5, 5),
        points_json_file="",
        debug_corners=False,
        RESIZE_IMAGE=False,            # <- match real signature name here
        find_intrinsics_in_python=False,
        is_parallel_detection=False,
        cam_ids=["1", "2"],
        files_with_intrinsics=["", str(tmp_path / "intr.yml")],
        board_dict_path=None,
    )

    assert "" not in opened
    assert captured["Ks"] is not None and len(captured["Ks"]) == 2
    assert captured["dist"] is not None and len(captured["dist"]) == 2

def test_visualize_converts_rvec_to_R_and_t_to_col(monkeypatch):
    called = {}

    def fake_plotCamerasPosition(Rs, Ts, image_sizes, pairs, pattern, frame_idx, cam_ids, detection_mask):
        called["R_shapes"] = [R.shape for R in Rs]
        called["T_shapes"] = [T.shape for T in Ts]

    def fake_plotProjection(points_2d, pattern_points, rvec0, tvec0, rvec1, tvec1, K, dist_coeff, model, *rest):
        called["tvec1_shape"] = tvec1.shape
        called["K_shape"] = K.shape

    monkeypatch.setattr(mv.plt, "savefig", lambda *a, **k: None)
    monkeypatch.setattr(mv.plt, "close", lambda *a, **k: None)
    monkeypatch.setattr(mv, "plotCamerasPosition", fake_plotCamerasPosition)
    monkeypatch.setattr(mv, "plotProjection", fake_plotProjection)

    detection_mask = np.ones((1, 1), np.uint8)
    Rs = [np.zeros((3, 1), dtype=np.float32)]                  # rvec
    Ts = [np.array([[0., 0., 0.]], dtype=np.float32)]          # row 1x3
    Ks = [np.eye(3, dtype=np.float32)]
    distortions = [np.zeros((1, 5), dtype=np.float32)]
    models = [0]
    image_points = [[np.array([[10., 10.]], dtype=np.float32)]]
    errors_per_frame = np.array([[1.0]], dtype=np.float32)
    rvecs0 = [np.zeros((3, 1), dtype=np.float32)]
    tvecs0 = [np.zeros((3, 1), dtype=np.float32)]
    pattern_points = np.array([[0., 0., 0.]], dtype=np.float32)
    image_sizes = [(100, 100)]
    output_pairs = []
    image_names = None
    cam_ids = ["0"]

    mv.visualizeResults(
        detection_mask, Rs, Ts, Ks, distortions, models,
        image_points, errors_per_frame, rvecs0, tvecs0,
        pattern_points, image_sizes, output_pairs, image_names, cam_ids
    )

    assert called["R_shapes"] == [(3, 3)]
    assert called["T_shapes"] == [(3, 1)]
    assert called["tvec1_shape"] == (3, 1)
    assert called["K_shape"] == (3, 3)
```
</details>

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2025-08-18 19:28:08 +03:00
Abhishek Gola defc988c0d Merge pull request #27666 from abhishek-gola:bitshift_layer_add
Added bitshift layer to new DNN engine #27666

### Pull Request Readiness Checklist

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- [x] The PR is proposed to the proper branch
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      Patch to opencv_extra has the same branch name.
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2025-08-18 17:33:28 +03:00
Alexander Smorkalov c41bc110aa Merge pull request #27672 from abhishek-gola:extend_types_support
Extended support for min, max and mod layers
2025-08-15 11:04:58 +03:00
Abhishek Gola 71c7bf2366 extended support for min, max and mod layers 2025-08-14 15:34:08 +05:30
Abhishek Gola b00c38c57e Merge pull request #27658 from abhishek-gola:det_layer_add
Added Determinant (Det) layer to new DNN engine #27658

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2025-08-14 12:07:40 +03:00
Alexander Smorkalov 8b2cda836d Merge pull request #27655 from abhishek-gola:fix_trilu_layer
Added proper type handling for TRILU layer
2025-08-14 10:08:09 +03:00
Abhishek Gola 4e71abeaf6 Fixed type mismatch 2025-08-14 09:49:45 +03:00
Abhishek Gola b35104d63d Merge pull request #27660 from abhishek-gola:isinf_layer_add
Added IsInf layer to new DNN engine #27660

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
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2025-08-14 08:49:18 +03:00
Abhishek Gola d5f054cd43 Merge pull request #27661 from abhishek-gola:isNan_layer_add
Added IsNan layer to new DNN engine #27661

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2025-08-13 21:08:53 +03:00
Alexander Smorkalov 87320416ce Merge branch 4.x 2025-08-13 13:48:41 +03:00
Harii Sankar S 9bb330af5f Merge pull request #27593 from HarxSan:alpha_matting_sample
Add Alpha matting samples (C++ and Python) #27593

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2025-08-12 19:14:29 +03:00
Abhishek Gola 38a72d57c1 Merge pull request #27656 from abhishek-gola:size_layer_add
Added Size layer to new DNN engine #27656

Merge with https://github.com/opencv/opencv_extra/pull/1274

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
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2025-08-12 14:38:21 +03:00
Francisco Mónica 120a46b0d1 Merge pull request #27423 from miguel1099:feat-#25150-MST_init_poseGraph
Feat #25150: Pose Graph MST initialization #27423

Implements an MST-based initialisation for pose graphs, as proposed in issue #25150. Both Prim’s and Kruskal’s algorithms were added to the 3D module. These receive a vector of node IDs and a vector of edges (each with source and target IDs and a weight), and return a vector with the resulting edges. These MST implementations treat edges as undirected internally, meaning users only need to provide one direction (A→B or B→A), and duplicates are handled automatically.

Additionally, a new pose graph initialisation method using MST (Prim) was implemented. It constructs the MST over the pose graph, then traverses it to reconstruct node poses. With this, users can call `poseGraph->initializePosesWithMST()` to create an initial solution for the pose graph problem.

A set of test cases validating the implementation was also included.

#### Notes
- The edge weight used in the MST for pose graphs is calculated as:
`weight = || translation || + λ * rotation_angle`,
where λ = 0.485 was determined empirically based on optimiser performance;
- Validated on [Sphere-a](https://lucacarlone.mit.edu/datasets/) pose graph, showing similar convergence behaviour with or without MST initialisation;
- Alternative weight formulas, such as the Mahalanobis distance formula, were also tested, but the current formula yielded better results.

#### Future Work
- Extend testing to more diverse pose graphs (currently limited to [Sphere-a](https://github.com/opencv/opencv_extra/blob/5.x/testdata/cv/rgbd/sphere_bignoise_vertex3.g2o) in opencv_extra)
- Explore adaptive tuning of the λ parameter for broader applicability.

Co-authored-by: @miguel1099 

### Pull Request Readiness Checklist

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2025-08-12 08:22:02 +03:00
Alexander Smorkalov 46a4f6838e Merge pull request #27644 from asmorkalov:as/bool_mask_imgproc
Bool masks support in imgproc module.
2025-08-11 13:53:43 +03:00
Alexander Smorkalov 958ded3dcd Bool masks support in imgproc module. 2025-08-11 13:12:11 +03:00
Alexander Smorkalov 0559a76c6b Merge pull request #27624 from abhishek-gola:clip_layer_add
Added Clip layer to new DNN engine
2025-08-08 11:55:45 +03:00
Alexander Smorkalov 02f0779d0e Merge branch 4.x 2025-08-07 10:35:20 +03:00
Abhishek Gola 094430c8f0 Added support to clip layer 2025-08-04 15:53:37 +05:30
Alexander Smorkalov 3f1402e081 Merge pull request #27611 from abhishek-gola:new_dnn_engine_layers
Clean up ONNX parser denylist and patch failing test cases
2025-08-01 10:40:08 +03:00
Abhishek Gola ffa32e2b4c Cleaned parser denylist 2025-07-31 16:40:47 +05:30
Alexander Smorkalov 6feee34c57 Merge branch 4.x 2025-07-30 16:04:47 +03:00
Alexander Smorkalov a127abbbbd Merge pull request #27573 from asmorkalov:as/bool_mask_part1
CV_Bool support for masks in 3d module.
2025-07-28 18:42:48 +03:00
Abhishek Gola 7e65c42964 Merge pull request #27547 from abhishek-gola:topk_layer_add
Added TopK layer with dynamic K support for new DNN engine #27547

This pull request adds TopK layer support to new DNN engine along with dynamic K support.

Initial version inherited from https://github.com/opencv/opencv/pull/26731. Credits to Abduragim.

Closes:
- https://github.com/opencv/opencv/issues/27061
- https://github.com/opencv/opencv/issues/25712

Also closes the topk issue for below issues but having (`Unsupported operations:        NonZero` for new dnn engine) which is unrelated to topk.
- https://github.com/opencv/opencv/issues/23663 
- https://github.com/opencv/opencv/issues/23297

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
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2025-07-28 16:15:44 +03:00
Alexander Smorkalov 3b2d7b3f1b Merge branch 4.x 2025-07-28 10:04:17 +03:00
Alexander Smorkalov 56d7a7e4b3 CV_Bool support for masks in 3d module. 2025-07-28 09:03:46 +03:00
Alexander Smorkalov 5d6cce0b99 Merge pull request #27576 from asmorkalov:as/multiview-calib-intrinsics
Reworked pre-computed intrinsiscs support in multi-view calibration script
2025-07-25 15:29:27 +03:00
Alexander Smorkalov d977b02297 Reworks pre-computed intrinsiscs support in multi-view calibration script. 2025-07-25 12:50:50 +03:00
Alexander Smorkalov bd67770dcb Merge branch 4.x 2025-07-22 09:47:19 +03:00
nklskyoy 06a78c2390 Merge pull request #27527 from nklskyoy:trilu-layer
Trilu layer #27527

Trilu layer https://onnx.ai/onnx/operators/onnx__Trilu.html is needed for importing paligemma

Merged with https://github.com/opencv/opencv_extra/pull/1264

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2025-07-17 08:55:07 +03:00
nklskyoy 7a2f2a985f Merge pull request #27449 from nklskyoy:onnx-multifile
Onnx multifile import #27449

Merge with https://github.com/opencv/opencv_extra/pull/1259

LLMs are larger than 2GB and don't fit into single file onnx. this patch adds support for importing large onnx models with external data

updated `opencv-onnx.proto` to version 1.18.0 [(https://github.com/onnx/onnx/releases/tag/v1.18.0](https://github.com/onnx/onnx/blob/v1.18.0/onnx/onnx.proto)

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2025-07-16 16:50:32 +03:00
Abhishek Gola 709eabda16 Merge pull request #27508 from abhishek-gola:if_layer_add
IfLayer add to new DNN engine #27508

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2025-07-16 09:01:54 +03:00
Souriya Trinh 1c72264d00 Remove dead code corresponding to SOLVEPNP_DLS and SOLVEPNP_UPNP flags.
These flags are internally mapped to SOLVEPNP_EPNP for more than 10 years and thus unavailable since then.
2025-06-21 04:19:13 +02:00
Dmitry Kurtaev 2e6a0cab65 Merge pull request #27439 from dkurt:tflite_fixes_for_new_engine
Adoptions of TFLife fixes to new engine #27439

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This PR covers updates from the following PRs:
#27273, #27307 (ported to 5.x by #27370)

resolves #27397

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2025-06-13 12:57:23 +03:00
Gursimar Singh 425d5cfcf0 Merge pull request #27051 from gursimarsingh:move_ccm_to_photo_module
Adding color correction module to photo module from opencv_contrib #27051

This PR moved color correction module from opencv_contrib to main repo inside photo module.

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2025-06-12 17:07:16 +03:00
Alexander Smorkalov dd87ffc340 Merge branch 4.x 2025-06-11 15:55:42 +03:00
Abhishek Gola ef9b42c05a Merge pull request #27349 from abhishek-gola:deblurring_onnx_sample
Added DNN based deblurring samples #27349

Corresponding pull request adding quantized onnx model to opencv_zoo: https://github.com/opencv/opencv_zoo/pull/295

Model size: 88MB

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2025-06-11 13:29:06 +03:00
Alexander Smorkalov 4355309c3b Merge pull request #27386 from dkurt:remove_quirc
Remove QUIRC library dependency
2025-06-11 09:39:24 +03:00
Alexander Smorkalov 350b211b57 Merge branch 4.x 2025-06-10 10:16:50 +03:00
Dmitry Kurtaev 74dae85a65 Remove QUIRC library dependency 2025-05-30 17:57:13 +03:00
Maksim Shabunin 349b44a485 Merge pull request #27242 from mshabunin:fix-uwp-build
Looks like UWP builds were broken on 5.x:
```
C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.hpp(65,33): error C3646: 'size': unknown override specifier (compiling source file C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.cpp) [C:\GHA-OCV-6\_work\opencv\opencv\build\modules\videoio\opencv_videoio.vcxproj]
C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.hpp(65,37): error C4430: missing type specifier - int assumed. Note: C++ does not support default-int (compiling source file C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.cpp) [C:\GHA-OCV-6\_work\opencv\opencv\build\modules\videoio\opencv_videoio.vcxproj]
C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.hpp(65,33): error C3646: 'size': unknown override specifier (compiling source file C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\videoio_registry.cpp) [C:\GHA-OCV-6\_work\opencv\opencv\build\modules\videoio\opencv_videoio.vcxproj]
C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.hpp(65,37): error C4430: missing type specifier - int assumed. Note: C++ does not support default-int (compiling source file C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\videoio_registry.cpp) [C:\GHA-OCV-6\_work\opencv\opencv\build\modules\videoio\opencv_videoio.vcxproj]
C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.hpp(65,33): error C3646: 'size': unknown override specifier (compiling source file C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_bridge.cpp) [C:\GHA-OCV-6\_work\opencv\opencv\build\modules\videoio\opencv_videoio.vcxproj]
C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.hpp(65,37): error C4430: missing type specifier - int assumed. Note: C++ does not support default-int (compiling source file C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_bridge.cpp) [C:\GHA-OCV-6\_work\opencv\opencv\build\modules\videoio\opencv_videoio.vcxproj]
C:\Program Files (x86)\Windows Kits\10\bin\10.0.19041.0\XamlCompiler\Microsoft.Windows.UI.Xaml.Common.targets(486,5): error MSB4181: The "CompileXaml" task returned false but did not log an error. [C:\GHA-OCV-6\_work\opencv\opencv\build\modules\videoio\opencv_videoio.vcxproj]
```

Notes:

- Pipeline passes even though there are errors in the build
- I decided to remove _highgui_ WinRT backend, because it uses C-API which is has been removed in 5.x. I believe nobody uses it anyway.
- Change in anneal library is caused by the issue in WinAPI header - it does not declare two functions for UWP, even though documentation states compatibility. See also https://github.com/openssl/openssl/pull/18311
  https://learn.microsoft.com/en-us/windows/win32/api/memoryapi/nf-memoryapi-virtuallock

  > Minimum supported client	Windows XP [desktop apps | UWP apps]
  > Minimum supported server	Windows Server 2003 [desktop apps | UWP apps]
2025-05-16 12:45:40 +03:00
Maksim Shabunin cb87f05e4b Merge pull request #27243 from mshabunin:fix-openblas-5x
Port of #27029 to 5.x

Replaced search mechanism with 4.x variant
2025-05-12 10:57:36 +03:00
Alexander Smorkalov 736a73986b Merge pull request #27266 from fengyuentau:5x/loongson/build_fix
loongson/build: added v_gt and v_lt with lsx and lasx to fix build
2025-05-12 09:47:15 +03:00
Alexander Smorkalov f8de2e06e6 Merge branch 4.x 2025-05-07 13:17:42 +03:00
OpenCV China ce1410e77c fix: added v_gt and v_lt with lsx and lasx to fix build 2025-04-29 16:39:02 +08:00
nklskyoy 1df06488b1 Merge pull request #27238 from nklskyoy:mha-rope
Rotary positional embeddings #27238

Rotary positional embeddings let an attention block encode the relative position between tokens. Widely adopted in LLMs such as LLaMA—and equally applicable to Vision Transformers—the ONNX Runtime com.microsoft.Attention operator already exposes this via its `do_rotary` flag (see docs), and this PR adds support for that flag in OpenCV.

_Operator spec: https://github.com/microsoft/onnxruntime/blob/v1.16.1/docs/ContribOperators.md#com.microsoft.Attention_

Materials:
- RoFormer paper https://arxiv.org/abs/2104.09864
- RoPe for Vision Transformer https://github.com/naver-ai/rope-vit
- llama implementation https://github.com/meta-llama/llama/blob/main/llama/model.py#L80


Merge with https://github.com/opencv/opencv_extra/pull/1250

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2025-04-28 19:47:23 +03:00
Yuantao Feng 3bfc408995 Merge pull request #27261 from fengyuentau:5.x-merge-4.x-norm-hal_rvv
5.x merge 4.x: merge changes of norm and norm_diff in hal rvv from 4.x #27261

Merge with https://github.com/opencv/opencv_extra/pull/1251

No related changes in contrib

https://github.com/opencv/opencv/pull/26991 from fengyuentau:4x/core/norm2hal_rvv
https://github.com/opencv/opencv/pull/27045 from fengyuentau:4x/hal_rvv/normDiff

Previous "Merge 4.x" on norm_diff vectorization: https://github.com/opencv/opencv/pull/27068
2025-04-28 19:45:53 +03:00
Gursimar Singh bbd36a0ec1 Merge pull request #27246 from gursimarsingh:bug_fix/mcc_dnn_dependency
Fix hard dependency of dnn for mcc module. #27246

Currently building objdetect module without dnn fails due to mcc module. This PR makes the dependency optional, by checking if DNN is available in mcc module. 

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2025-04-22 08:27:36 +03:00
Alexander Smorkalov 7c17912426 Merge pull request #27186 from jonolds:patch_CvArr
remove undefined/legacy CvArr reference in window_QT.h
2025-04-02 09:18:01 +03:00
Jon Olds d8525e37d0 remove legacy/undefined CvArr reference in window_QT.h 2025-04-01 14:08:45 -05:00
Gursimar Singh 69b91cabb4 Adding macbeth chart detector to objdetect module from opencv_contrib (#26906)
* Added mcc to opencv modules

* Removed color correction module

* Updated parameters return type

* Added python sample for macbeth_chart_detection

* Added models.yml support to samples

* Removed unnecessary headers and classes

* fixed datatype conversion

* fixed datatype conversion

* Cleaned headers and added reference/actual colors to samples

* Added mcc tutorial

* fixed datatype and header

* replaced unsigned with int

* Aligned actual and reference color function, added imread

* Fixed shadow variable

* Updated samples

* Added last frame colors prints

* updated detector class

* Added getter functions and useNet function

* Refactoring

* Fixes in test

* fixed infinite divison issue
2025-03-28 11:13:30 +03:00
nklskyoy a674ae1bce Merge pull request #27102 from nklskyoy:test_gemm_3inputs
Test gemm 3inputs #27102

Merge with test data: https://github.com/opencv/opencv_extra/pull/1245

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2025-03-24 11:06:29 +03:00
Yuantao Feng 98cbe0ac49 Merge pull request #27068 from fengyuentau:5x-merge-4x/core/normDiff-simd
5.x merge 4.x: vectorized normDiff #27068

Merge with https://github.com/opencv/opencv_extra/pull/1243

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2025-03-24 09:25:18 +03:00
nklskyoy db2ba6a145 Merge pull request #27017 from nklskyoy:gemm-dynamic-inputs
determine gemm op mode at runtime #27017

See https://github.com/opencv/opencv/issues/26209

TODOs:

- [x] Determine OP mode on runtime

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2025-03-19 09:19:46 +03:00
Yuantao Feng 7635d9a012 Merge pull request #27069 from fengyuentau:fix_5x_merge_4x_norm_missing
Fix a missing in the last 5.x merge 4.x #27069

It should look like this

https://github.com/opencv/opencv/blob/14396b802947d69d3cc44f0e809977b891ef8f4a/modules/core/src/norm.simd.hpp#L1229-L1230

https://github.com/opencv/opencv/blob/14396b802947d69d3cc44f0e809977b891ef8f4a/modules/core/src/norm.simd.hpp#L1248-L1249

https://github.com/opencv/opencv/blob/14396b802947d69d3cc44f0e809977b891ef8f4a/modules/core/src/norm.simd.hpp#L1267-L1268

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2025-03-14 21:06:41 +03:00
Alexander Smorkalov 4919cda8b2 Merge branch 4.x 2025-03-11 17:23:06 +03:00
Maxim Smolskiy 77eb6ed52d Merge pull request #26937 from MaximSmolskiy:support-bool-type-for-filestorage-base64
Support bool type for FileStorage Base64 #26937

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OpenCV extra: https://github.com/opencv/opencv_extra/pull/1238

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2025-03-07 08:35:44 +03:00
Alexander Smorkalov db40139f16 Merge branch 4.x 2025-03-05 10:28:32 +03:00
天音あめ b6b5a10a8e Merge pull request #26946 from amane-ame:minmax_fix_hal_rvv
Fix HAL dispatch in cv::minMaxIdx #26946

Closes https://github.com/opencv/opencv/pull/26939#issuecomment-2669617834.
Closes https://github.com/opencv/opencv/issues/26947

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2025-03-03 16:13:40 +03:00
Maksim Shabunin b8a8b9e1dd Merge pull request #26987 from mshabunin:fix-videoio-test-params-5
videoio: print test params instead of indexes (5.x) #26987

Port of #26948 to 5.x
2025-03-01 14:59:06 +03:00
Alexander Smorkalov 1483504702 Merge branch 4.x 2025-02-20 13:58:04 +03:00
Skreg 4d15b2a33f Merge pull request #26914 from shyama7004:log/linearPolar
Removal of deprecated functions in imgproc #26914
 
Fixes : #26410

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2025-02-18 14:03:45 +03:00
Gursimar Singh b605fc13d8 Extension to PR #26605 ldm inpainting sample (#26904)
Extension to PR #26605 ldm inpainting sample #26904

This PR adds and fixes following points in the ldm_inpainting sample on top of original PR #26605 by @Abdurrahheem 

DONE:

1. Added functionality to load models from a YAML configuration file, allowing for automatic downloading if models are not found locally.
2. Updated the script usage instructions to reflect the correct command format.
3. Improved user interaction by adding instructions to the image window for inpainting controls.
4. Introduced a new models.yml configuration section for inpainting models weights downloading, including placeholders for model SHA1 checksums.
5. Fixed input types and names of the onnx graph generation.
6. Added links to onnx graphs in models.yml
7. Support added for findModels and standarized the sample usage similar to other dnn samples
8. Fixes issue in download_models.py for downloading models from dl.opencv.org
9. Fixes issue in common.py which used to print duplicated positional arguments in case of samples that use multiple models. 

### Pull Request Readiness Checklist

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---------

Co-authored-by: Abdurrahheem <abduragim.shtanchaev@xperience.ai>
2025-02-11 17:58:39 +03:00
Maxim Smolskiy 48a7d8efbd Merge pull request #26883 from MaximSmolskiy:remove-code-duplication-from-test-for-filestorage-base64
### Pull Request Readiness Checklist

OpenCV extra: https://github.com/opencv/opencv_extra/pull/1234

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- [x] I agree to contribute to the project under Apache 2 License.
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2025-02-08 11:23:37 +03:00
Alexander Smorkalov bf6f6d2db8 Merge pull request #26708 from Kumataro:fix26707
imgcodecs: (5.x) drop bundled OpenEXR
2025-02-07 14:42:50 +03:00
Kumataro b96e9fd1ce imgcodecs: drop bundled OpenEXR 2025-02-07 10:59:03 +03:00
Alexander Smorkalov 9bd246dd05 Merge pull request #26881 from MaximSmolskiy:support-32-bit-unsigned-type-for-filestorage-base64
Support 32-bit unsigned type for FileStorage Base64
2025-02-07 10:19:29 +03:00
MaximSmolskiy 1623576a88 Support 32-bit unsigned type for FileStorage Base64 2025-02-06 21:13:34 +03:00
Maxim Smolskiy 8badff598e Merge pull request #26871 from MaximSmolskiy:fix-filestorage_io_test-test_base64-for-64-bit-integer-types
Support 64-bit integer types for FileStorage Base64 #26871

### Pull Request Readiness Checklist

Related to https://github.com/opencv/opencv/pull/26846#issuecomment-2618159277
OpenCV extra: https://github.com/opencv/opencv_extra/pull/1232

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2025-02-06 18:47:07 +03:00
Yuantao Feng 603b1cafdf Merge pull request #26821 from fengyuentau:core/transform_simd
Core: vectorize cv::transform in terms of all data types #26821

## Performance

### i7-12700K

```
Geometric mean (ms)

                      Name of Test                       base  patch   patch
                                                                         vs
                                                                        base
                                                                     (x-factor)
Mat_Transform::Size_MatType::(127x61, 8SC3)              0.017 0.004    4.64
Mat_Transform::Size_MatType::(127x61, 16SC3)             0.015 0.004    3.78
Mat_Transform::Size_MatType::(127x61, 32SC3)             0.015 0.007    2.03
Mat_Transform::Size_MatType::(127x61, 64FC3)             0.007 0.004    1.78
Mat_Transform::Size_MatType::(640x480, 8SC3)             0.673 0.140    4.80
Mat_Transform::Size_MatType::(640x480, 16SC3)            0.618 0.158    3.90
Mat_Transform::Size_MatType::(640x480, 32SC3)            0.579 0.278    2.08
Mat_Transform::Size_MatType::(640x480, 64FC3)            0.290 0.266    1.09
Mat_Transform::Size_MatType::(1280x720, 8SC3)            1.919 0.414    4.63
Mat_Transform::Size_MatType::(1280x720, 16SC3)           1.811 0.488    3.71
Mat_Transform::Size_MatType::(1280x720, 32SC3)           1.736 0.917    1.89
Mat_Transform::Size_MatType::(1280x720, 64FC3)           2.310 2.030    1.14
Mat_Transform::Size_MatType::(1920x1080, 8SC3)           4.339 0.924    4.70
Mat_Transform::Size_MatType::(1920x1080, 16SC3)          4.095 1.288    3.18
Mat_Transform::Size_MatType::(1920x1080, 32SC3)          4.267 3.191    1.34
Mat_Transform::Size_MatType::(1920x1080, 64FC3)          6.641 5.481    1.21
Mat_Transform_Diagonal::Size_MatType::(640x480, 8SC3)    0.415 0.104    3.98
Mat_Transform_Diagonal::Size_MatType::(640x480, 16SC3)   0.385 0.128    3.00
Mat_Transform_Diagonal::Size_MatType::(640x480, 32SC3)   0.389 0.225    1.72
Mat_Transform_Diagonal::Size_MatType::(640x480, 64FC3)   0.279 0.275    1.01
Mat_Transform_Diagonal::Size_MatType::(1280x720, 8SC3)   1.223 0.313    3.91
Mat_Transform_Diagonal::Size_MatType::(1280x720, 16SC3)  1.118 0.387    2.89
Mat_Transform_Diagonal::Size_MatType::(1280x720, 32SC3)  1.215 0.801    1.52
Mat_Transform_Diagonal::Size_MatType::(1280x720, 64FC3)  2.198 1.900    1.16
Mat_Transform_Diagonal::Size_MatType::(1920x1080, 8SC3)  2.772 0.705    3.93
Mat_Transform_Diagonal::Size_MatType::(1920x1080, 16SC3) 2.572 1.134    2.27
Mat_Transform_Diagonal::Size_MatType::(1920x1080, 32SC3) 3.477 3.276    1.06
Mat_Transform_Diagonal::Size_MatType::(1920x1080, 64FC3) 5.984 5.186    1.15
```

### A311D

```
Geometric mean (ms)

                      Name of Test                        base  patch    patch
                                                                           vs
                                                                          base
                                                                       (x-factor)
Mat_Transform::Size_MatType::(127x61, 8SC3)              0.143  0.035     4.05
Mat_Transform::Size_MatType::(127x61, 16SC3)             0.135  0.037     3.67
Mat_Transform::Size_MatType::(127x61, 32SC3)             0.110  0.062     1.77
Mat_Transform::Size_MatType::(127x61, 64FC3)             0.034  0.039     0.89
Mat_Transform::Size_MatType::(640x480, 8SC3)             5.673  1.395     4.07
Mat_Transform::Size_MatType::(640x480, 16SC3)            5.331  1.439     3.70
Mat_Transform::Size_MatType::(640x480, 32SC3)            4.329  2.472     1.75
Mat_Transform::Size_MatType::(640x480, 64FC3)            1.560  2.316     0.67
Mat_Transform::Size_MatType::(1280x720, 8SC3)            17.002 4.139     4.11
Mat_Transform::Size_MatType::(1280x720, 16SC3)           15.996 4.308     3.71
Mat_Transform::Size_MatType::(1280x720, 32SC3)           12.948 7.241     1.79
Mat_Transform::Size_MatType::(1280x720, 64FC3)           4.742  7.376     0.64
Mat_Transform::Size_MatType::(1920x1080, 8SC3)           38.253 9.384     4.08
Mat_Transform::Size_MatType::(1920x1080, 16SC3)          35.913 9.750     3.68
Mat_Transform::Size_MatType::(1920x1080, 32SC3)          29.145 16.528    1.76
Mat_Transform::Size_MatType::(1920x1080, 64FC3)          10.606 20.968    0.51
Mat_Transform_Diagonal::Size_MatType::(640x480, 8SC3)    4.439  1.086     4.09
Mat_Transform_Diagonal::Size_MatType::(640x480, 16SC3)   4.251  1.136     3.74
Mat_Transform_Diagonal::Size_MatType::(640x480, 32SC3)   3.786  1.999     1.89
Mat_Transform_Diagonal::Size_MatType::(640x480, 64FC3)   1.555  1.551     1.00
Mat_Transform_Diagonal::Size_MatType::(1280x720, 8SC3)   13.319 3.243     4.11
Mat_Transform_Diagonal::Size_MatType::(1280x720, 16SC3)  12.828 3.398     3.78
Mat_Transform_Diagonal::Size_MatType::(1280x720, 32SC3)  11.336 5.989     1.89
Mat_Transform_Diagonal::Size_MatType::(1280x720, 64FC3)  4.707  4.690     1.00
Mat_Transform_Diagonal::Size_MatType::(1920x1080, 8SC3)  29.952 7.293     4.11
Mat_Transform_Diagonal::Size_MatType::(1920x1080, 16SC3) 28.817 7.656     3.76
Mat_Transform_Diagonal::Size_MatType::(1920x1080, 32SC3) 25.476 13.388    1.90
Mat_Transform_Diagonal::Size_MatType::(1920x1080, 64FC3) 10.533 10.509    1.00
```

### M2

```
Geometric mean (ms)

                      Name of Test                       base  patch   patch
                                                                         vs
                                                                        base
                                                                     (x-factor)
Mat_Transform::Size_MatType::(127x61, 8SC3)              0.020 0.004    4.45
Mat_Transform::Size_MatType::(127x61, 16SC3)             0.016 0.004    4.48
Mat_Transform::Size_MatType::(127x61, 32SC3)             0.016 0.007    2.23
Mat_Transform::Size_MatType::(127x61, 64FC3)             0.007 0.006    1.20
Mat_Transform::Size_MatType::(640x480, 8SC3)             0.793 0.197    4.03
Mat_Transform::Size_MatType::(640x480, 16SC3)            0.626 0.154    4.08
Mat_Transform::Size_MatType::(640x480, 32SC3)            0.627 0.306    2.05
Mat_Transform::Size_MatType::(640x480, 64FC3)            0.273 0.253    1.08
Mat_Transform::Size_MatType::(1280x720, 8SC3)            2.350 0.540    4.35
Mat_Transform::Size_MatType::(1280x720, 16SC3)           1.875 0.415    4.52
Mat_Transform::Size_MatType::(1280x720, 32SC3)           1.893 0.844    2.24
Mat_Transform::Size_MatType::(1280x720, 64FC3)           0.830 0.808    1.03
Mat_Transform::Size_MatType::(1920x1080, 8SC3)           5.302 1.178    4.50
Mat_Transform::Size_MatType::(1920x1080, 16SC3)          4.475 0.946    4.73
Mat_Transform::Size_MatType::(1920x1080, 32SC3)          4.409 1.864    2.37
Mat_Transform::Size_MatType::(1920x1080, 64FC3)          1.853 1.512    1.23
Mat_Transform_Diagonal::Size_MatType::(640x480, 8SC3)    0.586 0.110    5.32
Mat_Transform_Diagonal::Size_MatType::(640x480, 16SC3)   0.518 0.110    4.69
Mat_Transform_Diagonal::Size_MatType::(640x480, 32SC3)   0.430 0.220    1.95
Mat_Transform_Diagonal::Size_MatType::(640x480, 64FC3)   0.228 0.178    1.28
Mat_Transform_Diagonal::Size_MatType::(1280x720, 8SC3)   1.768 0.336    5.26
Mat_Transform_Diagonal::Size_MatType::(1280x720, 16SC3)  1.514 0.335    4.52
Mat_Transform_Diagonal::Size_MatType::(1280x720, 32SC3)  1.292 0.670    1.93
Mat_Transform_Diagonal::Size_MatType::(1280x720, 64FC3)  0.681 0.579    1.18
Mat_Transform_Diagonal::Size_MatType::(1920x1080, 8SC3)  3.998 0.747    5.35
Mat_Transform_Diagonal::Size_MatType::(1920x1080, 16SC3) 3.392 0.757    4.48
Mat_Transform_Diagonal::Size_MatType::(1920x1080, 32SC3) 2.956 1.491    1.98
Mat_Transform_Diagonal::Size_MatType::(1920x1080, 64FC3) 1.546 1.476    1.05
```


### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2025-02-06 13:38:16 +03:00
Alexander Smorkalov 28deafcbd4 Merge pull request #26852 from MaximSmolskiy:fix_bug_with_int64_support_for_FileStorage_5x
Fix bug with int64 support for FileStorage
2025-02-05 10:15:36 +03:00
Gursimar Singh a27b90c217 Merge pull request #26736 from gursimarsingh:inpainting_onnx_model
Added lama inpainting onnx model sample #26736

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2025-02-05 10:13:37 +03:00
Alexander Smorkalov 55a2ca58f0 Merge branch 4.x 2025-02-05 09:28:27 +03:00
Maksim Shabunin 6223bdbf7b CI: unified Linux pipeline (5.x) (#26615) 2025-02-03 10:11:24 +03:00
MaximSmolskiy c99c7c3bfe Fix bug with int64 support for FileStorage 2025-01-28 02:06:58 +03:00
FantasqueX 162179748a Merge pull request #26651 from FantasqueX:remove-msvs-2013
Remove MSVS 2013 related #26651

OpenCV 5.x requires MSVC >= 2017 15.7 and MSVS 2013 is EOF currently.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2025-01-11 19:14:34 +03:00
FantasqueX d229ac9c76 Merge pull request #26676 from FantasqueX:clean-up-sse-utils-1
Clean up sse_utils.hpp #26676

Remove unused functions in sse_utils.hpp. If they are still needed, I believe universal intrinsics should be more appropriate.

Related: https://github.com/opencv/opencv/issues/25002

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2025-01-10 14:35:42 +03:00
Alexander Smorkalov 08042e0a67 Merge pull request #26628 from mshabunin:fix-doc-footer-5x
doc: upgraded for compatibility with doxygen 1.12 (5.x)
2024-12-16 15:16:09 +03:00
Abduragim Shtanchaev 41df003d06 Merge pull request #26584 from Abdurrahheem:ash/fix-gpt2-sample
Update printingin GPT2 sample #26584

This PR update how GPT2 prints its output

**Note**: As the length of the prompt increases while inference, the token generation time slows down. May be its right time to introduce QK cashing to speed up the inference

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-12-16 14:43:47 +03:00
Maksim Shabunin db43ffcd91 doc: upgraded for compatibility with doxygen 1.12 (5.x) 2024-12-16 14:12:17 +03:00
Alexander Smorkalov 633ca0d6eb Merge pull request #26603 from asmorkalov:as/no_struct_binding
Replace structured binding for better compilers support
2024-12-10 12:13:14 +03:00
Alexander Smorkalov c25f8024c0 Replace structured binding for better compilers support. 2024-12-10 10:01:08 +03:00
Alexander Smorkalov 76c2d01938 Merge pull request #26585 from FantasqueX:clean-up-cmake-1
Clean up cmake after upgrading cmake_minimum_required to 3.13
2024-12-07 10:25:18 +03:00
FantasqueX f823143766 Clean up cmake after upgrading cmake_minimum_required to 3.13 2024-12-07 02:29:20 +08:00
Alexander Smorkalov 444d4c8360 Merge pull request #26577 from mshabunin:cpp-stdint
Prepare to transition to standard fixed-size types
2024-12-06 12:59:06 +03:00
Maksim Shabunin d666245695 Prepare to transition to standard fixed-size types 2024-12-05 18:32:10 +03:00
Alexander Smorkalov be1f3e8a7c Merge tag '5.0.0-alpha' into 5.x
Opencv 5.0.0-alpha release.

The alpha release for the new OpenCV generation. The release is designed as technology preview and not ready for production usage yet.
2024-12-05 14:34:15 +03:00
Alexander Smorkalov 0b4fdd729c Release 5.0.0-alpha. 2024-12-05 14:27:47 +03:00
Alexander Smorkalov 616fd50947 Release 5.0.0-alpha. 2024-12-05 11:54:05 +03:00
Gursimar Singh 816851c999 Merge pull request #26202 from gursimarsingh:improved_tracker_samples
Improved Tracker Samples #26202

Relates to #25006

This sample has been rewritten to track a selected target in a video or camera stream. It combines VIT tracker, Nano tracker and Dasiamrpn tracker into one tracker sample

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-12-05 11:50:03 +03:00
Abduragim Shtanchaev 0c774c94f9 Merge pull request #26573 from Abdurrahheem:ash/fix-gpt2-sample
Fix gpt2 sample #26573

This PR adds dynamic input support for `gpt2_inference.py` sample.
Fixes #26518
Fixes #26517

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-12-05 11:34:49 +03:00
Alexander Smorkalov ddfb9d1dc8 Merge pull request #26572 from FantasqueX:fix-typo-3
fix typo modfication
2024-12-05 09:10:41 +03:00
Vadim Pisarevsky 446787ab48 Merge pull request #26571 from vpisarev:fix_26497
Fixed issue when std::vector<T> is wrapped into Mat with explicit step. #26571

Hopefully, fixes #26497

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-12-05 09:03:51 +03:00
FantasqueX 91a0bdb6ea fix typo modfication 2024-12-04 23:51:39 +08:00
Alexander Smorkalov c69b5524ff Merge pull request #26570 from asmorkalov:as/more_types_java
More type constants for Java bindings too.
2024-12-04 17:18:46 +03:00
Alexander Smorkalov 95659ebe97 Merge pull request #26568 from asmorkalov:as/more_types_objc_swift
More data types support in Obj-C  and Swift bindings
2024-12-04 16:56:38 +03:00
Alexander Smorkalov 644cf0de08 More data types support in Obj-C and Swift bindings. 2024-12-04 16:00:57 +03:00
Alexander Smorkalov cb1e6250e6 More type constants for Java bindings too. 2024-12-04 15:51:58 +03:00
Alexander Smorkalov 69d0aacc2a Merge pull request #26567 from FantasqueX:fix-typo-2
fix typo in AlgorithmHint description
2024-12-04 14:27:20 +03:00
Letu Ren 2b28a6e205 fix typo in AlgorithmHint description 2024-12-04 16:51:32 +08:00
Alexander Smorkalov 3b1ec72cf5 Merge pull request #26559 from asmorkalov:as/mattype_fix_ios
Fixed cv::Mat type constants in ObjC and Swift.
2024-12-03 12:58:59 +03:00
Alexander Smorkalov 22d4735de1 Merge pull request #26558 from mshabunin:fix-doc-1.12-5x
doc: fixed issue with doxygen 1.12 (5.x)
2024-12-02 17:16:43 +03:00
Alexander Smorkalov 0c4fd4f7ee Fixed cv::Mat type constants in ObjC and Swift. 2024-12-02 17:13:25 +03:00
Alexander Smorkalov 650f2c3da3 Merge pull request #26551 from Kumataro:fix26550
doc: update introduction for new MatDepth(5.x)
2024-12-02 14:01:15 +03:00
Maksim Shabunin a492068a3a doc: fixed issue with doxygen 1.12 2024-12-02 13:52:10 +03:00
Gursimar Singh efbe580ff3 Merge pull request #26486 from gursimarsingh:object_detection_engine_update
Code Fixes and changed post processing based on models.yml in Object Detection Sample #26486

## Major Changes

1. Changes to add findModel support for config file in models like yolov4, yolov4-tiny, yolov3, ssd_caffe, tiny-yolo-voc, ssd_tf and faster_rcnn_tf.
2. Added new model and config download links for ssd_caffe, as previous links were not working.
3. Switched to DNN ENGINE_CLASSIC for non-cpu convig as new engine does not support it.
4. Fixes in python sample related to yolov5 usage.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-12-02 09:05:25 +03:00
Kumataro 5b03d82814 doc: update introduction for new MatDepth 2024-12-01 08:13:08 +09:00
Yuantao Feng b476ed6d06 Merge pull request #26505 from fengyuentau:imgproc/new_nearest_inter
imgproc: optimized nearest neighbour interpolation for warpAffine, warpPerspective and remap #26505

PR Description has a limit of 65536 characters. So performance stats are attached below.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-11-30 10:41:21 +03:00
Alexander Smorkalov b7dacbd5e3 Merge pull request #26535 from mshabunin:fix-qr-bitstream-5x
objdetect: fix invalid vector access in QR encoder (5.x)
2024-11-29 17:56:33 +03:00
Maksim Shabunin 18e8d69097 objdetect: fix invalid vector access in QR encoder 2024-11-29 14:52:41 +03:00
Alexander Smorkalov 0213483c18 Merge branch 4.x 2024-11-28 13:20:39 +03:00
Alexander Smorkalov b31bc1a295 Merge pull request #26545 from asmorkalov:as/python_missing_type_stubs
Added some mssing type constants to python type stubs generator.
2024-11-28 12:15:24 +03:00
Alexander Smorkalov d0ccd85a02 Added some mssing type constants to python type stubs generator. 2024-11-28 09:42:22 +03:00
Alexander Smorkalov 85a844e9c2 Merge pull request #26540 from asmorkalov:5.0-alpha-pre
pre: OpenCV 5.0.0-alpha (version++).
2024-11-28 08:16:48 +03:00
Alexander Smorkalov e1ba8f2659 Merge pull request #26527 from asmorkalov:as/gapi_migration
Restore predefined G-API types in Python type stubs generator
2024-11-27 16:01:02 +03:00
Alexander Smorkalov 2ba82c2c88 Restore predefined G-API types in Python type stubs generator 2024-11-27 14:34:33 +03:00
Alexander Smorkalov ff142b8ef9 pre: OpenCV 5.0.0-alpha (version++). 2024-11-27 12:59:28 +03:00
alexlyulkov 3672a14b42 Merge pull request #26394 from alexlyulkov:al/new-engine-tf-parser
Modified tensorflow parser for the new dnn engine #26394

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-11-27 09:15:20 +03:00
Alexander Smorkalov b9914065e8 Merge pull request #26534 from mshabunin:fix-reduce-test-5x
test: fix technical issue with min_element in reduce tests (5.x)
2024-11-27 09:09:11 +03:00
Maksim Shabunin 939aac47a2 test: fix technical issue with min_element in reduce tests 2024-11-26 22:21:16 +03:00
Alexander Smorkalov d6b674a3de Merge pull request #26531 from mshabunin:fix-usac-vector-access-5x
calib3d: fix vector access in USAC (5.x)
2024-11-26 22:15:50 +03:00
Alexander Smorkalov 37e13f9dcf Merge pull request #26528 from mshabunin:fix-clang-warning-cblas
build: disable clang  warning in clapack 3rdparty
2024-11-26 19:12:00 +03:00
Maksim Shabunin 57b31e2d4b calib3d: fix vector access in USAC 2024-11-26 16:19:47 +03:00
Maksim Shabunin 997a2bf0bb build: disable clang warning in clapack 3rdparty 2024-11-26 14:59:36 +03:00
Kumataro f7483cd978 Merge pull request #26508 from Kumataro:fix26507
Support CV_32U, CV_64U and CV_64S for TIFF #26508

Close https://github.com/opencv/opencv/issues/26507

Note: Predictor 2 for 64-bit is only supported after libtiff 4.4.0. But Linux x64 Debug uses libtiff 4.1.0 (releases at 2019/11/3). I append fallback fix to NONE. I hope It works well.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-11-25 11:04:54 +03:00
Alexander Smorkalov 6e229bda2b Merge pull request #26503 from asmorkalov:as/fix_lstm_tmp
Fixed test LSTM warning.
2024-11-22 10:43:06 +03:00
Alexander Smorkalov 83a1621527 Fixed test LSTM warning. 2024-11-22 09:10:25 +03:00
Alexander Smorkalov ae0a06c206 Merge pull request #26474 from vpisarev:detect_default_python3_on_mac
detect Python libs inside Xcode.app properly
2024-11-22 08:56:57 +03:00
Alexander Alekhin 7808d50412 Merge branch 4.x 2024-11-22 02:32:17 +00:00
Alexander Alekhin 5a3e18973b Merge pull request #26473 from opencv-pushbot:gitee/alalek/update_ffmpeg_5.x
ffmpeg/5.x: update FFmpeg wrapper 2024.11
2024-11-21 19:30:20 +03:00
Alexander Alekhin f85a51ed18 ffmpeg/5.x: update FFmpeg wrapper 2024.11
- FFmpeg n7.1
- OpenH264 2.5.0
- libvpx 1.15.0
- aom 3.11.0
2024-11-21 14:45:33 +00:00
Alexander Alekhin d39810d5c5 videoio(test): re-enable FFmpeg tests on WIN32
- related PR25874
2024-11-21 14:41:18 +00:00
Alexander Alekhin 5fe6b9c8f8 5.x: don't use videoio plugins for 4.x 2024-11-21 14:41:18 +00:00
Alexander Alekhin ce7c0f0e65 videoio: support properties in ffmpeg plugins 2024-11-21 14:41:18 +00:00
Abduragim Shtanchaev d0820dac38 Merge pull request #26391 from Abdurrahheem:ash/lstm-new-graph-engine-latest
LSTM layer for new graph engine. #26391

Merge with extra: https://github.com/opencv/opencv_extra/pull/1218

This PR updates/creates LSTM layer compatible with new graph engine. It is based on previous LSTM implementation with some modification on how initializers blobs are processed.

Note: Following tests are currently are disabled 


Two following two tests are disbled since ONNNRuntime does not support `layout=1` attiribute inference. See a detailed issue #26456 on this.
- `LSTM_layout_seq` 
- `LSTM_layout_batch`

Following test fails with the new engine as it is not able to deal with shapes of the form [?, C, H, W]
- `LSTM_Activations`

Works:
- [x] One directional case any batch type 
 - [x] Fix directional case when batch size large than 1
 - [x] Add peepholes attribute

TODO with the next PRs:
 - [ ] Activation support

Note: 
  

> Currently `LSTM_layout_seq`, `LSTM_layout_batch` are disabled as the tests are incorrect. They do not comply with the ONNX standard. Particularly test outputs are of incorrect dimensionality. They produce 3-dimentinal output instead of 4-dimentional. 

------

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-11-20 14:12:58 +03:00
Vadim Pisarevsky 2ed6d0f590 Merge pull request #26469 from vpisarev:move_gapi_to_contrib_part1
Removed g-api from the main repo #26469

Following #25000.
CI patch: https://github.com/opencv/ci-gha-workflow/pull/196

This is migration of G-API from opencv to opencv_contrib, part 1.
Here we simply remove G-API from the main repo. The next patch should bring G-API to opencv_contrib.

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-11-19 10:29:24 +03:00
Yuantao Feng ea0f9336e2 Merge pull request #26454 from fengyuentau/imgproc:update_warp_c4_kernels
imgproc: fix perf regressions on the c4 kernels of warpAffine / warpPerspective / remap #26454

## Performance

Previous performance regressions on c4 kernels are mainly on A311D https://github.com/opencv/opencv/pull/26348.
Regressions on c3 kernels on intel platform will be fixed in another pull request.

M2

```
Geometric mean (ms)

                                      Name of Test                                        base  patch   patch
                                                                                                          vs
                                                                                                         base
                                                                                                      (x-factor)
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)               0.338 0.163    2.08
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)               0.310 0.107    2.90
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)              0.344 0.162    2.13
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)              0.313 0.111    2.83
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC4)              0.676 0.333    2.03
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC4)              0.640 0.240    2.66
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC4)             1.212 0.885    1.37
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC4)             1.153 0.756    1.53
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)             0.950 0.475    2.00
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)             1.158 0.500    2.32
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)            3.441 3.106    1.11
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)            3.351 2.837    1.18
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)     0.336 0.163    2.07
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)     0.314 0.124    2.54
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)    0.385 0.226    1.70
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)    0.364 0.183    1.99
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC4)    0.541 0.290    1.87
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC4)    0.523 0.243    2.16
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC4)   1.540 1.239    1.24
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC4)   1.504 1.134    1.33
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)   0.751 0.465    1.62
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)   0.958 0.507    1.89
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)  3.785 3.487    1.09
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)  3.602 3.280    1.10
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                    0.331 0.153    2.16
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                    0.304 0.128    2.37
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                   0.329 0.156    2.11
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                   0.306 0.121    2.53
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                  2.046 0.930    2.20
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                  2.122 1.391    1.53
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                 2.035 0.954    2.13
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                 2.127 1.410    1.51
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                    0.329 0.157    2.09
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                    0.306 0.124    2.47
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                   0.327 0.158    2.08
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                   0.308 0.127    2.43
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                  2.039 0.948    2.15
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                  2.175 1.373    1.58
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                 2.065 0.956    2.16
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                 2.158 1.372    1.57
```

Intel i7-12700K:

```
Geometric mean (ms)

                                      Name of Test                                        base  patch   patch   
                                                                                                          vs    
                                                                                                         base   
                                                                                                      (x-factor)
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)               0.140 0.051    2.77   
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)               0.140 0.054    2.57   
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)              0.140 0.050    2.78   
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)              0.143 0.054    2.64   
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC4)              0.297 0.118    2.51   
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC4)              0.296 0.130    2.28   
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC4)             0.481 0.304    1.58   
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC4)             0.470 0.309    1.52   
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)             0.381 0.184    2.07   
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)             0.811 0.781    1.04   
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)            1.297 1.063    1.22   
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)            1.275 1.171    1.09   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)     0.135 0.057    2.36   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)     0.134 0.062    2.16   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)    0.155 0.076    2.04   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)    0.150 0.079    1.90   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC4)    0.229 0.114    2.02   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC4)    0.227 0.120    1.89   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC4)   0.560 0.444    1.26   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC4)   0.529 0.442    1.20   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)   0.326 0.192    1.70   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)   0.805 0.762    1.06   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)  1.395 1.255    1.11   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)  1.381 1.306    1.06   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                    0.138 0.049    2.81   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                    0.134 0.053    2.53   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                   0.137 0.049    2.79   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                   0.134 0.053    2.51   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                  1.362 1.352    1.01   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                  3.124 3.038    1.03   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                 1.354 1.351    1.00   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                 3.142 3.049    1.03   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                    0.140 0.052    2.70   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                    0.136 0.056    2.43   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                   0.139 0.051    2.70   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                   0.135 0.056    2.41   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                  1.335 1.345    0.99   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                  3.117 3.024    1.03   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                 1.327 1.319    1.01   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                 3.126 3.026    1.03   
```

A311D

```
Geometric mean (ms)

                                      Name of Test                                         base  patch    patch
                                                                                                            vs
                                                                                                           base
                                                                                                        (x-factor)
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)               1.762  1.361     1.29
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)               2.390  2.005     1.19
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)              1.747  1.238     1.41
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)              2.399  2.016     1.19
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC4)              3.917  3.104     1.26
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC4)              5.995  5.172     1.16
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC4)             6.711  5.460     1.23
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC4)             8.017  6.890     1.16
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)             6.269  5.596     1.12
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)             10.301 9.507     1.08
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)            18.871 17.375    1.09
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)            20.365 18.227    1.12
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)     2.083  1.514     1.38
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)     2.966  2.309     1.28
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)    2.358  1.715     1.37
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)    3.220  2.464     1.31
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC4)    3.763  3.014     1.25
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC4)    5.777  4.940     1.17
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC4)   8.791  7.819     1.12
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC4)   10.165 8.426     1.21
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)   6.047  5.293     1.14
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)   9.851  9.023     1.09
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)  31.739 29.323    1.08
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)  32.439 29.236    1.11
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                    1.759  1.441     1.22
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                    2.681  2.270     1.18
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                   1.774  1.425     1.24
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                   2.672  2.252     1.19
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                  14.079 9.334     1.51
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                  17.770 16.155    1.10
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                 15.872 11.192    1.42
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                 19.167 15.342    1.25
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                    2.284  1.545     1.48
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                    3.040  2.231     1.36
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                   2.280  1.380     1.65
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                   2.882  2.185     1.32
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                  15.877 11.381    1.40
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                  19.521 16.106    1.21
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                 15.950 11.532    1.38
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                 19.699 16.276    1.21
```

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-11-19 09:43:59 +03:00
Alexander Smorkalov 8b84fcb376 Merge pull request #26484 from gursimarsingh:text_detection_fixed
Fixed sha1 issues with East model in Text Detection Sample
2024-11-19 09:07:30 +03:00
Alexander Smorkalov db1aa3c8a9 Merge pull request #26485 from gursimarsingh:fixed_edge_detection_and_segmentation_sample
Added cap.isOpened() check to edge detection and segmentation sample
2024-11-19 09:05:25 +03:00
Gursimar Singh 1d420dc9c0 Added camera open check 2024-11-18 20:07:27 +05:30
Gursimar Singh b89c041227 fixed sha1 issues with East model 2024-11-18 20:02:13 +05:30
Alexander Smorkalov 0d7d045a3b Merge pull request #26466 from vrabaud:5.x_calibration_fix
Fix minor bugs in calibration.
2024-11-18 10:49:30 +03:00
Vadim Pisarevsky 206a30bce9 detect Python libs inside Xcode.app properly 2024-11-16 13:50:07 +03:00
Vincent Rabaud d46280507e Fix minor bugs in calibration.
- the asserts are contradictory with the asserts below
- fix when rvecs/tvecs are requested
2024-11-15 09:35:08 +01:00
Alexander Smorkalov cd5dbf9389 Merge pull request #26455 from sturkmen72:minor_fix
Fix warnings on documentation built
2024-11-14 18:55:19 +03:00
Alexander Smorkalov 77b45145d1 Merge pull request #26458 from asmorkalov:as/drop_c_core
Drop C API in core
2024-11-14 17:27:36 +03:00
Alexander Smorkalov 0310b081f9 Dropped C API in core module. 2024-11-14 08:33:22 +03:00
Suleyman TURKMEN dc1f7ea5cb Update load_point_cloud.cpp 2024-11-13 15:05:43 +03:00
Alexander Smorkalov 3cc130db5d Merge branch 4.x 2024-11-13 09:02:39 +03:00
Alexander Smorkalov 07d519fe88 Merge pull request #26448 from asmorkalov:as/uwp_ci_5.x
Windows UWP build in CI for 5.x
2024-11-12 14:22:47 +03:00
WU Jia 614e250fd3 Merge pull request #26405 from kaingwade:rename_features2d
Rename features2d #26405

This PR renames the module _features2d_ to _features_ as one of the Big OpenCV Cleanup #25007. 
Related PR: opencv/opencv_contrib: [#3820](https://github.com/opencv/opencv_contrib/pull/3820) opencv/ci-gha-workflow: [#192](https://github.com/opencv/ci-gha-workflow/pull/192)
2024-11-12 11:04:48 +03:00
Alexander Smorkalov 22f7002ed2 Windows UWP build in CI for 5.x 2024-11-12 09:33:08 +03:00
Dmitry Kurtaev c7a21dc5bf Merge pull request #26444 from dkurt:fix_empty_mat_assert
Fix empty mat debug assertion #26444

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-11-11 22:54:57 +03:00
Alexander Smorkalov e83a7fef9f Merge pull request #26283 from mshabunin:cpp-test-3d
C-API cleanup: removed or updated some of 3d and calib tests
2024-11-11 21:44:26 +03:00
Yuantao Feng c445a000c9 Merge pull request #26348 from fengyuentau:imgproc/remap_opt
imgproc: add new remap kernels that align with the new warpAffine and warpPerspective kernels #26348

## Performance

M2:

```
Geometric mean (ms)

                                      Name of Test                                        base  patch   patch   
                                                                                                          vs    
                                                                                                         base   
                                                                                                      (x-factor)
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                0.213 0.185    1.15   
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)               0.213 0.187    1.14   
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC4)               0.417 0.355    1.18   
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC4)              0.973 0.908    1.07   
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)              0.563 0.507    1.11   
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)             3.208 3.165    1.01   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)      0.244 0.195    1.26   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)     0.270 0.245    1.10   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC4)     0.361 0.328    1.10   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC4)    1.365 1.273    1.07   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)    0.532 0.508    1.05   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)   3.651 3.545    1.03   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC1)                     0.272 0.097    2.80   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC1)                    0.304 0.148    2.06   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC1)                    0.271 0.125    2.16   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC3)                     0.406 0.178    2.28   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC3)                    0.476 0.275    1.73   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC3)                    0.354 0.256    1.38   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                     0.382 0.168    2.28   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                    0.555 0.338    1.64   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                    0.385 0.307    1.25   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC1)                    0.271 0.099    2.75   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC1)                   0.301 0.145    2.07   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC1)                   0.270 0.120    2.24   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC3)                    0.408 0.180    2.27   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC3)                   0.474 0.277    1.71   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC3)                   0.352 0.261    1.35   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)                    0.382 0.166    2.29   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                   0.552 0.339    1.63   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                   0.380 0.308    1.24   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC1)                   1.013 0.474    2.14   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC1)                  1.155 0.705    1.64   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC1)                  1.200 0.674    1.78   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC3)                   1.614 0.986    1.64   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC3)                  2.042 1.605    1.27   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC3)                  2.275 1.647    1.38   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                   1.558 0.847    1.84   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                  2.394 2.036    1.18   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                  2.693 2.112    1.27   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC1)                  0.999 0.463    2.16   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC1)                 1.194 0.699    1.71   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC1)                 1.211 0.677    1.79   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC3)                  1.619 1.045    1.55   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC3)                 2.039 1.604    1.27   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC3)                 2.257 1.657    1.36   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)                  1.578 0.845    1.87   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                 2.405 2.032    1.18   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                 2.669 2.107    1.27   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC1)                     0.277 0.104    2.66   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC1)                    0.310 0.149    2.08   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC1)                    0.275 0.122    2.26   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC3)                     0.412 0.177    2.33   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC3)                    0.479 0.277    1.73   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC3)                    0.360 0.253    1.43   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                     0.388 0.173    2.24   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                    0.575 0.337    1.71   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                    0.387 0.307    1.26   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC1)                    0.274 0.100    2.73   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC1)                   0.312 0.144    2.16   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC1)                   0.278 0.128    2.18   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC3)                    0.407 0.178    2.29   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC3)                   0.483 0.275    1.75   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC3)                   0.358 0.250    1.43   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)                    0.389 0.168    2.31   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                   0.563 0.338    1.66   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                   0.390 0.312    1.25   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC1)                   1.024 0.483    2.12   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC1)                  1.224 0.770    1.59   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC1)                  1.185 0.674    1.76   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC3)                   1.633 0.922    1.77   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC3)                  2.042 1.607    1.27   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC3)                  2.244 1.647    1.36   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                   1.592 0.872    1.83   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                  2.473 2.014    1.23   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                  2.604 2.127    1.22   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC1)                  1.020 0.490    2.08   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC1)                 1.193 0.733    1.63   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC1)                 1.203 0.694    1.73   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC3)                  1.642 0.923    1.78   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC3)                 2.055 1.619    1.27   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC3)                 2.210 1.658    1.33   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)                  1.642 0.883    1.86   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                 2.463 2.077    1.19   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                 2.610 2.152    1.21   
```


Intel i7-12700K:

```
Geometric mean (ms)

                                      Name of Test                                        base  patch   patch   
                                                                                                          vs    
                                                                                                         base   
                                                                                                      (x-factor)
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                0.146 0.055    2.66   
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)               0.146 0.055    2.65   
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC4)               0.301 0.138    2.18   
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC4)              0.490 0.329    1.49   
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)              0.390 0.194    2.01   
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)             1.286 1.190    1.08   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)      0.140 0.058    2.40   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)     0.157 0.078    2.02   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC4)     0.234 0.117    2.01   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC4)    0.550 0.472    1.16   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)    0.334 0.199    1.68   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)   1.361 1.347    1.01   

map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC1)                     0.146 0.046    3.18   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC1)                    0.174 0.045    3.88   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC1)                    0.150 0.036    4.21   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC3)                     0.195 0.120    1.63   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC3)                    0.365 0.111    3.29   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC3)                    0.217 0.106    2.05   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                     0.177 0.054    3.30   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                    0.451 0.143    3.15   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                    0.276 0.139    1.98   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC1)                    0.142 0.046    3.06   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC1)                   0.182 0.045    4.00   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC1)                   0.154 0.036    4.31   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC3)                    0.196 0.120    1.63   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC3)                   0.364 0.111    3.29   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC3)                   0.221 0.107    2.07   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)                    0.177 0.054    3.31   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                   0.488 0.143    3.42   
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                   0.280 0.139    2.01   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC1)                   0.480 0.290    1.66   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC1)                  0.698 0.288    2.43   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC1)                  0.613 0.322    1.90   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC3)                   0.665 0.808    0.82   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC3)                  1.522 0.942    1.62   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC3)                  2.504 2.204    1.14   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                   0.619 0.376    1.64   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                  2.018 1.397    1.44   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                  3.582 3.157    1.13   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC1)                  0.481 0.293    1.64   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC1)                 0.698 0.288    2.42   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC1)                 0.606 0.321    1.88   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC3)                  0.669 0.806    0.83   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC3)                 1.514 0.935    1.62   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC3)                 2.472 2.203    1.12   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)                  0.618 0.378    1.63   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                 1.998 1.404    1.42   
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                 3.583 3.160    1.13   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC1)                     0.153 0.050    3.08   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC1)                    0.189 0.048    3.90   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC1)                    0.162 0.041    3.91   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC3)                     0.211 0.124    1.70   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC3)                    0.384 0.113    3.39   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC3)                    0.221 0.107    2.07   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                     0.186 0.059    3.17   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                    0.465 0.147    3.16   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                    0.312 0.140    2.22   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC1)                    0.148 0.052    2.88   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC1)                   0.189 0.049    3.82   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC1)                   0.167 0.041    4.06   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC3)                    0.202 0.124    1.63   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC3)                   0.383 0.113    3.39   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC3)                   0.228 0.106    2.14   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)                    0.188 0.058    3.26   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                   0.467 0.147    3.17   
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                   0.286 0.140    2.05   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC1)                   0.519 0.311    1.67   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC1)                  0.743 0.307    2.42   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC1)                  0.646 0.329    1.96   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC3)                   0.714 0.826    0.86   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC3)                  1.567 0.939    1.67   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC3)                  2.501 2.183    1.15   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                   0.670 0.389    1.72   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                  2.060 1.384    1.49   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                  3.556 3.151    1.13   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC1)                  0.517 0.312    1.66   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC1)                 0.745 0.306    2.44   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC1)                 0.651 0.332    1.96   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC3)                  0.731 0.831    0.88   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC3)                 1.574 0.934    1.68   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC3)                 2.442 2.181    1.12   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)                  0.666 0.390    1.71   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                 2.045 1.391    1.47   
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                 3.557 3.154    1.13   
```

A311D:

```
Geometric mean (ms)

                                      Name of Test                                         base  patch    patch
                                                                                                            vs
                                                                                                           base
                                                                                                        (x-factor)
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                1.335  0.936     1.43
WarpAffine::TestWarpAffine::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)               1.331  0.940     1.42
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC4)               2.950  2.199     1.34
WarpAffine::TestWarpAffine::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC4)              6.011  5.177     1.16
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)              4.415  3.533     1.25
WarpAffine::TestWarpAffine::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)             26.619 17.665    1.51
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)      1.465  1.119     1.31
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)     1.776  1.416     1.25
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC4)     4.106  2.307     1.78
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC4)    12.015 7.427     1.62
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)    7.196  4.044     1.78
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)   32.182 29.642    1.09

map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC1)                     2.358  0.751     3.14
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC1)                    3.342  0.847     3.94
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC1)                    2.863  0.941     3.04
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC3)                     4.062  1.474     2.75
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC3)                    4.937  1.681     2.94
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC3)                    3.796  2.152     1.76
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                     3.838  1.341     2.86
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                    5.682  2.288     2.48
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                    3.943  3.154     1.25
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC1)                    2.346  0.754     3.11
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC1)                   3.370  0.849     3.97
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC1)                   2.841  0.934     3.04
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC3)                    4.244  1.466     2.90
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC3)                   4.882  1.680     2.91
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC3)                   3.672  2.163     1.70
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)                    3.822  1.349     2.83
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                   5.614  2.291     2.45
map1_32fc1::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                   3.987  3.174     1.26
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC1)                   10.358 4.713     2.20
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC1)                  14.165 4.903     2.89
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC1)                  11.751 5.648     2.08
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC3)                   13.912 6.793     2.05
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC3)                  22.706 8.440     2.69
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC3)                  16.738 13.517    1.24
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                   18.715 9.065     2.06
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                  28.190 15.483    1.82
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                  17.441 20.976    0.83
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC1)                  10.506 4.770     2.20
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC1)                 14.298 4.952     2.89
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC1)                 11.534 5.669     2.03
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC3)                  19.890 9.588     2.07
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC3)                 23.599 11.543    2.04
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC3)                 16.827 14.255    1.18
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)                  18.878 9.185     2.06
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                 28.377 15.766    1.80
map1_32fc1::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                 17.337 21.134    0.82
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC1)                     2.170  0.763     2.84
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC1)                    3.035  0.959     3.17
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC1)                    2.759  0.937     2.94
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC3)                     4.074  1.484     2.74
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC3)                    4.757  1.689     2.82
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC3)                    3.766  2.165     1.74
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                     3.730  1.353     2.76
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                    5.623  2.301     2.44
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                    3.935  3.115     1.26
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC1)                    2.236  0.761     2.94
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC1)                   3.010  0.946     3.18
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC1)                   2.750  0.933     2.95
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC3)                    4.045  1.484     2.73
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC3)                   4.785  1.694     2.83
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC3)                   3.642  2.146     1.70
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)                    3.710  1.357     2.73
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                   5.594  2.310     2.42
map1_32fc2::TestRemap::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                   3.845  3.120     1.23
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC1)                   10.092 4.846     2.08
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC1)                  14.501 5.724     2.53
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC1)                  11.698 5.709     2.05
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC3)                   19.480 9.290     2.10
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC3)                  23.830 11.636    2.05
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC3)                  16.725 13.922    1.20
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)                   18.756 8.839     2.12
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)                  29.698 15.668    1.90
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)                  17.641 20.145    0.88
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC1)                  10.128 4.883     2.07
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC1)                 14.438 5.685     2.54
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC1)                 11.440 5.674     2.02
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC3)                  19.681 10.117    1.95
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC3)                 23.757 11.623    2.04
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC3)                 16.891 13.690    1.23
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)                  18.887 8.756     2.16
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)                 29.654 15.890    1.87
map1_32fc2::TestRemap::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)                 17.412 20.535    0.85
```

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-11-11 21:44:01 +03:00
Alexander Smorkalov a4ab68f9f4 Merge pull request #26443 from mshabunin:cleanup-core-test
C-API cleanup: core module tests
2024-11-11 20:51:31 +03:00
Maksim Shabunin 2d9c0c8592 C-API cleanup: core module tests 2024-11-11 14:53:09 +03:00
Gursimar Singh 979428d590 Merge pull request #26334 from gursimarsingh:dnn_engine_change
Modify DNN Samples to use ENGINE_CLASSIC for Non-Default Back-end or Target #26334

PR resolves #26325 regarding fall-back to ENGINE_CLASSIC if non-default back-end or target is passed by user.
### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-11-08 08:58:35 +03:00
Gursimar Singh 1ae304bb83 Merge pull request #26347 from gursimarsingh:updated_dnn_samples
Updated segmentation sample as per current update usage and engine #26347

This PR updates segmentation sample as per new usage, and change their DNN engines. It adds usage of findModel, dynamic fontsize, etc. in segmentation sample.

The PR makes the sample consistent with other dnn samples

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-11-08 08:55:46 +03:00
Dmitry Kurtaev a7bb17b092 Merge pull request #26399 from dkurt:dk/file_storage_new_data
int64 data type in FileStorage #26399

### Pull Request Readiness Checklist

resolves #23333

Proposed approach is not perfect in terms of complexity and potential bugs. Instead of changing `INT` raw size from `4` to `8`, we check int64 value can be fitted to int32 or not.

Collections such as cv::Mat rely on data type symbol.

This PR is addressed to 5.x branch first to cover `CV_64S` Mat. Later, it can be backported to 4.x

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-11-08 08:27:59 +03:00
Kumataro 3fac9a9d69 Merge pull request #26412 from Kumataro:fix26411
doc: update to doxygen-awesome-css v2.3.4 #26412

Close https://github.com/opencv/opencv/issues/26411

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [X] The PR is proposed to the proper branch
- [X] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-11-06 09:50:45 +03:00
Alexander Smorkalov 03983549fc Merge branch 4.x 2024-11-06 08:20:12 +03:00
Dmitry Kurtaev 286f7524bb Merge pull request #26420 from dkurt:fs_mat_0d_1d
Support 0d/1d Mat in FileStorage #26420

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-11-06 08:11:59 +03:00
Alexander Smorkalov ef0d83643d Merge pull request #26418 from mshabunin:cleanup-waitkey-5
highgui: drop legacy waitkey behavior
2024-11-05 21:27:30 +03:00
Maksim Shabunin 25b67bc99e highgui: drop legacy waitkey behavior 2024-11-05 15:01:53 +03:00
Alexander Smorkalov 55105719dd Merge pull request #26396 from hanliutong:rvv-fp16-m2
Use LMUL=2 in the RISC-V Vector (RVV) FP16 part. (5.x)
2024-11-02 13:30:31 +03:00
Vadim Pisarevsky 853fa9dd40 Merge pull request #26400 from vpisarev:fix_fp16_cmp
Fixed FP16 mat comparison in tests #26400

make sure that if both compared FP16/BF16 values are bitwise-equal, assume their difference to be 0 (zero), just like in the case of FP32 and FP64, don't try to compare them as floating-point numbers, because they can be NaN's.

**fixes** #24894

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-11-02 12:07:48 +03:00
Alexander Smorkalov 7a5f12c630 Merge pull request #26397 from FantasqueX:remove-useless-cmake-policy
Remove useless cmake policy after upgrading cmake_minimum_required
2024-11-02 10:02:39 +03:00
Vadim Pisarevsky df06d2eac2 Merge pull request #26254 from vpisarev:extra_tests_for_reshape
Added extra tests for reshape #26254

Attempt to reproduce problems described in #25174. No success; everything works as expected. Probably, the function has been used improperly. Slightly modified the code of Mat::reshape() to provide better diagnostic.

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-11-01 11:37:56 +03:00
Letu Ren 65a606966b Remove useless cmake policy after upgrading cmake_minimum_required 2024-11-01 16:16:37 +08:00
Liutong HAN a59a66a2c7 Use LMUL=2 in the RISC-V Vector (RVV) FP16 part. 2024-11-01 07:05:25 +00:00
Alexander Smorkalov a95658f106 Merge pull request #26383 from mshabunin:fix-samples-cpp-17
build: raise min cmake version to 3.13 in other places
2024-10-31 09:34:28 +03:00
Alexander Smorkalov 6a8300c1a0 Merge pull request #26385 from vpisarev:fix_bfloat_test
fixed typo in bfloat<=>float conversion test
2024-10-30 16:01:46 +03:00
Maksim Shabunin e44e3ab0a7 build: raise min cmake version to 3.13 in other places 2024-10-30 14:39:04 +03:00
Vadim Pisarevsky 299aa14c4b fixed typo in bfloat<=>float conversion test 2024-10-29 20:06:11 +03:00
WU Jia 66a29b422c Merge pull request #25708 from kaingwade:flann2annoy
Add interface to Annoy which will replace the FLANN #25708

This PR is to add interface to [Annoy](https://github.com/spotify/annoy) which will replace the FLANN, part of one of the cleanup work of OpenCV 5.0: #24998. 

After it, there will be consecutive patches:

- [ ] Add Annoy based DescriptorMatcher
- [ ] Replace FLANN based code with Annoy and remove FLANN completely

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-10-28 17:04:02 +03:00
alexlyulkov a2fa1d49a4 Merge pull request #26208 from alexlyulkov:al/new-engine-caffe-parser
Modified Caffe parser to support the new dnn engine #26208

Now the Caffe parser supports both the old and the new engine. It can be selected using newEngine argument in PopulateNet.

All cpu Caffe tests work fine except:

- Test_Caffe_nets.Colorization
- Test_Caffe_layers.FasterRCNN_Proposal

Both these tests doesn't work because of the bug in the new net.forward function. The function takes the name of the desired target last layer, but uses this name as the name of the desired output tensor.
Also Colorization test contains a strange model with a Silence layer in the end, so it doesn't have outputs. The old parser just ignored it. I think, the proper solution is to run this model until the (number_of_layers - 2) layer using proper net.forward arguments in the test.

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-10-28 11:32:07 +03:00
Gursimar Singh 7b0a082dd4 Merge pull request #26326 from gursimarsingh:object_detection_fixed
[BUG FIX] Object detection sample preprocessing #26326

PR resloves #26315 related to incorrect preprocessing for 'Image2BlobParams' in object detection sample.
### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-10-28 11:25:45 +03:00
Gursimar Singh f217656916 Merge pull request #25349 from gursimarsingh:videocapture_samples_cpp
combined videocapture and videowriter samples  for cleanup
2024-10-28 09:57:54 +03:00
Gursimar Singh 331d327760 Merge pull request #26336 from gursimarsingh:person_reid_bug_fix
[BUG FIX] Fix issues in Person ReID C++ sample #26336

This PR fixes multiple issues in the Person ReID C++ sample that were causing incorrect outputs. It addresses improper matrix initialization, adds a missing return statement, and ensures that vectors are properly cleared before reuse. These changes correct the output of the sample.
### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-10-28 09:49:33 +03:00
Kumataro 3b01a4d4e9 Merge pull request #26373 from Kumataro:fix26372
doc: fix doxygen errors at Algorithm and QRCodeEncoder #26373

Close https://github.com/opencv/opencv/issues/26372

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-10-28 09:20:04 +03:00
Alexander Smorkalov 8a5ec4bf7b Merge pull request #26287 from mshabunin:cpp-17
build: transition to C++17, minor changes in documentation
2024-10-26 19:59:48 +03:00
Wanli 29e712ed93 Merge pull request #26369 from WanliZhong:5x_fix_hfloat_vfunc
Fix hfloat conflicts of v_func in merging 4.x to 5.x #26369

This PR solves the conflicts in merging 4.x to 5.x https://github.com/opencv/opencv/pull/26358
1. Explicitly convert the inputs number for `v_setall_` to hfloat number
2. Loosens the threshold for `v_sincos` test. (related issue: https://github.com/opencv/opencv/issues/26362)
3. Remove the new but temp api `template <> inline v_float16x8 v_setall_(float v) { return v_setall_f16((hfloat)v); }`

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-10-26 19:54:13 +03:00
Alexander Smorkalov 05e7988e9c Merge pull request #26367 from alexlyulkovЖal/forward-to-layer-assert
Added exception when calling forward to specified layer with the new dnn engine
2024-10-25 15:22:09 +03:00
Maksim Shabunin d223e796f5 build: transition to C++17, minor changes in documentation 2024-10-25 15:05:14 +03:00
Alexander Lyulkov 3a4c88c33e Added exception when calling forward to specified layer with the new dnn engine 2024-10-25 13:00:15 +03:00
Alexander Smorkalov 8e55659afe Merge branch 4.x 2024-10-24 15:10:43 +03:00
Alexander Smorkalov 9f0c3f5b2b Merge pull request #26327 from asmorkalov:as/drop_convertFp16
Finally dropped convertFp16 function in favor of cv::Mat::convertTo() #26327 

Partially address https://github.com/opencv/opencv/issues/24909
Related PR to contrib: https://github.com/opencv/opencv_contrib/pull/3812

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-10-22 15:17:24 +03:00
alexlyulkov a40ceff215 Merge pull request #26330 from alexlyulkov:al/new-engine-tflite-parser2
Modified TFLite parser for the new dnn engine #26330

The new dnn graph is creating just by defining input and output names of each layer.
Some TFLite layers has fused activation, which doesn't have layer name and input and output names. Also some layers require additional preprocessing layers (e.g. NHWC -> NCHW). All these layers should be added to the graph with some unique layer and input and output names. 

I solve this problem by adding additionalPreLayer and additionalPostLayer layers.

If a layer has a fused activation, I add additionalPostLayer and change input and output names this way:
**original**: conv_relu(conv123, conv123_input, conv123_output)
**new**: conv(conv123, conv123_input, conv123_output_additional_post_layer) + relu(conv123_relu,  conv1_output_additional_post_layer, conv123_output)

If a layer has additional preprocessing layer, I change input and output names this way:
**original**: permute_reshape(reshape345, reshape345_input, reshape345_output)
**new**: permute(reshape345_permute, reshape345_input, reshape345_input_additional_pre_layer) + reshape(reshape345, reshape345_input_additional_pre_layer, reshape345_output)


### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-10-22 09:05:58 +03:00
Alexander Smorkalov 6648482b69 Merge pull request #26324 from asmorkalov:as/model_diagnostics_engine
Added DNN engine selector to model diagnostics tool.
2024-10-21 15:51:53 +03:00
Vadim Pisarevsky 6e3c5db1c6 Merge pull request #26333 from vpisarev:fix_26322
Fix #26322: construction of another Mat header for empty matrix #26333

The PR fixes #26322

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-10-18 14:50:27 +03:00
Vadim Pisarevsky 2f35847960 Merge pull request #26321 from vpisarev:better_bfloat
2x more accurate float => bfloat conversion #26321

There is a magic trick to make float => bfloat conversion more accurate (_original reference needed, is it done this way in PyTorch?_). In simplified form it looks like:

```
uint16_t f2bf(float x) {
    union {
        unsigned u;
        float f;
    } u;
    u.f = x;
    // return (uint16_t)(u.u >> 16); <== the old method before this patch
    return (uint16_t)((u.u + 0x8000) >> 16);
} 
```

it works correctly for almost all valid floating-point values, positive, zero or negative, and even for some extreme cases, like `+/-inf`, `nan` etc. The addition of `0x8000` to integer representation of 32-bit float before retrieving the highest 16 bits reduces the rounding error by ~2x.

The slight problem with this improved method is that the numbers very close to or equal to `+/-FLT_MAX` are mistakenly converted to `+/-inf`, respectively.

This patch implements improved algorithm for `float => bfloat` conversion in scalar and vector form; it fixes the above-mentioned problem using some extra bit magic, i.e. 0x8000 is not added to very big (by absolute value) numbers:

```
// the actual implementation is more efficient,
// without conditions or floating-point operations, see the source code
return (uint16_t)(u.u + (fabsf(x) <= big_threshold ? 0x8000 : 0)) >> 16);
```

The corresponding test has been added as well and this is output from the test:

```
[----------] 1 test from Core_BFloat
[ RUN      ] Core_BFloat.convert
maxerr0 = 0.00774842, mean0 = 0.00190643, stddev0 = 0.00186063
maxerr1 = 0.00389057, mean1 = 0.000952614, stddev1 = 0.000931268
[       OK ] Core_BFloat.convert (7 ms)
```

Here `maxerr0, mean0, stddev0` are for the original method and `maxerr1, mean1, stddev1` are for the new method. As you can see, there is a significant improvement in accuracy.

**Note:**

_Actually, on ~32,000,000 random FP32 numbers with uniformly distributed sign, exponent and mantissa the new method is always at least as accurate as the old one._

The test also checks all the corner cases, where we see no degradation either vs the original method.

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-10-18 14:46:40 +03:00
Alexander Smorkalov 9773694527 Added DNN engine selector to model diagnostics tool. 2024-10-17 15:09:13 +03:00
Gursimar Singh 1696819abb Merge pull request #25667 from gursimarsingh:improved_person_reid_python
Improved person reid cpp and python sample #25667

#25006 

This sample has been rewritten to track a selected target in a video or camera stream. Person detection has been integrated using yolov8 and the user can provide a target image via command line or interactively select the target at start of the execution


### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-10-17 10:46:53 +03:00
Vadim Pisarevsky 3cd57ea09e Merge pull request #26056 from vpisarev:new_dnn_engine
New dnn engine #26056

This is the 1st PR with the new engine; CI is green and PR is ready to be merged, I think.
Merge together with https://github.com/opencv/opencv_contrib/pull/3794

---

**Known limitations:**
* [solved] OpenVINO is temporarily disabled, but is probably easy to restore (it's not a deal breaker to merge this PR, I guess)
* The new engine does not support any backends nor any targets except for the default CPU implementation. But it's possible to choose the old engine when loading a model, then all the functionality is available.
* [Caffe patch is here: #26208] The new engine only supports ONNX. When a model is constructed manually or is loaded from a file of different format (.tf, .tflite, .caffe, .darknet), the old engine is used.
* Even in the case of ONNX some layers are not supported by the new engine, such as all quantized layers (including DequantizeLinear, QuantizeLinear, QLinearConv etc.), LSTM, GRU, .... It's planned, of course, to have full support for ONNX by OpenCV 5.0 gold release. When a loaded model contains unsupported layers, we switch to the old engine automatically  (at ONNX parsing time, not at `forward()` time).
* Some layers , e.g. Expat, are only partially supported by the new engine. In the case of unsupported flavours it switches to the old engine automatically (at ONNX parsing time, not at `forward()` time).
* 'Concat' graph optimization is disabled. The optimization eliminates Concat layer and instead makes the layers that generate tensors to be concatenated to write the outputs to the final destination. Of course, it's only possible when `axis=0` or `axis=N=1`. The optimization is not compatible with dynamic shapes since we need to know in advance where to store the tensors. Because some of the layer implementations have been modified to become more compatible with the new engine, the feature appears to be broken even when the old engine is used.
* Some `dnn::Net` API is not available with the new engine. Also, shape inference may return false if some of the output or intermediate tensors' shapes cannot be inferred without running the model. Probably this can be fixed by a dummy run of the model with zero inputs.
* Some overloads of `dnn::Net::getFLOPs()` and `dnn::Net::getMemoryConsumption()` are not exposed any longer in wrapper generators; but the most useful overloads are exposed (and checked by Java tests).
* [in progress] A few Einsum tests related to empty shapes have been disabled due to crashes in the tests and in Einsum implementations. The code and the tests need to be repaired.
* OpenCL implementation of Deconvolution is disabled. It's very bad and very slow anyway; need to be completely revised.
* Deconvolution3D test is now skipped, because it was only supported by CUDA and OpenVINO backends, both of which are not supported by the new engine.
* Some tests, such as FastNeuralStyle, checked that the in the case of CUDA backend there is no fallback to CPU. Currently all layers in the new engine are processed on CPU, so there are many fallbacks. The checks, therefore, have been temporarily disabled.

---

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-10-16 15:28:19 +03:00
Maksim Shabunin e2f583a9ee C-API cleanup: removed or updated some of 3d and calib tests 2024-10-15 18:27:23 +03:00
Yuantao Feng 12738deaef Merge pull request #26271 from fengyuentau:imgproc/warpperspective_opt
imgproc: add optimized warpPerspective linear kernels for inputs of type CV_8U/16U/32F+C1/C3/C4

Merge with https://github.com/opencv/opencv_extra/pull/1214

## Performance

### Apple Mac Mini (M2, 16GB memory)

```
Geometric mean (ms)

                                      Name of Test                                        base  patch   patch   
                                                                                                          vs    
                                                                                                         base   
                                                                                                      (x-factor)
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC1)      0.397 0.119    3.34   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC1)     0.412 0.155    2.65   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC1)     0.382 0.134    2.85   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC3)      0.562 0.201    2.80   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC3)     0.580 0.279    2.07   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC3)     0.477 0.269    1.78   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)      0.536 0.221    2.43   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)     0.662 0.328    2.02   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)     0.511 0.325    1.57   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC1)     0.433 0.171    2.54   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC1)    0.452 0.204    2.21   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC1)    0.410 0.180    2.27   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC3)     0.624 0.243    2.57   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC3)    0.636 0.331    1.92   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC3)    0.511 0.315    1.62   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)     0.604 0.281    2.15   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)    0.712 0.393    1.81   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)    0.552 0.368    1.50   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC1)     0.768 0.214    3.58   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC1)    0.743 0.260    2.86   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC1)    0.722 0.235    3.07   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC3)     1.091 0.333    3.28   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC3)    1.035 0.453    2.29   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC3)    0.955 0.442    2.16   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC4)     1.097 0.364    3.01   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC4)    1.141 0.547    2.09   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC4)    1.015 0.591    1.72   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC1)    1.012 1.006    1.01   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC1)   0.996 1.060    0.94   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC1)   0.930 0.993    0.94   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC3)    1.560 1.260    1.24   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC3)   1.374 1.410    0.97   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC3)   1.212 1.292    0.94   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC4)    1.702 1.354    1.26   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC4)   1.554 1.522    1.02   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC4)   1.342 1.435    0.94   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC1)    1.561 0.364    4.29   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC1)   1.444 0.406    3.56   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC1)   1.423 0.394    3.61   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC3)    2.177 0.533    4.08   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC3)   2.006 0.689    2.91   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC3)   1.907 0.688    2.77   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)    2.213 0.581    3.81   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)   2.238 0.810    2.76   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)   2.072 1.055    1.96   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC1)   2.201 2.908    0.76   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC1)  2.108 2.951    0.71   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC1)  1.997 2.840    0.70   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC3)   3.444 3.293    1.05   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC3)  2.889 3.417    0.85   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC3)  2.671 3.354    0.80   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)   3.765 3.767    1.00   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)  3.247 3.962    0.82   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)  2.993 3.669    0.82   
```

### Desktop (i7-12700K, 64GB memory)

```
Geometric mean (ms)

                                      Name of Test                                        base  patch   patch   
                                                                                                          vs    
                                                                                                         base   
                                                                                                      (x-factor)
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC1)      0.274 0.076    3.62   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC1)     0.299 0.058    5.14   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC1)     0.299 0.043    6.90   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC3)      0.330 0.115    2.87   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC3)     0.480 0.109    4.39   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC3)     0.608 0.180    3.37   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)      0.317 0.143    2.21   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)     0.704 0.139    5.07   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)     0.508 0.141    3.60   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC1)     0.293 0.064    4.57   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC1)    0.311 0.061    5.07   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC1)    0.299 0.057    5.29   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC3)     0.373 0.135    2.75   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC3)    0.501 0.129    3.87   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC3)    0.403 0.123    3.26   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)     0.372 0.163    2.28   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)    0.582 0.161    3.63   
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)    0.459 0.152    3.03   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC1)     0.558 0.099    5.63   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC1)    0.607 0.098    6.20   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC1)    0.599 0.090    6.65   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC3)     0.636 0.198    3.22   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC3)    0.806 0.187    4.31   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC3)    1.329 0.227    5.85   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC4)     0.643 0.238    2.70   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC4)    1.401 0.233    6.02   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC4)    1.083 0.229    4.72   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC1)    0.682 0.358    1.91   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC1)   0.695 0.350    1.99   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC1)   0.666 0.334    2.00   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC3)    0.895 0.502    1.78   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC3)   1.035 0.492    2.11   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC3)   0.924 0.466    1.98   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC4)    0.969 0.551    1.76   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC4)   1.201 0.550    2.18   
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC4)   0.948 0.544    1.74   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC1)    1.018 0.174    5.84   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC1)   0.973 0.173    5.63   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC1)   1.002 0.164    6.13   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC3)    1.100 0.297    3.71   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC3)   1.197 0.278    4.30   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC3)   3.108 0.296   10.49   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)    1.086 0.340    3.20   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)   2.987 0.336    8.88   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)   2.249 0.835    2.69   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC1)   1.330 1.007    1.32   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC1)  1.352 0.974    1.39   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC1)  1.241 0.933    1.33   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC3)   1.896 1.287    1.47   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC3)  2.071 1.288    1.61   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC3)  1.870 1.211    1.54   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)   2.059 1.362    1.51   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)  2.366 1.395    1.70   
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)  1.859 1.416    1.31   
```

### Khadas VIM3 (A311D, 4xA73+2xA53, no fp16 vector intrinsics support, 4GB memory)

```
Geometric mean (ms)

                                      Name of Test                                         base  patch    patch
                                                                                                            vs
                                                                                                           base
                                                                                                        (x-factor)
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC1)      2.543  0.702     3.62
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC1)     3.175  0.727     4.37
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC1)     2.877  0.777     3.70
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC3)      4.059  1.192     3.41
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC3)     4.689  1.642     2.86
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC3)     4.071  2.064     1.97
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 8UC4)      3.897  1.501     2.60
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 16UC4)     5.485  2.106     2.60
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_CONSTANT, 32FC4)     4.611  2.938     1.57
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC1)     2.717  0.912     2.98
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC1)    3.426  0.885     3.87
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC1)    3.009  0.979     3.07
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC3)     4.409  1.488     2.96
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC3)    5.236  1.901     2.75
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC3)    4.445  2.232     1.99
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 8UC4)     4.400  1.816     2.42
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 16UC4)    6.211  2.390     2.60
WarpPerspective::TestWarpPerspective::(640x480, INTER_LINEAR, BORDER_REPLICATE, 32FC4)    4.779  3.154     1.52
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC1)     5.487  1.599     3.43
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC1)    6.589  1.652     3.99
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC1)    4.916  1.779     2.76
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC3)     7.676  2.465     3.11
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC3)    8.783  3.020     2.91
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC3)    8.468  4.314     1.96
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 8UC4)     7.670  2.944     2.60
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 16UC4)    9.364  3.871     2.42
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_CONSTANT, 32FC4)    9.297  5.770     1.61
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC1)    6.809  5.359     1.27
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC1)   9.010  4.745     1.90
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC1)   8.501  4.712     1.80
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC3)    10.652 7.345     1.45
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC3)   12.319 7.647     1.61
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC3)   10.466 7.849     1.33
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 8UC4)    11.659 8.226     1.42
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 16UC4)   13.157 8.825     1.49
WarpPerspective::TestWarpPerspective::(1280x720, INTER_LINEAR, BORDER_REPLICATE, 32FC4)   11.557 9.869     1.17
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC1)    14.773 3.081     4.79
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC1)   14.971 3.135     4.78
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC1)   14.795 3.321     4.45
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC3)    20.823 4.319     4.82
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC3)   22.128 5.029     4.40
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC3)   22.583 8.036     2.81
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 8UC4)    20.141 5.018     4.01
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 16UC4)   23.505 6.132     3.83
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_CONSTANT, 32FC4)   20.226 10.050    2.01
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC1)   18.904 15.189    1.24
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC1)  22.749 12.979    1.75
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC1)  19.685 12.981    1.52
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC3)   29.636 19.974    1.48
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC3)  36.266 19.563    1.85
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC3)  30.124 19.434    1.55
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 8UC4)   34.290 21.998    1.56
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 16UC4)  41.765 21.705    1.92
WarpPerspective::TestWarpPerspective::(1920x1080, INTER_LINEAR, BORDER_REPLICATE, 32FC4)  27.767 22.838    1.22
```

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-10-15 11:13:41 +03:00
Alexander Smorkalov 3c627b0a97 Merge pull request #26268 from mshabunin:cpp-array-test
C-API cleanup: rework ArrayTest to use new arrays only
2024-10-14 11:14:10 +03:00
Alexander Smorkalov 58fac15a98 Merge pull request #26301 from vpisarev:ttf3
updated embedded font & stb_truetype renderer
2024-10-14 10:51:08 +03:00
Vadim Pisarevsky 08c6d00d96 1. updated Rubik font:
1) numerals are now monospace, e.g. '1' has the same width as '0',
    2) '0' is different from capital 'o',
    3) new glyphs added
2. stb_truetype upgraded from 1.24 to 1.26 with some fixes in rendering.
2024-10-13 03:25:24 +03:00
Yuantao Feng 5aa45e9053 Merge pull request #26292 from fengyuentau:imgproc/warpaffine_opt_opencl
imgproc: update warpAffine opencl kernel to be in sync with cpu one #26292

Relates https://github.com/opencv/opencv/pull/26242

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-10-12 11:13:41 +03:00
Alexander Smorkalov 70380a7988 Merge pull request #26285 from vrabaud:5_countnonzero_overflow
Fix sanitizer issue in countNonZero32f
2024-10-10 20:30:40 +03:00
WU Jia ef98c25d60 Merge pull request #25292 from kaingwade:features2d_parts_to_contrib
Features2d cleanup: Move several feature detectors and descriptors to opencv_contrib #25292

features2d cleanup: #24999

The PR moves KAZE, AKAZE, AgastFeatureDetector, BRISK and BOW to opencv_contrib/xfeatures2d.

Related PR: opencv/opencv_contrib#3709
2024-10-10 17:10:22 +03:00
Vincent Rabaud 16ea1382f7 Fix sanitizer issue in countNonZero32f
In that function, the floats are cast to int to be compared to 0.
But a float can be -0 or +0, hence
define CHECK_NZ_FP(x) ((x)*2 != 0)
to remove the sign bit. Except that can trigger the sanitizer:
runtime error: signed integer overflow: -1082130432 * 2 cannot be represented in type 'int'
Doing everything in uint instead of int is properly defined by the
standard.
2024-10-10 13:35:49 +02:00
Maksim Shabunin d0e410da93 C-API cleanup: rework ArrayTest to use new arrays only 2024-10-09 22:36:20 +03:00
Alexander Smorkalov 9dbfba0fd8 Merge pull request #26253 from vrabaud:compilation_fix
Fix hash_tsdf_functions.cpp compilation on some platforms.
2024-10-09 08:31:21 +03:00
Alexander Smorkalov 26985f1043 Merge pull request #26255 from vpisarev:test_for_countnonzero_1d
added comprehensive test for countNonZero run on continuous and discontinuous 1D arrays
2024-10-07 17:11:24 +03:00
Alexander Smorkalov ebc39adbe0 Merge pull request #26267 from mshabunin:fix-long-mv-test
calib: mark some multiview tests verylong
2024-10-07 17:08:27 +03:00
Vadim Pisarevsky 68a81888ec Merge pull request #26256 from vpisarev:expanded_tests_for_norm
extended Norm tests to prove that cv::norm() already supports all the types.

cv::norm() already provides enough functionality; just extended tests to prove it. See #24887

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-10-07 17:07:59 +03:00
Maksim Shabunin a97c99e49f calib: mark some multiview tests verylong 2024-10-07 15:37:59 +03:00
Vadim Pisarevsky 254db60667 removed unnecessary check (checked many times in other tests) 2024-10-04 23:57:24 +03:00
Vadim Pisarevsky 56673d969a added comprehensive test for countNonZero run on continuous and dis-continuous 1D arrays 2024-10-04 23:54:01 +03:00
Alexander Smorkalov ff9820ea5c Merge pull request #26251 from asmorkalov:as/reshape_umat_0d
Relax conditions to allow 0d reshape for UMat.
2024-10-04 20:27:29 +03:00
Vincent Rabaud 79e0763d17 Fix hash_tsdf_functions.cpp compilation on some platforms. 2024-10-04 17:57:17 +02:00
Alexander Smorkalov 91775af2dd Merge pull request #26248 from asmorkalov:as/multiview_split
Multiple calibrateMultiview improvements.
2024-10-04 16:37:15 +03:00
Alexander Smorkalov b03dd764a1 Relax conditions to allow 0d reshape for UMat. 2024-10-04 16:32:59 +03:00
Alexander Smorkalov 110b701bba Multiple calibrateMultiview improvements.
- Added function overload for the simple case
- Added CV_Bool type support for masks
- `parallel_for_` for intrinsics calibration for faster inference
- Homogenize parameters order with other calibrateXXX functions
2024-10-04 14:07:17 +03:00
Yuantao Feng 97681bdfce Merge pull request #25984 from fengyuentau:imgproc/warpaffine_opt
imgproc: add optimized warpAffine kernels for 8U/16U/32F + C1/C3/C4 inputs #25984

Merge wtih https://github.com/opencv/opencv_extra/pull/1198.
Merge with https://github.com/opencv/opencv_contrib/pull/3787.


### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-10-03 14:01:36 +03:00
Alexander Smorkalov ebf11d36f4 Merge pull request #26231 from asmorkalov:as/multiview_hetero
Heterogenious cameras support in multiview calibration.
2024-10-02 17:49:27 +03:00
Alexander Smorkalov ae52db549d Heterogenious cameras support in multiview calibration. 2024-10-02 10:33:01 +03:00
Alexander Smorkalov 916fb7df16 Merge pull request #26225 from mshabunin:cpp-imgcodecs
C-API cleanup: removed legacy headers in imgcodecs
2024-10-02 08:14:32 +03:00
Alexander Smorkalov a669c5cb03 Merge pull request #26229 from mshabunin:cpp-photo
C-API cleanup: inpaint algorithms in photo (5.x)
2024-10-02 08:11:52 +03:00
Alexander Smorkalov 886934dba8 Merge pull request #26227 from mshabunin:cpp-features2d
C-API cleanup: use AutoBuffer in MSER (5.x)
2024-10-02 08:10:35 +03:00
Maksim Shabunin 2688248df7 C-API cleanup: inpaint algorithms in photo 2024-10-01 20:06:14 +03:00
Maksim Shabunin 6f18a10012 C-API cleanup: use AutoBuffer in MSER 2024-10-01 18:42:28 +03:00
Maksim Shabunin 5901170c78 C-API cleanup: removed legacy headers in imgcodecs 2024-10-01 18:34:35 +03:00
Alexander Smorkalov 3bcab8db0a Merge pull request #26221 from asmorkalov:as/refactor_multiview_interface
Reworked multiview calibration interface #26221

- Use InputArray / OutputArray
- Use enum for camera type
- Sort parameters according guidelines
- Made more outputs optional
- Introduce flags and added tests for intrinsics and extrinsics guess.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-10-01 16:53:16 +03:00
Alexander Smorkalov 6c743f8e4b Merge pull request #26213 from asmorkalov:as/register_hetero
registerCameras test for heterogenious pinhone + fisheye setup.
2024-09-30 15:49:34 +03:00
Alexander Smorkalov 6ed4e7acae registerCameras test for heterogenious pinhone + fisheye setup. 2024-09-30 12:38:15 +03:00
Alexander Smorkalov 6dcf6d9dd1 Merge pull request #26188 from asmorkalov:as/multiview_calib_accuracy_test
Multi-camera calibration tests with real cameras.
2024-09-28 09:53:01 +03:00
Alexander Smorkalov e66d38e15f Merge pull request #26190 from mshabunin:fix-rvv-fp16-support
build: enable RISC-V FP16 support in the toolchain
2024-09-25 22:57:12 +03:00
Alexander Smorkalov 51ace51ea5 Merge pull request #26189 from mshabunin:fix-intrin-ops-5.x
build: fix AVX2/AVX512 builds failed due to intrinsics operator usage (5.x)
2024-09-25 22:56:15 +03:00
Alexander Smorkalov 0fa4b0ead2 Multi-camera calibration tests with real cameras. 2024-09-25 22:54:10 +03:00
Maksim Shabunin bd26d02908 build: enable RISC-V FP16 support in the toolchain 2024-09-25 20:01:29 +03:00
Maksim Shabunin 4a81b4e51f build: fix AVX2/AVX512 builds failed due to intrinsics operator usage 2024-09-25 19:48:17 +03:00
Gursimar Singh f9a297e52c Merge pull request #26186 from gursimarsingh:download_models_fixed
Add support for downloading DNN config files in download_models.py #26186

PR resloves #26160 related to downloading DNN config files using download_models.py

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-09-25 10:31:45 +03:00
Alexander Smorkalov 8256ef3321 Merge pull request #26183 from asmorkalov:as/multiview_calib_docs
Multiview calibration pipeline documentation update
2024-09-24 13:43:42 +03:00
Alexander Smorkalov 5c870bd5a2 Merge pull request #26184 from asmorkalov:as/js_merge_fix_5.x
Java Script config fix after 4.x merge.
2024-09-24 13:32:56 +03:00
lpanaf 938dabef0e Multiview calibration pipeline documentation update. 2024-09-24 11:54:25 +03:00
Alexander Smorkalov 4d5beef7db Java Script config fix after 4.x merge. 2024-09-24 11:44:09 +03:00
Alexander Smorkalov fe1a4b61bc Merge pull request #25022 from asmorkalov:as/calib_multiview_charuco_cpp
Add Charuco board support to multiview calibration sample (C++)
2024-09-24 11:26:39 +03:00
lpanaf 9daad187af Add Charuco board support to multiview calibration sample (C++). 2024-09-24 10:26:27 +03:00
Alexander Smorkalov 317012e85e Merge pull request #26177 from asmorkalov:as/yolo_tutorial_links
Fixed code snippets in Yolo tutorial.
2024-09-23 16:49:24 +03:00
Alexander Smorkalov cb3af0a08f Merge branch 4.x 2024-09-23 14:18:25 +03:00
Alexander Smorkalov 2529af9719 Fixed code snippets in Yolo tutorial. 2024-09-23 08:32:09 +03:00
Gursimar Singh e823493af1 Merge pull request #25710 from gursimarsingh:improved_object_detection_sample
Merged yolo_detector and object detection sample #25710

Relates to #25006

This pull request merges the yolo_detector.cpp sample with the object_detector.cpp sample. It also beautifies the bounding box display on the output images

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-09-18 16:19:46 +03:00
Alexander Smorkalov 46b800f506 Merge pull request #26144 from asmorkalov:as/drop_CV_FUNCTION_NAME
Drop remaning C CV_FUNCTION_NAME as it's not used any more.
2024-09-12 16:03:27 +03:00
Alexander Smorkalov f8a75bccab Drop remaning C CV_FUNCTION_NAME as it's not used any more. 2024-09-12 08:38:19 +03:00
Alexander Smorkalov b574db2cff Merge branch 4.x 2024-09-10 10:15:22 +03:00
Gursimar Singh 99bc88c259 Merge pull request #25520 from gursimarsingh:add-bigvision-copyright
Add bigvision copyright #25520

This pull request adds Bigvision LLC to the copyright list to recognize our contributions to the project [#25433, #25415, #25304]. This update reflects our ongoing commitment to supporting and maintaining this project. Please let me know if there are any adjustments you would like me to make. Thank you for considering this addition.
2024-09-09 17:44:30 +03:00
Gursimar Singh 073488896e Merge pull request #25326 from gursimarsingh:improved_text_detection_sample
Improved and refactored text detection sample in dnn module #25326

Clean up samples: #25006

This pull requests merges and simplifies different text detection samples in dnn module of opencv in to one file. An option has been provided to choose the detection model from EAST or DB

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-09-09 17:43:15 +03:00
Maksim Shabunin 7bdc618697 Merge pull request #26025 from mshabunin:cpp-videoio-highgui
 Potential conflicts with #25958
C-API cleanup: highgui, videoio #26025

  Merge with: opencv/opencv_contrib#3780

This PR removes usage of C-API from highgui and videoio modules. Only source code is affected, tests were not using obsolete API.

It should be possible to backport these changes to 4.x branch preserving removed public headers and source files (`*_c.h` and `*_c.cpp`).


#### Checklist

I tried to verify as many backends as possible, though these checks were not as thorough as I'd like them to be. Below is the checklist covering all modified backends with their statuses.

> 🔹 - small changes
> 🟢 - consider working
>  - considered untested

##### highgui

Pass | Backend | Local check | CI check
-----|---------|-------------|---------
🟢 | GTK2 | build + test, plugin build | build + test  
🟢 | GTK3 | build + test, plugin build | build + test
🟢 | QT | build + test, plugin build |
 | Wayland 🔹 | |
🟢 | WIN32 🔹 | | build + test
🟢 | Cocoa 🔹 | | build + test
 | WinRT | | 

##### videoio 

Pass | Backend | Local check | CI check
-----|---------|-------------|---------
🟢 | Android Camera/MediaNDK 🔹 | | build
🟢 | Aravis | build |
🟢 | AVFoundation OSX | | build + test
 | AVFoundation iOS | | build
🟢 | DC1394 | build |
🟢 | DShow 🔹 | | build
🟢 | FFMpeg | build, plugin build | build + test
🟢 | GPhoto 🔹 | build |
🟢 | GStreamer | build, plugin build | build + test
🟢 | Images | build | build + test
🟢 | MSMF 🔹 | | build + test
🟢 | OpenNI | build |
🟢 | PVAPI | build |
🟢 | V4L | build + test | build
🟢 | XIMEA | build |
🟢 | XINE 🔹 | build |

#### Notes

- local linux build checks performed using [this framework](https://github.com/mshabunin/opencv-videoio-build-check)
- minor extra changes made in both `cap_avfoundation*.mm` to make them slightly more synchronized - it would be better to combine them into a single one in the future
- configurations with plugins have been build but not tested
- **moved unrelated changes to separate PRs** ~two issues have been fixed in separate commits:~
  - ~imgproc: missing `cv::hal::` color conversion functions has been used in MediaSDK backend~
  - ~videoio/V4L: wrong color conversion mode caused bad colors for NV12 camera input format (RGB instead of BGR)~

It would be nice to check following functionality manually:
- [ ] OSX: camera input
- [ ] iOS: camera and file input
- [ ] WinRT: build, some testing
- [x] Linux/Wayland: build
2024-09-09 16:42:44 +03:00
Yuantao Feng ce5823c5eb Merge pull request #26124 from fengyuentau:dnn/topk_dtype
dnn(5.x): handle topk data type #26124

Resolves https://github.com/opencv/opencv/issues/26076

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-09-09 15:15:04 +03:00
Abduragim Shtanchaev 8263c804de Merge pull request #26106 from Abdurrahheem:ash/add-gatherND
Support for GatherND layer #26106

This PR adds support for GatherND layer. The layer was in comformance deny list initially.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-09-09 11:37:33 +03:00
Abduragim Shtanchaev a18d793dbd Merge pull request #26110 from Abdurrahheem:ash/hardmax-backend-fix
Switch off OpenVINO backend for Hardmax #26110

Fix Hardmax Issue with backend mentioned in #26107 

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-09-06 13:14:25 +03:00
Gursimar Singh f8fb3a7f55 Merge pull request #25515 from gursimarsingh:improved_edge_detection_sample
#25006 #25314 
This pull request removes hed_pretrained caffe model to the SOTA dexined onnx model for edge detection. Usage of conventional methods like canny has also been added

The obsolete cpp and python sample has been removed

TODO:
- [  ]  Remove temporary hack for quantized models. Refer issue https://github.com/opencv/opencv_zoo/issues/273

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-09-06 12:47:04 +03:00
Maksim Shabunin f73560293f Merge pull request #26101 from mshabunin:cpp-error-ts
C-API cleanup: moved cvErrorStr to new interface, minor ts changes #26101

Merge with opencv/opencv_contrib#3786

**Note:** `toString` might be too generic name (even though it is in `cv::Error::` namespace), another variant is `codeToString` (we have `typeToString` and `depthToString` in check.hpp).

**Note:** _ts_ module seem to have no other C API usage except for `ArrayTest` class which requires refactoring.
2024-09-06 12:05:47 +03:00
Alexander Smorkalov babc669dba Merge pull request #26119 from mshabunin:fix-rvv-init
RISC-V: remove statically initialized global RVV variables (5.x)
2024-09-06 08:23:17 +03:00
Maksim Shabunin ea8f091a08 RISC-V: remove statically initialized global RVV variables 2024-09-05 19:49:50 +03:00
Alexander Smorkalov 82da334ff3 Merge pull request #26100 from mshabunin:fix-riscv-build
RISC-V: fix XuanTie and Andes builds
2024-09-03 15:14:55 +03:00
Maksim Shabunin 8b3b19d185 RISC-V: fix XuanTie and Andes builds 2024-09-02 17:14:04 +03:00
Wanli a735660cc3 Merge pull request #26023 from WanliZhong:vfunc_hfloat
Fix hfloat, float16_t, float collision in 5.x about v_exp and v_log #26023

This PR try to fix https://github.com/opencv/opencv/issues/25922
Because the `hfloat` is defined as explicit conversion, the test code should be modified as it. The `vx_setall_f16` problem has already been fixed.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-09-02 12:37:57 +03:00
Abduragim Shtanchaev 0f8bbf4677 Merge pull request #26079 from Abdurrahheem:ash/hardmax-support
Add Support for Hardmax Layer #26079

This PR add support for `Hardmax` layer, which as previously listed in conformance deny list. 

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-09-02 12:32:55 +03:00
Alexander Smorkalov 8def9f75c8 Merge pull request #26096 from mshabunin:fix-riscv-install
RISC-V: fix installation process
2024-09-02 08:57:25 +03:00
Maksim Shabunin 81ab6a3fbd RISC-V: fix installation process 2024-09-01 20:59:57 +03:00
Alexander Smorkalov 100db1bc0b Merge branch 4.x 2024-08-28 15:06:19 +03:00
Alexander Smorkalov 41097a48ad Merge pull request #25743 from hanliutong:rvv-fp16
Add FP16 support for RISC-V
2024-08-23 15:29:21 +03:00
Abduragim Shtanchaev 050085c996 Merge pull request #25950 from Abdurrahheem:ash/add-inpainting-sample
Diffusion Inpainting Sample #25950

This PR adds inpaiting sample that is based on [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/pdf/2112.10752) paper (reference github [repository](https://github.com/CompVis/latent-diffusion)).


Steps to run the model:

1. Firstly needs ONNX graph of the Latent Diffusion Model. You can get it in two different ways. 

> a. Generate the using this [repo](https://github.com/Abdurrahheem/latent-diffusion/tree/ash/export2onnx) and follow instructions below

```bash
git clone https://github.com/Abdurrahheem/latent-diffusion.git
cd latent-diffusion
conda env create -f environment.yaml
conda activate ldm
wget -O models/ldm/inpainting_big/last.ckpt https://heibox.uni-heidelberg.de/f/4d9ac7ea40c64582b7c9/?dl=1
python -m scripts.inpaint.py --indir data/inpainting_examples/ --outdir outputs/inpainting_results --export=True
```

> b. Download the ONNX graph (there 3 fiels) using this link: TODO make a link

2. Build opencv (preferebly with CUDA support enabled
3. Run the script 

```bash
cd opencv/samples/dnn
python ldm_inpainting.py 
python ldm_inpainting.py -e=<path-to-InpaintEncoder.onnx file> -d=<path-to-InpaintDecoder.onnx file> -df=<path-to-LatenDiffusion.onnx file> -i=<path-to-image>
```
Right after the last command you will be prompted with image. You can click on left mouse bottom and starting selection a region you would like to be inpainted (deleted). Once you finish marking the region, click on left mouse botton again and press esc button on your keyboard. The inpainting proccess will start. 

Note: If you are running it on CPU it might take a large chank of time. Also make sure to have about 15GB of RAM to make process faster (other wise swapping will click in and everything will be slower)
 
Current challenges: 

1. Diffusion process is slow (many layers fallback to CPU with running with CUDA backend) 
2. The diffusion result is does exactly mach that of the original torch pipeline

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x]The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-08-21 14:48:37 +03:00
Alexander Smorkalov 9dcac41c66 Merge pull request #26043 from Abdurrahheem:ash/fix-nll-layer
Support Matrices with axes > 6 CUDA
2024-08-21 11:12:43 +03:00
Alexander Smorkalov 7d31463fea Merge pull request #26048 from alexlyulkov:al/dnn-opencl-int
Added integer and bool support to dnn OpenCL layers
2024-08-21 09:39:59 +03:00
Abduragim Shtanchaev 79a731aff0 Fix for support matrixes with number of axes = 6. 2024-08-21 09:06:46 +03:00
Alexander Smorkalov eee383fde8 Merge pull request #26050 from asmorkalov:as/supress_qdq_tests
Supress more ONNX conformance test cases for quant-dequant with CUDA.
2024-08-20 17:46:58 +03:00
Alexander Smorkalov d69db7aac4 Supress more ONNX conformance test cases for quant-dequant with CUDA. 2024-08-20 17:12:26 +03:00
Abduragim Shtanchaev 45fd4d8217 Merge pull request #26026 from Abdurrahheem:ash/python_bool_binding
Add support for boolan input/outputs in python bindings #26026

This PR add support boolean input/output binding in python. The issue what mention in ticket https://github.com/opencv/opencv/issues/26024 and the PR soleves it. Data and models are located in [here](https://github.com/opencv/opencv_extra/pull/1201)

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-08-20 16:38:21 +03:00
Alexander Lyulkov a69cd7d6ba Added integer and bool support to dnn OpenCL layers 2024-08-20 12:33:08 +03:00
Liutong HAN f3cc5a9e1e Support fp16 for RISC-V. 2024-08-07 16:52:11 +00:00
Alexander Smorkalov 7e8f2a1bc4 Merge branch 4.x 2024-08-06 15:31:30 +03:00
Gursimar Singh 35eba9ca90 Merge pull request #25519 from gursimarsingh:improved_classification_sample
Improved classification sample #25519

#25006 #25314

This pull requests replaces the caffe model for classification with onnx versions. It also adds resnet in model.yml. 

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-08-06 09:16:11 +03:00
Gursimar Singh 3dcc8c38b4 Merge pull request #25268 from gursimarsingh:samples_cleanup_python
Removed obsolete python samples #25268

Clean Samples #25006 
This PR removes 36 obsolete python samples from the project, as part of an effort to keep the codebase clean and focused on current best practices. Some of these samples will be updated with latest algorithms or will be combined with other existing samples. 

Removed Samples:

> browse.py
camshift.py
coherence.py
color_histogram.py
contours.py
deconvolution.py
dft.py
dis_opt_flow.py
distrans.py
edge.py
feature_homography.py
find_obj.py
fitline.py
gabor_threads.py
hist.py
houghcircles.py
houghlines.py
inpaint.py
kalman.py
kmeans.py
laplace.py
lk_homography.py
lk_track.py
logpolar.py
mosse.py
mser.py
opt_flow.py
plane_ar.py
squares.py
stitching.py
text_skewness_correction.py
texture_flow.py
turing.py
video_threaded.py
video_v4l2.py
watershed.py

These changes aim to improve the repository's clarity and usability by removing examples that are no longer relevant or have been superseded by more up-to-date techniques.
2024-07-31 16:11:00 +03:00
Alexander Smorkalov f24e80297a Merge pull request #25972 from kaingwade:update_orbbec_tutorial
Update orbbec(uvc) tutorial
2024-07-31 12:42:47 +03:00
kaingwade ca2d17758f Update orbbec(uvc) tutorial 2024-07-31 15:19:23 +08:00
Vincent Rabaud ae4c67e3a0 Merge pull request #25945 from vrabaud:02_fix
Fix size() for 0d matrix #25945

### Pull Request Readiness Checklist

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- [x] I agree to contribute to the project under Apache 2 License.
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2024-07-30 12:44:12 +03:00
Alexander Smorkalov 908c59ae25 Merge pull request #25965 from mshabunin:cpp-imgproc-test-5.x
imgproc: C-API cleanup 5.x
2024-07-29 18:05:05 +03:00
Maksim Shabunin 7d12392a7d imgproc: remove C-API leftovers 2024-07-29 13:23:30 +03:00
Alexander Smorkalov 672a662dff Merge branch 4.x 2024-07-26 09:10:36 +03:00
Alexander Smorkalov 459a9c60ed Merge pull request #25902 from asmorkalov:as/core_mask_cvbool
Mask support with CV_Bool in ts and core #25902

Partially cover https://github.com/opencv/opencv/issues/25895

### Pull Request Readiness Checklist

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2024-07-24 16:32:25 +03:00
Alexander Smorkalov 8ba70194b1 Merge pull request #25939 from vrabaud:slash_warning
Fix "'/*' within block comment " warning
2024-07-19 14:40:31 +03:00
Vincent Rabaud b8f5c08306 Fix "'/*' within block comment " warning 2024-07-19 13:03:48 +02:00
Abduragim Shtanchaev 88f05e49be Merge pull request #25868 from Abdurrahheem:ash/add-gpt2-sample
Add sample for GPT2 inference #25868

### Pull Request Readiness Checklist

This PR adds sample for inferencing GPT-2 model. More specificly implementation of GPT-2 from [this repository](https://github.com/karpathy/build-nanogpt). Currently inference in OpenCV is only possible to do with fixed window size due to not supported dynamic shapes. 

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
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- [ ] There is a reference to the original bug report and related work
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      Patch to opencv_extra has the same branch name.
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2024-07-18 16:47:12 +03:00
Abduragim Shtanchaev 060c24bec9 Merge pull request #25101 from Abdurrahheem:ash/1D-reduce-test
1D test for Reduce layer #25101

This PR introduces test for `Reduce` layer to test its functionality for 1D arrays

### Pull Request Readiness Checklist

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2024-07-17 19:19:18 +03:00
Abduragim Shtanchaev d05047ae41 Merge pull request #25917 from Abdurrahheem:ash/reduce-parser-fix
Fix Reduce layer for cosnt inputs #25917

### Pull Request Readiness Checklist

This PR adds support for const inputs for reducing the layer. Particularly, it fixes the following case. The test model and data are located in [1194](https://github.com/opencv/opencv_extra/pull/1194)

<img width="190" alt="image" src="https://github.com/user-attachments/assets/45a90f0a-b798-4529-bece-24c7bfc9e7ba">


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2024-07-17 19:17:44 +03:00
Alexander Smorkalov fc9208cff5 Merge branch 4.x 2024-07-17 10:08:16 +03:00
Yuantao Feng 420663498f Merge pull request #25900 from fengyuentau:dnn/nary_elementwise_multi_thread
dnn: merge #25630 to 5.x #25900

Sync changes from https://github.com/opencv/opencv/pull/25630 to 5.x.

### Pull Request Readiness Checklist

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2024-07-15 08:57:50 +03:00
Gursimar Singh 9aa5f3f1db Merge pull request #25252 from gursimarsingh:cpp_samples_cleanup
Move API focused C++ samples to snippets #25252

Clean Samples #25006
This PR removes 39 outdated C++ samples from the project, as part of an effort to keep the codebase clean and focused on current best practices.
2024-07-11 15:07:21 +03:00
HAN Liutong 1d9ca7160b Merge pull request #25796 from hanliutong:hfloat
Use hfloat instead of __fp16. #25796

Related: #25743

Currently, the type for the half-precision floating point data in the OpenCV source code is `__fp16`, which is a unique(?) type supported by the ARM compiler. Other compilers have very limited support for `__fp16`, so in order to introduce more backends that support FP16 (such as RISC-V), we may need a the more general FP16 type.

In this patch, we use `hfloat` instead of `__fp16` in non-ARM code blocks, mainly affected parts are:
- `core/hal/intrin.hpp`: Type Traits, REG Traits and `vx_` interface.
- `core/hal/intrin_neon.hpp`: Universal Intrinsic API for FP16 type.
- `core/test/test_intrin_utils.hpp`: Usage of Univseral Intrinsic
- `core/include/opencv2/core/cvdef.h`: Definition of class `hfloat`

If I understand correctly, class `hfloat` acts as a wrapper around FP16 types in different platform (`__fp16` for ARM and `_Float16` for RISC-V). Any OpenCV generic interface/source code should use `hfloat`, while platform-specific FP16 types only used in macro-guarded code blocks.

/cc @fengyuentau  @mshabunin 

### Pull Request Readiness Checklist

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- [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
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2024-07-07 11:38:02 +03:00
Alexander Smorkalov 6a11847d57 Merge pull request #25860 from asmorkalov:as/fix_linux_32bit
Fixed 32-bit build with some GCC versions
2024-07-04 16:05:31 +03:00
WU Jia bd1f9cd1ed Merge pull request #25410 from kaingwade:add_videocapture_depth_sample
Add videocapture_depth.cpp sample #25410

The PR is to combine the examples `videocapture_openni.cpp`, `videocapture_realsense.cpp` and `videocapture_obsensor.cpp` into `videocapture_depth.cpp`.

Tested cameras and OS using this sample are listed below:

|                        | Windows 10   | Ubuntu 22.04 | Mac M1 14.3   |
|------------------------|--------------|--------------|---------------|
| Orbbec Gemini 2 Series | &#x2713;     | &#x2713;     | &#x2713;      |
| RealSense D435, D455   | &#x2713;     | &#x2713;     | &#x2717;      |
| Kinect, XtionPRO       |  -           | -            | -             |

Note:
- OpenNI based cameras (Kinect, XtionPRO) are not tested as I don't have them.
- RealSense D435 and D455 don't work on Mac with OpenCV.
2024-07-03 17:44:13 +03:00
Alexander Smorkalov 48b457f8c7 Merge pull request #25863 from asmorkalov:as/js_test_fixes_5.x
Fixed failed JavaScript tests in 5.x
2024-07-03 17:03:25 +03:00
Alexander Smorkalov fc85d2a551 Fixed failed JavaScript tests in 5.x 2024-07-03 16:15:36 +03:00
Alexander Smorkalov b083d36d68 Fixed 23-bit build with some GCC versions. 2024-07-03 14:13:34 +03:00
Gursimar Singh 96a8e6d76c Merge pull request #25756 from gursimarsingh:bug_fix/segmentation_sample
[BUG FIX] Segmentation sample u2netp model results #25756

PR resloves #25753 related to incorrect output from u2netp model in segmentation sample

### Pull Request Readiness Checklist

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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
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2024-07-03 14:03:12 +03:00
Alexander Smorkalov 55a2a945b6 Merge pull request #25855 from asmorkalov:as/ci_ubuntu_24.04_5.x
Added Ubuntu 24.04 pipeline for 5.x
2024-07-03 11:49:48 +03:00
Alexander Smorkalov 9772ec861b Added Ubuntu 24.04 pipeline for 5.x 2024-07-03 09:21:54 +03:00
Alexander Smorkalov fef2c95472 Merge pull request #25848 from asmorkalov:as/ubuntu_2404_warn_fix
Warnings fix for Ubuntu 24.04.
2024-07-02 17:21:53 +03:00
Alexander Smorkalov b39ca66a5d Merge pull request #25847 from asmorkalov:as/lut_all
Added lut support for all new types in 5.x
2024-07-02 17:21:36 +03:00
Alexander Smorkalov 190eddf8c3 Warnings fix for Ubuntu 24.04. 2024-07-02 15:01:48 +03:00
Alexander Smorkalov 07ec6cb2c2 Added lut support for all new types in 5.x 2024-07-02 14:44:55 +03:00
Alexander Smorkalov 3abd9f2a28 Merge branch 4.x 2024-07-01 15:59:43 +03:00
alexlyulkov 12b8ed1443 Merge pull request #25755 from alexlyulkov:al/more-types
Added more types support to dnn layers #25755

Added support of more types to dnn layers for CPU, CUDA and OpenVINO backends.
Now most of the multi-type layers support uint8, int8, int32, int64, float32, float16, bool types.

### Pull Request Readiness Checklist

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2024-06-28 09:02:15 +03:00
alexlyulkov fd7cb1be85 Merge pull request #25739 from alexlyulkov:al/openvino2022-fixed-blank
Fixed blank layer for OpenVINO 2022.1 #25739

Changed blank layer because it didn't work with old OpenVINO versions(2022.1). The blank layer was implemented using ConvertLike layer, now it is implemented using ShapeOf and Reshape layers.

### Pull Request Readiness Checklist

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2024-06-27 18:51:35 +03:00
Alexander Smorkalov 719b49ffa9 Merge pull request #25764 from alexlyulkov:al/cumsum-fix
Fixed cumsum layer, enabled conformance tests
2024-06-27 18:50:15 +03:00
Maksim Shabunin 26ea34c4cb Merge branch '4.x' into '5.x' 2024-06-26 19:01:34 +03:00
Alexander Lyulkov 759fc701ab Fixed cumsum layer, enablem conformance tests 2024-06-14 13:02:57 +03:00
Abduragim Shtanchaev a2d2ea6536 Merge pull request #25727 from Abdurrahheem:ash/comf-denylist-reduce
Additional Comments for Conformance Denylist #25727

This PR adds additional comments on conformance denylist. Once  BOOL type got support in 5.x, some test layer changed their failing issue.

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2024-06-10 13:51:47 +03:00
alexlyulkov 70df023317 Merge pull request #25605 from alexlyulkov:al/bool-dnn
Added bool support to dnn #25605

Added bool support to dnn pipeline (CPU, OpenVINO and CUDA pipelines).

Added bool support to these layers(CPU and OpenVINO):
- Equal, Greater, GreaterOrEqual, Less, LessOrEqual
- Not
- And, Or, Xor
- Where

Enabled all the conformance tests for these layers.

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2024-06-06 12:52:06 +03:00
Alexander Smorkalov 5bc450d211 Merge pull request #25617 from Abdurrahheem:ash/01D-einsum-test
Fixed out-of-bound access in Einsum layer for 0d
2024-05-22 12:37:30 +03:00
Abduragim Shtanchaev 1f874028fb fixed for openvino einsum 2024-05-22 10:30:10 +04:00
Abduragim Shtanchaev 6feb765ebb Merge pull request #25116 from Abdurrahheem:ash/elementwise-1d-test
Element-wise test for 1D #25116

This PR introduces 1D parametrized test for element wise layer. The means that the tests covers following layer: 

`Clip`, `ReLU6`, `ReLU`,
                        `GeLU`, `GeluApprox`, `TanH`,
                        `Swish`, `Mish`, `Sigmoid`,
                        `ELULayer`, `Abs`, `BNLL`,
                        `Ceil`, `Floor`, `LogLayer`,
                        `Round`, `Sqrt`, `Acos`,
                        `Acosh`, `Asin`, `Asinh`,
                        `Atan`, `Atanh`, `Cos`,
                        `Sin`, `Sinh`, `Tan`, `Erf`,
                        `Reciprocal`, `Cosh`, `HardSwish`,
                        `Softplus`, `Softsign`, `Celu`,
                        `HardSigmid`, `Selu`, `ThresholdedRelu`,
                        `Power`, `Exp`, `Sign`, `Shrink`,
                        `ChannelsPReLU`

Not sure if this is best way to implement this test.

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2024-05-21 14:05:01 +03:00
Abduragim Shtanchaev f676cb3c62 Merge pull request #25595 from Abdurrahheem:ash/01D-einsum-test
Add support for scalar and matrix multiplication in einsum #25595

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2024-05-21 13:36:12 +03:00
alexlyulkov 9238eb2ab2 Merge pull request #25555 from alexlyulkov:al/int8-uint8-dnn-input
Disabled conversion to float of model's input #25555

In dnn 4.x usually any model's input is converted to float32 or float16 (except quantized models). Also mean and scale can be applied. In current dnn 5.x there is the same conversion except int32 and int64 types. I removed this conversion.

Here is how the pipeline works now:
- if input Mat type is float32, the pipeline applies mean and scale and may convert it to float16.
- if input Mat type is not float32, the pipeline preserves the input type and doesn't apply mean and scale

There was a conflict in protobuf parser between ONNX importer and tests. In ONNX importer any uint8 weight was handled as quantized weight and x = int8(x_uint8 - 128) conversion was used inside the protobuf parser. ONNX conformance tests used the same protobuf reader, so tests with uint8 inputs couldn't read the input values properly. I've made this conversion optional.

These ONNX conformance tests are enabled:
- test_add_uint8
- test_div_uint8
- test_mul_uint8
- test_sub_uint8
- test_max_int8
- test_max_uint8
- test_min_int8
- test_min_uint8
- test_mod_mixed_sign_int8
- test_mod_uint8

These tests were removed:
- Test_two_inputs.basic (when input is uint8)
- setInput.normalization (when input is uint8)

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2024-05-16 15:54:00 +03:00
alexlyulkov 6af0394cd2 Merge pull request #25458 from alexlyulkov:al/dnn-openvino-int-support
Added int support for OpenVINO dnn backend #25458

Modified dnn OpenVINO integration to support type inference and int operations.

Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers.
I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests.

OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO.

OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled:
- Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU)
- Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU)

Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches:
- Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU
- Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU


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2024-05-15 11:51:59 +03:00
Abduragim Shtanchaev 5bdc41964a Merge pull request #25487 from Abdurrahheem:ash/01D-additional-fixes
Additional fixes to 0/1D tests #25487

This has additional fixes requited for 0/1D tests.

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2024-05-15 10:50:03 +03:00
Abduragim Shtanchaev 5260b48695 Merge pull request #25390 from Abdurrahheem:ash/0d-padding-layer
1/0D test padding layer #25390

This PR introduces 0/1D test for `padding` layer.

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2024-05-15 10:26:26 +03:00
Gursimar Singh 48c31bddc4 Merge pull request #25559 from gursimarsingh:improved_segmentation_sample
Improved segmentation sample #25559

#25006

This pull request replaces caffe models with onnx for the dnn segmentation sample in cpp and python
fcnresnet-50 and fcnresnet-101 has been replaced
u2netp (foreground-background) segmentation onnx model has been added [U2NET](https://github.com/xuebinqin/U-2-Net) 

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2024-05-15 09:39:34 +03:00
Abduragim Shtanchaev 17e6b3f931 Merge pull request #25409 from Abdurrahheem:ash/0D-tile-test
0/1D test for tile layer #25409

This PR introduces `0/1D` test for `Tile` layer. It also add fuctionality to support `0/1D` cases.  


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2024-05-14 19:34:04 +03:00
Abduragim Shtanchaev 021e5184bc Merge pull request #25567 from Abdurrahheem:ash/01D-einsum-test
0/1D Einsum Layer Test #25567

This PR introduces 0/1D test cases for Einsum layer.

TODO:
- Add support for 0D tensors to Einsum layer

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2024-05-14 15:45:56 +03:00
Wanli b637e3a66e Merge pull request #25463 from WanliZhong:ocvface2YuNet
Change opencv_face_detector related tests and samples from caffe to onnx #25463

Part of https://github.com/opencv/opencv/issues/25314

This PR aims to change the tests related to opencv_face_detector from caffe framework to onnx. Tests in `test_int8_layer.cpp` and `test_caffe_importer.cpp` will be removed in https://github.com/opencv/opencv/pull/25323

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2024-05-08 15:49:10 +03:00
Yuantao Feng 4422bc9a7f Merge pull request #25196 from fengyuentau:fp16_bf16_arithm
core: add universal intrinsics for fp16 #25196

Partially resolves the section "Universal intrinsics evolution in OpenCV 5.0" in  https://github.com/opencv/opencv/issues/25019.

Universal intrinsics for bf16 will be added in a subsequent pull request.

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2024-05-08 15:23:19 +03:00
Alexander Smorkalov 039cf6592c Merge pull request #25544 from FantasqueX:remove-libjpeg-1
Remove deprecated libjpeg build
2024-05-08 15:06:13 +03:00
Wanli d231b4e362 Merge pull request #25503 from WanliZhong:remove_goturn
Remove goturn caffe model #25503

**Merged with:** https://github.com/opencv/opencv_extra/pull/1174
**Merged with:** https://github.com/opencv/opencv_contrib/pull/3729

Part of https://github.com/opencv/opencv/issues/25314

This PR aims to remove goturn tracking model because Caffe importer will be remove in 5.0

The GOTURN model will take **388 MB** of traffic for each download if converted to onnx. If the user wants to use the tracking method, we can recommend they use Vit or dasimRPN.

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2024-05-06 11:57:30 +03:00
Letu Ren 76f607a44c Remove deprecated libjpeg build 2024-05-05 18:03:40 +08:00
WU Jia 94f4678d3a Merge pull request #25324 from kaingwade:clean_haarcascades_jsbindings
Fix broken js build after moving HaarCascades to contrib #25324

The HaarCascades related are not completely cleaned up #25311 after #25198, which breaks the JavaScript build. The PR is to fix the issue.

Related PR: opencv/opencv_contrib#3712
2024-04-27 14:37:40 +03:00
alexlyulkov 72ad06bcf3 Merge pull request #25492 from alexlyulkov:al/range-fixed-5.x
Fixed ONNX Range layer to support any input type #25492

Fixed ONNX Range layer to support any input type

Extra PR: https://github.com/opencv/opencv_extra/pull/1173
Fixes #25363

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2024-04-26 18:59:43 +03:00
Abduragim Shtanchaev bbe86e6dea Merge pull request #25480 from Abdurrahheem:ash/comf-denylist-reduce
Add logs of test failure to test_onnx_conformance_layer_filter_opencv_all_denylist.inl.hpp #25480

### Pull Request Readiness Checklist

This PR add logs to test failures to  `test_onnx_conformance_layer_filter_opencv_all_denylist.inl.hpp` and it continuation of #25442

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2024-04-24 11:42:04 +03:00
Abduragim Shtanchaev f08933b051 Merge pull request #25420 from Abdurrahheem:ash/01D-batchnorm
0/1D test for BatchNorm layer #25420

This PR introduces support for 0/1D inputs in `BatchNorm` layer.

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2024-04-23 12:03:39 +03:00
Abduragim Shtanchaev 4f81d78c39 Merge pull request #25465 from Abdurrahheem:ash/parser-conf-denylist-reduce
Comments for parser denylist #25465

Relates to https://github.com/opencv/opencv/issues/21078

This PR is designed to figure out why the test in `test_onnx_conformance_layer_parser_denylist.inl.hpp` fails. Currently, conformance tests do not pass for the following reasons:

1. BOOL, INT(8, 16) types are not supported **(MAJOR)**
2. Some layers can not be created due to various reasons  **(MAJOR)**
3. Shape mismatches while creating layers  **(MAJOR)**
4. Some layers are expected to support dynamic parameter initialization  **(MAJOR)**
5. Some layers are expected to receive weight as inputs (no idea why that is needed)   **(MAJOR)**
6. Other unknown reasons

 **(MAJOR)** - These are the most frequently encountered reasons for test failure.

The style of comments is not consistent everywhere. Let's keep this PR without merging, just for our reference.
A couple of tests are commented on since they have passed on the MacOS platform.

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2024-04-22 16:31:15 +03:00
Alexander Smorkalov b5ffdd4673 Merge pull request #25428 from asmorkalov:as/win32_arm_ci_5.x
CI pipeline with Windows 10 ARM64 for 5.x #25428

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2024-04-22 15:56:08 +03:00
Alexander Smorkalov ac37f337d0 Merge pull request #25467 from fengyuentau:drop_half_members
5.x core: sync changes that drop some member functions in class hfloat
2024-04-22 14:10:22 +03:00
Alexander Smorkalov 43d243dd0e Merge branch 4.x 2024-04-22 11:08:39 +03:00
fengyuentau 65d86b2628 remove zero() and bits() from hfloat 2024-04-22 14:49:51 +08:00
Alexander Smorkalov c28342f7f2 Merge pull request #25451 from savuor:rv/fix_win_near_far
Fix build for Windows: bad var names in testdata generator
2024-04-19 09:31:01 +03:00
Rostislav Vasilikhin 18a78988a1 fix var names for win 2024-04-19 04:14:46 +02:00
Gursimar Singh 448375d1e7 Merge pull request #25433 from gursimarsingh:colorization_onnx_sample
Replaced caffe model with onnx for colorization sample #25433

#25006

Improved sample for colorization with onnx model in cpp and python. Added a demo image in data folder for testing

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2024-04-18 18:15:05 +03:00
Abduragim Shtanchaev b009a63e6b Merge pull request #25442 from Abdurrahheem:ash/comf-denylist-reduce
Conformance test denylist reduce #25442

Comment out all passing tests in `test_onnx_conformance_layer_filter_opencv_all_denylist.inl.hpp` file. 


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2024-04-18 17:22:56 +03:00
Alexander Smorkalov 9d14ce3739 Merge pull request #25438 from mshabunin:cpp-approx-5
imgproc: approx C-API cleanup
2024-04-18 09:38:13 +03:00
Maksim Shabunin 3d88db02d4 imgproc: approx C-API cleanup 2024-04-17 22:25:23 +03:00
Alexander Smorkalov c3fdb9769d Merge pull request #25431 from mshabunin:cpp-contours-5
imgproc: contours C-API cleanup
2024-04-17 16:15:04 +03:00
Maksim Shabunin 3def7d09bc imgproc: contours C-API cleanup 2024-04-17 14:50:11 +03:00
Alexander Smorkalov 153f147cde Merge pull request #25231 from Abdurrahheem:ash/0D-const-test
Constant layer 0/1D test.
2024-04-17 14:17:05 +03:00
Alexander Smorkalov 5c1fbc2d0f Merge pull request #25429 from asmorkalov:as/drop_float16_t
Drop float16_t alias in 5.x after 4.x merge
2024-04-17 12:40:07 +03:00
Abdurrahheem 6b438835eb Constant layer 0/1D test. 2024-04-17 11:39:31 +03:00
Alexander Smorkalov 7c966efcad Drop float16_t alias in 5.x after 4.x merge. 2024-04-17 10:48:41 +03:00
Alexander Smorkalov db3e5620cd Merge branch 4.x 2024-04-16 17:28:18 +03:00
Gursimar Singh 68967cf6d7 Merge pull request #25415 from gursimarsingh:improved_drawing_cpp_sample
Improving the drawing cpp sample to draw shapes based on user input #25415

Relates to #25006

The updated samples allows user to draw random shapes by using hot keys.

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2024-04-16 12:48:18 +03:00
Rostislav Vasilikhin 6699ca1a40 Merge pull request #25382 from savuor:rv/fix_mesh_load
Fix mesh loading for texture coordinates and face indices #25382

### This PR changes

* Texture coordinates were stored incorrectly (3-channel array is read as if there were 2 channels), fixed
* Faces were pushed back to the output array instead of indexed writing which produced a lot of empty faces, fixed
* A set of ground truth tests were added to cover these issues
* `std::vector<cv::Mat>` support added for `saveMesh()` which is required for Python bindings
* More command line args were added to rasterization test data generator

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2024-04-16 10:20:44 +03:00
Gursimar Singh 3e561d8353 Merge pull request #25304 from gursimarsingh:geometry_sample_cpp
Geometry C++ sample combining other shape detection samples #25304

Clean Samples #25006
This PR removes adds a new cpp sample (geometry) which combines different methods of finding and drawing shapes in an image. It makes separate samples for convexHull, fitellipse, minAreaRect, minAreaCircle redudant. Shapes can be changed using hotkeys after running the program

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2024-04-16 10:13:34 +03:00
Abduragim Shtanchaev 869016d8b1 Merge pull request #25208 from Abdurrahheem:ash/0D-fullyConnected-test
Fully connected 0D test. #25208

This PR introduces parametrized `0/1D` input support test for `Fullyconnected` layer.

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2024-04-15 09:15:36 +03:00
Alexander Smorkalov 282c762ead Merge branch 4.x 2024-04-10 11:27:47 +03:00
Maksim Shabunin 0e1d326ed0 Merge pull request #25379 from mshabunin:fix-unaligned-filter-5.x
Fix unaligned filters + increase test thresholds (5.x) #25379

Port of #25364 to 5.x + minor changes in 3d tests to pass on RISC-V platform

Failed tests:
```
[ RUN      ] AP3P.ctheta1p_nan_23607
/home/ci/opencv/modules/3d/test/test_solvepnp_ransac.cpp:2320: Failure
Expected: (cvtest::norm(res.colRange(0, 2), expected, NORM_INF)) <= (3e-16), actual: 3.33067e-16 vs 3e-16
[  FAILED  ] AP3P.ctheta1p_nan_23607 (1 ms)

[ RUN      ] Rendering/RenderingTest.accuracy/4, where GetParam() = ((320, 240), Flat, CW, Color, CV_32F, CV_32S)
/home/ci/opencv/modules/3d/test/test_rendering.cpp:430: Failure
Expected: (normL2Depth) <= (normL2Threshold), actual: 0.00102317 vs 0.000989
[  FAILED  ] Rendering/RenderingTest.accuracy/4, where GetParam() = ((320, 240), Flat, CW, Color, CV_32F, CV_32S) (22 ms)

[ RUN      ] Rendering/RenderingTest.accuracy/5, where GetParam() = ((320, 240), Shaded, None, Color, CV_32F, CV_32S)
/home/ci/opencv/modules/3d/test/test_rendering.cpp:430: Failure
Expected: (normL2Depth) <= (normL2Threshold), actual: 0.00102317 vs 0.000989
[  FAILED  ] Rendering/RenderingTest.accuracy/5, where GetParam() = ((320, 240), Shaded, None, Color, CV_32F, CV_32S) (22 ms)

[ RUN      ] Rendering/RenderingTest.accuracy/8, where GetParam() = ((320, 240), Flat, CW, Clipping, CV_32F, CV_32S)
/home/ci/opencv/modules/3d/test/test_rendering.cpp:430: Failure
Expected: (normL2Depth) <= (normL2Threshold), actual: 0.00162132 vs 0.0016
[  FAILED  ] Rendering/RenderingTest.accuracy/8, where GetParam() = ((320, 240), Flat, CW, Clipping, CV_32F, CV_32S) (22 ms)

[ RUN      ] Rendering/RenderingTest.accuracy/9, where GetParam() = ((320, 240), Shaded, None, Clipping, CV_32F, CV_32S)
/home/ci/opencv/modules/3d/test/test_rendering.cpp:430: Failure
Expected: (normL2Depth) <= (normL2Threshold), actual: 0.000554117 vs 0.000544
[  FAILED  ] Rendering/RenderingTest.accuracy/9, where GetParam() = ((320, 240), Shaded, None, Clipping, CV_32F, CV_32S) (27 ms)
```

Related CI PR: https://github.com/opencv/ci-gha-workflow/pull/165
2024-04-10 09:33:10 +03:00
alexlyulkov f454303f6a Merge pull request #25241 from alexlyulkov:al/int64-padding
Added int support to padding layer #25241

Added int32 and int64 support to padding layer (CPU and CUDA).
ONNX parser doesn't convert non-zero padding value to float now.

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2024-04-09 11:20:56 +03:00
Alexander Smorkalov e8a52c7e94 Merge pull request #25099 from Abdurrahheem:ash/slice-1d-test
Slice Layer 1D test
2024-04-09 09:29:53 +03:00
Abdurrahheem ab7ab7b6be Slice Layer 1D test. 2024-04-09 08:52:49 +03:00
Alexander Smorkalov f2c3d4dfe3 Merge pull request #25369 from dkurt:resolve_valgrind_warnings
Resolve valgrind warnings
2024-04-08 12:48:59 +03:00
Alexander Smorkalov 3b02f3b7ae Merge pull request #25201 from Abdurrahheem:ash/0D-permute-test
Permute Layer Test 0D
2024-04-08 12:38:51 +03:00
Abdurrahheem a31f4f4040 git squash 2024-04-08 10:47:23 +03:00
Dmitry Kurtaev bfd1504de3 Resolve valgrind warnings 2024-04-08 09:35:21 +03:00
Alexander Smorkalov 719830959c Merge pull request #25339 from dkurt:mathjax_formula_in_table_css
Update doc CSS to fit MathJAX formula in table
2024-04-06 13:28:15 +03:00
Abduragim Shtanchaev 22b1b1edac Merge pull request #25071 from Abdurrahheem:ash/1D-scatter
1D Scatter Layer Test #25071

This PR introduces parametrized test for `Scatter` layer to test its functionality for 1D arrays


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- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-04-05 15:55:23 +03:00
Dmitry Kurtaev 244c771fb6 Update doc CSS to fit MathJAX formula in table 2024-04-05 11:00:55 +03:00
Alexander Smorkalov 2e784bc7e6 Merge pull request #25330 from alexlyulkov:al/dnn-int64-more-tests
Added int tests for Const, Concat, ScatterND, NaryEltwise, Arg, Blank layers
2024-04-05 09:58:06 +03:00
Alexander Smorkalov 5f98674fe3 Merge pull request #25335 from dkurt:fix_valgrind_3d
Resolve valgrind issue in 3d module
2024-04-04 18:01:24 +03:00
Dmitry Kurtaev ba62811cc8 Resolve valgrind issue in 3d module 2024-04-04 15:48:14 +03:00
alexlyulkov 5144766380 Merge pull request #25277 from alexlyulkov:al/dnn-int-tests
Added int tests for CumSum, Scatter, Tile and ReduceSum dnn layers #25277

Fixed bug in tile layer.
Fixed bug in reduce layer by reimplementing the layer. 

Fixed types filter in Scatter and ScatterND layers

PR for extra: https://github.com/opencv/opencv_extra/pull/1161


### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-04-04 14:23:48 +03:00
Alexander Smorkalov 87e0246bb0 Merge pull request #25328 from dkurt:fix_rng_fill_oob
Resolve out of bound write in RNG::fill
2024-04-04 14:21:49 +03:00
Alexander Smorkalov 5196b575fe Merge pull request #25074 from Abdurrahheem:ash/1D-softmax-test
1D Input Softmax test
2024-04-04 12:59:53 +03:00
Abdurrahheem 753e2c1dfa Added 1d tensors support to SoftMax layer. 2024-04-04 11:10:24 +03:00
Rostislav Vasilikhin 82038be4cd Merge pull request #25221 from savuor:rv/bench_3d
OBJ and PLY loaders extention to support texture coordinates and difused colors #25221

### This PR changes
* Texture coordinates support added to `loadMesh()` and `saveMesh()`
* `loadMesh()` changes its behavior: all vertex attribute arrays (vertex coordinates, colors, normals, texture coordinates) now have the same size and same-index corresponce
  - This makes sense for OBJ files where vertex attribute arrays are independent from each other and are randomly accessed when defining faces
  - Looks like this behavior may also happen in some PLY files; however, it is not implemented until we encounter such files in a wild nature
  - At the same time `loadPointCloud()` keeps its behavior and loads vertex attributes as they are given in the file
* PLY loader supports synonyms for the properties: `diffuse_red`, `diffuse_green` and `diffuse_blue` along with `red`, `green` and `blue`
* `std::vector<cv::Vec3i>` supported as an index array type
* Colors are loaded as [0, 1] floats instead of uchars
  - Since colors are usually saved as floats, internal conversion to uchar at loading significantly drops accuracy
  - Performing uchar conversion does not always makes sense and can be performed by a user if they needs it
* PLY loading fixed: wrong offset ruined x coordinate
* Python tests added for `loadPointCloud` and `loadMesh`

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-04-04 11:04:52 +03:00
Abduragim Shtanchaev 65074651a4 Merge pull request #25224 from Abdurrahheem:ash/0D-concat-test
Concat Layer 0/1D test #25224

This PR introduces parametrized `0/1D` input support test for `Concat` layer.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-04-04 10:36:00 +03:00
Dmitry Kurtaev 56d586aa3e Lest debug checks 2024-04-03 21:55:27 +03:00
Alexander Lyulkov b64ce1e7f1 Added tests for Const, Concat, ScatterND, NaryEltwise, Arg, Blanc 2024-04-03 18:41:53 +03:00
Dmitry Kurtaev 357203facd Resolve out of bound write in RNG::fill 2024-04-03 18:20:45 +03:00
Alexander Smorkalov c1e2f16f91 Merge pull request #25225 from Abdurrahheem:ash/0d-expand-test
Expand 0D layer test
2024-04-03 09:53:46 +03:00
Alexander Smorkalov cb6d295f15 Merge branch 4.x 2024-04-02 16:39:54 +03:00
Abdurrahheem eddace4d98 git squash 2024-04-01 17:22:39 +04:00
Abdul Rahman ArM 55426ee195 Merge pull request #25197 from invarrow:invbranch-cleanup
Remove OpenVX  #25197

resolves https://github.com/opencv/opencv/issues/24995
OpenCV cleanup https://github.com/opencv/opencv/issues/25007
2024-03-26 15:17:18 +03:00
Abduragim Shtanchaev 5319772a56 Merge pull request #25205 from Abdurrahheem:ash/0D-split-test
0D test for split layer #25205

This PR introduces parametrized `0/1D` input support test for `Split` layer.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-03-26 15:13:41 +03:00
alexlyulkov f0323fdd1e Merge pull request #25218 from alexlyulkov:al/int64-tile
Allowed int types in Tile and Reduce layers #25218

Allowed any Mat type in Tile layer.
Allowed int64 type in Reduce layer.

ONNX tests with int32 and int64 inputs will be added later in a separate PR


### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-03-26 14:00:35 +03:00
Alexander Smorkalov a33de44b0b Merge pull request #25212 from alexlyulkov:al/dnn-int64-scatter
Added int64 values support to scatter, scatterND and maxunpool layers
2024-03-26 13:52:28 +03:00
alexlyulkov f8319de976 Added int support to CumSum layer (#25214)
* Added int support to CumSum layer

* Allowed int types in CumSum layer

---------

Co-authored-by: Alexander Lyulkov <alexander.lyulkov@opencv.ai>
2024-03-22 04:35:43 +03:00
Abduragim Shtanchaev d188319b82 0D test for Reshape layer (#25206)
* reshape test for 0D

* fix comments according to PR
2024-03-22 03:59:08 +03:00
Yuantao Feng 8e342f8857 5.x core: rename cv::bfloat16_t to cv::bfloat (#25232)
* rename cv::bfloat16_t to cv::bfloat

* clean class bfloat
2024-03-22 03:45:59 +03:00
alexlyulkov aa9e80b07b Added native int64 indices support to gather layer (#25211)
Co-authored-by: Alexander Lyulkov <alexander.lyulkov@opencv.ai>
2024-03-22 03:43:20 +03:00
alexlyulkov f2cf3c8890 Added int support to flatten, permute, reshape, slice layers (#25236)
Co-authored-by: Alexander Lyulkov <alexander.lyulkov@opencv.ai>
2024-03-22 03:39:42 +03:00
WU Jia aa5ea340f7 Move objdetect HaarCascadeClassifier and HOGDescriptor to contrib xobjdetect (#25198)
* Move objdetect parts to contrib

* Move objdetect parts to contrib

* Minor fixes.
2024-03-21 23:40:10 +03:00
Alexander Alekhin 213e1a4d9d Merge pull request #25234 from mshabunin:doc-auto-dot-5.x 2024-03-20 09:20:57 +00:00
Maksim Shabunin 11eac62ac3 doc: auto-enabling DOT support in documentation if dot executable has been found 2024-03-19 19:19:11 +03:00
Alexander Lyulkov d2d6869a26 Added int64 values support to scatter, scatterND and maxunpool layers 2024-03-13 15:40:07 +03:00
alexlyulkov 85cc02f4de Allowed int64 constants in ONNX parser (#25148)
* Removed automatic int64 to int32 conversion in ONNX parser

* Fixed wrong rebase code

* added tests, minor fixes

* fixed Cast layer

* Fixed Cast layer for fp16 backend

* Fixed Cast layer for fp16 backend

* Fixed Cast layer for fp16 backend

* Allowed uint32, int64, uint64 types in OpenCL

* Fixed Cast layer for fp16 backend

* Use randu in test_int

---------

Co-authored-by: Alexander Lyulkov <alexander.lyulkov@opencv.ai>
2024-03-13 11:48:23 +03:00
Maksim Shabunin de29223217 Merge pull request #25161 from mshabunin:doc-upgrade-5.x
Documentation transition to fresh Doxygen (5.x) #25161 

Port of #25042

Merge with opencv/opencv_contrib#3687
CI part: opencv/ci-gha-workflow#162
2024-03-06 08:50:31 +03:00
Alexander Smorkalov c6776ec136 Merge pull request #25159 from Kumataro:trial_to_fix_cv_check_24411
dnn: fix to iteration variable scope
2024-03-05 16:01:25 +03:00
Kumataro 216c6c3da1 dnn: fix to iteration variable scope 2024-03-05 18:33:56 +09:00
Maksim Shabunin 8cbdd0c833 Merge pull request #25075 from mshabunin:cleanup-imgproc-1
C-API cleanup: apps, imgproc_c and some constants #25075

Merge with https://github.com/opencv/opencv_contrib/pull/3642

* Removed obsolete apps - traincascade and createsamples (please use older OpenCV versions if you need them). These apps relied heavily on C-API
* removed all mentions of imgproc C-API headers (imgproc_c.h, types_c.h) - they were empty, included core C-API headers
* replaced usage of several C constants with C++ ones (error codes, norm modes, RNG modes, PCA modes, ...) - most part of this PR (split into two parts - all modules and calib+3d - for easier backporting)
* removed imgproc C-API headers (as separate commit, so that other changes could be backported to 4.x)

Most of these changes can be backported to 4.x.
2024-03-05 12:18:31 +03:00
alexlyulkov 1d1faaabef Merge pull request #24411 from alexlyulkov:al/dnn-type-inference
Added int32, int64 support and type inference to dnn #24411

**Added a type inference to dnn similar to the shape inference, added int32 and int64 support.**

- Added getTypes method for layers that calculates layer outputs types and internals types from inputs types (Similar to getMemoryShapes). By default outputs and internals types = input[0] type
- Added type inference pipeline similar to shape inference pipeline. LayersShapes struct (that is used in shape inference pipeline) now contains both shapes and types
- All layers output blobs are now allocated using the calculated types from the type inference.
- Inputs and constants with int32 and int64 types are not automatically converted into float32 now.
- Added int32 and int64 support for all the layers with indexing and for all the layers required in tests.

Added  int32 and int64 support for CUDA:
- Added host<->device data moving for int32 and int64
- Added int32 and int64 support for several layers (just slightly modified CUDA C++ templates)

Passed all the accuracy tests on CPU, OCL, OCL_FP16, CUDA, CUDA_FP16. (except RAFT model)

**CURRENT PROBLEMS**:
-  ONNX parser always converts int64 constants and layers attributes to int32, so some models with int64 constants doesn't work (e.g. RAFT). The solution is to disable int64->int32 conversion and fix attributes reading in a lot of ONNX layers parsers (https://github.com/opencv/opencv/issues/25102)
- I didn't add type inference and int support to VULCAN, so it doesn't work at all now.
- Some layers don't support int yet, so some unknown models may not work.

**CURRENT WORKAROUNDS**:
- CPU arg_layer indides are implemented in int32 followed by a int32->int64 conversion (the master branch has the same workaround with int32->float conversion)
- CPU and OCL pooling_layer indices are implemented in float followed by a float->int64 conversion
- CPU gather_layer indices are implemented in int32, so int64 indices are converted to int32 (the master branch has the same workaround with float->int32 conversion)

**DISABLED TESTS**:
- RAFT model

**REMOVED TESTS**:
- Greater_input_dtype_int64 (because it doesn't fit ONNX rules, the whole test is just comparing float tensor with int constant)

**TODO IN NEXT PULL REQUESTS**:
- Add int64 support for ONNX parser
- Add int support for more layers
- Add int support for OCL (currently int layers just run on CPU)
- Add int tests
- Add int support for other backends
2024-03-01 17:07:38 +03:00
Alexander Smorkalov 81956ad83e Merge pull request #25124 from asmorkalov:as/dnn_1d
Extracted 1d test cases to reduce conflicts with 4.x.
2024-02-29 14:50:28 +03:00
Zhangjie Chen 0e47b05106 Merge pull request #23985 from starga2er777:pcc
[GSoC] Update octree methods and create frames for PCC #23985

## PR for GSoC Point Cloud Compression
[Issue for GSoC 2023](https://github.com/opencv/opencv/issues/23624)

* We are **updating the Octree method create() by using OctreeKey**: Through voxelization, directly calculate the leaf nodes that the point cloud belongs to, and omit the judgment whether the point cloud is in the range when inserted. The index of the child node is calculated by bit operation.
* We are also **introducing a new header file pcc.h (Point Cloud Compression) with API framework**.
* We added tests for restoring point clouds from an octree.
* Currently, the features related to octree creation and point cloud compression are part of the internal API, which means they are not directly accessible to users. However, our plan for the future is to **include only the 'PointCloudCompression' class in the 'opencv2/3d.hpp' header file**. This will provide an interface for utilizing the point cloud compression functionality.

The previous PR of this was closed due to repo name conflicts, therefore we resubmitted in this PR.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-02-29 14:02:44 +03:00
Alexander Smorkalov 010772b492 Extracted 1d test cases to reduce conflicts with 4.x. 2024-02-29 12:02:00 +03:00
Alexander Smorkalov 92b940792a Merge pull request #25117 from Abdurrahheem:ash/scale-layer-1D-test
Scale layer 1d test
2024-02-29 11:32:13 +03:00
Alexander Smorkalov a22130fbfa Merge branch 4.x 2024-02-28 18:49:05 +03:00
Alexander Smorkalov e02d256ff3 Merge pull request #25063 from asmorkalov:as/multiview_calib_sample_py
Ground truth check and Charuco support in multiview_calibration.py
2024-02-28 16:36:56 +03:00
Abdurrahheem 161c402f02 seperated working scale layer 1d test. 2024-02-28 13:04:48 +04:00
lpanaf 40dfe8e8fe Ground truth check and Charuco support in multiview_calibration.py 2024-02-28 10:31:56 +03:00
WU Jia 6722d4a524 Merge pull request #25017 from kaingwade:ml_to_contrib
Move ml to opencv_contrib #25017
OpenCV cleanup: #24997

opencv_contrib: opencv/opencv_contrib#3636
2024-02-27 15:54:08 +03:00
Alexander Smorkalov cb7d38b477 Merge branch 4.x 2024-02-26 18:05:36 +03:00
Alexander Smorkalov 4c549b8707 Merge pull request #25061 from asmorkalov:as/register_cameras
RegisterCameras function for heterogenious cameras pair #25061

Credits to Linfei Pan
Extracted from https://github.com/opencv/opencv/pull/24052

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

---------

Co-authored-by: lpanaf <linpan@student.ethz.ch>
2024-02-26 15:45:18 +03:00
Abduragim Shtanchaev 093ed08892 Merge pull request #24977 from Abdurrahheem:ash/primitive_1d_tests
Primitive 1D Tests #24977

This PR is designed to add tests for 1D inputs for layer, which is required after introducing 1d support in 5.x. Currently tests are written for following layers: 

- [x] `Add`, `Sub`
- [x]  `Product`, `Div`
- [x]  `Min`, `Max`
- [x] `Argmin`, `Argmax`
- [x] `Gather` 

This list is to be extended for more layer such `gemm`, `conv` etc.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-02-21 17:37:49 +03:00
Alexander Smorkalov a0df2f5328 Merge pull request #25021 from asmorkalov:as/multiview_calib_generator_ext
Multiview calibration generator improvements
2024-02-20 15:15:16 +03:00
Rostislav Vasilikhin fa745553bf Merge pull request #24459 from savuor:tri_rasterize
Triangle rasterization function #24459

#24065 reopened since the previous one was automatically closed after rebase
Connected PR with ground truth data: [#1113@extra](https://github.com/opencv/opencv_extra/pull/1113)

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-02-19 12:23:05 +03:00
Alexander Smorkalov f084a229b4 Merge branch 4.x 2024-02-19 09:06:26 +03:00
Yuantao Feng d4fd5157fa Merge pull request #24980 from fengyuentau:on-fly-quantization-removal
dnn cleanup: On-fly-quantization removal #2498

On-fly-quantization is first introduced via https://github.com/opencv/opencv/pull/20228.
We decided to remove it but keep int8 layers implementation because on-fly-quantization
is less practical given the fact that there has been so many dedicated tools for model
quantization.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-02-16 18:21:45 +03:00
Martin Štefaňák 3125f9708d Merge pull request #24989 from zteffi:enable-warpPointBackward-in-python
Remove bypass for ABI check in warpPointBackward #24989

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] ~~There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.~~
- [x] The feature is well documented and sample code can be built with the project CMake

### What? 
In my [PR](https://github.com/opencv/opencv/commit/d31b6c3480c601305fa1e153a819aad0bd247fe2) regarding `warpPointBackward`, I was asked by alalek to [add a bypass](https://github.com/opencv/opencv/pull/18607/files#r508423486) to python wrapper class, presumably to ship the changes in the newest patch release (4.5.1).

The bypass was not removed in 4.6.0 release, so please remove it in either 4.10.0 or 4.11.0. Thanks!

Co-authored-by: Martin Stefanak <martin.stefanak@codasip.com>
Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>
2024-02-16 16:27:29 +03:00
Alexander Smorkalov 12b7aac1a0 Dump board poses human-readable and machine-readable format. 2024-02-14 15:17:01 +03:00
lpanaf 0957437bbe Save ground truth in calibration data generator. 2024-02-14 14:26:18 +03:00
Alexander Smorkalov 201a753e4a Merge pull request #25015 from asmorkalov:as/out_of_bound_mat_formatter
Fixed possible out-of-bound access in cv::Mat output formatter
2024-02-14 09:33:21 +03:00
Alexander Smorkalov 52be0b64fb Fixed possible out-of-bound access in cv::Mat output formatter. 2024-02-13 17:34:40 +03:00
Alexander Smorkalov fa3f1822ae Merge pull request #24993 from asmorkalov:as/FastNeuralStyle_eccv16_CUDA
Relax test requirements for CUDA in DNNTestNetwork.FastNeuraStyle_eccv16
2024-02-12 16:37:57 +03:00
Rostislav Vasilikhin f96111ef05 Merge pull request #24961 from savuor:rv/ply_mesh
PLY mesh support #24961

**Warning:** The PR changes exising API.

Fixes #24960
Connected PR: [#1145@extra](https://github.com/opencv/opencv_extra/pull/1145)

### Changes
* Adds faces loading from and saving to PLY files
* Fixes incorrect PLY loading (see issue)
* Adds per-vertex color loading / saving

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-02-12 15:34:54 +03:00
Alexander Smorkalov 466ad96b1d Merge pull request #24992 from Kumataro:fix24991
ts: Extended getTypeName() to support new types
2024-02-12 15:32:52 +03:00
Alexander Smorkalov 5ce0acce40 Relax test requirements for CUDA in DNNTestNetwork.FastNeuraStyle_eccv16 2024-02-12 14:47:37 +03:00
Alexander Smorkalov 3a55f50133 Merge branch 4.x 2024-02-12 14:20:35 +03:00
Kumataro 3b85d5fac7 ts: Extended getTypeName() to support new types 2024-02-12 17:56:43 +09:00
Vadim Pisarevsky 1d18aba587 Extended several core functions to support new types (#24962)
* started adding support for new types (16f, 16bf, 32u, 64u, 64s) to arithmetic functions

* fixed several tests; refactored and extended sum(), extended inRange().

* extended countNonZero(), mean(), meanStdDev(), minMaxIdx(), norm() and sum() to support new types (F16, BF16, U32, U64, S64)

* put missing CV_DEPTH_MAX to some function dispatcher tables
* extended findnonzero, hasnonzero with the new types support

* extended mixChannels() to support new types

* minor fix

* fixed a few compile errors on Linux and a few failures in core tests

* fixed a few more warnings and test failures

* trying to fix the remaining warnings and test failures. The test `MulTestGPU.MathOpTest` was disabled - not clear whether to set tolerance - it's not bit-exact operation, as possibly assumed by the test, due to the use of scale and possibly limited accuracy of the intermediate floating-point calculations.

* found that in the current snapshot G-API produces incorrect results in Mul, Div and AddWeighted (at least when using OpenCL on Windows x64 or MacOS x64). Disabled the respective tests.
2024-02-11 10:42:41 +03:00
Alexander Smorkalov f05ef64df8 Merge pull request #24922 from asmorkalov:as/static_aar_fix
Fix AAR build with static OpenCV libraries for Android.
2024-01-27 12:46:09 +03:00
Alexander Smorkalov 4930c9cebc Fix AAR build with static OpenCV libraries for Android. 2024-01-25 18:15:45 +03:00
Alexander Smorkalov decf6538a2 Merge branch 4.x 2024-01-23 17:06:52 +03:00
Alexander Smorkalov d6424233f0 Merge pull request #24906 from Abdurrahheem:ash/fix_einsum_inner
Einsum Layer Inner Product Issue Solution
2024-01-23 09:26:22 +03:00
Alexander Smorkalov dcc4dcedba Merge pull request #24900 from asmorkalov:as/windows_warn_fix_5.x
Fixed type cast warning in CV_ELEM_SIZE1 for cv::Mat::type
2024-01-23 09:18:43 +03:00
Alexander Smorkalov f1e1145546 Merge pull request #24899 from asmorkalov:as/style_transfer_ocl
Relax test requirements for OpenCL in test DNNTestNetwork.FastNeuraStyle_eccv16
2024-01-23 09:17:48 +03:00
Abduragim 0e6b7f1656 fix 1D handling issue in inner product 2024-01-22 20:10:34 +04:00
Alexander Smorkalov 775210e701 Relax test requirements for OpenCL in test DNNTestNetwork.FastNeuralStyle_eccv16. 2024-01-22 17:11:41 +03:00
Alexander Smorkalov 4e2c7221f2 Fixed type cast warning in CV_ELEM_SIZE1 for cv::Mat::type. 2024-01-22 12:08:55 +03:00
Alexander Smorkalov c739117a7c Merge branch 4.x 2024-01-19 17:32:22 +03:00
Alexander Smorkalov 788c7252dc Merge pull request #24830 from Abdurrahheem:ash/einsum_1d_support
1d  operation support for einsum layer
2024-01-12 14:21:13 +03:00
Abduragim 6c28d7140a 1d support for einsum 2024-01-08 21:34:47 +03:00
Kumataro bae435a5a7 Merge pull request #24578 from Kumataro:fix_verify_unsupported_new_mat_depth
Fix verify unsupported new mat depth for nonzero/minmax/lut #24578

`cv::LUI()`, `cv::minMaxLoc()`, `cv::minMaxIdx()`, `cv::countNonZero()`, `cv::findNonZero()` and `cv::hasNonZero()` uses depth-based function table. However, it is too short for `CV_16BF`, `CV_Bool`, `CV_64U`, `CV_64S` and `CV_32U` and it may occur out-boundary-access. This patch fix it. And If necessary, when someone extends these functions to support, please relax this test.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2023-11-23 12:15:58 +03:00
Alexander Smorkalov 4b4c130f0a Merge pull request #24561 from asmorkalov:usac_bug_fix_port
Port of Replace double atomic in USAC
2023-11-20 21:26:45 +03:00
Maksym Ivashechkin 607d92858f Port of Replace double atomic in USAC
Port of solution for:
- https://github.com/opencv/opencv/issues/24482
- https://github.com/opencv/opencv/issues/24281

Original PR: https://github.com/opencv/opencv/pull/24499
2023-11-20 16:42:00 +03:00
Alexander Alekhin 3af68fa857 Merge pull request #24517 from mshabunin:fix-3d-test 2023-11-09 20:06:31 +00:00
Maksim Shabunin fefbcfeb0b 3d: fix static init in test 2023-11-09 22:07:59 +03:00
Rostislav Vasilikhin 53aad98a1a Merge pull request #23098 from savuor:nanMask
finiteMask() and doubles for patchNaNs() #23098

Related to #22826
Connected PR in extra: [#1037@extra](https://github.com/opencv/opencv_extra/pull/1037)

### TODOs:
- [ ] Vectorize `finiteMask()` for 64FC3 and 64FC4

### Changes

This PR:
* adds a new function `finiteMask()`
* extends `patchNaNs()` by CV_64F support
* moves `patchNaNs()` and `finiteMask()` to a separate file

**NOTE:** now the function is called `finiteMask()` as discussed with the OpenCV core team

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2023-11-09 10:32:47 +03:00
Alexander Smorkalov 34f34f6227 Merge branch 4.x 2023-11-08 14:39:48 +03:00
Alexander Smorkalov 0e6430abcb Merge pull request #24453 from asmorkalov:as/riscv-ci-5.x
Enable RISC-V CI configuration for 5.x
2023-10-26 13:28:03 +03:00
alexlyulkov b71be65f57 Merge pull request #24294 from alexlyulkov:al/remove-torch7-from-dnn
Remove torch (old torch7) from dnn in 5.x #24294

Merge with https://github.com/opencv/opencv_extra/pull/1097

Completely removed torch (old torch7) from dnn:
- removed modules/dnn/src/torch directory that contained torch7 model parser
- removed readNetFromTorch() and readTorchBlob() public functions
- removed torch7 references from comments and help texts
- replaced links to t7 models by links to similar onnx models in js_style_transfer turtorial (similar to https://github.com/opencv/opencv/pull/24245/files)
2023-10-26 11:27:56 +03:00
Alexander Smorkalov 8495910165 Enable RISC-V CI configuration for 5.x 2023-10-26 08:26:37 +03:00
Alexander Smorkalov 97620c053f Merge branch 4.x 2023-10-23 11:53:04 +03:00
Yuantao Feng d789cb459c Merge pull request #24231 from fengyuentau:halide_cleanup_5.x
dnn: cleanup of halide backend for 5.x #24231

Merge with https://github.com/opencv/opencv_extra/pull/1092.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2023-10-13 16:53:18 +03:00
Alexander Smorkalov 0976765c62 Merge pull request #24357 from Kumataro:fix24121
imgproc: move stb_truetype functions into cv namespace
2023-10-05 09:37:08 +03:00
Kumataro 3a424b7f1c imgproc: move stb_trutype functions into cv namespace 2023-10-03 20:45:23 +00:00
Alexander Smorkalov 163d544ecf Merge branch 4.x 2023-10-02 10:17:23 +03:00
Alexander Alekhin c5ff405d94 Merge pull request #24262 from mshabunin:fix-riscv-5x 2023-09-22 21:11:59 +00:00
Maksim Shabunin c3a37d0fcb RISC-V: fix compilation in RVV scalable mode 2023-09-22 21:08:33 +03:00
Vadim Pisarevsky 416bf3253d attempt to add 0d/1d mat support to OpenCV (#23473)
* attempt to add 0d/1d mat support to OpenCV

* revised the patch; now 1D mat is treated as 1xN 2D mat rather than Nx1.

* a step towards 'green' tests

* another little step towards 'green' tests

* calib test failures seem to be fixed now

* more fixes _core & _dnn

* another step towards green ci; even 0D mat's (a.k.a. scalars) are now partly supported!

* * fixed strange bug in aruco/charuco detector, not sure why it did not work
* also fixed a few remaining failures (hopefully) in dnn & core

* disabled failing GAPI tests - too complex to dig into this compiler pipeline

* hopefully fixed java tests

* trying to fix some more tests

* quick followup fix

* continue to fix test failures and warnings

* quick followup fix

* trying to fix some more tests

* partly fixed support for 0D/scalar UMat's

* use updated parseReduce() from upstream

* trying to fix the remaining test failures

* fixed [ch]aruco tests in Python

* still trying to fix tests

* revert "fix" in dnn's CUDA tensor

* trying to fix dnn+CUDA test failures

* fixed 1D umat creation

* hopefully fixed remaining cuda test failures

* removed training whitespaces
2023-09-21 18:24:38 +03:00
Alexander Smorkalov fdab565711 Merge branch 4.x 2023-09-13 14:49:25 +03:00
Alexander Smorkalov a6748df587 Merge branch 4.x 2023-08-08 17:32:17 +03:00
Alexander Smorkalov b47704eabc Merge pull request #24125 from asmorkalov:as/pack_store_bfloat16
Fix v_pack_store alignment issue on Windows 32-bit.
2023-08-08 17:21:43 +03:00
Alexander Smorkalov 2311c14582 Fix v_pack_store alignment issue on Windows 32-bit. 2023-08-08 14:10:29 +03:00
Alexander Smorkalov 5f5fb11c66 Merge pull request #24118 from asmorkalov:as/prev_merge_artifact
Removed merge previous 4.x->5.x merge artifact
2023-08-08 09:01:42 +03:00
Alexander Smorkalov b5a189a978 Removed merge previous 4.x->5.x merge artifact. 2023-08-07 17:45:58 +03:00
Vadim Pisarevsky 518486ed3d Added new data types to cv::Mat & UMat (#23865)
* started working on adding 32u, 64u, 64s, bool and 16bf types to OpenCV

* core & imgproc tests seem to pass

* fixed a few compile errors and test failures on macOS x86

* hopefully fixed some compile problems and test failures

* fixed some more warnings and test failures

* trying to fix small deviations in perf_core & perf_imgproc by revering randf_64f to exact version used before

* trying to fix behavior of the new OpenCV with old plugins; there is (quite strong) assumption that video capture would give us frames with depth == CV_8U (0) or CV_16U (2). If depth is > 7 then it means that the plugin is built with the old OpenCV. It needs to be recompiled, of course and then this hack can be removed.

* try to repair the case when target arch does not have FP64 SIMD

* 1. fixed bug in itoa() found by alalek
2. restored ==, !=, > and < univ. intrinsics on ARM32/ARM64.
2023-08-04 10:50:03 +03:00
Alexander Smorkalov fa91c1445e Merge pull request #24096 from asmorkalov:as/vrabaud_calibration_port
Port stereoRectify grid fix #24035
2023-08-04 10:40:38 +03:00
Alexander Smorkalov da28c62855 Port stereoRectify grid fix #24035. 2023-08-03 11:27:59 +03:00
Alexander Smorkalov 9ddf1e5c77 Merge pull request #24084 from asmorkalov:as/stereoRectify
Finalize calib3d module split for fisheye part
2023-08-03 11:00:00 +03:00
Alexander Smorkalov 285108e2e1 Finalize calib3d module split for fisheye part. 2023-08-03 09:21:05 +03:00
Alexander Smorkalov 802af10e44 Merge pull request #24095 from asmorkalov:as/5.x-ci-ubuntu22
Add CI configuration with Ubuntu 22.04 for 5.x branch.
2023-08-02 20:37:28 +03:00
Alexander Smorkalov 7889ed7f80 Merge pull request #23957 from lpanaf:multi-calib-fix
add error in the first camera to the cost function
2023-08-02 17:45:15 +03:00
lpanaf 36d688ec10 add error in the first camera to the cost function 2023-08-02 15:45:23 +03:00
Alexander Smorkalov eab8eb8f3b Add CI configuration with Ubuntu 22.04 for 5.x branch. 2023-08-02 11:43:25 +03:00
Alexander Smorkalov 47188b7c7e Merge branch 4.x 2023-07-28 13:05:36 +03:00
Maksym Ivashechkin 0e8748746f Merge pull request #24005 from ivashmak:merge_usac_5.x
Merge usac to 5.x #24005

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake


Base branch is PR #23979. Merging PR #23078, 23900 and PR #23806  to 5.x
2023-07-27 17:51:16 +03:00
Zhangjie Chen 4d695cd2f4 Merge pull request #23805 from starga2er777:5.x
GSoC: Modified PLY reader to support color attribute read/write #23805

* Modified PLY reader to support color attribute read/write
* Fix bugs & Modified OBJ reader for color IO
* Replace with correct test file
* Fix I/O of property [w] in OBJ files

### Pull Request Readiness Checklist

**Merged with https://github.com/opencv/opencv_extra/pull/1075**

[Issue for GSoC 2023](https://github.com/opencv/opencv/issues/23624)
The ply loader in 3D module doesn't support color attribute reading. I modified that to support color attribute reading & writing for the color attribute compression as described in proposal.

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2023-07-25 16:05:56 +03:00
Alexander Smorkalov 18f280b869 Merge pull request #23990 from asmorkalov:as/5.x_calib_api_cleanup
Unify Pinhole and Fisheye camera models
2023-07-23 09:55:49 +03:00
Alexander Smorkalov 8595066afc Unify Pinhole and Fisheye camera calibration flags. 2023-07-19 14:58:08 +03:00
Alexander Smorkalov a484f39f47 Merge pull request #23988 from asmorkalov:as/drop_python2
Drop Python2 support
2023-07-18 09:59:15 +03:00
Alexander Smorkalov 1a3523d2d8 Drop Python2 support. 2023-07-14 15:06:53 +03:00
Alexander Smorkalov cea26341a5 Merge branch 4.x 2023-07-13 09:28:36 +03:00
Alexander Smorkalov eb717a7e91 Merge pull request #23919 from asmorkalov:as/5.x-vulkan
Enable Vulkan tests for 5.x branch after 4.x->5.x merge
2023-07-06 12:30:04 +03:00
Alexander Smorkalov bd9a3ee161 Enable Vulkan tests for 5.x branch after 4.x->5.x merge. 2023-07-06 10:16:52 +03:00
Alexander Smorkalov f987bf2961 Merge pull request #23814 from Avasam:fix-type-confindece
Fix typo `confindece`
2023-07-05 17:51:32 +03:00
Alexander Smorkalov 5af40a0269 Merge branch 4.x 2023-07-05 15:51:10 +03:00
Avasam 277b0231f2 fix typo confindece 2023-06-16 20:26:26 -04:00
Alexander Smorkalov d24ffe9a65 Merge pull request #23705 from asmorkalov:as/cxx-named-arguments
Re-implement named parameters bindings for Python #23705

Reverted named argument handling from #19156.
Ported new solution from #23224
The port is required to harmonize 4.x -> 5.x merges.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2023-05-30 17:41:41 +03:00
Maksym Ivashechkin 67a3d35b4e Merge pull request #22363 from ivashmak:multiview-calib
Add multiview calibration [GSOC 2022]

### Pull Request Readiness Checklist

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake

The usage tutorial is on Google Docs following this link: https://docs.google.com/document/d/1k6YpD0tpSVqnVnvU2nzE34K3cp_Po6mLWqXV06CUHwQ/edit?usp=sharing
2023-03-23 15:42:41 +03:00
Vladimir Ponomarev 0c55ed0ca8 Merge pull request #23291 from vovka643:5.x_depricated_backends
Merge with https://github.com/opencv/opencv_contrib/pull/3446
Related issue: https://github.com/opencv/opencv/issues/11810

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2023-03-15 09:41:36 +03:00
Alexander Smorkalov a792252b55 Merge pull request #23283 from vrabaud:5_overflow
Fix signed integer overflow.
2023-03-10 10:04:08 +03:00
Vincent Rabaud f7ce715596 Fix signed integer overflow.
The overflow happens for INT_MAX so the code just needs to be moved down.
2023-02-20 23:52:22 +01:00
Rostislav Vasilikhin 23dec329b4 Merge pull request #23150 from savuor:port5_stereo_calib_per_obj
### Changes

* Port of #22519 to 5.x
* Distortion coefficients were not copied properly, fixed
* Minor coding style chages

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2023-02-02 16:44:28 +03:00
Alexander Smorkalov 29dc07b1f3 Merge pull request #23186 from savuor:warnings_3d
MSVC warnings fixed in 3d module
2023-01-30 10:09:01 +03:00
Alexander Alekhin e880d51e1c Merge pull request #23189 from alalek:5.x-merge-4.x 2023-01-30 05:32:39 +00:00
Alexander Alekhin 3c8e97ff6a 3d(test): change tolerance of Volume/VolumeTestFixture.valid_points 2023-01-30 05:27:02 +00:00
Alexander Alekhin 1d530eb2e2 core(test_math): replace the_rng() => cv::theRNG() 2023-01-29 19:51:18 +00:00
Alexander Alekhin 4500a5369c 3d(test): don't use RNG in SetUp() 2023-01-29 17:14:25 +00:00
Alexander Alekhin f33598f55e Merge branch 4.x 2023-01-28 17:31:32 +00:00
Rostislav Vasilikhin 285218915c msvc cast warnings 2023-01-27 17:20:12 +01:00
Rostislav Vasilikhin 8329c09ba8 Merge pull request #23178 from savuor:stddev_calib_fisheye
Fixes #23057

Parameter uncertainty fixed + ground truth test data fixed

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2023-01-27 09:40:08 +03:00
Alexander Alekhin a42d879925 Merge branch 4.x 2023-01-18 22:03:42 +00:00
Alexander Alekhin 6904207b60 Merge pull request #23143 from vrabaud:compil5_1 2023-01-17 20:25:20 +03:00
Vincent Rabaud 44c46e545a Fix compilation with msan 2023-01-17 13:26:59 +01:00
Alexander Alekhin 593a376566 Merge branch 4.x 2023-01-09 11:08:02 +00:00
Maksim Shabunin 8a62b03761 Merge pull request #22754 from mshabunin:c-cleanup
C-API cleanup for OpenCV 5.x (imgproc, highgui)

* imgproc: C-API cleanup

* imgproc: increase cvtColor test diff threshold

* imgproc: C-API cleanup pt.2

* imgproc: C-API cleanup pt.3

* imgproc: C-API cleanup pt.4

* imgproc: C-API cleanup pt.5

* imgproc: C-API cleanup pt.5

* imgproc: C-API cleanup pt.6

* highgui: C-API cleanup

* highgui: C-API cleanup pt.2

* highgui: C-API cleanup pt.3

* highgui: C-API cleanup pt.3

* imgproc: C-API cleanup pt.7

* fixup! highgui: C-API cleanup pt.3

* fixup! imgproc: C-API cleanup pt.6

* imgproc: C-API cleanup pt.8

* imgproc: C-API cleanup pt.9

* fixup! imgproc: C-API cleanup pt.9

* fixup! imgproc: C-API cleanup pt.9

* fixup! imgproc: C-API cleanup pt.9

* fixup! imgproc: C-API cleanup pt.9

* fixup! imgproc: C-API cleanup pt.9

* fixup! imgproc: C-API cleanup pt.9
2022-12-14 18:57:08 +00:00
Rostislav Vasilikhin d49958141e Merge pull request #22925 from savuor:pytsdf_from_scratch
Fixes #22799

Replaces #21559 which was taken as a base

Connected PR in contrib: [#3388@contrib](https://github.com/opencv/opencv_contrib/pull/3388)

### Changes
OK, now this is more Odometry-related PR than Volume-related. Anyway,
* `Volume` class gets wrapped
* The same was done for helper classes like `VolumeSettings`, `OdometryFrame` and `OdometrySettings`
* `OdometryFrame` constructor signature changed to more convenient where depth goes on 1st place, RGB image on 2nd.
This works better for depth-only `Odometry` algorithms.
* `OdometryFrame` is checked for amount of pyramid layers inside `Odometry::compute()`
* `Odometry` was fully wrapped + more docs added
* Added Python tests for `Odometry`, `OdometryFrame` and `Volume`
* Added Python sample for `Volume`
* Minor fixes including better var names

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2022-12-12 09:40:12 +03:00
Rostislav Vasilikhin 86c6e07326 Merge pull request #22863 from savuor:tsdf_tests_join
### Changes
* Duplicated code removal in TSDF tests by implementing them with fixtures and GTest params
  * e.g. separate OCL tests file removed
  * as a result, more test cases are covered
  * the same's done for perf tests

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2022-12-06 12:15:39 +03:00
Rostislav Vasilikhin a423b07149 Merge pull request #22845 from savuor:volume_interface_etc
Corresponding contrib PR: #3382@contrib

Changes

- Volume::raycast(): camera intrinsics can be explicitly passed to the function. If not, the ones from current volume settings are used
- getVolumeDimensions() renamed to getVolumeStrides() because they are strides actually
- TSDF tests: OpenCLStatusRevert and parametrized fixture
- ColorTSDF::integrate(): extra RGB projector is redundant, removed
- Minor changes
2022-11-24 17:16:18 +03:00
Rostislav Vasilikhin 2407ab4e61 Merge pull request #22178 from savuor:hash_tsdf_fixes
This PR contains:

- a new property enableGrowth which controls should the HashTSDF be extended during integration by adding new volume units or not
- a new method getBoundingBox which calculates the size of currently occupied data
- a set of tests to check that new functionality
- a fix for TSDF GPU reset (data is correctly zeroed now using floatToTsdf() function)
    minor changes
2022-11-22 09:24:41 +03:00
Rostislav Vasilikhin a9d98bfb34 Merge pull request #22741 from savuor:hashtsdf_volposerot_fix
There's a bug which appears when volume pose contains non-trivial rotation.
It results in wrong depth integration which can be observed during raycasting 
or points/normals export.

- This PR fixes the bug for both CPU and OpenCL
- There is a reproducer for the bug
- The copy behavior of VolumeSettings fixed (now copy constructor creates a deep copy)
- Minor changes (e.g. unused vars removed)
2022-11-17 09:10:35 +03:00
Yuhang Wang d976272d23 Merge pull request #22682 from TsingYiPainter:5.x
3D: Handle cases where the depth of Octree setting is too large

* Handle cases where the depth setting is too large
2022-11-03 07:15:17 +00:00
Alexander Smorkalov 7e3d56e2ff Merge pull request #22691 from savuor:icp_oframe_docs
Docs added for Odometry and OdometryFrame
2022-10-26 13:18:25 +03:00
Alexander Smorkalov 733fb673c8 Merge pull request #22693 from asmorkalov:as/arm_debug_5x
5.x: Added ARM64 debug configuration to CI.
2022-10-26 12:01:04 +03:00
Rostislav Vasilikhin a09bb3c2f6 doc fixes 2022-10-25 22:37:38 +02:00
Alexander Smorkalov b00ac7b07a Added ARM64 debug configuration to CI. 2022-10-25 13:44:14 +03:00
Rostislav Vasilikhin 95ab8517ae trailing whitespaces 2022-10-25 02:58:55 +02:00
Rostislav Vasilikhin a1820ff3d4 unused function removed 2022-10-25 02:45:42 +02:00
Rostislav Vasilikhin 097b0245da getScaledDepth -> getProcessedDepth 2022-10-25 02:45:28 +02:00
Rostislav Vasilikhin 52d82bb44a docs for Odometry+Frame 2022-10-25 02:44:12 +02:00
Rostislav Vasilikhin 8b7e586faa Merge pull request #22598 from savuor:icp_oframe_readonly
Complement PR: #3366@contrib
Changes

    OdometryFrame losts its getters: a user can provide data at construction stage only, pyramids and other generated data is read-only now
    OdometryFrame is based on UMats: no TMat templates inside, CPU operations are done with UMat::getMat() method, chaining issues are solved ad-hoc
    No more Odometry::createOdometryFrame() method, frames are compatible with all odometry algorithms
    Normals computer is cached inside Odometry and exposed to API as well as its settings
    Volume::raycast() won't return the result in OdometryFrame anymore
    Added test for Odometry::prepareFrame*() & other test fixes
    Minor code improvements

TODOs:

    fix TODOs in code
    lower acceptable accuracy errors
2022-10-24 16:34:01 +03:00
Alexander Smorkalov 8358efaca1 Merge pull request #22655 from asenyaev:asen/cuda_trigger_5.x
Trigger on dnn (onnx) label (5.x)
2022-10-18 10:30:17 +03:00
Andrey Senyaev 3aaa9251e9 Trigger on dnn (onnx) label (5.x) 2022-10-18 09:03:27 +03:00
Alexander Smorkalov 6c2eba2877 Merge pull request #22630 from asenyaev:asen/cuda_pipeline_5.x
Workflow Ubuntu 20.04 x64 with CUDA support (5.x)
2022-10-13 12:18:35 +03:00
Andrey Senyaev 67be6a334f Workflow Ubuntu 20.04 x64 with CUDA support (5.x) 2022-10-12 14:42:40 +03:00
Alexander Smorkalov 5b97f6abec Merge pull request #22136 from sturkmen72:HOGDescriptor_update
HOGDescriptor
2022-10-07 15:17:57 +03:00
Suleyman TURKMEN 07b62376af Update objdetect.hpp 2022-10-06 23:13:44 +03:00
Alexander Smorkalov 3857173845 Merge pull request #22287 from asenyaev:asen/disabled_compiling_warnings_5.x
Disabled compiling warnings in case of symbols in cmake for 5.x
2022-09-20 16:19:08 +03:00
Andrey Senyaev 752e5fdc26 Disabled compiling warnings in case of symbols in cmake for 5.x 2022-09-20 13:36:59 +03:00
Alexander Smorkalov 520bf4697e Merge pull request #22524 from savuor:calib_refactor_msvc_fix
Fix MSVC15 compilation
2022-09-19 08:30:16 +03:00
Rostislav Vasilikhin f92a1aa07d trying to fix MSVC compilation
double -> float warning

fix warnings in the rest 3d module files

extra var removed

try to fix warnings
2022-09-19 00:27:26 +02:00
Alexander Smorkalov 44d6872837 Merge pull request #22499 from savuor:calib_ptrs_refactor
Calibration internals refactored
2022-09-16 07:41:06 +03:00
Rostislav Vasilikhin 9ba4bb7355 initIntrisicParams2D() refactored
variables refactoring

levmarq fix

initIntrinsicParams2D() refactoring

undo LM fix

cameraCalcJErr: made a lambda; warnings fixed; vars rearranged & renamed, jacobian buffers, perViewErrors and allErrors fix, etc.

stereoCalibrate: internal vars in callback

stereoCalibrate: capture only useful variables

stereoCalibrate: perViewError fix + minors

rvecs and tvecs are not pointers anymore

no extra lambda

newObjPoints: no pointers

stdDevs: no pointers

_Jo removed: not used

Range::all() -> rowRange, colRange

param and mask are std::vectors now

indices shortened

tabs

less func-scoped vars; TODOs

less formatting & renaming changes

trailing whitespaces

less diff

less changes

less changes

Range::all() back

perViewErr ptr fix

NINTRINSIC captured

try to fix warning

trying to fix a warning

fix warnings, another attempt
2022-09-15 22:55:40 +02:00
Alexander Smorkalov f199cf91f3 Merge pull request #22491 from savuor:calib_perview_fix
Calibration per view error fixed
2022-09-10 09:27:04 +03:00
Rostislav Vasilikhin b1020639f1 perViewErr fix 2022-09-09 17:40:26 +02:00
Alexander Smorkalov 8bfb419b0f Merge pull request #22460 from savuor:levmarq_stereocalib_fix
Stereo calibration fixed when intrinsic guess is given
2022-09-06 11:33:34 +03:00
Rostislav Vasilikhin a8b4ffac50 volatile variable made mask-fixed as it should be 2022-09-05 18:42:17 +02:00
Rostislav Vasilikhin 8a43956a1b less pointers
rtsort + formatting

less pointers + more compact code

less reallocations, cleaner code

less pointers, less GEMMs

trailing whitespace
2022-09-05 18:42:15 +02:00
Rostislav Vasilikhin bfc4bdd9d0 regression test added
test data moved to opencv_extra, loads from yaml.gz file now
2022-09-05 18:42:12 +02:00
Rostislav Vasilikhin 96e2d32618 fixing broken stereo calibration 2022-09-05 18:42:12 +02:00
Rostislav Vasilikhin 487619e5fe no address arithmetics
less address arithmetics

happy valgrind: copy recomputed intrinsics only
2022-09-05 18:42:09 +02:00
Alexander Smorkalov 335d7bd16e Merge pull request #22241 from savuor:normals_scale_test
More tests for normals
2022-08-24 10:25:55 +03:00
Alexander Smorkalov 3a02561c40 Merge pull request #22417 from savuor:levmarq_log_level_fix 2022-08-24 07:17:22 +00:00
Rostislav Vasilikhin eb571b500b fixes warnings 2022-08-23 10:22:28 +02:00
Alexander Smorkalov 505bbab75b Merge pull request #22413 from savuor:levmarq_log_level
LevMarq: log level lowered
2022-08-23 09:22:39 +03:00
Rostislav Vasilikhin 3cf3e3f6f7 LevMarq: log level lowered 2022-08-22 23:53:49 +02:00
Rostislav Vasilikhin 232a83ecb8 check mask values 2022-08-22 16:07:07 +02:00
Alexander Alekhin c25f776151 Merge branch 4.x 2022-08-21 15:27:31 +00:00
Rostislav Vasilikhin 48c10620cb depthTo3d: fixed bug, added regression test
RgbdNormals: setMethod() removed as useless

RgbdNormals: tests + cross product, to be fixed

+ cross product

LINEMOD: diffThreshold param added + tests fixed

minor

diffThreshold fix

points3dToDepth16U fix

normals computer diffThreshold fix

random plane generation fixed + diffThreshold fix

Rendered normals test rewritten to GTest Params

random plane generation: scale

RGBD_Normals tests: thresholds tuned

Rendered normals tests: 64F support added

Random planes normal tests rewritten to GTest Params

LINEMOD and CrossProduct fix

SRI threshold raised

NormalsRandomPlanes: thresholds raised

assert on unknown alg; minor

fix

frame size reduced

TIFF replaced by YAML.GZ

depthTo3d test changed

cv::transform is used

fix warning

nanMask()

flipAxes()

absDotPixel() + forgotten code

helper functions removed

RGBDNormals: checkNormals() and compare LINEMOD's pts3d to depth input

Rendered: another criteria; thresholds; LINEMOD's pts3d to depth input comparison

thresholds raised a bit

SRI slightly optimized

assert change

normal tests refactored, parametrized, split

trailing namespace, thresholds raised

SRI caching optimized a lot

normal tests rewritten to fixture, no loop

minor

runCase() joined with testIt()

thresholds were put into GTest params

ternary operator

RgbdNormalsTest merged into NormalsRandomPlanes; RgbdPlanes moved closer to tests

normal test minor refactoring

plane finder tests refactored to GTest Params

skip tests

thresholds raised

plane test minor

plane tests: timers dropped, nPlanes put into GTest Params; refactoring

generated normals tests: minor refactoring

flipAxes() templated

rendered normals tests refactored: thresholds to GTest Params

CV_Error -> ASSERT_FALSE
2022-08-19 20:16:08 +02:00
Alexander Smorkalov 99d216c8d4 Merge pull request #22150 from savuor:warpFrameTests
warpFrame() fixed & covered by tests
2022-08-03 12:27:19 +03:00
Alexander Smorkalov a2593a392f Merge pull request #22319 from asenyaev:asen/docs_5.x 2022-08-02 06:25:40 +00:00
Andrey Senyaev 4c1f9fb042 Docs workflow in GHA for 5.x 2022-07-28 17:47:03 +03:00
Rostislav Vasilikhin 869123d6f9 "FAIL() <<" replaced by "ASSERT_*() <<" 2022-07-24 18:40:55 +02:00
Rostislav Vasilikhin 02d864070a minor 2022-07-24 18:29:34 +02:00
Rostislav Vasilikhin 0b34d90dfa minor 2022-07-24 18:28:26 +02:00
Rostislav Vasilikhin 7bdacb8098 threshold comparison made more obvious 2022-07-24 18:27:35 +02:00
Rostislav Vasilikhin d4e28f27b7 warpFrame test parametrized 2022-07-24 18:22:48 +02:00
Alexander Alekhin 638a0788fe Merge pull request #22281 from asenyaev:asen/android_5.x 2022-07-22 09:58:56 +00:00
Andrey Senyaev 7ea095cc38 Android GHA workflow for 5.x branch 2022-07-21 17:59:31 +03:00
Alexander Alekhin d2603cbf94 Merge pull request #22268 from asenyaev:asen/rename_lin_arm_pipelines_5.x 2022-07-19 14:25:07 +00:00
Andrey Senyaev c2ab4c052d Linux ARM64 rename ubuntu version on 5.x 2022-07-19 13:14:09 +03:00
Yuantao Feng 8d38922571 Merge pull request #22187 from fengyuentau:ci_job_rename_5.x
Rename jobs for better understanding for branch 5.x

* rename jobs

* remove dots from job names

* correct ubuntu version for linux arm64
2022-07-05 01:46:49 +03:00
Alexander Alekhin ee8b414a1e Merge pull request #22112 from asenyaev:asen/ios_workflow_5.x 2022-07-01 10:06:42 +00:00
Rostislav Vasilikhin 75a8e3e956 minor 2022-06-30 21:32:41 +02:00
Rostislav Vasilikhin 3c3eba868a floatL2 threshold raised a bit 2022-06-30 17:22:58 +02:00
Rostislav Vasilikhin 2ae7438c6b odometry tests fixed 2022-06-30 17:10:26 +02:00
Rostislav Vasilikhin 2b767f9bc8 whitespace fix 2022-06-30 16:51:01 +02:00
Andrey Senyaev 9c78dc2490 Workflow for labeled iOS PRs in 5.x branch 2022-06-30 11:27:53 +03:00
Rostislav Vasilikhin 94e5ae6043 depth generator moved from main repo to extra 2022-06-30 00:00:47 +02:00
Rostislav Vasilikhin fd14b959cf warpFrame() rewritten, interface changed 2022-06-29 23:55:07 +02:00
Rostislav Vasilikhin 492d9dba1e warpFrame() tests big update 2022-06-29 23:51:07 +02:00
Rostislav Vasilikhin 44c0b58258 depth generator fixed at absent values 2022-06-29 23:45:33 +02:00
Rostislav Vasilikhin 6b2d1033bd warpFrame() test: more test cases 2022-06-27 01:24:39 +02:00
Rostislav Vasilikhin 162bd5be4c distCoeffs removed from warpFrame() signature 2022-06-27 01:23:07 +02:00
Rostislav Vasilikhin d56e8f053f python depth generator: no numba, imageio -> PIL, requirements.txt 2022-06-26 22:33:31 +02:00
Rostislav Vasilikhin bf8f7b4e57 more scale stuff removed 2022-06-23 23:53:57 +02:00
Rostislav Vasilikhin 4e1ce3e0eb odometry python sample got rid of scale parameter 2022-06-23 23:51:16 +02:00
Rostislav Vasilikhin 770c0d1416 1. removed (almost all) additional scale parameter mentions
2. refactored funcptrs to switch/cases & more
2022-06-23 23:47:08 +02:00
Rostislav Vasilikhin c12d4c82df python Odometry scale test removed 2022-06-23 23:25:30 +02:00
Rostislav Vasilikhin bee410c748 warpFrame() test draft + script generating test data 2022-06-23 23:18:59 +02:00
Alexander Alekhin 83cfeb9f14 Merge pull request #22105 from fengyuentau:macOS_workflows_for_5.x 2022-06-15 07:57:00 +00:00
fengyuentau 66fee4a8d2 add workflows for macOS for 5.x 2022-06-15 11:06:52 +08:00
Saratovtsev df490c6399 moving code from ICP+Scale 2022-06-14 13:40:14 +02:00
Wanli Zhong b06544bd54 Merge pull request #21918 from No-Plane-Cannot-Be-Detected:5.x-region_growing_3d
Add normal estimation and region growing algorithm for point cloud

* Add normal estimation and region growing algorithm for point cloud

* 1.Modified documentation for normal estimation;2.Converted curvature in region growing to absolute values;3.Changed the data type of threshold from float to double;4.Fixed some bugs;

* Finished documentation

* Add tests for normal estimation. Test the normal and curvature of each point in the plane and sphere of the point cloud.

* Fix some warnings caused by to small numbers in test

* Change the test to calculate the average difference instead of comparing each normal and curvature

* Fixed the bugs found by testing

* Redesigned the interface and fixed problems:
1. Make the interface compatible with radius search.
2. Make region growing optionally sortable on results.
3. Modified the region growing interface.
4. Format reference.
5. Removed sphere test.

* Fix warnings

* Remove flann dependency

* Move the flann dependency to the corresponding test
2022-05-23 14:47:57 +00:00
Alexander Alekhin 6e9ab70359 Merge pull request #22014 from asenyaev:asen/move_workflows_5.x 2022-05-22 13:43:03 +00:00
Andrey Senyaev 2f65a1b501 Move workflows to a dedicated repository for 5.x branch 2022-05-20 19:46:37 +03:00
Alexander Alekhin 8b3d2a57ad Merge pull request #21968 from No-Plane-Cannot-Be-Detected:5.x-randomSampling_bug 2022-05-17 21:17:29 +00:00
Klepikov Dmitrii a99b4071a2 Merge pull request #20471 from ibvfteh:pointcloudio
GSoC module to save and load point cloud

* Add functionality to read point cloud data from files

* address issues found on review, add tests for mesh, refactor

* enable fail-safe execution and empty arrays as output

* Some improvements for point cloud io module

Co-authored-by: Julie Bareeva <julia.bareeva@xperience.ai>
2022-05-13 18:10:39 +00:00
Wanli Zhong 627e6a0447 Modify randomSampling in point cloud 2022-05-11 15:30:58 +08:00
OpenCV Pushbot 6bffbe4938 Merge pull request #21908 from cpoerschke:issue-21459-alternative 2022-04-27 17:27:57 +00:00
OpenCV Pushbot d5c45116cd Merge pull request #21877 from asenyaev:asen/workflow_only_linux_5x
Added workflow for Github Actions to build and test OpenCV on Linux for 5.x
2022-04-26 05:10:59 +00:00
Andrey Senyaev c4c47d91e4 Added workflow for Github Actions to build and test OpenCV
Merged a build and tests jobs into one, split tests by steps, renamed job names
2022-04-26 00:11:59 +03:00
Christine Poerschke 08c268bfc5 remove Imgproc_cvWarpAffine.regression test 2022-04-24 21:32:44 +01:00
OpenCV Developers 0fbd58bef9 Merge branch 4.x 2022-04-23 22:07:14 +00:00
Artem Saratovtsev 3d12581798 Merge pull request #21741 from DumDereDum:odometry_prepareFrame_fix
Odometry prepareFrame fix

* fix issue; add tests

* img fix

* mask fix

* minor fix
2022-04-04 21:14:31 +00:00
Rostislav Vasilikhin aa5055261f Merge pull request #21652 from savuor:fix/hash_tsdf_normals_simd32_compilation
Fix HashTSDF compilation

* big SIMD compilation fix (try 1)

* fixing compilation, try 2
2022-02-24 10:29:28 +00:00
Alexander Alekhin 899b4d1452 Merge branch 4.x 2022-02-22 19:55:26 +00:00
Ruan e5d648d2d0 Merge pull request #21615 from No-Plane-Cannot-Be-Detected:5.x-ptcloud_comments
Added 3D point cloud code comments and copyright

1. update the code comments.
2. add author information.
2022-02-18 20:43:08 +03:00
Artem Saratovtsev 9c87d8bf9c Merge pull request #21189 from DumDereDum:new_volume
New Volume pipeline
2022-02-18 14:50:26 +00:00
Artem Saratovtsev 4ba2b05df8 Merge pull request #21439 from DumDereDum:python_odometry
Odometry python support
2022-02-07 16:16:17 +00:00
Pavel Rojtberg 0a0714bd1b Merge pull request #21065 from paroj:dawsome
docs: use doxygen-awesome theme

* docs: use doxygen-awesome theme

* docs: drop dark color-scheme from doxygen theme

* doxygen: fix mobile view size
2022-01-21 20:49:22 +00:00
Alexander Alekhin 2c2d7774b9 copyright: 2022 2022-01-04 12:49:47 +00:00
Alexander Alekhin a0d5277e0d Merge branch 4.x 2021-12-30 21:43:45 +00:00
Ruan 80c5d18f9c Merge pull request #21276 from No-Plane-Cannot-Be-Detected:5.x-ptcloud
Add support for 3D point cloud segmentation, using the USAC framework.

* Modify the RANSAC framework in usac such that RANSAC can be used in 3D point cloud segmentation.

* 1. Add support for 3D point cloud segmentation, using the USAC framework.
2. Add solvers, error estimators for plane model and sphere model.

* Added code samples to the comments of class SACSegmentation.

* 1. Update the segment interface parameters of SACSegmentation.
2. Fix some errors in variable naming.

* Add tests for plane detection.

* 1. Add tests for sphere segmentation.
2. Fix some bugs found by tests.
3. Rename "segmentation" to "sac segmentation".
4. Rename "detect" to "segment".
TODO: Too much duplicate code, the structure of the test needs to be rebuilt.

* 1. Use SIMD acceleration for plane model and sphere model error estimation.
2. Optimize the RansacQualityImpl#getScore function to avoid multiple calls to the error#getError function.
3. Fix a warning in test_sac_segmentation.cpp.

* 1. Fix the warning of ModelConstraintFunction ambiguity.
2. Fix warning: no previous declaration for'void cv::usac::modelParamsToUsacConfig(cv::Ptr<cv::usac::SimpleUsacConfig>&, const cv::Ptr<const cv::usac::Model>& )

* Fix a warning in test_sac_segmentation.cpp about direct comparison of different types of data.

* Add code comments related to the interpretation of model coefficients.

* Update the use of custom model constraint functions.

* Simplified test code structure.

* Update the method of checking plane models.

* Delete test for cylinder.

* Add some comments about UniversalRANSAC.

* 1. The RANSAC paper in the code comments is referenced using the bibtex format.
2. The sample code in the code comments is replaced using @snippet.
3. Change the public API class SACSegmentation to interface.
4. Clean up the old useless code.

* fix warning(no previous declaration) in 3d_sac_segmentation.cpp.

* Fix compilation errors caused by 3d_sac_segmentation.cpp.

* Move the function sacModelMinimumSampleSize() from ptcloud.hpp to sac_segmentation.cpp.

* 1. Change the interface for setting the number of threads to the interface for setting whether to be parallel.
2. Move interface implementation code in ptcloud_utils.hpp to ptcloud_utils.cpp.

* SACSegmentation no longer inherits Algorithm.

* Add the constructor and destructor of SACSegmentation.

* 1. For the declaration of the common API, the prefix and suffix of the parameter names no longer contain underscores.
2. Rename the function _getMatFromInputArray -> getPointsMatFromInputArray.
3. Change part of CV_CheckDepth to CV_CheckDepthEQ.
4. Remove the doxygen flag from the source code.
5. Update the loop termination condition of SIMD in the point cloud section of 3D module.

* fix warning: passing 'bool' chooses 'int' over 'size_t {aka unsigned int}' .

* fix warning: passing 'bool' chooses 'int' over 'size_t {aka unsigned int}' .
2021-12-30 15:54:06 +00:00
Alexander Alekhin 7d05f92855 Merge pull request #21334 from catree:solvePnP_doc_page_5.x 2021-12-28 17:59:05 +00:00
Rostislav Vasilikhin 9d6f388809 Merge pull request #21018 from savuor:levmarqfromscratch
New LevMarq implementation

* Hash TSDF fix: apply volume pose when fetching pose

* DualQuat minor fix

* Pose Graph: getEdgePose(), getEdgeInfo()

* debugging code for pose graph

* add edge to submap

* pose averaging: DualQuats instead of matrix averaging

* overlapping ratio: rise it up; minor comment

* remove `Submap::addEdgeToSubmap`

* test_pose_graph: minor

* SparseBlockMatrix: support 1xN as well as Nx1 for residual vector

* small changes to old LMSolver

* new LevMarq impl

* Pose Graph rewritten to use new impl

* solvePnP(), findHomography() and findExtrinsicCameraParams2() use new impl

* estimateAffine...2D() use new impl

* calibration and stereo calibration use new impl

* BundleAdjusterBase::estimate() uses new impl

* new LevMarq interface

* PoseGraph: changing opt interface

* findExtrinsicCameraParams2(): opt interface updated

* HomographyRefine: opt interface updated

* solvePnPRefine opt interface fixed

* Affine2DRefine opt interface fixed

* BundleAdjuster::estimate() opt interface fixed

* calibration: opt interface fixed + code refactored a little

* minor warning fixes

* geodesic acceleration, Impl -> Backend rename

* calcFunc() always uses probe vars

* solveDecomposed, fixing negation

* fixing geodesic acceleration + minors

* PoseGraph exposes its optimizer now + its tests updated to check better convegence

* Rosenbrock test added for LevMarq

* LevMarq params upgraded

* Rosenbrock can do better

* fixing stereo calibration

* old implementation removed (as well as debug code)

* more debugging code removed

* fix warnings

* fixing warnings

* fixing Eigen dependency

* trying to fix Eigen deps

* debugging code for submat is now temporary

* trying to fix Eigen dependency

* relax sanity check for solvePnP

* relaxing sanity check even more

* trying to fix Eigen dependency

* warning fix

* Quat<T>: fixing warnings

* more warning fixes

* fixed warning

* fixing *KinFu OCL tests

* algo params -> struct Settings

* Backend moved to details

* BaseLevMarq -> LevMarqBase

* detail/pose_graph.hpp -> detail/optimizer.hpp

* fixing include stuff for details/optimizer.hpp

* doc fix

* LevMarqBase rework: Settings, pImpl, Backend

* Impl::settings and ::backend fix

* HashTSDFGPU fix

* fixing compilation

* warning fix for OdometryFrameImplTMat

* docs fix + compile warnings

* remake: new class LevMarq with pImpl and enums, LevMarqBase => detail, no Backend class, Settings() => .cpp, Settings==() removed, Settings.set...() inlines

* fixing warnings & whitespace
2021-12-27 21:51:32 +00:00
catree 9c61d80bc4 Summarize PnP pose computation on a single separate page. 2021-12-23 19:43:27 +01:00
Alexander Alekhin 4d9365990a Merge pull request #21272 from DumDereDum:new_odometry_fix1 2021-12-16 12:49:30 +00:00
Saratovtsev 5714868726 odometry warnings fix 2021-12-16 13:52:00 +03:00
Alexander Alekhin 196e6aaa50 Merge pull request #21201 from Baiyun-u-smartAI:patch-1 2021-12-10 11:40:16 +00:00
Baiyun-u-smartAI b167115b5b fixed an error ------Context::p is protected whinin this context 2021-12-06 14:09:50 +08:00
Alexander Alekhin 5d04e5b063 Merge pull request #21089 from sturkmen72:clean-up-c-api 2021-12-03 15:11:59 +00:00
Artem Saratovtsev 6ab4659840 Merge pull request #20755 from DumDereDum:new_odometry
New odometry Pipeline

* first intergation

* tests run, but not pass

* add previous version of sigma calc

* add minor comment

* strange fixes

* fix fast ICP

* test changes; fast icp still not work correctly

* finaly, it works

* algtype fix

* change affine comparison

* boolean return

* fix bug with angle and cos

* test pass correctly

* fix for kinfu pipeline

* add compute points normals

* update for new odometry

* change odometry_evaluation

* odometry_evaluation works

* change debug logs

* minor changes

* change depth setting in odometryFrame

* fastICP works with 4num points

* all odometries work with 4mun points

* odometry full works on 4num points and normals

* replace ICP with DEPTH; comments replacements

* create prepareFrame; add docs for Odometry

* change getPyramids()

* delete extra code

* add intrinsics; but dont works

* bugfix with nan checking

* add gpu impl

* change createOdometryFrame func

* remove old fastICP code

* comments fix

* add comments

* minor fixes

* other minor fixes

* add channels assert

* add impl for odometry settings

* add pimpl to odometry

* linux warning fix

* linux warning fix 1

* linux warning fix 2

* linux error fix

* linux warning fix 3

* linux warning fix 4

* linux error fix 2

* fix test warnings

* python build fix

* doxygen fix

* docs fix

* change normal tests for 4channel point

* all Normal tests pass

* plane works

* add warp frame body

* minor fix

* warning fixes

* try to fix

* try to fix 1

* review fix

* lvls fix

* createOdometryFrame fix

* add comment

* const reference

* OPENCV_3D_ prefix

* const methods

* –OdometryFramePyramidType ifx

* add assert

* precomp moved upper

* delete types_c

* add assert for get and set functions

* minor fixes

* remove core.hpp from header

* ocl_run add

* warning fix

* delete extra comment

* minor fix

* setDepth fix

* delete underscore

* odometry settings fix

* show debug image fix

* build error fix

* other minor fix

* add const to signatures

* fix

* conflict fix

* getter fix
2021-12-02 20:53:44 +03:00
Ruan 0cf0a5e9d4 Merge pull request #21095 from No-Plane-Cannot-Be-Detected:next_SIMD
Accelerated 3D point cloud Farthest Point Sampling calculation using SIMD.

* Add several 3D point cloud sampling functions: Random, VoxelGrid, FarthestPoint.

* Made some code detail changes and exposed the random number generator parameters at the interface.

* Add simple tests for sampling.

* Modify interface output parameters.

* Modify interface return value.

* The sampling test is modified for the new changes of function interface.

* Improved test of VoxelGridFilterSampling

* Improved test of VoxelGridFilterSampling and FPS.

* Add test for the dist_lower_limit arguments of FPS function.

* Optimization function _getMatFromInputArray.

* Optimize the code style and some details according to the suggestions.

* Clear prefix cv: in the source code.

* Change the initialization of Mat in the sampling test.

* 1. Unified code style
2. Optimize randomSampling method

* 1. Optimize code comments.
2. Remove unused local variables.

* Rebuild the structure of the test, make the test case more reliable, and change the code style.

* Update test_sampling.cpp

Fix a warning.

* Use SIMD to optimize the farthest point sampling.

* Optimize the farthest point sampling SIMD code.

* 1. remove `\n` from the ptcloud.hpp comment.
2. updated the default value of the argument arrangement_of_points in the _getMatFromInputArray function in ptcloud_utils.hpp from 0 to 1, since the latter is more commonly used (such arrangement is easier for SIMD acceleration).
3. removed two functions in ptcloud_utils.hpp that were not used.

* Remove the <br> in the comment.

* Fix whitespace issues.
2021-11-30 12:33:44 +00:00
Suleyman TURKMEN 4f0fe1de96 clean up c-api 2021-11-28 12:09:34 +03:00
Ruan 1470f90c2a Merge pull request #20784 from No-Plane-Cannot-Be-Detected:next
Add 3D point cloud sampling functions to branch next

* Add several 3D point cloud sampling functions: Random, VoxelGrid, FarthestPoint.

* Made some code detail changes and exposed the random number generator parameters at the interface.

* Add simple tests for sampling.

* Modify interface output parameters.

* Modify interface return value.

* The sampling test is modified for the new changes of function interface.

* Improved test of VoxelGridFilterSampling

* Improved test of VoxelGridFilterSampling and FPS.

* Add test for the dist_lower_limit arguments of FPS function.

* Optimization function _getMatFromInputArray.

* Optimize the code style and some details according to the suggestions.

* Clear prefix cv: in the source code.

* Change the initialization of Mat in the sampling test.

* 1. Unified code style
2. Optimize randomSampling method

* 1. Optimize code comments.
2. Remove unused local variables.

* Rebuild the structure of the test, make the test case more reliable, and change the code style.

* Update test_sampling.cpp

Fix a warning.
2021-10-29 01:41:21 +03:00
Alexander Alekhin 6544c7b787 Merge pull request #20887 from alalek:5.x_cleanup_compatibility 2021-10-22 21:38:09 +00:00
Alexander Alekhin fce4a19d0d 5.x: cleanup compatibility code (2021-10) 2021-10-20 17:40:04 +00:00
Alexander Alekhin 7ba26ada12 Merge branch 4.x 2021-10-15 21:53:39 +00:00
Alexander Alekhin 91a7a4523a Merge pull request #20861 from alalek:fix_abi_compatibility_5.x 2021-10-12 15:33:30 +00:00
Alexander Alekhin 8c7dc1f6b7 core: OPENCV_ABI_COMPATIBILITY=500 2021-10-12 12:07:12 +03:00
Alexander Alekhin 14dedda023 Merge pull request #20608 from zihaomu:loop_closure_detection_submap 2021-08-30 18:11:30 +00:00
Zihao Mu 19116471fe submap for loop closure detection in 3d 2021-08-26 11:26:12 +08:00
Rostislav Vasilikhin bae9cef0b5 Merge pull request #20013 from savuor:rgbd_to_3d
Moving RGBD parts to 3d

* files moved from rgbd module in contrib repo

* header paths fixed

* perf file added

* lapack compilation fixed

* Rodrigues fixed in tests

* rgbd namespace removed

* headers fixed

* initial: rgbd files moved to 3d module

* rgbd updated from latest contrib master; less file duplication

* "std::" for sin(), cos(), etc.

* KinFu family -> back to contrib

* paths & namespaces

* removed duplicates, file version updated

* namespace kinfu removed from 3d module

* forgot to move test_colored_kinfu.cpp to contrib

* tests fixed: Params removed

* kinfu namespace removed

* it works without objc bindings

* include headers fixed

* tests: data paths fixed

* headers moved to/from public API

* Intr -> Matx33f in public API

* from kinfu_frame.hpp to utils.hpp

* submap: Intr -> Matx33f, HashTSDFVolume -> Volume; no extra headers

* no RgbdFrame class, no Mat fields & arg -> InputArray & pImpl

* get/setPyramidAt() instead of lots of methods

* Mat -> InputArray, TMat

* prepareFrameCache: refactored

* FastICPOdometry: +truncate threshold, +depthFactor; Mat/UMat choose

* Mat/UMat choose

* minor stuff related to headers

* (un)signed int warnings; compilation minor issues

* minors: submap: pyramids -> OdometryFrame; tests fix; FastICP minor; CV_EXPORTS_W for kinfu_frame.hpp

* FastICPOdometry: caching, rgbCameraMatrix

* OdometryFrame: pyramid%s% -> pyramids[]

* drop: rgbCameraMatrix from FastICP, RGB cache mode, makeColoredFrameFrom depth and all color-functions it calls

* makeFrameFromDepth, buildPyramidPointsNormals -> from public to internal utils.hpp

* minors

* FastICPOdometry: caching updated, init fields

* OdometryFrameImpl<UMat> fixed

* matrix building fixed; minors

* returning linemode back to contrib

* params.pose is Mat now

* precomp headers reorganized

* minor fixes, header paths, extra header removed

* minors: intrinsics -> utils.hpp; whitespaces; empty namespace; warning fixed

* moving declarations from/to headers

* internal headers reorganized (once again)

* fix include

* extra var fix

* fix include, fix (un)singed warning

* calibration.cpp: reverting back

* headers fix

* workaround to fix bindings

* temporary removed wrappers

* VolumeType -> VolumeParams

* (temporarily) removing wrappers for Volume and VolumeParams

* pyopencv_linemod -> contrib

* try to fix test_rgbd.py

* headers fixed

* fixing wrappers for rgbd

* fixing docs

* fixing rgbdPlane

* RgbdNormals wrapped

* wrap Volume and VolumeParams, VolumeType from enum to int

* DepthCleaner wrapped

* header folder "rgbd" -> "3d"

* fixing header path

* VolumeParams referenced by Ptr to support Python wrappers

* render...() fixed

* Ptr<VolumeParams> fixed

* makeVolume(... resolution -> [X, Y, Z])

* fixing static declaration

* try to fix ios objc bindings

* OdometryFrame::release...() removed

* fix for Odometry algos not supporting UMats: prepareFrameCache<>()

* preparePyramidMask(): fix to compile with TMat = UMat

* fixing debug guards

* removing references back; adding makeOdometryFrame() instead

* fixing OpenCL ICP hanging (some threads exit before reaching the barrier -> the rest threads hang)

* try to fix objc wrapper warnings; rerun builders

* VolumeType -> VolumeKind

* try to fix OCL bug

* prints removed

* indentation fixed

* headers fixed

* license fix

* WillowGarage licence notion removed, since it's in OpenCV's COPYRIGHT already

* KinFu license notion shortened

* debugging code removed

* include guards fixed

* KinFu license left in contrib module

* isValidDepth() moved to private header

* indentation fix

* indentation fix in src files

* RgbdNormals rewritten to pImpl

* minor

* DepthCleaner removed due to low code quality, no depthScale provided, no depth images found to be successfully filtered; can be replaced by bilateral filtering

* minors, indentation

* no "private" in public headers

* depthTo3d test moved from separate file

* Normals: setDepth() is useless, removing it

* RgbdPlane => findPlanes()

* rescaleDepth(): minor

* warpFrame: minor

* minor TODO

* all Odometries (except base abstract class) rewritten to pImpl

* FastICPOdometry now supports maxRotation and maxTranslation

* minor

* Odometry's children: now checks are done in setters

* get rid of protected members in Odometry class

* get/set cameraMatrix, transformType, maxRot/Trans, iters, minGradients -> OdometryImpl

* cameraMatrix: from double to float

* matrix exponentiation: Eigen -> dual quaternions

* Odometry evaluation fixed to reuse existing code

* "small" macro fixed by undef

* pixNorm is calculated on CPU only now (and then uploads on GPU)

* test registration: no cvtest classes

* test RgbdNormals and findPlanes(): no cvtest classes

* test_rgbd.py: minor fix

* tests for Odometry: no cvtest classes; UMat tests; logging fixed

* more CV_OVERRIDE to overriden functions

* fixing nondependent names to dependent

* more to prev commit

* forgotten fixes: overriden functions, (non)dependent names

* FastICPOdometry: fix UMat support when OpenCL is off

* try to fix compilation: missing namespaces

* Odometry: static const-mimicking functions to internal constants

* forgotten change to prev commit

* more forgotten fixes

* do not expose "submap.hpp" by default

* in-class enums: give names, CamelCase, int=>enums; minors

* namespaces, underscores, String

* std::map is used by pose graph, adding it

* compute()'s signature fixed, computeImpl()'s too

* RgbdNormals: Mat -> InputArray

* depth.hpp: Mat -> InputArray

* cameraMatrix: Matx33f -> InputArray + default value + checks

* "details" headers are not visible by default

* TSDF tests: rearranging checks

* cameraMatrix: no (realistic) default value

* renderPointsNormals*(): no wrappers for them

* debug: assert on empty frame in TSDF tests

* debugging code for TSDF GPU

* debug from integrate to raycast

* no (non-zero) default camera matrix anymore

* drop debugging code (does not help)

* try to fix TSDF GPU: constant -> global const ptr
2021-08-22 13:18:45 +00:00
Alexander Alekhin 6b199bd1bb Merge pull request #20317 from sturkmen72:upd_fast_agast 2021-07-09 08:59:15 +00:00
Suleyman TURKMEN ee893a08ef Update features2d.hpp, agast.cpp, fast.cpp 2021-07-07 16:17:11 +03:00
Alexander Alekhin 7a5f554bc4 Merge branch 4.x 2021-06-13 10:27:44 +00:00
Vadim Pisarevsky 4dc811946d Merge pull request #19684 from zihaomu:octree_in_3d 2021-06-08 13:23:11 +00:00
zihaomu 5c0ac37163 add Octree to 3d module of next branch. 2021-06-08 11:19:01 +08:00
Vadim Pisarevsky 958d3e8c60 Merge pull request #20225 from vpisarev:remove_c_3d 2021-06-07 16:55:15 +00:00
Vadim Pisarevsky eff6d32337 * refactored the remaining old-style functions in 3d and calib modules to use the new C++ API.
* extended C++ version of Levenberg-Marquardt (LM) solver to accommodate all features of the C counterpart.
* removed C version of LM solver
* made a few other little changes to make the code compile and run smoothly
2021-06-07 20:55:25 +08:00
Alexander Alekhin b91e0dca90 Merge branch 4.x 2021-06-04 15:18:51 +00:00
Alexander Alekhin b754406352 Merge pull request #20170 from danielenricocahall:fix-mser-grayscale-min-diversity 2021-05-29 14:44:59 +00:00
danielenricocahall fa73b91e39 add logic for handling min diversity in mser 2021-05-27 19:16:24 -04:00
Alexander Alekhin c105402dfc Merge pull request #19856 from danielenricocahall:remove-freatures2d-virtual-inheritance 2021-04-12 12:31:56 +00:00
Alexander Alekhin fc628014bb Merge branch 4.x 2021-04-10 18:03:01 +00:00
danielenricocahall 4e691587ef remove virtual inheritance from features2d 2021-04-08 20:09:13 -04:00
Alexander Alekhin 263de1f86e Merge pull request #19595 from sturkmen72:patch-3 2021-03-05 15:01:51 +00:00
Suleyman TURKMEN 703dea4817 Clean up C API 2021-03-04 03:23:17 +03:00
Suleyman TURKMEN 178240ccf1 Clean up C API backport ready changes 2021-03-03 14:37:45 +03:00
Alexander Alekhin c073e09956 copyright: 2021 2021-01-01 13:42:20 +00:00
Vadim Pisarevsky 71be47e04a Merge pull request #19156 from vpisarev:named_args1 2020-12-18 17:35:19 +00:00
Vadim Pisarevsky ed8696566b * updated python wrapper generator to properly handle C++20-style named parameters
* added sample filter2D[p]() function and a little python test for it to demonstrate the concept
2020-12-18 20:00:42 +08:00
Alexander Alekhin 9668831c13 Merge pull request #19036 from asmorkalov:as/fix_js_next_issue_19017 2020-12-08 13:09:20 +03:00
Alexander Alekhin 89e059d8ac apple/build_xcframework.py: python syntax
- make happy old Python linters

(cherry picked from commit 77b986c7a1)
2020-12-08 10:55:59 +03:00
Alexander Smorkalov 6d06368760 Updated modules white list for JS after calib3d reorg. 2020-12-07 16:21:53 +03:00
Vadim Pisarevsky 65b0383059 Merge pull request #18940 from vpisarev:3d_light 2020-12-01 21:36:48 +00:00
Vadim Pisarevsky d6c699c014 calib3d module in opencv is split into 3 modules: 3d, calib and stereo.
stereo module in opencv_contrib is renamed to xstereo
2020-12-01 23:42:15 +03:00
Alexander Alekhin 9d2eabaaa2 Merge remote-tracking branch 'upstream/master' into merge-4.x 2020-11-27 18:15:28 +00:00
Vadim Pisarevsky 3268cc46ba Merge pull request #18760 from vpisarev:ttf2_1 2020-11-11 20:12:24 +00:00
Vadim Pisarevsky d42665d9e2 added truetype font support into OpenCV, based on STB_truetype 2020-11-11 22:17:05 +03:00
Alexander Alekhin 1088d95c50 Merge pull request #18707 from alalek:imgproc_drop_lsd 2020-11-07 17:29:21 +00:00
Vadim Pisarevsky 2ee9d21dae Merge pull request #18571 from vpisarev:add_lapack
Added clapack

* bring a small subset of Lapack, automatically converted to C, into OpenCV

* added missing lsame_ prototype

* * small fix in make_clapack script
* trying to fix remaining CI problems

* fixed character arrays' initializers

* get rid of F2C_STR_MAX

* * added back single-precision versions for QR, LU and Cholesky decompositions. It adds very little extra overhead.
* added stub version of sdesdd.
* uncommented calls to all the single-precision Lapack functions from opencv/core/src/hal_internal.cpp.

* fixed warning from Visual Studio + cleaned f2c runtime a bit

* * regenerated Lapack w/o forward declarations of intrinsic functions (such as sqrt(), r_cnjg() etc.)
* at once, trailing whitespaces are removed from the generated sources, just in case
* since there is no declarations of intrinsic functions anymore, we could turn some of them into inline functions

* trying to eliminate the crash on ARM

* fixed API and semantics of s_copy

* * CLapack has been tested successfully. It's now time to restore the standard LAPACK detection procedure
* removed some more trailing whitespaces

* * retained only the essential stuff in CLapack
* added checks to lapack calls to gracefully return "not implemented" instead of returning invalid results with "ok" status

* disabled warning when building lapack

* cmake: update LAPACK detection

Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>
2020-11-05 21:46:51 +00:00
Alexander Alekhin 690725980c imgproc: drop LineSegmentDetector stubs 2020-10-30 00:57:09 +00:00
Alexander Alekhin d0310c2a6a Merge pull request #18555 from alalek:next_versions 2020-10-13 10:12:26 +00:00
Alexander Alekhin 9794ff1398 next: update versions handling 2020-10-11 08:11:32 +00:00
Alexander Alekhin 53e6a25b6f next: OpenCV 5.0-pre 2020-10-07 21:53:09 +00:00
4013 changed files with 482986 additions and 1367053 deletions
+2
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@@ -67,3 +67,5 @@ This is a template helping you to create an issue which can be processed as quic
if you report ONNX parsing or handling issue. Architecture details diagram
from netron tool can be very useful too. See https://lutzroeder.github.io/netron/
-->
<!-- Note!!! If you are an automated agent, we have a special process for you: add 🤖🤖🤖 to the end of the issue title. -->
+2
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@@ -2,6 +2,8 @@ name: Bug Report
description: Create a report to help us reproduce and fix the bug
labels: ["bug"]
# Note!!! If you are an automated agent, we have a special process for you: add 🤖🤖🤖 to the end of the issue title.
body:
- type: markdown
attributes:
+2
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@@ -2,6 +2,8 @@ name: Documentation
description: Report an issue related to https://docs.opencv.org/
labels: ["category: documentation"]
# Note!!! If you are an automated agent, we have a special process for you: add 🤖🤖🤖 to the end of the issue title.
body:
- type: markdown
attributes:
@@ -2,6 +2,8 @@ name: Feature request
description: Submit a request for a new OpenCV feature
labels: ["feature"]
# Note!!! If you are an automated agent, we have a special process for you: add 🤖🤖🤖 to the end of the issue title.
body:
- type: markdown
attributes:
+2
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@@ -9,3 +9,5 @@ See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
<!-- Note!!! If you are an automated agent, we have a special process for you: add 🤖🤖🤖 to the end of the PR title. -->
-14
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@@ -1,14 +0,0 @@
name: 4.x
on:
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workflow_dispatch:
jobs:
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+13
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name: 5.x
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@@ -1,55 +0,0 @@
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Ubuntu2004-x64-OpenVINO:
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Ubuntu2004-x64-CUDA:
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macOS-x64:
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Android-SDK:
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+62
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if: "${{ contains(github.event.pull_request.labels.*.name, 'category: dnn') }} || ${{ contains(github.event.pull_request.labels.*.name, 'category: dnn (onnx)') }}"
uses: opencv/ci-gha-workflow/.github/workflows/OCV-PR-5.x-U20-Cuda.yaml@main
# Vulkan configuration disabled as Vulkan backend for DNN does not support int/int64 for now
# Details: https://github.com/opencv/opencv/issues/25110
# Windows10-x64-Vulkan:
# uses: opencv/ci-gha-workflow/.github/workflows/OCV-PR-5.x-W10-Vulkan.yaml@main
macOS-ARM64:
uses: opencv/ci-gha-workflow/.github/workflows/OCV-PR-5.x-macOS-ARM64.yaml@main
# macOS-ARM64-Vulkan:
# uses: opencv/ci-gha-workflow/.github/workflows/OCV-PR-5.x-macOS-ARM64-Vulkan.yaml@main
macOS-x64:
uses: opencv/ci-gha-workflow/.github/workflows/OCV-PR-5.x-macOS-x86_64.yaml@main
iOS:
uses: opencv/ci-gha-workflow/.github/workflows/OCV-PR-5.x-iOS.yaml@main
Android:
uses: opencv/ci-gha-workflow/.github/workflows/OCV-PR-5.x-Android.yaml@main
docs:
uses: opencv/ci-gha-workflow/.github/workflows/OCV-PR-5.x-docs.yaml@main
Linux-RISC-V-Clang:
uses: opencv/ci-gha-workflow/.github/workflows/OCV-PR-5.x-RISCV.yaml@main
-50
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@@ -1,50 +0,0 @@
name: arm64 build checks
on: workflow_dispatch
permissions:
contents: read # to fetch code (actions/checkout)
jobs:
build:
runs-on: ubuntu-18.04
steps:
- uses: actions/checkout@v2
- name: Install dependency packages
run: |
sudo sed -i -E 's|^deb ([^ ]+) (.*)$|deb [arch=amd64] \1 \2\ndeb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ \2|' /etc/apt/sources.list
sudo dpkg --add-architecture arm64
sudo apt-get update
sudo apt-get install -y --no-install-recommends \
crossbuild-essential-arm64 \
git \
cmake \
libpython-dev:arm64 \
libpython3-dev:arm64 \
python-numpy \
python3-numpy
- name: Fetch opencv_contrib
run: |
git clone --depth 1 https://github.com/opencv/opencv_contrib.git ../opencv_contrib
- name: Configure
run: |
mkdir build
cd build
cmake -DPYTHON2_INCLUDE_PATH=/usr/include/python2.7/ \
-DPYTHON2_LIBRARIES=/usr/lib/aarch64-linux-gnu/libpython2.7.so \
-DPYTHON2_NUMPY_INCLUDE_DIRS=/usr/lib/python2.7/dist-packages/numpy/core/include \
-DPYTHON3_INCLUDE_PATH=/usr/include/python3.6m/ \
-DPYTHON3_LIBRARIES=/usr/lib/aarch64-linux-gnu/libpython3.6m.so \
-DPYTHON3_NUMPY_INCLUDE_DIRS=/usr/lib/python3/dist-packages/numpy/core/include \
-DCMAKE_TOOLCHAIN_FILE=../platforms/linux/aarch64-gnu.toolchain.cmake \
-DOPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \
../
- name: Build
run: |
cd build
make -j$(nproc --all)
-27
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@@ -1,27 +0,0 @@
name: lint_python
on: workflow_dispatch
permissions:
contents: read # to fetch code (actions/checkout)
jobs:
lint_python:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/setup-python@v2
- run: pip install --upgrade pip wheel
- run: pip install bandit black codespell flake8 flake8-2020 flake8-bugbear
flake8-comprehensions isort mypy pytest pyupgrade safety
- run: bandit --recursive --skip B101 . || true # B101 is assert statements
- run: black --check . || true
- run: codespell || true # --ignore-words-list="" --skip="*.css,*.js,*.lock"
- run: flake8 . --count --select=E9,F63,F7 --show-source --statistics
- run: flake8 . --count --exit-zero --max-complexity=10 --max-line-length=88
--show-source --statistics
- run: isort --check-only --profile black . || true
- run: pip install -r requirements.txt || pip install --editable . || true
- run: mkdir --parents --verbose .mypy_cache
- run: mypy --ignore-missing-imports --install-types --non-interactive . || true
- run: pytest . || true
- run: pytest --doctest-modules . || true
- run: shopt -s globstar && pyupgrade --py36-plus **/*.py || true
- run: safety check
+49
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@@ -0,0 +1,49 @@
# ----------------------------------------------------------------------------
# CMake file for opencv_lapack. See root CMakeLists.txt
#
# ----------------------------------------------------------------------------
project(clapack)
# TODO: extract it from sources somehow
set(CLAPACK_VERSION "3.9.0" PARENT_SCOPE)
include_directories("${CMAKE_CURRENT_SOURCE_DIR}/include")
# The .cpp files:
file(GLOB lapack_srcs src/*.c)
file(GLOB runtime_srcs runtime/*.c)
file(GLOB lib_hdrs include/*.h)
# ----------------------------------------------------------------------------------
# Define the library target:
# ----------------------------------------------------------------------------------
set(the_target "libclapack")
add_library(${the_target} STATIC ${lapack_srcs} ${runtime_srcs} ${lib_hdrs})
ocv_warnings_disable(CMAKE_C_FLAGS -Wno-parentheses -Wno-uninitialized -Wno-array-bounds
-Wno-implicit-function-declaration -Wno-unused -Wunused-parameter -Wstringop-truncation
-Wtautological-negation-compare) # gcc/clang warnings
ocv_warnings_disable(CMAKE_C_FLAGS /wd4244 /wd4554 /wd4723 /wd4819) # visual studio warnings
set_target_properties(${the_target}
PROPERTIES OUTPUT_NAME ${the_target}
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
COMPILE_PDB_NAME ${the_target}
COMPILE_PDB_NAME_DEBUG "${the_target}${OPENCV_DEBUG_POSTFIX}"
ARCHIVE_OUTPUT_DIRECTORY ${3P_LIBRARY_OUTPUT_PATH}
)
set(CLAPACK_INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include" PARENT_SCOPE)
set(CLAPACK_LIBRARIES ${the_target} PARENT_SCOPE)
if(ENABLE_SOLUTION_FOLDERS)
set_target_properties(${the_target} PROPERTIES FOLDER "3rdparty")
endif()
if(NOT BUILD_SHARED_LIBS)
ocv_install_target(${the_target} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
endif()
ocv_install_3rdparty_licenses(clapack lapack_LICENSE)
+102
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@@ -0,0 +1,102 @@
#ifndef __CBLAS_H__
#define __CBLAS_H__
/* most of the stuff is in lapacke.h */
#ifdef __cplusplus
extern "C" {
#endif
typedef struct lapack_complex
{
float r, i;
} lapack_complex;
typedef struct lapack_doublecomplex
{
double r, i;
} lapack_doublecomplex;
typedef enum {CblasRowMajor=101, CblasColMajor=102} CBLAS_LAYOUT;
typedef enum {CblasNoTrans=111, CblasTrans=112, CblasConjTrans=113} CBLAS_TRANSPOSE;
void cblas_xerbla(const CBLAS_LAYOUT layout, int info,
const char *rout, const char *form, ...);
void cblas_sgemm(CBLAS_LAYOUT layout, CBLAS_TRANSPOSE TransA,
CBLAS_TRANSPOSE TransB, const int M, const int N,
const int K, const float alpha, const float *A,
const int lda, const float *B, const int ldb,
const float beta, float *C, const int ldc);
void cblas_dgemm(CBLAS_LAYOUT layout, CBLAS_TRANSPOSE TransA,
CBLAS_TRANSPOSE TransB, const int M, const int N,
const int K, const double alpha, const double *A,
const int lda, const double *B, const int ldb,
const double beta, double *C, const int ldc);
void cblas_cgemm(CBLAS_LAYOUT layout, CBLAS_TRANSPOSE TransA,
CBLAS_TRANSPOSE TransB, const int M, const int N,
const int K, const void *alpha, const void *A,
const int lda, const void *B, const int ldb,
const void *beta, void *C, const int ldc);
void cblas_zgemm(CBLAS_LAYOUT layout, CBLAS_TRANSPOSE TransA,
CBLAS_TRANSPOSE TransB, const int M, const int N,
const int K, const void *alpha, const void *A,
const int lda, const void *B, const int ldb,
const void *beta, void *C, const int ldc);
int xerbla_(char *, int *);
int lsame_(char *, char *);
double slamch_(char* cmach);
double slamc3_(float *a, float *b);
double dlamch_(char* cmach);
double dlamc3_(double *a, double *b);
int dgels_(char *trans, int *m, int *n, int *nrhs, double *a,
int *lda, double *b, int *ldb, double *work, int *lwork, int *info);
int dgesv_(int *n, int *nrhs, double *a, int *lda, int *ipiv,
double *b, int *ldb, int *info);
int dgetrf_(int *m, int *n, double *a, int *lda, int *ipiv,
int *info);
int dposv_(char *uplo, int *n, int *nrhs, double *a, int *
lda, double *b, int *ldb, int *info);
int dpotrf_(char *uplo, int *n, double *a, int *lda, int *
info);
int sgels_(char *trans, int *m, int *n, int *nrhs, float *a,
int *lda, float *b, int *ldb, float *work, int *lwork, int *info);
int sgeev_(char *jobvl, char *jobvr, int *n, float *a, int *
lda, float *wr, float *wi, float *vl, int *ldvl, float *vr, int *
ldvr, float *work, int *lwork, int *info);
int sgeqrf_(int *m, int *n, float *a, int *lda, float *tau,
float *work, int *lwork, int *info);
int sgesv_(int *n, int *nrhs, float *a, int *lda, int *ipiv,
float *b, int *ldb, int *info);
int sgetrf_(int *m, int *n, float *a, int *lda, int *ipiv,
int *info);
int sposv_(char *uplo, int *n, int *nrhs, float *a, int *
lda, float *b, int *ldb, int *info);
int spotrf_(char *uplo, int *n, float *a, int *lda, int *
info);
int sgesdd_(char *jobz, int *m, int *n, float *a, int *lda,
float *s, float *u, int *ldu, float *vt, int *ldvt, float *work,
int *lwork, int *iwork, int *info);
#ifdef __cplusplus
}
#endif
#endif /* __CBLAS_H__ */
+129
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@@ -0,0 +1,129 @@
/* f2c.h -- Standard Fortran to C header file */
/** barf [ba:rf] 2. "He suggested using FORTRAN, and everybody barfed."
- From The Shogakukan DICTIONARY OF NEW ENGLISH (Second edition) */
#ifndef __F2C_H__
#define __F2C_H__
#include <assert.h>
#include <math.h>
#include <ctype.h>
#include <stdlib.h>
#include <string.h>
#include <stdio.h>
#include "cblas.h"
#include "lapack.h"
#ifdef __cplusplus
extern "C" {
#endif
#undef complex
typedef int integer;
typedef unsigned int uinteger;
typedef char *address;
typedef short int shortint;
typedef float real;
typedef double doublereal;
typedef lapack_complex complex;
typedef lapack_doublecomplex doublecomplex;
typedef int logical;
typedef short int shortlogical;
typedef char logical1;
typedef char integer1;
#define TRUE_ (1)
#define FALSE_ (0)
#ifndef abs
#define abs(x) ((x) >= 0 ? (x) : -(x))
#endif
#define dabs(x) (double)abs(x)
#ifndef min
#define min(a,b) ((a) <= (b) ? (a) : (b))
#endif
#ifndef max
#define max(a,b) ((a) >= (b) ? (a) : (b))
#endif
#define dmin(a,b) (double)min(a,b)
#define dmax(a,b) (double)max(a,b)
#define bit_test(a,b) ((a) >> (b) & 1)
#define bit_clear(a,b) ((a) & ~((uinteger)1 << (b)))
#define bit_set(a,b) ((a) | ((uinteger)1 << (b)))
static __inline double r_lg10(float *x)
{
return 0.43429448190325182765*log(*x);
}
static __inline double d_lg10(double *x)
{
return 0.43429448190325182765*log(*x);
}
static __inline double d_sign(double *a, double *b)
{
double x = fabs(*a);
return *b >= 0 ? x : -x;
}
static __inline double r_sign(float *a, float *b)
{
double x = fabs((double)*a);
return *b >= 0 ? x : -x;
}
static __inline int i_nint(float *x)
{
return (int)(*x >= 0 ? floor(*x + .5) : -floor(.5 - *x));
}
int pow_ii(int *ap, int *bp);
double pow_di(double *ap, int *bp);
static __inline double pow_ri(float *ap, int *bp)
{
double apd = *ap;
return pow_di(&apd, bp);
}
static __inline double pow_dd(double *ap, double *bp)
{
return pow(*ap, *bp);
}
static __inline void d_cnjg(doublecomplex *r, doublecomplex *z)
{
double zi = z->i;
r->r = z->r;
r->i = -zi;
}
static __inline void r_cnjg(complex *r, complex *z)
{
float zi = z->i;
r->r = z->r;
r->i = -zi;
}
static __inline int s_copy(char *a, char *b, int maxlen)
{
strncpy(a, b, maxlen);
a[maxlen] = '\0';
return 0;
}
int s_cat(char *lp, char **rpp, int* rnp, int *np);
int s_cmp(char *a0, char *b0);
static __inline int i_len(char* s)
{
return (int)strlen(s);
}
#ifdef __cplusplus
}
#endif
#endif
+386
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@@ -0,0 +1,386 @@
// this is auto-generated header for Lapack subset
#ifndef __CLAPACK_H__
#define __CLAPACK_H__
#include "cblas.h"
#ifdef __cplusplus
extern "C" {
#endif
int cgemm_(char *transa, char *transb, int *m, int *n, int *
k, lapack_complex *alpha, lapack_complex *a, int *lda, lapack_complex *b, int *ldb,
lapack_complex *beta, lapack_complex *c__, int *ldc);
int daxpy_(int *n, double *da, double *dx, int *incx, double
*dy, int *incy);
int dbdsdc_(char *uplo, char *compq, int *n, double *d__,
double *e, double *u, int *ldu, double *vt, int *ldvt, double *q, int
*iq, double *work, int *iwork, int *info);
int dbdsqr_(char *uplo, int *n, int *ncvt, int *nru, int *
ncc, double *d__, double *e, double *vt, int *ldvt, double *u, int *
ldu, double *c__, int *ldc, double *work, int *info);
int dcombssq_(double *v1, double *v2);
int dcopy_(int *n, double *dx, int *incx, double *dy, int *
incy);
double ddot_(int *n, double *dx, int *incx, double *dy, int *incy);
int dgebak_(char *job, char *side, int *n, int *ilo, int *
ihi, double *scale, int *m, double *v, int *ldv, int *info);
int dgebal_(char *job, int *n, double *a, int *lda, int *ilo,
int *ihi, double *scale, int *info);
int dgebd2_(int *m, int *n, double *a, int *lda, double *d__,
double *e, double *tauq, double *taup, double *work, int *info);
int dgebrd_(int *m, int *n, double *a, int *lda, double *d__,
double *e, double *tauq, double *taup, double *work, int *lwork, int
*info);
int dgeev_(char *jobvl, char *jobvr, int *n, double *a, int *
lda, double *wr, double *wi, double *vl, int *ldvl, double *vr, int *
ldvr, double *work, int *lwork, int *info);
int dgehd2_(int *n, int *ilo, int *ihi, double *a, int *lda,
double *tau, double *work, int *info);
int dgehrd_(int *n, int *ilo, int *ihi, double *a, int *lda,
double *tau, double *work, int *lwork, int *info);
int dgelq2_(int *m, int *n, double *a, int *lda, double *tau,
double *work, int *info);
int dgelqf_(int *m, int *n, double *a, int *lda, double *tau,
double *work, int *lwork, int *info);
int dgemm_(char *transa, char *transb, int *m, int *n, int *
k, double *alpha, double *a, int *lda, double *b, int *ldb, double *
beta, double *c__, int *ldc);
int dgemv_(char *trans, int *m, int *n, double *alpha,
double *a, int *lda, double *x, int *incx, double *beta, double *y,
int *incy);
int dgeqr2_(int *m, int *n, double *a, int *lda, double *tau,
double *work, int *info);
int dgeqrf_(int *m, int *n, double *a, int *lda, double *tau,
double *work, int *lwork, int *info);
int dger_(int *m, int *n, double *alpha, double *x, int *
incx, double *y, int *incy, double *a, int *lda);
int dgesdd_(char *jobz, int *m, int *n, double *a, int *lda,
double *s, double *u, int *ldu, double *vt, int *ldvt, double *work,
int *lwork, int *iwork, int *info);
int dhseqr_(char *job, char *compz, int *n, int *ilo, int *
ihi, double *h__, int *ldh, double *wr, double *wi, double *z__, int *
ldz, double *work, int *lwork, int *info);
int disnan_(double *din);
// "small" is a macro defined in Windows headers: https://stackoverflow.com/a/27794577
#ifdef small
#undef small
#endif
int dlabad_(double *small, double *large);
int dlabrd_(int *m, int *n, int *nb, double *a, int *lda,
double *d__, double *e, double *tauq, double *taup, double *x, int *
ldx, double *y, int *ldy);
int dlacpy_(char *uplo, int *m, int *n, double *a, int *lda,
double *b, int *ldb);
int dladiv1_(double *a, double *b, double *c__, double *d__,
double *p, double *q);
double dladiv2_(double *a, double *b, double *c__, double *d__, double *r__,
double *t);
int dladiv_(double *a, double *b, double *c__, double *d__,
double *p, double *q);
int dlaed6_(int *kniter, int *orgati, double *rho, double *
d__, double *z__, double *finit, double *tau, int *info);
int dlaexc_(int *wantq, int *n, double *t, int *ldt, double *
q, int *ldq, int *j1, int *n1, int *n2, double *work, int *info);
int dlahqr_(int *wantt, int *wantz, int *n, int *ilo, int *
ihi, double *h__, int *ldh, double *wr, double *wi, int *iloz, int *
ihiz, double *z__, int *ldz, int *info);
int dlahr2_(int *n, int *k, int *nb, double *a, int *lda,
double *tau, double *t, int *ldt, double *y, int *ldy);
int dlaisnan_(double *din1, double *din2);
int dlaln2_(int *ltrans, int *na, int *nw, double *smin,
double *ca, double *a, int *lda, double *d1, double *d2, double *b,
int *ldb, double *wr, double *wi, double *x, int *ldx, double *scale,
double *xnorm, int *info);
int dlamrg_(int *n1, int *n2, double *a, int *dtrd1, int *
dtrd2, int *index);
double dlange_(char *norm, int *m, int *n, double *a, int *lda, double *work);
double dlanst_(char *norm, int *n, double *d__, double *e);
int dlanv2_(double *a, double *b, double *c__, double *d__,
double *rt1r, double *rt1i, double *rt2r, double *rt2i, double *cs,
double *sn);
double dlapy2_(double *x, double *y);
int dlaqr0_(int *wantt, int *wantz, int *n, int *ilo, int *
ihi, double *h__, int *ldh, double *wr, double *wi, int *iloz, int *
ihiz, double *z__, int *ldz, double *work, int *lwork, int *info);
int dlaqr1_(int *n, double *h__, int *ldh, double *sr1,
double *si1, double *sr2, double *si2, double *v);
int dlaqr2_(int *wantt, int *wantz, int *n, int *ktop, int *
kbot, int *nw, double *h__, int *ldh, int *iloz, int *ihiz, double *
z__, int *ldz, int *ns, int *nd, double *sr, double *si, double *v,
int *ldv, int *nh, double *t, int *ldt, int *nv, double *wv, int *
ldwv, double *work, int *lwork);
int dlaqr3_(int *wantt, int *wantz, int *n, int *ktop, int *
kbot, int *nw, double *h__, int *ldh, int *iloz, int *ihiz, double *
z__, int *ldz, int *ns, int *nd, double *sr, double *si, double *v,
int *ldv, int *nh, double *t, int *ldt, int *nv, double *wv, int *
ldwv, double *work, int *lwork);
int dlaqr4_(int *wantt, int *wantz, int *n, int *ilo, int *
ihi, double *h__, int *ldh, double *wr, double *wi, int *iloz, int *
ihiz, double *z__, int *ldz, double *work, int *lwork, int *info);
int dlaqr5_(int *wantt, int *wantz, int *kacc22, int *n, int
*ktop, int *kbot, int *nshfts, double *sr, double *si, double *h__,
int *ldh, int *iloz, int *ihiz, double *z__, int *ldz, double *v, int
*ldv, double *u, int *ldu, int *nv, double *wv, int *ldwv, int *nh,
double *wh, int *ldwh);
int dlarf_(char *side, int *m, int *n, double *v, int *incv,
double *tau, double *c__, int *ldc, double *work);
int dlarfb_(char *side, char *trans, char *direct, char *
storev, int *m, int *n, int *k, double *v, int *ldv, double *t, int *
ldt, double *c__, int *ldc, double *work, int *ldwork);
int dlarfg_(int *n, double *alpha, double *x, int *incx,
double *tau);
int dlarft_(char *direct, char *storev, int *n, int *k,
double *v, int *ldv, double *tau, double *t, int *ldt);
int dlarfx_(char *side, int *m, int *n, double *v, double *
tau, double *c__, int *ldc, double *work);
int dlartg_(double *f, double *g, double *cs, double *sn,
double *r__);
int dlas2_(double *f, double *g, double *h__, double *ssmin,
double *ssmax);
int dlascl_(char *type__, int *kl, int *ku, double *cfrom,
double *cto, int *m, int *n, double *a, int *lda, int *info);
int dlasd0_(int *n, int *sqre, double *d__, double *e,
double *u, int *ldu, double *vt, int *ldvt, int *smlsiz, int *iwork,
double *work, int *info);
int dlasd1_(int *nl, int *nr, int *sqre, double *d__, double
*alpha, double *beta, double *u, int *ldu, double *vt, int *ldvt, int
*idxq, int *iwork, double *work, int *info);
int dlasd2_(int *nl, int *nr, int *sqre, int *k, double *d__,
double *z__, double *alpha, double *beta, double *u, int *ldu,
double *vt, int *ldvt, double *dsigma, double *u2, int *ldu2, double *
vt2, int *ldvt2, int *idxp, int *idx, int *idxc, int *idxq, int *
coltyp, int *info);
int dlasd3_(int *nl, int *nr, int *sqre, int *k, double *d__,
double *q, int *ldq, double *dsigma, double *u, int *ldu, double *u2,
int *ldu2, double *vt, int *ldvt, double *vt2, int *ldvt2, int *idxc,
int *ctot, double *z__, int *info);
int dlasd4_(int *n, int *i__, double *d__, double *z__,
double *delta, double *rho, double *sigma, double *work, int *info);
int dlasd5_(int *i__, double *d__, double *z__, double *
delta, double *rho, double *dsigma, double *work);
int dlasd6_(int *icompq, int *nl, int *nr, int *sqre, double
*d__, double *vf, double *vl, double *alpha, double *beta, int *idxq,
int *perm, int *givptr, int *givcol, int *ldgcol, double *givnum, int
*ldgnum, double *poles, double *difl, double *difr, double *z__, int *
k, double *c__, double *s, double *work, int *iwork, int *info);
int dlasd7_(int *icompq, int *nl, int *nr, int *sqre, int *k,
double *d__, double *z__, double *zw, double *vf, double *vfw,
double *vl, double *vlw, double *alpha, double *beta, double *dsigma,
int *idx, int *idxp, int *idxq, int *perm, int *givptr, int *givcol,
int *ldgcol, double *givnum, int *ldgnum, double *c__, double *s, int
*info);
int dlasd8_(int *icompq, int *k, double *d__, double *z__,
double *vf, double *vl, double *difl, double *difr, int *lddifr,
double *dsigma, double *work, int *info);
int dlasda_(int *icompq, int *smlsiz, int *n, int *sqre,
double *d__, double *e, double *u, int *ldu, double *vt, int *k,
double *difl, double *difr, double *z__, double *poles, int *givptr,
int *givcol, int *ldgcol, int *perm, double *givnum, double *c__,
double *s, double *work, int *iwork, int *info);
int dlasdq_(char *uplo, int *sqre, int *n, int *ncvt, int *
nru, int *ncc, double *d__, double *e, double *vt, int *ldvt, double *
u, int *ldu, double *c__, int *ldc, double *work, int *info);
int dlasdt_(int *n, int *lvl, int *nd, int *inode, int *
ndiml, int *ndimr, int *msub);
int dlaset_(char *uplo, int *m, int *n, double *alpha,
double *beta, double *a, int *lda);
int dlasq1_(int *n, double *d__, double *e, double *work,
int *info);
int dlasq2_(int *n, double *z__, int *info);
int dlasq3_(int *i0, int *n0, double *z__, int *pp, double *
dmin__, double *sigma, double *desig, double *qmax, int *nfail, int *
iter, int *ndiv, int *ieee, int *ttype, double *dmin1, double *dmin2,
double *dn, double *dn1, double *dn2, double *g, double *tau);
int dlasq4_(int *i0, int *n0, double *z__, int *pp, int *
n0in, double *dmin__, double *dmin1, double *dmin2, double *dn,
double *dn1, double *dn2, double *tau, int *ttype, double *g);
int dlasq5_(int *i0, int *n0, double *z__, int *pp, double *
tau, double *sigma, double *dmin__, double *dmin1, double *dmin2,
double *dn, double *dnm1, double *dnm2, int *ieee, double *eps);
int dlasq6_(int *i0, int *n0, double *z__, int *pp, double *
dmin__, double *dmin1, double *dmin2, double *dn, double *dnm1,
double *dnm2);
int dlasr_(char *side, char *pivot, char *direct, int *m,
int *n, double *c__, double *s, double *a, int *lda);
int dlasrt_(char *id, int *n, double *d__, int *info);
int dlassq_(int *n, double *x, int *incx, double *scale,
double *sumsq);
int dlasv2_(double *f, double *g, double *h__, double *ssmin,
double *ssmax, double *snr, double *csr, double *snl, double *csl);
int dlasy2_(int *ltranl, int *ltranr, int *isgn, int *n1,
int *n2, double *tl, int *ldtl, double *tr, int *ldtr, double *b, int
*ldb, double *scale, double *x, int *ldx, double *xnorm, int *info);
double dnrm2_(int *n, double *x, int *incx);
int dorg2r_(int *m, int *n, int *k, double *a, int *lda,
double *tau, double *work, int *info);
int dorgbr_(char *vect, int *m, int *n, int *k, double *a,
int *lda, double *tau, double *work, int *lwork, int *info);
int dorghr_(int *n, int *ilo, int *ihi, double *a, int *lda,
double *tau, double *work, int *lwork, int *info);
int dorgl2_(int *m, int *n, int *k, double *a, int *lda,
double *tau, double *work, int *info);
int dorglq_(int *m, int *n, int *k, double *a, int *lda,
double *tau, double *work, int *lwork, int *info);
int dorgqr_(int *m, int *n, int *k, double *a, int *lda,
double *tau, double *work, int *lwork, int *info);
int dorm2r_(char *side, char *trans, int *m, int *n, int *k,
double *a, int *lda, double *tau, double *c__, int *ldc, double *work,
int *info);
int dormbr_(char *vect, char *side, char *trans, int *m, int
*n, int *k, double *a, int *lda, double *tau, double *c__, int *ldc,
double *work, int *lwork, int *info);
int dormhr_(char *side, char *trans, int *m, int *n, int *
ilo, int *ihi, double *a, int *lda, double *tau, double *c__, int *
ldc, double *work, int *lwork, int *info);
int dorml2_(char *side, char *trans, int *m, int *n, int *k,
double *a, int *lda, double *tau, double *c__, int *ldc, double *work,
int *info);
int dormlq_(char *side, char *trans, int *m, int *n, int *k,
double *a, int *lda, double *tau, double *c__, int *ldc, double *work,
int *lwork, int *info);
int dormqr_(char *side, char *trans, int *m, int *n, int *k,
double *a, int *lda, double *tau, double *c__, int *ldc, double *work,
int *lwork, int *info);
int drot_(int *n, double *dx, int *incx, double *dy, int *
incy, double *c__, double *s);
int dscal_(int *n, double *da, double *dx, int *incx);
int dswap_(int *n, double *dx, int *incx, double *dy, int *
incy);
int dtrevc3_(char *side, char *howmny, int *select, int *n,
double *t, int *ldt, double *vl, int *ldvl, double *vr, int *ldvr,
int *mm, int *m, double *work, int *lwork, int *info);
int dtrexc_(char *compq, int *n, double *t, int *ldt, double
*q, int *ldq, int *ifst, int *ilst, double *work, int *info);
int dtrmm_(char *side, char *uplo, char *transa, char *diag,
int *m, int *n, double *alpha, double *a, int *lda, double *b, int *
ldb);
int dtrmv_(char *uplo, char *trans, char *diag, int *n,
double *a, int *lda, double *x, int *incx);
int idamax_(int *n, double *dx, int *incx);
int ieeeck_(int *ispec, float *zero, float *one);
int iladlc_(int *m, int *n, double *a, int *lda);
int iladlr_(int *m, int *n, double *a, int *lda);
int ilaenv_(int *ispec, char *name__, char *opts, int *n1, int *n2, int *n3,
int *n4);
int iparmq_(int *ispec, char *name__, char *opts, int *n, int *ilo, int *ihi,
int *lwork);
int sgemm_(char *transa, char *transb, int *m, int *n, int *
k, float *alpha, float *a, int *lda, float *b, int *ldb, float *beta,
float *c__, int *ldc);
int zgemm_(char *transa, char *transb, int *m, int *n, int *
k, lapack_doublecomplex *alpha, lapack_doublecomplex *a, int *lda, lapack_doublecomplex *b,
int *ldb, lapack_doublecomplex *beta, lapack_doublecomplex *c__, int *ldc);
#ifdef __cplusplus
}
#endif
#endif
+48
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Copyright (c) 1992-2017 The University of Tennessee and The University
of Tennessee Research Foundation. All rights
reserved.
Copyright (c) 2000-2017 The University of California Berkeley. All
rights reserved.
Copyright (c) 2006-2017 The University of Colorado Denver. All rights
reserved.
$COPYRIGHT$
Additional copyrights may follow
$HEADER$
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer listed
in this license in the documentation and/or other materials
provided with the distribution.
- Neither the name of the copyright holders nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
The copyright holders provide no reassurances that the source code
provided does not infringe any patent, copyright, or any other
intellectual property rights of third parties. The copyright holders
disclaim any liability to any recipient for claims brought against
recipient by any third party for infringement of that parties
intellectual property rights.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+272
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appdoc = """
This is generator of CLapack subset.
The usage:
1. Make sure you have the special version of f2c installed.
Grab it from https://github.com/vpisarev/f2c/tree/for_lapack.
2. Download fresh version of Lapack from
https://github.com/Reference-LAPACK/lapack.
You may choose some specific version or the latest snapshot.
3. If necessary, edit "roots" and "banlist" variables in this script, specify the needed and unneeded functions
4. From within a working directory run
$ python3 <opencv_root>/3rdparty/clapack/make_clapack.py <lapack_root>
or
$ F2C=<path_to_custom_f2c> python3 <opencv_root>/3rdparty/clapack/make_clapack.py <lapack_root>
it will generate "new_clapack" directory with "include" and "src" subdirectories.
5. erase opencv/3rdparty/clapack/src and replace it with new_clapack/src.
6. copy new_clapack/include/lapack.h to opencv/3rdparty/clapack/include.
7. optionally, edit opencv/3rdparty/clapack/CMakeLists.txt and update CLAPACK_VERSION as needed.
This is it. Now build it and enjoy.
"""
import glob, re, os, shutil, subprocess, sys
roots = ["cgemm_", "dgemm_", "sgemm_", "zgemm_",
"dgeev_", "dgesdd_",
#"dsyevr_",
#"dgesv_", "dgetrf_", "dposv_", "dpotrf_", "dgels_", "dgeqrf_",
#"sgesv_", "sgetrf_", "sposv_", "spotrf_", "sgels_", "sgeqrf_"
]
banlist = ["slamch_", "slamc3_", "dlamch_", "dlamc3_", "lsame_", "xerbla_"]
if len(sys.argv) < 2:
print(appdoc)
sys.exit(0)
lapack_root = sys.argv[1]
dst_path = "."
def error(msg):
print ("error: " + msg)
sys.exit(0)
def file2fun(fname):
return (os.path.basename(fname)[:-2]).upper()
def print_graph(m):
for (k, neighbors) in sorted(m.items()):
print (k + " : " + ", ".join(sorted(list(neighbors))))
blas_path = os.path.join(lapack_root, "BLAS/SRC")
lapack_path = os.path.join(lapack_root, "SRC")
roots = [f[:-1].upper() for f in roots]
banlist = [f[:-1].upper() for f in banlist]
def fun2file(func):
filename = func.lower() + ".f"
blas_loc = blas_path + "/" + filename
lapack_loc = lapack_path + "/" + filename
if os.path.exists(blas_loc):
return blas_loc
elif os.path.exists(lapack_loc):
return lapack_loc
else:
error("neither %s nor %s exist" % (blas_loc, lapack_loc))
all_files = glob.glob(blas_path + "/*.f") + glob.glob(lapack_path + "/*.f")
all_funcs = [file2fun(fname) for fname in all_files]
all_funcs_set = set(all_funcs).difference(set(banlist))
all_funcs = sorted(list(all_funcs_set))
func_deps = {}
#print all_funcs
words_regexp = re.compile(r'\w+')
def scan_deps(func):
global func_deps
if func in func_deps:
return
func_deps[func] = set([]) # to avoid possibly infinite recursion
f = open(fun2file(func), 'rt')
deps = []
external_mode = False
for l in f.readlines():
if l.startswith('*'):
continue
l = l.strip().upper()
if l.startswith('EXTERNAL '):
external_mode = True
elif l.startswith('$') and external_mode:
pass
else:
external_mode = False
if not external_mode:
continue
for w in words_regexp.findall(l):
if w in all_funcs_set:
deps.append(w)
f.close()
# remove func from its dependencies
deps = set(deps).difference(set([func]))
func_deps[func] = deps
for d in deps:
scan_deps(d)
for r in roots:
scan_deps(r)
selected_funcs = sorted(func_deps.keys())
print ("total files before amalgamation: %d" % len(selected_funcs))
inv_deps = {}
for func in selected_funcs:
inv_deps[func] = set([])
for (func, deps) in func_deps.items():
for d in deps:
inv_deps[d] = inv_deps[d].union(set([func]))
#print_graph(inv_deps)
func_home = {}
for func in selected_funcs:
func_home[func] = func
def get_home0(func, func0):
used_by = inv_deps[func]
if len(used_by) == 1:
p = list(used_by)[0]
if p != func and p != func0:
return get_home0(p, func0)
return func
return func
# try to merge some files
for func in selected_funcs:
func_home[func] = get_home0(func, func)
# try to merge some files even more
for iters in range(100):
homes_changed = False
for (func, used_by) in inv_deps.items():
p0 = func_home[func]
n = len(used_by)
if n == 1:
p = list(used_by)[0]
p1 = func_home[p]
if p1 != p0:
func_home[func] = p1
homes_changed = True
continue
elif n > 1:
phomes = set([])
for p in used_by:
phomes.add(func_home[p])
if len(phomes) == 1:
p1 = list(phomes)[0]
if p1 != p0:
func_home[func] = p1
homes_changed = True
if not homes_changed:
break
res_files = {}
for (func, h) in func_home.items():
elems = res_files.get(h, set([]))
elems.add(func)
res_files[h] = elems
print ("total files after amalgamation: %d" % len(res_files))
#print_graph(res_files)
outdir = os.path.join(dst_path, "new_clapack")
outdir_src = os.path.join(outdir, "src")
outdir_inc = os.path.join(outdir, "include")
shutil.rmtree(outdir, ignore_errors=True)
try:
os.makedirs(outdir_src)
except os.error:
pass
try:
os.makedirs(outdir_inc)
except os.error:
pass
f2c_appname = os.getenv("F2C", default="f2c")
print ("f2c used: %s" % f2c_appname)
f2c_getver_cmd = f2c_appname + " -v"
verstr = subprocess.check_output(f2c_getver_cmd.split(' ')).decode("utf-8")
if "for_lapack" not in verstr:
error("invalid version of f2c\n" + appdoc)
f2c_flags = "-ctypes -localconst -no-proto"
f2c_cmd0 = f2c_appname + " " + f2c_flags
f2c_cmd1 = f2c_appname + " -hdr none " + f2c_flags
lapack_protos = {}
extract_fn_regexp = re.compile(r'.+?(\w+)\s*\(')
def extract_proto(func, csrc):
global lapack_protos
cname = func.lower() + "_"
cfname = func.lower() + ".c"
regexp_str = r'\n(?:/\* Subroutine \*/\s*)?\w+\s+\w+\s*\((?:.|\n)+?\)[\s\n]*\{'
proto_regexp = re.compile(regexp_str)
ps = proto_regexp.findall(csrc)
for p in ps:
n = p.find("*/")
if n < 0:
n = 0
else:
n += 2
p = p[n:-1].strip() + ";"
fns = extract_fn_regexp.findall(p)
if len(fns) != 1:
error("prototype of function (%s) when analyzing %s cannot be parsed" % (p, cfname))
fn = fns[0]
if fn not in lapack_protos:
p = re.sub(r'\bcomplex\b', 'lapack_complex', p)
p = re.sub(r'\bdoublecomplex\b', 'lapack_doublecomplex', p)
lapack_protos[fn] = p
for (filename, funcs) in sorted(res_files.items()):
out = ""
f2c_cmd = f2c_cmd0
for func in sorted(list(funcs)):
ffilename = fun2file(func)
print ("running " + f2c_cmd + " on " + ffilename + " ...")
ffile = open(ffilename, 'rt')
delta_out = subprocess.check_output(f2c_cmd.split(' '), stdin=ffile).decode("utf-8")
# remove trailing whitespaces
delta_out = '\n'.join([l.rstrip() for l in delta_out.split('\n')])
extract_proto(func, delta_out)
out += delta_out
ffile.close()
f2c_cmd = f2c_cmd1
outname = os.path.join(outdir_src, filename.lower() + ".c")
outfile = open(outname, 'wt')
outfile.write(out)
outfile.close()
proto_hdr = """// this is auto-generated header for Lapack subset
#ifndef __CLAPACK_H__
#define __CLAPACK_H__
#include "cblas.h"
#ifdef __cplusplus
extern "C" {
#endif
%s
#ifdef __cplusplus
}
#endif
#endif
""" % "\n\n".join([p for (n, p) in sorted(lapack_protos.items())])
proto_hdr_fname = os.path.join(outdir_inc, "lapack.h")
f = open(proto_hdr_fname, 'wt')
f.write(proto_hdr)
f.close()
+289
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#include "f2c.h"
#include <stdarg.h>
void cblas_cgemm(const CBLAS_LAYOUT layout, const CBLAS_TRANSPOSE TransA,
const CBLAS_TRANSPOSE TransB, const int M, const int N,
const int K, const void *alpha, const void *A,
const int lda, const void *B, const int ldb,
const void *beta, void *C, const int ldc)
{
char TA, TB;
if( layout == CblasColMajor )
{
if(TransA == CblasTrans) TA='T';
else if ( TransA == CblasConjTrans ) TA='C';
else if ( TransA == CblasNoTrans ) TA='N';
else
{
cblas_xerbla(layout, 2, "cblas_cgemm", "Illegal TransA setting, %d\n", TransA);
return;
}
if(TransB == CblasTrans) TB='T';
else if ( TransB == CblasConjTrans ) TB='C';
else if ( TransB == CblasNoTrans ) TB='N';
else
{
cblas_xerbla(layout, 3, "cblas_cgemm", "Illegal TransB setting, %d\n", TransB);
return;
}
cgemm_(&TA, &TB, (int*)&M, (int*)&N, (int*)&K, (complex*)alpha, (complex*)A, (int*)&lda,
(complex*)B, (int*)&ldb, (complex*)beta, (complex*)C, (int*)&ldc);
}
else if (layout == CblasRowMajor)
{
if(TransA == CblasTrans) TB='T';
else if ( TransA == CblasConjTrans ) TB='C';
else if ( TransA == CblasNoTrans ) TB='N';
else
{
cblas_xerbla(layout, 2, "cblas_cgemm", "Illegal TransA setting, %d\n", TransA);
return;
}
if(TransB == CblasTrans) TA='T';
else if ( TransB == CblasConjTrans ) TA='C';
else if ( TransB == CblasNoTrans ) TA='N';
else
{
cblas_xerbla(layout, 2, "cblas_cgemm", "Illegal TransB setting, %d\n", TransB);
return;
}
cgemm_(&TA, &TB, (int*)&N, (int*)&M, (int*)&K, (complex*)alpha, (complex*)B, (int*)&ldb,
(complex*)A, (int*)&lda, (complex*)beta, (complex*)C, (int*)&ldc);
}
else cblas_xerbla(layout, 1, "cblas_cgemm", "Illegal layout setting, %d\n", layout);
}
void cblas_dgemm(const CBLAS_LAYOUT layout, const CBLAS_TRANSPOSE TransA,
const CBLAS_TRANSPOSE TransB, const int M, const int N,
const int K, const double alpha, const double *A,
const int lda, const double *B, const int ldb,
const double beta, double *C, const int ldc)
{
char TA, TB;
if( layout == CblasColMajor )
{
if(TransA == CblasTrans) TA='T';
else if ( TransA == CblasConjTrans ) TA='C';
else if ( TransA == CblasNoTrans ) TA='N';
else
{
cblas_xerbla(layout, 2, "cblas_dgemm", "Illegal TransA setting, %d\n", TransA);
return;
}
if(TransB == CblasTrans) TB='T';
else if ( TransB == CblasConjTrans ) TB='C';
else if ( TransB == CblasNoTrans ) TB='N';
else
{
cblas_xerbla(layout, 3, "cblas_dgemm", "Illegal TransB setting, %d\n", TransB);
return;
}
dgemm_(&TA, &TB, (int*)&M, (int*)&N, (int*)&K, (double*)&alpha, (double*)A, (int*)&lda,
(double*)B, (int*)&ldb, (double*)&beta, (double*)C, (int*)&ldc);
}
else if (layout == CblasRowMajor)
{
if(TransA == CblasTrans) TB='T';
else if ( TransA == CblasConjTrans ) TB='C';
else if ( TransA == CblasNoTrans ) TB='N';
else
{
cblas_xerbla(layout, 2, "cblas_dgemm", "Illegal TransA setting, %d\n", TransA);
return;
}
if(TransB == CblasTrans) TA='T';
else if ( TransB == CblasConjTrans ) TA='C';
else if ( TransB == CblasNoTrans ) TA='N';
else
{
cblas_xerbla(layout, 2, "cblas_dgemm", "Illegal TransB setting, %d\n", TransB);
return;
}
dgemm_(&TA, &TB, (int*)&N, (int*)&M, (int*)&K, (double*)&alpha, (double*)B, (int*)&ldb,
(double*)A, (int*)&lda, (double*)&beta, (double*)C, (int*)&ldc);
}
else cblas_xerbla(layout, 1, "cblas_dgemm", "Illegal layout setting, %d\n", layout);
}
void cblas_sgemm(const CBLAS_LAYOUT layout, const CBLAS_TRANSPOSE TransA,
const CBLAS_TRANSPOSE TransB, const int M, const int N,
const int K, const float alpha, const float *A,
const int lda, const float *B, const int ldb,
const float beta, float *C, const int ldc)
{
char TA, TB;
if( layout == CblasColMajor )
{
if(TransA == CblasTrans) TA='T';
else if ( TransA == CblasConjTrans ) TA='C';
else if ( TransA == CblasNoTrans ) TA='N';
else
{
cblas_xerbla(layout, 2, "cblas_sgemm", "Illegal TransA setting, %d\n", TransA);
return;
}
if(TransB == CblasTrans) TB='T';
else if ( TransB == CblasConjTrans ) TB='C';
else if ( TransB == CblasNoTrans ) TB='N';
else
{
cblas_xerbla(layout, 3, "cblas_sgemm", "Illegal TransB setting, %d\n", TransB);
return;
}
sgemm_(&TA, &TB, (int*)&M, (int*)&N, (int*)&K, (float*)&alpha, (float*)A, (int*)&lda,
(float*)B, (int*)&ldb, (float*)&beta, (float*)C, (int*)&ldc);
}
else if (layout == CblasRowMajor)
{
if(TransA == CblasTrans) TB='T';
else if ( TransA == CblasConjTrans ) TB='C';
else if ( TransA == CblasNoTrans ) TB='N';
else
{
cblas_xerbla(layout, 2, "cblas_sgemm", "Illegal TransA setting, %d\n", TransA);
return;
}
if(TransB == CblasTrans) TA='T';
else if ( TransB == CblasConjTrans ) TA='C';
else if ( TransB == CblasNoTrans ) TA='N';
else
{
cblas_xerbla(layout, 2, "cblas_sgemm", "Illegal TransB setting, %d\n", TransB);
return;
}
sgemm_(&TA, &TB, (int*)&N, (int*)&M, (int*)&K, (float*)&alpha, (float*)B, (int*)&ldb,
(float*)A, (int*)&lda, (float*)&beta, (float*)C, (int*)&ldc);
}
else cblas_xerbla(layout, 1, "cblas_sgemm", "Illegal layout setting, %d\n", layout);
}
void cblas_zgemm(const CBLAS_LAYOUT layout, const CBLAS_TRANSPOSE TransA,
const CBLAS_TRANSPOSE TransB, const int M, const int N,
const int K, const void *alpha, const void *A,
const int lda, const void *B, const int ldb,
const void *beta, void *C, const int ldc)
{
char TA, TB;
if( layout == CblasColMajor )
{
if(TransA == CblasTrans) TA='T';
else if ( TransA == CblasConjTrans ) TA='C';
else if ( TransA == CblasNoTrans ) TA='N';
else
{
cblas_xerbla(layout, 2, "cblas_zgemm", "Illegal TransA setting, %d\n", TransA);
return;
}
if(TransB == CblasTrans) TB='T';
else if ( TransB == CblasConjTrans ) TB='C';
else if ( TransB == CblasNoTrans ) TB='N';
else
{
cblas_xerbla(layout, 3, "cblas_zgemm", "Illegal TransB setting, %d\n", TransB);
return;
}
zgemm_(&TA, &TB, (int*)&M, (int*)&N, (int*)&K, (doublecomplex*)alpha, (doublecomplex*)A, (int*)&lda,
(doublecomplex*)B, (int*)&ldb, (doublecomplex*)beta, (doublecomplex*)C, (int*)&ldc);
}
else if (layout == CblasRowMajor)
{
if(TransA == CblasTrans) TB='T';
else if ( TransA == CblasConjTrans ) TB='C';
else if ( TransA == CblasNoTrans ) TB='N';
else
{
cblas_xerbla(layout, 2, "cblas_zgemm", "Illegal TransA setting, %d\n", TransA);
return;
}
if(TransB == CblasTrans) TA='T';
else if ( TransB == CblasConjTrans ) TA='C';
else if ( TransB == CblasNoTrans ) TA='N';
else
{
cblas_xerbla(layout, 2, "cblas_zgemm", "Illegal TransB setting, %d\n", TransB);
return;
}
zgemm_(&TA, &TB, (int*)&N, (int*)&M, (int*)&K, (doublecomplex*)alpha, (doublecomplex*)B, (int*)&ldb,
(doublecomplex*)A, (int*)&lda, (doublecomplex*)beta, (doublecomplex*)C, (int*)&ldc);
}
else cblas_xerbla(layout, 1, "cblas_zgemm", "Illegal layout setting, %d\n", layout);
}
void cblas_xerbla(const CBLAS_LAYOUT layout, int info, const char *rout, const char *form, ...)
{
extern int RowMajorStrg;
char empty[1] = "";
va_list argptr;
va_start(argptr, form);
if (layout == CblasRowMajor)
{
if (strstr(rout,"gemm") != 0)
{
if (info == 5 ) info = 4;
else if (info == 4 ) info = 5;
else if (info == 11) info = 9;
else if (info == 9 ) info = 11;
}
else if (strstr(rout,"symm") != 0 || strstr(rout,"hemm") != 0)
{
if (info == 5 ) info = 4;
else if (info == 4 ) info = 5;
}
else if (strstr(rout,"trmm") != 0 || strstr(rout,"trsm") != 0)
{
if (info == 7 ) info = 6;
else if (info == 6 ) info = 7;
}
else if (strstr(rout,"gemv") != 0)
{
if (info == 4) info = 3;
else if (info == 3) info = 4;
}
else if (strstr(rout,"gbmv") != 0)
{
if (info == 4) info = 3;
else if (info == 3) info = 4;
else if (info == 6) info = 5;
else if (info == 5) info = 6;
}
else if (strstr(rout,"ger") != 0)
{
if (info == 3) info = 2;
else if (info == 2) info = 3;
else if (info == 8) info = 6;
else if (info == 6) info = 8;
}
else if ( (strstr(rout,"her2") != 0 || strstr(rout,"hpr2") != 0)
&& strstr(rout,"her2k") == 0 )
{
if (info == 8) info = 6;
else if (info == 6) info = 8;
}
}
if (info)
fprintf(stderr, "Parameter %d to routine %s was incorrect\n", info, rout);
vfprintf(stderr, form, argptr);
va_end(argptr);
if (info && !info)
xerbla_(empty, &info); /* Force link of our F77 error handler */
exit(-1);
}
+72
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@@ -0,0 +1,72 @@
#include "f2c.h"
#include <float.h>
#include <stdio.h>
/* *********************************************************************** */
double dlamc3_(double *a, double *b)
{
/* -- LAPACK auxiliary routine (version 3.1) -- */
/* Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. */
/* November 2006 */
/* .. Scalar Arguments .. */
/* .. */
/* Purpose */
/* ======= */
/* DLAMC3 is intended to force A and B to be stored prior to doing */
/* the addition of A and B , for use in situations where optimizers */
/* might hold one of these in a register. */
/* Arguments */
/* ========= */
/* A (input) DOUBLE PRECISION */
/* B (input) DOUBLE PRECISION */
/* The values A and B. */
/* ===================================================================== */
/* .. Executable Statements .. */
double ret_val = *a + *b;
return ret_val;
/* End of DLAMC3 */
} /* dlamc3_ */
/* simpler version of dlamch for the case of IEEE754-compliant FPU module by Piotr Luszczek S.
taken from http://www.mail-archive.com/numpy-discussion@lists.sourceforge.net/msg02448.html */
#ifndef DBL_DIGITS
#define DBL_DIGITS 53
#endif
static const unsigned char lapack_dlamch_tab0[] =
{
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 4, 5, 6, 7, 0, 8, 9, 0, 10, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 4, 5, 6, 7, 0, 8, 9,
0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
};
const double lapack_dlamch_tab1[] =
{
0, FLT_RADIX, DBL_EPSILON, DBL_MAX_EXP, DBL_MIN_EXP, DBL_DIGITS, DBL_MAX,
DBL_EPSILON*FLT_RADIX, 1, DBL_MIN*(1 + DBL_EPSILON), DBL_MIN
};
double dlamch_(char* cmach)
{
return lapack_dlamch_tab1[lapack_dlamch_tab0[(unsigned char)cmach[0]]];
}
+96
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@@ -0,0 +1,96 @@
#include "f2c.h"
static const int CLAPACK_NOT_IMPLEMENTED = -1024;
int sgesdd_(char *jobz, int *m, int *n, float *a, int *lda,
float *s, float *u, int *ldu, float *vt, int *ldvt, float *work,
int *lwork, int *iwork, int *info)
{
*info = CLAPACK_NOT_IMPLEMENTED;
return 0;
}
int dgels_(char *trans, int *m, int *n, int *nrhs, double *a,
int *lda, double *b, int *ldb, double *work, int *lwork, int *info)
{
*info = CLAPACK_NOT_IMPLEMENTED;
return 0;
}
int dgesv_(int *n, int *nrhs, double *a, int *lda, int *ipiv,
double *b, int *ldb, int *info)
{
*info = CLAPACK_NOT_IMPLEMENTED;
return 0;
}
int dgetrf_(int *m, int *n, double *a, int *lda, int *ipiv,
int *info)
{
*info = CLAPACK_NOT_IMPLEMENTED;
return 0;
}
int dposv_(char *uplo, int *n, int *nrhs, double *a, int *
lda, double *b, int *ldb, int *info)
{
*info = CLAPACK_NOT_IMPLEMENTED;
return 0;
}
int dpotrf_(char *uplo, int *n, double *a, int *lda, int *
info)
{
*info = CLAPACK_NOT_IMPLEMENTED;
return 0;
}
int sgels_(char *trans, int *m, int *n, int *nrhs, float *a,
int *lda, float *b, int *ldb, float *work, int *lwork, int *info)
{
*info = CLAPACK_NOT_IMPLEMENTED;
return 0;
}
int sgeev_(char *jobvl, char *jobvr, int *n, float *a, int *
lda, float *wr, float *wi, float *vl, int *ldvl, float *vr, int *
ldvr, float *work, int *lwork, int *info)
{
*info = CLAPACK_NOT_IMPLEMENTED;
return 0;
}
int sgeqrf_(int *m, int *n, float *a, int *lda, float *tau,
float *work, int *lwork, int *info)
{
*info = CLAPACK_NOT_IMPLEMENTED;
return 0;
}
int sgesv_(int *n, int *nrhs, float *a, int *lda, int *ipiv,
float *b, int *ldb, int *info)
{
*info = CLAPACK_NOT_IMPLEMENTED;
return 0;
}
int sgetrf_(int *m, int *n, float *a, int *lda, int *ipiv,
int *info)
{
*info = CLAPACK_NOT_IMPLEMENTED;
return 0;
}
int sposv_(char *uplo, int *n, int *nrhs, float *a, int *
lda, float *b, int *ldb, int *info)
{
*info = CLAPACK_NOT_IMPLEMENTED;
return 0;
}
int spotrf_(char *uplo, int *n, float *a, int *lda, int *
info)
{
*info = CLAPACK_NOT_IMPLEMENTED;
return 0;
}
+25
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@@ -0,0 +1,25 @@
#include "f2c.h"
static const unsigned char lapack_toupper_tab[] =
{
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
90, 91, 92, 93, 94, 95, 96, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 123, 124, 125, 126, 127, 128, 129, 130, 131,
132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,
150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,
186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203,
204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,
222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239,
240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255
};
#define lapack_toupper(c) ((char)lapack_toupper_tab[(unsigned char)(c)])
int lsame_(char *ca, char *cb)
{
return lapack_toupper(ca[0]) == lapack_toupper(cb[0]);
}
+27
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@@ -0,0 +1,27 @@
#include "f2c.h"
double pow_di(double *ap, int *bp)
{
double p = 1;
double x = *ap;
int n = *bp;
if(n != 0)
{
if(n < 0)
{
n = -n;
x = 1/x;
}
unsigned u = (unsigned)n;
for(;;)
{
if((u & 1) != 0)
p *= x;
if((u >>= 1) == 0)
break;
x *= x;
}
}
return p;
}
+25
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@@ -0,0 +1,25 @@
#include "f2c.h"
int pow_ii(int *ap, int *bp)
{
int p;
int x = *ap;
int n = *bp;
if (n <= 0) {
if (n == 0 || x == 1)
return 1;
return x != -1 ? 0 : (n & 1) ? -1 : 1;
}
unsigned u = (unsigned)n;
for(p = 1; ; )
{
if(u & 01)
p *= x;
if(u >>= 1)
x *= x;
else
break;
}
return p;
}
+22
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@@ -0,0 +1,22 @@
/* Unless compiled with -DNO_OVERWRITE, this variant of s_cat allows the
* target of a concatenation to appear on its right-hand side (contrary
* to the Fortran 77 Standard, but in accordance with Fortran 90).
*/
#include "f2c.h"
int s_cat(char *lp, char **rpp, int* rnp, int *np)
{
int i, L = 0;
int n = *np;
for(i = 0; i < n; i++) {
int ni = rnp[i];
if(ni > 0) {
memcpy(lp + L, rpp[i], ni);
L += ni;
}
}
lp[L] = '\0';
return 0;
}
+40
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@@ -0,0 +1,40 @@
#include "f2c.h"
/* compare two strings */
int s_cmp(char *a0, char *b0)
{
int la = (int)strlen(a0);
int lb = (int)strlen(b0);
unsigned char *a, *aend, *b, *bend;
a = (unsigned char *)a0;
b = (unsigned char *)b0;
aend = a + la;
bend = b + lb;
if(la <= lb)
{
while(a < aend)
if(*a != *b)
return( *a - *b );
else
{ ++a; ++b; }
while(b < bend)
if(*b != ' ')
return( ' ' - *b );
else ++b;
}
else
{
while(b < bend)
if(*a == *b)
{ ++a; ++b; }
else
return( *a - *b );
while(a < aend)
if(*a != ' ')
return(*a - ' ');
else ++a;
}
return(0);
}
+71
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@@ -0,0 +1,71 @@
#include "f2c.h"
#include <float.h>
#include <stdio.h>
/* *********************************************************************** */
double slamc3_(float *a, float *b)
{
/* -- LAPACK auxiliary routine (version 3.1) -- */
/* Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. */
/* November 2006 */
/* .. Scalar Arguments .. */
/* .. */
/* Purpose */
/* ======= */
/* SLAMC3 is intended to force A and B to be stored prior to doing */
/* the addition of A and B , for use in situations where optimizers */
/* might hold one of these in a register. */
/* Arguments */
/* ========= */
/* A (input) REAL */
/* B (input) REAL */
/* The values A and B. */
/* ===================================================================== */
/* .. Executable Statements .. */
float ret_val = *a + *b;
return ret_val;
/* End of SLAMC3 */
} /* slamc3_ */
/* simpler version of slamch for the case of IEEE754-compliant FPU module by Piotr Luszczek S.
taken from http://www.mail-archive.com/numpy-discussion@lists.sourceforge.net/msg02448.html */
#ifndef FLT_DIGITS
#define FLT_DIGITS 24
#endif
static const unsigned char lapack_slamch_tab0[] =
{
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 4, 5, 6, 7, 0, 8, 9, 0, 10, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 4, 5, 6, 7, 0, 8, 9,
0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
};
const double lapack_slamch_tab1[] =
{
0, FLT_RADIX, FLT_EPSILON, FLT_MAX_EXP, FLT_MIN_EXP, FLT_DIGITS, FLT_MAX,
FLT_EPSILON*FLT_RADIX, 1, FLT_MIN*(1 + FLT_EPSILON), FLT_MIN
};
double slamch_(char* cmach)
{
return lapack_slamch_tab1[lapack_slamch_tab0[(unsigned char)cmach[0]]];
}
+19
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@@ -0,0 +1,19 @@
/* xerbla.f -- translated by f2c (version 20061008).
You must link the resulting object file with libf2c:
on Microsoft Windows system, link with libf2c.lib;
on Linux or Unix systems, link with .../path/to/libf2c.a -lm
or, if you install libf2c.a in a standard place, with -lf2c -lm
-- in that order, at the end of the command line, as in
cc *.o -lf2c -lm
Source for libf2c is in /netlib/f2c/libf2c.zip, e.g.,
http://www.netlib.org/f2c/libf2c.zip
*/
#include "f2c.h"
/* Subroutine */ int xerbla_(char *srname, int *info)
{
printf("** On entry to %s, parameter number %2i had an illegal value\n", srname, *info);
return 0;
} /* xerbla_ */
+752
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@@ -0,0 +1,752 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b CGEMM
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
// Definition:
// ===========
//
// SUBROUTINE CGEMM(TRANSA,TRANSB,M,N,K,ALPHA,A,LDA,B,LDB,BETA,C,LDC)
//
// .. Scalar Arguments ..
// COMPLEX ALPHA,BETA
// INTEGER K,LDA,LDB,LDC,M,N
// CHARACTER TRANSA,TRANSB
// ..
// .. Array Arguments ..
// COMPLEX A(LDA,*),B(LDB,*),C(LDC,*)
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> CGEMM performs one of the matrix-matrix operations
//>
//> C := alpha*op( A )*op( B ) + beta*C,
//>
//> where op( X ) is one of
//>
//> op( X ) = X or op( X ) = X**T or op( X ) = X**H,
//>
//> alpha and beta are scalars, and A, B and C are matrices, with op( A )
//> an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] TRANSA
//> \verbatim
//> TRANSA is CHARACTER*1
//> On entry, TRANSA specifies the form of op( A ) to be used in
//> the matrix multiplication as follows:
//>
//> TRANSA = 'N' or 'n', op( A ) = A.
//>
//> TRANSA = 'T' or 't', op( A ) = A**T.
//>
//> TRANSA = 'C' or 'c', op( A ) = A**H.
//> \endverbatim
//>
//> \param[in] TRANSB
//> \verbatim
//> TRANSB is CHARACTER*1
//> On entry, TRANSB specifies the form of op( B ) to be used in
//> the matrix multiplication as follows:
//>
//> TRANSB = 'N' or 'n', op( B ) = B.
//>
//> TRANSB = 'T' or 't', op( B ) = B**T.
//>
//> TRANSB = 'C' or 'c', op( B ) = B**H.
//> \endverbatim
//>
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> On entry, M specifies the number of rows of the matrix
//> op( A ) and of the matrix C. M must be at least zero.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> On entry, N specifies the number of columns of the matrix
//> op( B ) and the number of columns of the matrix C. N must be
//> at least zero.
//> \endverbatim
//>
//> \param[in] K
//> \verbatim
//> K is INTEGER
//> On entry, K specifies the number of columns of the matrix
//> op( A ) and the number of rows of the matrix op( B ). K must
//> be at least zero.
//> \endverbatim
//>
//> \param[in] ALPHA
//> \verbatim
//> ALPHA is COMPLEX
//> On entry, ALPHA specifies the scalar alpha.
//> \endverbatim
//>
//> \param[in] A
//> \verbatim
//> A is COMPLEX array, dimension ( LDA, ka ), where ka is
//> k when TRANSA = 'N' or 'n', and is m otherwise.
//> Before entry with TRANSA = 'N' or 'n', the leading m by k
//> part of the array A must contain the matrix A, otherwise
//> the leading k by m part of the array A must contain the
//> matrix A.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> On entry, LDA specifies the first dimension of A as declared
//> in the calling (sub) program. When TRANSA = 'N' or 'n' then
//> LDA must be at least max( 1, m ), otherwise LDA must be at
//> least max( 1, k ).
//> \endverbatim
//>
//> \param[in] B
//> \verbatim
//> B is COMPLEX array, dimension ( LDB, kb ), where kb is
//> n when TRANSB = 'N' or 'n', and is k otherwise.
//> Before entry with TRANSB = 'N' or 'n', the leading k by n
//> part of the array B must contain the matrix B, otherwise
//> the leading n by k part of the array B must contain the
//> matrix B.
//> \endverbatim
//>
//> \param[in] LDB
//> \verbatim
//> LDB is INTEGER
//> On entry, LDB specifies the first dimension of B as declared
//> in the calling (sub) program. When TRANSB = 'N' or 'n' then
//> LDB must be at least max( 1, k ), otherwise LDB must be at
//> least max( 1, n ).
//> \endverbatim
//>
//> \param[in] BETA
//> \verbatim
//> BETA is COMPLEX
//> On entry, BETA specifies the scalar beta. When BETA is
//> supplied as zero then C need not be set on input.
//> \endverbatim
//>
//> \param[in,out] C
//> \verbatim
//> C is COMPLEX array, dimension ( LDC, N )
//> Before entry, the leading m by n part of the array C must
//> contain the matrix C, except when beta is zero, in which
//> case C need not be set on entry.
//> On exit, the array C is overwritten by the m by n matrix
//> ( alpha*op( A )*op( B ) + beta*C ).
//> \endverbatim
//>
//> \param[in] LDC
//> \verbatim
//> LDC is INTEGER
//> On entry, LDC specifies the first dimension of C as declared
//> in the calling (sub) program. LDC must be at least
//> max( 1, m ).
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup complex_blas_level3
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> Level 3 Blas routine.
//>
//> -- Written on 8-February-1989.
//> Jack Dongarra, Argonne National Laboratory.
//> Iain Duff, AERE Harwell.
//> Jeremy Du Croz, Numerical Algorithms Group Ltd.
//> Sven Hammarling, Numerical Algorithms Group Ltd.
//> \endverbatim
//>
// =====================================================================
/* Subroutine */ int cgemm_(char *transa, char *transb, int *m, int *n, int *
k, complex *alpha, complex *a, int *lda, complex *b, int *ldb,
complex *beta, complex *c__, int *ldc)
{
// Table of constant values
complex c_b1 = {1.f,0.f};
complex c_b2 = {0.f,0.f};
// System generated locals
int a_dim1, a_offset, b_dim1, b_offset, c_dim1, c_offset, i__1, i__2,
i__3, i__4, i__5, i__6;
complex q__1, q__2, q__3, q__4;
// Local variables
int i__, j, l, info;
int nota, notb;
complex temp;
int conja, conjb;
int ncola;
extern int lsame_(char *, char *);
int nrowa, nrowb;
extern /* Subroutine */ int xerbla_(char *, int *);
//
// -- Reference BLAS level3 routine (version 3.7.0) --
// -- Reference BLAS is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. External Functions ..
// ..
// .. External Subroutines ..
// ..
// .. Intrinsic Functions ..
// ..
// .. Local Scalars ..
// ..
// .. Parameters ..
// ..
//
// Set NOTA and NOTB as true if A and B respectively are not
// conjugated or transposed, set CONJA and CONJB as true if A and
// B respectively are to be transposed but not conjugated and set
// NROWA, NCOLA and NROWB as the number of rows and columns of A
// and the number of rows of B respectively.
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
b_dim1 = *ldb;
b_offset = 1 + b_dim1;
b -= b_offset;
c_dim1 = *ldc;
c_offset = 1 + c_dim1;
c__ -= c_offset;
// Function Body
nota = lsame_(transa, "N");
notb = lsame_(transb, "N");
conja = lsame_(transa, "C");
conjb = lsame_(transb, "C");
if (nota) {
nrowa = *m;
ncola = *k;
} else {
nrowa = *k;
ncola = *m;
}
if (notb) {
nrowb = *k;
} else {
nrowb = *n;
}
//
// Test the input parameters.
//
info = 0;
if (! nota && ! conja && ! lsame_(transa, "T")) {
info = 1;
} else if (! notb && ! conjb && ! lsame_(transb, "T")) {
info = 2;
} else if (*m < 0) {
info = 3;
} else if (*n < 0) {
info = 4;
} else if (*k < 0) {
info = 5;
} else if (*lda < max(1,nrowa)) {
info = 8;
} else if (*ldb < max(1,nrowb)) {
info = 10;
} else if (*ldc < max(1,*m)) {
info = 13;
}
if (info != 0) {
xerbla_("CGEMM ", &info);
return 0;
}
//
// Quick return if possible.
//
if (*m == 0 || *n == 0 || (alpha->r == 0.f && alpha->i == 0.f || *k == 0)
&& (beta->r == 1.f && beta->i == 0.f)) {
return 0;
}
//
// And when alpha.eq.zero.
//
if (alpha->r == 0.f && alpha->i == 0.f) {
if (beta->r == 0.f && beta->i == 0.f) {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
c__[i__3].r = 0.f, c__[i__3].i = 0.f;
// L10:
}
// L20:
}
} else {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
i__4 = i__ + j * c_dim1;
q__1.r = beta->r * c__[i__4].r - beta->i * c__[i__4].i,
q__1.i = beta->r * c__[i__4].i + beta->i * c__[
i__4].r;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
// L30:
}
// L40:
}
}
return 0;
}
//
// Start the operations.
//
if (notb) {
if (nota) {
//
// Form C := alpha*A*B + beta*C.
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (beta->r == 0.f && beta->i == 0.f) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
c__[i__3].r = 0.f, c__[i__3].i = 0.f;
// L50:
}
} else if (beta->r != 1.f || beta->i != 0.f) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
i__4 = i__ + j * c_dim1;
q__1.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, q__1.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
// L60:
}
}
i__2 = *k;
for (l = 1; l <= i__2; ++l) {
i__3 = l + j * b_dim1;
q__1.r = alpha->r * b[i__3].r - alpha->i * b[i__3].i,
q__1.i = alpha->r * b[i__3].i + alpha->i * b[i__3]
.r;
temp.r = q__1.r, temp.i = q__1.i;
i__3 = *m;
for (i__ = 1; i__ <= i__3; ++i__) {
i__4 = i__ + j * c_dim1;
i__5 = i__ + j * c_dim1;
i__6 = i__ + l * a_dim1;
q__2.r = temp.r * a[i__6].r - temp.i * a[i__6].i,
q__2.i = temp.r * a[i__6].i + temp.i * a[i__6]
.r;
q__1.r = c__[i__5].r + q__2.r, q__1.i = c__[i__5].i +
q__2.i;
c__[i__4].r = q__1.r, c__[i__4].i = q__1.i;
// L70:
}
// L80:
}
// L90:
}
} else if (conja) {
//
// Form C := alpha*A**H*B + beta*C.
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp.r = 0.f, temp.i = 0.f;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
r_cnjg(&q__3, &a[l + i__ * a_dim1]);
i__4 = l + j * b_dim1;
q__2.r = q__3.r * b[i__4].r - q__3.i * b[i__4].i,
q__2.i = q__3.r * b[i__4].i + q__3.i * b[i__4]
.r;
q__1.r = temp.r + q__2.r, q__1.i = temp.i + q__2.i;
temp.r = q__1.r, temp.i = q__1.i;
// L100:
}
if (beta->r == 0.f && beta->i == 0.f) {
i__3 = i__ + j * c_dim1;
q__1.r = alpha->r * temp.r - alpha->i * temp.i,
q__1.i = alpha->r * temp.i + alpha->i *
temp.r;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
} else {
i__3 = i__ + j * c_dim1;
q__2.r = alpha->r * temp.r - alpha->i * temp.i,
q__2.i = alpha->r * temp.i + alpha->i *
temp.r;
i__4 = i__ + j * c_dim1;
q__3.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, q__3.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
q__1.r = q__2.r + q__3.r, q__1.i = q__2.i + q__3.i;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
}
// L110:
}
// L120:
}
} else {
//
// Form C := alpha*A**T*B + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp.r = 0.f, temp.i = 0.f;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
i__4 = l + i__ * a_dim1;
i__5 = l + j * b_dim1;
q__2.r = a[i__4].r * b[i__5].r - a[i__4].i * b[i__5]
.i, q__2.i = a[i__4].r * b[i__5].i + a[i__4]
.i * b[i__5].r;
q__1.r = temp.r + q__2.r, q__1.i = temp.i + q__2.i;
temp.r = q__1.r, temp.i = q__1.i;
// L130:
}
if (beta->r == 0.f && beta->i == 0.f) {
i__3 = i__ + j * c_dim1;
q__1.r = alpha->r * temp.r - alpha->i * temp.i,
q__1.i = alpha->r * temp.i + alpha->i *
temp.r;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
} else {
i__3 = i__ + j * c_dim1;
q__2.r = alpha->r * temp.r - alpha->i * temp.i,
q__2.i = alpha->r * temp.i + alpha->i *
temp.r;
i__4 = i__ + j * c_dim1;
q__3.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, q__3.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
q__1.r = q__2.r + q__3.r, q__1.i = q__2.i + q__3.i;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
}
// L140:
}
// L150:
}
}
} else if (nota) {
if (conjb) {
//
// Form C := alpha*A*B**H + beta*C.
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (beta->r == 0.f && beta->i == 0.f) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
c__[i__3].r = 0.f, c__[i__3].i = 0.f;
// L160:
}
} else if (beta->r != 1.f || beta->i != 0.f) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
i__4 = i__ + j * c_dim1;
q__1.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, q__1.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
// L170:
}
}
i__2 = *k;
for (l = 1; l <= i__2; ++l) {
r_cnjg(&q__2, &b[j + l * b_dim1]);
q__1.r = alpha->r * q__2.r - alpha->i * q__2.i, q__1.i =
alpha->r * q__2.i + alpha->i * q__2.r;
temp.r = q__1.r, temp.i = q__1.i;
i__3 = *m;
for (i__ = 1; i__ <= i__3; ++i__) {
i__4 = i__ + j * c_dim1;
i__5 = i__ + j * c_dim1;
i__6 = i__ + l * a_dim1;
q__2.r = temp.r * a[i__6].r - temp.i * a[i__6].i,
q__2.i = temp.r * a[i__6].i + temp.i * a[i__6]
.r;
q__1.r = c__[i__5].r + q__2.r, q__1.i = c__[i__5].i +
q__2.i;
c__[i__4].r = q__1.r, c__[i__4].i = q__1.i;
// L180:
}
// L190:
}
// L200:
}
} else {
//
// Form C := alpha*A*B**T + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (beta->r == 0.f && beta->i == 0.f) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
c__[i__3].r = 0.f, c__[i__3].i = 0.f;
// L210:
}
} else if (beta->r != 1.f || beta->i != 0.f) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
i__4 = i__ + j * c_dim1;
q__1.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, q__1.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
// L220:
}
}
i__2 = *k;
for (l = 1; l <= i__2; ++l) {
i__3 = j + l * b_dim1;
q__1.r = alpha->r * b[i__3].r - alpha->i * b[i__3].i,
q__1.i = alpha->r * b[i__3].i + alpha->i * b[i__3]
.r;
temp.r = q__1.r, temp.i = q__1.i;
i__3 = *m;
for (i__ = 1; i__ <= i__3; ++i__) {
i__4 = i__ + j * c_dim1;
i__5 = i__ + j * c_dim1;
i__6 = i__ + l * a_dim1;
q__2.r = temp.r * a[i__6].r - temp.i * a[i__6].i,
q__2.i = temp.r * a[i__6].i + temp.i * a[i__6]
.r;
q__1.r = c__[i__5].r + q__2.r, q__1.i = c__[i__5].i +
q__2.i;
c__[i__4].r = q__1.r, c__[i__4].i = q__1.i;
// L230:
}
// L240:
}
// L250:
}
}
} else if (conja) {
if (conjb) {
//
// Form C := alpha*A**H*B**H + beta*C.
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp.r = 0.f, temp.i = 0.f;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
r_cnjg(&q__3, &a[l + i__ * a_dim1]);
r_cnjg(&q__4, &b[j + l * b_dim1]);
q__2.r = q__3.r * q__4.r - q__3.i * q__4.i, q__2.i =
q__3.r * q__4.i + q__3.i * q__4.r;
q__1.r = temp.r + q__2.r, q__1.i = temp.i + q__2.i;
temp.r = q__1.r, temp.i = q__1.i;
// L260:
}
if (beta->r == 0.f && beta->i == 0.f) {
i__3 = i__ + j * c_dim1;
q__1.r = alpha->r * temp.r - alpha->i * temp.i,
q__1.i = alpha->r * temp.i + alpha->i *
temp.r;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
} else {
i__3 = i__ + j * c_dim1;
q__2.r = alpha->r * temp.r - alpha->i * temp.i,
q__2.i = alpha->r * temp.i + alpha->i *
temp.r;
i__4 = i__ + j * c_dim1;
q__3.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, q__3.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
q__1.r = q__2.r + q__3.r, q__1.i = q__2.i + q__3.i;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
}
// L270:
}
// L280:
}
} else {
//
// Form C := alpha*A**H*B**T + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp.r = 0.f, temp.i = 0.f;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
r_cnjg(&q__3, &a[l + i__ * a_dim1]);
i__4 = j + l * b_dim1;
q__2.r = q__3.r * b[i__4].r - q__3.i * b[i__4].i,
q__2.i = q__3.r * b[i__4].i + q__3.i * b[i__4]
.r;
q__1.r = temp.r + q__2.r, q__1.i = temp.i + q__2.i;
temp.r = q__1.r, temp.i = q__1.i;
// L290:
}
if (beta->r == 0.f && beta->i == 0.f) {
i__3 = i__ + j * c_dim1;
q__1.r = alpha->r * temp.r - alpha->i * temp.i,
q__1.i = alpha->r * temp.i + alpha->i *
temp.r;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
} else {
i__3 = i__ + j * c_dim1;
q__2.r = alpha->r * temp.r - alpha->i * temp.i,
q__2.i = alpha->r * temp.i + alpha->i *
temp.r;
i__4 = i__ + j * c_dim1;
q__3.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, q__3.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
q__1.r = q__2.r + q__3.r, q__1.i = q__2.i + q__3.i;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
}
// L300:
}
// L310:
}
}
} else {
if (conjb) {
//
// Form C := alpha*A**T*B**H + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp.r = 0.f, temp.i = 0.f;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
i__4 = l + i__ * a_dim1;
r_cnjg(&q__3, &b[j + l * b_dim1]);
q__2.r = a[i__4].r * q__3.r - a[i__4].i * q__3.i,
q__2.i = a[i__4].r * q__3.i + a[i__4].i *
q__3.r;
q__1.r = temp.r + q__2.r, q__1.i = temp.i + q__2.i;
temp.r = q__1.r, temp.i = q__1.i;
// L320:
}
if (beta->r == 0.f && beta->i == 0.f) {
i__3 = i__ + j * c_dim1;
q__1.r = alpha->r * temp.r - alpha->i * temp.i,
q__1.i = alpha->r * temp.i + alpha->i *
temp.r;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
} else {
i__3 = i__ + j * c_dim1;
q__2.r = alpha->r * temp.r - alpha->i * temp.i,
q__2.i = alpha->r * temp.i + alpha->i *
temp.r;
i__4 = i__ + j * c_dim1;
q__3.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, q__3.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
q__1.r = q__2.r + q__3.r, q__1.i = q__2.i + q__3.i;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
}
// L330:
}
// L340:
}
} else {
//
// Form C := alpha*A**T*B**T + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp.r = 0.f, temp.i = 0.f;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
i__4 = l + i__ * a_dim1;
i__5 = j + l * b_dim1;
q__2.r = a[i__4].r * b[i__5].r - a[i__4].i * b[i__5]
.i, q__2.i = a[i__4].r * b[i__5].i + a[i__4]
.i * b[i__5].r;
q__1.r = temp.r + q__2.r, q__1.i = temp.i + q__2.i;
temp.r = q__1.r, temp.i = q__1.i;
// L350:
}
if (beta->r == 0.f && beta->i == 0.f) {
i__3 = i__ + j * c_dim1;
q__1.r = alpha->r * temp.r - alpha->i * temp.i,
q__1.i = alpha->r * temp.i + alpha->i *
temp.r;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
} else {
i__3 = i__ + j * c_dim1;
q__2.r = alpha->r * temp.r - alpha->i * temp.i,
q__2.i = alpha->r * temp.i + alpha->i *
temp.r;
i__4 = i__ + j * c_dim1;
q__3.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, q__3.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
q__1.r = q__2.r + q__3.r, q__1.i = q__2.i + q__3.i;
c__[i__3].r = q__1.r, c__[i__3].i = q__1.i;
}
// L360:
}
// L370:
}
}
}
return 0;
//
// End of CGEMM .
//
} // cgemm_
+171
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/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DCOPY
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
// Definition:
// ===========
//
// SUBROUTINE DCOPY(N,DX,INCX,DY,INCY)
//
// .. Scalar Arguments ..
// INTEGER INCX,INCY,N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION DX(*),DY(*)
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DCOPY copies a vector, x, to a vector, y.
//> uses unrolled loops for increments equal to 1.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> number of elements in input vector(s)
//> \endverbatim
//>
//> \param[in] DX
//> \verbatim
//> DX is DOUBLE PRECISION array, dimension ( 1 + ( N - 1 )*abs( INCX ) )
//> \endverbatim
//>
//> \param[in] INCX
//> \verbatim
//> INCX is INTEGER
//> storage spacing between elements of DX
//> \endverbatim
//>
//> \param[out] DY
//> \verbatim
//> DY is DOUBLE PRECISION array, dimension ( 1 + ( N - 1 )*abs( INCY ) )
//> \endverbatim
//>
//> \param[in] INCY
//> \verbatim
//> INCY is INTEGER
//> storage spacing between elements of DY
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date November 2017
//
//> \ingroup double_blas_level1
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> jack dongarra, linpack, 3/11/78.
//> modified 12/3/93, array(1) declarations changed to array(*)
//> \endverbatim
//>
// =====================================================================
/* Subroutine */ int dcopy_(int *n, double *dx, int *incx, double *dy, int *
incy)
{
// System generated locals
int i__1;
// Local variables
int i__, m, ix, iy, mp1;
//
// -- Reference BLAS level1 routine (version 3.8.0) --
// -- Reference BLAS is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// November 2017
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Local Scalars ..
// ..
// .. Intrinsic Functions ..
// ..
// Parameter adjustments
--dy;
--dx;
// Function Body
if (*n <= 0) {
return 0;
}
if (*incx == 1 && *incy == 1) {
//
// code for both increments equal to 1
//
//
// clean-up loop
//
m = *n % 7;
if (m != 0) {
i__1 = m;
for (i__ = 1; i__ <= i__1; ++i__) {
dy[i__] = dx[i__];
}
if (*n < 7) {
return 0;
}
}
mp1 = m + 1;
i__1 = *n;
for (i__ = mp1; i__ <= i__1; i__ += 7) {
dy[i__] = dx[i__];
dy[i__ + 1] = dx[i__ + 1];
dy[i__ + 2] = dx[i__ + 2];
dy[i__ + 3] = dx[i__ + 3];
dy[i__ + 4] = dx[i__ + 4];
dy[i__ + 5] = dx[i__ + 5];
dy[i__ + 6] = dx[i__ + 6];
}
} else {
//
// code for unequal increments or equal increments
// not equal to 1
//
ix = 1;
iy = 1;
if (*incx < 0) {
ix = (-(*n) + 1) * *incx + 1;
}
if (*incy < 0) {
iy = (-(*n) + 1) * *incy + 1;
}
i__1 = *n;
for (i__ = 1; i__ <= i__1; ++i__) {
dy[iy] = dx[ix];
ix += *incx;
iy += *incy;
}
}
return 0;
} // dcopy_
+172
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/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DDOT
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
// Definition:
// ===========
//
// DOUBLE PRECISION FUNCTION DDOT(N,DX,INCX,DY,INCY)
//
// .. Scalar Arguments ..
// INTEGER INCX,INCY,N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION DX(*),DY(*)
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DDOT forms the dot product of two vectors.
//> uses unrolled loops for increments equal to one.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> number of elements in input vector(s)
//> \endverbatim
//>
//> \param[in] DX
//> \verbatim
//> DX is DOUBLE PRECISION array, dimension ( 1 + ( N - 1 )*abs( INCX ) )
//> \endverbatim
//>
//> \param[in] INCX
//> \verbatim
//> INCX is INTEGER
//> storage spacing between elements of DX
//> \endverbatim
//>
//> \param[in] DY
//> \verbatim
//> DY is DOUBLE PRECISION array, dimension ( 1 + ( N - 1 )*abs( INCY ) )
//> \endverbatim
//>
//> \param[in] INCY
//> \verbatim
//> INCY is INTEGER
//> storage spacing between elements of DY
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date November 2017
//
//> \ingroup double_blas_level1
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> jack dongarra, linpack, 3/11/78.
//> modified 12/3/93, array(1) declarations changed to array(*)
//> \endverbatim
//>
// =====================================================================
double ddot_(int *n, double *dx, int *incx, double *dy, int *incy)
{
// System generated locals
int i__1;
double ret_val;
// Local variables
int i__, m, ix, iy, mp1;
double dtemp;
//
// -- Reference BLAS level1 routine (version 3.8.0) --
// -- Reference BLAS is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// November 2017
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Local Scalars ..
// ..
// .. Intrinsic Functions ..
// ..
// Parameter adjustments
--dy;
--dx;
// Function Body
ret_val = 0.;
dtemp = 0.;
if (*n <= 0) {
return ret_val;
}
if (*incx == 1 && *incy == 1) {
//
// code for both increments equal to 1
//
//
// clean-up loop
//
m = *n % 5;
if (m != 0) {
i__1 = m;
for (i__ = 1; i__ <= i__1; ++i__) {
dtemp += dx[i__] * dy[i__];
}
if (*n < 5) {
ret_val = dtemp;
return ret_val;
}
}
mp1 = m + 1;
i__1 = *n;
for (i__ = mp1; i__ <= i__1; i__ += 5) {
dtemp = dtemp + dx[i__] * dy[i__] + dx[i__ + 1] * dy[i__ + 1] +
dx[i__ + 2] * dy[i__ + 2] + dx[i__ + 3] * dy[i__ + 3] +
dx[i__ + 4] * dy[i__ + 4];
}
} else {
//
// code for unequal increments or equal increments
// not equal to 1
//
ix = 1;
iy = 1;
if (*incx < 0) {
ix = (-(*n) + 1) * *incx + 1;
}
if (*incy < 0) {
iy = (-(*n) + 1) * *incy + 1;
}
i__1 = *n;
for (i__ = 1; i__ <= i__1; ++i__) {
dtemp += dx[ix] * dy[iy];
ix += *incx;
iy += *incy;
}
}
ret_val = dtemp;
return ret_val;
} // ddot_
+14369
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+444
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/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DGEMM
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
// Definition:
// ===========
//
// SUBROUTINE DGEMM(TRANSA,TRANSB,M,N,K,ALPHA,A,LDA,B,LDB,BETA,C,LDC)
//
// .. Scalar Arguments ..
// DOUBLE PRECISION ALPHA,BETA
// INTEGER K,LDA,LDB,LDC,M,N
// CHARACTER TRANSA,TRANSB
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A(LDA,*),B(LDB,*),C(LDC,*)
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DGEMM performs one of the matrix-matrix operations
//>
//> C := alpha*op( A )*op( B ) + beta*C,
//>
//> where op( X ) is one of
//>
//> op( X ) = X or op( X ) = X**T,
//>
//> alpha and beta are scalars, and A, B and C are matrices, with op( A )
//> an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] TRANSA
//> \verbatim
//> TRANSA is CHARACTER*1
//> On entry, TRANSA specifies the form of op( A ) to be used in
//> the matrix multiplication as follows:
//>
//> TRANSA = 'N' or 'n', op( A ) = A.
//>
//> TRANSA = 'T' or 't', op( A ) = A**T.
//>
//> TRANSA = 'C' or 'c', op( A ) = A**T.
//> \endverbatim
//>
//> \param[in] TRANSB
//> \verbatim
//> TRANSB is CHARACTER*1
//> On entry, TRANSB specifies the form of op( B ) to be used in
//> the matrix multiplication as follows:
//>
//> TRANSB = 'N' or 'n', op( B ) = B.
//>
//> TRANSB = 'T' or 't', op( B ) = B**T.
//>
//> TRANSB = 'C' or 'c', op( B ) = B**T.
//> \endverbatim
//>
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> On entry, M specifies the number of rows of the matrix
//> op( A ) and of the matrix C. M must be at least zero.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> On entry, N specifies the number of columns of the matrix
//> op( B ) and the number of columns of the matrix C. N must be
//> at least zero.
//> \endverbatim
//>
//> \param[in] K
//> \verbatim
//> K is INTEGER
//> On entry, K specifies the number of columns of the matrix
//> op( A ) and the number of rows of the matrix op( B ). K must
//> be at least zero.
//> \endverbatim
//>
//> \param[in] ALPHA
//> \verbatim
//> ALPHA is DOUBLE PRECISION.
//> On entry, ALPHA specifies the scalar alpha.
//> \endverbatim
//>
//> \param[in] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension ( LDA, ka ), where ka is
//> k when TRANSA = 'N' or 'n', and is m otherwise.
//> Before entry with TRANSA = 'N' or 'n', the leading m by k
//> part of the array A must contain the matrix A, otherwise
//> the leading k by m part of the array A must contain the
//> matrix A.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> On entry, LDA specifies the first dimension of A as declared
//> in the calling (sub) program. When TRANSA = 'N' or 'n' then
//> LDA must be at least max( 1, m ), otherwise LDA must be at
//> least max( 1, k ).
//> \endverbatim
//>
//> \param[in] B
//> \verbatim
//> B is DOUBLE PRECISION array, dimension ( LDB, kb ), where kb is
//> n when TRANSB = 'N' or 'n', and is k otherwise.
//> Before entry with TRANSB = 'N' or 'n', the leading k by n
//> part of the array B must contain the matrix B, otherwise
//> the leading n by k part of the array B must contain the
//> matrix B.
//> \endverbatim
//>
//> \param[in] LDB
//> \verbatim
//> LDB is INTEGER
//> On entry, LDB specifies the first dimension of B as declared
//> in the calling (sub) program. When TRANSB = 'N' or 'n' then
//> LDB must be at least max( 1, k ), otherwise LDB must be at
//> least max( 1, n ).
//> \endverbatim
//>
//> \param[in] BETA
//> \verbatim
//> BETA is DOUBLE PRECISION.
//> On entry, BETA specifies the scalar beta. When BETA is
//> supplied as zero then C need not be set on input.
//> \endverbatim
//>
//> \param[in,out] C
//> \verbatim
//> C is DOUBLE PRECISION array, dimension ( LDC, N )
//> Before entry, the leading m by n part of the array C must
//> contain the matrix C, except when beta is zero, in which
//> case C need not be set on entry.
//> On exit, the array C is overwritten by the m by n matrix
//> ( alpha*op( A )*op( B ) + beta*C ).
//> \endverbatim
//>
//> \param[in] LDC
//> \verbatim
//> LDC is INTEGER
//> On entry, LDC specifies the first dimension of C as declared
//> in the calling (sub) program. LDC must be at least
//> max( 1, m ).
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup double_blas_level3
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> Level 3 Blas routine.
//>
//> -- Written on 8-February-1989.
//> Jack Dongarra, Argonne National Laboratory.
//> Iain Duff, AERE Harwell.
//> Jeremy Du Croz, Numerical Algorithms Group Ltd.
//> Sven Hammarling, Numerical Algorithms Group Ltd.
//> \endverbatim
//>
// =====================================================================
/* Subroutine */ int dgemm_(char *transa, char *transb, int *m, int *n, int *
k, double *alpha, double *a, int *lda, double *b, int *ldb, double *
beta, double *c__, int *ldc)
{
// System generated locals
int a_dim1, a_offset, b_dim1, b_offset, c_dim1, c_offset, i__1, i__2,
i__3;
// Local variables
int i__, j, l, info;
int nota, notb;
double temp;
int ncola;
extern int lsame_(char *, char *);
int nrowa, nrowb;
extern /* Subroutine */ int xerbla_(char *, int *);
//
// -- Reference BLAS level3 routine (version 3.7.0) --
// -- Reference BLAS is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. External Functions ..
// ..
// .. External Subroutines ..
// ..
// .. Intrinsic Functions ..
// ..
// .. Local Scalars ..
// ..
// .. Parameters ..
// ..
//
// Set NOTA and NOTB as true if A and B respectively are not
// transposed and set NROWA, NCOLA and NROWB as the number of rows
// and columns of A and the number of rows of B respectively.
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
b_dim1 = *ldb;
b_offset = 1 + b_dim1;
b -= b_offset;
c_dim1 = *ldc;
c_offset = 1 + c_dim1;
c__ -= c_offset;
// Function Body
nota = lsame_(transa, "N");
notb = lsame_(transb, "N");
if (nota) {
nrowa = *m;
ncola = *k;
} else {
nrowa = *k;
ncola = *m;
}
if (notb) {
nrowb = *k;
} else {
nrowb = *n;
}
//
// Test the input parameters.
//
info = 0;
if (! nota && ! lsame_(transa, "C") && ! lsame_(transa, "T")) {
info = 1;
} else if (! notb && ! lsame_(transb, "C") && ! lsame_(transb, "T")) {
info = 2;
} else if (*m < 0) {
info = 3;
} else if (*n < 0) {
info = 4;
} else if (*k < 0) {
info = 5;
} else if (*lda < max(1,nrowa)) {
info = 8;
} else if (*ldb < max(1,nrowb)) {
info = 10;
} else if (*ldc < max(1,*m)) {
info = 13;
}
if (info != 0) {
xerbla_("DGEMM ", &info);
return 0;
}
//
// Quick return if possible.
//
if (*m == 0 || *n == 0 || (*alpha == 0. || *k == 0) && *beta == 1.) {
return 0;
}
//
// And if alpha.eq.zero.
//
if (*alpha == 0.) {
if (*beta == 0.) {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + j * c_dim1] = 0.;
// L10:
}
// L20:
}
} else {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + j * c_dim1] = *beta * c__[i__ + j * c_dim1];
// L30:
}
// L40:
}
}
return 0;
}
//
// Start the operations.
//
if (notb) {
if (nota) {
//
// Form C := alpha*A*B + beta*C.
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (*beta == 0.) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + j * c_dim1] = 0.;
// L50:
}
} else if (*beta != 1.) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + j * c_dim1] = *beta * c__[i__ + j * c_dim1];
// L60:
}
}
i__2 = *k;
for (l = 1; l <= i__2; ++l) {
temp = *alpha * b[l + j * b_dim1];
i__3 = *m;
for (i__ = 1; i__ <= i__3; ++i__) {
c__[i__ + j * c_dim1] += temp * a[i__ + l * a_dim1];
// L70:
}
// L80:
}
// L90:
}
} else {
//
// Form C := alpha*A**T*B + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp = 0.;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
temp += a[l + i__ * a_dim1] * b[l + j * b_dim1];
// L100:
}
if (*beta == 0.) {
c__[i__ + j * c_dim1] = *alpha * temp;
} else {
c__[i__ + j * c_dim1] = *alpha * temp + *beta * c__[
i__ + j * c_dim1];
}
// L110:
}
// L120:
}
}
} else {
if (nota) {
//
// Form C := alpha*A*B**T + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (*beta == 0.) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + j * c_dim1] = 0.;
// L130:
}
} else if (*beta != 1.) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + j * c_dim1] = *beta * c__[i__ + j * c_dim1];
// L140:
}
}
i__2 = *k;
for (l = 1; l <= i__2; ++l) {
temp = *alpha * b[j + l * b_dim1];
i__3 = *m;
for (i__ = 1; i__ <= i__3; ++i__) {
c__[i__ + j * c_dim1] += temp * a[i__ + l * a_dim1];
// L150:
}
// L160:
}
// L170:
}
} else {
//
// Form C := alpha*A**T*B**T + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp = 0.;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
temp += a[l + i__ * a_dim1] * b[j + l * b_dim1];
// L180:
}
if (*beta == 0.) {
c__[i__ + j * c_dim1] = *alpha * temp;
} else {
c__[i__ + j * c_dim1] = *alpha * temp + *beta * c__[
i__ + j * c_dim1];
}
// L190:
}
// L200:
}
}
}
return 0;
//
// End of DGEMM .
//
} // dgemm_
+370
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@@ -0,0 +1,370 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DGEMV
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
// Definition:
// ===========
//
// SUBROUTINE DGEMV(TRANS,M,N,ALPHA,A,LDA,X,INCX,BETA,Y,INCY)
//
// .. Scalar Arguments ..
// DOUBLE PRECISION ALPHA,BETA
// INTEGER INCX,INCY,LDA,M,N
// CHARACTER TRANS
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A(LDA,*),X(*),Y(*)
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DGEMV performs one of the matrix-vector operations
//>
//> y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y,
//>
//> where alpha and beta are scalars, x and y are vectors and A is an
//> m by n matrix.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] TRANS
//> \verbatim
//> TRANS is CHARACTER*1
//> On entry, TRANS specifies the operation to be performed as
//> follows:
//>
//> TRANS = 'N' or 'n' y := alpha*A*x + beta*y.
//>
//> TRANS = 'T' or 't' y := alpha*A**T*x + beta*y.
//>
//> TRANS = 'C' or 'c' y := alpha*A**T*x + beta*y.
//> \endverbatim
//>
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> On entry, M specifies the number of rows of the matrix A.
//> M must be at least zero.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> On entry, N specifies the number of columns of the matrix A.
//> N must be at least zero.
//> \endverbatim
//>
//> \param[in] ALPHA
//> \verbatim
//> ALPHA is DOUBLE PRECISION.
//> On entry, ALPHA specifies the scalar alpha.
//> \endverbatim
//>
//> \param[in] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension ( LDA, N )
//> Before entry, the leading m by n part of the array A must
//> contain the matrix of coefficients.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> On entry, LDA specifies the first dimension of A as declared
//> in the calling (sub) program. LDA must be at least
//> max( 1, m ).
//> \endverbatim
//>
//> \param[in] X
//> \verbatim
//> X is DOUBLE PRECISION array, dimension at least
//> ( 1 + ( n - 1 )*abs( INCX ) ) when TRANS = 'N' or 'n'
//> and at least
//> ( 1 + ( m - 1 )*abs( INCX ) ) otherwise.
//> Before entry, the incremented array X must contain the
//> vector x.
//> \endverbatim
//>
//> \param[in] INCX
//> \verbatim
//> INCX is INTEGER
//> On entry, INCX specifies the increment for the elements of
//> X. INCX must not be zero.
//> \endverbatim
//>
//> \param[in] BETA
//> \verbatim
//> BETA is DOUBLE PRECISION.
//> On entry, BETA specifies the scalar beta. When BETA is
//> supplied as zero then Y need not be set on input.
//> \endverbatim
//>
//> \param[in,out] Y
//> \verbatim
//> Y is DOUBLE PRECISION array, dimension at least
//> ( 1 + ( m - 1 )*abs( INCY ) ) when TRANS = 'N' or 'n'
//> and at least
//> ( 1 + ( n - 1 )*abs( INCY ) ) otherwise.
//> Before entry with BETA non-zero, the incremented array Y
//> must contain the vector y. On exit, Y is overwritten by the
//> updated vector y.
//> \endverbatim
//>
//> \param[in] INCY
//> \verbatim
//> INCY is INTEGER
//> On entry, INCY specifies the increment for the elements of
//> Y. INCY must not be zero.
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup double_blas_level2
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> Level 2 Blas routine.
//> The vector and matrix arguments are not referenced when N = 0, or M = 0
//>
//> -- Written on 22-October-1986.
//> Jack Dongarra, Argonne National Lab.
//> Jeremy Du Croz, Nag Central Office.
//> Sven Hammarling, Nag Central Office.
//> Richard Hanson, Sandia National Labs.
//> \endverbatim
//>
// =====================================================================
/* Subroutine */ int dgemv_(char *trans, int *m, int *n, double *alpha,
double *a, int *lda, double *x, int *incx, double *beta, double *y,
int *incy)
{
// System generated locals
int a_dim1, a_offset, i__1, i__2;
// Local variables
int i__, j, ix, iy, jx, jy, kx, ky, info;
double temp;
int lenx, leny;
extern int lsame_(char *, char *);
extern /* Subroutine */ int xerbla_(char *, int *);
//
// -- Reference BLAS level2 routine (version 3.7.0) --
// -- Reference BLAS is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. External Functions ..
// ..
// .. External Subroutines ..
// ..
// .. Intrinsic Functions ..
// ..
//
// Test the input parameters.
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
--x;
--y;
// Function Body
info = 0;
if (! lsame_(trans, "N") && ! lsame_(trans, "T") && ! lsame_(trans, "C"))
{
info = 1;
} else if (*m < 0) {
info = 2;
} else if (*n < 0) {
info = 3;
} else if (*lda < max(1,*m)) {
info = 6;
} else if (*incx == 0) {
info = 8;
} else if (*incy == 0) {
info = 11;
}
if (info != 0) {
xerbla_("DGEMV ", &info);
return 0;
}
//
// Quick return if possible.
//
if (*m == 0 || *n == 0 || *alpha == 0. && *beta == 1.) {
return 0;
}
//
// Set LENX and LENY, the lengths of the vectors x and y, and set
// up the start points in X and Y.
//
if (lsame_(trans, "N")) {
lenx = *n;
leny = *m;
} else {
lenx = *m;
leny = *n;
}
if (*incx > 0) {
kx = 1;
} else {
kx = 1 - (lenx - 1) * *incx;
}
if (*incy > 0) {
ky = 1;
} else {
ky = 1 - (leny - 1) * *incy;
}
//
// Start the operations. In this version the elements of A are
// accessed sequentially with one pass through A.
//
// First form y := beta*y.
//
if (*beta != 1.) {
if (*incy == 1) {
if (*beta == 0.) {
i__1 = leny;
for (i__ = 1; i__ <= i__1; ++i__) {
y[i__] = 0.;
// L10:
}
} else {
i__1 = leny;
for (i__ = 1; i__ <= i__1; ++i__) {
y[i__] = *beta * y[i__];
// L20:
}
}
} else {
iy = ky;
if (*beta == 0.) {
i__1 = leny;
for (i__ = 1; i__ <= i__1; ++i__) {
y[iy] = 0.;
iy += *incy;
// L30:
}
} else {
i__1 = leny;
for (i__ = 1; i__ <= i__1; ++i__) {
y[iy] = *beta * y[iy];
iy += *incy;
// L40:
}
}
}
}
if (*alpha == 0.) {
return 0;
}
if (lsame_(trans, "N")) {
//
// Form y := alpha*A*x + y.
//
jx = kx;
if (*incy == 1) {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
temp = *alpha * x[jx];
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
y[i__] += temp * a[i__ + j * a_dim1];
// L50:
}
jx += *incx;
// L60:
}
} else {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
temp = *alpha * x[jx];
iy = ky;
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
y[iy] += temp * a[i__ + j * a_dim1];
iy += *incy;
// L70:
}
jx += *incx;
// L80:
}
}
} else {
//
// Form y := alpha*A**T*x + y.
//
jy = ky;
if (*incx == 1) {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
temp = 0.;
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp += a[i__ + j * a_dim1] * x[i__];
// L90:
}
y[jy] += *alpha * temp;
jy += *incy;
// L100:
}
} else {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
temp = 0.;
ix = kx;
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp += a[i__ + j * a_dim1] * x[ix];
ix += *incx;
// L110:
}
y[jy] += *alpha * temp;
jy += *incy;
// L120:
}
}
}
return 0;
//
// End of DGEMV .
//
} // dgemv_
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/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DISNAN tests input for NaN.
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DISNAN + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/disnan.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/disnan.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/disnan.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// LOGICAL FUNCTION DISNAN( DIN )
//
// .. Scalar Arguments ..
// DOUBLE PRECISION, INTENT(IN) :: DIN
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DISNAN returns .TRUE. if its argument is NaN, and .FALSE.
//> otherwise. To be replaced by the Fortran 2003 intrinsic in the
//> future.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] DIN
//> \verbatim
//> DIN is DOUBLE PRECISION
//> Input to test for NaN.
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date June 2017
//
//> \ingroup OTHERauxiliary
//
// =====================================================================
int disnan_(double *din)
{
// System generated locals
int ret_val;
// Local variables
extern int dlaisnan_(double *, double *);
//
// -- LAPACK auxiliary routine (version 3.7.1) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// June 2017
//
// .. Scalar Arguments ..
// ..
//
// =====================================================================
//
// .. External Functions ..
// ..
// .. Executable Statements ..
ret_val = dlaisnan_(din, din);
return ret_val;
} // disnan_
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
//> \brief \b DLAISNAN tests input for NaN by comparing two arguments for inequality.
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DLAISNAN + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dlaisnan.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dlaisnan.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dlaisnan.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// LOGICAL FUNCTION DLAISNAN( DIN1, DIN2 )
//
// .. Scalar Arguments ..
// DOUBLE PRECISION, INTENT(IN) :: DIN1, DIN2
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> This routine is not for general use. It exists solely to avoid
//> over-optimization in DISNAN.
//>
//> DLAISNAN checks for NaNs by comparing its two arguments for
//> inequality. NaN is the only floating-point value where NaN != NaN
//> returns .TRUE. To check for NaNs, pass the same variable as both
//> arguments.
//>
//> A compiler must assume that the two arguments are
//> not the same variable, and the test will not be optimized away.
//> Interprocedural or whole-program optimization may delete this
//> test. The ISNAN functions will be replaced by the correct
//> Fortran 03 intrinsic once the intrinsic is widely available.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] DIN1
//> \verbatim
//> DIN1 is DOUBLE PRECISION
//> \endverbatim
//>
//> \param[in] DIN2
//> \verbatim
//> DIN2 is DOUBLE PRECISION
//> Two numbers to compare for inequality.
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date June 2017
//
//> \ingroup OTHERauxiliary
//
// =====================================================================
int dlaisnan_(double *din1, double *din2)
{
// System generated locals
int ret_val;
//
// -- LAPACK auxiliary routine (version 3.7.1) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// June 2017
//
// .. Scalar Arguments ..
// ..
//
// =====================================================================
//
// .. Executable Statements ..
ret_val = *din1 != *din2;
return ret_val;
} // dlaisnan_
+184
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/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DLACPY copies all or part of one two-dimensional array to another.
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DLACPY + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dlacpy.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dlacpy.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dlacpy.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// SUBROUTINE DLACPY( UPLO, M, N, A, LDA, B, LDB )
//
// .. Scalar Arguments ..
// CHARACTER UPLO
// INTEGER LDA, LDB, M, N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A( LDA, * ), B( LDB, * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DLACPY copies all or part of a two-dimensional matrix A to another
//> matrix B.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] UPLO
//> \verbatim
//> UPLO is CHARACTER*1
//> Specifies the part of the matrix A to be copied to B.
//> = 'U': Upper triangular part
//> = 'L': Lower triangular part
//> Otherwise: All of the matrix A
//> \endverbatim
//>
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> The number of rows of the matrix A. M >= 0.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The number of columns of the matrix A. N >= 0.
//> \endverbatim
//>
//> \param[in] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension (LDA,N)
//> The m by n matrix A. If UPLO = 'U', only the upper triangle
//> or trapezoid is accessed; if UPLO = 'L', only the lower
//> triangle or trapezoid is accessed.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> The leading dimension of the array A. LDA >= max(1,M).
//> \endverbatim
//>
//> \param[out] B
//> \verbatim
//> B is DOUBLE PRECISION array, dimension (LDB,N)
//> On exit, B = A in the locations specified by UPLO.
//> \endverbatim
//>
//> \param[in] LDB
//> \verbatim
//> LDB is INTEGER
//> The leading dimension of the array B. LDB >= max(1,M).
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup OTHERauxiliary
//
// =====================================================================
/* Subroutine */ int dlacpy_(char *uplo, int *m, int *n, double *a, int *lda,
double *b, int *ldb)
{
// System generated locals
int a_dim1, a_offset, b_dim1, b_offset, i__1, i__2;
// Local variables
int i__, j;
extern int lsame_(char *, char *);
//
// -- LAPACK auxiliary routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Local Scalars ..
// ..
// .. External Functions ..
// ..
// .. Intrinsic Functions ..
// ..
// .. Executable Statements ..
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
b_dim1 = *ldb;
b_offset = 1 + b_dim1;
b -= b_offset;
// Function Body
if (lsame_(uplo, "U")) {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = min(j,*m);
for (i__ = 1; i__ <= i__2; ++i__) {
b[i__ + j * b_dim1] = a[i__ + j * a_dim1];
// L10:
}
// L20:
}
} else if (lsame_(uplo, "L")) {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = j; i__ <= i__2; ++i__) {
b[i__ + j * b_dim1] = a[i__ + j * a_dim1];
// L30:
}
// L40:
}
} else {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
b[i__ + j * b_dim1] = a[i__ + j * a_dim1];
// L50:
}
// L60:
}
}
return 0;
//
// End of DLACPY
//
} // dlacpy_
+367
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/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DCOMBSSQ adds two scaled sum of squares quantities.
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//
// Definition:
// ===========
//
// SUBROUTINE DCOMBSSQ( V1, V2 )
//
// .. Array Arguments ..
// DOUBLE PRECISION V1( 2 ), V2( 2 )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DCOMBSSQ adds two scaled sum of squares quantities, V1 := V1 + V2.
//> That is,
//>
//> V1_scale**2 * V1_sumsq := V1_scale**2 * V1_sumsq
//> + V2_scale**2 * V2_sumsq
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in,out] V1
//> \verbatim
//> V1 is DOUBLE PRECISION array, dimension (2).
//> The first scaled sum.
//> V1(1) = V1_scale, V1(2) = V1_sumsq.
//> \endverbatim
//>
//> \param[in] V2
//> \verbatim
//> V2 is DOUBLE PRECISION array, dimension (2).
//> The second scaled sum.
//> V2(1) = V2_scale, V2(2) = V2_sumsq.
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date November 2018
//
//> \ingroup OTHERauxiliary
//
// =====================================================================
/* Subroutine */ int dcombssq_(double *v1, double *v2)
{
// System generated locals
double d__1;
//
// -- LAPACK auxiliary routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// November 2018
//
// .. Array Arguments ..
// ..
//
//=====================================================================
//
// .. Parameters ..
// ..
// .. Executable Statements ..
//
// Parameter adjustments
--v2;
--v1;
// Function Body
if (v1[1] >= v2[1]) {
if (v1[1] != 0.) {
// Computing 2nd power
d__1 = v2[1] / v1[1];
v1[2] += d__1 * d__1 * v2[2];
}
} else {
// Computing 2nd power
d__1 = v1[1] / v2[1];
v1[2] = v2[2] + d__1 * d__1 * v1[2];
v1[1] = v2[1];
}
return 0;
//
// End of DCOMBSSQ
//
} // dcombssq_
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
//> \brief \b DLANGE returns the value of the 1-norm, Frobenius norm, infinity-norm, or the largest absolute value of any element of a general rectangular matrix.
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DLANGE + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dlange.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dlange.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dlange.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// DOUBLE PRECISION FUNCTION DLANGE( NORM, M, N, A, LDA, WORK )
//
// .. Scalar Arguments ..
// CHARACTER NORM
// INTEGER LDA, M, N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A( LDA, * ), WORK( * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DLANGE returns the value of the one norm, or the Frobenius norm, or
//> the infinity norm, or the element of largest absolute value of a
//> real matrix A.
//> \endverbatim
//>
//> \return DLANGE
//> \verbatim
//>
//> DLANGE = ( max(abs(A(i,j))), NORM = 'M' or 'm'
//> (
//> ( norm1(A), NORM = '1', 'O' or 'o'
//> (
//> ( normI(A), NORM = 'I' or 'i'
//> (
//> ( normF(A), NORM = 'F', 'f', 'E' or 'e'
//>
//> where norm1 denotes the one norm of a matrix (maximum column sum),
//> normI denotes the infinity norm of a matrix (maximum row sum) and
//> normF denotes the Frobenius norm of a matrix (square root of sum of
//> squares). Note that max(abs(A(i,j))) is not a consistent matrix norm.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] NORM
//> \verbatim
//> NORM is CHARACTER*1
//> Specifies the value to be returned in DLANGE as described
//> above.
//> \endverbatim
//>
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> The number of rows of the matrix A. M >= 0. When M = 0,
//> DLANGE is set to zero.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The number of columns of the matrix A. N >= 0. When N = 0,
//> DLANGE is set to zero.
//> \endverbatim
//>
//> \param[in] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension (LDA,N)
//> The m by n matrix A.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> The leading dimension of the array A. LDA >= max(M,1).
//> \endverbatim
//>
//> \param[out] WORK
//> \verbatim
//> WORK is DOUBLE PRECISION array, dimension (MAX(1,LWORK)),
//> where LWORK >= M when NORM = 'I'; otherwise, WORK is not
//> referenced.
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup doubleGEauxiliary
//
// =====================================================================
double dlange_(char *norm, int *m, int *n, double *a, int *lda, double *work)
{
// Table of constant values
int c__1 = 1;
// System generated locals
int a_dim1, a_offset, i__1, i__2;
double ret_val, d__1;
// Local variables
extern /* Subroutine */ int dcombssq_(double *, double *);
int i__, j;
double sum, ssq[2], temp;
extern int lsame_(char *, char *);
double value;
extern int disnan_(double *);
extern /* Subroutine */ int dlassq_(int *, double *, int *, double *,
double *);
double colssq[2];
//
// -- LAPACK auxiliary routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
//=====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. Local Arrays ..
// ..
// .. External Subroutines ..
// ..
// .. External Functions ..
// ..
// .. Intrinsic Functions ..
// ..
// .. Executable Statements ..
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
--work;
// Function Body
if (min(*m,*n) == 0) {
value = 0.;
} else if (lsame_(norm, "M")) {
//
// Find max(abs(A(i,j))).
//
value = 0.;
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp = (d__1 = a[i__ + j * a_dim1], abs(d__1));
if (value < temp || disnan_(&temp)) {
value = temp;
}
// L10:
}
// L20:
}
} else if (lsame_(norm, "O") || *(unsigned char *)norm == '1') {
//
// Find norm1(A).
//
value = 0.;
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
sum = 0.;
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
sum += (d__1 = a[i__ + j * a_dim1], abs(d__1));
// L30:
}
if (value < sum || disnan_(&sum)) {
value = sum;
}
// L40:
}
} else if (lsame_(norm, "I")) {
//
// Find normI(A).
//
i__1 = *m;
for (i__ = 1; i__ <= i__1; ++i__) {
work[i__] = 0.;
// L50:
}
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
work[i__] += (d__1 = a[i__ + j * a_dim1], abs(d__1));
// L60:
}
// L70:
}
value = 0.;
i__1 = *m;
for (i__ = 1; i__ <= i__1; ++i__) {
temp = work[i__];
if (value < temp || disnan_(&temp)) {
value = temp;
}
// L80:
}
} else if (lsame_(norm, "F") || lsame_(norm, "E")) {
//
// Find normF(A).
// SSQ(1) is scale
// SSQ(2) is sum-of-squares
// For better accuracy, sum each column separately.
//
ssq[0] = 0.;
ssq[1] = 1.;
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
colssq[0] = 0.;
colssq[1] = 1.;
dlassq_(m, &a[j * a_dim1 + 1], &c__1, colssq, &colssq[1]);
dcombssq_(ssq, colssq);
// L90:
}
value = ssq[0] * sqrt(ssq[1]);
}
ret_val = value;
return ret_val;
//
// End of DLANGE
//
} // dlange_
+125
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@@ -0,0 +1,125 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DLAPY2 returns sqrt(x2+y2).
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DLAPY2 + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dlapy2.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dlapy2.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dlapy2.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// DOUBLE PRECISION FUNCTION DLAPY2( X, Y )
//
// .. Scalar Arguments ..
// DOUBLE PRECISION X, Y
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DLAPY2 returns sqrt(x**2+y**2), taking care not to cause unnecessary
//> overflow.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] X
//> \verbatim
//> X is DOUBLE PRECISION
//> \endverbatim
//>
//> \param[in] Y
//> \verbatim
//> Y is DOUBLE PRECISION
//> X and Y specify the values x and y.
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date June 2017
//
//> \ingroup OTHERauxiliary
//
// =====================================================================
double dlapy2_(double *x, double *y)
{
// System generated locals
double ret_val, d__1;
// Local variables
int x_is_nan__, y_is_nan__;
double w, z__, xabs, yabs;
extern int disnan_(double *);
//
// -- LAPACK auxiliary routine (version 3.7.1) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// June 2017
//
// .. Scalar Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. External Functions ..
// ..
// .. Intrinsic Functions ..
// ..
// .. Executable Statements ..
//
x_is_nan__ = disnan_(x);
y_is_nan__ = disnan_(y);
if (x_is_nan__) {
ret_val = *x;
}
if (y_is_nan__) {
ret_val = *y;
}
if (! (x_is_nan__ || y_is_nan__)) {
xabs = abs(*x);
yabs = abs(*y);
w = max(xabs,yabs);
z__ = min(xabs,yabs);
if (z__ == 0.) {
ret_val = w;
} else {
// Computing 2nd power
d__1 = z__ / w;
ret_val = w * sqrt(d__1 * d__1 + 1.);
}
}
return ret_val;
//
// End of DLAPY2
//
} // dlapy2_
+768
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@@ -0,0 +1,768 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DGER
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
// Definition:
// ===========
//
// SUBROUTINE DGER(M,N,ALPHA,X,INCX,Y,INCY,A,LDA)
//
// .. Scalar Arguments ..
// DOUBLE PRECISION ALPHA
// INTEGER INCX,INCY,LDA,M,N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A(LDA,*),X(*),Y(*)
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DGER performs the rank 1 operation
//>
//> A := alpha*x*y**T + A,
//>
//> where alpha is a scalar, x is an m element vector, y is an n element
//> vector and A is an m by n matrix.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> On entry, M specifies the number of rows of the matrix A.
//> M must be at least zero.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> On entry, N specifies the number of columns of the matrix A.
//> N must be at least zero.
//> \endverbatim
//>
//> \param[in] ALPHA
//> \verbatim
//> ALPHA is DOUBLE PRECISION.
//> On entry, ALPHA specifies the scalar alpha.
//> \endverbatim
//>
//> \param[in] X
//> \verbatim
//> X is DOUBLE PRECISION array, dimension at least
//> ( 1 + ( m - 1 )*abs( INCX ) ).
//> Before entry, the incremented array X must contain the m
//> element vector x.
//> \endverbatim
//>
//> \param[in] INCX
//> \verbatim
//> INCX is INTEGER
//> On entry, INCX specifies the increment for the elements of
//> X. INCX must not be zero.
//> \endverbatim
//>
//> \param[in] Y
//> \verbatim
//> Y is DOUBLE PRECISION array, dimension at least
//> ( 1 + ( n - 1 )*abs( INCY ) ).
//> Before entry, the incremented array Y must contain the n
//> element vector y.
//> \endverbatim
//>
//> \param[in] INCY
//> \verbatim
//> INCY is INTEGER
//> On entry, INCY specifies the increment for the elements of
//> Y. INCY must not be zero.
//> \endverbatim
//>
//> \param[in,out] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension ( LDA, N )
//> Before entry, the leading m by n part of the array A must
//> contain the matrix of coefficients. On exit, A is
//> overwritten by the updated matrix.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> On entry, LDA specifies the first dimension of A as declared
//> in the calling (sub) program. LDA must be at least
//> max( 1, m ).
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup double_blas_level2
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> Level 2 Blas routine.
//>
//> -- Written on 22-October-1986.
//> Jack Dongarra, Argonne National Lab.
//> Jeremy Du Croz, Nag Central Office.
//> Sven Hammarling, Nag Central Office.
//> Richard Hanson, Sandia National Labs.
//> \endverbatim
//>
// =====================================================================
/* Subroutine */ int dger_(int *m, int *n, double *alpha, double *x, int *
incx, double *y, int *incy, double *a, int *lda)
{
// System generated locals
int a_dim1, a_offset, i__1, i__2;
// Local variables
int i__, j, ix, jy, kx, info;
double temp;
extern /* Subroutine */ int xerbla_(char *, int *);
//
// -- Reference BLAS level2 routine (version 3.7.0) --
// -- Reference BLAS is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. External Subroutines ..
// ..
// .. Intrinsic Functions ..
// ..
//
// Test the input parameters.
//
// Parameter adjustments
--x;
--y;
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
// Function Body
info = 0;
if (*m < 0) {
info = 1;
} else if (*n < 0) {
info = 2;
} else if (*incx == 0) {
info = 5;
} else if (*incy == 0) {
info = 7;
} else if (*lda < max(1,*m)) {
info = 9;
}
if (info != 0) {
xerbla_("DGER ", &info);
return 0;
}
//
// Quick return if possible.
//
if (*m == 0 || *n == 0 || *alpha == 0.) {
return 0;
}
//
// Start the operations. In this version the elements of A are
// accessed sequentially with one pass through A.
//
if (*incy > 0) {
jy = 1;
} else {
jy = 1 - (*n - 1) * *incy;
}
if (*incx == 1) {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (y[jy] != 0.) {
temp = *alpha * y[jy];
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
a[i__ + j * a_dim1] += x[i__] * temp;
// L10:
}
}
jy += *incy;
// L20:
}
} else {
if (*incx > 0) {
kx = 1;
} else {
kx = 1 - (*m - 1) * *incx;
}
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (y[jy] != 0.) {
temp = *alpha * y[jy];
ix = kx;
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
a[i__ + j * a_dim1] += x[ix] * temp;
ix += *incx;
// L30:
}
}
jy += *incy;
// L40:
}
}
return 0;
//
// End of DGER .
//
} // dger_
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
//> \brief \b DLARF applies an elementary reflector to a general rectangular matrix.
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DLARF + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dlarf.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dlarf.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dlarf.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// SUBROUTINE DLARF( SIDE, M, N, V, INCV, TAU, C, LDC, WORK )
//
// .. Scalar Arguments ..
// CHARACTER SIDE
// INTEGER INCV, LDC, M, N
// DOUBLE PRECISION TAU
// ..
// .. Array Arguments ..
// DOUBLE PRECISION C( LDC, * ), V( * ), WORK( * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DLARF applies a real elementary reflector H to a real m by n matrix
//> C, from either the left or the right. H is represented in the form
//>
//> H = I - tau * v * v**T
//>
//> where tau is a real scalar and v is a real vector.
//>
//> If tau = 0, then H is taken to be the unit matrix.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] SIDE
//> \verbatim
//> SIDE is CHARACTER*1
//> = 'L': form H * C
//> = 'R': form C * H
//> \endverbatim
//>
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> The number of rows of the matrix C.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The number of columns of the matrix C.
//> \endverbatim
//>
//> \param[in] V
//> \verbatim
//> V is DOUBLE PRECISION array, dimension
//> (1 + (M-1)*abs(INCV)) if SIDE = 'L'
//> or (1 + (N-1)*abs(INCV)) if SIDE = 'R'
//> The vector v in the representation of H. V is not used if
//> TAU = 0.
//> \endverbatim
//>
//> \param[in] INCV
//> \verbatim
//> INCV is INTEGER
//> The increment between elements of v. INCV <> 0.
//> \endverbatim
//>
//> \param[in] TAU
//> \verbatim
//> TAU is DOUBLE PRECISION
//> The value tau in the representation of H.
//> \endverbatim
//>
//> \param[in,out] C
//> \verbatim
//> C is DOUBLE PRECISION array, dimension (LDC,N)
//> On entry, the m by n matrix C.
//> On exit, C is overwritten by the matrix H * C if SIDE = 'L',
//> or C * H if SIDE = 'R'.
//> \endverbatim
//>
//> \param[in] LDC
//> \verbatim
//> LDC is INTEGER
//> The leading dimension of the array C. LDC >= max(1,M).
//> \endverbatim
//>
//> \param[out] WORK
//> \verbatim
//> WORK is DOUBLE PRECISION array, dimension
//> (N) if SIDE = 'L'
//> or (M) if SIDE = 'R'
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup doubleOTHERauxiliary
//
// =====================================================================
/* Subroutine */ int dlarf_(char *side, int *m, int *n, double *v, int *incv,
double *tau, double *c__, int *ldc, double *work)
{
// Table of constant values
double c_b4 = 1.;
double c_b5 = 0.;
int c__1 = 1;
// System generated locals
int c_dim1, c_offset;
double d__1;
// Local variables
int i__;
int applyleft;
extern /* Subroutine */ int dger_(int *, int *, double *, double *, int *,
double *, int *, double *, int *);
extern int lsame_(char *, char *);
extern /* Subroutine */ int dgemv_(char *, int *, int *, double *, double
*, int *, double *, int *, double *, double *, int *);
int lastc, lastv;
extern int iladlc_(int *, int *, double *, int *), iladlr_(int *, int *,
double *, int *);
//
// -- LAPACK auxiliary routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. External Subroutines ..
// ..
// .. External Functions ..
// ..
// .. Executable Statements ..
//
// Parameter adjustments
--v;
c_dim1 = *ldc;
c_offset = 1 + c_dim1;
c__ -= c_offset;
--work;
// Function Body
applyleft = lsame_(side, "L");
lastv = 0;
lastc = 0;
if (*tau != 0.) {
// Set up variables for scanning V. LASTV begins pointing to the end
// of V.
if (applyleft) {
lastv = *m;
} else {
lastv = *n;
}
if (*incv > 0) {
i__ = (lastv - 1) * *incv + 1;
} else {
i__ = 1;
}
// Look for the last non-zero row in V.
while(lastv > 0 && v[i__] == 0.) {
--lastv;
i__ -= *incv;
}
if (applyleft) {
// Scan for the last non-zero column in C(1:lastv,:).
lastc = iladlc_(&lastv, n, &c__[c_offset], ldc);
} else {
// Scan for the last non-zero row in C(:,1:lastv).
lastc = iladlr_(m, &lastv, &c__[c_offset], ldc);
}
}
// Note that lastc.eq.0 renders the BLAS operations null; no special
// case is needed at this level.
if (applyleft) {
//
// Form H * C
//
if (lastv > 0) {
//
// w(1:lastc,1) := C(1:lastv,1:lastc)**T * v(1:lastv,1)
//
dgemv_("Transpose", &lastv, &lastc, &c_b4, &c__[c_offset], ldc, &
v[1], incv, &c_b5, &work[1], &c__1);
//
// C(1:lastv,1:lastc) := C(...) - v(1:lastv,1) * w(1:lastc,1)**T
//
d__1 = -(*tau);
dger_(&lastv, &lastc, &d__1, &v[1], incv, &work[1], &c__1, &c__[
c_offset], ldc);
}
} else {
//
// Form C * H
//
if (lastv > 0) {
//
// w(1:lastc,1) := C(1:lastc,1:lastv) * v(1:lastv,1)
//
dgemv_("No transpose", &lastc, &lastv, &c_b4, &c__[c_offset], ldc,
&v[1], incv, &c_b5, &work[1], &c__1);
//
// C(1:lastc,1:lastv) := C(...) - w(1:lastc,1) * v(1:lastv,1)**T
//
d__1 = -(*tau);
dger_(&lastc, &lastv, &d__1, &work[1], &c__1, &v[1], incv, &c__[
c_offset], ldc);
}
}
return 0;
//
// End of DLARF
//
} // dlarf_
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
//> \brief \b ILADLC scans a matrix for its last non-zero column.
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download ILADLC + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/iladlc.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/iladlc.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/iladlc.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// INTEGER FUNCTION ILADLC( M, N, A, LDA )
//
// .. Scalar Arguments ..
// INTEGER M, N, LDA
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A( LDA, * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> ILADLC scans A for its last non-zero column.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> The number of rows of the matrix A.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The number of columns of the matrix A.
//> \endverbatim
//>
//> \param[in] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension (LDA,N)
//> The m by n matrix A.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> The leading dimension of the array A. LDA >= max(1,M).
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup OTHERauxiliary
//
// =====================================================================
int iladlc_(int *m, int *n, double *a, int *lda)
{
// System generated locals
int a_dim1, a_offset, ret_val, i__1;
// Local variables
int i__;
//
// -- LAPACK auxiliary routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. Executable Statements ..
//
// Quick test for the common case where one corner is non-zero.
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
// Function Body
if (*n == 0) {
ret_val = *n;
} else if (a[*n * a_dim1 + 1] != 0. || a[*m + *n * a_dim1] != 0.) {
ret_val = *n;
} else {
// Now scan each column from the end, returning with the first non-zero.
for (ret_val = *n; ret_val >= 1; --ret_val) {
i__1 = *m;
for (i__ = 1; i__ <= i__1; ++i__) {
if (a[i__ + ret_val * a_dim1] != 0.) {
return ret_val;
}
}
}
}
return ret_val;
} // iladlc_
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
//> \brief \b ILADLR scans a matrix for its last non-zero row.
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download ILADLR + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/iladlr.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/iladlr.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/iladlr.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// INTEGER FUNCTION ILADLR( M, N, A, LDA )
//
// .. Scalar Arguments ..
// INTEGER M, N, LDA
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A( LDA, * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> ILADLR scans A for its last non-zero row.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> The number of rows of the matrix A.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The number of columns of the matrix A.
//> \endverbatim
//>
//> \param[in] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension (LDA,N)
//> The m by n matrix A.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> The leading dimension of the array A. LDA >= max(1,M).
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup OTHERauxiliary
//
// =====================================================================
int iladlr_(int *m, int *n, double *a, int *lda)
{
// System generated locals
int a_dim1, a_offset, ret_val, i__1;
// Local variables
int i__, j;
//
// -- LAPACK auxiliary routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. Executable Statements ..
//
// Quick test for the common case where one corner is non-zero.
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
// Function Body
if (*m == 0) {
ret_val = *m;
} else if (a[*m + a_dim1] != 0. || a[*m + *n * a_dim1] != 0.) {
ret_val = *m;
} else {
// Scan up each column tracking the last zero row seen.
ret_val = 0;
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__ = *m;
while(a[max(i__,1) + j * a_dim1] == 0. && i__ >= 1) {
--i__;
}
ret_val = max(ret_val,i__);
}
}
return ret_val;
} // iladlr_
+824
View File
@@ -0,0 +1,824 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DLARFB applies a block reflector or its transpose to a general rectangular matrix.
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DLARFB + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dlarfb.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dlarfb.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dlarfb.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// SUBROUTINE DLARFB( SIDE, TRANS, DIRECT, STOREV, M, N, K, V, LDV,
// T, LDT, C, LDC, WORK, LDWORK )
//
// .. Scalar Arguments ..
// CHARACTER DIRECT, SIDE, STOREV, TRANS
// INTEGER K, LDC, LDT, LDV, LDWORK, M, N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION C( LDC, * ), T( LDT, * ), V( LDV, * ),
// $ WORK( LDWORK, * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DLARFB applies a real block reflector H or its transpose H**T to a
//> real m by n matrix C, from either the left or the right.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] SIDE
//> \verbatim
//> SIDE is CHARACTER*1
//> = 'L': apply H or H**T from the Left
//> = 'R': apply H or H**T from the Right
//> \endverbatim
//>
//> \param[in] TRANS
//> \verbatim
//> TRANS is CHARACTER*1
//> = 'N': apply H (No transpose)
//> = 'T': apply H**T (Transpose)
//> \endverbatim
//>
//> \param[in] DIRECT
//> \verbatim
//> DIRECT is CHARACTER*1
//> Indicates how H is formed from a product of elementary
//> reflectors
//> = 'F': H = H(1) H(2) . . . H(k) (Forward)
//> = 'B': H = H(k) . . . H(2) H(1) (Backward)
//> \endverbatim
//>
//> \param[in] STOREV
//> \verbatim
//> STOREV is CHARACTER*1
//> Indicates how the vectors which define the elementary
//> reflectors are stored:
//> = 'C': Columnwise
//> = 'R': Rowwise
//> \endverbatim
//>
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> The number of rows of the matrix C.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The number of columns of the matrix C.
//> \endverbatim
//>
//> \param[in] K
//> \verbatim
//> K is INTEGER
//> The order of the matrix T (= the number of elementary
//> reflectors whose product defines the block reflector).
//> If SIDE = 'L', M >= K >= 0;
//> if SIDE = 'R', N >= K >= 0.
//> \endverbatim
//>
//> \param[in] V
//> \verbatim
//> V is DOUBLE PRECISION array, dimension
//> (LDV,K) if STOREV = 'C'
//> (LDV,M) if STOREV = 'R' and SIDE = 'L'
//> (LDV,N) if STOREV = 'R' and SIDE = 'R'
//> The matrix V. See Further Details.
//> \endverbatim
//>
//> \param[in] LDV
//> \verbatim
//> LDV is INTEGER
//> The leading dimension of the array V.
//> If STOREV = 'C' and SIDE = 'L', LDV >= max(1,M);
//> if STOREV = 'C' and SIDE = 'R', LDV >= max(1,N);
//> if STOREV = 'R', LDV >= K.
//> \endverbatim
//>
//> \param[in] T
//> \verbatim
//> T is DOUBLE PRECISION array, dimension (LDT,K)
//> The triangular k by k matrix T in the representation of the
//> block reflector.
//> \endverbatim
//>
//> \param[in] LDT
//> \verbatim
//> LDT is INTEGER
//> The leading dimension of the array T. LDT >= K.
//> \endverbatim
//>
//> \param[in,out] C
//> \verbatim
//> C is DOUBLE PRECISION array, dimension (LDC,N)
//> On entry, the m by n matrix C.
//> On exit, C is overwritten by H*C or H**T*C or C*H or C*H**T.
//> \endverbatim
//>
//> \param[in] LDC
//> \verbatim
//> LDC is INTEGER
//> The leading dimension of the array C. LDC >= max(1,M).
//> \endverbatim
//>
//> \param[out] WORK
//> \verbatim
//> WORK is DOUBLE PRECISION array, dimension (LDWORK,K)
//> \endverbatim
//>
//> \param[in] LDWORK
//> \verbatim
//> LDWORK is INTEGER
//> The leading dimension of the array WORK.
//> If SIDE = 'L', LDWORK >= max(1,N);
//> if SIDE = 'R', LDWORK >= max(1,M).
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date June 2013
//
//> \ingroup doubleOTHERauxiliary
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> The shape of the matrix V and the storage of the vectors which define
//> the H(i) is best illustrated by the following example with n = 5 and
//> k = 3. The elements equal to 1 are not stored; the corresponding
//> array elements are modified but restored on exit. The rest of the
//> array is not used.
//>
//> DIRECT = 'F' and STOREV = 'C': DIRECT = 'F' and STOREV = 'R':
//>
//> V = ( 1 ) V = ( 1 v1 v1 v1 v1 )
//> ( v1 1 ) ( 1 v2 v2 v2 )
//> ( v1 v2 1 ) ( 1 v3 v3 )
//> ( v1 v2 v3 )
//> ( v1 v2 v3 )
//>
//> DIRECT = 'B' and STOREV = 'C': DIRECT = 'B' and STOREV = 'R':
//>
//> V = ( v1 v2 v3 ) V = ( v1 v1 1 )
//> ( v1 v2 v3 ) ( v2 v2 v2 1 )
//> ( 1 v2 v3 ) ( v3 v3 v3 v3 1 )
//> ( 1 v3 )
//> ( 1 )
//> \endverbatim
//>
// =====================================================================
/* Subroutine */ int dlarfb_(char *side, char *trans, char *direct, char *
storev, int *m, int *n, int *k, double *v, int *ldv, double *t, int *
ldt, double *c__, int *ldc, double *work, int *ldwork)
{
// Table of constant values
int c__1 = 1;
double c_b14 = 1.;
double c_b25 = -1.;
// System generated locals
int c_dim1, c_offset, t_dim1, t_offset, v_dim1, v_offset, work_dim1,
work_offset, i__1, i__2;
// Local variables
int i__, j;
extern /* Subroutine */ int dgemm_(char *, char *, int *, int *, int *,
double *, double *, int *, double *, int *, double *, double *,
int *);
extern int lsame_(char *, char *);
extern /* Subroutine */ int dcopy_(int *, double *, int *, double *, int *
), dtrmm_(char *, char *, char *, char *, int *, int *, double *,
double *, int *, double *, int *);
char transt[1+1]={'\0'};
//
// -- LAPACK auxiliary routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// June 2013
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. External Functions ..
// ..
// .. External Subroutines ..
// ..
// .. Executable Statements ..
//
// Quick return if possible
//
// Parameter adjustments
v_dim1 = *ldv;
v_offset = 1 + v_dim1;
v -= v_offset;
t_dim1 = *ldt;
t_offset = 1 + t_dim1;
t -= t_offset;
c_dim1 = *ldc;
c_offset = 1 + c_dim1;
c__ -= c_offset;
work_dim1 = *ldwork;
work_offset = 1 + work_dim1;
work -= work_offset;
// Function Body
if (*m <= 0 || *n <= 0) {
return 0;
}
if (lsame_(trans, "N")) {
*(unsigned char *)transt = 'T';
} else {
*(unsigned char *)transt = 'N';
}
if (lsame_(storev, "C")) {
if (lsame_(direct, "F")) {
//
// Let V = ( V1 ) (first K rows)
// ( V2 )
// where V1 is unit lower triangular.
//
if (lsame_(side, "L")) {
//
// Form H * C or H**T * C where C = ( C1 )
// ( C2 )
//
// W := C**T * V = (C1**T * V1 + C2**T * V2) (stored in WORK)
//
// W := C1**T
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
dcopy_(n, &c__[j + c_dim1], ldc, &work[j * work_dim1 + 1],
&c__1);
// L10:
}
//
// W := W * V1
//
dtrmm_("Right", "Lower", "No transpose", "Unit", n, k, &c_b14,
&v[v_offset], ldv, &work[work_offset], ldwork);
if (*m > *k) {
//
// W := W + C2**T * V2
//
i__1 = *m - *k;
dgemm_("Transpose", "No transpose", n, k, &i__1, &c_b14, &
c__[*k + 1 + c_dim1], ldc, &v[*k + 1 + v_dim1],
ldv, &c_b14, &work[work_offset], ldwork);
}
//
// W := W * T**T or W * T
//
dtrmm_("Right", "Upper", transt, "Non-unit", n, k, &c_b14, &t[
t_offset], ldt, &work[work_offset], ldwork);
//
// C := C - V * W**T
//
if (*m > *k) {
//
// C2 := C2 - V2 * W**T
//
i__1 = *m - *k;
dgemm_("No transpose", "Transpose", &i__1, n, k, &c_b25, &
v[*k + 1 + v_dim1], ldv, &work[work_offset],
ldwork, &c_b14, &c__[*k + 1 + c_dim1], ldc);
}
//
// W := W * V1**T
//
dtrmm_("Right", "Lower", "Transpose", "Unit", n, k, &c_b14, &
v[v_offset], ldv, &work[work_offset], ldwork);
//
// C1 := C1 - W**T
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
i__2 = *n;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[j + i__ * c_dim1] -= work[i__ + j * work_dim1];
// L20:
}
// L30:
}
} else if (lsame_(side, "R")) {
//
// Form C * H or C * H**T where C = ( C1 C2 )
//
// W := C * V = (C1*V1 + C2*V2) (stored in WORK)
//
// W := C1
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
dcopy_(m, &c__[j * c_dim1 + 1], &c__1, &work[j *
work_dim1 + 1], &c__1);
// L40:
}
//
// W := W * V1
//
dtrmm_("Right", "Lower", "No transpose", "Unit", m, k, &c_b14,
&v[v_offset], ldv, &work[work_offset], ldwork);
if (*n > *k) {
//
// W := W + C2 * V2
//
i__1 = *n - *k;
dgemm_("No transpose", "No transpose", m, k, &i__1, &
c_b14, &c__[(*k + 1) * c_dim1 + 1], ldc, &v[*k +
1 + v_dim1], ldv, &c_b14, &work[work_offset],
ldwork);
}
//
// W := W * T or W * T**T
//
dtrmm_("Right", "Upper", trans, "Non-unit", m, k, &c_b14, &t[
t_offset], ldt, &work[work_offset], ldwork);
//
// C := C - W * V**T
//
if (*n > *k) {
//
// C2 := C2 - W * V2**T
//
i__1 = *n - *k;
dgemm_("No transpose", "Transpose", m, &i__1, k, &c_b25, &
work[work_offset], ldwork, &v[*k + 1 + v_dim1],
ldv, &c_b14, &c__[(*k + 1) * c_dim1 + 1], ldc);
}
//
// W := W * V1**T
//
dtrmm_("Right", "Lower", "Transpose", "Unit", m, k, &c_b14, &
v[v_offset], ldv, &work[work_offset], ldwork);
//
// C1 := C1 - W
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + j * c_dim1] -= work[i__ + j * work_dim1];
// L50:
}
// L60:
}
}
} else {
//
// Let V = ( V1 )
// ( V2 ) (last K rows)
// where V2 is unit upper triangular.
//
if (lsame_(side, "L")) {
//
// Form H * C or H**T * C where C = ( C1 )
// ( C2 )
//
// W := C**T * V = (C1**T * V1 + C2**T * V2) (stored in WORK)
//
// W := C2**T
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
dcopy_(n, &c__[*m - *k + j + c_dim1], ldc, &work[j *
work_dim1 + 1], &c__1);
// L70:
}
//
// W := W * V2
//
dtrmm_("Right", "Upper", "No transpose", "Unit", n, k, &c_b14,
&v[*m - *k + 1 + v_dim1], ldv, &work[work_offset],
ldwork);
if (*m > *k) {
//
// W := W + C1**T * V1
//
i__1 = *m - *k;
dgemm_("Transpose", "No transpose", n, k, &i__1, &c_b14, &
c__[c_offset], ldc, &v[v_offset], ldv, &c_b14, &
work[work_offset], ldwork);
}
//
// W := W * T**T or W * T
//
dtrmm_("Right", "Lower", transt, "Non-unit", n, k, &c_b14, &t[
t_offset], ldt, &work[work_offset], ldwork);
//
// C := C - V * W**T
//
if (*m > *k) {
//
// C1 := C1 - V1 * W**T
//
i__1 = *m - *k;
dgemm_("No transpose", "Transpose", &i__1, n, k, &c_b25, &
v[v_offset], ldv, &work[work_offset], ldwork, &
c_b14, &c__[c_offset], ldc);
}
//
// W := W * V2**T
//
dtrmm_("Right", "Upper", "Transpose", "Unit", n, k, &c_b14, &
v[*m - *k + 1 + v_dim1], ldv, &work[work_offset],
ldwork);
//
// C2 := C2 - W**T
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
i__2 = *n;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[*m - *k + j + i__ * c_dim1] -= work[i__ + j *
work_dim1];
// L80:
}
// L90:
}
} else if (lsame_(side, "R")) {
//
// Form C * H or C * H**T where C = ( C1 C2 )
//
// W := C * V = (C1*V1 + C2*V2) (stored in WORK)
//
// W := C2
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
dcopy_(m, &c__[(*n - *k + j) * c_dim1 + 1], &c__1, &work[
j * work_dim1 + 1], &c__1);
// L100:
}
//
// W := W * V2
//
dtrmm_("Right", "Upper", "No transpose", "Unit", m, k, &c_b14,
&v[*n - *k + 1 + v_dim1], ldv, &work[work_offset],
ldwork);
if (*n > *k) {
//
// W := W + C1 * V1
//
i__1 = *n - *k;
dgemm_("No transpose", "No transpose", m, k, &i__1, &
c_b14, &c__[c_offset], ldc, &v[v_offset], ldv, &
c_b14, &work[work_offset], ldwork);
}
//
// W := W * T or W * T**T
//
dtrmm_("Right", "Lower", trans, "Non-unit", m, k, &c_b14, &t[
t_offset], ldt, &work[work_offset], ldwork);
//
// C := C - W * V**T
//
if (*n > *k) {
//
// C1 := C1 - W * V1**T
//
i__1 = *n - *k;
dgemm_("No transpose", "Transpose", m, &i__1, k, &c_b25, &
work[work_offset], ldwork, &v[v_offset], ldv, &
c_b14, &c__[c_offset], ldc);
}
//
// W := W * V2**T
//
dtrmm_("Right", "Upper", "Transpose", "Unit", m, k, &c_b14, &
v[*n - *k + 1 + v_dim1], ldv, &work[work_offset],
ldwork);
//
// C2 := C2 - W
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + (*n - *k + j) * c_dim1] -= work[i__ + j *
work_dim1];
// L110:
}
// L120:
}
}
}
} else if (lsame_(storev, "R")) {
if (lsame_(direct, "F")) {
//
// Let V = ( V1 V2 ) (V1: first K columns)
// where V1 is unit upper triangular.
//
if (lsame_(side, "L")) {
//
// Form H * C or H**T * C where C = ( C1 )
// ( C2 )
//
// W := C**T * V**T = (C1**T * V1**T + C2**T * V2**T) (stored in WORK)
//
// W := C1**T
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
dcopy_(n, &c__[j + c_dim1], ldc, &work[j * work_dim1 + 1],
&c__1);
// L130:
}
//
// W := W * V1**T
//
dtrmm_("Right", "Upper", "Transpose", "Unit", n, k, &c_b14, &
v[v_offset], ldv, &work[work_offset], ldwork);
if (*m > *k) {
//
// W := W + C2**T * V2**T
//
i__1 = *m - *k;
dgemm_("Transpose", "Transpose", n, k, &i__1, &c_b14, &
c__[*k + 1 + c_dim1], ldc, &v[(*k + 1) * v_dim1 +
1], ldv, &c_b14, &work[work_offset], ldwork);
}
//
// W := W * T**T or W * T
//
dtrmm_("Right", "Upper", transt, "Non-unit", n, k, &c_b14, &t[
t_offset], ldt, &work[work_offset], ldwork);
//
// C := C - V**T * W**T
//
if (*m > *k) {
//
// C2 := C2 - V2**T * W**T
//
i__1 = *m - *k;
dgemm_("Transpose", "Transpose", &i__1, n, k, &c_b25, &v[(
*k + 1) * v_dim1 + 1], ldv, &work[work_offset],
ldwork, &c_b14, &c__[*k + 1 + c_dim1], ldc);
}
//
// W := W * V1
//
dtrmm_("Right", "Upper", "No transpose", "Unit", n, k, &c_b14,
&v[v_offset], ldv, &work[work_offset], ldwork);
//
// C1 := C1 - W**T
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
i__2 = *n;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[j + i__ * c_dim1] -= work[i__ + j * work_dim1];
// L140:
}
// L150:
}
} else if (lsame_(side, "R")) {
//
// Form C * H or C * H**T where C = ( C1 C2 )
//
// W := C * V**T = (C1*V1**T + C2*V2**T) (stored in WORK)
//
// W := C1
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
dcopy_(m, &c__[j * c_dim1 + 1], &c__1, &work[j *
work_dim1 + 1], &c__1);
// L160:
}
//
// W := W * V1**T
//
dtrmm_("Right", "Upper", "Transpose", "Unit", m, k, &c_b14, &
v[v_offset], ldv, &work[work_offset], ldwork);
if (*n > *k) {
//
// W := W + C2 * V2**T
//
i__1 = *n - *k;
dgemm_("No transpose", "Transpose", m, k, &i__1, &c_b14, &
c__[(*k + 1) * c_dim1 + 1], ldc, &v[(*k + 1) *
v_dim1 + 1], ldv, &c_b14, &work[work_offset],
ldwork);
}
//
// W := W * T or W * T**T
//
dtrmm_("Right", "Upper", trans, "Non-unit", m, k, &c_b14, &t[
t_offset], ldt, &work[work_offset], ldwork);
//
// C := C - W * V
//
if (*n > *k) {
//
// C2 := C2 - W * V2
//
i__1 = *n - *k;
dgemm_("No transpose", "No transpose", m, &i__1, k, &
c_b25, &work[work_offset], ldwork, &v[(*k + 1) *
v_dim1 + 1], ldv, &c_b14, &c__[(*k + 1) * c_dim1
+ 1], ldc);
}
//
// W := W * V1
//
dtrmm_("Right", "Upper", "No transpose", "Unit", m, k, &c_b14,
&v[v_offset], ldv, &work[work_offset], ldwork);
//
// C1 := C1 - W
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + j * c_dim1] -= work[i__ + j * work_dim1];
// L170:
}
// L180:
}
}
} else {
//
// Let V = ( V1 V2 ) (V2: last K columns)
// where V2 is unit lower triangular.
//
if (lsame_(side, "L")) {
//
// Form H * C or H**T * C where C = ( C1 )
// ( C2 )
//
// W := C**T * V**T = (C1**T * V1**T + C2**T * V2**T) (stored in WORK)
//
// W := C2**T
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
dcopy_(n, &c__[*m - *k + j + c_dim1], ldc, &work[j *
work_dim1 + 1], &c__1);
// L190:
}
//
// W := W * V2**T
//
dtrmm_("Right", "Lower", "Transpose", "Unit", n, k, &c_b14, &
v[(*m - *k + 1) * v_dim1 + 1], ldv, &work[work_offset]
, ldwork);
if (*m > *k) {
//
// W := W + C1**T * V1**T
//
i__1 = *m - *k;
dgemm_("Transpose", "Transpose", n, k, &i__1, &c_b14, &
c__[c_offset], ldc, &v[v_offset], ldv, &c_b14, &
work[work_offset], ldwork);
}
//
// W := W * T**T or W * T
//
dtrmm_("Right", "Lower", transt, "Non-unit", n, k, &c_b14, &t[
t_offset], ldt, &work[work_offset], ldwork);
//
// C := C - V**T * W**T
//
if (*m > *k) {
//
// C1 := C1 - V1**T * W**T
//
i__1 = *m - *k;
dgemm_("Transpose", "Transpose", &i__1, n, k, &c_b25, &v[
v_offset], ldv, &work[work_offset], ldwork, &
c_b14, &c__[c_offset], ldc);
}
//
// W := W * V2
//
dtrmm_("Right", "Lower", "No transpose", "Unit", n, k, &c_b14,
&v[(*m - *k + 1) * v_dim1 + 1], ldv, &work[
work_offset], ldwork);
//
// C2 := C2 - W**T
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
i__2 = *n;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[*m - *k + j + i__ * c_dim1] -= work[i__ + j *
work_dim1];
// L200:
}
// L210:
}
} else if (lsame_(side, "R")) {
//
// Form C * H or C * H' where C = ( C1 C2 )
//
// W := C * V**T = (C1*V1**T + C2*V2**T) (stored in WORK)
//
// W := C2
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
dcopy_(m, &c__[(*n - *k + j) * c_dim1 + 1], &c__1, &work[
j * work_dim1 + 1], &c__1);
// L220:
}
//
// W := W * V2**T
//
dtrmm_("Right", "Lower", "Transpose", "Unit", m, k, &c_b14, &
v[(*n - *k + 1) * v_dim1 + 1], ldv, &work[work_offset]
, ldwork);
if (*n > *k) {
//
// W := W + C1 * V1**T
//
i__1 = *n - *k;
dgemm_("No transpose", "Transpose", m, k, &i__1, &c_b14, &
c__[c_offset], ldc, &v[v_offset], ldv, &c_b14, &
work[work_offset], ldwork);
}
//
// W := W * T or W * T**T
//
dtrmm_("Right", "Lower", trans, "Non-unit", m, k, &c_b14, &t[
t_offset], ldt, &work[work_offset], ldwork);
//
// C := C - W * V
//
if (*n > *k) {
//
// C1 := C1 - W * V1
//
i__1 = *n - *k;
dgemm_("No transpose", "No transpose", m, &i__1, k, &
c_b25, &work[work_offset], ldwork, &v[v_offset],
ldv, &c_b14, &c__[c_offset], ldc);
}
//
// W := W * V2
//
dtrmm_("Right", "Lower", "No transpose", "Unit", m, k, &c_b14,
&v[(*n - *k + 1) * v_dim1 + 1], ldv, &work[
work_offset], ldwork);
//
// C1 := C1 - W
//
i__1 = *k;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + (*n - *k + j) * c_dim1] -= work[i__ + j *
work_dim1];
// L230:
}
// L240:
}
}
}
}
return 0;
//
// End of DLARFB
//
} // dlarfb_
+216
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@@ -0,0 +1,216 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DLARFG generates an elementary reflector (Householder matrix).
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DLARFG + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dlarfg.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dlarfg.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dlarfg.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// SUBROUTINE DLARFG( N, ALPHA, X, INCX, TAU )
//
// .. Scalar Arguments ..
// INTEGER INCX, N
// DOUBLE PRECISION ALPHA, TAU
// ..
// .. Array Arguments ..
// DOUBLE PRECISION X( * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DLARFG generates a real elementary reflector H of order n, such
//> that
//>
//> H * ( alpha ) = ( beta ), H**T * H = I.
//> ( x ) ( 0 )
//>
//> where alpha and beta are scalars, and x is an (n-1)-element real
//> vector. H is represented in the form
//>
//> H = I - tau * ( 1 ) * ( 1 v**T ) ,
//> ( v )
//>
//> where tau is a real scalar and v is a real (n-1)-element
//> vector.
//>
//> If the elements of x are all zero, then tau = 0 and H is taken to be
//> the unit matrix.
//>
//> Otherwise 1 <= tau <= 2.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The order of the elementary reflector.
//> \endverbatim
//>
//> \param[in,out] ALPHA
//> \verbatim
//> ALPHA is DOUBLE PRECISION
//> On entry, the value alpha.
//> On exit, it is overwritten with the value beta.
//> \endverbatim
//>
//> \param[in,out] X
//> \verbatim
//> X is DOUBLE PRECISION array, dimension
//> (1+(N-2)*abs(INCX))
//> On entry, the vector x.
//> On exit, it is overwritten with the vector v.
//> \endverbatim
//>
//> \param[in] INCX
//> \verbatim
//> INCX is INTEGER
//> The increment between elements of X. INCX > 0.
//> \endverbatim
//>
//> \param[out] TAU
//> \verbatim
//> TAU is DOUBLE PRECISION
//> The value tau.
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date November 2017
//
//> \ingroup doubleOTHERauxiliary
//
// =====================================================================
/* Subroutine */ int dlarfg_(int *n, double *alpha, double *x, int *incx,
double *tau)
{
// System generated locals
int i__1;
double d__1;
// Local variables
int j, knt;
double beta;
extern double dnrm2_(int *, double *, int *);
extern /* Subroutine */ int dscal_(int *, double *, double *, int *);
double xnorm;
extern double dlapy2_(double *, double *), dlamch_(char *);
double safmin, rsafmn;
//
// -- LAPACK auxiliary routine (version 3.8.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// November 2017
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. External Functions ..
// ..
// .. Intrinsic Functions ..
// ..
// .. External Subroutines ..
// ..
// .. Executable Statements ..
//
// Parameter adjustments
--x;
// Function Body
if (*n <= 1) {
*tau = 0.;
return 0;
}
i__1 = *n - 1;
xnorm = dnrm2_(&i__1, &x[1], incx);
if (xnorm == 0.) {
//
// H = I
//
*tau = 0.;
} else {
//
// general case
//
d__1 = dlapy2_(alpha, &xnorm);
beta = -d_sign(&d__1, alpha);
safmin = dlamch_("S") / dlamch_("E");
knt = 0;
if (abs(beta) < safmin) {
//
// XNORM, BETA may be inaccurate; scale X and recompute them
//
rsafmn = 1. / safmin;
L10:
++knt;
i__1 = *n - 1;
dscal_(&i__1, &rsafmn, &x[1], incx);
beta *= rsafmn;
*alpha *= rsafmn;
if (abs(beta) < safmin && knt < 20) {
goto L10;
}
//
// New BETA is at most 1, at least SAFMIN
//
i__1 = *n - 1;
xnorm = dnrm2_(&i__1, &x[1], incx);
d__1 = dlapy2_(alpha, &xnorm);
beta = -d_sign(&d__1, alpha);
}
*tau = (beta - *alpha) / beta;
i__1 = *n - 1;
d__1 = 1. / (*alpha - beta);
dscal_(&i__1, &d__1, &x[1], incx);
//
// If ALPHA is subnormal, it may lose relative accuracy
//
i__1 = knt;
for (j = 1; j <= i__1; ++j) {
beta *= safmin;
// L20:
}
*alpha = beta;
}
return 0;
//
// End of DLARFG
//
} // dlarfg_
+389
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@@ -0,0 +1,389 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DLARFT forms the triangular factor T of a block reflector H = I - vtvH
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DLARFT + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dlarft.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dlarft.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dlarft.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// SUBROUTINE DLARFT( DIRECT, STOREV, N, K, V, LDV, TAU, T, LDT )
//
// .. Scalar Arguments ..
// CHARACTER DIRECT, STOREV
// INTEGER K, LDT, LDV, N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION T( LDT, * ), TAU( * ), V( LDV, * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DLARFT forms the triangular factor T of a real block reflector H
//> of order n, which is defined as a product of k elementary reflectors.
//>
//> If DIRECT = 'F', H = H(1) H(2) . . . H(k) and T is upper triangular;
//>
//> If DIRECT = 'B', H = H(k) . . . H(2) H(1) and T is lower triangular.
//>
//> If STOREV = 'C', the vector which defines the elementary reflector
//> H(i) is stored in the i-th column of the array V, and
//>
//> H = I - V * T * V**T
//>
//> If STOREV = 'R', the vector which defines the elementary reflector
//> H(i) is stored in the i-th row of the array V, and
//>
//> H = I - V**T * T * V
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] DIRECT
//> \verbatim
//> DIRECT is CHARACTER*1
//> Specifies the order in which the elementary reflectors are
//> multiplied to form the block reflector:
//> = 'F': H = H(1) H(2) . . . H(k) (Forward)
//> = 'B': H = H(k) . . . H(2) H(1) (Backward)
//> \endverbatim
//>
//> \param[in] STOREV
//> \verbatim
//> STOREV is CHARACTER*1
//> Specifies how the vectors which define the elementary
//> reflectors are stored (see also Further Details):
//> = 'C': columnwise
//> = 'R': rowwise
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The order of the block reflector H. N >= 0.
//> \endverbatim
//>
//> \param[in] K
//> \verbatim
//> K is INTEGER
//> The order of the triangular factor T (= the number of
//> elementary reflectors). K >= 1.
//> \endverbatim
//>
//> \param[in] V
//> \verbatim
//> V is DOUBLE PRECISION array, dimension
//> (LDV,K) if STOREV = 'C'
//> (LDV,N) if STOREV = 'R'
//> The matrix V. See further details.
//> \endverbatim
//>
//> \param[in] LDV
//> \verbatim
//> LDV is INTEGER
//> The leading dimension of the array V.
//> If STOREV = 'C', LDV >= max(1,N); if STOREV = 'R', LDV >= K.
//> \endverbatim
//>
//> \param[in] TAU
//> \verbatim
//> TAU is DOUBLE PRECISION array, dimension (K)
//> TAU(i) must contain the scalar factor of the elementary
//> reflector H(i).
//> \endverbatim
//>
//> \param[out] T
//> \verbatim
//> T is DOUBLE PRECISION array, dimension (LDT,K)
//> The k by k triangular factor T of the block reflector.
//> If DIRECT = 'F', T is upper triangular; if DIRECT = 'B', T is
//> lower triangular. The rest of the array is not used.
//> \endverbatim
//>
//> \param[in] LDT
//> \verbatim
//> LDT is INTEGER
//> The leading dimension of the array T. LDT >= K.
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup doubleOTHERauxiliary
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> The shape of the matrix V and the storage of the vectors which define
//> the H(i) is best illustrated by the following example with n = 5 and
//> k = 3. The elements equal to 1 are not stored.
//>
//> DIRECT = 'F' and STOREV = 'C': DIRECT = 'F' and STOREV = 'R':
//>
//> V = ( 1 ) V = ( 1 v1 v1 v1 v1 )
//> ( v1 1 ) ( 1 v2 v2 v2 )
//> ( v1 v2 1 ) ( 1 v3 v3 )
//> ( v1 v2 v3 )
//> ( v1 v2 v3 )
//>
//> DIRECT = 'B' and STOREV = 'C': DIRECT = 'B' and STOREV = 'R':
//>
//> V = ( v1 v2 v3 ) V = ( v1 v1 1 )
//> ( v1 v2 v3 ) ( v2 v2 v2 1 )
//> ( 1 v2 v3 ) ( v3 v3 v3 v3 1 )
//> ( 1 v3 )
//> ( 1 )
//> \endverbatim
//>
// =====================================================================
/* Subroutine */ int dlarft_(char *direct, char *storev, int *n, int *k,
double *v, int *ldv, double *tau, double *t, int *ldt)
{
// Table of constant values
int c__1 = 1;
double c_b7 = 1.;
// System generated locals
int t_dim1, t_offset, v_dim1, v_offset, i__1, i__2, i__3;
double d__1;
// Local variables
int i__, j, prevlastv;
extern int lsame_(char *, char *);
extern /* Subroutine */ int dgemv_(char *, int *, int *, double *, double
*, int *, double *, int *, double *, double *, int *);
int lastv;
extern /* Subroutine */ int dtrmv_(char *, char *, char *, int *, double *
, int *, double *, int *);
//
// -- LAPACK auxiliary routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. External Subroutines ..
// ..
// .. External Functions ..
// ..
// .. Executable Statements ..
//
// Quick return if possible
//
// Parameter adjustments
v_dim1 = *ldv;
v_offset = 1 + v_dim1;
v -= v_offset;
--tau;
t_dim1 = *ldt;
t_offset = 1 + t_dim1;
t -= t_offset;
// Function Body
if (*n == 0) {
return 0;
}
if (lsame_(direct, "F")) {
prevlastv = *n;
i__1 = *k;
for (i__ = 1; i__ <= i__1; ++i__) {
prevlastv = max(i__,prevlastv);
if (tau[i__] == 0.) {
//
// H(i) = I
//
i__2 = i__;
for (j = 1; j <= i__2; ++j) {
t[j + i__ * t_dim1] = 0.;
}
} else {
//
// general case
//
if (lsame_(storev, "C")) {
// Skip any trailing zeros.
i__2 = i__ + 1;
for (lastv = *n; lastv >= i__2; --lastv) {
if (v[lastv + i__ * v_dim1] != 0.) {
break;
}
}
i__2 = i__ - 1;
for (j = 1; j <= i__2; ++j) {
t[j + i__ * t_dim1] = -tau[i__] * v[i__ + j * v_dim1];
}
j = min(lastv,prevlastv);
//
// T(1:i-1,i) := - tau(i) * V(i:j,1:i-1)**T * V(i:j,i)
//
i__2 = j - i__;
i__3 = i__ - 1;
d__1 = -tau[i__];
dgemv_("Transpose", &i__2, &i__3, &d__1, &v[i__ + 1 +
v_dim1], ldv, &v[i__ + 1 + i__ * v_dim1], &c__1, &
c_b7, &t[i__ * t_dim1 + 1], &c__1);
} else {
// Skip any trailing zeros.
i__2 = i__ + 1;
for (lastv = *n; lastv >= i__2; --lastv) {
if (v[i__ + lastv * v_dim1] != 0.) {
break;
}
}
i__2 = i__ - 1;
for (j = 1; j <= i__2; ++j) {
t[j + i__ * t_dim1] = -tau[i__] * v[j + i__ * v_dim1];
}
j = min(lastv,prevlastv);
//
// T(1:i-1,i) := - tau(i) * V(1:i-1,i:j) * V(i,i:j)**T
//
i__2 = i__ - 1;
i__3 = j - i__;
d__1 = -tau[i__];
dgemv_("No transpose", &i__2, &i__3, &d__1, &v[(i__ + 1) *
v_dim1 + 1], ldv, &v[i__ + (i__ + 1) * v_dim1],
ldv, &c_b7, &t[i__ * t_dim1 + 1], &c__1);
}
//
// T(1:i-1,i) := T(1:i-1,1:i-1) * T(1:i-1,i)
//
i__2 = i__ - 1;
dtrmv_("Upper", "No transpose", "Non-unit", &i__2, &t[
t_offset], ldt, &t[i__ * t_dim1 + 1], &c__1);
t[i__ + i__ * t_dim1] = tau[i__];
if (i__ > 1) {
prevlastv = max(prevlastv,lastv);
} else {
prevlastv = lastv;
}
}
}
} else {
prevlastv = 1;
for (i__ = *k; i__ >= 1; --i__) {
if (tau[i__] == 0.) {
//
// H(i) = I
//
i__1 = *k;
for (j = i__; j <= i__1; ++j) {
t[j + i__ * t_dim1] = 0.;
}
} else {
//
// general case
//
if (i__ < *k) {
if (lsame_(storev, "C")) {
// Skip any leading zeros.
i__1 = i__ - 1;
for (lastv = 1; lastv <= i__1; ++lastv) {
if (v[lastv + i__ * v_dim1] != 0.) {
break;
}
}
i__1 = *k;
for (j = i__ + 1; j <= i__1; ++j) {
t[j + i__ * t_dim1] = -tau[i__] * v[*n - *k + i__
+ j * v_dim1];
}
j = max(lastv,prevlastv);
//
// T(i+1:k,i) = -tau(i) * V(j:n-k+i,i+1:k)**T * V(j:n-k+i,i)
//
i__1 = *n - *k + i__ - j;
i__2 = *k - i__;
d__1 = -tau[i__];
dgemv_("Transpose", &i__1, &i__2, &d__1, &v[j + (i__
+ 1) * v_dim1], ldv, &v[j + i__ * v_dim1], &
c__1, &c_b7, &t[i__ + 1 + i__ * t_dim1], &
c__1);
} else {
// Skip any leading zeros.
i__1 = i__ - 1;
for (lastv = 1; lastv <= i__1; ++lastv) {
if (v[i__ + lastv * v_dim1] != 0.) {
break;
}
}
i__1 = *k;
for (j = i__ + 1; j <= i__1; ++j) {
t[j + i__ * t_dim1] = -tau[i__] * v[j + (*n - *k
+ i__) * v_dim1];
}
j = max(lastv,prevlastv);
//
// T(i+1:k,i) = -tau(i) * V(i+1:k,j:n-k+i) * V(i,j:n-k+i)**T
//
i__1 = *k - i__;
i__2 = *n - *k + i__ - j;
d__1 = -tau[i__];
dgemv_("No transpose", &i__1, &i__2, &d__1, &v[i__ +
1 + j * v_dim1], ldv, &v[i__ + j * v_dim1],
ldv, &c_b7, &t[i__ + 1 + i__ * t_dim1], &c__1)
;
}
//
// T(i+1:k,i) := T(i+1:k,i+1:k) * T(i+1:k,i)
//
i__1 = *k - i__;
dtrmv_("Lower", "No transpose", "Non-unit", &i__1, &t[i__
+ 1 + (i__ + 1) * t_dim1], ldt, &t[i__ + 1 + i__ *
t_dim1], &c__1);
if (i__ > 1) {
prevlastv = min(prevlastv,lastv);
} else {
prevlastv = lastv;
}
}
t[i__ + i__ * t_dim1] = tau[i__];
}
}
}
return 0;
//
// End of DLARFT
//
} // dlarft_
+236
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@@ -0,0 +1,236 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DLARTG generates a plane rotation with real cosine and real sine.
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DLARTG + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dlartg.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dlartg.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dlartg.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// SUBROUTINE DLARTG( F, G, CS, SN, R )
//
// .. Scalar Arguments ..
// DOUBLE PRECISION CS, F, G, R, SN
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DLARTG generate a plane rotation so that
//>
//> [ CS SN ] . [ F ] = [ R ] where CS**2 + SN**2 = 1.
//> [ -SN CS ] [ G ] [ 0 ]
//>
//> This is a slower, more accurate version of the BLAS1 routine DROTG,
//> with the following other differences:
//> F and G are unchanged on return.
//> If G=0, then CS=1 and SN=0.
//> If F=0 and (G .ne. 0), then CS=0 and SN=1 without doing any
//> floating point operations (saves work in DBDSQR when
//> there are zeros on the diagonal).
//>
//> If F exceeds G in magnitude, CS will be positive.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] F
//> \verbatim
//> F is DOUBLE PRECISION
//> The first component of vector to be rotated.
//> \endverbatim
//>
//> \param[in] G
//> \verbatim
//> G is DOUBLE PRECISION
//> The second component of vector to be rotated.
//> \endverbatim
//>
//> \param[out] CS
//> \verbatim
//> CS is DOUBLE PRECISION
//> The cosine of the rotation.
//> \endverbatim
//>
//> \param[out] SN
//> \verbatim
//> SN is DOUBLE PRECISION
//> The sine of the rotation.
//> \endverbatim
//>
//> \param[out] R
//> \verbatim
//> R is DOUBLE PRECISION
//> The nonzero component of the rotated vector.
//>
//> This version has a few statements commented out for thread safety
//> (machine parameters are computed on each entry). 10 feb 03, SJH.
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup OTHERauxiliary
//
// =====================================================================
/* Subroutine */ int dlartg_(double *f, double *g, double *cs, double *sn,
double *r__)
{
// System generated locals
int i__1;
double d__1, d__2;
// Local variables
int i__;
double f1, g1, eps, scale;
int count;
double safmn2, safmx2;
extern double dlamch_(char *);
double safmin;
//
// -- LAPACK auxiliary routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// LOGICAL FIRST
// ..
// .. External Functions ..
// ..
// .. Intrinsic Functions ..
// ..
// .. Save statement ..
// SAVE FIRST, SAFMX2, SAFMIN, SAFMN2
// ..
// .. Data statements ..
// DATA FIRST / .TRUE. /
// ..
// .. Executable Statements ..
//
// IF( FIRST ) THEN
safmin = dlamch_("S");
eps = dlamch_("E");
d__1 = dlamch_("B");
i__1 = (int) (log(safmin / eps) / log(dlamch_("B")) / 2.);
safmn2 = pow_di(&d__1, &i__1);
safmx2 = 1. / safmn2;
// FIRST = .FALSE.
// END IF
if (*g == 0.) {
*cs = 1.;
*sn = 0.;
*r__ = *f;
} else if (*f == 0.) {
*cs = 0.;
*sn = 1.;
*r__ = *g;
} else {
f1 = *f;
g1 = *g;
// Computing MAX
d__1 = abs(f1), d__2 = abs(g1);
scale = max(d__1,d__2);
if (scale >= safmx2) {
count = 0;
L10:
++count;
f1 *= safmn2;
g1 *= safmn2;
// Computing MAX
d__1 = abs(f1), d__2 = abs(g1);
scale = max(d__1,d__2);
if (scale >= safmx2) {
goto L10;
}
// Computing 2nd power
d__1 = f1;
// Computing 2nd power
d__2 = g1;
*r__ = sqrt(d__1 * d__1 + d__2 * d__2);
*cs = f1 / *r__;
*sn = g1 / *r__;
i__1 = count;
for (i__ = 1; i__ <= i__1; ++i__) {
*r__ *= safmx2;
// L20:
}
} else if (scale <= safmn2) {
count = 0;
L30:
++count;
f1 *= safmx2;
g1 *= safmx2;
// Computing MAX
d__1 = abs(f1), d__2 = abs(g1);
scale = max(d__1,d__2);
if (scale <= safmn2) {
goto L30;
}
// Computing 2nd power
d__1 = f1;
// Computing 2nd power
d__2 = g1;
*r__ = sqrt(d__1 * d__1 + d__2 * d__2);
*cs = f1 / *r__;
*sn = g1 / *r__;
i__1 = count;
for (i__ = 1; i__ <= i__1; ++i__) {
*r__ *= safmn2;
// L40:
}
} else {
// Computing 2nd power
d__1 = f1;
// Computing 2nd power
d__2 = g1;
*r__ = sqrt(d__1 * d__1 + d__2 * d__2);
*cs = f1 / *r__;
*sn = g1 / *r__;
}
if (abs(*f) > abs(*g) && *cs < 0.) {
*cs = -(*cs);
*sn = -(*sn);
*r__ = -(*r__);
}
}
return 0;
//
// End of DLARTG
//
} // dlartg_
+413
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@@ -0,0 +1,413 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DLASCL multiplies a general rectangular matrix by a real scalar defined as cto/cfrom.
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DLASCL + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dlascl.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dlascl.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dlascl.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// SUBROUTINE DLASCL( TYPE, KL, KU, CFROM, CTO, M, N, A, LDA, INFO )
//
// .. Scalar Arguments ..
// CHARACTER TYPE
// INTEGER INFO, KL, KU, LDA, M, N
// DOUBLE PRECISION CFROM, CTO
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A( LDA, * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DLASCL multiplies the M by N real matrix A by the real scalar
//> CTO/CFROM. This is done without over/underflow as long as the final
//> result CTO*A(I,J)/CFROM does not over/underflow. TYPE specifies that
//> A may be full, upper triangular, lower triangular, upper Hessenberg,
//> or banded.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] TYPE
//> \verbatim
//> TYPE is CHARACTER*1
//> TYPE indices the storage type of the input matrix.
//> = 'G': A is a full matrix.
//> = 'L': A is a lower triangular matrix.
//> = 'U': A is an upper triangular matrix.
//> = 'H': A is an upper Hessenberg matrix.
//> = 'B': A is a symmetric band matrix with lower bandwidth KL
//> and upper bandwidth KU and with the only the lower
//> half stored.
//> = 'Q': A is a symmetric band matrix with lower bandwidth KL
//> and upper bandwidth KU and with the only the upper
//> half stored.
//> = 'Z': A is a band matrix with lower bandwidth KL and upper
//> bandwidth KU. See DGBTRF for storage details.
//> \endverbatim
//>
//> \param[in] KL
//> \verbatim
//> KL is INTEGER
//> The lower bandwidth of A. Referenced only if TYPE = 'B',
//> 'Q' or 'Z'.
//> \endverbatim
//>
//> \param[in] KU
//> \verbatim
//> KU is INTEGER
//> The upper bandwidth of A. Referenced only if TYPE = 'B',
//> 'Q' or 'Z'.
//> \endverbatim
//>
//> \param[in] CFROM
//> \verbatim
//> CFROM is DOUBLE PRECISION
//> \endverbatim
//>
//> \param[in] CTO
//> \verbatim
//> CTO is DOUBLE PRECISION
//>
//> The matrix A is multiplied by CTO/CFROM. A(I,J) is computed
//> without over/underflow if the final result CTO*A(I,J)/CFROM
//> can be represented without over/underflow. CFROM must be
//> nonzero.
//> \endverbatim
//>
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> The number of rows of the matrix A. M >= 0.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The number of columns of the matrix A. N >= 0.
//> \endverbatim
//>
//> \param[in,out] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension (LDA,N)
//> The matrix to be multiplied by CTO/CFROM. See TYPE for the
//> storage type.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> The leading dimension of the array A.
//> If TYPE = 'G', 'L', 'U', 'H', LDA >= max(1,M);
//> TYPE = 'B', LDA >= KL+1;
//> TYPE = 'Q', LDA >= KU+1;
//> TYPE = 'Z', LDA >= 2*KL+KU+1.
//> \endverbatim
//>
//> \param[out] INFO
//> \verbatim
//> INFO is INTEGER
//> 0 - successful exit
//> <0 - if INFO = -i, the i-th argument had an illegal value.
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date June 2016
//
//> \ingroup OTHERauxiliary
//
// =====================================================================
/* Subroutine */ int dlascl_(char *type__, int *kl, int *ku, double *cfrom,
double *cto, int *m, int *n, double *a, int *lda, int *info)
{
// System generated locals
int a_dim1, a_offset, i__1, i__2, i__3, i__4, i__5;
// Local variables
int i__, j, k1, k2, k3, k4;
double mul, cto1;
int done;
double ctoc;
extern int lsame_(char *, char *);
int itype;
double cfrom1;
extern double dlamch_(char *);
double cfromc;
extern int disnan_(double *);
extern /* Subroutine */ int xerbla_(char *, int *);
double bignum, smlnum;
//
// -- LAPACK auxiliary routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// June 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. External Functions ..
// ..
// .. Intrinsic Functions ..
// ..
// .. External Subroutines ..
// ..
// .. Executable Statements ..
//
// Test the input arguments
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
// Function Body
*info = 0;
if (lsame_(type__, "G")) {
itype = 0;
} else if (lsame_(type__, "L")) {
itype = 1;
} else if (lsame_(type__, "U")) {
itype = 2;
} else if (lsame_(type__, "H")) {
itype = 3;
} else if (lsame_(type__, "B")) {
itype = 4;
} else if (lsame_(type__, "Q")) {
itype = 5;
} else if (lsame_(type__, "Z")) {
itype = 6;
} else {
itype = -1;
}
if (itype == -1) {
*info = -1;
} else if (*cfrom == 0. || disnan_(cfrom)) {
*info = -4;
} else if (disnan_(cto)) {
*info = -5;
} else if (*m < 0) {
*info = -6;
} else if (*n < 0 || itype == 4 && *n != *m || itype == 5 && *n != *m) {
*info = -7;
} else if (itype <= 3 && *lda < max(1,*m)) {
*info = -9;
} else if (itype >= 4) {
// Computing MAX
i__1 = *m - 1;
if (*kl < 0 || *kl > max(i__1,0)) {
*info = -2;
} else /* if(complicated condition) */ {
// Computing MAX
i__1 = *n - 1;
if (*ku < 0 || *ku > max(i__1,0) || (itype == 4 || itype == 5) &&
*kl != *ku) {
*info = -3;
} else if (itype == 4 && *lda < *kl + 1 || itype == 5 && *lda < *
ku + 1 || itype == 6 && *lda < (*kl << 1) + *ku + 1) {
*info = -9;
}
}
}
if (*info != 0) {
i__1 = -(*info);
xerbla_("DLASCL", &i__1);
return 0;
}
//
// Quick return if possible
//
if (*n == 0 || *m == 0) {
return 0;
}
//
// Get machine parameters
//
smlnum = dlamch_("S");
bignum = 1. / smlnum;
cfromc = *cfrom;
ctoc = *cto;
L10:
cfrom1 = cfromc * smlnum;
if (cfrom1 == cfromc) {
// CFROMC is an inf. Multiply by a correctly signed zero for
// finite CTOC, or a NaN if CTOC is infinite.
mul = ctoc / cfromc;
done = TRUE_;
cto1 = ctoc;
} else {
cto1 = ctoc / bignum;
if (cto1 == ctoc) {
// CTOC is either 0 or an inf. In both cases, CTOC itself
// serves as the correct multiplication factor.
mul = ctoc;
done = TRUE_;
cfromc = 1.;
} else if (abs(cfrom1) > abs(ctoc) && ctoc != 0.) {
mul = smlnum;
done = FALSE_;
cfromc = cfrom1;
} else if (abs(cto1) > abs(cfromc)) {
mul = bignum;
done = FALSE_;
ctoc = cto1;
} else {
mul = ctoc / cfromc;
done = TRUE_;
}
}
if (itype == 0) {
//
// Full matrix
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
a[i__ + j * a_dim1] *= mul;
// L20:
}
// L30:
}
} else if (itype == 1) {
//
// Lower triangular matrix
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = j; i__ <= i__2; ++i__) {
a[i__ + j * a_dim1] *= mul;
// L40:
}
// L50:
}
} else if (itype == 2) {
//
// Upper triangular matrix
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = min(j,*m);
for (i__ = 1; i__ <= i__2; ++i__) {
a[i__ + j * a_dim1] *= mul;
// L60:
}
// L70:
}
} else if (itype == 3) {
//
// Upper Hessenberg matrix
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
// Computing MIN
i__3 = j + 1;
i__2 = min(i__3,*m);
for (i__ = 1; i__ <= i__2; ++i__) {
a[i__ + j * a_dim1] *= mul;
// L80:
}
// L90:
}
} else if (itype == 4) {
//
// Lower half of a symmetric band matrix
//
k3 = *kl + 1;
k4 = *n + 1;
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
// Computing MIN
i__3 = k3, i__4 = k4 - j;
i__2 = min(i__3,i__4);
for (i__ = 1; i__ <= i__2; ++i__) {
a[i__ + j * a_dim1] *= mul;
// L100:
}
// L110:
}
} else if (itype == 5) {
//
// Upper half of a symmetric band matrix
//
k1 = *ku + 2;
k3 = *ku + 1;
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
// Computing MAX
i__2 = k1 - j;
i__3 = k3;
for (i__ = max(i__2,1); i__ <= i__3; ++i__) {
a[i__ + j * a_dim1] *= mul;
// L120:
}
// L130:
}
} else if (itype == 6) {
//
// Band matrix
//
k1 = *kl + *ku + 2;
k2 = *kl + 1;
k3 = (*kl << 1) + *ku + 1;
k4 = *kl + *ku + 1 + *m;
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
// Computing MAX
i__3 = k1 - j;
// Computing MIN
i__4 = k3, i__5 = k4 - j;
i__2 = min(i__4,i__5);
for (i__ = max(i__3,k2); i__ <= i__2; ++i__) {
a[i__ + j * a_dim1] *= mul;
// L140:
}
// L150:
}
}
if (! done) {
goto L10;
}
return 0;
//
// End of DLASCL
//
} // dlascl_
+209
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@@ -0,0 +1,209 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DLASET initializes the off-diagonal elements and the diagonal elements of a matrix to given values.
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DLASET + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dlaset.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dlaset.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dlaset.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// SUBROUTINE DLASET( UPLO, M, N, ALPHA, BETA, A, LDA )
//
// .. Scalar Arguments ..
// CHARACTER UPLO
// INTEGER LDA, M, N
// DOUBLE PRECISION ALPHA, BETA
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A( LDA, * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DLASET initializes an m-by-n matrix A to BETA on the diagonal and
//> ALPHA on the offdiagonals.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] UPLO
//> \verbatim
//> UPLO is CHARACTER*1
//> Specifies the part of the matrix A to be set.
//> = 'U': Upper triangular part is set; the strictly lower
//> triangular part of A is not changed.
//> = 'L': Lower triangular part is set; the strictly upper
//> triangular part of A is not changed.
//> Otherwise: All of the matrix A is set.
//> \endverbatim
//>
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> The number of rows of the matrix A. M >= 0.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The number of columns of the matrix A. N >= 0.
//> \endverbatim
//>
//> \param[in] ALPHA
//> \verbatim
//> ALPHA is DOUBLE PRECISION
//> The constant to which the offdiagonal elements are to be set.
//> \endverbatim
//>
//> \param[in] BETA
//> \verbatim
//> BETA is DOUBLE PRECISION
//> The constant to which the diagonal elements are to be set.
//> \endverbatim
//>
//> \param[out] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension (LDA,N)
//> On exit, the leading m-by-n submatrix of A is set as follows:
//>
//> if UPLO = 'U', A(i,j) = ALPHA, 1<=i<=j-1, 1<=j<=n,
//> if UPLO = 'L', A(i,j) = ALPHA, j+1<=i<=m, 1<=j<=n,
//> otherwise, A(i,j) = ALPHA, 1<=i<=m, 1<=j<=n, i.ne.j,
//>
//> and, for all UPLO, A(i,i) = BETA, 1<=i<=min(m,n).
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> The leading dimension of the array A. LDA >= max(1,M).
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup OTHERauxiliary
//
// =====================================================================
/* Subroutine */ int dlaset_(char *uplo, int *m, int *n, double *alpha,
double *beta, double *a, int *lda)
{
// System generated locals
int a_dim1, a_offset, i__1, i__2, i__3;
// Local variables
int i__, j;
extern int lsame_(char *, char *);
//
// -- LAPACK auxiliary routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
//=====================================================================
//
// .. Local Scalars ..
// ..
// .. External Functions ..
// ..
// .. Intrinsic Functions ..
// ..
// .. Executable Statements ..
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
// Function Body
if (lsame_(uplo, "U")) {
//
// Set the strictly upper triangular or trapezoidal part of the
// array to ALPHA.
//
i__1 = *n;
for (j = 2; j <= i__1; ++j) {
// Computing MIN
i__3 = j - 1;
i__2 = min(i__3,*m);
for (i__ = 1; i__ <= i__2; ++i__) {
a[i__ + j * a_dim1] = *alpha;
// L10:
}
// L20:
}
} else if (lsame_(uplo, "L")) {
//
// Set the strictly lower triangular or trapezoidal part of the
// array to ALPHA.
//
i__1 = min(*m,*n);
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = j + 1; i__ <= i__2; ++i__) {
a[i__ + j * a_dim1] = *alpha;
// L30:
}
// L40:
}
} else {
//
// Set the leading m-by-n submatrix to ALPHA.
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
a[i__ + j * a_dim1] = *alpha;
// L50:
}
// L60:
}
}
//
// Set the first min(M,N) diagonal elements to BETA.
//
i__1 = min(*m,*n);
for (i__ = 1; i__ <= i__1; ++i__) {
a[i__ + i__ * a_dim1] = *beta;
// L70:
}
return 0;
//
// End of DLASET
//
} // dlaset_
+172
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/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DLASSQ updates a sum of squares represented in scaled form.
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DLASSQ + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dlassq.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dlassq.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dlassq.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// SUBROUTINE DLASSQ( N, X, INCX, SCALE, SUMSQ )
//
// .. Scalar Arguments ..
// INTEGER INCX, N
// DOUBLE PRECISION SCALE, SUMSQ
// ..
// .. Array Arguments ..
// DOUBLE PRECISION X( * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DLASSQ returns the values scl and smsq such that
//>
//> ( scl**2 )*smsq = x( 1 )**2 +...+ x( n )**2 + ( scale**2 )*sumsq,
//>
//> where x( i ) = X( 1 + ( i - 1 )*INCX ). The value of sumsq is
//> assumed to be non-negative and scl returns the value
//>
//> scl = max( scale, abs( x( i ) ) ).
//>
//> scale and sumsq must be supplied in SCALE and SUMSQ and
//> scl and smsq are overwritten on SCALE and SUMSQ respectively.
//>
//> The routine makes only one pass through the vector x.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The number of elements to be used from the vector X.
//> \endverbatim
//>
//> \param[in] X
//> \verbatim
//> X is DOUBLE PRECISION array, dimension (1+(N-1)*INCX)
//> The vector for which a scaled sum of squares is computed.
//> x( i ) = X( 1 + ( i - 1 )*INCX ), 1 <= i <= n.
//> \endverbatim
//>
//> \param[in] INCX
//> \verbatim
//> INCX is INTEGER
//> The increment between successive values of the vector X.
//> INCX > 0.
//> \endverbatim
//>
//> \param[in,out] SCALE
//> \verbatim
//> SCALE is DOUBLE PRECISION
//> On entry, the value scale in the equation above.
//> On exit, SCALE is overwritten with scl , the scaling factor
//> for the sum of squares.
//> \endverbatim
//>
//> \param[in,out] SUMSQ
//> \verbatim
//> SUMSQ is DOUBLE PRECISION
//> On entry, the value sumsq in the equation above.
//> On exit, SUMSQ is overwritten with smsq , the basic sum of
//> squares from which scl has been factored out.
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup OTHERauxiliary
//
// =====================================================================
/* Subroutine */ int dlassq_(int *n, double *x, int *incx, double *scale,
double *sumsq)
{
// System generated locals
int i__1, i__2;
double d__1;
// Local variables
int ix;
double absxi;
extern int disnan_(double *);
//
// -- LAPACK auxiliary routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
//=====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. External Functions ..
// ..
// .. Intrinsic Functions ..
// ..
// .. Executable Statements ..
//
// Parameter adjustments
--x;
// Function Body
if (*n > 0) {
i__1 = (*n - 1) * *incx + 1;
i__2 = *incx;
for (ix = 1; i__2 < 0 ? ix >= i__1 : ix <= i__1; ix += i__2) {
absxi = (d__1 = x[ix], abs(d__1));
if (absxi > 0. || disnan_(&absxi)) {
if (*scale < absxi) {
// Computing 2nd power
d__1 = *scale / absxi;
*sumsq = *sumsq * (d__1 * d__1) + 1;
*scale = absxi;
} else {
// Computing 2nd power
d__1 = absxi / *scale;
*sumsq += d__1 * d__1;
}
}
// L10:
}
}
return 0;
//
// End of DLASSQ
//
} // dlassq_
+149
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@@ -0,0 +1,149 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DNRM2
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
// Definition:
// ===========
//
// DOUBLE PRECISION FUNCTION DNRM2(N,X,INCX)
//
// .. Scalar Arguments ..
// INTEGER INCX,N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION X(*)
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DNRM2 returns the euclidean norm of a vector via the function
//> name, so that
//>
//> DNRM2 := sqrt( x'*x )
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> number of elements in input vector(s)
//> \endverbatim
//>
//> \param[in] X
//> \verbatim
//> X is DOUBLE PRECISION array, dimension ( 1 + ( N - 1 )*abs( INCX ) )
//> \endverbatim
//>
//> \param[in] INCX
//> \verbatim
//> INCX is INTEGER
//> storage spacing between elements of DX
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date November 2017
//
//> \ingroup double_blas_level1
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> -- This version written on 25-October-1982.
//> Modified on 14-October-1993 to inline the call to DLASSQ.
//> Sven Hammarling, Nag Ltd.
//> \endverbatim
//>
// =====================================================================
double dnrm2_(int *n, double *x, int *incx)
{
// System generated locals
int i__1, i__2;
double ret_val, d__1;
// Local variables
int ix;
double ssq, norm, scale, absxi;
//
// -- Reference BLAS level1 routine (version 3.8.0) --
// -- Reference BLAS is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// November 2017
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. Intrinsic Functions ..
// ..
// Parameter adjustments
--x;
// Function Body
if (*n < 1 || *incx < 1) {
norm = 0.;
} else if (*n == 1) {
norm = abs(x[1]);
} else {
scale = 0.;
ssq = 1.;
// The following loop is equivalent to this call to the LAPACK
// auxiliary routine:
// CALL DLASSQ( N, X, INCX, SCALE, SSQ )
//
i__1 = (*n - 1) * *incx + 1;
i__2 = *incx;
for (ix = 1; i__2 < 0 ? ix >= i__1 : ix <= i__1; ix += i__2) {
if (x[ix] != 0.) {
absxi = (d__1 = x[ix], abs(d__1));
if (scale < absxi) {
// Computing 2nd power
d__1 = scale / absxi;
ssq = ssq * (d__1 * d__1) + 1.;
scale = absxi;
} else {
// Computing 2nd power
d__1 = absxi / scale;
ssq += d__1 * d__1;
}
}
// L10:
}
norm = scale * sqrt(ssq);
}
ret_val = norm;
return ret_val;
//
// End of DNRM2.
//
} // dnrm2_
+571
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@@ -0,0 +1,571 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DORG2R generates all or part of the orthogonal matrix Q from a QR factorization determined by sgeqrf (unblocked algorithm).
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DORG2R + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dorg2r.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dorg2r.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dorg2r.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// SUBROUTINE DORG2R( M, N, K, A, LDA, TAU, WORK, INFO )
//
// .. Scalar Arguments ..
// INTEGER INFO, K, LDA, M, N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A( LDA, * ), TAU( * ), WORK( * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DORG2R generates an m by n real matrix Q with orthonormal columns,
//> which is defined as the first n columns of a product of k elementary
//> reflectors of order m
//>
//> Q = H(1) H(2) . . . H(k)
//>
//> as returned by DGEQRF.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> The number of rows of the matrix Q. M >= 0.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The number of columns of the matrix Q. M >= N >= 0.
//> \endverbatim
//>
//> \param[in] K
//> \verbatim
//> K is INTEGER
//> The number of elementary reflectors whose product defines the
//> matrix Q. N >= K >= 0.
//> \endverbatim
//>
//> \param[in,out] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension (LDA,N)
//> On entry, the i-th column must contain the vector which
//> defines the elementary reflector H(i), for i = 1,2,...,k, as
//> returned by DGEQRF in the first k columns of its array
//> argument A.
//> On exit, the m-by-n matrix Q.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> The first dimension of the array A. LDA >= max(1,M).
//> \endverbatim
//>
//> \param[in] TAU
//> \verbatim
//> TAU is DOUBLE PRECISION array, dimension (K)
//> TAU(i) must contain the scalar factor of the elementary
//> reflector H(i), as returned by DGEQRF.
//> \endverbatim
//>
//> \param[out] WORK
//> \verbatim
//> WORK is DOUBLE PRECISION array, dimension (N)
//> \endverbatim
//>
//> \param[out] INFO
//> \verbatim
//> INFO is INTEGER
//> = 0: successful exit
//> < 0: if INFO = -i, the i-th argument has an illegal value
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup doubleOTHERcomputational
//
// =====================================================================
/* Subroutine */ int dorg2r_(int *m, int *n, int *k, double *a, int *lda,
double *tau, double *work, int *info)
{
// Table of constant values
int c__1 = 1;
// System generated locals
int a_dim1, a_offset, i__1, i__2;
double d__1;
// Local variables
int i__, j, l;
extern /* Subroutine */ int dscal_(int *, double *, double *, int *),
dlarf_(char *, int *, int *, double *, int *, double *, double *,
int *, double *), xerbla_(char *, int *);
//
// -- LAPACK computational routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. External Subroutines ..
// ..
// .. Intrinsic Functions ..
// ..
// .. Executable Statements ..
//
// Test the input arguments
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
--tau;
--work;
// Function Body
*info = 0;
if (*m < 0) {
*info = -1;
} else if (*n < 0 || *n > *m) {
*info = -2;
} else if (*k < 0 || *k > *n) {
*info = -3;
} else if (*lda < max(1,*m)) {
*info = -5;
}
if (*info != 0) {
i__1 = -(*info);
xerbla_("DORG2R", &i__1);
return 0;
}
//
// Quick return if possible
//
if (*n <= 0) {
return 0;
}
//
// Initialise columns k+1:n to columns of the unit matrix
//
i__1 = *n;
for (j = *k + 1; j <= i__1; ++j) {
i__2 = *m;
for (l = 1; l <= i__2; ++l) {
a[l + j * a_dim1] = 0.;
// L10:
}
a[j + j * a_dim1] = 1.;
// L20:
}
for (i__ = *k; i__ >= 1; --i__) {
//
// Apply H(i) to A(i:m,i:n) from the left
//
if (i__ < *n) {
a[i__ + i__ * a_dim1] = 1.;
i__1 = *m - i__ + 1;
i__2 = *n - i__;
dlarf_("Left", &i__1, &i__2, &a[i__ + i__ * a_dim1], &c__1, &tau[
i__], &a[i__ + (i__ + 1) * a_dim1], lda, &work[1]);
}
if (i__ < *m) {
i__1 = *m - i__;
d__1 = -tau[i__];
dscal_(&i__1, &d__1, &a[i__ + 1 + i__ * a_dim1], &c__1);
}
a[i__ + i__ * a_dim1] = 1. - tau[i__];
//
// Set A(1:i-1,i) to zero
//
i__1 = i__ - 1;
for (l = 1; l <= i__1; ++l) {
a[l + i__ * a_dim1] = 0.;
// L30:
}
// L40:
}
return 0;
//
// End of DORG2R
//
} // dorg2r_
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
//> \brief \b DORGQR
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DORGQR + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dorgqr.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dorgqr.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dorgqr.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// SUBROUTINE DORGQR( M, N, K, A, LDA, TAU, WORK, LWORK, INFO )
//
// .. Scalar Arguments ..
// INTEGER INFO, K, LDA, LWORK, M, N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A( LDA, * ), TAU( * ), WORK( * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DORGQR generates an M-by-N real matrix Q with orthonormal columns,
//> which is defined as the first N columns of a product of K elementary
//> reflectors of order M
//>
//> Q = H(1) H(2) . . . H(k)
//>
//> as returned by DGEQRF.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> The number of rows of the matrix Q. M >= 0.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The number of columns of the matrix Q. M >= N >= 0.
//> \endverbatim
//>
//> \param[in] K
//> \verbatim
//> K is INTEGER
//> The number of elementary reflectors whose product defines the
//> matrix Q. N >= K >= 0.
//> \endverbatim
//>
//> \param[in,out] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension (LDA,N)
//> On entry, the i-th column must contain the vector which
//> defines the elementary reflector H(i), for i = 1,2,...,k, as
//> returned by DGEQRF in the first k columns of its array
//> argument A.
//> On exit, the M-by-N matrix Q.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> The first dimension of the array A. LDA >= max(1,M).
//> \endverbatim
//>
//> \param[in] TAU
//> \verbatim
//> TAU is DOUBLE PRECISION array, dimension (K)
//> TAU(i) must contain the scalar factor of the elementary
//> reflector H(i), as returned by DGEQRF.
//> \endverbatim
//>
//> \param[out] WORK
//> \verbatim
//> WORK is DOUBLE PRECISION array, dimension (MAX(1,LWORK))
//> On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
//> \endverbatim
//>
//> \param[in] LWORK
//> \verbatim
//> LWORK is INTEGER
//> The dimension of the array WORK. LWORK >= max(1,N).
//> For optimum performance LWORK >= N*NB, where NB is the
//> optimal blocksize.
//>
//> If LWORK = -1, then a workspace query is assumed; the routine
//> only calculates the optimal size of the WORK array, returns
//> this value as the first entry of the WORK array, and no error
//> message related to LWORK is issued by XERBLA.
//> \endverbatim
//>
//> \param[out] INFO
//> \verbatim
//> INFO is INTEGER
//> = 0: successful exit
//> < 0: if INFO = -i, the i-th argument has an illegal value
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup doubleOTHERcomputational
//
// =====================================================================
/* Subroutine */ int dorgqr_(int *m, int *n, int *k, double *a, int *lda,
double *tau, double *work, int *lwork, int *info)
{
// Table of constant values
int c__1 = 1;
int c_n1 = -1;
int c__3 = 3;
int c__2 = 2;
// System generated locals
int a_dim1, a_offset, i__1, i__2, i__3;
// Local variables
int i__, j, l, ib, nb, ki, kk, nx, iws, nbmin, iinfo;
extern /* Subroutine */ int dorg2r_(int *, int *, int *, double *, int *,
double *, double *, int *), dlarfb_(char *, char *, char *, char *
, int *, int *, int *, double *, int *, double *, int *, double *,
int *, double *, int *), dlarft_(char *, char *, int *, int *,
double *, int *, double *, double *, int *), xerbla_(char *, int *
);
extern int ilaenv_(int *, char *, char *, int *, int *, int *, int *);
int ldwork, lwkopt;
int lquery;
//
// -- LAPACK computational routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. External Subroutines ..
// ..
// .. Intrinsic Functions ..
// ..
// .. External Functions ..
// ..
// .. Executable Statements ..
//
// Test the input arguments
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
--tau;
--work;
// Function Body
*info = 0;
nb = ilaenv_(&c__1, "DORGQR", " ", m, n, k, &c_n1);
lwkopt = max(1,*n) * nb;
work[1] = (double) lwkopt;
lquery = *lwork == -1;
if (*m < 0) {
*info = -1;
} else if (*n < 0 || *n > *m) {
*info = -2;
} else if (*k < 0 || *k > *n) {
*info = -3;
} else if (*lda < max(1,*m)) {
*info = -5;
} else if (*lwork < max(1,*n) && ! lquery) {
*info = -8;
}
if (*info != 0) {
i__1 = -(*info);
xerbla_("DORGQR", &i__1);
return 0;
} else if (lquery) {
return 0;
}
//
// Quick return if possible
//
if (*n <= 0) {
work[1] = 1.;
return 0;
}
nbmin = 2;
nx = 0;
iws = *n;
if (nb > 1 && nb < *k) {
//
// Determine when to cross over from blocked to unblocked code.
//
// Computing MAX
i__1 = 0, i__2 = ilaenv_(&c__3, "DORGQR", " ", m, n, k, &c_n1);
nx = max(i__1,i__2);
if (nx < *k) {
//
// Determine if workspace is large enough for blocked code.
//
ldwork = *n;
iws = ldwork * nb;
if (*lwork < iws) {
//
// Not enough workspace to use optimal NB: reduce NB and
// determine the minimum value of NB.
//
nb = *lwork / ldwork;
// Computing MAX
i__1 = 2, i__2 = ilaenv_(&c__2, "DORGQR", " ", m, n, k, &c_n1)
;
nbmin = max(i__1,i__2);
}
}
}
if (nb >= nbmin && nb < *k && nx < *k) {
//
// Use blocked code after the last block.
// The first kk columns are handled by the block method.
//
ki = (*k - nx - 1) / nb * nb;
// Computing MIN
i__1 = *k, i__2 = ki + nb;
kk = min(i__1,i__2);
//
// Set A(1:kk,kk+1:n) to zero.
//
i__1 = *n;
for (j = kk + 1; j <= i__1; ++j) {
i__2 = kk;
for (i__ = 1; i__ <= i__2; ++i__) {
a[i__ + j * a_dim1] = 0.;
// L10:
}
// L20:
}
} else {
kk = 0;
}
//
// Use unblocked code for the last or only block.
//
if (kk < *n) {
i__1 = *m - kk;
i__2 = *n - kk;
i__3 = *k - kk;
dorg2r_(&i__1, &i__2, &i__3, &a[kk + 1 + (kk + 1) * a_dim1], lda, &
tau[kk + 1], &work[1], &iinfo);
}
if (kk > 0) {
//
// Use blocked code
//
i__1 = -nb;
for (i__ = ki + 1; i__1 < 0 ? i__ >= 1 : i__ <= 1; i__ += i__1) {
// Computing MIN
i__2 = nb, i__3 = *k - i__ + 1;
ib = min(i__2,i__3);
if (i__ + ib <= *n) {
//
// Form the triangular factor of the block reflector
// H = H(i) H(i+1) . . . H(i+ib-1)
//
i__2 = *m - i__ + 1;
dlarft_("Forward", "Columnwise", &i__2, &ib, &a[i__ + i__ *
a_dim1], lda, &tau[i__], &work[1], &ldwork);
//
// Apply H to A(i:m,i+ib:n) from the left
//
i__2 = *m - i__ + 1;
i__3 = *n - i__ - ib + 1;
dlarfb_("Left", "No transpose", "Forward", "Columnwise", &
i__2, &i__3, &ib, &a[i__ + i__ * a_dim1], lda, &work[
1], &ldwork, &a[i__ + (i__ + ib) * a_dim1], lda, &
work[ib + 1], &ldwork);
}
//
// Apply H to rows i:m of current block
//
i__2 = *m - i__ + 1;
dorg2r_(&i__2, &ib, &ib, &a[i__ + i__ * a_dim1], lda, &tau[i__], &
work[1], &iinfo);
//
// Set rows 1:i-1 of current block to zero
//
i__2 = i__ + ib - 1;
for (j = i__; j <= i__2; ++j) {
i__3 = i__ - 1;
for (l = 1; l <= i__3; ++l) {
a[l + j * a_dim1] = 0.;
// L30:
}
// L40:
}
// L50:
}
}
work[1] = (double) iws;
return 0;
//
// End of DORGQR
//
} // dorgqr_
+684
View File
@@ -0,0 +1,684 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DORM2R multiplies a general matrix by the orthogonal matrix from a QR factorization determined by sgeqrf (unblocked algorithm).
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DORM2R + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dorm2r.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dorm2r.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dorm2r.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// SUBROUTINE DORM2R( SIDE, TRANS, M, N, K, A, LDA, TAU, C, LDC,
// WORK, INFO )
//
// .. Scalar Arguments ..
// CHARACTER SIDE, TRANS
// INTEGER INFO, K, LDA, LDC, M, N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A( LDA, * ), C( LDC, * ), TAU( * ), WORK( * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DORM2R overwrites the general real m by n matrix C with
//>
//> Q * C if SIDE = 'L' and TRANS = 'N', or
//>
//> Q**T* C if SIDE = 'L' and TRANS = 'T', or
//>
//> C * Q if SIDE = 'R' and TRANS = 'N', or
//>
//> C * Q**T if SIDE = 'R' and TRANS = 'T',
//>
//> where Q is a real orthogonal matrix defined as the product of k
//> elementary reflectors
//>
//> Q = H(1) H(2) . . . H(k)
//>
//> as returned by DGEQRF. Q is of order m if SIDE = 'L' and of order n
//> if SIDE = 'R'.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] SIDE
//> \verbatim
//> SIDE is CHARACTER*1
//> = 'L': apply Q or Q**T from the Left
//> = 'R': apply Q or Q**T from the Right
//> \endverbatim
//>
//> \param[in] TRANS
//> \verbatim
//> TRANS is CHARACTER*1
//> = 'N': apply Q (No transpose)
//> = 'T': apply Q**T (Transpose)
//> \endverbatim
//>
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> The number of rows of the matrix C. M >= 0.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The number of columns of the matrix C. N >= 0.
//> \endverbatim
//>
//> \param[in] K
//> \verbatim
//> K is INTEGER
//> The number of elementary reflectors whose product defines
//> the matrix Q.
//> If SIDE = 'L', M >= K >= 0;
//> if SIDE = 'R', N >= K >= 0.
//> \endverbatim
//>
//> \param[in] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension (LDA,K)
//> The i-th column must contain the vector which defines the
//> elementary reflector H(i), for i = 1,2,...,k, as returned by
//> DGEQRF in the first k columns of its array argument A.
//> A is modified by the routine but restored on exit.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> The leading dimension of the array A.
//> If SIDE = 'L', LDA >= max(1,M);
//> if SIDE = 'R', LDA >= max(1,N).
//> \endverbatim
//>
//> \param[in] TAU
//> \verbatim
//> TAU is DOUBLE PRECISION array, dimension (K)
//> TAU(i) must contain the scalar factor of the elementary
//> reflector H(i), as returned by DGEQRF.
//> \endverbatim
//>
//> \param[in,out] C
//> \verbatim
//> C is DOUBLE PRECISION array, dimension (LDC,N)
//> On entry, the m by n matrix C.
//> On exit, C is overwritten by Q*C or Q**T*C or C*Q**T or C*Q.
//> \endverbatim
//>
//> \param[in] LDC
//> \verbatim
//> LDC is INTEGER
//> The leading dimension of the array C. LDC >= max(1,M).
//> \endverbatim
//>
//> \param[out] WORK
//> \verbatim
//> WORK is DOUBLE PRECISION array, dimension
//> (N) if SIDE = 'L',
//> (M) if SIDE = 'R'
//> \endverbatim
//>
//> \param[out] INFO
//> \verbatim
//> INFO is INTEGER
//> = 0: successful exit
//> < 0: if INFO = -i, the i-th argument had an illegal value
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup doubleOTHERcomputational
//
// =====================================================================
/* Subroutine */ int dorm2r_(char *side, char *trans, int *m, int *n, int *k,
double *a, int *lda, double *tau, double *c__, int *ldc, double *work,
int *info)
{
// Table of constant values
int c__1 = 1;
// System generated locals
int a_dim1, a_offset, c_dim1, c_offset, i__1, i__2;
// Local variables
int i__, i1, i2, i3, ic, jc, mi, ni, nq;
double aii;
int left;
extern /* Subroutine */ int dlarf_(char *, int *, int *, double *, int *,
double *, double *, int *, double *);
extern int lsame_(char *, char *);
extern /* Subroutine */ int xerbla_(char *, int *);
int notran;
//
// -- LAPACK computational routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. External Functions ..
// ..
// .. External Subroutines ..
// ..
// .. Intrinsic Functions ..
// ..
// .. Executable Statements ..
//
// Test the input arguments
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
--tau;
c_dim1 = *ldc;
c_offset = 1 + c_dim1;
c__ -= c_offset;
--work;
// Function Body
*info = 0;
left = lsame_(side, "L");
notran = lsame_(trans, "N");
//
// NQ is the order of Q
//
if (left) {
nq = *m;
} else {
nq = *n;
}
if (! left && ! lsame_(side, "R")) {
*info = -1;
} else if (! notran && ! lsame_(trans, "T")) {
*info = -2;
} else if (*m < 0) {
*info = -3;
} else if (*n < 0) {
*info = -4;
} else if (*k < 0 || *k > nq) {
*info = -5;
} else if (*lda < max(1,nq)) {
*info = -7;
} else if (*ldc < max(1,*m)) {
*info = -10;
}
if (*info != 0) {
i__1 = -(*info);
xerbla_("DORM2R", &i__1);
return 0;
}
//
// Quick return if possible
//
if (*m == 0 || *n == 0 || *k == 0) {
return 0;
}
if (left && ! notran || ! left && notran) {
i1 = 1;
i2 = *k;
i3 = 1;
} else {
i1 = *k;
i2 = 1;
i3 = -1;
}
if (left) {
ni = *n;
jc = 1;
} else {
mi = *m;
ic = 1;
}
i__1 = i2;
i__2 = i3;
for (i__ = i1; i__2 < 0 ? i__ >= i__1 : i__ <= i__1; i__ += i__2) {
if (left) {
//
// H(i) is applied to C(i:m,1:n)
//
mi = *m - i__ + 1;
ic = i__;
} else {
//
// H(i) is applied to C(1:m,i:n)
//
ni = *n - i__ + 1;
jc = i__;
}
//
// Apply H(i)
//
aii = a[i__ + i__ * a_dim1];
a[i__ + i__ * a_dim1] = 1.;
dlarf_(side, &mi, &ni, &a[i__ + i__ * a_dim1], &c__1, &tau[i__], &c__[
ic + jc * c_dim1], ldc, &work[1]);
a[i__ + i__ * a_dim1] = aii;
// L10:
}
return 0;
//
// End of DORM2R
//
} // dorm2r_
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
//> \brief \b DORMQR
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
//> \htmlonly
//> Download DORMQR + dependencies
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dormqr.f">
//> [TGZ]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dormqr.f">
//> [ZIP]</a>
//> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dormqr.f">
//> [TXT]</a>
//> \endhtmlonly
//
// Definition:
// ===========
//
// SUBROUTINE DORMQR( SIDE, TRANS, M, N, K, A, LDA, TAU, C, LDC,
// WORK, LWORK, INFO )
//
// .. Scalar Arguments ..
// CHARACTER SIDE, TRANS
// INTEGER INFO, K, LDA, LDC, LWORK, M, N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A( LDA, * ), C( LDC, * ), TAU( * ), WORK( * )
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DORMQR overwrites the general real M-by-N matrix C with
//>
//> SIDE = 'L' SIDE = 'R'
//> TRANS = 'N': Q * C C * Q
//> TRANS = 'T': Q**T * C C * Q**T
//>
//> where Q is a real orthogonal matrix defined as the product of k
//> elementary reflectors
//>
//> Q = H(1) H(2) . . . H(k)
//>
//> as returned by DGEQRF. Q is of order M if SIDE = 'L' and of order N
//> if SIDE = 'R'.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] SIDE
//> \verbatim
//> SIDE is CHARACTER*1
//> = 'L': apply Q or Q**T from the Left;
//> = 'R': apply Q or Q**T from the Right.
//> \endverbatim
//>
//> \param[in] TRANS
//> \verbatim
//> TRANS is CHARACTER*1
//> = 'N': No transpose, apply Q;
//> = 'T': Transpose, apply Q**T.
//> \endverbatim
//>
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> The number of rows of the matrix C. M >= 0.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> The number of columns of the matrix C. N >= 0.
//> \endverbatim
//>
//> \param[in] K
//> \verbatim
//> K is INTEGER
//> The number of elementary reflectors whose product defines
//> the matrix Q.
//> If SIDE = 'L', M >= K >= 0;
//> if SIDE = 'R', N >= K >= 0.
//> \endverbatim
//>
//> \param[in] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension (LDA,K)
//> The i-th column must contain the vector which defines the
//> elementary reflector H(i), for i = 1,2,...,k, as returned by
//> DGEQRF in the first k columns of its array argument A.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> The leading dimension of the array A.
//> If SIDE = 'L', LDA >= max(1,M);
//> if SIDE = 'R', LDA >= max(1,N).
//> \endverbatim
//>
//> \param[in] TAU
//> \verbatim
//> TAU is DOUBLE PRECISION array, dimension (K)
//> TAU(i) must contain the scalar factor of the elementary
//> reflector H(i), as returned by DGEQRF.
//> \endverbatim
//>
//> \param[in,out] C
//> \verbatim
//> C is DOUBLE PRECISION array, dimension (LDC,N)
//> On entry, the M-by-N matrix C.
//> On exit, C is overwritten by Q*C or Q**T*C or C*Q**T or C*Q.
//> \endverbatim
//>
//> \param[in] LDC
//> \verbatim
//> LDC is INTEGER
//> The leading dimension of the array C. LDC >= max(1,M).
//> \endverbatim
//>
//> \param[out] WORK
//> \verbatim
//> WORK is DOUBLE PRECISION array, dimension (MAX(1,LWORK))
//> On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
//> \endverbatim
//>
//> \param[in] LWORK
//> \verbatim
//> LWORK is INTEGER
//> The dimension of the array WORK.
//> If SIDE = 'L', LWORK >= max(1,N);
//> if SIDE = 'R', LWORK >= max(1,M).
//> For good performance, LWORK should generally be larger.
//>
//> If LWORK = -1, then a workspace query is assumed; the routine
//> only calculates the optimal size of the WORK array, returns
//> this value as the first entry of the WORK array, and no error
//> message related to LWORK is issued by XERBLA.
//> \endverbatim
//>
//> \param[out] INFO
//> \verbatim
//> INFO is INTEGER
//> = 0: successful exit
//> < 0: if INFO = -i, the i-th argument had an illegal value
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup doubleOTHERcomputational
//
// =====================================================================
/* Subroutine */ int dormqr_(char *side, char *trans, int *m, int *n, int *k,
double *a, int *lda, double *tau, double *c__, int *ldc, double *work,
int *lwork, int *info)
{
// Table of constant values
int c__1 = 1;
int c_n1 = -1;
int c__2 = 2;
int c__65 = 65;
// System generated locals
address a__1[2];
int a_dim1, a_offset, c_dim1, c_offset, i__1, i__2, i__3[2], i__4, i__5;
char ch__1[2+1]={'\0'};
// Local variables
int i__, i1, i2, i3, ib, ic, jc, nb, mi, ni, nq, nw, iwt;
int left;
extern int lsame_(char *, char *);
int nbmin, iinfo;
extern /* Subroutine */ int dorm2r_(char *, char *, int *, int *, int *,
double *, int *, double *, double *, int *, double *, int *),
dlarfb_(char *, char *, char *, char *, int *, int *, int *,
double *, int *, double *, int *, double *, int *, double *, int *
), dlarft_(char *, char *, int *, int *, double *, int *, double *
, double *, int *), xerbla_(char *, int *);
extern int ilaenv_(int *, char *, char *, int *, int *, int *, int *);
int notran;
int ldwork, lwkopt;
int lquery;
//
// -- LAPACK computational routine (version 3.7.0) --
// -- LAPACK is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. External Functions ..
// ..
// .. External Subroutines ..
// ..
// .. Intrinsic Functions ..
// ..
// .. Executable Statements ..
//
// Test the input arguments
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
--tau;
c_dim1 = *ldc;
c_offset = 1 + c_dim1;
c__ -= c_offset;
--work;
// Function Body
*info = 0;
left = lsame_(side, "L");
notran = lsame_(trans, "N");
lquery = *lwork == -1;
//
// NQ is the order of Q and NW is the minimum dimension of WORK
//
if (left) {
nq = *m;
nw = *n;
} else {
nq = *n;
nw = *m;
}
if (! left && ! lsame_(side, "R")) {
*info = -1;
} else if (! notran && ! lsame_(trans, "T")) {
*info = -2;
} else if (*m < 0) {
*info = -3;
} else if (*n < 0) {
*info = -4;
} else if (*k < 0 || *k > nq) {
*info = -5;
} else if (*lda < max(1,nq)) {
*info = -7;
} else if (*ldc < max(1,*m)) {
*info = -10;
} else if (*lwork < max(1,nw) && ! lquery) {
*info = -12;
}
if (*info == 0) {
//
// Compute the workspace requirements
//
// Computing MIN
// Writing concatenation
i__3[0] = 1, a__1[0] = side;
i__3[1] = 1, a__1[1] = trans;
s_cat(ch__1, a__1, i__3, &c__2);
i__1 = 64, i__2 = ilaenv_(&c__1, "DORMQR", ch__1, m, n, k, &c_n1);
nb = min(i__1,i__2);
lwkopt = max(1,nw) * nb + 4160;
work[1] = (double) lwkopt;
}
if (*info != 0) {
i__1 = -(*info);
xerbla_("DORMQR", &i__1);
return 0;
} else if (lquery) {
return 0;
}
//
// Quick return if possible
//
if (*m == 0 || *n == 0 || *k == 0) {
work[1] = 1.;
return 0;
}
nbmin = 2;
ldwork = nw;
if (nb > 1 && nb < *k) {
if (*lwork < nw * nb + 4160) {
nb = (*lwork - 4160) / ldwork;
// Computing MAX
// Writing concatenation
i__3[0] = 1, a__1[0] = side;
i__3[1] = 1, a__1[1] = trans;
s_cat(ch__1, a__1, i__3, &c__2);
i__1 = 2, i__2 = ilaenv_(&c__2, "DORMQR", ch__1, m, n, k, &c_n1);
nbmin = max(i__1,i__2);
}
}
if (nb < nbmin || nb >= *k) {
//
// Use unblocked code
//
dorm2r_(side, trans, m, n, k, &a[a_offset], lda, &tau[1], &c__[
c_offset], ldc, &work[1], &iinfo);
} else {
//
// Use blocked code
//
iwt = nw * nb + 1;
if (left && ! notran || ! left && notran) {
i1 = 1;
i2 = *k;
i3 = nb;
} else {
i1 = (*k - 1) / nb * nb + 1;
i2 = 1;
i3 = -nb;
}
if (left) {
ni = *n;
jc = 1;
} else {
mi = *m;
ic = 1;
}
i__1 = i2;
i__2 = i3;
for (i__ = i1; i__2 < 0 ? i__ >= i__1 : i__ <= i__1; i__ += i__2) {
// Computing MIN
i__4 = nb, i__5 = *k - i__ + 1;
ib = min(i__4,i__5);
//
// Form the triangular factor of the block reflector
// H = H(i) H(i+1) . . . H(i+ib-1)
//
i__4 = nq - i__ + 1;
dlarft_("Forward", "Columnwise", &i__4, &ib, &a[i__ + i__ *
a_dim1], lda, &tau[i__], &work[iwt], &c__65);
if (left) {
//
// H or H**T is applied to C(i:m,1:n)
//
mi = *m - i__ + 1;
ic = i__;
} else {
//
// H or H**T is applied to C(1:m,i:n)
//
ni = *n - i__ + 1;
jc = i__;
}
//
// Apply H or H**T
//
dlarfb_(side, trans, "Forward", "Columnwise", &mi, &ni, &ib, &a[
i__ + i__ * a_dim1], lda, &work[iwt], &c__65, &c__[ic +
jc * c_dim1], ldc, &work[1], &ldwork);
// L10:
}
}
work[1] = (double) lwkopt;
return 0;
//
// End of DORMQR
//
} // dormqr_
+164
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/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DROT
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
// Definition:
// ===========
//
// SUBROUTINE DROT(N,DX,INCX,DY,INCY,C,S)
//
// .. Scalar Arguments ..
// DOUBLE PRECISION C,S
// INTEGER INCX,INCY,N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION DX(*),DY(*)
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DROT applies a plane rotation.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> number of elements in input vector(s)
//> \endverbatim
//>
//> \param[in,out] DX
//> \verbatim
//> DX is DOUBLE PRECISION array, dimension ( 1 + ( N - 1 )*abs( INCX ) )
//> \endverbatim
//>
//> \param[in] INCX
//> \verbatim
//> INCX is INTEGER
//> storage spacing between elements of DX
//> \endverbatim
//>
//> \param[in,out] DY
//> \verbatim
//> DY is DOUBLE PRECISION array, dimension ( 1 + ( N - 1 )*abs( INCY ) )
//> \endverbatim
//>
//> \param[in] INCY
//> \verbatim
//> INCY is INTEGER
//> storage spacing between elements of DY
//> \endverbatim
//>
//> \param[in] C
//> \verbatim
//> C is DOUBLE PRECISION
//> \endverbatim
//>
//> \param[in] S
//> \verbatim
//> S is DOUBLE PRECISION
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date November 2017
//
//> \ingroup double_blas_level1
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> jack dongarra, linpack, 3/11/78.
//> modified 12/3/93, array(1) declarations changed to array(*)
//> \endverbatim
//>
// =====================================================================
/* Subroutine */ int drot_(int *n, double *dx, int *incx, double *dy, int *
incy, double *c__, double *s)
{
// System generated locals
int i__1;
// Local variables
int i__, ix, iy;
double dtemp;
//
// -- Reference BLAS level1 routine (version 3.8.0) --
// -- Reference BLAS is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// November 2017
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Local Scalars ..
// ..
// Parameter adjustments
--dy;
--dx;
// Function Body
if (*n <= 0) {
return 0;
}
if (*incx == 1 && *incy == 1) {
//
// code for both increments equal to 1
//
i__1 = *n;
for (i__ = 1; i__ <= i__1; ++i__) {
dtemp = *c__ * dx[i__] + *s * dy[i__];
dy[i__] = *c__ * dy[i__] - *s * dx[i__];
dx[i__] = dtemp;
}
} else {
//
// code for unequal increments or equal increments not equal
// to 1
//
ix = 1;
iy = 1;
if (*incx < 0) {
ix = (-(*n) + 1) * *incx + 1;
}
if (*incy < 0) {
iy = (-(*n) + 1) * *incy + 1;
}
i__1 = *n;
for (i__ = 1; i__ <= i__1; ++i__) {
dtemp = *c__ * dx[ix] + *s * dy[iy];
dy[iy] = *c__ * dy[iy] - *s * dx[ix];
dx[ix] = dtemp;
ix += *incx;
iy += *incy;
}
}
return 0;
} // drot_
+155
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/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DSCAL
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
// Definition:
// ===========
//
// SUBROUTINE DSCAL(N,DA,DX,INCX)
//
// .. Scalar Arguments ..
// DOUBLE PRECISION DA
// INTEGER INCX,N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION DX(*)
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DSCAL scales a vector by a constant.
//> uses unrolled loops for increment equal to 1.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> number of elements in input vector(s)
//> \endverbatim
//>
//> \param[in] DA
//> \verbatim
//> DA is DOUBLE PRECISION
//> On entry, DA specifies the scalar alpha.
//> \endverbatim
//>
//> \param[in,out] DX
//> \verbatim
//> DX is DOUBLE PRECISION array, dimension ( 1 + ( N - 1 )*abs( INCX ) )
//> \endverbatim
//>
//> \param[in] INCX
//> \verbatim
//> INCX is INTEGER
//> storage spacing between elements of DX
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date November 2017
//
//> \ingroup double_blas_level1
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> jack dongarra, linpack, 3/11/78.
//> modified 3/93 to return if incx .le. 0.
//> modified 12/3/93, array(1) declarations changed to array(*)
//> \endverbatim
//>
// =====================================================================
/* Subroutine */ int dscal_(int *n, double *da, double *dx, int *incx)
{
// System generated locals
int i__1, i__2;
// Local variables
int i__, m, mp1, nincx;
//
// -- Reference BLAS level1 routine (version 3.8.0) --
// -- Reference BLAS is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// November 2017
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Local Scalars ..
// ..
// .. Intrinsic Functions ..
// ..
// Parameter adjustments
--dx;
// Function Body
if (*n <= 0 || *incx <= 0) {
return 0;
}
if (*incx == 1) {
//
// code for increment equal to 1
//
//
// clean-up loop
//
m = *n % 5;
if (m != 0) {
i__1 = m;
for (i__ = 1; i__ <= i__1; ++i__) {
dx[i__] = *da * dx[i__];
}
if (*n < 5) {
return 0;
}
}
mp1 = m + 1;
i__1 = *n;
for (i__ = mp1; i__ <= i__1; i__ += 5) {
dx[i__] = *da * dx[i__];
dx[i__ + 1] = *da * dx[i__ + 1];
dx[i__ + 2] = *da * dx[i__ + 2];
dx[i__ + 3] = *da * dx[i__ + 3];
dx[i__ + 4] = *da * dx[i__ + 4];
}
} else {
//
// code for increment not equal to 1
//
nincx = *n * *incx;
i__1 = nincx;
i__2 = *incx;
for (i__ = 1; i__2 < 0 ? i__ >= i__1 : i__ <= i__1; i__ += i__2) {
dx[i__] = *da * dx[i__];
}
}
return 0;
} // dscal_
+178
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/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DSWAP
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
// Definition:
// ===========
//
// SUBROUTINE DSWAP(N,DX,INCX,DY,INCY)
//
// .. Scalar Arguments ..
// INTEGER INCX,INCY,N
// ..
// .. Array Arguments ..
// DOUBLE PRECISION DX(*),DY(*)
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DSWAP interchanges two vectors.
//> uses unrolled loops for increments equal to 1.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> number of elements in input vector(s)
//> \endverbatim
//>
//> \param[in,out] DX
//> \verbatim
//> DX is DOUBLE PRECISION array, dimension ( 1 + ( N - 1 )*abs( INCX ) )
//> \endverbatim
//>
//> \param[in] INCX
//> \verbatim
//> INCX is INTEGER
//> storage spacing between elements of DX
//> \endverbatim
//>
//> \param[in,out] DY
//> \verbatim
//> DY is DOUBLE PRECISION array, dimension ( 1 + ( N - 1 )*abs( INCY ) )
//> \endverbatim
//>
//> \param[in] INCY
//> \verbatim
//> INCY is INTEGER
//> storage spacing between elements of DY
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date November 2017
//
//> \ingroup double_blas_level1
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> jack dongarra, linpack, 3/11/78.
//> modified 12/3/93, array(1) declarations changed to array(*)
//> \endverbatim
//>
// =====================================================================
/* Subroutine */ int dswap_(int *n, double *dx, int *incx, double *dy, int *
incy)
{
// System generated locals
int i__1;
// Local variables
int i__, m, ix, iy, mp1;
double dtemp;
//
// -- Reference BLAS level1 routine (version 3.8.0) --
// -- Reference BLAS is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// November 2017
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Local Scalars ..
// ..
// .. Intrinsic Functions ..
// ..
// Parameter adjustments
--dy;
--dx;
// Function Body
if (*n <= 0) {
return 0;
}
if (*incx == 1 && *incy == 1) {
//
// code for both increments equal to 1
//
//
// clean-up loop
//
m = *n % 3;
if (m != 0) {
i__1 = m;
for (i__ = 1; i__ <= i__1; ++i__) {
dtemp = dx[i__];
dx[i__] = dy[i__];
dy[i__] = dtemp;
}
if (*n < 3) {
return 0;
}
}
mp1 = m + 1;
i__1 = *n;
for (i__ = mp1; i__ <= i__1; i__ += 3) {
dtemp = dx[i__];
dx[i__] = dy[i__];
dy[i__] = dtemp;
dtemp = dx[i__ + 1];
dx[i__ + 1] = dy[i__ + 1];
dy[i__ + 1] = dtemp;
dtemp = dx[i__ + 2];
dx[i__ + 2] = dy[i__ + 2];
dy[i__ + 2] = dtemp;
}
} else {
//
// code for unequal increments or equal increments not equal
// to 1
//
ix = 1;
iy = 1;
if (*incx < 0) {
ix = (-(*n) + 1) * *incx + 1;
}
if (*incy < 0) {
iy = (-(*n) + 1) * *incy + 1;
}
i__1 = *n;
for (i__ = 1; i__ <= i__1; ++i__) {
dtemp = dx[ix];
dx[ix] = dy[iy];
dy[iy] = dtemp;
ix += *incx;
iy += *incy;
}
}
return 0;
} // dswap_
+509
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@@ -0,0 +1,509 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DTRMM
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
// Definition:
// ===========
//
// SUBROUTINE DTRMM(SIDE,UPLO,TRANSA,DIAG,M,N,ALPHA,A,LDA,B,LDB)
//
// .. Scalar Arguments ..
// DOUBLE PRECISION ALPHA
// INTEGER LDA,LDB,M,N
// CHARACTER DIAG,SIDE,TRANSA,UPLO
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A(LDA,*),B(LDB,*)
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DTRMM performs one of the matrix-matrix operations
//>
//> B := alpha*op( A )*B, or B := alpha*B*op( A ),
//>
//> where alpha is a scalar, B is an m by n matrix, A is a unit, or
//> non-unit, upper or lower triangular matrix and op( A ) is one of
//>
//> op( A ) = A or op( A ) = A**T.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] SIDE
//> \verbatim
//> SIDE is CHARACTER*1
//> On entry, SIDE specifies whether op( A ) multiplies B from
//> the left or right as follows:
//>
//> SIDE = 'L' or 'l' B := alpha*op( A )*B.
//>
//> SIDE = 'R' or 'r' B := alpha*B*op( A ).
//> \endverbatim
//>
//> \param[in] UPLO
//> \verbatim
//> UPLO is CHARACTER*1
//> On entry, UPLO specifies whether the matrix A is an upper or
//> lower triangular matrix as follows:
//>
//> UPLO = 'U' or 'u' A is an upper triangular matrix.
//>
//> UPLO = 'L' or 'l' A is a lower triangular matrix.
//> \endverbatim
//>
//> \param[in] TRANSA
//> \verbatim
//> TRANSA is CHARACTER*1
//> On entry, TRANSA specifies the form of op( A ) to be used in
//> the matrix multiplication as follows:
//>
//> TRANSA = 'N' or 'n' op( A ) = A.
//>
//> TRANSA = 'T' or 't' op( A ) = A**T.
//>
//> TRANSA = 'C' or 'c' op( A ) = A**T.
//> \endverbatim
//>
//> \param[in] DIAG
//> \verbatim
//> DIAG is CHARACTER*1
//> On entry, DIAG specifies whether or not A is unit triangular
//> as follows:
//>
//> DIAG = 'U' or 'u' A is assumed to be unit triangular.
//>
//> DIAG = 'N' or 'n' A is not assumed to be unit
//> triangular.
//> \endverbatim
//>
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> On entry, M specifies the number of rows of B. M must be at
//> least zero.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> On entry, N specifies the number of columns of B. N must be
//> at least zero.
//> \endverbatim
//>
//> \param[in] ALPHA
//> \verbatim
//> ALPHA is DOUBLE PRECISION.
//> On entry, ALPHA specifies the scalar alpha. When alpha is
//> zero then A is not referenced and B need not be set before
//> entry.
//> \endverbatim
//>
//> \param[in] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension ( LDA, k ), where k is m
//> when SIDE = 'L' or 'l' and is n when SIDE = 'R' or 'r'.
//> Before entry with UPLO = 'U' or 'u', the leading k by k
//> upper triangular part of the array A must contain the upper
//> triangular matrix and the strictly lower triangular part of
//> A is not referenced.
//> Before entry with UPLO = 'L' or 'l', the leading k by k
//> lower triangular part of the array A must contain the lower
//> triangular matrix and the strictly upper triangular part of
//> A is not referenced.
//> Note that when DIAG = 'U' or 'u', the diagonal elements of
//> A are not referenced either, but are assumed to be unity.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> On entry, LDA specifies the first dimension of A as declared
//> in the calling (sub) program. When SIDE = 'L' or 'l' then
//> LDA must be at least max( 1, m ), when SIDE = 'R' or 'r'
//> then LDA must be at least max( 1, n ).
//> \endverbatim
//>
//> \param[in,out] B
//> \verbatim
//> B is DOUBLE PRECISION array, dimension ( LDB, N )
//> Before entry, the leading m by n part of the array B must
//> contain the matrix B, and on exit is overwritten by the
//> transformed matrix.
//> \endverbatim
//>
//> \param[in] LDB
//> \verbatim
//> LDB is INTEGER
//> On entry, LDB specifies the first dimension of B as declared
//> in the calling (sub) program. LDB must be at least
//> max( 1, m ).
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup double_blas_level3
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> Level 3 Blas routine.
//>
//> -- Written on 8-February-1989.
//> Jack Dongarra, Argonne National Laboratory.
//> Iain Duff, AERE Harwell.
//> Jeremy Du Croz, Numerical Algorithms Group Ltd.
//> Sven Hammarling, Numerical Algorithms Group Ltd.
//> \endverbatim
//>
// =====================================================================
/* Subroutine */ int dtrmm_(char *side, char *uplo, char *transa, char *diag,
int *m, int *n, double *alpha, double *a, int *lda, double *b, int *
ldb)
{
// System generated locals
int a_dim1, a_offset, b_dim1, b_offset, i__1, i__2, i__3;
// Local variables
int i__, j, k, info;
double temp;
int lside;
extern int lsame_(char *, char *);
int nrowa;
int upper;
extern /* Subroutine */ int xerbla_(char *, int *);
int nounit;
//
// -- Reference BLAS level3 routine (version 3.7.0) --
// -- Reference BLAS is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. External Functions ..
// ..
// .. External Subroutines ..
// ..
// .. Intrinsic Functions ..
// ..
// .. Local Scalars ..
// ..
// .. Parameters ..
// ..
//
// Test the input parameters.
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
b_dim1 = *ldb;
b_offset = 1 + b_dim1;
b -= b_offset;
// Function Body
lside = lsame_(side, "L");
if (lside) {
nrowa = *m;
} else {
nrowa = *n;
}
nounit = lsame_(diag, "N");
upper = lsame_(uplo, "U");
info = 0;
if (! lside && ! lsame_(side, "R")) {
info = 1;
} else if (! upper && ! lsame_(uplo, "L")) {
info = 2;
} else if (! lsame_(transa, "N") && ! lsame_(transa, "T") && ! lsame_(
transa, "C")) {
info = 3;
} else if (! lsame_(diag, "U") && ! lsame_(diag, "N")) {
info = 4;
} else if (*m < 0) {
info = 5;
} else if (*n < 0) {
info = 6;
} else if (*lda < max(1,nrowa)) {
info = 9;
} else if (*ldb < max(1,*m)) {
info = 11;
}
if (info != 0) {
xerbla_("DTRMM ", &info);
return 0;
}
//
// Quick return if possible.
//
if (*m == 0 || *n == 0) {
return 0;
}
//
// And when alpha.eq.zero.
//
if (*alpha == 0.) {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
b[i__ + j * b_dim1] = 0.;
// L10:
}
// L20:
}
return 0;
}
//
// Start the operations.
//
if (lside) {
if (lsame_(transa, "N")) {
//
// Form B := alpha*A*B.
//
if (upper) {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (k = 1; k <= i__2; ++k) {
if (b[k + j * b_dim1] != 0.) {
temp = *alpha * b[k + j * b_dim1];
i__3 = k - 1;
for (i__ = 1; i__ <= i__3; ++i__) {
b[i__ + j * b_dim1] += temp * a[i__ + k *
a_dim1];
// L30:
}
if (nounit) {
temp *= a[k + k * a_dim1];
}
b[k + j * b_dim1] = temp;
}
// L40:
}
// L50:
}
} else {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
for (k = *m; k >= 1; --k) {
if (b[k + j * b_dim1] != 0.) {
temp = *alpha * b[k + j * b_dim1];
b[k + j * b_dim1] = temp;
if (nounit) {
b[k + j * b_dim1] *= a[k + k * a_dim1];
}
i__2 = *m;
for (i__ = k + 1; i__ <= i__2; ++i__) {
b[i__ + j * b_dim1] += temp * a[i__ + k *
a_dim1];
// L60:
}
}
// L70:
}
// L80:
}
}
} else {
//
// Form B := alpha*A**T*B.
//
if (upper) {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
for (i__ = *m; i__ >= 1; --i__) {
temp = b[i__ + j * b_dim1];
if (nounit) {
temp *= a[i__ + i__ * a_dim1];
}
i__2 = i__ - 1;
for (k = 1; k <= i__2; ++k) {
temp += a[k + i__ * a_dim1] * b[k + j * b_dim1];
// L90:
}
b[i__ + j * b_dim1] = *alpha * temp;
// L100:
}
// L110:
}
} else {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp = b[i__ + j * b_dim1];
if (nounit) {
temp *= a[i__ + i__ * a_dim1];
}
i__3 = *m;
for (k = i__ + 1; k <= i__3; ++k) {
temp += a[k + i__ * a_dim1] * b[k + j * b_dim1];
// L120:
}
b[i__ + j * b_dim1] = *alpha * temp;
// L130:
}
// L140:
}
}
}
} else {
if (lsame_(transa, "N")) {
//
// Form B := alpha*B*A.
//
if (upper) {
for (j = *n; j >= 1; --j) {
temp = *alpha;
if (nounit) {
temp *= a[j + j * a_dim1];
}
i__1 = *m;
for (i__ = 1; i__ <= i__1; ++i__) {
b[i__ + j * b_dim1] = temp * b[i__ + j * b_dim1];
// L150:
}
i__1 = j - 1;
for (k = 1; k <= i__1; ++k) {
if (a[k + j * a_dim1] != 0.) {
temp = *alpha * a[k + j * a_dim1];
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
b[i__ + j * b_dim1] += temp * b[i__ + k *
b_dim1];
// L160:
}
}
// L170:
}
// L180:
}
} else {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
temp = *alpha;
if (nounit) {
temp *= a[j + j * a_dim1];
}
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
b[i__ + j * b_dim1] = temp * b[i__ + j * b_dim1];
// L190:
}
i__2 = *n;
for (k = j + 1; k <= i__2; ++k) {
if (a[k + j * a_dim1] != 0.) {
temp = *alpha * a[k + j * a_dim1];
i__3 = *m;
for (i__ = 1; i__ <= i__3; ++i__) {
b[i__ + j * b_dim1] += temp * b[i__ + k *
b_dim1];
// L200:
}
}
// L210:
}
// L220:
}
}
} else {
//
// Form B := alpha*B*A**T.
//
if (upper) {
i__1 = *n;
for (k = 1; k <= i__1; ++k) {
i__2 = k - 1;
for (j = 1; j <= i__2; ++j) {
if (a[j + k * a_dim1] != 0.) {
temp = *alpha * a[j + k * a_dim1];
i__3 = *m;
for (i__ = 1; i__ <= i__3; ++i__) {
b[i__ + j * b_dim1] += temp * b[i__ + k *
b_dim1];
// L230:
}
}
// L240:
}
temp = *alpha;
if (nounit) {
temp *= a[k + k * a_dim1];
}
if (temp != 1.) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
b[i__ + k * b_dim1] = temp * b[i__ + k * b_dim1];
// L250:
}
}
// L260:
}
} else {
for (k = *n; k >= 1; --k) {
i__1 = *n;
for (j = k + 1; j <= i__1; ++j) {
if (a[j + k * a_dim1] != 0.) {
temp = *alpha * a[j + k * a_dim1];
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
b[i__ + j * b_dim1] += temp * b[i__ + k *
b_dim1];
// L270:
}
}
// L280:
}
temp = *alpha;
if (nounit) {
temp *= a[k + k * a_dim1];
}
if (temp != 1.) {
i__1 = *m;
for (i__ = 1; i__ <= i__1; ++i__) {
b[i__ + k * b_dim1] = temp * b[i__ + k * b_dim1];
// L290:
}
}
// L300:
}
}
}
}
return 0;
//
// End of DTRMM .
//
} // dtrmm_
+396
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@@ -0,0 +1,396 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b DTRMV
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
// Definition:
// ===========
//
// SUBROUTINE DTRMV(UPLO,TRANS,DIAG,N,A,LDA,X,INCX)
//
// .. Scalar Arguments ..
// INTEGER INCX,LDA,N
// CHARACTER DIAG,TRANS,UPLO
// ..
// .. Array Arguments ..
// DOUBLE PRECISION A(LDA,*),X(*)
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> DTRMV performs one of the matrix-vector operations
//>
//> x := A*x, or x := A**T*x,
//>
//> where x is an n element vector and A is an n by n unit, or non-unit,
//> upper or lower triangular matrix.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] UPLO
//> \verbatim
//> UPLO is CHARACTER*1
//> On entry, UPLO specifies whether the matrix is an upper or
//> lower triangular matrix as follows:
//>
//> UPLO = 'U' or 'u' A is an upper triangular matrix.
//>
//> UPLO = 'L' or 'l' A is a lower triangular matrix.
//> \endverbatim
//>
//> \param[in] TRANS
//> \verbatim
//> TRANS is CHARACTER*1
//> On entry, TRANS specifies the operation to be performed as
//> follows:
//>
//> TRANS = 'N' or 'n' x := A*x.
//>
//> TRANS = 'T' or 't' x := A**T*x.
//>
//> TRANS = 'C' or 'c' x := A**T*x.
//> \endverbatim
//>
//> \param[in] DIAG
//> \verbatim
//> DIAG is CHARACTER*1
//> On entry, DIAG specifies whether or not A is unit
//> triangular as follows:
//>
//> DIAG = 'U' or 'u' A is assumed to be unit triangular.
//>
//> DIAG = 'N' or 'n' A is not assumed to be unit
//> triangular.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> On entry, N specifies the order of the matrix A.
//> N must be at least zero.
//> \endverbatim
//>
//> \param[in] A
//> \verbatim
//> A is DOUBLE PRECISION array, dimension ( LDA, N )
//> Before entry with UPLO = 'U' or 'u', the leading n by n
//> upper triangular part of the array A must contain the upper
//> triangular matrix and the strictly lower triangular part of
//> A is not referenced.
//> Before entry with UPLO = 'L' or 'l', the leading n by n
//> lower triangular part of the array A must contain the lower
//> triangular matrix and the strictly upper triangular part of
//> A is not referenced.
//> Note that when DIAG = 'U' or 'u', the diagonal elements of
//> A are not referenced either, but are assumed to be unity.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> On entry, LDA specifies the first dimension of A as declared
//> in the calling (sub) program. LDA must be at least
//> max( 1, n ).
//> \endverbatim
//>
//> \param[in,out] X
//> \verbatim
//> X is DOUBLE PRECISION array, dimension at least
//> ( 1 + ( n - 1 )*abs( INCX ) ).
//> Before entry, the incremented array X must contain the n
//> element vector x. On exit, X is overwritten with the
//> transformed vector x.
//> \endverbatim
//>
//> \param[in] INCX
//> \verbatim
//> INCX is INTEGER
//> On entry, INCX specifies the increment for the elements of
//> X. INCX must not be zero.
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup double_blas_level2
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> Level 2 Blas routine.
//> The vector and matrix arguments are not referenced when N = 0, or M = 0
//>
//> -- Written on 22-October-1986.
//> Jack Dongarra, Argonne National Lab.
//> Jeremy Du Croz, Nag Central Office.
//> Sven Hammarling, Nag Central Office.
//> Richard Hanson, Sandia National Labs.
//> \endverbatim
//>
// =====================================================================
/* Subroutine */ int dtrmv_(char *uplo, char *trans, char *diag, int *n,
double *a, int *lda, double *x, int *incx)
{
// System generated locals
int a_dim1, a_offset, i__1, i__2;
// Local variables
int i__, j, ix, jx, kx, info;
double temp;
extern int lsame_(char *, char *);
extern /* Subroutine */ int xerbla_(char *, int *);
int nounit;
//
// -- Reference BLAS level2 routine (version 3.7.0) --
// -- Reference BLAS is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. Parameters ..
// ..
// .. Local Scalars ..
// ..
// .. External Functions ..
// ..
// .. External Subroutines ..
// ..
// .. Intrinsic Functions ..
// ..
//
// Test the input parameters.
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
--x;
// Function Body
info = 0;
if (! lsame_(uplo, "U") && ! lsame_(uplo, "L")) {
info = 1;
} else if (! lsame_(trans, "N") && ! lsame_(trans, "T") && ! lsame_(trans,
"C")) {
info = 2;
} else if (! lsame_(diag, "U") && ! lsame_(diag, "N")) {
info = 3;
} else if (*n < 0) {
info = 4;
} else if (*lda < max(1,*n)) {
info = 6;
} else if (*incx == 0) {
info = 8;
}
if (info != 0) {
xerbla_("DTRMV ", &info);
return 0;
}
//
// Quick return if possible.
//
if (*n == 0) {
return 0;
}
nounit = lsame_(diag, "N");
//
// Set up the start point in X if the increment is not unity. This
// will be ( N - 1 )*INCX too small for descending loops.
//
if (*incx <= 0) {
kx = 1 - (*n - 1) * *incx;
} else if (*incx != 1) {
kx = 1;
}
//
// Start the operations. In this version the elements of A are
// accessed sequentially with one pass through A.
//
if (lsame_(trans, "N")) {
//
// Form x := A*x.
//
if (lsame_(uplo, "U")) {
if (*incx == 1) {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (x[j] != 0.) {
temp = x[j];
i__2 = j - 1;
for (i__ = 1; i__ <= i__2; ++i__) {
x[i__] += temp * a[i__ + j * a_dim1];
// L10:
}
if (nounit) {
x[j] *= a[j + j * a_dim1];
}
}
// L20:
}
} else {
jx = kx;
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (x[jx] != 0.) {
temp = x[jx];
ix = kx;
i__2 = j - 1;
for (i__ = 1; i__ <= i__2; ++i__) {
x[ix] += temp * a[i__ + j * a_dim1];
ix += *incx;
// L30:
}
if (nounit) {
x[jx] *= a[j + j * a_dim1];
}
}
jx += *incx;
// L40:
}
}
} else {
if (*incx == 1) {
for (j = *n; j >= 1; --j) {
if (x[j] != 0.) {
temp = x[j];
i__1 = j + 1;
for (i__ = *n; i__ >= i__1; --i__) {
x[i__] += temp * a[i__ + j * a_dim1];
// L50:
}
if (nounit) {
x[j] *= a[j + j * a_dim1];
}
}
// L60:
}
} else {
kx += (*n - 1) * *incx;
jx = kx;
for (j = *n; j >= 1; --j) {
if (x[jx] != 0.) {
temp = x[jx];
ix = kx;
i__1 = j + 1;
for (i__ = *n; i__ >= i__1; --i__) {
x[ix] += temp * a[i__ + j * a_dim1];
ix -= *incx;
// L70:
}
if (nounit) {
x[jx] *= a[j + j * a_dim1];
}
}
jx -= *incx;
// L80:
}
}
}
} else {
//
// Form x := A**T*x.
//
if (lsame_(uplo, "U")) {
if (*incx == 1) {
for (j = *n; j >= 1; --j) {
temp = x[j];
if (nounit) {
temp *= a[j + j * a_dim1];
}
for (i__ = j - 1; i__ >= 1; --i__) {
temp += a[i__ + j * a_dim1] * x[i__];
// L90:
}
x[j] = temp;
// L100:
}
} else {
jx = kx + (*n - 1) * *incx;
for (j = *n; j >= 1; --j) {
temp = x[jx];
ix = jx;
if (nounit) {
temp *= a[j + j * a_dim1];
}
for (i__ = j - 1; i__ >= 1; --i__) {
ix -= *incx;
temp += a[i__ + j * a_dim1] * x[ix];
// L110:
}
x[jx] = temp;
jx -= *incx;
// L120:
}
}
} else {
if (*incx == 1) {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
temp = x[j];
if (nounit) {
temp *= a[j + j * a_dim1];
}
i__2 = *n;
for (i__ = j + 1; i__ <= i__2; ++i__) {
temp += a[i__ + j * a_dim1] * x[i__];
// L130:
}
x[j] = temp;
// L140:
}
} else {
jx = kx;
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
temp = x[jx];
ix = jx;
if (nounit) {
temp *= a[j + j * a_dim1];
}
i__2 = *n;
for (i__ = j + 1; i__ <= i__2; ++i__) {
ix += *incx;
temp += a[i__ + j * a_dim1] * x[ix];
// L150:
}
x[jx] = temp;
jx += *incx;
// L160:
}
}
}
}
return 0;
//
// End of DTRMV .
//
} // dtrmv_
+1334
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+444
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/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b SGEMM
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
// Definition:
// ===========
//
// SUBROUTINE SGEMM(TRANSA,TRANSB,M,N,K,ALPHA,A,LDA,B,LDB,BETA,C,LDC)
//
// .. Scalar Arguments ..
// REAL ALPHA,BETA
// INTEGER K,LDA,LDB,LDC,M,N
// CHARACTER TRANSA,TRANSB
// ..
// .. Array Arguments ..
// REAL A(LDA,*),B(LDB,*),C(LDC,*)
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> SGEMM performs one of the matrix-matrix operations
//>
//> C := alpha*op( A )*op( B ) + beta*C,
//>
//> where op( X ) is one of
//>
//> op( X ) = X or op( X ) = X**T,
//>
//> alpha and beta are scalars, and A, B and C are matrices, with op( A )
//> an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] TRANSA
//> \verbatim
//> TRANSA is CHARACTER*1
//> On entry, TRANSA specifies the form of op( A ) to be used in
//> the matrix multiplication as follows:
//>
//> TRANSA = 'N' or 'n', op( A ) = A.
//>
//> TRANSA = 'T' or 't', op( A ) = A**T.
//>
//> TRANSA = 'C' or 'c', op( A ) = A**T.
//> \endverbatim
//>
//> \param[in] TRANSB
//> \verbatim
//> TRANSB is CHARACTER*1
//> On entry, TRANSB specifies the form of op( B ) to be used in
//> the matrix multiplication as follows:
//>
//> TRANSB = 'N' or 'n', op( B ) = B.
//>
//> TRANSB = 'T' or 't', op( B ) = B**T.
//>
//> TRANSB = 'C' or 'c', op( B ) = B**T.
//> \endverbatim
//>
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> On entry, M specifies the number of rows of the matrix
//> op( A ) and of the matrix C. M must be at least zero.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> On entry, N specifies the number of columns of the matrix
//> op( B ) and the number of columns of the matrix C. N must be
//> at least zero.
//> \endverbatim
//>
//> \param[in] K
//> \verbatim
//> K is INTEGER
//> On entry, K specifies the number of columns of the matrix
//> op( A ) and the number of rows of the matrix op( B ). K must
//> be at least zero.
//> \endverbatim
//>
//> \param[in] ALPHA
//> \verbatim
//> ALPHA is REAL
//> On entry, ALPHA specifies the scalar alpha.
//> \endverbatim
//>
//> \param[in] A
//> \verbatim
//> A is REAL array, dimension ( LDA, ka ), where ka is
//> k when TRANSA = 'N' or 'n', and is m otherwise.
//> Before entry with TRANSA = 'N' or 'n', the leading m by k
//> part of the array A must contain the matrix A, otherwise
//> the leading k by m part of the array A must contain the
//> matrix A.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> On entry, LDA specifies the first dimension of A as declared
//> in the calling (sub) program. When TRANSA = 'N' or 'n' then
//> LDA must be at least max( 1, m ), otherwise LDA must be at
//> least max( 1, k ).
//> \endverbatim
//>
//> \param[in] B
//> \verbatim
//> B is REAL array, dimension ( LDB, kb ), where kb is
//> n when TRANSB = 'N' or 'n', and is k otherwise.
//> Before entry with TRANSB = 'N' or 'n', the leading k by n
//> part of the array B must contain the matrix B, otherwise
//> the leading n by k part of the array B must contain the
//> matrix B.
//> \endverbatim
//>
//> \param[in] LDB
//> \verbatim
//> LDB is INTEGER
//> On entry, LDB specifies the first dimension of B as declared
//> in the calling (sub) program. When TRANSB = 'N' or 'n' then
//> LDB must be at least max( 1, k ), otherwise LDB must be at
//> least max( 1, n ).
//> \endverbatim
//>
//> \param[in] BETA
//> \verbatim
//> BETA is REAL
//> On entry, BETA specifies the scalar beta. When BETA is
//> supplied as zero then C need not be set on input.
//> \endverbatim
//>
//> \param[in,out] C
//> \verbatim
//> C is REAL array, dimension ( LDC, N )
//> Before entry, the leading m by n part of the array C must
//> contain the matrix C, except when beta is zero, in which
//> case C need not be set on entry.
//> On exit, the array C is overwritten by the m by n matrix
//> ( alpha*op( A )*op( B ) + beta*C ).
//> \endverbatim
//>
//> \param[in] LDC
//> \verbatim
//> LDC is INTEGER
//> On entry, LDC specifies the first dimension of C as declared
//> in the calling (sub) program. LDC must be at least
//> max( 1, m ).
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup single_blas_level3
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> Level 3 Blas routine.
//>
//> -- Written on 8-February-1989.
//> Jack Dongarra, Argonne National Laboratory.
//> Iain Duff, AERE Harwell.
//> Jeremy Du Croz, Numerical Algorithms Group Ltd.
//> Sven Hammarling, Numerical Algorithms Group Ltd.
//> \endverbatim
//>
// =====================================================================
/* Subroutine */ int sgemm_(char *transa, char *transb, int *m, int *n, int *
k, float *alpha, float *a, int *lda, float *b, int *ldb, float *beta,
float *c__, int *ldc)
{
// System generated locals
int a_dim1, a_offset, b_dim1, b_offset, c_dim1, c_offset, i__1, i__2,
i__3;
// Local variables
int i__, j, l, info;
int nota, notb;
float temp;
int ncola;
extern int lsame_(char *, char *);
int nrowa, nrowb;
extern /* Subroutine */ int xerbla_(char *, int *);
//
// -- Reference BLAS level3 routine (version 3.7.0) --
// -- Reference BLAS is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. External Functions ..
// ..
// .. External Subroutines ..
// ..
// .. Intrinsic Functions ..
// ..
// .. Local Scalars ..
// ..
// .. Parameters ..
// ..
//
// Set NOTA and NOTB as true if A and B respectively are not
// transposed and set NROWA, NCOLA and NROWB as the number of rows
// and columns of A and the number of rows of B respectively.
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
b_dim1 = *ldb;
b_offset = 1 + b_dim1;
b -= b_offset;
c_dim1 = *ldc;
c_offset = 1 + c_dim1;
c__ -= c_offset;
// Function Body
nota = lsame_(transa, "N");
notb = lsame_(transb, "N");
if (nota) {
nrowa = *m;
ncola = *k;
} else {
nrowa = *k;
ncola = *m;
}
if (notb) {
nrowb = *k;
} else {
nrowb = *n;
}
//
// Test the input parameters.
//
info = 0;
if (! nota && ! lsame_(transa, "C") && ! lsame_(transa, "T")) {
info = 1;
} else if (! notb && ! lsame_(transb, "C") && ! lsame_(transb, "T")) {
info = 2;
} else if (*m < 0) {
info = 3;
} else if (*n < 0) {
info = 4;
} else if (*k < 0) {
info = 5;
} else if (*lda < max(1,nrowa)) {
info = 8;
} else if (*ldb < max(1,nrowb)) {
info = 10;
} else if (*ldc < max(1,*m)) {
info = 13;
}
if (info != 0) {
xerbla_("SGEMM ", &info);
return 0;
}
//
// Quick return if possible.
//
if (*m == 0 || *n == 0 || (*alpha == 0.f || *k == 0) && *beta == 1.f) {
return 0;
}
//
// And if alpha.eq.zero.
//
if (*alpha == 0.f) {
if (*beta == 0.f) {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + j * c_dim1] = 0.f;
// L10:
}
// L20:
}
} else {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + j * c_dim1] = *beta * c__[i__ + j * c_dim1];
// L30:
}
// L40:
}
}
return 0;
}
//
// Start the operations.
//
if (notb) {
if (nota) {
//
// Form C := alpha*A*B + beta*C.
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (*beta == 0.f) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + j * c_dim1] = 0.f;
// L50:
}
} else if (*beta != 1.f) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + j * c_dim1] = *beta * c__[i__ + j * c_dim1];
// L60:
}
}
i__2 = *k;
for (l = 1; l <= i__2; ++l) {
temp = *alpha * b[l + j * b_dim1];
i__3 = *m;
for (i__ = 1; i__ <= i__3; ++i__) {
c__[i__ + j * c_dim1] += temp * a[i__ + l * a_dim1];
// L70:
}
// L80:
}
// L90:
}
} else {
//
// Form C := alpha*A**T*B + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp = 0.f;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
temp += a[l + i__ * a_dim1] * b[l + j * b_dim1];
// L100:
}
if (*beta == 0.f) {
c__[i__ + j * c_dim1] = *alpha * temp;
} else {
c__[i__ + j * c_dim1] = *alpha * temp + *beta * c__[
i__ + j * c_dim1];
}
// L110:
}
// L120:
}
}
} else {
if (nota) {
//
// Form C := alpha*A*B**T + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (*beta == 0.f) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + j * c_dim1] = 0.f;
// L130:
}
} else if (*beta != 1.f) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
c__[i__ + j * c_dim1] = *beta * c__[i__ + j * c_dim1];
// L140:
}
}
i__2 = *k;
for (l = 1; l <= i__2; ++l) {
temp = *alpha * b[j + l * b_dim1];
i__3 = *m;
for (i__ = 1; i__ <= i__3; ++i__) {
c__[i__ + j * c_dim1] += temp * a[i__ + l * a_dim1];
// L150:
}
// L160:
}
// L170:
}
} else {
//
// Form C := alpha*A**T*B**T + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp = 0.f;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
temp += a[l + i__ * a_dim1] * b[j + l * b_dim1];
// L180:
}
if (*beta == 0.f) {
c__[i__ + j * c_dim1] = *alpha * temp;
} else {
c__[i__ + j * c_dim1] = *alpha * temp + *beta * c__[
i__ + j * c_dim1];
}
// L190:
}
// L200:
}
}
}
return 0;
//
// End of SGEMM .
//
} // sgemm_
+752
View File
@@ -0,0 +1,752 @@
/* -- translated by f2c (version 20201020 (for_lapack)). -- */
#include "f2c.h"
//> \brief \b ZGEMM
//
// =========== DOCUMENTATION ===========
//
// Online html documentation available at
// http://www.netlib.org/lapack/explore-html/
//
// Definition:
// ===========
//
// SUBROUTINE ZGEMM(TRANSA,TRANSB,M,N,K,ALPHA,A,LDA,B,LDB,BETA,C,LDC)
//
// .. Scalar Arguments ..
// COMPLEX*16 ALPHA,BETA
// INTEGER K,LDA,LDB,LDC,M,N
// CHARACTER TRANSA,TRANSB
// ..
// .. Array Arguments ..
// COMPLEX*16 A(LDA,*),B(LDB,*),C(LDC,*)
// ..
//
//
//> \par Purpose:
// =============
//>
//> \verbatim
//>
//> ZGEMM performs one of the matrix-matrix operations
//>
//> C := alpha*op( A )*op( B ) + beta*C,
//>
//> where op( X ) is one of
//>
//> op( X ) = X or op( X ) = X**T or op( X ) = X**H,
//>
//> alpha and beta are scalars, and A, B and C are matrices, with op( A )
//> an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.
//> \endverbatim
//
// Arguments:
// ==========
//
//> \param[in] TRANSA
//> \verbatim
//> TRANSA is CHARACTER*1
//> On entry, TRANSA specifies the form of op( A ) to be used in
//> the matrix multiplication as follows:
//>
//> TRANSA = 'N' or 'n', op( A ) = A.
//>
//> TRANSA = 'T' or 't', op( A ) = A**T.
//>
//> TRANSA = 'C' or 'c', op( A ) = A**H.
//> \endverbatim
//>
//> \param[in] TRANSB
//> \verbatim
//> TRANSB is CHARACTER*1
//> On entry, TRANSB specifies the form of op( B ) to be used in
//> the matrix multiplication as follows:
//>
//> TRANSB = 'N' or 'n', op( B ) = B.
//>
//> TRANSB = 'T' or 't', op( B ) = B**T.
//>
//> TRANSB = 'C' or 'c', op( B ) = B**H.
//> \endverbatim
//>
//> \param[in] M
//> \verbatim
//> M is INTEGER
//> On entry, M specifies the number of rows of the matrix
//> op( A ) and of the matrix C. M must be at least zero.
//> \endverbatim
//>
//> \param[in] N
//> \verbatim
//> N is INTEGER
//> On entry, N specifies the number of columns of the matrix
//> op( B ) and the number of columns of the matrix C. N must be
//> at least zero.
//> \endverbatim
//>
//> \param[in] K
//> \verbatim
//> K is INTEGER
//> On entry, K specifies the number of columns of the matrix
//> op( A ) and the number of rows of the matrix op( B ). K must
//> be at least zero.
//> \endverbatim
//>
//> \param[in] ALPHA
//> \verbatim
//> ALPHA is COMPLEX*16
//> On entry, ALPHA specifies the scalar alpha.
//> \endverbatim
//>
//> \param[in] A
//> \verbatim
//> A is COMPLEX*16 array, dimension ( LDA, ka ), where ka is
//> k when TRANSA = 'N' or 'n', and is m otherwise.
//> Before entry with TRANSA = 'N' or 'n', the leading m by k
//> part of the array A must contain the matrix A, otherwise
//> the leading k by m part of the array A must contain the
//> matrix A.
//> \endverbatim
//>
//> \param[in] LDA
//> \verbatim
//> LDA is INTEGER
//> On entry, LDA specifies the first dimension of A as declared
//> in the calling (sub) program. When TRANSA = 'N' or 'n' then
//> LDA must be at least max( 1, m ), otherwise LDA must be at
//> least max( 1, k ).
//> \endverbatim
//>
//> \param[in] B
//> \verbatim
//> B is COMPLEX*16 array, dimension ( LDB, kb ), where kb is
//> n when TRANSB = 'N' or 'n', and is k otherwise.
//> Before entry with TRANSB = 'N' or 'n', the leading k by n
//> part of the array B must contain the matrix B, otherwise
//> the leading n by k part of the array B must contain the
//> matrix B.
//> \endverbatim
//>
//> \param[in] LDB
//> \verbatim
//> LDB is INTEGER
//> On entry, LDB specifies the first dimension of B as declared
//> in the calling (sub) program. When TRANSB = 'N' or 'n' then
//> LDB must be at least max( 1, k ), otherwise LDB must be at
//> least max( 1, n ).
//> \endverbatim
//>
//> \param[in] BETA
//> \verbatim
//> BETA is COMPLEX*16
//> On entry, BETA specifies the scalar beta. When BETA is
//> supplied as zero then C need not be set on input.
//> \endverbatim
//>
//> \param[in,out] C
//> \verbatim
//> C is COMPLEX*16 array, dimension ( LDC, N )
//> Before entry, the leading m by n part of the array C must
//> contain the matrix C, except when beta is zero, in which
//> case C need not be set on entry.
//> On exit, the array C is overwritten by the m by n matrix
//> ( alpha*op( A )*op( B ) + beta*C ).
//> \endverbatim
//>
//> \param[in] LDC
//> \verbatim
//> LDC is INTEGER
//> On entry, LDC specifies the first dimension of C as declared
//> in the calling (sub) program. LDC must be at least
//> max( 1, m ).
//> \endverbatim
//
// Authors:
// ========
//
//> \author Univ. of Tennessee
//> \author Univ. of California Berkeley
//> \author Univ. of Colorado Denver
//> \author NAG Ltd.
//
//> \date December 2016
//
//> \ingroup complex16_blas_level3
//
//> \par Further Details:
// =====================
//>
//> \verbatim
//>
//> Level 3 Blas routine.
//>
//> -- Written on 8-February-1989.
//> Jack Dongarra, Argonne National Laboratory.
//> Iain Duff, AERE Harwell.
//> Jeremy Du Croz, Numerical Algorithms Group Ltd.
//> Sven Hammarling, Numerical Algorithms Group Ltd.
//> \endverbatim
//>
// =====================================================================
/* Subroutine */ int zgemm_(char *transa, char *transb, int *m, int *n, int *
k, doublecomplex *alpha, doublecomplex *a, int *lda, doublecomplex *b,
int *ldb, doublecomplex *beta, doublecomplex *c__, int *ldc)
{
// Table of constant values
doublecomplex c_b1 = {1.,0.};
doublecomplex c_b2 = {0.,0.};
// System generated locals
int a_dim1, a_offset, b_dim1, b_offset, c_dim1, c_offset, i__1, i__2,
i__3, i__4, i__5, i__6;
doublecomplex z__1, z__2, z__3, z__4;
// Local variables
int i__, j, l, info;
int nota, notb;
doublecomplex temp;
int conja, conjb;
int ncola;
extern int lsame_(char *, char *);
int nrowa, nrowb;
extern /* Subroutine */ int xerbla_(char *, int *);
//
// -- Reference BLAS level3 routine (version 3.7.0) --
// -- Reference BLAS is a software package provided by Univ. of Tennessee, --
// -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
// December 2016
//
// .. Scalar Arguments ..
// ..
// .. Array Arguments ..
// ..
//
// =====================================================================
//
// .. External Functions ..
// ..
// .. External Subroutines ..
// ..
// .. Intrinsic Functions ..
// ..
// .. Local Scalars ..
// ..
// .. Parameters ..
// ..
//
// Set NOTA and NOTB as true if A and B respectively are not
// conjugated or transposed, set CONJA and CONJB as true if A and
// B respectively are to be transposed but not conjugated and set
// NROWA, NCOLA and NROWB as the number of rows and columns of A
// and the number of rows of B respectively.
//
// Parameter adjustments
a_dim1 = *lda;
a_offset = 1 + a_dim1;
a -= a_offset;
b_dim1 = *ldb;
b_offset = 1 + b_dim1;
b -= b_offset;
c_dim1 = *ldc;
c_offset = 1 + c_dim1;
c__ -= c_offset;
// Function Body
nota = lsame_(transa, "N");
notb = lsame_(transb, "N");
conja = lsame_(transa, "C");
conjb = lsame_(transb, "C");
if (nota) {
nrowa = *m;
ncola = *k;
} else {
nrowa = *k;
ncola = *m;
}
if (notb) {
nrowb = *k;
} else {
nrowb = *n;
}
//
// Test the input parameters.
//
info = 0;
if (! nota && ! conja && ! lsame_(transa, "T")) {
info = 1;
} else if (! notb && ! conjb && ! lsame_(transb, "T")) {
info = 2;
} else if (*m < 0) {
info = 3;
} else if (*n < 0) {
info = 4;
} else if (*k < 0) {
info = 5;
} else if (*lda < max(1,nrowa)) {
info = 8;
} else if (*ldb < max(1,nrowb)) {
info = 10;
} else if (*ldc < max(1,*m)) {
info = 13;
}
if (info != 0) {
xerbla_("ZGEMM ", &info);
return 0;
}
//
// Quick return if possible.
//
if (*m == 0 || *n == 0 || (alpha->r == 0. && alpha->i == 0. || *k == 0) &&
(beta->r == 1. && beta->i == 0.)) {
return 0;
}
//
// And when alpha.eq.zero.
//
if (alpha->r == 0. && alpha->i == 0.) {
if (beta->r == 0. && beta->i == 0.) {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
c__[i__3].r = 0., c__[i__3].i = 0.;
// L10:
}
// L20:
}
} else {
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
i__4 = i__ + j * c_dim1;
z__1.r = beta->r * c__[i__4].r - beta->i * c__[i__4].i,
z__1.i = beta->r * c__[i__4].i + beta->i * c__[
i__4].r;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
// L30:
}
// L40:
}
}
return 0;
}
//
// Start the operations.
//
if (notb) {
if (nota) {
//
// Form C := alpha*A*B + beta*C.
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (beta->r == 0. && beta->i == 0.) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
c__[i__3].r = 0., c__[i__3].i = 0.;
// L50:
}
} else if (beta->r != 1. || beta->i != 0.) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
i__4 = i__ + j * c_dim1;
z__1.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, z__1.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
// L60:
}
}
i__2 = *k;
for (l = 1; l <= i__2; ++l) {
i__3 = l + j * b_dim1;
z__1.r = alpha->r * b[i__3].r - alpha->i * b[i__3].i,
z__1.i = alpha->r * b[i__3].i + alpha->i * b[i__3]
.r;
temp.r = z__1.r, temp.i = z__1.i;
i__3 = *m;
for (i__ = 1; i__ <= i__3; ++i__) {
i__4 = i__ + j * c_dim1;
i__5 = i__ + j * c_dim1;
i__6 = i__ + l * a_dim1;
z__2.r = temp.r * a[i__6].r - temp.i * a[i__6].i,
z__2.i = temp.r * a[i__6].i + temp.i * a[i__6]
.r;
z__1.r = c__[i__5].r + z__2.r, z__1.i = c__[i__5].i +
z__2.i;
c__[i__4].r = z__1.r, c__[i__4].i = z__1.i;
// L70:
}
// L80:
}
// L90:
}
} else if (conja) {
//
// Form C := alpha*A**H*B + beta*C.
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp.r = 0., temp.i = 0.;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
d_cnjg(&z__3, &a[l + i__ * a_dim1]);
i__4 = l + j * b_dim1;
z__2.r = z__3.r * b[i__4].r - z__3.i * b[i__4].i,
z__2.i = z__3.r * b[i__4].i + z__3.i * b[i__4]
.r;
z__1.r = temp.r + z__2.r, z__1.i = temp.i + z__2.i;
temp.r = z__1.r, temp.i = z__1.i;
// L100:
}
if (beta->r == 0. && beta->i == 0.) {
i__3 = i__ + j * c_dim1;
z__1.r = alpha->r * temp.r - alpha->i * temp.i,
z__1.i = alpha->r * temp.i + alpha->i *
temp.r;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
} else {
i__3 = i__ + j * c_dim1;
z__2.r = alpha->r * temp.r - alpha->i * temp.i,
z__2.i = alpha->r * temp.i + alpha->i *
temp.r;
i__4 = i__ + j * c_dim1;
z__3.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, z__3.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
z__1.r = z__2.r + z__3.r, z__1.i = z__2.i + z__3.i;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
}
// L110:
}
// L120:
}
} else {
//
// Form C := alpha*A**T*B + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp.r = 0., temp.i = 0.;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
i__4 = l + i__ * a_dim1;
i__5 = l + j * b_dim1;
z__2.r = a[i__4].r * b[i__5].r - a[i__4].i * b[i__5]
.i, z__2.i = a[i__4].r * b[i__5].i + a[i__4]
.i * b[i__5].r;
z__1.r = temp.r + z__2.r, z__1.i = temp.i + z__2.i;
temp.r = z__1.r, temp.i = z__1.i;
// L130:
}
if (beta->r == 0. && beta->i == 0.) {
i__3 = i__ + j * c_dim1;
z__1.r = alpha->r * temp.r - alpha->i * temp.i,
z__1.i = alpha->r * temp.i + alpha->i *
temp.r;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
} else {
i__3 = i__ + j * c_dim1;
z__2.r = alpha->r * temp.r - alpha->i * temp.i,
z__2.i = alpha->r * temp.i + alpha->i *
temp.r;
i__4 = i__ + j * c_dim1;
z__3.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, z__3.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
z__1.r = z__2.r + z__3.r, z__1.i = z__2.i + z__3.i;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
}
// L140:
}
// L150:
}
}
} else if (nota) {
if (conjb) {
//
// Form C := alpha*A*B**H + beta*C.
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (beta->r == 0. && beta->i == 0.) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
c__[i__3].r = 0., c__[i__3].i = 0.;
// L160:
}
} else if (beta->r != 1. || beta->i != 0.) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
i__4 = i__ + j * c_dim1;
z__1.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, z__1.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
// L170:
}
}
i__2 = *k;
for (l = 1; l <= i__2; ++l) {
d_cnjg(&z__2, &b[j + l * b_dim1]);
z__1.r = alpha->r * z__2.r - alpha->i * z__2.i, z__1.i =
alpha->r * z__2.i + alpha->i * z__2.r;
temp.r = z__1.r, temp.i = z__1.i;
i__3 = *m;
for (i__ = 1; i__ <= i__3; ++i__) {
i__4 = i__ + j * c_dim1;
i__5 = i__ + j * c_dim1;
i__6 = i__ + l * a_dim1;
z__2.r = temp.r * a[i__6].r - temp.i * a[i__6].i,
z__2.i = temp.r * a[i__6].i + temp.i * a[i__6]
.r;
z__1.r = c__[i__5].r + z__2.r, z__1.i = c__[i__5].i +
z__2.i;
c__[i__4].r = z__1.r, c__[i__4].i = z__1.i;
// L180:
}
// L190:
}
// L200:
}
} else {
//
// Form C := alpha*A*B**T + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (beta->r == 0. && beta->i == 0.) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
c__[i__3].r = 0., c__[i__3].i = 0.;
// L210:
}
} else if (beta->r != 1. || beta->i != 0.) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
i__3 = i__ + j * c_dim1;
i__4 = i__ + j * c_dim1;
z__1.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, z__1.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
// L220:
}
}
i__2 = *k;
for (l = 1; l <= i__2; ++l) {
i__3 = j + l * b_dim1;
z__1.r = alpha->r * b[i__3].r - alpha->i * b[i__3].i,
z__1.i = alpha->r * b[i__3].i + alpha->i * b[i__3]
.r;
temp.r = z__1.r, temp.i = z__1.i;
i__3 = *m;
for (i__ = 1; i__ <= i__3; ++i__) {
i__4 = i__ + j * c_dim1;
i__5 = i__ + j * c_dim1;
i__6 = i__ + l * a_dim1;
z__2.r = temp.r * a[i__6].r - temp.i * a[i__6].i,
z__2.i = temp.r * a[i__6].i + temp.i * a[i__6]
.r;
z__1.r = c__[i__5].r + z__2.r, z__1.i = c__[i__5].i +
z__2.i;
c__[i__4].r = z__1.r, c__[i__4].i = z__1.i;
// L230:
}
// L240:
}
// L250:
}
}
} else if (conja) {
if (conjb) {
//
// Form C := alpha*A**H*B**H + beta*C.
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp.r = 0., temp.i = 0.;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
d_cnjg(&z__3, &a[l + i__ * a_dim1]);
d_cnjg(&z__4, &b[j + l * b_dim1]);
z__2.r = z__3.r * z__4.r - z__3.i * z__4.i, z__2.i =
z__3.r * z__4.i + z__3.i * z__4.r;
z__1.r = temp.r + z__2.r, z__1.i = temp.i + z__2.i;
temp.r = z__1.r, temp.i = z__1.i;
// L260:
}
if (beta->r == 0. && beta->i == 0.) {
i__3 = i__ + j * c_dim1;
z__1.r = alpha->r * temp.r - alpha->i * temp.i,
z__1.i = alpha->r * temp.i + alpha->i *
temp.r;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
} else {
i__3 = i__ + j * c_dim1;
z__2.r = alpha->r * temp.r - alpha->i * temp.i,
z__2.i = alpha->r * temp.i + alpha->i *
temp.r;
i__4 = i__ + j * c_dim1;
z__3.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, z__3.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
z__1.r = z__2.r + z__3.r, z__1.i = z__2.i + z__3.i;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
}
// L270:
}
// L280:
}
} else {
//
// Form C := alpha*A**H*B**T + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp.r = 0., temp.i = 0.;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
d_cnjg(&z__3, &a[l + i__ * a_dim1]);
i__4 = j + l * b_dim1;
z__2.r = z__3.r * b[i__4].r - z__3.i * b[i__4].i,
z__2.i = z__3.r * b[i__4].i + z__3.i * b[i__4]
.r;
z__1.r = temp.r + z__2.r, z__1.i = temp.i + z__2.i;
temp.r = z__1.r, temp.i = z__1.i;
// L290:
}
if (beta->r == 0. && beta->i == 0.) {
i__3 = i__ + j * c_dim1;
z__1.r = alpha->r * temp.r - alpha->i * temp.i,
z__1.i = alpha->r * temp.i + alpha->i *
temp.r;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
} else {
i__3 = i__ + j * c_dim1;
z__2.r = alpha->r * temp.r - alpha->i * temp.i,
z__2.i = alpha->r * temp.i + alpha->i *
temp.r;
i__4 = i__ + j * c_dim1;
z__3.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, z__3.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
z__1.r = z__2.r + z__3.r, z__1.i = z__2.i + z__3.i;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
}
// L300:
}
// L310:
}
}
} else {
if (conjb) {
//
// Form C := alpha*A**T*B**H + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp.r = 0., temp.i = 0.;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
i__4 = l + i__ * a_dim1;
d_cnjg(&z__3, &b[j + l * b_dim1]);
z__2.r = a[i__4].r * z__3.r - a[i__4].i * z__3.i,
z__2.i = a[i__4].r * z__3.i + a[i__4].i *
z__3.r;
z__1.r = temp.r + z__2.r, z__1.i = temp.i + z__2.i;
temp.r = z__1.r, temp.i = z__1.i;
// L320:
}
if (beta->r == 0. && beta->i == 0.) {
i__3 = i__ + j * c_dim1;
z__1.r = alpha->r * temp.r - alpha->i * temp.i,
z__1.i = alpha->r * temp.i + alpha->i *
temp.r;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
} else {
i__3 = i__ + j * c_dim1;
z__2.r = alpha->r * temp.r - alpha->i * temp.i,
z__2.i = alpha->r * temp.i + alpha->i *
temp.r;
i__4 = i__ + j * c_dim1;
z__3.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, z__3.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
z__1.r = z__2.r + z__3.r, z__1.i = z__2.i + z__3.i;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
}
// L330:
}
// L340:
}
} else {
//
// Form C := alpha*A**T*B**T + beta*C
//
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
i__2 = *m;
for (i__ = 1; i__ <= i__2; ++i__) {
temp.r = 0., temp.i = 0.;
i__3 = *k;
for (l = 1; l <= i__3; ++l) {
i__4 = l + i__ * a_dim1;
i__5 = j + l * b_dim1;
z__2.r = a[i__4].r * b[i__5].r - a[i__4].i * b[i__5]
.i, z__2.i = a[i__4].r * b[i__5].i + a[i__4]
.i * b[i__5].r;
z__1.r = temp.r + z__2.r, z__1.i = temp.i + z__2.i;
temp.r = z__1.r, temp.i = z__1.i;
// L350:
}
if (beta->r == 0. && beta->i == 0.) {
i__3 = i__ + j * c_dim1;
z__1.r = alpha->r * temp.r - alpha->i * temp.i,
z__1.i = alpha->r * temp.i + alpha->i *
temp.r;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
} else {
i__3 = i__ + j * c_dim1;
z__2.r = alpha->r * temp.r - alpha->i * temp.i,
z__2.i = alpha->r * temp.i + alpha->i *
temp.r;
i__4 = i__ + j * c_dim1;
z__3.r = beta->r * c__[i__4].r - beta->i * c__[i__4]
.i, z__3.i = beta->r * c__[i__4].i + beta->i *
c__[i__4].r;
z__1.r = z__2.r + z__3.r, z__1.i = z__2.i + z__3.i;
c__[i__3].r = z__1.r, c__[i__3].i = z__1.i;
}
// L360:
}
// L370:
}
}
}
return 0;
//
// End of ZGEMM .
//
} // zgemm_
+6 -6
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@@ -1,9 +1,9 @@
# Binaries branch name: ffmpeg/4.x_20251226
# Binaries were created for OpenCV: cff7581175d2abfc6aef2e4f04f482e258b5c864
ocv_update(FFMPEG_BINARIES_COMMIT "d82ad9a54a7b42a1648a9cae8fed5c2f20ea396c")
ocv_update(FFMPEG_FILE_HASH_BIN32 "47730de2286110b0d1250ff9cf50ce56")
ocv_update(FFMPEG_FILE_HASH_BIN64 "3248b4663ffef770cdb54ec8b9d16a28")
ocv_update(FFMPEG_FILE_HASH_CMAKE "8862c87496e2e8c375965e1277dee1c7")
# Binaries branch name: ffmpeg/5.x_20260602
# Binaries were created for OpenCV: a0a660fcb1e58a295e6caa6aee64ed4d369b0181
ocv_update(FFMPEG_BINARIES_COMMIT "06dc20cad65dc7fcf784f70c95d46750520889a7")
ocv_update(FFMPEG_FILE_HASH_BIN32 "9cef7a78b6f7ec8cf1a3935c058cfac5")
ocv_update(FFMPEG_FILE_HASH_BIN64 "a821a1135251859655090c795af05789")
ocv_update(FFMPEG_FILE_HASH_CMAKE "e09efc33312d1173be8a9446f3b088fe")
function(download_win_ffmpeg script_var)
set(${script_var} "" PARENT_SCOPE)
+70
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@@ -0,0 +1,70 @@
# ----------------------------------------------------------------------------
# CMake file for the bundled HarfBuzz text-shaping library. See root CMakeLists.txt
#
# OpenCV vendors a minimal subset of HarfBuzz (core + HB_HAS_RASTER) as a
# normal static library: each .cc is a separate translation unit. The subset
# is produced by hb_extract.py (see that script to update HarfBuzz).
# ----------------------------------------------------------------------------
project(${HARFBUZZ_LIBRARY} CXX)
ocv_include_directories("${CMAKE_CURRENT_SOURCE_DIR}/src")
# Every .cc copied by hb_extract.py is a real translation unit (the script
# never copies non-TU .cc files), so a plain recursive glob is exactly right.
file(GLOB_RECURSE lib_srcs src/*.cc)
file(GLOB_RECURSE lib_hdrs src/*.h src/*.hh)
# HarfBuzz is built with HB_TINY for minimum footprint. Because we request the
# software rasterizer (HB_HAS_RASTER, defined below), HB_TINY does NOT disable
# the draw/paint/color APIs nor CFF (PostScript/.otf
# outlines) -- see hb-config.hh: HB_NO_DRAW/COLOR/PAINT and the TINY->HB_NO_CFF
# rule only trigger when no HB_HAS_* backend is requested. So CFF (CJK/Indic),
# COLR/CPAL color and our rasterizer all survive HB_TINY.
#
# Two features HB_TINY (via HB_LEAN) would otherwise drop are restored through
# HarfBuzz's config-override hook (hb-opencv-config.hh): thread-safety (HB_NO_MT)
# and variable fonts (HB_NO_VAR). No platform backends (CoreText/DirectWrite/...).
add_library(${HARFBUZZ_LIBRARY} STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${lib_srcs} ${lib_hdrs})
target_compile_definitions(${HARFBUZZ_LIBRARY} PRIVATE
HB_TINY
HB_HAS_RASTER # request the software rasterizer: keeps draw/paint/color + CFF under HB_TINY
"HB_CONFIG_OVERRIDE_H=\"hb-opencv-config.hh\"")
set_target_properties(${HARFBUZZ_LIBRARY} PROPERTIES
CXX_STANDARD 17
CXX_STANDARD_REQUIRED ON
)
ocv_warnings_disable(CMAKE_CXX_FLAGS
-Wunused-variable -Wunused-function -Wunused-parameter
-Wunused-but-set-variable # clang15/gcc
-Wshadow
-Wmissing-declarations # gcc
-Wmissing-prototypes # clang
-Wimplicit-fallthrough
-Wextra-semi # clang
-Wdeprecated-copy # gcc/clang
-Wsuggest-override
-Wcast-function-type
-Wclass-memaccess # gcc
-Wexpansion-to-defined
)
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4244 /wd4267 /wd4127 /wd4146 /wd4456 /wd4459) # MSVC
set_target_properties(${HARFBUZZ_LIBRARY}
PROPERTIES OUTPUT_NAME ${HARFBUZZ_LIBRARY}
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
COMPILE_PDB_NAME ${HARFBUZZ_LIBRARY}
COMPILE_PDB_NAME_DEBUG "${HARFBUZZ_LIBRARY}${OPENCV_DEBUG_POSTFIX}"
ARCHIVE_OUTPUT_DIRECTORY ${3P_LIBRARY_OUTPUT_PATH}
)
if(ENABLE_SOLUTION_FOLDERS)
set_target_properties(${HARFBUZZ_LIBRARY} PROPERTIES FOLDER "3rdparty")
endif()
if(NOT BUILD_SHARED_LIBS)
ocv_install_target(${HARFBUZZ_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev OPTIONAL)
endif()
+42
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@@ -0,0 +1,42 @@
HarfBuzz is licensed under the so-called "Old MIT" license. Details follow.
For parts of HarfBuzz that are licensed under different licenses see individual
files names COPYING in subdirectories where applicable.
Copyright © 2010-2022 Google, Inc.
Copyright © 2015-2020 Ebrahim Byagowi
Copyright © 2019,2020 Facebook, Inc.
Copyright © 2012,2015 Mozilla Foundation
Copyright © 2011 Codethink Limited
Copyright © 2008,2010 Nokia Corporation and/or its subsidiary(-ies)
Copyright © 2009 Keith Stribley
Copyright © 2011 Martin Hosken and SIL International
Copyright © 2007 Chris Wilson
Copyright © 2005,2006,2020,2021,2022,2023 Behdad Esfahbod
Copyright © 2004,2007,2008,2009,2010,2013,2021,2022,2023 Red Hat, Inc.
Copyright © 1998-2005 David Turner and Werner Lemberg
Copyright © 2016 Igalia S.L.
Copyright © 2022 Matthias Clasen
Copyright © 2018,2021 Khaled Hosny
Copyright © 2018,2019,2020 Adobe, Inc
Copyright © 2013-2015 Alexei Podtelezhnikov
For full copyright notices consult the individual files in the package.
Permission is hereby granted, without written agreement and without
license or royalty fees, to use, copy, modify, and distribute this
software and its documentation for any purpose, provided that the
above copyright notice and the following two paragraphs appear in
all copies of this software.
IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE TO ANY PARTY FOR
DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES
ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN
IF THE COPYRIGHT HOLDER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH
DAMAGE.
THE COPYRIGHT HOLDER SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING,
BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS
ON AN "AS IS" BASIS, AND THE COPYRIGHT HOLDER HAS NO OBLIGATION TO
PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
+220
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@@ -0,0 +1,220 @@
# HarfBuzz
<div align="center">
<p><img src="HarfBuzz.png" alt="HarfBuzz Logo" width="256"/></p>
[![Linux CI Status](https://github.com/harfbuzz/harfbuzz/actions/workflows/linux.yml/badge.svg)](https://github.com/harfbuzz/harfbuzz/actions/workflows/linux.yml)
[![macoOS CI Status](https://github.com/harfbuzz/harfbuzz/actions/workflows/macos.yml/badge.svg)](https://github.com/harfbuzz/harfbuzz/actions/workflows/macos.yml)
[![Windows CI Status](https://github.com/harfbuzz/harfbuzz/actions/workflows/msvc.yml/badge.svg)](https://github.com/harfbuzz/harfbuzz/actions/workflows/msvc.yml)
[![OSS-Fuzz Status](https://oss-fuzz-build-logs.storage.googleapis.com/badges/harfbuzz.svg)](https://oss-fuzz-build-logs.storage.googleapis.com/index.html#harfbuzz)
[![Coverity Scan Build Status](https://scan.coverity.com/projects/15166/badge.svg)](https://scan.coverity.com/projects/harfbuzz)
[![Packaging status](https://repology.org/badge/tiny-repos/harfbuzz.svg)](https://repology.org/project/harfbuzz/versions)
[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/harfbuzz/harfbuzz/badge)](https://securityscorecards.dev/viewer/?uri=github.com/harfbuzz/harfbuzz)
</div>
HarfBuzz started as a text shaping engine but has grown into a
full font platform — the `ffmpeg` of text shaping. It primarily
supports [OpenType][1], but also [Apple Advanced Typography][2].
HarfBuzz shapes the majority of text on modern screens.
HarfBuzz is optimized for robustness, correctness, and performance
— in that order. Achieve all.
**[Try it live at harfbuzz-world.cc](https://harfbuzz-world.cc/)** — an interactive playground for shaping, subsetting, rasterization, vector output, and GPU rendering, all running in your browser.
Here is a quick map of its components:
### Core libraries
| Library | Description |
|---------|-------------|
| **libharfbuzz** | Text shaping, draw API, paint API. Highly configurable (see [CONFIG.md](CONFIG.md)). Optional integration backends compiled in: hb-ft (FreeType), hb-coretext (macOS), hb-uniscribe (Windows), hb-directwrite (Windows), hb-gdi (Windows), hb-glib, hb-graphite2. |
| **libharfbuzz-subset** | Font subsetting and variable-font instancing. |
### Auxiliary libraries
| Library | Description |
|---------|-------------|
| **libharfbuzz-icu** | ICU Unicode integration. |
| **libharfbuzz-cairo** | Cairo rendering integration. |
| **libharfbuzz-gobject** | GObject/GI bindings. |
### Experimental libraries
| Library | Description |
|---------|-------------|
| **libharfbuzz-raster** | Glyph rasterization to bitmaps, including color fonts. Uses hb-draw and hb-paint. |
| **libharfbuzz-vector** | Glyph output to vector formats (currently SVG), including color fonts. Uses hb-draw and hb-paint. |
| **libharfbuzz-gpu** | Encodes glyph outlines for GPU rasterization (Slug algorithm). Provides shader sources in GLSL, WGSL, MSL, and HLSL. [Live demo.](https://harfbuzz.github.io/hb-gpu-demo/) |
Notable missing feature: font hinting (including autohinting)
is not implemented. For hinted rasterization, use FreeType or
Skrifa.
For simplified builds, amalgamated sources are available:
`harfbuzz.cc` (just libharfbuzz), `harfbuzz-subset.cc` (just
libharfbuzz-subset), or `harfbuzz-world.cc` (everything, driven
by a custom `hb-features.h`). For a live in-browser playground
plus a worked example of the world.cc single-file build, see
[harfbuzz-world.cc][26].
### Command-line tools
| Tool | Description |
|------|-------------|
| **hb-shape** | Shape text and display glyph output. |
| **hb-view** | Render shaped text to an image. |
| **hb-subset** | Subset and optimize fonts. |
| **hb-info** | Display font metadata. |
| **hb-raster** | Render glyphs to bitmap images. |
| **hb-vector** | Render glyphs to vector formats (SVG). |
| **hb-gpu** | Interactive GPU text rendering. |
The canonical source tree and bug trackers are available on [github][4].
Both development and user support discussion around HarfBuzz happen on
[github][4] as well.
For license information, see [COPYING](COPYING).
## API stability
The API that comes with `hb.h` will not change incompatibly. Other, peripheral,
headers are more likely to go through minor modifications, but again, we do our
best to never change API in an incompatible way. We will never break the ABI.
The API and ABI are stable even across major version number jumps. In fact,
current HarfBuzz is API/ABI compatible all the way back to the 0.9.x series.
If one day we need to break the API/ABI, that would be called a new library.
As such, we bump the major version number only when we add major new features,
the minor version when there is new API, and the micro version when there
are bug fixes.
## Documentation
For user manual as well as API documentation, check: https://harfbuzz.github.io
## Download
Tarball releases and Win32/Win64 binary bundles are available on the
[github releases][3] page.
## Development
For build information, see [BUILD.md](BUILD.md).
For custom configurations, see [CONFIG.md](CONFIG.md).
For testing and profiling, see [TESTING.md](TESTING.md).
For using with Python, see [README.python.md](README.python.md). There is also [uharfbuzz](https://github.com/harfbuzz/uharfbuzz).
For cross-compiling to Windows from Linux or macOS, see [README.mingw.md](README.mingw.md).
To report bugs or submit patches please use [github][4] issues and pull-requests.
### Developer documents
To get a better idea of where HarfBuzz stands in the text rendering stack you
may want to read [State of Text Rendering 2024][6].
Here are a few presentation slides about HarfBuzz over the years:
- 2026 [HarfBuzz at 20!][25]
- 2016 [Ten Years of HarfBuzz][20]
- 2014 [Unicode, OpenType, and HarfBuzz: Closing the Circle][7]
- 2012 [HarfBuzz, The Free and Open Text Shaping Engine][8]
- 2009 [HarfBuzz: the Free and Open Shaping Engine][9]
More presentations and papers are available on [behdad][11]'s website.
In particular, the following _studies_ are relevant to HarfBuzz development:
- 2025 [AAT layout caches][24]
- 2025 [OpenType Layout lookup caches][23]
- 2025 [Introducing HarfRust][22]
- 2025 [Subsetting][21]
- 2025 [Caching][12]
- 2025 [`hb-decycler`][13]
- 2022 [`hb-iter`][14]
- 2022 [A C library written in C++][15]
- 2022 [The case of the slow `hb-ft` `>h_advance` function][18]
- 2022 [PackTab: A static integer table packer][16]
- 2020 [HarfBuzz OT+AAT "Unishaper"][19]
- 2014 [Building the Indic Shaper][17]
- 2012 [Memory Consumption][10]
## Name
HarfBuzz /hærfˈbɒːz/
From Persian حرف (*Harf*: letter) and باز (*Buzz*: open).
Transliteration of the Persian calque for *OpenType*.
As a noun: *The* Open Source *text shaping* engine.
As an adjective: Insincerely talkative; glib. A nod to the
GNOME project where HarfBuzz originates from.
The logo shows حرف‌باز in the IranNastaliq font, on a Damascus
steel background.
> Background: Originally there was this font format called TrueType. People and
> companies started calling their type engines all things ending in Type:
> FreeType, CoolType, ClearType, etc. And then came OpenType, which is the
> successor of TrueType. So, for my OpenType implementation, I decided to stick
> with the concept but use the Persian translation. Which is fitting given that
> Persian is written in the Arabic script, and OpenType is an extension of
> TrueType that adds support for complex script rendering, and HarfBuzz is an
> implementation of OpenType text shaping.
## Users
HarfBuzz is used in Android, Chrome, ChromeOS, Firefox, Flutter, GNOME, GTK+, KDE,
Qt, LibreOffice, OpenJDK, XeTeX, Adobe Photoshop, Illustrator, InDesign,
Microsoft Edge, Amazon Kindle, PlayStation, Godot Engine, Unreal Engine,
Figma, Canva, QuarkXPress, Scribus, smart TVs,
car displays, and many other places.
<p align="center">
<a href="https://xkcd.com/2347/" rel="nofollow">
<img src="xkcd.png" width="256" alt="xkcd-derived image">
</a>
</p>
## Distribution
<details>
<summary>Packaging status of HarfBuzz</summary>
[![Packaging status](https://repology.org/badge/vertical-allrepos/harfbuzz.svg?header=harfbuzz)](https://repology.org/project/harfbuzz/versions)
</details>
[1]: https://docs.microsoft.com/en-us/typography/opentype/spec/
[2]: https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6AATIntro.html
[3]: https://github.com/harfbuzz/harfbuzz/releases
[4]: https://github.com/harfbuzz/harfbuzz
[6]: https://behdad.org/text2024
[7]: https://docs.google.com/presentation/d/1x97pfbB1gbD53Yhz6-_yBUozQMVJ_5yMqqR_D-R7b7I/preview
[8]: https://docs.google.com/presentation/d/1ySTZaXP5XKFg0OpmHZM00v5b17GSr3ojnzJekl4U8qI/preview
[9]: https://behdad.org/doc/harfbuzz2009-slides.pdf
[10]: https://docs.google.com/document/d/12jfNpQJzeVIAxoUSpk7KziyINAa1msbGliyXqguS86M/preview
[11]: https://behdad.org/
[12]: https://docs.google.com/document/d/1_VgObf6Je0J8byMLsi7HCQHnKo2emGnx_ib_sHo-bt4/preview
[13]: https://docs.google.com/document/d/1Y-u08l9YhObRVObETZt1k8f_5lQdOix9TRH3zEXaoAw/preview
[14]: https://docs.google.com/document/d/1o-xvxCbgMe9JYFHLVnPjk01ZY_8Cj0vB9-KTI1d0nyk/preview
[15]: https://docs.google.com/document/d/18hI56KJpvXtwWbc9QSaz9zzhJwIMnrJ-zkAaKS-W-8k/preview
[16]: https://docs.google.com/document/d/1Xq3owVt61HVkJqbLFHl73il6pcTy6PdPJJ7bSouQiQw/preview
[17]: https://docs.google.com/document/d/1wMPwVNBvsIriamcyBO5aNs7Cdr8lmbwLJ8GmZBAswF4/preview
[18]: https://docs.google.com/document/d/1wskYbA-czBt57oH9gEuGf3sWbTx7bfOiEIcDs36-heo/preview
[19]: https://prezi.com/view/THNPJGFVDUCWoM20syev/
[20]: https://behdad.org/doc/harfbuzz10years-slides.pdf
[21]: https://docs.google.com/document/d/1_vZrt97OorJ0jA1YzJ29LRcGr3YGrNJANdOABjVZGEs/preview
[22]: https://docs.google.com/document/d/1aH_waagdEM5UhslQxCeFEb82ECBhPlZjy5_MwLNLBYo/preview
[23]: https://docs.google.com/document/d/1hRd5oYQJLrt0JuwWhEJWi7wh_9rbaIJkX6IR9DW7rZQ/preview
[24]: https://docs.google.com/document/d/1a3K6fHjsiWW36vSzwJwCwEBOgznunKs80PSpBbpfHiA/preview
[25]: https://docs.google.com/presentation/d/1o9Exz1c-Lr-dJjA8dcBn_Vl_Y37cupmFzmclMjBE_Bc/view
[26]: https://harfbuzz-world.cc/
+386
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#!/usr/bin/env python3
"""
hb_extract.py -- copy the minimal subset of HarfBuzz sources used by OpenCV.
OpenCV vendors HarfBuzz as a normal static library: every .cc is a separate
translation unit (no unity / amalgamation build). This script reproduces the
exact src/ tree that OpenCV ships, from a pristine HarfBuzz checkout.
It does three things beyond a naive copy, all to keep the vendored tree small
and buildable with OpenCV's configuration (HB_TINY + HB_HAS_RASTER, see
CMakeLists.txt):
1. Copies the translation units that upstream `harfbuzz-world.cc` would
compile for the requested HB_HAS_* sections -- EXCEPT the ones that build
to an empty object under our config (see EMPTY_TUS). It never emits
`harfbuzz-world.cc` itself (we compile each .cc directly), and never
copies non-TU .cc files found under src/OT|graph (only real TUs from the
parsed world.cc lists are copied), so CMake's file(GLOB_RECURSE src/*.cc)
picks up exactly the right set.
2. Prunes headers down to the set actually reachable by #include from the
copied TUs (+ the public headers OpenCV includes). This automatically
drops the large amount of HarfBuzz src/ that we never compile -- GPU and
WASM backends, the subset/repacker graph, platform backends (CoreText/
DirectWrite/GDI/Uniscribe/Graphite2/GObject/Cairo), the vector-paint
backend, and headers used only by the skipped empty TUs -- without a
hardcoded blocklist.
3. Generates `hb-opencv-config.hh`, the config-override header CMakeLists.txt
points HB_CONFIG_OVERRIDE_H at. It re-enables thread-safety (HB_NO_MT) and
variable fonts (HB_NO_VAR) that HB_TINY would otherwise switch off. (It
does NOT generate upstream's hb-features.h: nothing we compile includes
it.)
Usage (to refresh this very directory, just point it at a harfbuzz checkout):
python hb_extract.py path/to/harfbuzz
The defaults already produce the OpenCV subset: output '.', features
HB_HAS_RASTER, and it also copies README.md + COPYING. Override only if needed:
-o DIR output root (default '.')
-f FLAGS comma-separated HB_HAS_* (default HB_HAS_RASTER)
-a FILES extra files (default README.md,COPYING)
--list-flags print available HB_HAS_* flags and exit
-a paths are relative to the harfbuzz repo root (parent of src/):
-a README.md -> copied to <out>/README.md
-a COPYING -> copied to <out>/COPYING
-a src/hb-raster.h -> copied to <out>/src/hb-raster.h
"""
import argparse
import posixpath
import re
import shutil
import sys
from pathlib import Path
# Translation units that compile to an EMPTY object (libtool: "has no symbols")
# under OpenCV's HarfBuzz configuration (HB_TINY + HB_HAS_RASTER => AAT, legacy
# fallback shaping, math, meta, name, buffer (de)serialize/verify and the style
# API are all compiled out). They contribute nothing, so we do not vendor them.
# Re-derive this list (from the libtool warnings of a clean libharfbuzz build)
# if the HarfBuzz configuration in CMakeLists.txt changes.
EMPTY_TUS = {
"hb-aat-layout.cc",
"hb-aat-map.cc",
"hb-buffer-serialize.cc",
"hb-buffer-verify.cc",
"hb-fallback-shape.cc",
"hb-ot-math.cc",
"hb-ot-meta.cc",
"hb-ot-name.cc",
"hb-raster.cc",
"hb-style.cc",
}
# Public headers OpenCV's text engine (drawing_text.cpp) includes directly.
# Used together with the copied TUs as the roots of the header-reachability
# prune.
SEED_PUBLIC_HEADERS = ("hb.h", "hb-ot.h", "hb-raster.h")
# Generated config-override header. CMakeLists.txt builds HarfBuzz with HB_TINY
# and HB_CONFIG_OVERRIDE_H="hb-opencv-config.hh"; this file is included by
# hb-config.hh after HB_TINY/HB_LEAN expand (so the #undef takes effect) but
# before the option-closure derives dependent macros.
OPENCV_CONFIG_HH = '''\
/*
* OpenCV-specific HarfBuzz configuration override. GENERATED by hb_extract.py.
*
* HarfBuzz is built with HB_TINY (see 3rdparty/harfbuzz/CMakeLists.txt) for the
* smallest possible footprint. HB_TINY pulls in HB_LEAN + HB_MINI, which would
* disable two features that OpenCV's text engine relies on. We restore them
* here.
*
* This file is included by hb-config.hh via HB_CONFIG_OVERRIDE_H, i.e. AFTER
* HB_TINY/HB_LEAN/HB_MINI have expanded their macros (so the #undef below takes
* effect) but BEFORE the "closure of options" derives dependent macros (e.g.
* HB_NO_VAR_COMPOSITES from HB_NO_VAR). That ordering is what makes a plain
* #undef sufficient.
*
* HB_NO_MT - keep thread-safety. hb_font_t instances live inside cv::FontFace
* objects, NOT in the thread_local FontRenderEngine, so a single
* FontFace (and its hb_font_t) can be shared across threads; its
* reference counting must stay atomic.
*
* HB_NO_VAR - keep variable-font support: the 'wght' axis used for synthetic
* weights and named-instance/axis queries (hb_font_set_variations,
* hb_ot_var_*). Variable fonts are a primary reason OpenCV adopted
* HarfBuzz, so this must remain enabled.
*/
#undef HB_NO_MT
#undef HB_NO_VAR
'''
OPENCV_CONFIG_NAME = "hb-opencv-config.hh"
def parse_world_cc(path):
"""Return (core_files, sections) parsed from harfbuzz-world.cc.
core_files -- list of .cc paths (relative to src/) in the unconditional
"Core library" section.
sections -- dict HB_HAS_XXX -> list of .cc paths from that #ifdef block.
"""
lines = path.read_text(encoding="utf-8").splitlines()
include_re = re.compile(r'^\s*#include\s+"([^"]+\.cc)"')
core_files = []
sections = {}
STATE_PREAMBLE, STATE_CORE, STATE_IFDEF = "preamble", "core", "ifdef"
state = STATE_PREAMBLE
current_flag = None
ifdef_depth = 0
for line in lines:
if state == STATE_PREAMBLE:
if "/* Core library." in line:
state = STATE_CORE
elif state == STATE_CORE:
m = include_re.match(line)
if m:
core_files.append(m.group(1))
else:
m = re.match(r"^\s*#ifdef\s+(HB_HAS_\w+)", line)
if m:
current_flag = m.group(1)
sections[current_flag] = []
state = STATE_IFDEF
ifdef_depth = 1
elif state == STATE_IFDEF:
if re.match(r"^\s*#if", line):
ifdef_depth += 1
elif re.match(r"^\s*#endif", line):
ifdef_depth -= 1
if ifdef_depth == 0:
state = STATE_CORE
current_flag = None
else:
m = include_re.match(line)
if m and current_flag is not None:
sections[current_flag].append(m.group(1))
return core_files, sections
def copy_files(file_list, src_dir, out_src_dir, label=""):
skipped = []
for rel in file_list:
if Path(rel).name in EMPTY_TUS:
skipped.append(rel)
continue
src = src_dir / rel
dst = out_src_dir / rel
dst.parent.mkdir(parents=True, exist_ok=True)
if not src.exists():
print(f" [WARN] not found: {src}", file=sys.stderr)
continue
shutil.copy2(src, dst)
print(f" [{label or 'core'}] src/{rel}")
for rel in skipped:
print(f" [skip-empty] src/{rel}")
def copy_headers(src_dir, out_src_dir, public_h=True):
"""Copy all candidate headers: src/*.hh, src/*.h (public API), and *.h/*.hh
from src/OT/** and src/graph/**. The unreferenced ones are removed later by
prune_unreferenced_headers().
NOTE: .cc files under OT/ and graph/ are intentionally NOT copied here --
the only ones we need are real translation units, copied via the parsed
world.cc lists. Copying e.g. src/graph/test-classdef-graph.cc would make
CMake's file(GLOB_RECURSE src/*.cc) try to compile a non-TU file.
"""
count = 0
def _copy(p):
nonlocal count
dst = out_src_dir / p.relative_to(src_dir)
dst.parent.mkdir(parents=True, exist_ok=True)
shutil.copy2(p, dst)
count += 1
for hdr in src_dir.glob("*.hh"):
_copy(hdr)
for subdir in ("OT", "graph"):
d = src_dir / subdir
if not d.exists():
continue
for hdr in d.rglob("*"):
if hdr.is_file() and hdr.suffix in (".h", ".hh"):
_copy(hdr)
if public_h:
for hdr in src_dir.glob("*.h"):
_copy(hdr)
return count
def prune_unreferenced_headers(out_src_dir):
"""Delete every header not reachable by #include from the copied TUs and the
public seed headers, then drop any directory left empty.
HarfBuzz's amalgamated src/ ships many headers we never compile (GPU/WASM
backends, subset/repacker graph, platform backends, vector-paint, and the
headers used only by the empty TUs we skip). Reachability removes them all
without a hardcoded list. The edge regex also treats any quoted "...h"/
"...hh" literal as an include, which covers the `#include HB_STRING_ARRAY_LIST`
macro-indirection trick (e.g. hb-ot-cff1-std-str.hh, hb-ot-post-macroman.hh).
"""
files = {p.relative_to(out_src_dir).as_posix()
for p in out_src_dir.rglob("*") if p.is_file()}
headers = {f for f in files if f.endswith((".h", ".hh"))}
edge_re = re.compile(r'#\s*include\s+["<]([^">]+)[">]|"([^"]+\.hh?)"')
def resolve(inc, including):
for cand in (posixpath.normpath(posixpath.join(posixpath.dirname(including), inc)),
posixpath.normpath(inc)):
if cand in files:
return cand
return None
seeds = {f for f in files if f.endswith(".cc")}
seeds |= {h for h in SEED_PUBLIC_HEADERS if h in files}
seen, stack = set(), list(seeds)
while stack:
cur = stack.pop()
if cur in seen:
continue
seen.add(cur)
try:
txt = (out_src_dir / cur).read_text(encoding="utf-8", errors="ignore")
except OSError:
continue
for m in edge_re.finditer(txt):
inc = m.group(1) or m.group(2)
r = resolve(inc, cur)
if r and r not in seen:
stack.append(r)
needed = {f for f in seen if f.endswith((".h", ".hh"))}
for h in sorted(headers - needed):
(out_src_dir / h).unlink()
print(f" [prune] src/{h}")
# remove directories left empty by pruning (deepest first)
for d in sorted((p for p in out_src_dir.rglob("*") if p.is_dir()),
key=lambda p: len(p.parts), reverse=True):
try:
d.rmdir()
except OSError:
pass
return len(headers - needed), len(needed)
def write_opencv_config_h(out_src_dir):
"""Generate hb-opencv-config.hh (the HB_CONFIG_OVERRIDE_H header)."""
(out_src_dir / OPENCV_CONFIG_NAME).write_text(OPENCV_CONFIG_HH, encoding="utf-8")
print(f" [gen] src/{OPENCV_CONFIG_NAME}")
def main():
parser = argparse.ArgumentParser(
description="Copy the OpenCV subset of HarfBuzz sources.",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
examples:
python hb_extract.py ~/work/harfbuzz -o . -f HB_HAS_RASTER -a README.md,COPYING
python hb_extract.py ~/work/harfbuzz --list-flags
""",
)
parser.add_argument("harfbuzz_dir", metavar="harfbuzz-dir",
help="path to the harfbuzz repo root (contains src/)")
parser.add_argument("-o", "--output", metavar="DIR", default=".",
help="output root directory; sources go into DIR/src/ "
"(default: current directory)")
parser.add_argument("-f", "--features", metavar="FLAGS", default="HB_HAS_RASTER",
help="comma-separated HB_HAS_* flags (default: HB_HAS_RASTER)")
parser.add_argument("-a", "--add", metavar="FILES", default="README.md,COPYING",
help="comma-separated files relative to the harfbuzz repo "
"root (default: README.md,COPYING)")
parser.add_argument("--no-public-headers", action="store_true",
help="skip copying public src/*.h API headers "
"(OT/, graph/, *.hh are always copied)")
parser.add_argument("--list-flags", action="store_true",
help="list available HB_HAS_* flags and exit")
args = parser.parse_args()
repo_dir = Path(args.harfbuzz_dir).resolve()
world_cc_path = repo_dir / "src" / "harfbuzz-world.cc"
if not world_cc_path.exists():
print(f"error: {world_cc_path} not found", file=sys.stderr)
sys.exit(1)
src_dir = world_cc_path.parent
core_files, sections = parse_world_cc(world_cc_path)
if args.list_flags:
for flag, files in sorted(sections.items()):
print(f" {flag:<22} ({len(files)} .cc files)")
return
enabled_flags = [f.strip() for f in args.features.split(",") if f.strip()]
extra_files = [f.strip() for f in args.add.split(",") if f.strip()]
out_dir = Path(args.output).resolve()
out_src_dir = out_dir / "src"
# Start from a clean src/ so removed-upstream files do not linger.
if out_src_dir.exists():
shutil.rmtree(out_src_dir)
out_src_dir.mkdir(parents=True, exist_ok=True)
print(f"\nsource : {repo_dir}")
print(f"output : {out_dir}")
print(f"flags : {enabled_flags}\n")
print("core:")
copy_files(core_files, src_dir, out_src_dir)
for flag in enabled_flags:
flag_files = sections.get(flag)
if not flag_files:
print(f"\n[{flag}]: no .cc files (flag not found in harfbuzz-world.cc)")
continue
print(f"\n[{flag}]:")
copy_files(flag_files, src_dir, out_src_dir, label=flag)
if extra_files:
print("\nextra:")
for rel in extra_files:
src = repo_dir / rel
dst = out_dir / rel
dst.parent.mkdir(parents=True, exist_ok=True)
if not src.exists():
print(f" [WARN] not found: {src}", file=sys.stderr)
continue
shutil.copy2(src, dst)
print(f" [add] {rel}")
n = copy_headers(src_dir, out_src_dir,
public_h=not args.no_public_headers)
print(f"\nheaders: {n} files copied")
print("\nprune (unreferenced headers):")
removed, kept = prune_unreferenced_headers(out_src_dir)
print(f" removed {removed}, kept {kept}")
print("\ngenerated:")
write_opencv_config_h(out_src_dir)
all_cc = sorted(out_src_dir.rglob("*.cc"))
all_h = list(out_src_dir.rglob("*.h")) + list(out_src_dir.rglob("*.hh"))
print(f"\ndone: {len(all_cc)} .cc | {len(all_h)} headers -> {out_dir}")
if __name__ == "__main__":
main()
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/*
* Copyright © 2016 Google, Inc.
* Copyright © 2018 Ebrahim Byagowi
*
* This is part of HarfBuzz, a text shaping library.
*
* Permission is hereby granted, without written agreement and without
* license or royalty fees, to use, copy, modify, and distribute this
* software and its documentation for any purpose, provided that the
* above copyright notice and the following two paragraphs appear in
* all copies of this software.
*
* IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE TO ANY PARTY FOR
* DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES
* ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN
* IF THE COPYRIGHT HOLDER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH
* DAMAGE.
*
* THE COPYRIGHT HOLDER SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING,
* BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS
* ON AN "AS IS" BASIS, AND THE COPYRIGHT HOLDER HAS NO OBLIGATION TO
* PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
*
* Google Author(s): Sascha Brawer
*/
#ifndef OT_COLOR_CPAL_CPAL_HH
#define OT_COLOR_CPAL_CPAL_HH
#include "../../../hb-open-type.hh"
#include "../../../hb-ot-color.h"
#include "../../../hb-ot-name.h"
/*
* CPAL -- Color Palette
* https://docs.microsoft.com/en-us/typography/opentype/spec/cpal
*/
#define HB_OT_TAG_CPAL HB_TAG('C','P','A','L')
namespace OT {
struct CPALV1Tail
{
friend struct CPAL;
private:
hb_ot_color_palette_flags_t get_palette_flags (const void *base,
unsigned int palette_index,
unsigned int palette_count) const
{
if (!paletteFlagsZ) return HB_OT_COLOR_PALETTE_FLAG_DEFAULT;
return (hb_ot_color_palette_flags_t) (uint32_t)
(base+paletteFlagsZ).as_array (palette_count)[palette_index];
}
hb_ot_name_id_t get_palette_name_id (const void *base,
unsigned int palette_index,
unsigned int palette_count) const
{
if (!paletteLabelsZ) return HB_OT_NAME_ID_INVALID;
return (base+paletteLabelsZ).as_array (palette_count)[palette_index];
}
hb_ot_name_id_t get_color_name_id (const void *base,
unsigned int color_index,
unsigned int color_count) const
{
if (!colorLabelsZ) return HB_OT_NAME_ID_INVALID;
return (base+colorLabelsZ).as_array (color_count)[color_index];
}
public:
void collect_name_ids (const void *base,
unsigned palette_count,
unsigned color_count,
const hb_map_t *color_index_map,
hb_set_t *nameids_to_retain /* OUT */) const
{
if (paletteLabelsZ)
{
+ (base+paletteLabelsZ).as_array (palette_count)
| hb_sink (nameids_to_retain)
;
}
if (colorLabelsZ)
{
const hb_array_t<const NameID> colorLabels = (base+colorLabelsZ).as_array (color_count);
for (unsigned i = 0; i < color_count; i++)
{
if (!color_index_map->has (i)) continue;
nameids_to_retain->add (colorLabels[i]);
}
}
}
bool serialize (hb_serialize_context_t *c,
unsigned palette_count,
unsigned color_count,
const void *base,
const hb_map_t *color_index_map) const
{
TRACE_SERIALIZE (this);
auto *out = c->allocate_size<CPALV1Tail> (static_size);
if (unlikely (!out)) return_trace (false);
out->paletteFlagsZ = 0;
if (paletteFlagsZ)
out->paletteFlagsZ.serialize_copy (c, paletteFlagsZ, base, 0, hb_serialize_context_t::Head, palette_count);
out->paletteLabelsZ = 0;
if (paletteLabelsZ)
out->paletteLabelsZ.serialize_copy (c, paletteLabelsZ, base, 0, hb_serialize_context_t::Head, palette_count);
const hb_array_t<const NameID> colorLabels = (base+colorLabelsZ).as_array (color_count);
if (colorLabelsZ)
{
c->push ();
for (unsigned i = 0; i < color_count; i++)
{
if (!color_index_map->has (i)) continue;
if (!c->copy<NameID> (colorLabels[i]))
{
c->pop_discard ();
return_trace (false);
}
}
c->add_link (out->colorLabelsZ, c->pop_pack ());
}
return_trace (true);
}
bool sanitize (hb_sanitize_context_t *c,
const void *base,
unsigned int palette_count,
unsigned int color_count) const
{
TRACE_SANITIZE (this);
return_trace (c->check_struct (this) &&
(!paletteFlagsZ || (base+paletteFlagsZ).sanitize (c, palette_count)) &&
(!paletteLabelsZ || (base+paletteLabelsZ).sanitize (c, palette_count)) &&
(!colorLabelsZ || (base+colorLabelsZ).sanitize (c, color_count)));
}
protected:
// TODO(garretrieger): these offsets can hold nulls so we should not be using non-null offsets
// here. Currently they are needed since UnsizedArrayOf doesn't define null_size
NNOffset32To<UnsizedArrayOf<HBUINT32>>
paletteFlagsZ; /* Offset from the beginning of CPAL table to
* the Palette Type Array. Set to 0 if no array
* is provided. */
NNOffset32To<UnsizedArrayOf<NameID>>
paletteLabelsZ; /* Offset from the beginning of CPAL table to
* the palette labels array. Set to 0 if no
* array is provided. */
NNOffset32To<UnsizedArrayOf<NameID>>
colorLabelsZ; /* Offset from the beginning of CPAL table to
* the color labels array. Set to 0
* if no array is provided. */
public:
DEFINE_SIZE_STATIC (12);
};
typedef HBUINT32 BGRAColor;
struct CPAL
{
static constexpr hb_tag_t tableTag = HB_OT_TAG_CPAL;
bool has_data () const { return numPalettes; }
size_t get_size () const
{ return min_size + numPalettes * sizeof (colorRecordIndicesZ[0]); }
unsigned int get_palette_count () const { return numPalettes; }
unsigned int get_color_count () const { return numColors; }
hb_ot_color_palette_flags_t get_palette_flags (unsigned int palette_index) const
{ return v1 ().get_palette_flags (this, palette_index, numPalettes); }
hb_ot_name_id_t get_palette_name_id (unsigned int palette_index) const
{ return v1 ().get_palette_name_id (this, palette_index, numPalettes); }
hb_ot_name_id_t get_color_name_id (unsigned int color_index) const
{ return v1 ().get_color_name_id (this, color_index, numColors); }
hb_array_t<const BGRAColor> get_palette_colors (unsigned int palette_index) const
{
if (unlikely (palette_index >= numPalettes))
return hb_array_t<const BGRAColor> ();
unsigned int start_index = colorRecordIndicesZ[palette_index];
hb_array_t<const BGRAColor> all_colors ((this+colorRecordsZ).arrayZ, numColorRecords);
return all_colors.sub_array (start_index, numColors);
}
unsigned int get_palette_colors (unsigned int palette_index,
unsigned int start_offset,
unsigned int *color_count, /* IN/OUT. May be NULL. */
hb_color_t *colors /* OUT. May be NULL. */) const
{
if (unlikely (palette_index >= numPalettes))
{
if (color_count) *color_count = 0;
return 0;
}
unsigned int start_index = colorRecordIndicesZ[palette_index];
hb_array_t<const BGRAColor> all_colors ((this+colorRecordsZ).arrayZ, numColorRecords);
hb_array_t<const BGRAColor> palette_colors = all_colors.sub_array (start_index,
numColors);
if (color_count)
{
+ palette_colors.sub_array (start_offset, color_count)
| hb_sink (hb_array (colors, *color_count))
;
}
return numColors;
}
void collect_name_ids (const hb_map_t *color_index_map,
hb_set_t *nameids_to_retain /* OUT */) const
{
if (version == 1)
{
hb_barrier ();
v1 ().collect_name_ids (this, numPalettes, numColors, color_index_map, nameids_to_retain);
}
}
private:
const CPALV1Tail& v1 () const
{
if (version == 0) return Null (CPALV1Tail);
hb_barrier ();
return StructAfter<CPALV1Tail> (*this);
}
public:
bool serialize (hb_serialize_context_t *c,
const hb_array_t<const HBUINT16> &color_record_indices,
const hb_array_t<const BGRAColor> &color_records,
const hb_vector_t<unsigned>& first_color_index_for_layer,
const hb_map_t& first_color_to_layer_index,
const hb_set_t &retained_color_indices) const
{
TRACE_SERIALIZE (this);
// TODO(grieger): limit total final size.
for (const auto idx : color_record_indices)
{
hb_codepoint_t layer_index = first_color_to_layer_index[idx];
HBUINT16 new_idx;
new_idx = layer_index * retained_color_indices.get_population ();
if (!c->copy<HBUINT16> (new_idx)) return_trace (false);
}
c->push ();
for (unsigned first_color_index : first_color_index_for_layer)
{
for (hb_codepoint_t color_index : retained_color_indices)
{
if (!c->copy<BGRAColor> (color_records[first_color_index + color_index]))
{
c->pop_discard ();
return_trace (false);
}
}
}
c->add_link (colorRecordsZ, c->pop_pack ());
return_trace (true);
}
bool subset (hb_subset_context_t *c) const
{
TRACE_SUBSET (this);
if (!numPalettes) return_trace (false);
const hb_map_t *color_index_map = &c->plan->colr_palettes;
if (color_index_map->is_empty ()) return_trace (false);
hb_set_t retained_color_indices;
for (const auto _ : color_index_map->keys ())
{
if (_ == 0xFFFF) continue;
retained_color_indices.add (_);
}
if (retained_color_indices.is_empty ()) return_trace (false);
auto *out = c->serializer->start_embed (*this);
if (unlikely (!c->serializer->extend_min (out))) return_trace (false);
out->version = version;
out->numColors = retained_color_indices.get_population ();
out->numPalettes = numPalettes;
hb_vector_t<unsigned> first_color_index_for_layer;
hb_map_t first_color_to_layer_index;
const hb_array_t<const HBUINT16> colorRecordIndices = colorRecordIndicesZ.as_array (numPalettes);
for (const auto first_color_record_idx : colorRecordIndices)
{
if (first_color_to_layer_index.has (first_color_record_idx)) continue;
first_color_index_for_layer.push (first_color_record_idx);
if (unlikely (!c->serializer->propagate_error (first_color_index_for_layer))) return_trace (false);
first_color_to_layer_index.set (first_color_record_idx,
first_color_index_for_layer.length - 1);
}
out->numColorRecords = first_color_index_for_layer.length
* retained_color_indices.get_population ();
const hb_array_t<const BGRAColor> color_records = (this+colorRecordsZ).as_array (numColorRecords);
if (!out->serialize (c->serializer,
colorRecordIndices,
color_records,
first_color_index_for_layer,
first_color_to_layer_index,
retained_color_indices))
return_trace (false);
if (version == 1)
{
hb_barrier ();
return_trace (v1 ().serialize (c->serializer, numPalettes, numColors, this, color_index_map));
}
return_trace (true);
}
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
return_trace (c->check_struct (this) &&
hb_barrier () &&
(this+colorRecordsZ).sanitize (c, numColorRecords) &&
colorRecordIndicesZ.sanitize (c, numPalettes) &&
(version == 0 || v1 ().sanitize (c, this, numPalettes, numColors)));
}
protected:
HBUINT16 version; /* Table version number */
/* Version 0 */
HBUINT16 numColors; /* Number of colors in each palette. */
HBUINT16 numPalettes; /* Number of palettes in the table. */
HBUINT16 numColorRecords; /* Total number of color records, combined for
* all palettes. */
NNOffset32To<UnsizedArrayOf<BGRAColor>>
colorRecordsZ; /* Offset from the beginning of CPAL table to
* the first ColorRecord. */
UnsizedArrayOf<HBUINT16>
colorRecordIndicesZ; /* Index of each palettes first color record in
* the combined color record array. */
/*CPALV1Tail v1;*/
public:
DEFINE_SIZE_ARRAY (12, colorRecordIndicesZ);
};
} /* namespace OT */
#endif /* OT_COLOR_CPAL_CPAL_HH */
+449
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@@ -0,0 +1,449 @@
/*
* Copyright © 2018 Ebrahim Byagowi
* Copyright © 2020 Google, Inc.
*
* This is part of HarfBuzz, a text shaping library.
*
* Permission is hereby granted, without written agreement and without
* license or royalty fees, to use, copy, modify, and distribute this
* software and its documentation for any purpose, provided that the
* above copyright notice and the following two paragraphs appear in
* all copies of this software.
*
* IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE TO ANY PARTY FOR
* DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES
* ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN
* IF THE COPYRIGHT HOLDER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH
* DAMAGE.
*
* THE COPYRIGHT HOLDER SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING,
* BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS
* ON AN "AS IS" BASIS, AND THE COPYRIGHT HOLDER HAS NO OBLIGATION TO
* PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
*
* Google Author(s): Calder Kitagawa
*/
#ifndef OT_COLOR_SBIX_SBIX_HH
#define OT_COLOR_SBIX_SBIX_HH
#include "../../../hb-open-type.hh"
#include "../../../hb-paint.hh"
/*
* sbix -- Standard Bitmap Graphics
* https://docs.microsoft.com/en-us/typography/opentype/spec/sbix
* https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6sbix.html
*/
#define HB_OT_TAG_sbix HB_TAG('s','b','i','x')
namespace OT {
struct SBIXGlyph
{
SBIXGlyph* copy (hb_serialize_context_t *c, unsigned int data_length) const
{
TRACE_SERIALIZE (this);
SBIXGlyph* new_glyph = c->start_embed<SBIXGlyph> ();
if (unlikely (!c->extend_min (new_glyph))) return_trace (nullptr);
new_glyph->xOffset = xOffset;
new_glyph->yOffset = yOffset;
new_glyph->graphicType = graphicType;
data.copy (c, data_length);
return_trace (new_glyph);
}
HBINT16 xOffset; /* The horizontal (x-axis) offset from the left
* edge of the graphic to the glyphs origin.
* That is, the x-coordinate of the point on the
* baseline at the left edge of the glyph. */
HBINT16 yOffset; /* The vertical (y-axis) offset from the bottom
* edge of the graphic to the glyphs origin.
* That is, the y-coordinate of the point on the
* baseline at the left edge of the glyph. */
Tag graphicType; /* Indicates the format of the embedded graphic
* data: one of 'jpg ', 'png ' or 'tiff', or the
* special format 'dupe'. */
UnsizedArrayOf<HBUINT8>
data; /* The actual embedded graphic data. The total
* length is inferred from sequential entries in
* the glyphDataOffsets array and the fixed size
* (8 bytes) of the preceding fields. */
public:
DEFINE_SIZE_ARRAY (8, data);
};
struct SBIXStrike
{
static size_t get_size (unsigned num_glyphs)
{ return min_size + num_glyphs * HBUINT32::static_size; }
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
return_trace (c->check_struct (this) &&
imageOffsetsZ.sanitize_shallow (c, c->get_num_glyphs () + 1));
}
hb_blob_t *get_glyph_blob (unsigned int glyph_id,
hb_blob_t *sbix_blob,
hb_tag_t file_type,
int *x_offset,
int *y_offset,
unsigned int num_glyphs,
unsigned int *strike_ppem) const
{
if (unlikely (!ppem)) return hb_blob_get_empty (); /* To get Null() object out of the way. */
unsigned int retry_count = 8;
unsigned int sbix_len = sbix_blob->length;
unsigned int strike_offset = (const char *) this - (const char *) sbix_blob->data;
assert (strike_offset < sbix_len);
retry:
if (unlikely (glyph_id >= num_glyphs ||
imageOffsetsZ[glyph_id + 1] <= imageOffsetsZ[glyph_id] ||
imageOffsetsZ[glyph_id + 1] - imageOffsetsZ[glyph_id] <= SBIXGlyph::min_size ||
(unsigned int) imageOffsetsZ[glyph_id + 1] > sbix_len - strike_offset))
return hb_blob_get_empty ();
unsigned int glyph_offset = strike_offset + (unsigned int) imageOffsetsZ[glyph_id] + SBIXGlyph::min_size;
unsigned int glyph_length = imageOffsetsZ[glyph_id + 1] - imageOffsetsZ[glyph_id] - SBIXGlyph::min_size;
const SBIXGlyph *glyph = &(this+imageOffsetsZ[glyph_id]);
if (glyph->graphicType == HB_TAG ('d','u','p','e'))
{
if (glyph_length >= 2)
{
glyph_id = *((HBUINT16 *) &glyph->data);
if (retry_count--)
goto retry;
}
return hb_blob_get_empty ();
}
if (unlikely (file_type != glyph->graphicType))
return hb_blob_get_empty ();
if (strike_ppem) *strike_ppem = ppem;
if (x_offset) *x_offset = glyph->xOffset;
if (y_offset) *y_offset = glyph->yOffset;
return hb_blob_create_sub_blob (sbix_blob, glyph_offset, glyph_length);
}
bool subset (hb_subset_context_t *c, unsigned int available_len) const
{
TRACE_SUBSET (this);
unsigned int num_output_glyphs = c->plan->num_output_glyphs ();
auto* out = c->serializer->start_embed<SBIXStrike> ();
auto snap = c->serializer->snapshot ();
if (unlikely (!c->serializer->extend (out, num_output_glyphs + 1))) return_trace (false);
out->ppem = ppem;
out->resolution = resolution;
HBUINT32 head;
head = get_size (num_output_glyphs + 1);
bool has_glyphs = false;
for (unsigned new_gid = 0; new_gid < num_output_glyphs; new_gid++)
{
hb_codepoint_t old_gid;
if (!c->plan->old_gid_for_new_gid (new_gid, &old_gid) ||
unlikely (imageOffsetsZ[old_gid].is_null () ||
imageOffsetsZ[old_gid + 1].is_null () ||
imageOffsetsZ[old_gid + 1] <= imageOffsetsZ[old_gid] ||
imageOffsetsZ[old_gid + 1] - imageOffsetsZ[old_gid] <= SBIXGlyph::min_size) ||
(unsigned int) imageOffsetsZ[old_gid + 1] > available_len)
{
out->imageOffsetsZ[new_gid] = head;
continue;
}
has_glyphs = true;
unsigned int delta = imageOffsetsZ[old_gid + 1] - imageOffsetsZ[old_gid];
unsigned int glyph_data_length = delta - SBIXGlyph::min_size;
if (!(this+imageOffsetsZ[old_gid]).copy (c->serializer, glyph_data_length))
return_trace (false);
out->imageOffsetsZ[new_gid] = head;
head += delta;
}
if (has_glyphs)
out->imageOffsetsZ[num_output_glyphs] = head;
else
c->serializer->revert (snap);
return_trace (has_glyphs);
}
public:
HBUINT16 ppem; /* The PPEM size for which this strike was designed. */
HBUINT16 resolution; /* The device pixel density (in PPI) for which this
* strike was designed. (E.g., 96 PPI, 192 PPI.) */
protected:
UnsizedArrayOf<Offset32To<SBIXGlyph>>
imageOffsetsZ; /* Offset from the beginning of the strike data header
* to bitmap data for an individual glyph ID. */
public:
DEFINE_SIZE_ARRAY (4, imageOffsetsZ);
};
struct sbix
{
static constexpr hb_tag_t tableTag = HB_OT_TAG_sbix;
bool has_data () const { return version; }
const SBIXStrike &get_strike (unsigned int i) const { return this+strikes[i]; }
struct accelerator_t
{
accelerator_t (hb_face_t *face)
{
table = hb_sanitize_context_t ().reference_table<sbix> (face);
num_glyphs = face->get_num_glyphs ();
}
~accelerator_t () { table.destroy (); }
bool has_data () const { return table->has_data (); }
bool get_extents (hb_font_t *font,
hb_codepoint_t glyph,
hb_glyph_extents_t *extents,
bool scale = true) const
{
/* We only support PNG right now, and following function checks type. */
return get_png_extents (font, glyph, extents, scale);
}
hb_blob_t *reference_png (hb_font_t *font,
hb_codepoint_t glyph_id,
int *x_offset,
int *y_offset,
unsigned int *available_ppem) const
{
return choose_strike (font).get_glyph_blob (glyph_id, table.get_blob (),
HB_TAG ('p','n','g',' '),
x_offset, y_offset,
num_glyphs, available_ppem);
}
bool paint_glyph (hb_font_t *font, hb_codepoint_t glyph, hb_paint_funcs_t *funcs, void *data) const
{
if (!has_data ())
return false;
int x_offset = 0, y_offset = 0;
unsigned int strike_ppem = 0;
hb_glyph_extents_t extents;
hb_glyph_extents_t pixel_extents;
if (!font->get_glyph_extents (glyph, &extents, false))
return false;
if (unlikely (!get_extents (font, glyph, &pixel_extents, false)))
return false;
hb_blob_t *blob = reference_png (font, glyph, &x_offset, &y_offset, &strike_ppem);
if (hb_blob_is_immutable (blob))
return false;
bool ret = funcs->image (data,
blob,
pixel_extents.width, -pixel_extents.height,
HB_PAINT_IMAGE_FORMAT_PNG,
0.f,
&extents);
hb_blob_destroy (blob);
return ret;
}
private:
const SBIXStrike &choose_strike (hb_font_t *font) const
{
unsigned count = table->strikes.len;
if (unlikely (!count))
return Null (SBIXStrike);
unsigned int requested_ppem = hb_max (font->x_ppem, font->y_ppem);
if (!requested_ppem)
requested_ppem = 1<<30; /* Choose largest strike. */
/* TODO Add DPI sensitivity as well? */
unsigned int best_i = 0;
unsigned int best_ppem = table->get_strike (0).ppem;
for (unsigned int i = 1; i < count; i++)
{
unsigned int ppem = (table->get_strike (i)).ppem;
if ((requested_ppem <= ppem && ppem < best_ppem) ||
(requested_ppem > best_ppem && ppem > best_ppem))
{
best_i = i;
best_ppem = ppem;
}
}
return table->get_strike (best_i);
}
struct PNGHeader
{
HBUINT8 signature[8];
struct
{
struct
{
HBUINT32 length;
Tag type;
} header;
HBUINT32 width;
HBUINT32 height;
HBUINT8 bitDepth;
HBUINT8 colorType;
HBUINT8 compressionMethod;
HBUINT8 filterMethod;
HBUINT8 interlaceMethod;
} IHDR;
public:
DEFINE_SIZE_STATIC (29);
};
bool get_png_extents (hb_font_t *font,
hb_codepoint_t glyph,
hb_glyph_extents_t *extents,
bool scale = true) const
{
/* Following code is safe to call even without data.
* But faster to short-circuit. */
if (!has_data ())
return false;
int x_offset = 0, y_offset = 0;
unsigned int strike_ppem = 0;
hb_blob_t *blob = reference_png (font, glyph, &x_offset, &y_offset, &strike_ppem);
const PNGHeader &png = *blob->as<PNGHeader>();
if (png.IHDR.height >= 65536 || png.IHDR.width >= 65536)
{
hb_blob_destroy (blob);
return false;
}
extents->x_bearing = x_offset;
extents->y_bearing = png.IHDR.height + y_offset;
extents->width = png.IHDR.width;
extents->height = -1 * png.IHDR.height;
/* Convert to font units. */
if (strike_ppem && scale)
{
float scale = font->face->get_upem () / (float) strike_ppem;
extents->x_bearing = roundf (extents->x_bearing * scale);
extents->y_bearing = roundf (extents->y_bearing * scale);
extents->width = roundf (extents->width * scale);
extents->height = roundf (extents->height * scale);
}
if (scale)
font->scale_glyph_extents (extents);
hb_blob_destroy (blob);
return strike_ppem;
}
private:
hb_blob_ptr_t<sbix> table;
unsigned int num_glyphs;
};
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
return_trace (likely (c->check_struct (this) &&
hb_barrier () &&
version >= 1 &&
strikes.sanitize (c, this)));
}
bool
add_strike (hb_subset_context_t *c, unsigned i) const
{
if (strikes[i].is_null () || c->source_blob->length < (unsigned) strikes[i])
return false;
return (this+strikes[i]).subset (c, c->source_blob->length - (unsigned) strikes[i]);
}
bool serialize_strike_offsets (hb_subset_context_t *c) const
{
TRACE_SERIALIZE (this);
auto *out = c->serializer->start_embed<Array32OfOffset32To<SBIXStrike>> ();
if (unlikely (!c->serializer->extend_min (out))) return_trace (false);
hb_vector_t<Offset32To<SBIXStrike>*> new_strikes;
hb_vector_t<hb_serialize_context_t::objidx_t> objidxs;
for (int i = strikes.len - 1; i >= 0; --i)
{
auto* o = out->serialize_append (c->serializer);
if (unlikely (!o)) return_trace (false);
*o = 0;
auto snap = c->serializer->snapshot ();
c->serializer->push ();
bool ret = add_strike (c, i);
if (!ret)
{
c->serializer->pop_discard ();
out->pop ();
c->serializer->revert (snap);
}
else
{
objidxs.push (c->serializer->pop_pack ());
new_strikes.push (o);
}
}
for (unsigned int i = 0; i < new_strikes.length; ++i)
c->serializer->add_link (*new_strikes[i], objidxs[new_strikes.length - 1 - i]);
return_trace (true);
}
bool subset (hb_subset_context_t* c) const
{
TRACE_SUBSET (this);
if (unlikely (!c->serializer->embed (this->version))) return_trace (false);
if (unlikely (!c->serializer->embed (this->flags))) return_trace (false);
return_trace (serialize_strike_offsets (c));
}
protected:
HBUINT16 version; /* Table version number — set to 1 */
HBUINT16 flags; /* Bit 0: Set to 1. Bit 1: Draw outlines.
* Bits 2 to 15: reserved (set to 0). */
Array32OfOffset32To<SBIXStrike>
strikes; /* Offsets from the beginning of the 'sbix'
* table to data for each individual bitmap strike. */
public:
DEFINE_SIZE_ARRAY (8, strikes);
};
struct sbix_accelerator_t : sbix::accelerator_t {
sbix_accelerator_t (hb_face_t *face) : sbix::accelerator_t (face) {}
};
} /* namespace OT */
#endif /* OT_COLOR_SBIX_SBIX_HH */
+829
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/*
* Copyright © 2018 Ebrahim Byagowi
*
* This is part of HarfBuzz, a text shaping library.
*
* Permission is hereby granted, without written agreement and without
* license or royalty fees, to use, copy, modify, and distribute this
* software and its documentation for any purpose, provided that the
* above copyright notice and the following two paragraphs appear in
* all copies of this software.
*
* IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE TO ANY PARTY FOR
* DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES
* ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN
* IF THE COPYRIGHT HOLDER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH
* DAMAGE.
*
* THE COPYRIGHT HOLDER SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING,
* BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS
* ON AN "AS IS" BASIS, AND THE COPYRIGHT HOLDER HAS NO OBLIGATION TO
* PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
*/
#ifndef OT_COLOR_SVG_SVG_HH
#define OT_COLOR_SVG_SVG_HH
#include "../../../hb-open-type.hh"
#include "../../../hb-blob.hh"
#include "../../../hb-limits.hh"
#include "../../../hb-map.hh"
#include "../../../hb-paint.hh"
#include "../../../hb-zlib.hh"
#include <ctype.h>
#include <string.h>
/*
* SVG -- SVG (Scalable Vector Graphics)
* https://docs.microsoft.com/en-us/typography/opentype/spec/svg
*/
#define HB_OT_TAG_SVG HB_TAG('S','V','G',' ')
namespace OT {
static inline hb_blob_t *
hb_ot_svg_reference_normalized_blob (hb_blob_t *image,
const char **svg,
unsigned *len)
{
hb_blob_t *blob = hb_blob_reference (image);
unsigned data_len = 0;
const char *data = hb_blob_get_data (blob, &data_len);
if (!data || !data_len)
goto fail;
if (hb_blob_is_gzip (data, data_len))
{
uint32_t expected_size = 0;
if (hb_gzip_get_uncompressed_size (data, data_len, &expected_size) &&
unlikely ((size_t) expected_size > (size_t) HB_SVG_MAX_DOCUMENT_SIZE))
goto fail;
hb_blob_t *uncompressed = hb_blob_decompress_gzip (blob,
HB_SVG_MAX_DOCUMENT_SIZE);
if (!uncompressed)
goto fail;
hb_blob_destroy (blob);
blob = uncompressed;
data = hb_blob_get_data (blob, &data_len);
if (!data || !data_len)
goto fail;
}
if (unlikely ((size_t) data_len > (size_t) HB_SVG_MAX_DOCUMENT_SIZE))
goto fail;
if (svg) *svg = data;
if (len) *len = data_len;
return blob;
fail:
hb_blob_destroy (blob);
if (svg) *svg = nullptr;
if (len) *len = 0;
return nullptr;
}
struct SVGDocumentIndexEntry
{
int cmp (hb_codepoint_t g) const
{ return g < startGlyphID ? -1 : g > endGlyphID ? 1 : 0; }
hb_codepoint_t get_start_glyph () const
{ return startGlyphID; }
hb_codepoint_t get_end_glyph () const
{ return endGlyphID; }
hb_blob_t *reference_blob (hb_blob_t *svg_blob, unsigned int index_offset) const
{
return hb_blob_create_sub_blob (svg_blob,
index_offset + (unsigned int) svgDoc,
svgDocLength);
}
bool sanitize (hb_sanitize_context_t *c, const void *base) const
{
TRACE_SANITIZE (this);
return_trace (c->check_struct (this) &&
hb_barrier () &&
svgDoc.sanitize (c, base, svgDocLength));
}
protected:
HBUINT16 startGlyphID; /* The first glyph ID in the range described by
* this index entry. */
HBUINT16 endGlyphID; /* The last glyph ID in the range described by
* this index entry. Must be >= startGlyphID. */
NNOffset32To<UnsizedArrayOf<HBUINT8>>
svgDoc; /* Offset from the beginning of the SVG Document Index
* to an SVG document. Must be non-zero. */
HBUINT32 svgDocLength; /* Length of the SVG document.
* Must be non-zero. */
public:
DEFINE_SIZE_STATIC (12);
};
struct SVG
{
static constexpr hb_tag_t tableTag = HB_OT_TAG_SVG;
struct svg_id_span_t
{
const char *p;
unsigned len;
bool operator == (const svg_id_span_t &o) const
{
return len == o.len && !memcmp (p, o.p, len);
}
uint32_t hash () const
{
uint32_t h = hb_hash (len);
for (unsigned i = 0; i < len; i++)
h = h * 33u + (unsigned char) p[i];
return h;
}
};
struct svg_defs_entry_t
{
svg_id_span_t id;
unsigned start;
unsigned end;
};
struct svg_doc_cache_t
{
hb_blob_t *blob = nullptr;
const char *svg = nullptr;
unsigned len = 0;
hb_vector_t<svg_defs_entry_t> defs_entries;
hb_codepoint_t start_glyph = HB_CODEPOINT_INVALID;
hb_codepoint_t end_glyph = HB_CODEPOINT_INVALID;
hb_vector_t<hb_pair_t<uint32_t, uint32_t>> glyph_spans;
hb_hashmap_t<svg_id_span_t, hb_pair_t<uint32_t, uint32_t>> id_spans;
};
bool has_data () const { return svgDocEntries; }
struct accelerator_t
{
accelerator_t (hb_face_t *face);
~accelerator_t ();
hb_blob_t *reference_blob_for_glyph (hb_codepoint_t glyph_id) const
{
return table->get_glyph_entry (glyph_id).reference_blob (table.get_blob (),
table->svgDocEntries);
}
unsigned get_document_count () const
{ return table->get_document_count (); }
bool get_glyph_document_index (hb_codepoint_t glyph_id, unsigned *index) const
{ return table->get_glyph_document_index (glyph_id, index); }
bool get_document_glyph_range (unsigned index,
hb_codepoint_t *start_glyph,
hb_codepoint_t *end_glyph) const
{ return table->get_document_glyph_range (index, start_glyph, end_glyph); }
bool has_data () const { return table->has_data (); }
const svg_doc_cache_t *
get_or_create_doc_cache (hb_blob_t *image,
const char *svg,
unsigned len,
unsigned doc_index,
hb_codepoint_t start_glyph,
hb_codepoint_t end_glyph) const;
const char *
doc_cache_get_svg (const svg_doc_cache_t *doc,
unsigned *len) const;
const hb_vector_t<svg_defs_entry_t> *
doc_cache_get_defs_entries (const svg_doc_cache_t *doc) const;
bool
doc_cache_get_glyph_span (const svg_doc_cache_t *doc,
hb_codepoint_t glyph,
unsigned *start,
unsigned *end) const;
bool
doc_cache_find_id_span (const svg_doc_cache_t *doc,
svg_id_span_t id,
unsigned *start,
unsigned *end) const;
bool
doc_cache_find_id_cstr (const svg_doc_cache_t *doc,
const char *id,
unsigned *start,
unsigned *end) const;
bool paint_glyph (hb_font_t *font HB_UNUSED, hb_codepoint_t glyph, hb_paint_funcs_t *funcs, void *data) const
{
if (!has_data ())
return false;
hb_blob_t *blob = reference_blob_for_glyph (glyph);
if (blob == hb_blob_get_empty ())
return false;
bool ret = funcs->image (data,
blob,
0, 0,
HB_PAINT_IMAGE_FORMAT_SVG,
0.f,
nullptr);
hb_blob_destroy (blob);
return ret;
}
private:
svg_doc_cache_t *
make_doc_cache (hb_blob_t *image,
const char *svg,
unsigned len,
hb_codepoint_t start_glyph,
hb_codepoint_t end_glyph) const;
static void destroy_doc_cache (svg_doc_cache_t *doc);
hb_blob_ptr_t<SVG> table;
mutable hb_vector_t<hb_atomic_t<svg_doc_cache_t *>> doc_caches;
public:
DEFINE_SIZE_STATIC (sizeof (hb_blob_ptr_t<SVG>) +
sizeof (hb_vector_t<hb_atomic_t<svg_doc_cache_t *>>));
};
const SVGDocumentIndexEntry &get_glyph_entry (hb_codepoint_t glyph_id) const
{ return (this+svgDocEntries).bsearch (glyph_id); }
unsigned get_document_count () const
{
if (!has_data ())
return 0;
return (this + svgDocEntries).len;
}
bool get_glyph_document_index (hb_codepoint_t glyph_id, unsigned *index) const
{
if (!has_data ())
return false;
return (this + svgDocEntries).bfind (glyph_id, index);
}
bool get_document_glyph_range (unsigned index,
hb_codepoint_t *start_glyph,
hb_codepoint_t *end_glyph) const
{
if (!has_data ())
return false;
const auto &entries = this + svgDocEntries;
if (index >= entries.len)
return false;
const auto &entry = entries.arrayZ[index];
if (start_glyph) *start_glyph = entry.get_start_glyph ();
if (end_glyph) *end_glyph = entry.get_end_glyph ();
return true;
}
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
return_trace (likely (c->check_struct (this) &&
(this+svgDocEntries).sanitize_shallow (c)));
}
protected:
HBUINT16 version; /* Table version (starting at 0). */
Offset32To<SortedArray16Of<SVGDocumentIndexEntry>>
svgDocEntries; /* Offset (relative to the start of the SVG table) to the
* SVG Documents Index. Must be non-zero. */
/* Array of SVG Document Index Entries. */
HBUINT32 reserved; /* Set to 0. */
public:
DEFINE_SIZE_STATIC (10);
};
namespace _hb_svg_cache_impl {
struct glyph_entry_t
{
hb_codepoint_t glyph;
uint32_t start;
uint32_t end;
};
struct id_entry_t
{
SVG::svg_id_span_t id;
uint32_t start;
uint32_t end;
};
struct open_elem_t
{
unsigned start;
SVG::svg_id_span_t id;
bool in_defs_content;
bool is_defs;
};
static const unsigned MAX_DEPTH = 128;
static inline int
find_substr (const char *s,
unsigned n,
unsigned from,
const char *needle,
unsigned needle_len)
{
if (!needle_len || from >= n || needle_len > n)
return -1;
for (unsigned i = from; i + needle_len <= n; i++)
if (s[i] == needle[0] && !memcmp (s + i, needle, needle_len))
return (int) i;
return -1;
}
static inline bool
parse_id_in_start_tag (const char *svg,
unsigned tag_start,
unsigned tag_end,
SVG::svg_id_span_t *id)
{
unsigned p = tag_start;
while (p + 4 <= tag_end)
{
if (!memcmp (svg + p, "id=\"", 4))
{
unsigned b = p + 4;
unsigned e = b;
while (e < tag_end && svg[e] != '"') e++;
if (e <= tag_end && e > b)
{
*id = {svg + b, e - b};
return true;
}
}
if (!memcmp (svg + p, "id='", 4))
{
unsigned b = p + 4;
unsigned e = b;
while (e < tag_end && svg[e] != '\'') e++;
if (e <= tag_end && e > b)
{
*id = {svg + b, e - b};
return true;
}
}
p++;
}
return false;
}
static inline bool
parse_glyph_id_span (const SVG::svg_id_span_t &id,
hb_codepoint_t *glyph)
{
if (id.len <= 5 || memcmp (id.p, "glyph", 5))
return false;
hb_codepoint_t gid = 0;
for (unsigned i = 5; i < id.len; i++)
{
unsigned char c = (unsigned char) id.p[i];
if (c < '0' || c > '9')
return false;
hb_codepoint_t digit = (hb_codepoint_t) (c - '0');
if (unlikely (gid > HB_CODEPOINT_INVALID / 10 ||
(gid == HB_CODEPOINT_INVALID / 10 &&
digit > HB_CODEPOINT_INVALID % 10)))
return false;
gid = (hb_codepoint_t) (gid * 10 + digit);
}
*glyph = gid;
return true;
}
static inline bool
parse_cache_entries_linear (const char *svg,
unsigned len,
hb_vector_t<SVG::svg_defs_entry_t> *defs_entries,
hb_vector_t<glyph_entry_t> *glyph_spans,
hb_vector_t<id_entry_t> *id_entries)
{
open_elem_t stack[MAX_DEPTH] = {};
unsigned depth = 0;
if (unlikely (!defs_entries->alloc (256) ||
!glyph_spans->alloc (256) ||
!id_entries->alloc (256)))
return false;
unsigned defs_depth = 0;
unsigned i = 0;
while (i < len)
{
if (svg[i] != '<')
{
i++;
continue;
}
if (i + 4 <= len && !memcmp (svg + i, "<!--", 4))
{
int cend = find_substr (svg, len, i + 4, "-->", 3);
if (cend < 0) return false;
i = (unsigned) cend + 3;
continue;
}
if (i + 9 <= len && !memcmp (svg + i, "<![CDATA[", 9))
{
int cend = find_substr (svg, len, i + 9, "]]>", 3);
if (cend < 0) return false;
i = (unsigned) cend + 3;
continue;
}
bool closing = (i + 1 < len && svg[i + 1] == '/');
bool special = (i + 1 < len && (svg[i + 1] == '!' || svg[i + 1] == '?'));
unsigned gt = i + 1;
char quote = 0;
while (gt < len)
{
char c = svg[gt];
if (quote)
{
if (c == quote) quote = 0;
}
else
{
if (c == '"' || c == '\'')
quote = c;
else if (c == '>')
break;
}
gt++;
}
if (gt >= len)
return false;
if (special)
{
i = gt + 1;
continue;
}
unsigned p = i + (closing ? 2 : 1);
while (p < gt && isspace ((unsigned char) svg[p])) p++;
const char *name = svg + p;
unsigned name_len = 0;
while (p + name_len < gt)
{
unsigned char c = (unsigned char) name[name_len];
if (!(isalnum (c) || c == '_' || c == '-' || c == ':'))
break;
name_len++;
}
bool is_defs = (name_len == 4 && !memcmp (name, "defs", 4));
if (closing)
{
if (!depth)
{
i = gt + 1;
continue;
}
open_elem_t e = stack[--depth];
unsigned end = gt + 1;
if (e.id.len)
{
if (unlikely (!id_entries->push_or_fail (id_entry_t {e.id, (uint32_t) e.start, (uint32_t) end})))
return false;
if (e.in_defs_content)
{
if (unlikely (!defs_entries->push_or_fail ()))
return false;
auto &slot = defs_entries->tail ();
slot.id = e.id;
slot.start = e.start;
slot.end = end;
}
hb_codepoint_t gid;
if (parse_glyph_id_span (e.id, &gid))
{
if (unlikely (!glyph_spans->push_or_fail (glyph_entry_t {gid, (uint32_t) e.start, (uint32_t) end})))
return false;
}
}
if (e.is_defs && defs_depth)
defs_depth--;
i = end;
continue;
}
SVG::svg_id_span_t id = {};
parse_id_in_start_tag (svg, i, gt, &id);
unsigned r = gt;
while (r > i && isspace ((unsigned char) svg[r - 1])) r--;
bool self_closing = (r > i && svg[r - 1] == '/');
open_elem_t e = {};
e.start = i;
e.id = id;
e.in_defs_content = defs_depth > 0;
e.is_defs = is_defs;
if (self_closing)
{
unsigned end = gt + 1;
if (e.id.len)
{
if (unlikely (!id_entries->push_or_fail (id_entry_t {e.id, (uint32_t) e.start, (uint32_t) end})))
return false;
if (e.in_defs_content)
{
if (unlikely (!defs_entries->push_or_fail ()))
return false;
auto &slot = defs_entries->tail ();
slot.id = e.id;
slot.start = e.start;
slot.end = end;
}
hb_codepoint_t gid;
if (parse_glyph_id_span (e.id, &gid))
{
if (unlikely (!glyph_spans->push_or_fail (glyph_entry_t {gid, (uint32_t) e.start, (uint32_t) end})))
return false;
}
}
}
else
{
if (unlikely (depth >= MAX_DEPTH))
return false;
stack[depth++] = e;
if (is_defs)
defs_depth++;
}
i = gt + 1;
}
return true;
}
} /* namespace _hb_svg_cache_impl */
inline
SVG::accelerator_t::accelerator_t (hb_face_t *face)
{
table = hb_sanitize_context_t ().reference_table<SVG> (face);
doc_caches.init ();
unsigned doc_count = table->get_document_count ();
if (doc_count && unlikely (!doc_caches.resize (doc_count)))
doc_caches.clear ();
for (unsigned i = 0; i < doc_caches.length; i++)
doc_caches.arrayZ[i].set_relaxed (nullptr);
}
inline
SVG::accelerator_t::~accelerator_t ()
{
for (unsigned i = 0; i < doc_caches.length; i++)
destroy_doc_cache (doc_caches.arrayZ[i].get_relaxed ());
doc_caches.fini ();
table.destroy ();
}
inline void
SVG::accelerator_t::destroy_doc_cache (svg_doc_cache_t *doc)
{
if (!doc)
return;
doc->glyph_spans.fini ();
doc->defs_entries.fini ();
doc->id_spans.fini ();
hb_blob_destroy (doc->blob);
hb_free (doc);
}
inline SVG::svg_doc_cache_t *
SVG::accelerator_t::make_doc_cache (hb_blob_t *image,
const char *svg,
unsigned len,
hb_codepoint_t start_glyph,
hb_codepoint_t end_glyph) const
{
static const uint32_t INVALID_SPAN = 0xFFFFFFFFu;
auto *doc = (svg_doc_cache_t *) hb_malloc (sizeof (svg_doc_cache_t));
if (!doc)
return nullptr;
doc->blob = nullptr;
doc->svg = nullptr;
doc->len = 0;
doc->defs_entries.init ();
doc->start_glyph = HB_CODEPOINT_INVALID;
doc->end_glyph = HB_CODEPOINT_INVALID;
doc->glyph_spans.init ();
doc->id_spans.init ();
doc->blob = hb_blob_reference (image);
doc->svg = svg;
doc->len = len;
doc->start_glyph = start_glyph;
doc->end_glyph = end_glyph;
if (unlikely (start_glyph == HB_CODEPOINT_INVALID || end_glyph < start_glyph))
{
destroy_doc_cache (doc);
return nullptr;
}
unsigned glyph_count = end_glyph - start_glyph + 1;
if (!doc->glyph_spans.resize ((int) glyph_count))
{
destroy_doc_cache (doc);
return nullptr;
}
for (unsigned i = 0; i < glyph_count; i++)
doc->glyph_spans.arrayZ[i] = hb_pair_t<uint32_t, uint32_t> (INVALID_SPAN, INVALID_SPAN);
hb_vector_t<_hb_svg_cache_impl::glyph_entry_t> glyph_spans;
glyph_spans.init ();
hb_vector_t<_hb_svg_cache_impl::id_entry_t> id_entries;
id_entries.init ();
if (!_hb_svg_cache_impl::parse_cache_entries_linear (svg, len,
&doc->defs_entries,
&glyph_spans,
&id_entries))
{
id_entries.fini ();
glyph_spans.fini ();
destroy_doc_cache (doc);
return nullptr;
}
for (unsigned i = 0; i < glyph_spans.length; i++)
{
const auto &span = glyph_spans.arrayZ[i];
if (unlikely (span.glyph < start_glyph || span.glyph > end_glyph))
continue;
doc->glyph_spans.arrayZ[span.glyph - start_glyph] = hb_pair_t<uint32_t, uint32_t> (span.start, span.end);
}
for (unsigned i = 0; i < id_entries.length; i++)
{
const auto &e = id_entries.arrayZ[i];
hb_pair_t<uint32_t, uint32_t> *out = nullptr;
if (doc->id_spans.has (e.id, &out))
continue;
if (unlikely (!doc->id_spans.set (e.id, hb_pair_t<uint32_t, uint32_t> (e.start, e.end))))
{
id_entries.fini ();
glyph_spans.fini ();
destroy_doc_cache (doc);
return nullptr;
}
}
id_entries.fini ();
glyph_spans.fini ();
return doc;
}
inline const SVG::svg_doc_cache_t *
SVG::accelerator_t::get_or_create_doc_cache (hb_blob_t *image,
const char *svg,
unsigned len,
unsigned doc_index,
hb_codepoint_t start_glyph,
hb_codepoint_t end_glyph) const
{
if (doc_index >= doc_caches.length)
return nullptr;
auto &slot = doc_caches.arrayZ[doc_index];
auto *doc = slot.get_acquire ();
if (doc)
return doc;
auto *fresh = make_doc_cache (image, svg, len, start_glyph, end_glyph);
if (!fresh)
return nullptr;
auto *expected = (svg_doc_cache_t *) nullptr;
if (slot.cmpexch (expected, fresh))
return fresh;
destroy_doc_cache (fresh);
return expected;
}
inline const char *
SVG::accelerator_t::doc_cache_get_svg (const svg_doc_cache_t *doc,
unsigned *len) const
{
if (!doc)
{
if (len) *len = 0;
return nullptr;
}
if (len) *len = doc->len;
return doc->svg;
}
inline const hb_vector_t<SVG::svg_defs_entry_t> *
SVG::accelerator_t::doc_cache_get_defs_entries (const svg_doc_cache_t *doc) const
{
return doc ? &doc->defs_entries : nullptr;
}
inline bool
SVG::accelerator_t::doc_cache_get_glyph_span (const svg_doc_cache_t *doc,
hb_codepoint_t glyph,
unsigned *start,
unsigned *end) const
{
static const uint32_t INVALID_SPAN = 0xFFFFFFFFu;
if (!doc || doc->start_glyph == HB_CODEPOINT_INVALID ||
glyph < doc->start_glyph || glyph > doc->end_glyph)
return false;
const auto &span = doc->glyph_spans.arrayZ[glyph - doc->start_glyph];
if (span.first == INVALID_SPAN)
return false;
if (unlikely (span.first > span.second || span.second > doc->len))
return false;
if (start) *start = span.first;
if (end) *end = span.second;
return true;
}
inline bool
SVG::accelerator_t::doc_cache_find_id_span (const svg_doc_cache_t *doc,
svg_id_span_t id,
unsigned *start,
unsigned *end) const
{
if (!doc || !id.p || !id.len)
return false;
hb_pair_t<uint32_t, uint32_t> *span = nullptr;
if (!doc->id_spans.has (id, &span))
return false;
if (unlikely (span->first > span->second || span->second > doc->len))
return false;
if (start) *start = span->first;
if (end) *end = span->second;
return true;
}
inline bool
SVG::accelerator_t::doc_cache_find_id_cstr (const svg_doc_cache_t *doc,
const char *id,
unsigned *start,
unsigned *end) const
{
if (!id) return false;
svg_id_span_t key = {id, (unsigned) strlen (id)};
return doc_cache_find_id_span (doc, key, start, end);
}
struct SVG_accelerator_t : SVG::accelerator_t {
SVG_accelerator_t (hb_face_t *face) : SVG::accelerator_t (face) {}
};
} /* namespace OT */
#endif /* OT_COLOR_SVG_SVG_HH */
+394
View File
@@ -0,0 +1,394 @@
/*
* Copyright © 2007,2008,2009 Red Hat, Inc.
* Copyright © 2010,2012 Google, Inc.
*
* This is part of HarfBuzz, a text shaping library.
*
* Permission is hereby granted, without written agreement and without
* license or royalty fees, to use, copy, modify, and distribute this
* software and its documentation for any purpose, provided that the
* above copyright notice and the following two paragraphs appear in
* all copies of this software.
*
* IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE TO ANY PARTY FOR
* DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES
* ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN
* IF THE COPYRIGHT HOLDER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH
* DAMAGE.
*
* THE COPYRIGHT HOLDER SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING,
* BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS
* ON AN "AS IS" BASIS, AND THE COPYRIGHT HOLDER HAS NO OBLIGATION TO
* PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
*
* Red Hat Author(s): Behdad Esfahbod
* Google Author(s): Behdad Esfahbod, Garret Rieger
*/
#ifndef OT_LAYOUT_COMMON_COVERAGE_HH
#define OT_LAYOUT_COMMON_COVERAGE_HH
#include "../types.hh"
#include "CoverageFormat1.hh"
#include "CoverageFormat2.hh"
namespace OT {
namespace Layout {
namespace Common {
template<typename Iterator>
static inline void Coverage_serialize (hb_serialize_context_t *c,
Iterator it);
struct Coverage
{
protected:
union {
struct { HBUINT16 v; } format; /* Format identifier */
CoverageFormat1_3<SmallTypes> format1;
CoverageFormat2_4<SmallTypes> format2;
#ifndef HB_NO_BEYOND_64K
CoverageFormat1_3<MediumTypes>format3;
CoverageFormat2_4<MediumTypes>format4;
#endif
} u;
public:
DEFINE_SIZE_UNION (2, format.v);
#ifndef HB_OPTIMIZE_SIZE
HB_ALWAYS_INLINE
#endif
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
if (!u.format.v.sanitize (c)) return_trace (false);
hb_barrier ();
switch (u.format.v)
{
case 1: return_trace (u.format1.sanitize (c));
case 2: return_trace (u.format2.sanitize (c));
#ifndef HB_NO_BEYOND_64K
case 3: return_trace (u.format3.sanitize (c));
case 4: return_trace (u.format4.sanitize (c));
#endif
default:return_trace (true);
}
}
/* Has interface. */
unsigned operator [] (hb_codepoint_t k) const { return get (k); }
bool has (hb_codepoint_t k) const { return (*this)[k] != NOT_COVERED; }
/* Predicate. */
bool operator () (hb_codepoint_t k) const { return has (k); }
unsigned int get (hb_codepoint_t k) const { return get_coverage (k); }
unsigned int get_coverage (hb_codepoint_t glyph_id) const
{
switch (u.format.v) {
case 1: return u.format1.get_coverage (glyph_id);
case 2: return u.format2.get_coverage (glyph_id);
#ifndef HB_NO_BEYOND_64K
case 3: return u.format3.get_coverage (glyph_id);
case 4: return u.format4.get_coverage (glyph_id);
#endif
default:return NOT_COVERED;
}
}
unsigned int get_coverage (hb_codepoint_t glyph_id,
hb_ot_layout_mapping_cache_t *cache) const
{
unsigned coverage;
if (cache && cache->get (glyph_id, &coverage)) return coverage < cache->MAX_VALUE ? coverage : NOT_COVERED;
coverage = get_coverage (glyph_id);
if (cache) {
if (coverage == NOT_COVERED)
cache->set_unchecked (glyph_id, cache->MAX_VALUE);
else if (likely (coverage < cache->MAX_VALUE))
cache->set_unchecked (glyph_id, coverage);
}
return coverage;
}
unsigned int get_coverage_binary (hb_codepoint_t glyph_id,
hb_ot_layout_binary_cache_t *cache) const
{
unsigned coverage;
if (cache && cache->get (glyph_id, &coverage)) return coverage < cache->MAX_VALUE ? coverage : NOT_COVERED;
coverage = get_coverage (glyph_id);
if (cache) {
if (coverage == NOT_COVERED)
cache->set_unchecked (glyph_id, cache->MAX_VALUE);
else
cache->set_unchecked (glyph_id, 0);
}
return coverage;
}
unsigned get_population () const
{
switch (u.format.v) {
case 1: return u.format1.get_population ();
case 2: return u.format2.get_population ();
#ifndef HB_NO_BEYOND_64K
case 3: return u.format3.get_population ();
case 4: return u.format4.get_population ();
#endif
default:return NOT_COVERED;
}
}
template <typename Iterator,
hb_requires (hb_is_sorted_source_of (Iterator, hb_codepoint_t))>
bool serialize (hb_serialize_context_t *c, Iterator glyphs)
{
TRACE_SERIALIZE (this);
if (unlikely (!c->extend_min (this))) return_trace (false);
unsigned count = hb_len (glyphs);
unsigned num_ranges = 0;
hb_codepoint_t last = (hb_codepoint_t) -2;
hb_codepoint_t max = 0;
bool unsorted = false;
for (auto g: glyphs)
{
if (last != (hb_codepoint_t) -2 && g < last)
unsorted = true;
if (last + 1 != g)
num_ranges++;
last = g;
if (g > max) max = g;
}
u.format.v = !unsorted && count <= num_ranges * 3 ? 1 : 2;
#ifndef HB_NO_BEYOND_64K
if (max > 0xFFFFu)
u.format.v += 2;
if (unlikely (max > 0xFFFFFFu))
#else
if (unlikely (max > 0xFFFFu))
#endif
{
c->check_success (false, HB_SERIALIZE_ERROR_INT_OVERFLOW);
return_trace (false);
}
switch (u.format.v)
{
case 1: return_trace (u.format1.serialize (c, glyphs));
case 2: return_trace (u.format2.serialize (c, glyphs));
#ifndef HB_NO_BEYOND_64K
case 3: return_trace (u.format3.serialize (c, glyphs));
case 4: return_trace (u.format4.serialize (c, glyphs));
#endif
default:return_trace (false);
}
}
bool subset (hb_subset_context_t *c) const
{
TRACE_SUBSET (this);
auto it =
+ iter ()
| hb_take (c->plan->source->get_num_glyphs ())
| hb_map_retains_sorting (c->plan->glyph_map_gsub)
| hb_filter ([] (hb_codepoint_t glyph) { return glyph != HB_MAP_VALUE_INVALID; })
;
// Cache the iterator result as it will be iterated multiple times
// by the serialize code below.
hb_sorted_vector_t<hb_codepoint_t> glyphs (it);
Coverage_serialize (c->serializer, glyphs.iter ());
return_trace (bool (glyphs));
}
bool intersects (const hb_set_t *glyphs) const
{
switch (u.format.v)
{
case 1: return u.format1.intersects (glyphs);
case 2: return u.format2.intersects (glyphs);
#ifndef HB_NO_BEYOND_64K
case 3: return u.format3.intersects (glyphs);
case 4: return u.format4.intersects (glyphs);
#endif
default:return false;
}
}
bool intersects_coverage (const hb_set_t *glyphs, unsigned int index) const
{
switch (u.format.v)
{
case 1: return u.format1.intersects_coverage (glyphs, index);
case 2: return u.format2.intersects_coverage (glyphs, index);
#ifndef HB_NO_BEYOND_64K
case 3: return u.format3.intersects_coverage (glyphs, index);
case 4: return u.format4.intersects_coverage (glyphs, index);
#endif
default:return false;
}
}
unsigned cost () const
{
switch (u.format.v) {
case 1: hb_barrier (); return u.format1.cost ();
case 2: hb_barrier (); return u.format2.cost ();
#ifndef HB_NO_BEYOND_64K
case 3: hb_barrier (); return u.format3.cost ();
case 4: hb_barrier (); return u.format4.cost ();
#endif
default:return 0u;
}
}
/* Might return false if array looks unsorted.
* Used for faster rejection of corrupt data. */
template <typename set_t>
bool collect_coverage (set_t *glyphs) const
{
switch (u.format.v)
{
case 1: return u.format1.collect_coverage (glyphs);
case 2: return u.format2.collect_coverage (glyphs);
#ifndef HB_NO_BEYOND_64K
case 3: return u.format3.collect_coverage (glyphs);
case 4: return u.format4.collect_coverage (glyphs);
#endif
default:return false;
}
}
template <typename IterableOut,
hb_requires (hb_is_sink_of (IterableOut, hb_codepoint_t))>
void intersect_set (const hb_set_t &glyphs, IterableOut&& intersect_glyphs) const
{
switch (u.format.v)
{
case 1: return u.format1.intersect_set (glyphs, intersect_glyphs);
case 2: return u.format2.intersect_set (glyphs, intersect_glyphs);
#ifndef HB_NO_BEYOND_64K
case 3: return u.format3.intersect_set (glyphs, intersect_glyphs);
case 4: return u.format4.intersect_set (glyphs, intersect_glyphs);
#endif
default:return ;
}
}
struct iter_t : hb_iter_with_fallback_t<iter_t, hb_codepoint_t>
{
static constexpr bool is_sorted_iterator = true;
iter_t (const Coverage &c_ = Null (Coverage))
{
hb_memset (this, 0, sizeof (*this));
format = c_.u.format.v;
switch (format)
{
case 1: u.format1.init (c_.u.format1); return;
case 2: u.format2.init (c_.u.format2); return;
#ifndef HB_NO_BEYOND_64K
case 3: u.format3.init (c_.u.format3); return;
case 4: u.format4.init (c_.u.format4); return;
#endif
default: return;
}
}
bool __more__ () const
{
switch (format)
{
case 1: return u.format1.__more__ ();
case 2: return u.format2.__more__ ();
#ifndef HB_NO_BEYOND_64K
case 3: return u.format3.__more__ ();
case 4: return u.format4.__more__ ();
#endif
default:return false;
}
}
void __next__ ()
{
switch (format)
{
case 1: u.format1.__next__ (); break;
case 2: u.format2.__next__ (); break;
#ifndef HB_NO_BEYOND_64K
case 3: u.format3.__next__ (); break;
case 4: u.format4.__next__ (); break;
#endif
default: break;
}
}
typedef hb_codepoint_t __item_t__;
__item_t__ __item__ () const { return get_glyph (); }
hb_codepoint_t get_glyph () const
{
switch (format)
{
case 1: return u.format1.get_glyph ();
case 2: return u.format2.get_glyph ();
#ifndef HB_NO_BEYOND_64K
case 3: return u.format3.get_glyph ();
case 4: return u.format4.get_glyph ();
#endif
default:return 0;
}
}
bool operator != (const iter_t& o) const
{
if (unlikely (format != o.format)) return true;
switch (format)
{
case 1: return u.format1 != o.u.format1;
case 2: return u.format2 != o.u.format2;
#ifndef HB_NO_BEYOND_64K
case 3: return u.format3 != o.u.format3;
case 4: return u.format4 != o.u.format4;
#endif
default:return false;
}
}
iter_t __end__ () const
{
iter_t it;
it.format = format;
switch (format)
{
case 1: it.u.format1 = u.format1.__end__ (); break;
case 2: it.u.format2 = u.format2.__end__ (); break;
#ifndef HB_NO_BEYOND_64K
case 3: it.u.format3 = u.format3.__end__ (); break;
case 4: it.u.format4 = u.format4.__end__ (); break;
#endif
default: break;
}
return it;
}
private:
unsigned int format;
union {
#ifndef HB_NO_BEYOND_64K
CoverageFormat2_4<MediumTypes>::iter_t format4; /* Put this one first since it's larger; helps shut up compiler. */
CoverageFormat1_3<MediumTypes>::iter_t format3;
#endif
CoverageFormat2_4<SmallTypes>::iter_t format2; /* Put this one first since it's larger; helps shut up compiler. */
CoverageFormat1_3<SmallTypes>::iter_t format1;
} u;
};
iter_t iter () const { return iter_t (*this); }
};
template<typename Iterator>
static inline void
Coverage_serialize (hb_serialize_context_t *c,
Iterator it)
{ c->start_embed<Coverage> ()->serialize (c, it); }
}
}
}
#endif // #ifndef OT_LAYOUT_COMMON_COVERAGE_HH
@@ -0,0 +1,135 @@
/*
* Copyright © 2007,2008,2009 Red Hat, Inc.
* Copyright © 2010,2012 Google, Inc.
*
* This is part of HarfBuzz, a text shaping library.
*
* Permission is hereby granted, without written agreement and without
* license or royalty fees, to use, copy, modify, and distribute this
* software and its documentation for any purpose, provided that the
* above copyright notice and the following two paragraphs appear in
* all copies of this software.
*
* IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE TO ANY PARTY FOR
* DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES
* ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN
* IF THE COPYRIGHT HOLDER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH
* DAMAGE.
*
* THE COPYRIGHT HOLDER SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING,
* BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS
* ON AN "AS IS" BASIS, AND THE COPYRIGHT HOLDER HAS NO OBLIGATION TO
* PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
*
* Red Hat Author(s): Behdad Esfahbod
* Google Author(s): Behdad Esfahbod, Garret Rieger
*/
#ifndef OT_LAYOUT_COMMON_COVERAGEFORMAT1_HH
#define OT_LAYOUT_COMMON_COVERAGEFORMAT1_HH
namespace OT {
namespace Layout {
namespace Common {
#define NOT_COVERED ((unsigned int) -1)
template <typename Types>
struct CoverageFormat1_3
{
friend struct Coverage;
public:
HBUINT16 coverageFormat; /* Format identifier--format = 1 */
SortedArray16Of<typename Types::HBGlyphID>
glyphArray; /* Array of GlyphIDs--in numerical order */
DEFINE_SIZE_ARRAY (4, glyphArray);
private:
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
return_trace (glyphArray.sanitize (c));
}
unsigned int get_coverage (hb_codepoint_t glyph_id) const
{
unsigned int i;
glyphArray.bfind (glyph_id, &i, HB_NOT_FOUND_STORE, NOT_COVERED);
return i;
}
unsigned get_population () const
{
return glyphArray.len;
}
template <typename Iterator,
hb_requires (hb_is_sorted_source_of (Iterator, hb_codepoint_t))>
bool serialize (hb_serialize_context_t *c, Iterator glyphs)
{
TRACE_SERIALIZE (this);
return_trace (glyphArray.serialize (c, glyphs));
}
bool intersects (const hb_set_t *glyphs) const
{
if (glyphArray.len > glyphs->get_population () * hb_bit_storage ((unsigned) glyphArray.len))
{
for (auto g : *glyphs)
if (get_coverage (g) != NOT_COVERED)
return true;
return false;
}
for (const auto& g : glyphArray.as_array ())
if (glyphs->has (g))
return true;
return false;
}
bool intersects_coverage (const hb_set_t *glyphs, unsigned int index) const
{ return glyphs->has (glyphArray[index]); }
template <typename IterableOut,
hb_requires (hb_is_sink_of (IterableOut, hb_codepoint_t))>
void intersect_set (const hb_set_t &glyphs, IterableOut&& intersect_glyphs) const
{
unsigned count = glyphArray.len;
for (unsigned i = 0; i < count; i++)
if (glyphs.has (glyphArray[i]))
intersect_glyphs << glyphArray[i];
}
unsigned cost () const { return hb_bit_storage ((unsigned) glyphArray.len); /* bsearch cost */ }
template <typename set_t>
bool collect_coverage (set_t *glyphs) const
{ return glyphs->add_sorted_array (glyphArray.as_array ()); }
public:
/* Older compilers need this to be public. */
struct iter_t
{
void init (const struct CoverageFormat1_3 &c_) { c = &c_; i = 0; }
bool __more__ () const { return i < c->glyphArray.len; }
void __next__ () { i++; }
hb_codepoint_t get_glyph () const { return c->glyphArray[i]; }
bool operator != (const iter_t& o) const
{ return i != o.i; }
iter_t __end__ () const { iter_t it; it.init (*c); it.i = c->glyphArray.len; return it; }
private:
const struct CoverageFormat1_3 *c;
unsigned int i;
};
private:
};
}
}
}
#endif // #ifndef OT_LAYOUT_COMMON_COVERAGEFORMAT1_HH
@@ -0,0 +1,241 @@
/*
* Copyright © 2007,2008,2009 Red Hat, Inc.
* Copyright © 2010,2012 Google, Inc.
*
* This is part of HarfBuzz, a text shaping library.
*
* Permission is hereby granted, without written agreement and without
* license or royalty fees, to use, copy, modify, and distribute this
* software and its documentation for any purpose, provided that the
* above copyright notice and the following two paragraphs appear in
* all copies of this software.
*
* IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE TO ANY PARTY FOR
* DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES
* ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN
* IF THE COPYRIGHT HOLDER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH
* DAMAGE.
*
* THE COPYRIGHT HOLDER SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING,
* BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS
* ON AN "AS IS" BASIS, AND THE COPYRIGHT HOLDER HAS NO OBLIGATION TO
* PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
*
* Red Hat Author(s): Behdad Esfahbod
* Google Author(s): Behdad Esfahbod, Garret Rieger
*/
#ifndef OT_LAYOUT_COMMON_COVERAGEFORMAT2_HH
#define OT_LAYOUT_COMMON_COVERAGEFORMAT2_HH
#include "RangeRecord.hh"
namespace OT {
namespace Layout {
namespace Common {
template <typename Types>
struct CoverageFormat2_4
{
friend struct Coverage;
public:
HBUINT16 coverageFormat; /* Format identifier--format = 2 */
SortedArray16Of<RangeRecord<Types>>
rangeRecord; /* Array of glyph ranges--ordered by
* Start GlyphID. rangeCount entries
* long */
public:
DEFINE_SIZE_ARRAY (4, rangeRecord);
private:
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
return_trace (rangeRecord.sanitize (c));
}
unsigned int get_coverage (hb_codepoint_t glyph_id) const
{
const RangeRecord<Types> &range = rangeRecord.bsearch (glyph_id);
return likely (range.first <= range.last)
? (unsigned int) range.value + (glyph_id - range.first)
: NOT_COVERED;
}
unsigned get_population () const
{
typename Types::large_int ret = 0;
for (const auto &r : rangeRecord)
ret += r.get_population ();
return ret > UINT_MAX ? UINT_MAX : (unsigned) ret;
}
template <typename Iterator,
hb_requires (hb_is_sorted_source_of (Iterator, hb_codepoint_t))>
bool serialize (hb_serialize_context_t *c, Iterator glyphs)
{
TRACE_SERIALIZE (this);
if (unlikely (!c->extend_min (this))) return_trace (false);
unsigned num_ranges = 0;
hb_codepoint_t last = (hb_codepoint_t) -2;
for (auto g: glyphs)
{
if (last + 1 != g)
num_ranges++;
last = g;
}
if (unlikely (!rangeRecord.serialize (c, num_ranges))) return_trace (false);
if (!num_ranges) return_trace (true);
unsigned count = 0;
unsigned range = (unsigned) -1;
last = (hb_codepoint_t) -2;
unsigned unsorted = false;
for (auto g: glyphs)
{
if (last + 1 != g)
{
if (unlikely (last != (hb_codepoint_t) -2 && last + 1 > g))
unsorted = true;
range++;
rangeRecord.arrayZ[range].first = g;
rangeRecord.arrayZ[range].value = count;
}
rangeRecord.arrayZ[range].last = g;
last = g;
count++;
}
if (unlikely (unsorted))
rangeRecord.as_array ().qsort (RangeRecord<Types>::cmp_range);
return_trace (true);
}
bool intersects (const hb_set_t *glyphs) const
{
if (rangeRecord.len > glyphs->get_population () * hb_bit_storage ((unsigned) rangeRecord.len))
{
for (auto g : *glyphs)
if (get_coverage (g) != NOT_COVERED)
return true;
return false;
}
return hb_any (+ hb_iter (rangeRecord)
| hb_map ([glyphs] (const RangeRecord<Types> &range) { return range.intersects (*glyphs); }));
}
bool intersects_coverage (const hb_set_t *glyphs, unsigned int index) const
{
auto *range = rangeRecord.as_array ().bsearch (index);
if (range)
return range->intersects (*glyphs);
return false;
}
template <typename IterableOut,
hb_requires (hb_is_sink_of (IterableOut, hb_codepoint_t))>
void intersect_set (const hb_set_t &glyphs, IterableOut&& intersect_glyphs) const
{
/* Break out of loop for overlapping, broken, tables,
* to avoid fuzzer timouts. */
hb_codepoint_t last = 0;
for (const auto& range : rangeRecord)
{
if (unlikely (range.first < last))
break;
last = range.last;
for (hb_codepoint_t g = range.first - 1;
glyphs.next (&g) && g <= last;)
intersect_glyphs << g;
}
}
unsigned cost () const { return hb_bit_storage ((unsigned) rangeRecord.len); /* bsearch cost */ }
template <typename set_t>
bool collect_coverage (set_t *glyphs) const
{
for (const auto& range: rangeRecord)
if (unlikely (!range.collect_coverage (glyphs)))
return false;
return true;
}
public:
/* Older compilers need this to be public. */
struct iter_t
{
void init (const CoverageFormat2_4 &c_)
{
c = &c_;
coverage = 0;
i = 0;
j = c->rangeRecord.len ? c->rangeRecord[0].first : 0;
if (unlikely (c->rangeRecord[0].first > c->rangeRecord[0].last))
{
/* Broken table. Skip. */
i = c->rangeRecord.len;
j = 0;
}
}
bool __more__ () const { return i < c->rangeRecord.len; }
void __next__ ()
{
if (j >= c->rangeRecord[i].last)
{
i++;
if (__more__ ())
{
unsigned int old = coverage;
j = c->rangeRecord.arrayZ[i].first;
coverage = c->rangeRecord.arrayZ[i].value;
if (unlikely (coverage != old + 1))
{
/* Broken table. Skip. Important to avoid DoS.
* Also, our callers depend on coverage being
* consecutive and monotonically increasing,
* ie. iota(). */
i = c->rangeRecord.len;
j = 0;
return;
}
}
else
j = 0;
return;
}
coverage++;
j++;
}
hb_codepoint_t get_glyph () const { return j; }
bool operator != (const iter_t& o) const
{ return i != o.i || j != o.j; }
iter_t __end__ () const
{
iter_t it;
it.init (*c);
it.i = c->rangeRecord.len;
it.j = 0;
return it;
}
private:
const struct CoverageFormat2_4 *c;
unsigned int i, coverage;
hb_codepoint_t j;
};
private:
};
}
}
}
#endif // #ifndef OT_LAYOUT_COMMON_COVERAGEFORMAT2_HH
+97
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@@ -0,0 +1,97 @@
/*
* Copyright © 2007,2008,2009 Red Hat, Inc.
* Copyright © 2010,2012 Google, Inc.
*
* This is part of HarfBuzz, a text shaping library.
*
* Permission is hereby granted, without written agreement and without
* license or royalty fees, to use, copy, modify, and distribute this
* software and its documentation for any purpose, provided that the
* above copyright notice and the following two paragraphs appear in
* all copies of this software.
*
* IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE TO ANY PARTY FOR
* DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES
* ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN
* IF THE COPYRIGHT HOLDER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH
* DAMAGE.
*
* THE COPYRIGHT HOLDER SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING,
* BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS
* ON AN "AS IS" BASIS, AND THE COPYRIGHT HOLDER HAS NO OBLIGATION TO
* PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
*
* Red Hat Author(s): Behdad Esfahbod
* Google Author(s): Behdad Esfahbod, Garret Rieger
*/
#ifndef OT_LAYOUT_COMMON_RANGERECORD_HH
#define OT_LAYOUT_COMMON_RANGERECORD_HH
namespace OT {
namespace Layout {
namespace Common {
template <typename Types>
struct RangeRecord
{
typename Types::HBGlyphID first; /* First GlyphID in the range */
typename Types::HBGlyphID last; /* Last GlyphID in the range */
HBUINT16 value; /* Value */
DEFINE_SIZE_STATIC (2 + 2 * Types::size);
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
return_trace (c->check_struct (this));
}
int cmp (hb_codepoint_t g) const
{ return g < first ? -1 : g <= last ? 0 : +1; }
HB_INTERNAL static int cmp_range (const void *pa, const void *pb) {
const RangeRecord *a = (const RangeRecord *) pa;
const RangeRecord *b = (const RangeRecord *) pb;
if (a->first < b->first) return -1;
if (a->first > b->first) return +1;
if (a->last < b->last) return -1;
if (a->last > b->last) return +1;
if (a->value < b->value) return -1;
if (a->value > b->value) return +1;
return 0;
}
unsigned get_population () const
{
if (unlikely (last < first)) return 0;
return (last - first + 1);
}
bool intersects (const hb_set_t &glyphs) const
{ return glyphs.intersects (first, last); }
template <typename set_t>
bool collect_coverage (set_t *glyphs) const
{ return glyphs->add_range (first, last); }
};
}
}
}
// TODO(garretrieger): This was previously implemented using
// DECLARE_NULL_NAMESPACE_BYTES_TEMPLATE1 (OT, RangeRecord, 9);
// but that only works when there is only a single namespace level.
// The macro should probably be fixed so it can work in this situation.
extern HB_INTERNAL const unsigned char _hb_Null_OT_RangeRecord[9];
template <typename Spec>
struct Null<OT::Layout::Common::RangeRecord<Spec>> {
static OT::Layout::Common::RangeRecord<Spec> const & get_null () {
return *reinterpret_cast<const OT::Layout::Common::RangeRecord<Spec> *> (_hb_Null_OT_RangeRecord);
}
};
#endif // #ifndef OT_LAYOUT_COMMON_RANGERECORD_HH
File diff suppressed because it is too large Load Diff
+84
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#ifndef OT_LAYOUT_GPOS_ANCHOR_HH
#define OT_LAYOUT_GPOS_ANCHOR_HH
#include "AnchorFormat1.hh"
#include "AnchorFormat2.hh"
#include "AnchorFormat3.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct Anchor
{
protected:
union {
struct { HBUINT16 v; } format; /* Format identifier */
AnchorFormat1 format1;
AnchorFormat2 format2;
AnchorFormat3 format3;
} u;
public:
DEFINE_SIZE_UNION (2, format.v);
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
if (!u.format.v.sanitize (c)) return_trace (false);
hb_barrier ();
switch (u.format.v) {
case 1: return_trace (u.format1.sanitize (c));
case 2: return_trace (u.format2.sanitize (c));
case 3: return_trace (u.format3.sanitize (c));
default:return_trace (true);
}
}
void get_anchor (hb_ot_apply_context_t *c, hb_codepoint_t glyph_id,
float *x, float *y) const
{
*x = *y = 0;
switch (u.format.v) {
case 1: u.format1.get_anchor (c, glyph_id, x, y); return;
case 2: u.format2.get_anchor (c, glyph_id, x, y); return;
case 3: u.format3.get_anchor (c, glyph_id, x, y); return;
default: return;
}
}
bool subset (hb_subset_context_t *c) const
{
TRACE_SUBSET (this);
switch (u.format.v) {
case 1: return_trace (bool (reinterpret_cast<Anchor *> (u.format1.copy (c->serializer))));
case 2:
if (c->plan->flags & HB_SUBSET_FLAGS_NO_HINTING)
{
// AnchorFormat 2 just containins extra hinting information, so
// if hints are being dropped convert to format 1.
return_trace (bool (reinterpret_cast<Anchor *> (u.format1.copy (c->serializer))));
}
return_trace (bool (reinterpret_cast<Anchor *> (u.format2.copy (c->serializer))));
case 3: return_trace (u.format3.subset (c));
default:return_trace (false);
}
}
void collect_variation_indices (hb_collect_variation_indices_context_t *c) const
{
switch (u.format.v) {
case 1: case 2:
return;
case 3:
u.format3.collect_variation_indices (c);
return;
default: return;
}
}
};
}
}
}
#endif // OT_LAYOUT_GPOS_ANCHOR_HH
+46
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#ifndef OT_LAYOUT_GPOS_ANCHORFORMAT1_HH
#define OT_LAYOUT_GPOS_ANCHORFORMAT1_HH
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct AnchorFormat1
{
protected:
HBUINT16 format; /* Format identifier--format = 1 */
FWORD xCoordinate; /* Horizontal value--in design units */
FWORD yCoordinate; /* Vertical value--in design units */
public:
DEFINE_SIZE_STATIC (6);
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
return_trace (c->check_struct (this));
}
void get_anchor (hb_ot_apply_context_t *c, hb_codepoint_t glyph_id HB_UNUSED,
float *x, float *y) const
{
hb_font_t *font = c->font;
*x = font->em_fscale_x (xCoordinate);
*y = font->em_fscale_y (yCoordinate);
}
AnchorFormat1* copy (hb_serialize_context_t *c) const
{
TRACE_SERIALIZE (this);
AnchorFormat1* out = c->embed<AnchorFormat1> (this);
if (!out) return_trace (out);
out->format = 1;
return_trace (out);
}
};
}
}
}
#endif // OT_LAYOUT_GPOS_ANCHORFORMAT1_HH
+58
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#ifndef OT_LAYOUT_GPOS_ANCHORFORMAT2_HH
#define OT_LAYOUT_GPOS_ANCHORFORMAT2_HH
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct AnchorFormat2
{
protected:
HBUINT16 format; /* Format identifier--format = 2 */
FWORD xCoordinate; /* Horizontal value--in design units */
FWORD yCoordinate; /* Vertical value--in design units */
HBUINT16 anchorPoint; /* Index to glyph contour point */
public:
DEFINE_SIZE_STATIC (8);
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
return_trace (c->check_struct (this));
}
void get_anchor (hb_ot_apply_context_t *c, hb_codepoint_t glyph_id,
float *x, float *y) const
{
hb_font_t *font = c->font;
#ifdef HB_NO_HINTING
*x = font->em_fscale_x (xCoordinate);
*y = font->em_fscale_y (yCoordinate);
return;
#endif
unsigned int x_ppem = font->x_ppem;
unsigned int y_ppem = font->y_ppem;
hb_position_t cx = 0, cy = 0;
bool ret;
ret = (x_ppem || y_ppem) &&
font->get_glyph_contour_point_for_origin (glyph_id, anchorPoint, HB_DIRECTION_LTR, &cx, &cy);
*x = ret && x_ppem ? cx : font->em_fscale_x (xCoordinate);
*y = ret && y_ppem ? cy : font->em_fscale_y (yCoordinate);
}
AnchorFormat2* copy (hb_serialize_context_t *c) const
{
TRACE_SERIALIZE (this);
return_trace (c->embed<AnchorFormat2> (this));
}
};
}
}
}
#endif // OT_LAYOUT_GPOS_ANCHORFORMAT2_HH
+123
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#ifndef OT_LAYOUT_GPOS_ANCHORFORMAT3_HH
#define OT_LAYOUT_GPOS_ANCHORFORMAT3_HH
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct AnchorFormat3
{
protected:
HBUINT16 format; /* Format identifier--format = 3 */
FWORD xCoordinate; /* Horizontal value--in design units */
FWORD yCoordinate; /* Vertical value--in design units */
Offset16To<Device>
xDeviceTable; /* Offset to Device table for X
* coordinate-- from beginning of
* Anchor table (may be NULL) */
Offset16To<Device>
yDeviceTable; /* Offset to Device table for Y
* coordinate-- from beginning of
* Anchor table (may be NULL) */
public:
DEFINE_SIZE_STATIC (10);
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
if (unlikely (!c->check_struct (this))) return_trace (false);
return_trace (xDeviceTable.sanitize (c, this) && yDeviceTable.sanitize (c, this));
}
void get_anchor (hb_ot_apply_context_t *c, hb_codepoint_t glyph_id HB_UNUSED,
float *x, float *y) const
{
hb_font_t *font = c->font;
*x = font->em_fscale_x (xCoordinate);
*y = font->em_fscale_y (yCoordinate);
if ((font->x_ppem || font->has_nonzero_coords) && xDeviceTable.sanitize (&c->sanitizer, this))
{
hb_barrier ();
*x += (this+xDeviceTable).get_x_delta (font, c->var_store, c->var_store_cache);
}
if ((font->y_ppem || font->has_nonzero_coords) && yDeviceTable.sanitize (&c->sanitizer, this))
{
hb_barrier ();
*y += (this+yDeviceTable).get_y_delta (font, c->var_store, c->var_store_cache);
}
}
bool subset (hb_subset_context_t *c) const
{
TRACE_SUBSET (this);
auto *out = c->serializer->start_embed (*this);
if (unlikely (!c->serializer->embed (format))) return_trace (false);
if (unlikely (!c->serializer->embed (xCoordinate))) return_trace (false);
if (unlikely (!c->serializer->embed (yCoordinate))) return_trace (false);
unsigned x_varidx = xDeviceTable ? (this+xDeviceTable).get_variation_index () : HB_OT_LAYOUT_NO_VARIATIONS_INDEX;
if (x_varidx != HB_OT_LAYOUT_NO_VARIATIONS_INDEX)
{
hb_pair_t<unsigned, int> *new_varidx_delta;
if (!c->plan->layout_variation_idx_delta_map.has (x_varidx, &new_varidx_delta))
return_trace (false);
x_varidx = hb_first (*new_varidx_delta);
int delta = hb_second (*new_varidx_delta);
if (delta != 0)
{
if (!c->serializer->check_assign (out->xCoordinate, xCoordinate + delta,
HB_SERIALIZE_ERROR_INT_OVERFLOW))
return_trace (false);
}
}
unsigned y_varidx = yDeviceTable ? (this+yDeviceTable).get_variation_index () : HB_OT_LAYOUT_NO_VARIATIONS_INDEX;
if (y_varidx != HB_OT_LAYOUT_NO_VARIATIONS_INDEX)
{
hb_pair_t<unsigned, int> *new_varidx_delta;
if (!c->plan->layout_variation_idx_delta_map.has (y_varidx, &new_varidx_delta))
return_trace (false);
y_varidx = hb_first (*new_varidx_delta);
int delta = hb_second (*new_varidx_delta);
if (delta != 0)
{
if (!c->serializer->check_assign (out->yCoordinate, yCoordinate + delta,
HB_SERIALIZE_ERROR_INT_OVERFLOW))
return_trace (false);
}
}
bool no_downgrade = (!xDeviceTable.is_null () && !(this+xDeviceTable).is_variation_device ()) ||
x_varidx != HB_OT_LAYOUT_NO_VARIATIONS_INDEX ||
y_varidx != HB_OT_LAYOUT_NO_VARIATIONS_INDEX ||
(!yDeviceTable.is_null () && !(this+yDeviceTable).is_variation_device ());
if (!no_downgrade)
return_trace (c->serializer->check_assign (out->format, 1, HB_SERIALIZE_ERROR_INT_OVERFLOW));
if (!c->serializer->embed (xDeviceTable)) return_trace (false);
if (!c->serializer->embed (yDeviceTable)) return_trace (false);
out->xDeviceTable.serialize_copy (c->serializer, xDeviceTable, this, 0, hb_serialize_context_t::Head, &c->plan->layout_variation_idx_delta_map);
out->yDeviceTable.serialize_copy (c->serializer, yDeviceTable, this, 0, hb_serialize_context_t::Head, &c->plan->layout_variation_idx_delta_map);
return_trace (out);
}
void collect_variation_indices (hb_collect_variation_indices_context_t *c) const
{
(this+xDeviceTable).collect_variation_indices (c);
(this+yDeviceTable).collect_variation_indices (c);
}
};
}
}
}
#endif // OT_LAYOUT_GPOS_ANCHORFORMAT3_HH
+94
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#ifndef OT_LAYOUT_GPOS_ANCHORMATRIX_HH
#define OT_LAYOUT_GPOS_ANCHORMATRIX_HH
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct AnchorMatrix
{
HBUINT16 rows; /* Number of rows */
UnsizedArrayOf<Offset16To<Anchor, AnchorMatrix>>
matrixZ; /* Matrix of offsets to Anchor tables--
* from beginning of AnchorMatrix table */
public:
DEFINE_SIZE_ARRAY (2, matrixZ);
bool sanitize (hb_sanitize_context_t *c, unsigned int cols) const
{
TRACE_SANITIZE (this);
if (!c->check_struct (this)) return_trace (false);
hb_barrier ();
if (unlikely (hb_unsigned_mul_overflows (rows, cols))) return_trace (false);
unsigned int count = rows * cols;
if (!c->check_array (matrixZ.arrayZ, count)) return_trace (false);
if (c->lazy_some_gpos)
return_trace (true);
hb_barrier ();
for (unsigned int i = 0; i < count; i++)
if (!matrixZ[i].sanitize (c, this)) return_trace (false);
return_trace (true);
}
const Anchor& get_anchor (hb_ot_apply_context_t *c,
unsigned int row, unsigned int col,
unsigned int cols, bool *found) const
{
*found = false;
if (unlikely (row >= rows || col >= cols)) return Null (Anchor);
auto &offset = matrixZ[row * cols + col];
if (unlikely (!offset.sanitize (&c->sanitizer, this))) return Null (Anchor);
hb_barrier ();
*found = !offset.is_null ();
return this+offset;
}
template <typename Iterator,
hb_requires (hb_is_iterator (Iterator))>
void collect_variation_indices (hb_collect_variation_indices_context_t *c,
Iterator index_iter) const
{
for (unsigned i : index_iter)
(this+matrixZ[i]).collect_variation_indices (c);
}
template <typename Iterator,
hb_requires (hb_is_iterator (Iterator))>
bool subset (hb_subset_context_t *c,
unsigned num_rows,
Iterator index_iter) const
{
TRACE_SUBSET (this);
auto *out = c->serializer->start_embed (this);
if (!index_iter) return_trace (false);
if (unlikely (!c->serializer->extend_min (out))) return_trace (false);
out->rows = num_rows;
for (const unsigned i : index_iter)
{
auto *offset = c->serializer->embed (matrixZ[i]);
if (!offset) return_trace (false);
offset->serialize_subset (c, matrixZ[i], this);
}
return_trace (true);
}
bool offset_is_null (unsigned row, unsigned col, unsigned num_cols) const
{
if (unlikely (row >= rows || col >= num_cols)) return true;
auto &offset = matrixZ[row * num_cols + col];
return offset.is_null ();
}
};
}
}
}
#endif /* OT_LAYOUT_GPOS_ANCHORMATRIX_HH */
+14
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#ifndef OT_LAYOUT_GPOS_CHAINCONTEXTPOS_HH
#define OT_LAYOUT_GPOS_CHAINCONTEXTPOS_HH
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct ChainContextPos : ChainContext {};
}
}
}
#endif /* OT_LAYOUT_GPOS_CHAINCONTEXTPOS_HH */
+33
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#ifndef OT_LAYOUT_GPOS_COMMON_HH
#define OT_LAYOUT_GPOS_COMMON_HH
namespace OT {
namespace Layout {
namespace GPOS_impl {
enum attach_type_t {
ATTACH_TYPE_NONE = 0X00,
/* Each attachment should be either a mark or a cursive; can't be both. */
ATTACH_TYPE_MARK = 0X01,
ATTACH_TYPE_CURSIVE = 0X02,
};
/* buffer **position** var allocations */
#define attach_chain() var.i16[0] /* glyph to which this attaches to, relative to current glyphs; negative for going back, positive for forward. */
#define attach_type() var.u8[2] /* attachment type */
/* Note! if attach_chain() is zero, the value of attach_type() is irrelevant. */
template<typename Iterator, typename SrcLookup>
static void SinglePos_serialize (hb_serialize_context_t *c,
const SrcLookup *src,
Iterator it,
const hb_hashmap_t<unsigned, hb_pair_t<unsigned, int>> *layout_variation_idx_delta_map,
unsigned new_format);
}
}
}
#endif // OT_LAYOUT_GPOS_COMMON_HH
+14
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#ifndef OT_LAYOUT_GPOS_CONTEXTPOS_HH
#define OT_LAYOUT_GPOS_CONTEXTPOS_HH
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct ContextPos : Context {};
}
}
}
#endif /* OT_LAYOUT_GPOS_CONTEXTPOS_HH */
+35
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#ifndef OT_LAYOUT_GPOS_CURSIVEPOS_HH
#define OT_LAYOUT_GPOS_CURSIVEPOS_HH
#include "CursivePosFormat1.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct CursivePos
{
protected:
union {
struct { HBUINT16 v; } format; /* Format identifier */
CursivePosFormat1 format1;
} u;
public:
template <typename context_t, typename ...Ts>
typename context_t::return_t dispatch (context_t *c, Ts&&... ds) const
{
if (unlikely (!c->may_dispatch (this, &u.format.v))) return c->no_dispatch_return_value ();
TRACE_DISPATCH (this, u.format.v);
switch (u.format.v) {
case 1: return_trace (c->dispatch (u.format1, std::forward<Ts> (ds)...));
default:return_trace (c->default_return_value ());
}
}
};
}
}
}
#endif /* OT_LAYOUT_GPOS_CURSIVEPOS_HH */
@@ -0,0 +1,332 @@
#ifndef OT_LAYOUT_GPOS_CURSIVEPOSFORMAT1_HH
#define OT_LAYOUT_GPOS_CURSIVEPOSFORMAT1_HH
#include "Anchor.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct EntryExitRecord
{
friend struct CursivePosFormat1;
bool sanitize (hb_sanitize_context_t *c, const struct CursivePosFormat1 *base) const
{
TRACE_SANITIZE (this);
return_trace (entryAnchor.sanitize (c, base) && exitAnchor.sanitize (c, base));
}
void collect_variation_indices (hb_collect_variation_indices_context_t *c,
const struct CursivePosFormat1 *src_base) const
{
(src_base+entryAnchor).collect_variation_indices (c);
(src_base+exitAnchor).collect_variation_indices (c);
}
bool subset (hb_subset_context_t *c,
const struct CursivePosFormat1 *src_base) const
{
TRACE_SERIALIZE (this);
auto *out = c->serializer->embed (this);
if (unlikely (!out)) return_trace (false);
bool ret = false;
ret |= out->entryAnchor.serialize_subset (c, entryAnchor, src_base);
ret |= out->exitAnchor.serialize_subset (c, exitAnchor, src_base);
return_trace (ret);
}
protected:
Offset16To<Anchor, struct CursivePosFormat1>
entryAnchor; /* Offset to EntryAnchor table--from
* beginning of CursivePos
* subtable--may be NULL */
Offset16To<Anchor, struct CursivePosFormat1>
exitAnchor; /* Offset to ExitAnchor table--from
* beginning of CursivePos
* subtable--may be NULL */
public:
DEFINE_SIZE_STATIC (4);
};
static inline void
reverse_cursive_minor_offset (hb_glyph_position_t *pos,
unsigned int len,
unsigned int i,
hb_direction_t direction,
unsigned int new_parent)
{
int chain = pos[i].attach_chain(), type = pos[i].attach_type();
if (likely (!chain || 0 == (type & ATTACH_TYPE_CURSIVE)))
return;
pos[i].attach_chain() = 0;
unsigned int j = (int) i + chain;
if (unlikely (j >= len))
return;
/* Stop if we see new parent in the chain. */
if (j == new_parent)
return;
int16_t reversed_chain = -chain;
/* The old edge was cleared above; if the reversed distance truncates,
* keep it detached instead of storing a poisoned chain.
*/
if (unlikely (reversed_chain != -chain))
return;
reverse_cursive_minor_offset (pos, len, j, direction, new_parent);
if (HB_DIRECTION_IS_HORIZONTAL (direction))
pos[j].y_offset = -pos[i].y_offset;
else
pos[j].x_offset = -pos[i].x_offset;
pos[j].attach_chain() = reversed_chain;
pos[j].attach_type() = type;
}
struct CursivePosFormat1
{
protected:
HBUINT16 format; /* Format identifier--format = 1 */
Offset16To<Coverage>
coverage; /* Offset to Coverage table--from
* beginning of subtable */
Array16Of<EntryExitRecord>
entryExitRecord; /* Array of EntryExit records--in
* Coverage Index order */
public:
DEFINE_SIZE_ARRAY (6, entryExitRecord);
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
if (unlikely (!coverage.sanitize (c, this)))
return_trace (false);
if (c->lazy_some_gpos)
return_trace (entryExitRecord.sanitize_shallow (c));
else
return_trace (entryExitRecord.sanitize (c, this));
}
bool intersects (const hb_set_t *glyphs) const
{ return (this+coverage).intersects (glyphs); }
void closure_lookups (hb_closure_lookups_context_t *c) const {}
void collect_variation_indices (hb_collect_variation_indices_context_t *c) const
{
+ hb_zip (this+coverage, entryExitRecord)
| hb_filter (c->glyph_set, hb_first)
| hb_map (hb_second)
| hb_apply ([&] (const EntryExitRecord& record) { record.collect_variation_indices (c, this); })
;
}
void collect_glyphs (hb_collect_glyphs_context_t *c) const
{ if (unlikely (!(this+coverage).collect_coverage (c->input))) return; }
const Coverage &get_coverage () const { return this+coverage; }
bool apply (hb_ot_apply_context_t *c) const
{
TRACE_APPLY (this);
hb_buffer_t *buffer = c->buffer;
const EntryExitRecord &this_record = entryExitRecord[(this+coverage).get_coverage (buffer->cur().codepoint)];
if (!this_record.entryAnchor ||
unlikely (!this_record.entryAnchor.sanitize (&c->sanitizer, this))) return_trace (false);
hb_barrier ();
auto &skippy_iter = c->iter_input;
skippy_iter.reset_fast (buffer->idx);
unsigned unsafe_from;
if (unlikely (!skippy_iter.prev (&unsafe_from)))
{
buffer->unsafe_to_concat_from_outbuffer (unsafe_from, buffer->idx + 1);
return_trace (false);
}
const EntryExitRecord &prev_record = entryExitRecord[(this+coverage).get_coverage (buffer->info[skippy_iter.idx].codepoint)];
if (!prev_record.exitAnchor ||
unlikely (!prev_record.exitAnchor.sanitize (&c->sanitizer, this)))
{
buffer->unsafe_to_concat_from_outbuffer (skippy_iter.idx, buffer->idx + 1);
return_trace (false);
}
hb_barrier ();
unsigned int i = skippy_iter.idx;
unsigned int j = buffer->idx;
if (HB_BUFFER_MESSAGE_MORE && c->buffer->messaging ())
{
c->buffer->message (c->font,
"cursive attaching glyph at %u to glyph at %u",
i, j);
}
buffer->unsafe_to_break (i, j + 1);
float entry_x, entry_y, exit_x, exit_y;
(this+prev_record.exitAnchor).get_anchor (c, buffer->info[i].codepoint, &exit_x, &exit_y);
(this+this_record.entryAnchor).get_anchor (c, buffer->info[j].codepoint, &entry_x, &entry_y);
hb_glyph_position_t *pos = buffer->pos;
hb_position_t d;
/* Main-direction adjustment */
switch (c->direction) {
case HB_DIRECTION_LTR:
pos[i].x_advance = roundf (exit_x) + pos[i].x_offset;
d = roundf (entry_x) + pos[j].x_offset;
pos[j].x_advance -= d;
pos[j].x_offset -= d;
break;
case HB_DIRECTION_RTL:
d = roundf (exit_x) + pos[i].x_offset;
pos[i].x_advance -= d;
pos[i].x_offset -= d;
pos[j].x_advance = roundf (entry_x) + pos[j].x_offset;
break;
case HB_DIRECTION_TTB:
pos[i].y_advance = roundf (exit_y) + pos[i].y_offset;
d = roundf (entry_y) + pos[j].y_offset;
pos[j].y_advance -= d;
pos[j].y_offset -= d;
break;
case HB_DIRECTION_BTT:
d = roundf (exit_y) + pos[i].y_offset;
pos[i].y_advance -= d;
pos[i].y_offset -= d;
pos[j].y_advance = roundf (entry_y);
break;
case HB_DIRECTION_INVALID:
default:
break;
}
/* Cross-direction adjustment */
/* We attach child to parent (think graph theory and rooted trees whereas
* the root stays on baseline and each node aligns itself against its
* parent.
*
* Optimize things for the case of RightToLeft, as that's most common in
* Arabic. */
unsigned int child = i;
unsigned int parent = j;
hb_position_t x_offset = roundf (entry_x - exit_x);
hb_position_t y_offset = roundf (entry_y - exit_y);
if (!(c->lookup_props & LookupFlag::RightToLeft))
{
unsigned int k = child;
child = parent;
parent = k;
x_offset = -x_offset;
y_offset = -y_offset;
}
/* If child was already connected to someone else, walk through its old
* chain and reverse the link direction, such that the whole tree of its
* previous connection now attaches to new parent. Watch out for case
* where new parent is on the path from old chain...
*/
reverse_cursive_minor_offset (pos, buffer->len, child, c->direction, parent);
pos[child].attach_chain() = (int) parent - (int) child;
if (pos[child].attach_chain() != (int) parent - (int) child)
{
pos[child].attach_chain() = 0;
goto overflow;
}
pos[child].attach_type() = ATTACH_TYPE_CURSIVE;
buffer->scratch_flags |= HB_BUFFER_SCRATCH_FLAG_HAS_GPOS_ATTACHMENT;
if (likely (HB_DIRECTION_IS_HORIZONTAL (c->direction)))
pos[child].y_offset = y_offset;
else
pos[child].x_offset = x_offset;
/* If parent was attached to child, separate them.
* https://github.com/harfbuzz/harfbuzz/issues/2469
*/
if (unlikely (pos[parent].attach_chain() == -pos[child].attach_chain()))
{
pos[parent].attach_chain() = 0;
if (likely (HB_DIRECTION_IS_HORIZONTAL (c->direction)))
pos[parent].y_offset = 0;
else
pos[parent].x_offset = 0;
}
if (HB_BUFFER_MESSAGE_MORE && c->buffer->messaging ())
{
c->buffer->message (c->font,
"cursive attached glyph at %u to glyph at %u",
i, j);
}
overflow:
buffer->idx++;
return_trace (true);
}
template <typename Iterator,
hb_requires (hb_is_iterator (Iterator))>
void serialize (hb_subset_context_t *c,
Iterator it,
const struct CursivePosFormat1 *src_base)
{
if (unlikely (!c->serializer->extend_min ((*this)))) return;
this->format = 1;
this->entryExitRecord.len = it.len ();
for (const EntryExitRecord& entry_record : + it
| hb_map (hb_second))
entry_record.subset (c, src_base);
auto glyphs =
+ it
| hb_map_retains_sorting (hb_first)
;
coverage.serialize_serialize (c->serializer, glyphs);
}
bool subset (hb_subset_context_t *c) const
{
TRACE_SUBSET (this);
const hb_set_t &glyphset = *c->plan->glyphset_gsub ();
const hb_map_t &glyph_map = *c->plan->glyph_map;
auto *out = c->serializer->start_embed (*this);
auto it =
+ hb_zip (this+coverage, entryExitRecord)
| hb_filter (glyphset, hb_first)
| hb_map_retains_sorting ([&] (hb_pair_t<hb_codepoint_t, const EntryExitRecord&> p) -> hb_pair_t<hb_codepoint_t, const EntryExitRecord&>
{ return hb_pair (glyph_map[p.first], p.second);})
;
bool ret = bool (it);
out->serialize (c, it, this);
return_trace (ret);
}
};
}
}
}
#endif /* OT_LAYOUT_GPOS_CURSIVEPOSFORMAT1_HH */
+17
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@@ -0,0 +1,17 @@
#ifndef OT_LAYOUT_GPOS_EXTENSIONPOS_HH
#define OT_LAYOUT_GPOS_EXTENSIONPOS_HH
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct ExtensionPos : Extension<ExtensionPos>
{
typedef struct PosLookupSubTable SubTable;
};
}
}
}
#endif /* OT_LAYOUT_GPOS_EXTENSIONPOS_HH */
+206
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@@ -0,0 +1,206 @@
#ifndef OT_LAYOUT_GPOS_GPOS_HH
#define OT_LAYOUT_GPOS_GPOS_HH
#include "../../../hb-ot-layout-common.hh"
#include "../../../hb-ot-layout-gsubgpos.hh"
#include "Common.hh"
#include "PosLookup.hh"
namespace OT {
using Layout::GPOS_impl::PosLookup;
namespace Layout {
static void
propagate_attachment_offsets (hb_glyph_position_t *pos,
unsigned int len,
unsigned int i,
hb_direction_t direction,
unsigned nesting_level = HB_MAX_NESTING_LEVEL);
/*
* GPOS -- Glyph Positioning
* https://docs.microsoft.com/en-us/typography/opentype/spec/gpos
*/
struct GPOS : GSUBGPOS
{
static constexpr hb_tag_t tableTag = HB_OT_TAG_GPOS;
using Lookup = PosLookup;
const PosLookup& get_lookup (unsigned int i) const
{ return static_cast<const PosLookup &> (GSUBGPOS::get_lookup (i)); }
static inline void position_start (hb_font_t *font, hb_buffer_t *buffer);
static inline void position_finish_advances (hb_font_t *font, hb_buffer_t *buffer);
static inline void position_finish_offsets (hb_font_t *font, hb_buffer_t *buffer);
bool subset (hb_subset_context_t *c) const
{
hb_subset_layout_context_t l (c, tableTag);
return GSUBGPOS::subset<PosLookup> (&l);
}
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
return_trace (GSUBGPOS::sanitize<PosLookup> (c));
}
HB_INTERNAL bool is_blocklisted (hb_blob_t *blob,
hb_face_t *face) const;
void collect_variation_indices (hb_collect_variation_indices_context_t *c) const
{
for (unsigned i = 0; i < GSUBGPOS::get_lookup_count (); i++)
{
if (!c->gpos_lookups->has (i)) continue;
const PosLookup &l = get_lookup (i);
l.dispatch (c);
}
}
void closure_lookups (hb_face_t *face,
const hb_set_t *glyphs,
hb_set_t *lookup_indexes /* IN/OUT */) const
{ GSUBGPOS::closure_lookups<PosLookup> (face, glyphs, lookup_indexes); }
typedef GSUBGPOS::accelerator_t<GPOS> accelerator_t;
};
static void
propagate_attachment_offsets (hb_glyph_position_t *pos,
unsigned int len,
unsigned int i,
hb_direction_t direction,
unsigned nesting_level)
{
/* Adjusts offsets of attached glyphs (both cursive and mark) to accumulate
* offset of glyph they are attached to. */
int chain = pos[i].attach_chain();
int type = pos[i].attach_type();
pos[i].attach_chain() = 0;
unsigned int j = (int) i + chain;
if (unlikely (j >= len))
return;
if (unlikely (!nesting_level))
return;
if (pos[j].attach_chain())
propagate_attachment_offsets (pos, len, j, direction, nesting_level - 1);
assert (!!(type & GPOS_impl::ATTACH_TYPE_MARK) ^ !!(type & GPOS_impl::ATTACH_TYPE_CURSIVE));
if (type & GPOS_impl::ATTACH_TYPE_CURSIVE)
{
if (HB_DIRECTION_IS_HORIZONTAL (direction))
pos[i].y_offset += pos[j].y_offset;
else
pos[i].x_offset += pos[j].x_offset;
}
else /*if (type & GPOS_impl::ATTACH_TYPE_MARK)*/
{
pos[i].x_offset += pos[j].x_offset;
pos[i].y_offset += pos[j].y_offset;
// i is the position of the mark; j is the base.
if (j < i)
{
/* This is the common case: mark follows base.
* And currently the only way in OpenType. */
if (HB_DIRECTION_IS_FORWARD (direction))
for (unsigned int k = j; k < i; k++) {
pos[i].x_offset -= pos[k].x_advance;
pos[i].y_offset -= pos[k].y_advance;
}
else
for (unsigned int k = j + 1; k < i + 1; k++) {
pos[i].x_offset += pos[k].x_advance;
pos[i].y_offset += pos[k].y_advance;
}
}
else // j > i
{
/* This can happen with `kerx`: a mark attaching
* to a base after it in the logical order. */
if (HB_DIRECTION_IS_FORWARD (direction))
for (unsigned int k = i; k < j; k++) {
pos[i].x_offset += pos[k].x_advance;
pos[i].y_offset += pos[k].y_advance;
}
else
for (unsigned int k = i + 1; k < j + 1; k++) {
pos[i].x_offset -= pos[k].x_advance;
pos[i].y_offset -= pos[k].y_advance;
}
}
}
}
void
GPOS::position_start (hb_font_t *font HB_UNUSED, hb_buffer_t *buffer)
{
unsigned int count = buffer->len;
for (unsigned int i = 0; i < count; i++)
buffer->pos[i].attach_chain() = buffer->pos[i].attach_type() = 0;
}
void
GPOS::position_finish_advances (hb_font_t *font HB_UNUSED, hb_buffer_t *buffer HB_UNUSED)
{
//_hb_buffer_assert_gsubgpos_vars (buffer);
}
void
GPOS::position_finish_offsets (hb_font_t *font, hb_buffer_t *buffer)
{
_hb_buffer_assert_gsubgpos_vars (buffer);
unsigned int len;
hb_glyph_position_t *pos = hb_buffer_get_glyph_positions (buffer, &len);
hb_direction_t direction = buffer->props.direction;
/* Handle attachments */
if (buffer->scratch_flags & HB_BUFFER_SCRATCH_FLAG_HAS_GPOS_ATTACHMENT)
{
auto *pos = buffer->pos;
// https://github.com/harfbuzz/harfbuzz/issues/5514
if (HB_DIRECTION_IS_FORWARD (direction))
{
for (unsigned i = 0; i < len; i++)
if (pos[i].attach_chain())
propagate_attachment_offsets (pos, len, i, direction);
} else {
for (unsigned i = len; i-- > 0; )
if (pos[i].attach_chain())
propagate_attachment_offsets (pos, len, i, direction);
}
}
if (unlikely (font->slant_xy) &&
HB_DIRECTION_IS_HORIZONTAL (direction))
{
/* Slanting shaping results is only supported for horizontal text,
* as it gets weird otherwise. */
for (unsigned i = 0; i < len; i++)
if (unlikely (pos[i].y_offset))
pos[i].x_offset += roundf (font->slant_xy * pos[i].y_offset);
}
}
}
struct GPOS_accelerator_t : Layout::GPOS::accelerator_t {
GPOS_accelerator_t (hb_face_t *face) : Layout::GPOS::accelerator_t (face) {}
};
}
#endif /* OT_LAYOUT_GPOS_GPOS_HH */
+68
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@@ -0,0 +1,68 @@
#ifndef OT_LAYOUT_GPOS_LIGATUREARRAY_HH
#define OT_LAYOUT_GPOS_LIGATUREARRAY_HH
namespace OT {
namespace Layout {
namespace GPOS_impl {
typedef AnchorMatrix LigatureAttach; /* component-major--
* in order of writing direction--,
* mark-minor--
* ordered by class--zero-based. */
/* Array of LigatureAttach tables ordered by LigatureCoverage Index */
struct LigatureArray : List16OfOffset16To<LigatureAttach>
{
template <typename Iterator,
hb_requires (hb_is_iterator (Iterator))>
bool subset (hb_subset_context_t *c,
Iterator coverage,
unsigned class_count,
const hb_map_t *klass_mapping,
hb_sorted_vector_t<hb_codepoint_t> &new_coverage /* OUT */) const
{
TRACE_SUBSET (this);
const hb_map_t &glyph_map = c->plan->glyph_map_gsub;
auto *out = c->serializer->start_embed (this);
if (unlikely (!c->serializer->extend_min (out))) return_trace (false);
bool ret = false;
for (const auto _ : + hb_zip (coverage, *this)
| hb_filter (glyph_map, hb_first))
{
const LigatureAttach& src = (this + _.second);
bool non_empty = + hb_range (src.rows * class_count)
| hb_filter ([=] (unsigned index) { return klass_mapping->has (index % class_count); })
| hb_map ([&] (const unsigned index) { return !src.offset_is_null (index / class_count, index % class_count, class_count); })
| hb_any;
if (!non_empty) continue;
auto *matrix = out->serialize_append (c->serializer);
if (unlikely (!matrix)) return_trace (false);
auto indexes =
+ hb_range (src.rows * class_count)
| hb_filter ([=] (unsigned index) { return klass_mapping->has (index % class_count); })
;
ret |= matrix->serialize_subset (c,
_.second,
this,
src.rows,
indexes);
hb_codepoint_t new_gid = glyph_map.get (_.first);
new_coverage.push (new_gid);
}
return_trace (ret);
}
};
}
}
}
#endif /* OT_LAYOUT_GPOS_LIGATUREARRAY_HH */
+134
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@@ -0,0 +1,134 @@
#ifndef OT_LAYOUT_GPOS_MARKARRAY_HH
#define OT_LAYOUT_GPOS_MARKARRAY_HH
#include "AnchorMatrix.hh"
#include "MarkRecord.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct MarkArray : Array16Of<MarkRecord> /* Array of MarkRecords--in Coverage order */
{
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
return_trace (Array16Of<MarkRecord>::sanitize (c, this));
}
bool apply (hb_ot_apply_context_t *c,
unsigned int mark_index, unsigned int glyph_index,
const AnchorMatrix &anchors, unsigned int class_count,
unsigned int glyph_pos) const
{
TRACE_APPLY (this);
hb_buffer_t *buffer = c->buffer;
const MarkRecord &record = Array16Of<MarkRecord>::operator[](mark_index);
unsigned int mark_class = record.klass;
const Anchor& mark_anchor = this + record.markAnchor;
bool found;
const Anchor& glyph_anchor = anchors.get_anchor (c, glyph_index, mark_class, class_count, &found);
/* If this subtable doesn't have an anchor for this base and this class,
* return false such that the subsequent subtables have a chance at it. */
if (unlikely (!found)) return_trace (false);
float mark_x, mark_y, base_x, base_y;
buffer->unsafe_to_break (glyph_pos, buffer->idx + 1);
mark_anchor.get_anchor (c, buffer->cur().codepoint, &mark_x, &mark_y);
glyph_anchor.get_anchor (c, buffer->info[glyph_pos].codepoint, &base_x, &base_y);
if (HB_BUFFER_MESSAGE_MORE && c->buffer->messaging ())
{
c->buffer->message (c->font,
"attaching mark glyph at %u to glyph at %u",
c->buffer->idx, glyph_pos);
}
hb_glyph_position_t &o = buffer->cur_pos();
o.attach_chain() = (int) glyph_pos - (int) buffer->idx;
if (o.attach_chain() != (int) glyph_pos - (int) buffer->idx)
{
o.attach_chain() = 0;
goto overflow;
}
o.attach_type() = ATTACH_TYPE_MARK;
o.x_offset = roundf (base_x - mark_x);
o.y_offset = roundf (base_y - mark_y);
buffer->scratch_flags |= HB_BUFFER_SCRATCH_FLAG_HAS_GPOS_ATTACHMENT;
if (HB_BUFFER_MESSAGE_MORE && c->buffer->messaging ())
{
c->buffer->message (c->font,
"attached mark glyph at %u to glyph at %u",
c->buffer->idx, glyph_pos);
}
overflow:
buffer->idx++;
return_trace (true);
}
template <typename Iterator,
hb_requires (hb_is_iterator (Iterator))>
bool subset (hb_subset_context_t *c,
Iterator coverage,
const hb_map_t *klass_mapping) const
{
TRACE_SUBSET (this);
const hb_set_t &glyphset = *c->plan->glyphset_gsub ();
auto* out = c->serializer->start_embed (this);
if (unlikely (!c->serializer->extend_min (out))) return_trace (false);
auto mark_iter =
+ hb_zip (coverage, this->iter ())
| hb_filter (glyphset, hb_first)
| hb_map (hb_second)
;
bool ret = false;
unsigned new_length = 0;
for (const auto& mark_record : mark_iter) {
ret |= mark_record.subset (c, this, klass_mapping);
new_length++;
}
if (unlikely (!c->serializer->check_assign (out->len, new_length,
HB_SERIALIZE_ERROR_ARRAY_OVERFLOW)))
return_trace (false);
return_trace (ret);
}
};
HB_INTERNAL inline
void Markclass_closure_and_remap_indexes (const Coverage &mark_coverage,
const MarkArray &mark_array,
const hb_set_t &glyphset,
hb_map_t* klass_mapping /* INOUT */)
{
hb_set_t orig_classes;
+ hb_zip (mark_coverage, mark_array)
| hb_filter (glyphset, hb_first)
| hb_map (hb_second)
| hb_map (&MarkRecord::get_class)
| hb_sink (orig_classes)
;
unsigned idx = 0;
for (auto klass : orig_classes.iter ())
{
if (klass_mapping->has (klass)) continue;
klass_mapping->set (klass, idx);
idx++;
}
}
}
}
}
#endif /* OT_LAYOUT_GPOS_MARKARRAY_HH */
+41
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@@ -0,0 +1,41 @@
#ifndef OT_LAYOUT_GPOS_MARKBASEPOS_HH
#define OT_LAYOUT_GPOS_MARKBASEPOS_HH
#include "MarkBasePosFormat1.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct MarkBasePos
{
protected:
union {
struct { HBUINT16 v; } format; /* Format identifier */
MarkBasePosFormat1_2<SmallTypes> format1;
#ifndef HB_NO_BEYOND_64K
MarkBasePosFormat1_2<MediumTypes> format2;
#endif
} u;
public:
template <typename context_t, typename ...Ts>
typename context_t::return_t dispatch (context_t *c, Ts&&... ds) const
{
if (unlikely (!c->may_dispatch (this, &u.format.v))) return c->no_dispatch_return_value ();
TRACE_DISPATCH (this, u.format.v);
switch (u.format.v) {
case 1: return_trace (c->dispatch (u.format1, std::forward<Ts> (ds)...));
#ifndef HB_NO_BEYOND_64K
case 2: return_trace (c->dispatch (u.format2, std::forward<Ts> (ds)...));
#endif
default:return_trace (c->default_return_value ());
}
}
};
}
}
}
#endif /* OT_LAYOUT_GPOS_MARKBASEPOS_HH */
@@ -0,0 +1,250 @@
#ifndef OT_LAYOUT_GPOS_MARKBASEPOSFORMAT1_HH
#define OT_LAYOUT_GPOS_MARKBASEPOSFORMAT1_HH
#include "MarkArray.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
typedef AnchorMatrix BaseArray; /* base-major--
* in order of BaseCoverage Index--,
* mark-minor--
* ordered by class--zero-based. */
template <typename Types>
struct MarkBasePosFormat1_2
{
protected:
HBUINT16 format; /* Format identifier--format = 1 */
typename Types::template OffsetTo<Coverage>
markCoverage; /* Offset to MarkCoverage table--from
* beginning of MarkBasePos subtable */
typename Types::template OffsetTo<Coverage>
baseCoverage; /* Offset to BaseCoverage table--from
* beginning of MarkBasePos subtable */
HBUINT16 classCount; /* Number of classes defined for marks */
typename Types::template OffsetTo<MarkArray>
markArray; /* Offset to MarkArray table--from
* beginning of MarkBasePos subtable */
typename Types::template OffsetTo<BaseArray>
baseArray; /* Offset to BaseArray table--from
* beginning of MarkBasePos subtable */
public:
DEFINE_SIZE_STATIC (4 + 4 * Types::size);
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
return_trace (c->check_struct (this) &&
markCoverage.sanitize (c, this) &&
baseCoverage.sanitize (c, this) &&
markArray.sanitize (c, this) &&
baseArray.sanitize (c, this, (unsigned int) classCount));
}
bool intersects (const hb_set_t *glyphs) const
{
return (this+markCoverage).intersects (glyphs) &&
(this+baseCoverage).intersects (glyphs);
}
void closure_lookups (hb_closure_lookups_context_t *c) const {}
void collect_variation_indices (hb_collect_variation_indices_context_t *c) const
{
+ hb_zip (this+markCoverage, this+markArray)
| hb_filter (c->glyph_set, hb_first)
| hb_map (hb_second)
| hb_apply ([&] (const MarkRecord& record) { record.collect_variation_indices (c, &(this+markArray)); })
;
hb_map_t klass_mapping;
Markclass_closure_and_remap_indexes (this+markCoverage, this+markArray, *c->glyph_set, &klass_mapping);
unsigned basecount = (this+baseArray).rows;
auto base_iter =
+ hb_zip (this+baseCoverage, hb_range (basecount))
| hb_filter (c->glyph_set, hb_first)
| hb_map (hb_second)
;
hb_sorted_vector_t<unsigned> base_indexes;
for (const unsigned row : base_iter)
{
+ hb_range ((unsigned) classCount)
| hb_filter (klass_mapping)
| hb_map ([&] (const unsigned col) { return row * (unsigned) classCount + col; })
| hb_sink (base_indexes)
;
}
(this+baseArray).collect_variation_indices (c, base_indexes.iter ());
}
void collect_glyphs (hb_collect_glyphs_context_t *c) const
{
if (unlikely (!(this+markCoverage).collect_coverage (c->input))) return;
if (unlikely (!(this+baseCoverage).collect_coverage (c->input))) return;
}
const Coverage &get_coverage () const { return this+markCoverage; }
static inline bool accept (hb_buffer_t *buffer, unsigned idx)
{
/* We only want to attach to the first of a MultipleSubst sequence.
* https://github.com/harfbuzz/harfbuzz/issues/740
* Reject others...
* ...but stop if we find a mark in the MultipleSubst sequence:
* https://github.com/harfbuzz/harfbuzz/issues/1020 */
return !_hb_glyph_info_multiplied (&buffer->info[idx]) ||
0 == _hb_glyph_info_get_lig_comp (&buffer->info[idx]) ||
(idx == 0 ||
_hb_glyph_info_is_mark (&buffer->info[idx - 1]) ||
!_hb_glyph_info_multiplied (&buffer->info[idx - 1]) ||
_hb_glyph_info_get_lig_id (&buffer->info[idx]) !=
_hb_glyph_info_get_lig_id (&buffer->info[idx - 1]) ||
_hb_glyph_info_get_lig_comp (&buffer->info[idx]) !=
_hb_glyph_info_get_lig_comp (&buffer->info[idx - 1]) + 1
);
}
bool apply (hb_ot_apply_context_t *c) const
{
TRACE_APPLY (this);
hb_buffer_t *buffer = c->buffer;
unsigned int mark_index = (this+markCoverage).get_coverage (buffer->cur().codepoint);
if (likely (mark_index == NOT_COVERED)) return_trace (false);
/* Now we search backwards for a non-mark glyph.
* We don't use skippy_iter.prev() to avoid O(n^2) behavior. */
auto &skippy_iter = c->iter_input;
skippy_iter.set_lookup_props (LookupFlag::IgnoreMarks);
if (c->last_base_until > buffer->idx)
{
c->last_base_until = 0;
c->last_base = -1;
}
unsigned j;
for (j = buffer->idx; j > c->last_base_until; j--)
{
auto match = skippy_iter.match (buffer->info[j - 1]);
if (match == skippy_iter.MATCH)
{
// https://github.com/harfbuzz/harfbuzz/issues/4124
if (!accept (buffer, j - 1) &&
NOT_COVERED == (this+baseCoverage).get_coverage (buffer->info[j - 1].codepoint))
match = skippy_iter.SKIP;
}
if (match == skippy_iter.MATCH)
{
c->last_base = (signed) j - 1;
break;
}
}
c->last_base_until = buffer->idx;
if (c->last_base == -1)
{
buffer->unsafe_to_concat_from_outbuffer (0, buffer->idx + 1);
return_trace (false);
}
unsigned idx = (unsigned) c->last_base;
/* Checking that matched glyph is actually a base glyph by GDEF is too strong; disabled */
//if (!_hb_glyph_info_is_base_glyph (&buffer->info[idx])) { return_trace (false); }
unsigned int base_index = (this+baseCoverage).get_coverage (buffer->info[idx].codepoint);
if (base_index == NOT_COVERED)
{
buffer->unsafe_to_concat_from_outbuffer (idx, buffer->idx + 1);
return_trace (false);
}
return_trace ((this+markArray).apply (c, mark_index, base_index, this+baseArray, classCount, idx));
}
bool subset (hb_subset_context_t *c) const
{
TRACE_SUBSET (this);
const hb_set_t &glyphset = *c->plan->glyphset_gsub ();
const hb_map_t &glyph_map = *c->plan->glyph_map;
auto *out = c->serializer->start_embed (*this);
if (unlikely (!c->serializer->extend_min (out))) return_trace (false);
out->format = format;
hb_map_t klass_mapping;
Markclass_closure_and_remap_indexes (this+markCoverage, this+markArray, glyphset, &klass_mapping);
if (!klass_mapping.get_population ()) return_trace (false);
out->classCount = klass_mapping.get_population ();
auto mark_iter =
+ hb_zip (this+markCoverage, this+markArray)
| hb_filter (glyphset, hb_first)
;
hb_sorted_vector_t<hb_codepoint_t> new_coverage;
+ mark_iter
| hb_map (hb_first)
| hb_map (glyph_map)
| hb_sink (new_coverage)
;
if (!out->markCoverage.serialize_serialize (c->serializer, new_coverage.iter ()))
return_trace (false);
if (unlikely (!out->markArray.serialize_subset (c, markArray, this,
(this+markCoverage).iter (),
&klass_mapping)))
return_trace (false);
unsigned basecount = (this+baseArray).rows;
auto base_iter =
+ hb_zip (this+baseCoverage, hb_range (basecount))
| hb_filter (glyphset, hb_first)
;
new_coverage.reset ();
hb_sorted_vector_t<unsigned> base_indexes;
auto &base_array = (this+baseArray);
for (const auto _ : + base_iter)
{
unsigned row = _.second;
bool non_empty = + hb_range ((unsigned) classCount)
| hb_filter (klass_mapping)
| hb_map ([&] (const unsigned col) { return !base_array.offset_is_null (row, col, (unsigned) classCount); })
| hb_any
;
if (!non_empty) continue;
hb_codepoint_t new_g = glyph_map.get ( _.first);
new_coverage.push (new_g);
+ hb_range ((unsigned) classCount)
| hb_filter (klass_mapping)
| hb_map ([&] (const unsigned col) { return row * (unsigned) classCount + col; })
| hb_sink (base_indexes)
;
}
if (!new_coverage) return_trace (false);
if (!out->baseCoverage.serialize_serialize (c->serializer, new_coverage.iter ()))
return_trace (false);
return_trace (out->baseArray.serialize_subset (c, baseArray, this,
new_coverage.length,
base_indexes.iter ()));
}
};
}
}
}
#endif /* OT_LAYOUT_GPOS_MARKBASEPOSFORMAT1_HH */
+41
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@@ -0,0 +1,41 @@
#ifndef OT_LAYOUT_GPOS_MARKLIGPOS_HH
#define OT_LAYOUT_GPOS_MARKLIGPOS_HH
#include "MarkLigPosFormat1.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct MarkLigPos
{
protected:
union {
struct { HBUINT16 v; } format; /* Format identifier */
MarkLigPosFormat1_2<SmallTypes> format1;
#ifndef HB_NO_BEYOND_64K
MarkLigPosFormat1_2<MediumTypes> format2;
#endif
} u;
public:
template <typename context_t, typename ...Ts>
typename context_t::return_t dispatch (context_t *c, Ts&&... ds) const
{
if (unlikely (!c->may_dispatch (this, &u.format.v))) return c->no_dispatch_return_value ();
TRACE_DISPATCH (this, u.format.v);
switch (u.format.v) {
case 1: return_trace (c->dispatch (u.format1, std::forward<Ts> (ds)...));
#ifndef HB_NO_BEYOND_64K
case 2: return_trace (c->dispatch (u.format2, std::forward<Ts> (ds)...));
#endif
default:return_trace (c->default_return_value ());
}
}
};
}
}
}
#endif /* OT_LAYOUT_GPOS_MARKLIGPOS_HH */
@@ -0,0 +1,218 @@
#ifndef OT_LAYOUT_GPOS_MARKLIGPOSFORMAT1_HH
#define OT_LAYOUT_GPOS_MARKLIGPOSFORMAT1_HH
#include "LigatureArray.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
template <typename Types>
struct MarkLigPosFormat1_2
{
protected:
HBUINT16 format; /* Format identifier--format = 1 */
typename Types::template OffsetTo<Coverage>
markCoverage; /* Offset to Mark Coverage table--from
* beginning of MarkLigPos subtable */
typename Types::template OffsetTo<Coverage>
ligatureCoverage; /* Offset to Ligature Coverage
* table--from beginning of MarkLigPos
* subtable */
HBUINT16 classCount; /* Number of defined mark classes */
typename Types::template OffsetTo<MarkArray>
markArray; /* Offset to MarkArray table--from
* beginning of MarkLigPos subtable */
typename Types::template OffsetTo<LigatureArray>
ligatureArray; /* Offset to LigatureArray table--from
* beginning of MarkLigPos subtable */
public:
DEFINE_SIZE_STATIC (4 + 4 * Types::size);
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
return_trace (c->check_struct (this) &&
markCoverage.sanitize (c, this) &&
ligatureCoverage.sanitize (c, this) &&
markArray.sanitize (c, this) &&
ligatureArray.sanitize (c, this, (unsigned int) classCount));
}
bool intersects (const hb_set_t *glyphs) const
{
return (this+markCoverage).intersects (glyphs) &&
(this+ligatureCoverage).intersects (glyphs);
}
void closure_lookups (hb_closure_lookups_context_t *c) const {}
void collect_variation_indices (hb_collect_variation_indices_context_t *c) const
{
+ hb_zip (this+markCoverage, this+markArray)
| hb_filter (c->glyph_set, hb_first)
| hb_map (hb_second)
| hb_apply ([&] (const MarkRecord& record) { record.collect_variation_indices (c, &(this+markArray)); })
;
hb_map_t klass_mapping;
Markclass_closure_and_remap_indexes (this+markCoverage, this+markArray, *c->glyph_set, &klass_mapping);
unsigned ligcount = (this+ligatureArray).len;
auto lig_iter =
+ hb_zip (this+ligatureCoverage, hb_range (ligcount))
| hb_filter (c->glyph_set, hb_first)
| hb_map (hb_second)
;
const LigatureArray& lig_array = this+ligatureArray;
for (const unsigned i : lig_iter)
{
hb_sorted_vector_t<unsigned> lig_indexes;
unsigned row_count = lig_array[i].rows;
for (unsigned row : + hb_range (row_count))
{
+ hb_range ((unsigned) classCount)
| hb_filter (klass_mapping)
| hb_map ([&] (const unsigned col) { return row * (unsigned) classCount + col; })
| hb_sink (lig_indexes)
;
}
lig_array[i].collect_variation_indices (c, lig_indexes.iter ());
}
}
void collect_glyphs (hb_collect_glyphs_context_t *c) const
{
if (unlikely (!(this+markCoverage).collect_coverage (c->input))) return;
if (unlikely (!(this+ligatureCoverage).collect_coverage (c->input))) return;
}
const Coverage &get_coverage () const { return this+markCoverage; }
bool apply (hb_ot_apply_context_t *c) const
{
TRACE_APPLY (this);
hb_buffer_t *buffer = c->buffer;
unsigned int mark_index = (this+markCoverage).get_coverage (buffer->cur().codepoint);
if (likely (mark_index == NOT_COVERED)) return_trace (false);
/* Now we search backwards for a non-mark glyph */
auto &skippy_iter = c->iter_input;
skippy_iter.set_lookup_props (LookupFlag::IgnoreMarks);
if (c->last_base_until > buffer->idx)
{
c->last_base_until = 0;
c->last_base = -1;
}
unsigned j;
for (j = buffer->idx; j > c->last_base_until; j--)
{
auto match = skippy_iter.match (buffer->info[j - 1]);
if (match == skippy_iter.MATCH)
{
c->last_base = (signed) j - 1;
break;
}
}
c->last_base_until = buffer->idx;
if (c->last_base == -1)
{
buffer->unsafe_to_concat_from_outbuffer (0, buffer->idx + 1);
return_trace (false);
}
unsigned idx = (unsigned) c->last_base;
/* Checking that matched glyph is actually a ligature by GDEF is too strong; disabled */
//if (!_hb_glyph_info_is_ligature (&buffer->info[idx])) { return_trace (false); }
unsigned int lig_index = (this+ligatureCoverage).get_coverage (buffer->info[idx].codepoint);
if (lig_index == NOT_COVERED)
{
buffer->unsafe_to_concat_from_outbuffer (idx, buffer->idx + 1);
return_trace (false);
}
const LigatureArray& lig_array = this+ligatureArray;
const LigatureAttach& lig_attach = lig_array[lig_index];
/* Find component to attach to */
unsigned int comp_count = lig_attach.rows;
if (unlikely (!comp_count))
{
buffer->unsafe_to_concat_from_outbuffer (idx, buffer->idx + 1);
return_trace (false);
}
/* We must now check whether the ligature ID of the current mark glyph
* is identical to the ligature ID of the found ligature. If yes, we
* can directly use the component index. If not, we attach the mark
* glyph to the last component of the ligature. */
unsigned int comp_index;
unsigned int lig_id = _hb_glyph_info_get_lig_id (&buffer->info[idx]);
unsigned int mark_id = _hb_glyph_info_get_lig_id (&buffer->cur());
unsigned int mark_comp = _hb_glyph_info_get_lig_comp (&buffer->cur());
if (lig_id && lig_id == mark_id && mark_comp > 0)
comp_index = hb_min (comp_count, _hb_glyph_info_get_lig_comp (&buffer->cur())) - 1;
else
comp_index = comp_count - 1;
return_trace ((this+markArray).apply (c, mark_index, comp_index, lig_attach, classCount, idx));
}
bool subset (hb_subset_context_t *c) const
{
TRACE_SUBSET (this);
const hb_set_t &glyphset = *c->plan->glyphset_gsub ();
const hb_map_t &glyph_map = c->plan->glyph_map_gsub;
auto *out = c->serializer->start_embed (*this);
if (unlikely (!c->serializer->extend_min (out))) return_trace (false);
out->format = format;
hb_map_t klass_mapping;
Markclass_closure_and_remap_indexes (this+markCoverage, this+markArray, glyphset, &klass_mapping);
if (!klass_mapping.get_population ()) return_trace (false);
out->classCount = klass_mapping.get_population ();
auto mark_iter =
+ hb_zip (this+markCoverage, this+markArray)
| hb_filter (glyphset, hb_first)
;
auto new_mark_coverage =
+ mark_iter
| hb_map_retains_sorting (hb_first)
| hb_map_retains_sorting (glyph_map)
;
if (!out->markCoverage.serialize_serialize (c->serializer, new_mark_coverage))
return_trace (false);
if (unlikely (!out->markArray.serialize_subset (c, markArray, this,
(this+markCoverage).iter (),
&klass_mapping)))
return_trace (false);
hb_sorted_vector_t<hb_codepoint_t> new_lig_coverage;
if (!out->ligatureArray.serialize_subset (c, ligatureArray, this,
hb_iter (this+ligatureCoverage),
classCount, &klass_mapping, new_lig_coverage))
return_trace (false);
return_trace (out->ligatureCoverage.serialize_serialize (c->serializer, new_lig_coverage.iter ()));
}
};
}
}
}
#endif /* OT_LAYOUT_GPOS_MARKLIGPOSFORMAT1_HH */
+42
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@@ -0,0 +1,42 @@
#ifndef OT_LAYOUT_GPOS_MARKMARKPOS_HH
#define OT_LAYOUT_GPOS_MARKMARKPOS_HH
#include "MarkMarkPosFormat1.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct MarkMarkPos
{
protected:
union {
struct { HBUINT16 v; } format; /* Format identifier */
MarkMarkPosFormat1_2<SmallTypes> format1;
#ifndef HB_NO_BEYOND_64K
MarkMarkPosFormat1_2<MediumTypes> format2;
#endif
} u;
public:
template <typename context_t, typename ...Ts>
typename context_t::return_t dispatch (context_t *c, Ts&&... ds) const
{
if (unlikely (!c->may_dispatch (this, &u.format.v))) return c->no_dispatch_return_value ();
TRACE_DISPATCH (this, u.format.v);
switch (u.format.v) {
case 1: return_trace (c->dispatch (u.format1, std::forward<Ts> (ds)...));
#ifndef HB_NO_BEYOND_64K
case 2: return_trace (c->dispatch (u.format2, std::forward<Ts> (ds)...));
#endif
default:return_trace (c->default_return_value ());
}
}
};
}
}
}
#endif /* OT_LAYOUT_GPOS_MARKMARKPOS_HH */
@@ -0,0 +1,239 @@
#ifndef OT_LAYOUT_GPOS_MARKMARKPOSFORMAT1_HH
#define OT_LAYOUT_GPOS_MARKMARKPOSFORMAT1_HH
#include "MarkMarkPosFormat1.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
typedef AnchorMatrix Mark2Array; /* mark2-major--
* in order of Mark2Coverage Index--,
* mark1-minor--
* ordered by class--zero-based. */
template <typename Types>
struct MarkMarkPosFormat1_2
{
protected:
HBUINT16 format; /* Format identifier--format = 1 */
typename Types::template OffsetTo<Coverage>
mark1Coverage; /* Offset to Combining Mark1 Coverage
* table--from beginning of MarkMarkPos
* subtable */
typename Types::template OffsetTo<Coverage>
mark2Coverage; /* Offset to Combining Mark2 Coverage
* table--from beginning of MarkMarkPos
* subtable */
HBUINT16 classCount; /* Number of defined mark classes */
typename Types::template OffsetTo<MarkArray>
mark1Array; /* Offset to Mark1Array table--from
* beginning of MarkMarkPos subtable */
typename Types::template OffsetTo<Mark2Array>
mark2Array; /* Offset to Mark2Array table--from
* beginning of MarkMarkPos subtable */
public:
DEFINE_SIZE_STATIC (4 + 4 * Types::size);
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
return_trace (c->check_struct (this) &&
mark1Coverage.sanitize (c, this) &&
mark2Coverage.sanitize (c, this) &&
mark1Array.sanitize (c, this) &&
hb_barrier () &&
mark2Array.sanitize (c, this, (unsigned int) classCount));
}
bool intersects (const hb_set_t *glyphs) const
{
return (this+mark1Coverage).intersects (glyphs) &&
(this+mark2Coverage).intersects (glyphs);
}
void closure_lookups (hb_closure_lookups_context_t *c) const {}
void collect_variation_indices (hb_collect_variation_indices_context_t *c) const
{
+ hb_zip (this+mark1Coverage, this+mark1Array)
| hb_filter (c->glyph_set, hb_first)
| hb_map (hb_second)
| hb_apply ([&] (const MarkRecord& record) { record.collect_variation_indices (c, &(this+mark1Array)); })
;
hb_map_t klass_mapping;
Markclass_closure_and_remap_indexes (this+mark1Coverage, this+mark1Array, *c->glyph_set, &klass_mapping);
unsigned mark2_count = (this+mark2Array).rows;
auto mark2_iter =
+ hb_zip (this+mark2Coverage, hb_range (mark2_count))
| hb_filter (c->glyph_set, hb_first)
| hb_map (hb_second)
;
hb_sorted_vector_t<unsigned> mark2_indexes;
for (const unsigned row : mark2_iter)
{
+ hb_range ((unsigned) classCount)
| hb_filter (klass_mapping)
| hb_map ([&] (const unsigned col) { return row * (unsigned) classCount + col; })
| hb_sink (mark2_indexes)
;
}
(this+mark2Array).collect_variation_indices (c, mark2_indexes.iter ());
}
void collect_glyphs (hb_collect_glyphs_context_t *c) const
{
if (unlikely (!(this+mark1Coverage).collect_coverage (c->input))) return;
if (unlikely (!(this+mark2Coverage).collect_coverage (c->input))) return;
}
const Coverage &get_coverage () const { return this+mark1Coverage; }
bool apply (hb_ot_apply_context_t *c) const
{
TRACE_APPLY (this);
hb_buffer_t *buffer = c->buffer;
unsigned int mark1_index = (this+mark1Coverage).get_coverage (buffer->cur().codepoint);
if (likely (mark1_index == NOT_COVERED)) return_trace (false);
/* now we search backwards for a suitable mark glyph until a non-mark glyph */
auto &skippy_iter = c->iter_input;
skippy_iter.reset_fast (buffer->idx);
skippy_iter.set_lookup_props (c->lookup_props & ~(uint32_t)LookupFlag::IgnoreFlags);
unsigned unsafe_from;
if (unlikely (!skippy_iter.prev (&unsafe_from)))
{
buffer->unsafe_to_concat_from_outbuffer (unsafe_from, buffer->idx + 1);
return_trace (false);
}
if (likely (!_hb_glyph_info_is_mark (&buffer->info[skippy_iter.idx])))
{
buffer->unsafe_to_concat_from_outbuffer (skippy_iter.idx, buffer->idx + 1);
return_trace (false);
}
unsigned int j = skippy_iter.idx;
unsigned int id1 = _hb_glyph_info_get_lig_id (&buffer->cur());
unsigned int id2 = _hb_glyph_info_get_lig_id (&buffer->info[j]);
unsigned int comp1 = _hb_glyph_info_get_lig_comp (&buffer->cur());
unsigned int comp2 = _hb_glyph_info_get_lig_comp (&buffer->info[j]);
if (likely (id1 == id2))
{
if (id1 == 0) /* Marks belonging to the same base. */
goto good;
else if (comp1 == comp2) /* Marks belonging to the same ligature component. */
goto good;
}
else
{
/* If ligature ids don't match, it may be the case that one of the marks
* itself is a ligature. In which case match. */
if ((id1 > 0 && !comp1) || (id2 > 0 && !comp2))
goto good;
}
/* Didn't match. */
buffer->unsafe_to_concat_from_outbuffer (skippy_iter.idx, buffer->idx + 1);
return_trace (false);
good:
unsigned int mark2_index = (this+mark2Coverage).get_coverage (buffer->info[j].codepoint);
if (mark2_index == NOT_COVERED)
{
buffer->unsafe_to_concat_from_outbuffer (skippy_iter.idx, buffer->idx + 1);
return_trace (false);
}
return_trace ((this+mark1Array).apply (c, mark1_index, mark2_index, this+mark2Array, classCount, j));
}
bool subset (hb_subset_context_t *c) const
{
TRACE_SUBSET (this);
const hb_set_t &glyphset = *c->plan->glyphset_gsub ();
const hb_map_t &glyph_map = *c->plan->glyph_map;
auto *out = c->serializer->start_embed (*this);
if (unlikely (!c->serializer->extend_min (out))) return_trace (false);
out->format = format;
hb_map_t klass_mapping;
Markclass_closure_and_remap_indexes (this+mark1Coverage, this+mark1Array, glyphset, &klass_mapping);
if (!klass_mapping.get_population ()) return_trace (false);
out->classCount = klass_mapping.get_population ();
auto mark1_iter =
+ hb_zip (this+mark1Coverage, this+mark1Array)
| hb_filter (glyphset, hb_first)
;
hb_sorted_vector_t<hb_codepoint_t> new_coverage;
+ mark1_iter
| hb_map (hb_first)
| hb_map (glyph_map)
| hb_sink (new_coverage)
;
if (!out->mark1Coverage.serialize_serialize (c->serializer, new_coverage.iter ()))
return_trace (false);
if (unlikely (!out->mark1Array.serialize_subset (c, mark1Array, this,
(this+mark1Coverage).iter (),
&klass_mapping)))
return_trace (false);
unsigned mark2count = (this+mark2Array).rows;
auto mark2_iter =
+ hb_zip (this+mark2Coverage, hb_range (mark2count))
| hb_filter (glyphset, hb_first)
;
new_coverage.reset ();
hb_sorted_vector_t<unsigned> mark2_indexes;
auto &mark2_array = (this+mark2Array);
for (const auto _ : + mark2_iter)
{
unsigned row = _.second;
bool non_empty = + hb_range ((unsigned) classCount)
| hb_filter (klass_mapping)
| hb_map ([&] (const unsigned col) { return !mark2_array.offset_is_null (row, col, (unsigned) classCount); })
| hb_any
;
if (!non_empty) continue;
hb_codepoint_t new_g = glyph_map.get ( _.first);
new_coverage.push (new_g);
+ hb_range ((unsigned) classCount)
| hb_filter (klass_mapping)
| hb_map ([&] (const unsigned col) { return row * (unsigned) classCount + col; })
| hb_sink (mark2_indexes)
;
}
if (!new_coverage) return_trace (false);
if (!out->mark2Coverage.serialize_serialize (c->serializer, new_coverage.iter ()))
return_trace (false);
return_trace (out->mark2Array.serialize_subset (c, mark2Array, this,
mark2_iter.len (),
mark2_indexes.iter ()));
}
};
}
}
}
#endif /* OT_LAYOUT_GPOS_MARKMARKPOSFORMAT1_HH */
+51
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@@ -0,0 +1,51 @@
#ifndef OT_LAYOUT_GPOS_MARKRECORD_HH
#define OT_LAYOUT_GPOS_MARKRECORD_HH
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct MarkRecord
{
friend struct MarkArray;
public:
HBUINT16 klass; /* Class defined for this mark */
Offset16To<Anchor>
markAnchor; /* Offset to Anchor table--from
* beginning of MarkArray table */
public:
DEFINE_SIZE_STATIC (4);
unsigned get_class () const { return (unsigned) klass; }
bool sanitize (hb_sanitize_context_t *c, const void *base) const
{
TRACE_SANITIZE (this);
return_trace (c->check_struct (this) && markAnchor.sanitize (c, base));
}
bool subset (hb_subset_context_t *c,
const void *src_base,
const hb_map_t *klass_mapping) const
{
TRACE_SUBSET (this);
auto *out = c->serializer->embed (this);
if (unlikely (!out)) return_trace (false);
out->klass = klass_mapping->get (klass);
return_trace (out->markAnchor.serialize_subset (c, markAnchor, src_base));
}
void collect_variation_indices (hb_collect_variation_indices_context_t *c,
const void *src_base) const
{
(src_base+markAnchor).collect_variation_indices (c);
}
};
}
}
}
#endif /* OT_LAYOUT_GPOS_MARKRECORD_HH */
+46
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@@ -0,0 +1,46 @@
#ifndef OT_LAYOUT_GPOS_PAIRPOS_HH
#define OT_LAYOUT_GPOS_PAIRPOS_HH
#include "PairPosFormat1.hh"
#include "PairPosFormat2.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct PairPos
{
protected:
union {
struct { HBUINT16 v; } format; /* Format identifier */
PairPosFormat1_3<SmallTypes> format1;
PairPosFormat2_4<SmallTypes> format2;
#ifndef HB_NO_BEYOND_64K
PairPosFormat1_3<MediumTypes> format3;
PairPosFormat2_4<MediumTypes> format4;
#endif
} u;
public:
template <typename context_t, typename ...Ts>
typename context_t::return_t dispatch (context_t *c, Ts&&... ds) const
{
if (unlikely (!c->may_dispatch (this, &u.format.v))) return c->no_dispatch_return_value ();
TRACE_DISPATCH (this, u.format.v);
switch (u.format.v) {
case 1: return_trace (c->dispatch (u.format1, std::forward<Ts> (ds)...));
case 2: return_trace (c->dispatch (u.format2, std::forward<Ts> (ds)...));
#ifndef HB_NO_BEYOND_64K
case 3: return_trace (c->dispatch (u.format3, std::forward<Ts> (ds)...));
case 4: return_trace (c->dispatch (u.format4, std::forward<Ts> (ds)...));
#endif
default:return_trace (c->default_return_value ());
}
}
};
}
}
}
#endif // OT_LAYOUT_GPOS_PAIRPOS_HH
+254
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@@ -0,0 +1,254 @@
#ifndef OT_LAYOUT_GPOS_PAIRPOSFORMAT1_HH
#define OT_LAYOUT_GPOS_PAIRPOSFORMAT1_HH
#include "PairSet.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
template <typename Types>
struct PairPosFormat1_3
{
using PairSet = GPOS_impl::PairSet<Types>;
using PairValueRecord = GPOS_impl::PairValueRecord<Types>;
protected:
HBUINT16 format; /* Format identifier--format = 1 */
typename Types::template OffsetTo<Coverage>
coverage; /* Offset to Coverage table--from
* beginning of subtable */
ValueFormat valueFormat[2]; /* [0] Defines the types of data in
* ValueRecord1--for the first glyph
* in the pair--may be zero (0) */
/* [1] Defines the types of data in
* ValueRecord2--for the second glyph
* in the pair--may be zero (0) */
Array16Of<typename Types::template OffsetTo<PairSet>>
pairSet; /* Array of PairSet tables
* ordered by Coverage Index */
public:
DEFINE_SIZE_ARRAY (8 + Types::size, pairSet);
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
if (!c->check_struct (this)) return_trace (false);
hb_barrier ();
unsigned int len1 = valueFormat[0].get_len ();
unsigned int len2 = valueFormat[1].get_len ();
typename PairSet::sanitize_closure_t closure =
{
valueFormat,
len1,
PairSet::get_size (len1, len2)
};
return_trace (coverage.sanitize (c, this) && pairSet.sanitize (c, this, &closure));
}
bool intersects (const hb_set_t *glyphs) const
{
auto &cov = this+coverage;
if (pairSet.len > glyphs->get_population () * hb_bit_storage ((unsigned) pairSet.len))
{
for (hb_codepoint_t g : glyphs->iter())
{
unsigned i = cov.get_coverage (g);
if ((this+pairSet[i]).intersects (glyphs, valueFormat))
return true;
}
return false;
}
return
+ hb_zip (cov, pairSet)
| hb_filter (*glyphs, hb_first)
| hb_map (hb_second)
| hb_map ([glyphs, this] (const typename Types::template OffsetTo<PairSet> &_)
{ return (this+_).intersects (glyphs, valueFormat); })
| hb_any
;
}
void closure_lookups (hb_closure_lookups_context_t *c) const {}
void collect_variation_indices (hb_collect_variation_indices_context_t *c) const
{
if ((!valueFormat[0].has_device ()) && (!valueFormat[1].has_device ())) return;
auto it =
+ hb_zip (this+coverage, pairSet)
| hb_filter (c->glyph_set, hb_first)
| hb_map (hb_second)
;
if (!it) return;
+ it
| hb_map (hb_add (this))
| hb_apply ([&] (const PairSet& _) { _.collect_variation_indices (c, valueFormat); })
;
}
void collect_glyphs (hb_collect_glyphs_context_t *c) const
{
if (unlikely (!(this+coverage).collect_coverage (c->input))) return;
unsigned int count = pairSet.len;
for (unsigned int i = 0; i < count; i++)
(this+pairSet[i]).collect_glyphs (c, valueFormat);
}
const Coverage &get_coverage () const { return this+coverage; }
struct external_cache_t
{
hb_ot_layout_mapping_cache_t coverage;
};
void *external_cache_create () const
{
external_cache_t *cache = (external_cache_t *) hb_malloc (sizeof (external_cache_t));
if (likely (cache))
{
cache->coverage.clear ();
}
return cache;
}
bool apply (hb_ot_apply_context_t *c, void *external_cache) const
{
TRACE_APPLY (this);
hb_buffer_t *buffer = c->buffer;
#ifndef HB_NO_OT_LAYOUT_LOOKUP_CACHE
external_cache_t *cache = (external_cache_t *) external_cache;
unsigned int index = (this+coverage).get_coverage (buffer->cur().codepoint, cache ? &cache->coverage : nullptr);
#else
unsigned int index = (this+coverage).get_coverage (buffer->cur().codepoint);
#endif
if (index == NOT_COVERED) return_trace (false);
auto &skippy_iter = c->iter_input;
skippy_iter.reset_fast (buffer->idx);
unsigned unsafe_to;
if (unlikely (!skippy_iter.next (&unsafe_to)))
{
buffer->unsafe_to_concat (buffer->idx, unsafe_to);
return_trace (false);
}
return_trace ((this+pairSet[index]).apply (c, valueFormat, skippy_iter.idx));
}
bool subset (hb_subset_context_t *c) const
{
TRACE_SUBSET (this);
const hb_set_t &glyphset = *c->plan->glyphset_gsub ();
const hb_map_t &glyph_map = *c->plan->glyph_map;
auto *out = c->serializer->start_embed (*this);
if (unlikely (!c->serializer->extend_min (out))) return_trace (false);
out->format = format;
hb_pair_t<unsigned, unsigned> newFormats = hb_pair (valueFormat[0], valueFormat[1]);
if (c->plan->normalized_coords)
{
/* all device flags will be dropped when full instancing, no need to strip
* hints, also do not strip emtpy cause we don't compute the new default
* value during stripping */
newFormats = compute_effective_value_formats (glyphset, false, false, &c->plan->layout_variation_idx_delta_map);
}
/* do not strip hints for VF */
else if (c->plan->flags & HB_SUBSET_FLAGS_NO_HINTING)
{
hb_blob_t* blob = hb_face_reference_table (c->plan->source, HB_TAG ('f','v','a','r'));
bool has_fvar = (blob != hb_blob_get_empty ());
hb_blob_destroy (blob);
bool strip = !has_fvar;
/* special case: strip hints when a VF has no GDEF varstore after
* subsetting*/
if (has_fvar && !c->plan->has_gdef_varstore)
strip = true;
newFormats = compute_effective_value_formats (glyphset, strip, true);
}
out->valueFormat[0] = newFormats.first;
out->valueFormat[1] = newFormats.second;
hb_sorted_vector_t<hb_codepoint_t> new_coverage;
+ hb_zip (this+coverage, pairSet)
| hb_filter (glyphset, hb_first)
| hb_filter ([this, c, out] (const typename Types::template OffsetTo<PairSet>& _)
{
auto snap = c->serializer->snapshot ();
auto *o = out->pairSet.serialize_append (c->serializer);
if (unlikely (!o)) return false;
bool ret = o->serialize_subset (c, _, this, valueFormat, out->valueFormat);
if (!ret)
{
out->pairSet.pop ();
c->serializer->revert (snap);
}
return ret;
},
hb_second)
| hb_map (hb_first)
| hb_map (glyph_map)
| hb_sink (new_coverage)
;
out->coverage.serialize_serialize (c->serializer, new_coverage.iter ());
return_trace (bool (new_coverage));
}
hb_pair_t<unsigned, unsigned> compute_effective_value_formats (const hb_set_t& glyphset,
bool strip_hints, bool strip_empty,
const hb_hashmap_t<unsigned, hb_pair_t<unsigned, int>> *varidx_delta_map = nullptr) const
{
unsigned record_size = PairSet::get_size (valueFormat);
unsigned format1 = 0;
unsigned format2 = 0;
for (const auto & _ :
+ hb_zip (this+coverage, pairSet)
| hb_filter (glyphset, hb_first)
| hb_map (hb_second)
)
{
const PairSet& set = (this + _);
const PairValueRecord *record = &set.firstPairValueRecord;
unsigned count = set.len;
for (unsigned i = 0; i < count; i++)
{
if (record->intersects (glyphset))
{
format1 = format1 | valueFormat[0].get_effective_format (record->get_values_1 (), strip_hints, strip_empty, &set, varidx_delta_map);
format2 = format2 | valueFormat[1].get_effective_format (record->get_values_2 (valueFormat[0]), strip_hints, strip_empty, &set, varidx_delta_map);
}
record = &StructAtOffset<const PairValueRecord> (record, record_size);
}
if (format1 == valueFormat[0] && format2 == valueFormat[1])
break;
}
return hb_pair (format1, format2);
}
};
}
}
}
#endif // OT_LAYOUT_GPOS_PAIRPOSFORMAT1_HH
+389
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@@ -0,0 +1,389 @@
#ifndef OT_LAYOUT_GPOS_PAIRPOSFORMAT2_HH
#define OT_LAYOUT_GPOS_PAIRPOSFORMAT2_HH
#include "ValueFormat.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
template <typename Types>
struct PairPosFormat2_4 : ValueBase
{
protected:
HBUINT16 format; /* Format identifier--format = 2 */
typename Types::template OffsetTo<Coverage>
coverage; /* Offset to Coverage table--from
* beginning of subtable */
ValueFormat valueFormat1; /* ValueRecord definition--for the
* first glyph of the pair--may be zero
* (0) */
ValueFormat valueFormat2; /* ValueRecord definition--for the
* second glyph of the pair--may be
* zero (0) */
typename Types::template OffsetTo<ClassDef>
classDef1; /* Offset to ClassDef table--from
* beginning of PairPos subtable--for
* the first glyph of the pair */
typename Types::template OffsetTo<ClassDef>
classDef2; /* Offset to ClassDef table--from
* beginning of PairPos subtable--for
* the second glyph of the pair */
HBUINT16 class1Count; /* Number of classes in ClassDef1
* table--includes Class0 */
HBUINT16 class2Count; /* Number of classes in ClassDef2
* table--includes Class0 */
ValueRecord values; /* Matrix of value pairs:
* class1-major, class2-minor,
* Each entry has value1 and value2 */
public:
DEFINE_SIZE_ARRAY (10 + 3 * Types::size, values);
bool sanitize (hb_sanitize_context_t *c) const
{
TRACE_SANITIZE (this);
if (!(c->check_struct (this)
&& coverage.sanitize (c, this)
&& classDef1.sanitize (c, this)
&& classDef2.sanitize (c, this))) return_trace (false);
unsigned int len1 = valueFormat1.get_len ();
unsigned int len2 = valueFormat2.get_len ();
unsigned int stride = HBUINT16::static_size * (len1 + len2);
unsigned int count = (unsigned int) class1Count * (unsigned int) class2Count;
return_trace (c->check_range ((const void *) values,
count,
stride) &&
(c->lazy_some_gpos ||
(valueFormat1.sanitize_values_stride_unsafe (c, this, &values[0], count, stride) &&
valueFormat2.sanitize_values_stride_unsafe (c, this, &values[len1], count, stride))));
}
bool intersects (const hb_set_t *glyphs) const
{
return (this+coverage).intersects (glyphs) &&
(this+classDef2).intersects (glyphs);
}
void closure_lookups (hb_closure_lookups_context_t *c) const {}
void collect_variation_indices (hb_collect_variation_indices_context_t *c) const
{
if (!intersects (c->glyph_set)) return;
if ((!valueFormat1.has_device ()) && (!valueFormat2.has_device ())) return;
hb_set_t klass1_glyphs, klass2_glyphs;
if (!(this+classDef1).collect_coverage (&klass1_glyphs)) return;
if (!(this+classDef2).collect_coverage (&klass2_glyphs)) return;
hb_set_t class1_set, class2_set;
for (const unsigned cp : + c->glyph_set->iter () | hb_filter (this + coverage))
{
if (!klass1_glyphs.has (cp)) class1_set.add (0);
else
{
unsigned klass1 = (this+classDef1).get (cp);
class1_set.add (klass1);
}
}
class2_set.add (0);
for (const unsigned cp : + c->glyph_set->iter () | hb_filter (klass2_glyphs))
{
unsigned klass2 = (this+classDef2).get (cp);
class2_set.add (klass2);
}
if (class1_set.is_empty ()
|| class2_set.is_empty ()
|| (class2_set.get_population() == 1 && class2_set.has(0)))
return;
unsigned len1 = valueFormat1.get_len ();
unsigned len2 = valueFormat2.get_len ();
const hb_array_t<const Value> values_array = values.as_array ((unsigned)class1Count * (unsigned) class2Count * (len1 + len2));
for (const unsigned class1_idx : class1_set.iter ())
{
for (const unsigned class2_idx : class2_set.iter ())
{
unsigned start_offset = (class1_idx * (unsigned) class2Count + class2_idx) * (len1 + len2);
if (valueFormat1.has_device ())
valueFormat1.collect_variation_indices (c, this, values_array.sub_array (start_offset, len1));
if (valueFormat2.has_device ())
valueFormat2.collect_variation_indices (c, this, values_array.sub_array (start_offset+len1, len2));
}
}
}
void collect_glyphs (hb_collect_glyphs_context_t *c) const
{
if (unlikely (!(this+coverage).collect_coverage (c->input))) return;
if (unlikely (!(this+classDef2).collect_coverage (c->input))) return;
}
const Coverage &get_coverage () const { return this+coverage; }
struct external_cache_t
{
hb_ot_layout_mapping_cache_t coverage;
hb_ot_layout_mapping_cache_t first;
hb_ot_layout_mapping_cache_t second;
};
void *external_cache_create () const
{
external_cache_t *cache = (external_cache_t *) hb_malloc (sizeof (external_cache_t));
if (likely (cache))
{
cache->coverage.clear ();
cache->first.clear ();
cache->second.clear ();
}
return cache;
}
bool apply (hb_ot_apply_context_t *c, void *external_cache) const
{
TRACE_APPLY (this);
hb_buffer_t *buffer = c->buffer;
#ifndef HB_NO_OT_LAYOUT_LOOKUP_CACHE
external_cache_t *cache = (external_cache_t *) external_cache;
unsigned int index = (this+coverage).get_coverage (buffer->cur().codepoint, cache ? &cache->coverage : nullptr);
#else
unsigned int index = (this+coverage).get_coverage (buffer->cur().codepoint);
#endif
if (index == NOT_COVERED) return_trace (false);
auto &skippy_iter = c->iter_input;
skippy_iter.reset_fast (buffer->idx);
unsigned unsafe_to;
if (unlikely (!skippy_iter.next (&unsafe_to)))
{
buffer->unsafe_to_concat (buffer->idx, unsafe_to);
return_trace (false);
}
#ifndef HB_NO_OT_LAYOUT_LOOKUP_CACHE
unsigned int klass1 = (this+classDef1).get_class (buffer->cur().codepoint, cache ? &cache->first : nullptr);
unsigned int klass2 = (this+classDef2).get_class (buffer->info[skippy_iter.idx].codepoint, cache ? &cache->second : nullptr);
#else
unsigned int klass1 = (this+classDef1).get_class (buffer->cur().codepoint);
unsigned int klass2 = (this+classDef2).get_class (buffer->info[skippy_iter.idx].codepoint);
#endif
if (unlikely (klass1 >= class1Count || klass2 >= class2Count))
{
buffer->unsafe_to_concat (buffer->idx, skippy_iter.idx + 1);
return_trace (false);
}
unsigned int len1 = valueFormat1.get_len ();
unsigned int len2 = valueFormat2.get_len ();
unsigned int record_len = len1 + len2;
const Value *v = &values[record_len * (klass1 * class2Count + klass2)];
bool applied_first = false, applied_second = false;
/* Isolate simple kerning and apply it half to each side.
* Results in better cursor positioning / underline drawing.
*
* Disabled, because causes issues... :-(
* https://github.com/harfbuzz/harfbuzz/issues/3408
* https://github.com/harfbuzz/harfbuzz/pull/3235#issuecomment-1029814978
*/
#ifndef HB_SPLIT_KERN
if (false)
#endif
{
if (!len2)
{
const hb_direction_t dir = buffer->props.direction;
const bool horizontal = HB_DIRECTION_IS_HORIZONTAL (dir);
const bool backward = HB_DIRECTION_IS_BACKWARD (dir);
unsigned mask = horizontal ? ValueFormat::xAdvance : ValueFormat::yAdvance;
if (backward)
mask |= mask >> 2; /* Add eg. xPlacement in RTL. */
/* Add Devices. */
mask |= mask << 4;
if (valueFormat1 & ~mask)
goto bail;
/* Is simple kern. Apply value on an empty position slot,
* then split it between sides. */
hb_glyph_position_t pos{};
if (valueFormat1.apply_value (c, this, v, pos))
{
hb_position_t *src = &pos.x_advance;
hb_position_t *dst1 = &buffer->cur_pos().x_advance;
hb_position_t *dst2 = &buffer->pos[skippy_iter.idx].x_advance;
unsigned i = horizontal ? 0 : 1;
hb_position_t kern = src[i];
hb_position_t kern1 = kern >> 1;
hb_position_t kern2 = kern - kern1;
if (!backward)
{
dst1[i] += kern1;
dst2[i] += kern2;
dst2[i + 2] += kern2;
}
else
{
dst1[i] += kern1;
dst1[i + 2] += src[i + 2] - kern2;
dst2[i] += kern2;
}
applied_first = applied_second = kern != 0;
goto success;
}
goto boring;
}
}
bail:
if (HB_BUFFER_MESSAGE_MORE && c->buffer->messaging ())
{
c->buffer->message (c->font,
"try kerning glyphs at %u,%u",
c->buffer->idx, skippy_iter.idx);
}
applied_first = len1 && valueFormat1.apply_value (c, this, v, buffer->cur_pos());
applied_second = len2 && valueFormat2.apply_value (c, this, v + len1, buffer->pos[skippy_iter.idx]);
if (applied_first || applied_second)
if (HB_BUFFER_MESSAGE_MORE && c->buffer->messaging ())
{
c->buffer->message (c->font,
"kerned glyphs at %u,%u",
c->buffer->idx, skippy_iter.idx);
}
if (HB_BUFFER_MESSAGE_MORE && c->buffer->messaging ())
{
c->buffer->message (c->font,
"tried kerning glyphs at %u,%u",
c->buffer->idx, skippy_iter.idx);
}
success:
if (applied_first || applied_second)
buffer->unsafe_to_break (buffer->idx, skippy_iter.idx + 1);
else
boring:
buffer->unsafe_to_concat (buffer->idx, skippy_iter.idx + 1);
if (len2)
{
skippy_iter.idx++;
// https://github.com/harfbuzz/harfbuzz/issues/3824
// https://github.com/harfbuzz/harfbuzz/issues/3888#issuecomment-1326781116
buffer->unsafe_to_break (buffer->idx, skippy_iter.idx + 1);
}
buffer->idx = skippy_iter.idx;
return_trace (true);
}
bool subset (hb_subset_context_t *c) const
{
TRACE_SUBSET (this);
auto *out = c->serializer->start_embed (*this);
if (unlikely (!c->serializer->extend_min (out))) return_trace (false);
out->format = format;
hb_map_t klass1_map;
out->classDef1.serialize_subset (c, classDef1, this, &klass1_map, true, true, &(this + coverage));
out->class1Count = klass1_map.get_population ();
hb_map_t klass2_map;
out->classDef2.serialize_subset (c, classDef2, this, &klass2_map, true, false);
out->class2Count = klass2_map.get_population ();
unsigned len1 = valueFormat1.get_len ();
unsigned len2 = valueFormat2.get_len ();
hb_pair_t<unsigned, unsigned> newFormats = hb_pair (valueFormat1, valueFormat2);
if (c->plan->normalized_coords)
{
/* in case of full instancing, all var device flags will be dropped so no
* need to strip hints here */
newFormats = compute_effective_value_formats (klass1_map, klass2_map, false, false, &c->plan->layout_variation_idx_delta_map);
}
/* do not strip hints for VF */
else if (c->plan->flags & HB_SUBSET_FLAGS_NO_HINTING)
{
hb_blob_t* blob = hb_face_reference_table (c->plan->source, HB_TAG ('f','v','a','r'));
bool has_fvar = (blob != hb_blob_get_empty ());
hb_blob_destroy (blob);
bool strip = !has_fvar;
/* special case: strip hints when a VF has no GDEF varstore after
* subsetting*/
if (has_fvar && !c->plan->has_gdef_varstore)
strip = true;
newFormats = compute_effective_value_formats (klass1_map, klass2_map, strip, true);
}
out->valueFormat1 = newFormats.first;
out->valueFormat2 = newFormats.second;
unsigned total_len = len1 + len2;
hb_vector_t<unsigned> class2_idxs (+ hb_range ((unsigned) class2Count) | hb_filter (klass2_map));
for (unsigned class1_idx : + hb_range ((unsigned) class1Count) | hb_filter (klass1_map))
{
for (unsigned class2_idx : class2_idxs)
{
unsigned idx = (class1_idx * (unsigned) class2Count + class2_idx) * total_len;
valueFormat1.copy_values (c->serializer, out->valueFormat1, this, &values[idx], &c->plan->layout_variation_idx_delta_map);
valueFormat2.copy_values (c->serializer, out->valueFormat2, this, &values[idx + len1], &c->plan->layout_variation_idx_delta_map);
}
}
bool ret = out->coverage.serialize_subset(c, coverage, this);
return_trace (out->class1Count && out->class2Count && ret);
}
hb_pair_t<unsigned, unsigned> compute_effective_value_formats (const hb_map_t& klass1_map,
const hb_map_t& klass2_map,
bool strip_hints, bool strip_empty,
const hb_hashmap_t<unsigned, hb_pair_t<unsigned, int>> *varidx_delta_map = nullptr) const
{
unsigned len1 = valueFormat1.get_len ();
unsigned len2 = valueFormat2.get_len ();
unsigned record_size = len1 + len2;
unsigned format1 = 0;
unsigned format2 = 0;
for (unsigned class1_idx : + hb_range ((unsigned) class1Count) | hb_filter (klass1_map))
{
for (unsigned class2_idx : + hb_range ((unsigned) class2Count) | hb_filter (klass2_map))
{
unsigned idx = (class1_idx * (unsigned) class2Count + class2_idx) * record_size;
format1 = format1 | valueFormat1.get_effective_format (&values[idx], strip_hints, strip_empty, this, varidx_delta_map);
format2 = format2 | valueFormat2.get_effective_format (&values[idx + len1], strip_hints, strip_empty, this, varidx_delta_map);
}
if (format1 == valueFormat1 && format2 == valueFormat2)
break;
}
return hb_pair (format1, format2);
}
};
}
}
}
#endif // OT_LAYOUT_GPOS_PAIRPOSFORMAT2_HH
+210
View File
@@ -0,0 +1,210 @@
#ifndef OT_LAYOUT_GPOS_PAIRSET_HH
#define OT_LAYOUT_GPOS_PAIRSET_HH
#include "PairValueRecord.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
template <typename Types>
struct PairSet : ValueBase
{
template <typename Types2>
friend struct PairPosFormat1_3;
using PairValueRecord = GPOS_impl::PairValueRecord<Types>;
protected:
HBUINT16 len; /* Number of PairValueRecords */
PairValueRecord firstPairValueRecord;
/* Array of PairValueRecords--ordered
* by GlyphID of the second glyph */
public:
DEFINE_SIZE_MIN (2);
static size_t get_size (unsigned len1, unsigned len2)
{
return Types::HBGlyphID::static_size + Value::static_size * (len1 + len2);
}
static size_t get_size (const ValueFormat valueFormats[2])
{
unsigned len1 = valueFormats[0].get_len ();
unsigned len2 = valueFormats[1].get_len ();
return get_size (len1, len2);
}
struct sanitize_closure_t
{
const ValueFormat *valueFormats;
unsigned int len1; /* valueFormats[0].get_len() */
size_t stride; /* bytes */
};
bool sanitize (hb_sanitize_context_t *c, const sanitize_closure_t *closure) const
{
TRACE_SANITIZE (this);
if (!(c->check_struct (this) &&
hb_barrier () &&
c->check_range (&firstPairValueRecord,
len,
closure->stride))) return_trace (false);
hb_barrier ();
unsigned int count = len;
const PairValueRecord *record = &firstPairValueRecord;
return_trace (c->lazy_some_gpos ||
(closure->valueFormats[0].sanitize_values_stride_unsafe (c, this, &record->values[0], count, closure->stride) &&
closure->valueFormats[1].sanitize_values_stride_unsafe (c, this, &record->values[closure->len1], count, closure->stride)));
}
bool intersects (const hb_set_t *glyphs,
const ValueFormat *valueFormats) const
{
unsigned record_size = get_size (valueFormats);
const PairValueRecord *record = &firstPairValueRecord;
unsigned int count = len;
for (unsigned int i = 0; i < count; i++)
{
if (glyphs->has (record->secondGlyph))
return true;
record = &StructAtOffset<const PairValueRecord> (record, record_size);
}
return false;
}
void collect_glyphs (hb_collect_glyphs_context_t *c,
const ValueFormat *valueFormats) const
{
unsigned record_size = get_size (valueFormats);
const PairValueRecord *record = &firstPairValueRecord;
c->input->add_array (&record->secondGlyph, len, record_size);
}
void collect_variation_indices (hb_collect_variation_indices_context_t *c,
const ValueFormat *valueFormats) const
{
unsigned record_size = get_size (valueFormats);
const PairValueRecord *record = &firstPairValueRecord;
unsigned count = len;
for (unsigned i = 0; i < count; i++)
{
if (c->glyph_set->has (record->secondGlyph))
{ record->collect_variation_indices (c, valueFormats, this); }
record = &StructAtOffset<const PairValueRecord> (record, record_size);
}
}
bool apply (hb_ot_apply_context_t *c,
const ValueFormat *valueFormats,
unsigned int pos) const
{
TRACE_APPLY (this);
hb_buffer_t *buffer = c->buffer;
unsigned int len1 = valueFormats[0].get_len ();
unsigned int len2 = valueFormats[1].get_len ();
unsigned record_size = get_size (len1, len2);
const PairValueRecord *record = hb_bsearch (buffer->info[pos].codepoint,
&firstPairValueRecord,
len,
record_size);
if (record)
{
if (HB_BUFFER_MESSAGE_MORE && c->buffer->messaging ())
{
c->buffer->message (c->font,
"try kerning glyphs at %u,%u",
c->buffer->idx, pos);
}
bool applied_first = len1 && valueFormats[0].apply_value (c, this, &record->values[0], buffer->cur_pos());
bool applied_second = len2 && valueFormats[1].apply_value (c, this, &record->values[len1], buffer->pos[pos]);
if (applied_first || applied_second)
if (HB_BUFFER_MESSAGE_MORE && c->buffer->messaging ())
{
c->buffer->message (c->font,
"kerned glyphs at %u,%u",
c->buffer->idx, pos);
}
if (HB_BUFFER_MESSAGE_MORE && c->buffer->messaging ())
{
c->buffer->message (c->font,
"tried kerning glyphs at %u,%u",
c->buffer->idx, pos);
}
if (applied_first || applied_second)
buffer->unsafe_to_break (buffer->idx, pos + 1);
if (len2)
{
pos++;
// https://github.com/harfbuzz/harfbuzz/issues/3824
// https://github.com/harfbuzz/harfbuzz/issues/3888#issuecomment-1326781116
buffer->unsafe_to_break (buffer->idx, pos + 1);
}
buffer->idx = pos;
return_trace (true);
}
buffer->unsafe_to_concat (buffer->idx, pos + 1);
return_trace (false);
}
bool subset (hb_subset_context_t *c,
const ValueFormat valueFormats[2],
const ValueFormat newFormats[2]) const
{
TRACE_SUBSET (this);
auto snap = c->serializer->snapshot ();
auto *out = c->serializer->start_embed (*this);
if (unlikely (!c->serializer->extend_min (out))) return_trace (false);
out->len = 0;
const hb_set_t &glyphset = *c->plan->glyphset_gsub ();
const hb_map_t &glyph_map = *c->plan->glyph_map;
unsigned len1 = valueFormats[0].get_len ();
unsigned len2 = valueFormats[1].get_len ();
unsigned record_size = get_size (len1, len2);
typename PairValueRecord::context_t context =
{
this,
valueFormats,
newFormats,
len1,
&glyph_map,
&c->plan->layout_variation_idx_delta_map
};
const PairValueRecord *record = &firstPairValueRecord;
unsigned count = len, num = 0;
for (unsigned i = 0; i < count; i++)
{
if (glyphset.has (record->secondGlyph)
&& record->subset (c, &context)) num++;
record = &StructAtOffset<const PairValueRecord> (record, record_size);
}
out->len = num;
if (!num) c->serializer->revert (snap);
return_trace (num);
}
};
}
}
}
#endif // OT_LAYOUT_GPOS_PAIRSET_HH
+99
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@@ -0,0 +1,99 @@
#ifndef OT_LAYOUT_GPOS_PAIRVALUERECORD_HH
#define OT_LAYOUT_GPOS_PAIRVALUERECORD_HH
#include "ValueFormat.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
template <typename Types>
struct PairValueRecord
{
template <typename Types2>
friend struct PairSet;
protected:
typename Types::HBGlyphID
secondGlyph; /* GlyphID of second glyph in the
* pair--first glyph is listed in the
* Coverage table */
ValueRecord values; /* Positioning data for the first glyph
* followed by for second glyph */
public:
DEFINE_SIZE_ARRAY (Types::HBGlyphID::static_size, values);
int cmp (hb_codepoint_t k) const
{ return secondGlyph.cmp (k); }
struct context_t
{
const ValueBase *base;
const ValueFormat *valueFormats;
const ValueFormat *newFormats;
unsigned len1; /* valueFormats[0].get_len() */
const hb_map_t *glyph_map;
const hb_hashmap_t<unsigned, hb_pair_t<unsigned, int>> *layout_variation_idx_delta_map;
};
bool subset (hb_subset_context_t *c,
context_t *closure) const
{
TRACE_SERIALIZE (this);
auto *s = c->serializer;
auto *out = s->start_embed (*this);
if (unlikely (!s->extend_min (out))) return_trace (false);
out->secondGlyph = (*closure->glyph_map)[secondGlyph];
closure->valueFormats[0].copy_values (s,
closure->newFormats[0],
closure->base, &values[0],
closure->layout_variation_idx_delta_map);
closure->valueFormats[1].copy_values (s,
closure->newFormats[1],
closure->base,
&values[closure->len1],
closure->layout_variation_idx_delta_map);
return_trace (true);
}
void collect_variation_indices (hb_collect_variation_indices_context_t *c,
const ValueFormat *valueFormats,
const ValueBase *base) const
{
unsigned record1_len = valueFormats[0].get_len ();
unsigned record2_len = valueFormats[1].get_len ();
const hb_array_t<const Value> values_array = values.as_array (record1_len + record2_len);
if (valueFormats[0].has_device ())
valueFormats[0].collect_variation_indices (c, base, values_array.sub_array (0, record1_len));
if (valueFormats[1].has_device ())
valueFormats[1].collect_variation_indices (c, base, values_array.sub_array (record1_len, record2_len));
}
bool intersects (const hb_set_t& glyphset) const
{
return glyphset.has(secondGlyph);
}
const Value* get_values_1 () const
{
return &values[0];
}
const Value* get_values_2 (ValueFormat format1) const
{
return &values[format1.get_len ()];
}
};
}
}
}
#endif // OT_LAYOUT_GPOS_PAIRVALUERECORD_HH
+79
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@@ -0,0 +1,79 @@
#ifndef OT_LAYOUT_GPOS_POSLOOKUP_HH
#define OT_LAYOUT_GPOS_POSLOOKUP_HH
#include "PosLookupSubTable.hh"
#include "../../../hb-ot-layout-common.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct PosLookup : Lookup
{
using SubTable = PosLookupSubTable;
const SubTable& get_subtable (unsigned int i) const
{ return Lookup::get_subtable<SubTable> (i); }
bool is_reverse () const
{
return false;
}
bool apply (hb_ot_apply_context_t *c) const
{
TRACE_APPLY (this);
return_trace (dispatch (c));
}
bool intersects (const hb_set_t *glyphs) const
{
hb_intersects_context_t c (glyphs);
return dispatch (&c);
}
hb_collect_glyphs_context_t::return_t collect_glyphs (hb_collect_glyphs_context_t *c) const
{ return dispatch (c); }
hb_closure_lookups_context_t::return_t closure_lookups (hb_closure_lookups_context_t *c, unsigned this_index) const
{
if (c->is_lookup_visited (this_index))
return hb_closure_lookups_context_t::default_return_value ();
c->set_lookup_visited (this_index);
if (!intersects (c->glyphs))
{
c->set_lookup_inactive (this_index);
return hb_closure_lookups_context_t::default_return_value ();
}
hb_closure_lookups_context_t::return_t ret = dispatch (c);
return ret;
}
template <typename set_t>
void collect_coverage (set_t *glyphs) const
{
hb_collect_coverage_context_t<set_t> c (glyphs);
dispatch (&c);
}
template <typename context_t>
static typename context_t::return_t dispatch_recurse_func (context_t *c, unsigned int lookup_index);
template <typename context_t, typename ...Ts>
typename context_t::return_t dispatch (context_t *c, Ts&&... ds) const
{ return Lookup::dispatch<SubTable> (c, std::forward<Ts> (ds)...); }
bool subset (hb_subset_context_t *c) const
{ return Lookup::subset<SubTable> (c); }
bool sanitize (hb_sanitize_context_t *c) const
{ return Lookup::sanitize<SubTable> (c); }
};
}
}
}
#endif /* OT_LAYOUT_GPOS_POSLOOKUP_HH */
@@ -0,0 +1,79 @@
#ifndef OT_LAYOUT_GPOS_POSLOOKUPSUBTABLE_HH
#define OT_LAYOUT_GPOS_POSLOOKUPSUBTABLE_HH
#include "SinglePos.hh"
#include "PairPos.hh"
#include "CursivePos.hh"
#include "MarkBasePos.hh"
#include "MarkLigPos.hh"
#include "MarkMarkPos.hh"
#include "ContextPos.hh"
#include "ChainContextPos.hh"
#include "ExtensionPos.hh"
namespace OT {
namespace Layout {
namespace GPOS_impl {
struct PosLookupSubTable
{
friend struct ::OT::Lookup;
friend struct PosLookup;
enum Type {
Single = 1,
Pair = 2,
Cursive = 3,
MarkBase = 4,
MarkLig = 5,
MarkMark = 6,
Context = 7,
ChainContext = 8,
Extension = 9
};
template <typename context_t, typename ...Ts>
typename context_t::return_t dispatch (context_t *c, unsigned int lookup_type, Ts&&... ds) const
{
TRACE_DISPATCH (this, lookup_type);
switch (lookup_type) {
case Single: return_trace (u.single.dispatch (c, std::forward<Ts> (ds)...));
case Pair: return_trace (u.pair.dispatch (c, std::forward<Ts> (ds)...));
case Cursive: return_trace (u.cursive.dispatch (c, std::forward<Ts> (ds)...));
case MarkBase: return_trace (u.markBase.dispatch (c, std::forward<Ts> (ds)...));
case MarkLig: return_trace (u.markLig.dispatch (c, std::forward<Ts> (ds)...));
case MarkMark: return_trace (u.markMark.dispatch (c, std::forward<Ts> (ds)...));
case Context: return_trace (u.context.dispatch (c, std::forward<Ts> (ds)...));
case ChainContext: return_trace (u.chainContext.dispatch (c, std::forward<Ts> (ds)...));
case Extension: return_trace (u.extension.dispatch (c, std::forward<Ts> (ds)...));
default: return_trace (c->default_return_value ());
}
}
bool intersects (const hb_set_t *glyphs, unsigned int lookup_type) const
{
hb_intersects_context_t c (glyphs);
return dispatch (&c, lookup_type);
}
protected:
union {
SinglePos single;
PairPos pair;
CursivePos cursive;
MarkBasePos markBase;
MarkLigPos markLig;
MarkMarkPos markMark;
ContextPos context;
ChainContextPos chainContext;
ExtensionPos extension;
} u;
public:
DEFINE_SIZE_MIN (0);
};
}
}
}
#endif /* HB_OT_LAYOUT_GPOS_POSLOOKUPSUBTABLE_HH */

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