core: vectorize masked norm/normDiff for remaining depths - #29815
### Summary
- The masked `cv::norm()` / `cv::norm(a, b)` kernels in `norm.simd.hpp` had SIMD specializations only for `uchar`, `ushort` and `float`.
- `schar`, `short`, `int`, `double` and `uchar` L2 paths uses the scalar implementation.
### Changes
- Added new vectorized implementations of MaskedNorm{Inf,L1,L2}_SIMD for schar, short, int, and double.
- Added new vectorized implementation of MaskedNormL2_SIMD<uchar, int>.
- Added new MaskedNormDiff{Inf,L1,L2}_SIMD implementations for double.
- Added cn == 4 v_load_deinterleave paths to the uchar L1/L2 kernels.
- Replaced the single f64 accumulator in MaskedNormL1_SIMD<float,double> with four independent ones, so the widening adds can overlap instead of each waiting on the previous.
### Performance Benchmarks
<img width="715" height="709" alt="image" src="https://github.com/user-attachments/assets/46afea06-df98-4dd4-a346-dc8cd4a065c9" />
core: fix unsigned Rect intersection for disjoint rectangles (#11988) - #29069Fixes#11988
## Problem
`Rect_<_Tp>::operator&` / `operator&=` returned a non-empty rectangle when two **unsigned** rectangles do not overlap.
### Example:
```cpp
cv::Rect_<unsigned> r1(0, 0, 1, 1);
cv::Rect_<unsigned> r2(2, 2, 1, 1);
auto inter = r1 & r2; // was [1 x 1 from (2, 2)], expected empty
```
Root cause: the previous implementation subtracted edge coordinates before checking overlap. For `unsigned _Tp`, expressions like `width - (x_max - x_min)` can underflow when rectangles are disjoint.
### Solution
Add a check for underflow
### Tests
Added regression test `Core_Rect.test_unsigned_overflow in modules/core/test/test_misc.cpp`.
core: fix JacobiSVD SIMD accumulation to match scalar path - #29727
Replace FMA with mul-add in dotD/givensD to avoid Windows MSVC rounding drift.
- Address the FMA drift introduced in https://github.com/opencv/opencv/pull/29720
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Add VBLAS::dotD and VBLAS::givensD to vectorize JacobiSVDImpl_'s dot-product,
Givens rotation and norm loops with double-precision accumulation matching the
scalar path.
Dispatch lapack for both AVX512_SKX and AVX512_ICL.
The generated cv_cpu_helper.h sets CV_TRY_<OPT> to 1 both when <OPT> is a
dispatch target and when it is part of the baseline, but an opt_<OPT> namespace
is only emitted for the dispatch case: __ocv_add_dispatched_file() guards it
with CPU_DISPATCH_FINAL, and a baseline optimization is deliberately kept out of
that list. Code that hand-rolls "#if CV_TRY_<OPT> ... opt_<OPT>::fn" therefore
fails to compile as soon as the optimization lands in the baseline, e.g. with
-DCPU_BASELINE=AVX512_ICL or -march=native on an ICL capable CPU:
lut.dispatch.cpp:22:16: error: 'opt_AVX512_ICL' has not been declared
CV_CPU_CALL_<OPT>() already handles the namespace selection for a call. Add the
same thing for code that needs the function pointer instead:
CV_CPU_GET_FN_PTR_<OPT>(fn) per optimization
CV_CPU_GET_FN_PTR_BASELINE(fn) chain terminator
CV_CPU_DISPATCH_FN(fn, modes) full chain, mirrors CV_CPU_DISPATCH()
In the baseline case it resolves to cpu_baseline::fn, which is the <OPT> build
of the kernel, so no implementation is lost and no second copy is emitted. In
the dispatch case it keeps the cv::checkHardwareSupport() test and returns
opt_<OPT>::fn, and it compiles out when the optimization is unavailable.
Use it in the two places that hit this, which also removes the hand-written
preprocessor blocks. Generated code for the dispatch configuration is unchanged
(byte-identical .text with -DCPU_BASELINE=SSE3 -DCPU_DISPATCH=AVX512_ICL).
Fixes#29694
Implemented a check to enable instrumentation for IPP HAL #29542
To enable instrumentation for IPP HAL correctly it needed to generalize a part of `private.hpp` and put it to the separate file that can be included in `private.hpp` and `ipp_utils.hpp` both to avoid duplication.
Built with `-DENABLE_INSTRUMENTATION=ON` and ran `opencv_perf_imgproc --perf_instrument=1`:
| Test | IPP weight before | IPP weight after |
|---|---|---|
| `distanceTransform` (8U->32F) | 0.0% | ~76% |
| `ippSobel` | 0.0% | ~98% |
| `scharrViaSobelFilter` | 0.0% | ~90% |
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core: vectorize cv::reduce #27510
- [ ] ~reduceR_~ Dropped due to performance
- [x] reduceC_
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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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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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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.
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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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
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Optimized gemm implementation with Universal SIMD #29242
- vectorized GEMMSingleMul and GEMMBlockMul
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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.
---
Fixesopencv/opencv#21960
fix MSVC warning for minMaxIdx_simd #29379
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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.
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.
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Fast-path transposeND for identity and 2D transpose orders #29172
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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%)
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.
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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).
- 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.
imgproc: optimized LUT and equalizeHist with SIMD #29250
- optimized lut with SIMD
- support equalizeHist with v_lut
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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" />
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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.
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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
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