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).
* 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
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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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.
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
core: suppress LAPACK deprecation warnings on macOS #28203
### Description
On macOS (Apple Silicon) with newer Xcode/Clang, the CLAPACK interface is deprecated.
Compiling `hal_internal.cpp` triggers multiple warnings like:
`warning: 'sgesv_' is deprecated: first deprecated in macOS 13.3 - The CLAPACK interface is deprecated. [-Wdeprecated-declarations]`
Since migrating to the new Accelerate interface (Apple's recommended fix) requires significant changes to the HAL implementation and breaks the build if headers are mismatched, this PR takes the pragmatic approach. It suppresses the `-Wdeprecated-declarations` warning for this specific file using Clang pragmas to clean up the build output.
### Verification
- [x] Verified build is silent on macOS (M3).
- [x] Verified pragmas are correctly scoped within `HAVE_LAPACK` to prevent scope mismatch on non-LAPACK builds.
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Dropped OPENCV_FOR_OPENMP_DYNAMIC_DISABLE environment varibale in favor of standard OMP_DYNAMIC #28122
Fixes: https://github.com/opencv/opencv/issues/25717
Replaces: https://github.com/opencv/opencv/pull/28084
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Fix tempfile race condition on Windows (issue #19648) #28087
Fix tempfile race condition on Windows
Addresses issue #19648
Problem
The cv::tempfile() function on Windows used GetTempFileNameA() followed by an immediate DeleteFileA() call. This created a race condition where multiple OpenCV processes running simultaneously could receive the same temporary filename, leading to name collisions.
Root Cause
The previous implementation:
Called GetTempFileNameA() to generate a temp filename
Immediately deleted the file to free the name
Returned just the filename string
Between steps 2 and 3, another process could call GetTempFileNameA() and receive the same filename, causing a collision.
Solution
Replaced GetTempFileNameA() with GUID-based filename generation using CoCreateGuid(), following the same approach already used in GetTempFileNameWinRT() and Microsoft's recommendations for scenarios requiring many temp files.
Changes
modules/core/src/system.cpp:
Removed GetTempFileNameA() and DeleteFileA() calls
Added CoCreateGuid() to generate unique GUID-based filenames
Format: "ocv{GUID}" where GUID ensures uniqueness across processes
Benefits
Eliminates race condition in multi-process scenarios
No file I/O overhead from creating and deleting placeholder files
Consistent with WinRT implementation approach
Follows Microsoft best practices
Testing
Standard OpenCV test suite. The change only affects Windows temp file naming and maintains the same String return type and usage pattern.
make cuda::GpuMatND compatible with InputArray/OutputArray #23913
continuation of [PR#19259](https://github.com/opencv/opencv/pull/19259)
Make cuda::GpuMatND wrappable in InputArray/OutputArray
The goal for now is just wrapping, some functions are not supported (InputArray::size(), InputArray::convertTo(), InputArray::assign()...)
No new feature for cuda::GpuMatND
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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
### Pull Request Readiness Checklist
**Merge with contrib**: https://github.com/opencv/opencv_contrib/pull/4000
related: https://github.com/opencv/opencv/pull/27581
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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core: ARM64 loop unrolling in kmeans to improve Weighted Filter performance #27596
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- This PR improves the performance of the Weighted Filter function from the ximgproc module on Windows on ARM64.
- The optimization is achieved by unrolling two performance-critical loops in the generateCentersPP function in modules/core/src/kmeans.cpp, which is internally used by the Weighted Filter function.
- The unrolling is enabled only for ARM64 builds using #if defined(_M_ARM64) guards to preserve compatibility and maintain performance on other architectures.
**Performance Improvements:**
- Improves execution time for Weighted Filter performance tests on ARM64 without affecting other platforms.
<img width="772" height="558" alt="image" src="https://github.com/user-attachments/assets/ae28c0af-97d3-460b-ad5a-207d3fc6936f" />
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.
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Matt Van Horn
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