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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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.
core: add YAML 1.2 support for FileStorage #28482Fixes: #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
Rvv core norm #29057Fixesopencv/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`.
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" />
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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
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- 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
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
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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.
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core: fix heap-buffer-overflow in YAML parseKey for empty keys #28620
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### 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).
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.
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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.
modified Input/OutputArray methods to handle 'std::vector<T>' or 'std::vector<std::vector<T>>' properly #28242
This is port of #26408 with some further improvements (all switch-by-vector-type statements are consolidated in a single macro)
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core: fix solveCubic numerical instability via coefficient normalization (fixes#27748) #28117
Summary
This PR fixes numerical instability in `cv::solveCubic` when the leading coefficient `a` is non-zero but extremely small relative to other coefficients (Issue #27748).
It introduces a **normalization step** that scales all coefficients by their maximum magnitude before solving. This ensures robust detection of when the equation should degenerate to a quadratic solver, without breaking valid cubic equations that happen to have small coefficients (e.g., scaled by 1e-9).
The Problem (Issue #27748)
The previous implementation checked `if (a == 0)` to decide whether to use the cubic or quadratic formula.
- When `a` is extremely small (e.g., 1e-17) but not exactly zero, and other coefficients are normal (e.g., 5.0), the standard cubic formula suffers from catastrophic cancellation and overflow, producing incorrect roots (e.g., 1e14).
The Fix
1. Normalization: The solver now finds `max_coeff = max(|a|, |b|, |c|, |d|)` and scales all coefficients by `1.0 / max_coeff`.
2. Relative Threshold: It then checks `if (abs(a) < epsilon)` on the *normalized* coefficients.
Why this is better than previous attempts
In a previous attempt (PR #28057), a simple absolute check `abs(a) < epsilon` was proposed. That approach was rejected because it failed for scaled equations.
Fixes#27748
rvv_hal: fix flip inplace #28180
Fixes https://github.com/opencv/opencv/issues/28124
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core: add copyAt() for ROI operation #27318
Close https://github.com/opencv/opencv/issues/27320
Close https://github.com/opencv/opencv/issues/27298
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Force empty output type where it's defined in API #27972
The PR replaces:
- https://github.com/opencv/opencv/pull/27936
- https://github.com/opencv/opencv/pull/21059
Empty matrix has undefined type, so user code should not relay on the output type, if it's empty. The PR introduces some exceptions:
- copyTo documentation defines, that the method re-create output buffer and set it's type.
- convertTo has output type as parameter and output type is defined and expected.
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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core: support 16 bit LUT #27890
Close https://github.com/opencv/opencv/issues/26899
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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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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
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
core: support parsing back slash \ in parseKey in FileStorage (JSON) #27587Fixes#27585
### 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
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