To be on par with `cv::Mat`, let's add `cv::cuda::GpuMat::getStdAllocator()`
This is useful anyway, because when a user wants to use custom allocators, he might want to resort to the standard default allocator behaviour, not some other allocator that could have been set by `setDefaultAllocator()`
[GSoC] dnn: Blockwise quantization support #25644
This PR introduces blockwise quantization in DNN allowing the parsing of ONNX models quantized in blockwise style. In particular it modifies the `Quantize` and `Dequantize` operations. The related PR opencv/opencv_extra#1181 contains the test data.
Additional notes:
- The original quantization issue has been fixed. Previously, for 1D scale and zero-point, the operation applied was $y = int8(x/s - z)$ instead of $y = int8(x/s + z)$. Note that the operation was already correctly implemented when the scale and zero-point were scalars. The previous implementation failed the ONNX test cases, but now all have passed successfully. [Reference](https://github.com/onnx/onnx/blob/main/docs/Operators.md#QuantizeLinear)
- the function `block_repeat` broadcasts scale and zero-point to the input shape. It repeats all the elements of a given axis n times. This function generalizes the behavior of `repeat` from the core module which is defined just for 2 axis assuming `Mat` has 2 dimensions. If appropriate and useful, you might consider moving `block_repeat` to the core module.
- Now, the scale and zero-point can be taken as layer inputs. This increases the ONNX layers' coverage and enables us to run the ONNX test cases (previously disabled) being fully compliant with ONNX standards. Since they are now supported, I have enabled the test cases for: `test_dequantizelinear`, `test_dequantizelinear_axis`, `test_dequantizelinear_blocked`, `test_quantizelinear`, `test_quantizelinear_axis`, `test_quantizelinear_blocked` just in CPU backend. All of them pass successfully.
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modules/js/perf/perf_helpfunc.js and target tests, e.g. perf_gaussianBlur.js contained "const isNodeJs", leading to re-definition when using associated *.html files.
Search in two directions when try to add new quad in addOuterQuad #25807
In ChessBoardDetector::addOuterQuad, previous code try to connect new quad with inner quad, if possible, but only search for one direction. I have made three test images, one is normal(a.jpg), one lossed an outer quad(b.jpg), and then i flipped it vertically(c.jpg). Only last one fails. I fixed it by check two directions and row/col.
Here is the test code and images:
```
Mat img;
vector<Point2f> corners;
auto size = cv::Size(6, 6);
img = imread("D:/tmp/a.jpg", 0);
std::cout<<cv::findChessboardCorners(img, size, corners)<<"\n";
std::cout << corners.size() << "\n";
img = imread("D:/tmp/b.jpg", 0);
std::cout<<cv::findChessboardCorners(img, size, corners)<<"\n";
std::cout << corners.size() << "\n";
img = imread("D:/tmp/c.jpg", 0);
std::cout<<cv::findChessboardCorners(img, size, corners)<<"\n";
std::cout << corners.size() << "\n";
```
a
b
c
Properly check markers when none are provided. #25938
CharucoDetectorImpl::detectBoard finds temporary markers when none are provided but those are discarded when
charucoDetectorImpl::checkBoard is called.
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HAL for dot product added #25936
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videoio: fix cv::VideoWriter with FFmpeg encapsulation timestamps #25874
Fix https://github.com/opencv/opencv/issues/25873 by modifying `cv::VideoWriter` to use provided presentation indices (pts).
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dnn: optimize activations with v_exp #25881
Merge with https://github.com/opencv/opencv_extra/pull/1191.
This PR optimizes the following activations:
- [x] Swish
- [x] Mish
- [x] Elu
- [x] Celu
- [x] Selu
- [x] HardSwish
### Performance (Updated on 2024-07-18)
#### AmLogic A311D2 (ARM Cortex A73 + A53)
```
Geometric mean (ms)
Name of Test activations activations.patch activations.patch
vs
activations
(x-factor)
Celu::Layer_Elementwise::OCV/CPU 115.859 27.930 4.15
Elu::Layer_Elementwise::OCV/CPU 27.846 27.003 1.03
Gelu::Layer_Elementwise::OCV/CPU 0.657 0.602 1.09
HardSwish::Layer_Elementwise::OCV/CPU 31.885 6.781 4.70
Mish::Layer_Elementwise::OCV/CPU 35.729 32.089 1.11
Selu::Layer_Elementwise::OCV/CPU 61.955 27.850 2.22
Swish::Layer_Elementwise::OCV/CPU 30.819 26.688 1.15
```
#### Apple M1
```
Geometric mean (ms)
Name of Test activations activations.patch activations.patch
vs
activations
(x-factor)
Celu::Layer_Elementwise::OCV/CPU 16.184 2.118 7.64
Celu::Layer_Elementwise::OCV/CPU_FP16 16.280 2.123 7.67
Elu::Layer_Elementwise::OCV/CPU 9.123 1.878 4.86
Elu::Layer_Elementwise::OCV/CPU_FP16 9.085 1.897 4.79
Gelu::Layer_Elementwise::OCV/CPU 0.089 0.081 1.11
Gelu::Layer_Elementwise::OCV/CPU_FP16 0.086 0.074 1.17
HardSwish::Layer_Elementwise::OCV/CPU 1.560 1.555 1.00
HardSwish::Layer_Elementwise::OCV/CPU_FP16 1.536 1.523 1.01
Mish::Layer_Elementwise::OCV/CPU 6.077 2.476 2.45
Mish::Layer_Elementwise::OCV/CPU_FP16 5.990 2.496 2.40
Selu::Layer_Elementwise::OCV/CPU 11.351 1.976 5.74
Selu::Layer_Elementwise::OCV/CPU_FP16 11.533 1.985 5.81
Swish::Layer_Elementwise::OCV/CPU 4.687 1.890 2.48
Swish::Layer_Elementwise::OCV/CPU_FP16 4.715 1.873 2.52
```
#### Intel i7-12700K
```
Geometric mean (ms)
Name of Test activations activations.patch activations.patch
vs
activations
(x-factor)
Celu::Layer_Elementwise::OCV/CPU 17.106 3.560 4.81
Elu::Layer_Elementwise::OCV/CPU 5.064 3.478 1.46
Gelu::Layer_Elementwise::OCV/CPU 0.036 0.035 1.04
HardSwish::Layer_Elementwise::OCV/CPU 2.914 2.893 1.01
Mish::Layer_Elementwise::OCV/CPU 3.820 3.529 1.08
Selu::Layer_Elementwise::OCV/CPU 10.799 3.593 3.01
Swish::Layer_Elementwise::OCV/CPU 3.651 3.473 1.05
```
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Upgrade RISC-V Vector intrinsic and cleanup the obsolete RVV backend. #25883
This patch upgrade RISC-V Vector intrinsic from `v0.10` to `v0.12`/`v1.0`:
- Update cmake check and options;
- Upgrade RVV implement for Universal Intrinsic;
- Upgrade RVV optimized DNN kernel.
- Cleanup the obsolete RVV backend (`intrin_rvv.hpp`) and compatable header file.
With this patch, RVV backend require Clang 17+ or GCC 14+ (which means `__riscv_v_intrinsic >= 12000`, see https://godbolt.org/z/es7ncETE3)
This patch is test with Clang 17.0.6 (require extra `-DWITH_PNG=OFF` due to ICE), Clang 18.1.8 and GCC 14.1.0 on QEMU and k230 (with `--gtest_filter="*hal_*"`).
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Add a check for src == dst in ocl warpTransform #25898
As mentioned in #25853, when doing WarpAffine with Mat and UMat respectively, if you force the use of the in-place operation (so that src and dst are passed the same variables), Mat produces the correct results, but UMat produces unexpected results.
Obviously in-place operations are not possible with this transformation. When Mat performs the operation, if dst and src are the same variable, the function inherently makes a copy of src without telling the user.
74b50c7af0/modules/imgproc/src/imgwarp.cpp (L2831-L2834)
So I did the same check in UMat, but I'm not sure if it's appropriate, should we just do a copy operation without telling the user (even if the user thinks he's doing an in-place operation), or should we throw an exception to indicate that we shouldn't pass in two same variables here?
The possible reason for this problem is that there is a create function here, so it gives the developer the false impression that this create function has allocated new memory for dst, however it does not.
74b50c7af0/modules/imgproc/src/imgwarp.cpp (L2607-L2609)
Because by the time the check is done here, the function has returned back.
74b50c7af0/modules/core/src/umatrix.cpp (L668-L675)
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Add tutorial on using Orbbec 3D cameras (UVC) #25907
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code clean #25931
Align code and remove redundant CMake code
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Support OpenGL GTK3 New API #25822Fixes#20001
GSoC2024 Project
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calib3d: fix Rodrigues CV_32F and CV_64F type mismatch in projectPoints #25824Fixes#25318
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Added flag to GaussianBlur for faster but not bit-exact implementation #25792
Rationale:
Current implementation of GaussianBlur is almost always bit-exact. It helps to get predictable results according platforms, but prohibits most of approximations and optimization tricks.
The patch converts `borderType` parameter to more generic `flags` and introduces `GAUSS_ALLOW_APPROXIMATIONS` flag to allow not bit-exact implementation. With the flag IPP and generic HAL implementation are called first. The flag naming and location is a subject for discussion.
Replaces https://github.com/opencv/opencv/pull/22073
Possibly related issue: https://github.com/opencv/opencv/issues/24135
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Mark cv::Mat(Mat&&) as noexcept #25899
This fixes https://github.com/opencv/opencv/issues/25065
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