Avoid uninitialized value read in resize. #26084
When there is no point falling right, an hypothetical value is computed (but unused) using an uninitialized ofst. This triggers warnings in the sanitizers.
Including those values in the for loops is also possible but messy when SIMD is involved.
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Remove the redundant codes of cv::convertMaps and mRGBA2RGBA<uchar> #26071
(1) cv::convertMaps: the branch [else if( m1type == CV_32FC2 && dstm1type == CV_16SC2 ) if( nninterpolate )] is unreachable,
as the condition is satisfied in lines 1959 to 1961, calculated in advance and return directly.
(2) mRGBA2RGBA<uchar>: dst[0], dst[1], dst[2] and dst[3] is calculated repeatedly. Introduced in https://github.com/opencv/opencv/pull/13440
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Update test_tiff.cpp #26093
related #22090
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Added offset for HAL as ofs2idx expects 1-based index #26080
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DNN(ONNX): Enabled several OpenCL conformance tests #26053
The tests also work in 5.x
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Fixed the simd bugs of iPow8u and iPow16u #26061
Add the following cases in opencv_perf_core:
* OCL_PowFixture_iPow.iPow/0, where GetParam() = (640x480, 8UC1)
* OCL_PowFixture_iPow.iPow/2, where GetParam() = (640x480, 16UC1)
iPow8u and iPow16u failed to call to simd accelerating while executing.
Fix the bug by changing the input type of iPow_SIMD function.
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Einsum buffer allocation fix#26059
This PR fixed buffer allocation issue in Einsum layer that causes segmentation fault on 32bit platforms. Related issue #26008
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Imgproc: use double to determine whether the corners points are within src #26022close#26016
Related https://github.com/opencv/opencv_contrib/pull/3778
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Split Javascript white-list to support contrib modules #25986
Single whitelist converted to several per-module json files. They are concatenated automatically and can be overriden by user config.
Related to https://github.com/opencv/opencv/pull/25656
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Increase neighbors search radius for corners in ChessBoardDetector:findQuadNeighbors #26014
I didn't do everything right the way I wanted at #25991. I forgot that `edge_len` is edge **squared** length as well as `thresh_scale` is threshold for **squared** scale. So, I wanted to increase scale by `sqrt(2)` times (idea is to use quad diagonal instead of quad side) and therefore `thresh_scale` should be equal to `sqrt(2)^2 = 2`.
And refactor variables names to explicitly indicate that they are squared, so that no one else falls into this trap
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 `1/2%`:
```
cell_img_size = 100 (default)
before
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
after
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.952083 13710 14400 0.595984
Total detected time: 136.55770600000014 sec
----------------------------------------------------------------------------------------------------------------------------------------------
cell_img_size = 10
before
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
after
category detected chessboard total detected chessboard total chessboard average detected error
all 0.591389 8516 14400 4.155250
Total detected time: 2.700832999999997 sec
```
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dnn: add ONNX TopK #23279
Merge with https://github.com/opencv/opencv_extra/pull/1200
Partially fixes#22890 and #20258
To-do:
- [x] TopK forward impl
- [x] add tests
- [x] support Opset 1 & 10 if possible
- [ ] ~Support other backends~ (TopK has two outputs, which is not supported by other backends, such as openvino)
Perf:
M1 (time in millisecond)
| input shape | axis | dnn | ort |
| --------------- | ---- | ---- | ---- |
| (1000, 100) | 0 | 1.68 | 4.07 |
| (1000, 100) K5 | 0 | 1.13 | 0.12 |
| (1000, 100) | 1 | 0.96 | 0.77 |
| (100, 100, 100) | 0 | 10.00 | 31.13 |
| (100, 100, 100) | 1 | 7.33 | 9.17 |
| (100, 100, 100) | 2 | 7.52 | 9.48 |
M2 (time in milisecond)
| input shape | axis | dnn | ort |
| --------------- | ---- | ---- | ---- |
| (1000, 100) | 0 | 0.76 | 2.44 |
| (1000, 100) K5 | 0 | 0.68 | 0.07 |
| (1000, 100) | 1 | 0.41 | 0.50 |
| (100, 100, 100) | 0 | 4.83 | 17.52|
| (100, 100, 100) | 1 | 3.60 | 5.08 |
| (100, 100, 100) | 2 | 3.73 | 5.10 |
ONNXRuntime performance testing script: https://gist.github.com/fengyuentau/a119f94fd16721ec9974b8c7b0a45d4c
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Correct Bayer2Gray u8 SIMD #25968
SIMD version of CV_DESCALE is not correct. It should be implemented using v_dotprod.
What's more, the stop condition of vector operation should be `bayer < bayer_end - 14` because we just need to make sure result is safely stored into `dst`.
Closes: https://github.com/opencv/opencv/issues/25823
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Unified build.gradle files into one template #26009
Issue #24686
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Fix typos #26038
Fix typos
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imgproc: add specific error code when cvtColor is used on an image with an invalid number of channels #25981close#25971
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Add support for QNX #25832
Build and test instruction for QNX:
https://github.com/chachoi-world/qnx-ports/blob/main/opencv/README.md
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pyrDown: offset HAL added, IPP removed #25970Resolves#25976
### Changes
* HAL added for offset support so that border pixels can be fetched from outside of the image ROI (see `BORDER_ISOLATED` parameter)
* IPP removed since there is `pyrUp` instead of `pyrDown` and there's no easy way to fix this other than rewriting it from scratch
* replaced old C call by modern `cv::pyrDown`
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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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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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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()`
[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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Rostislav Vasilikhin
Vincent Rabaud
tingboliao
Suleyman TURKMEN
catree
Alexander Smorkalov
Maksim Shabunin
alexlyulkov
Liutong HAN
Hao Chen
Alexander Smorkalov
penghuiho
Abduragim Shtanchaev
Kumataro
Maxim Smolskiy
Yuantao Feng
FantasqueX
Ivan Afanasyev
ShengYu
Octopus136
Yuantao Feng
ericmariasis
James Choi
Rostislav Vasilikhin
Aven
stepkamipt
Junyan721113
chacha21
Daniele Affinita
gblikas
Alexander Lyulkov
武士风度的牛