Fixes#28396 : out-of-bounds read in SIMD type conversion #28397Fixes#28396Fixes#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
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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.
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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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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" />
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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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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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Fix missing vec_cvfo on IBM POWER9 due to unavailable VSX float64 conversion #27990
Replaces https://github.com/opencv/opencv/pull/27633
Closes https://github.com/opencv/opencv/issues/27635
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Clarified supported types in cv::patchNaNs() documentation #27959
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Updated the docs for cv::patchNaNs() to specify that both CV_32F and CV_64F types are supported. Fixes incomplete information.
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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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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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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Alexander Smorkalov
Vincent Rabaud
Anshu
Abhishek Gola
Vadim Pisarevsky
Murat Raimbekov
Adrian Kretz
Denis Barucic
pratham-mcw
Mark Harfouche
Kumataro
nishith-fujitsu
Pierre Chatelier
Alexander Smorkalov
abhijeetraj10-web
Mahi Dhiman
cudawarped
xybuild
Alexander Smorkalov