imgproc: Optimized remap interpolation #29507
- Add a SIMD dispatch file for remap and vectorize the single-channel (C1) in-bounds paths of bilinear, bicubic and lanczos4 interpolation (32F / 16U / 16S) using width-agnostic gather
- Dispatch the bilinear C1 path and drop the per-pixel weight-table gathers
- Widen the fixed-point coordinate map conversion
- 32F lanczos4 is kept on the scalar path: its vectorized 64-tap accumulation deviates beyond the set float accuracy tolerance
- Vectorize the bilinear inlier/outlier run detection so any-channel linear remap skips constant-status runs with SIMD instead of a per-pixel bounds test
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Distransform function IPP migration to HAL in 4.x #29463
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Fix calibration tutorial docs, decomposeProjectionMatrix, and convertMaps performance claims #29487Fixes#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
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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.
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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
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Extracting IPP to HAL for matchTemplate function in 4.x #29464
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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.
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dnn: add DynamicQuantizeLinear ONNX layer support #29018
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### 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
Fix#29452: Remove <complex.h> to prevent _Complex macro conflicts #29455
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---
**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.
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.
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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.
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N/A - bug fix, no new API.
Filter2D IPP migration to HAL for 4.x #29427
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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
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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.
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Alexander Smorkalov
Alexander Smorkalov
Madan mohan Manokar
Akansha Mallick
Uwez Khan
Varun Jaiswal
FAN YUCHEN
LiuChenjiayi
Prasad Ayush Kumar
Andrei Fedorov
yyyisyyy11
velonica0
Anushka
rmsalinas
MAAZIZ Adel Ayoub
orbisai0security
Timur Izmajlov