Added xxxApprox overloads for YUV color conversions in HAL and AlgorithmHint to cvtColor #25932
The xxxApprox to implement HAL functions with less bits for arithmetic of FP.
The hint was introduced in #25792 and #25911
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Improved classification sample #25519#25006#25314
This pull requests replaces the caffe model for classification with onnx versions. It also adds resnet in model.yml.
### Pull Request Readiness Checklist
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Improve corners matching in ChessBoardDetector::NeighborsFinder::findCornerNeighbor #25991
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Idea was mentioned in `Section III-B. New Heuristic for Quadrangle Linking` of `Rufli, Martin & Scaramuzza, Davide & Siegwart, Roland. (2008). Automatic Detection of Checkerboards on Blurred and Distorted Images. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. 3121-3126. 10.1109/IROS.2008.4650703` (https://rpg.ifi.uzh.ch/docs/IROS08_scaramuzza_b.pdf):
```
* For each candidate pair, focus on the quadrangles they belong to and draw two straight lines passing through the midsections of the respective quadrangle edges (see Fig. 6).
* If the candidate corner and the source corner are on the same side of every of the four straight lines drawn this way (this corresponds to the yellow shaded area in Fig. 6), then the corners are successfully matched.
```
By improving corners matching, we can increase the search radius (`thresh_scale`).
I tested this PR with benchmark
```
python3 objdetect_benchmark.py --configuration=generate_run --board_x=7 --path=res_chessboard --synthetic_object=chessboard
```
PR increases detected chessboards number by `3/7%`:
```
cell_img_size = 100 (default)
before
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.910417 13110 14400 0.599746
Total detected time: 147.50906700000002 sec
after
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.941667 13560 14400 0.596726
Total detected time: 136.68963200000007 sec
----------------------------------------------------------------------------------------------------------------------------------------------
cell_img_size = 10
before
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.539792 7773 14400 4.208237
Total detected time: 2.668964 sec
after
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.579167 8340 14400 4.198448
Total detected time: 2.535998999999999 sec
```
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Patch to opencv_extra has the same branch name.
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Current code using CMAKE_SOURCE_DIR and it works well if opencv is standalone CMake project,
but in case of building OpenCV as part of a larger CMake project (e.g. one that includes
opencv and opencv_contrib) this path is incorrect, unlike OpenCV_SOURCE_DIR
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()`
Removed obsolete python samples #25268
Clean Samples #25006
This PR removes 36 obsolete python samples from the project, as part of an effort to keep the codebase clean and focused on current best practices. Some of these samples will be updated with latest algorithms or will be combined with other existing samples.
Removed Samples:
> browse.py
camshift.py
coherence.py
color_histogram.py
contours.py
deconvolution.py
dft.py
dis_opt_flow.py
distrans.py
edge.py
feature_homography.py
find_obj.py
fitline.py
gabor_threads.py
hist.py
houghcircles.py
houghlines.py
inpaint.py
kalman.py
kmeans.py
laplace.py
lk_homography.py
lk_track.py
logpolar.py
mosse.py
mser.py
opt_flow.py
plane_ar.py
squares.py
stitching.py
text_skewness_correction.py
texture_flow.py
turing.py
video_threaded.py
video_v4l2.py
watershed.py
These changes aim to improve the repository's clarity and usability by removing examples that are no longer relevant or have been superseded by more up-to-date techniques.
[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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Fix size() for 0d matrix #25945
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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.
Mask support with CV_Bool in ts and core #25902
Partially cover https://github.com/opencv/opencv/issues/25895
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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).
### Pull Request Readiness Checklist
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Alexander Lyulkov
Alexander Smorkalov
Alexander Smorkalov
Gursimar Singh
Maxim Smolskiy
Aven
stepkamipt
Junyan721113
Alexander Alekhin
chacha21
kaingwade
Daniele Affinita
Vincent Rabaud
Maksim Shabunin
gblikas
Kumataro
武士风度的牛
Rostislav Vasilikhin
cudawarped