Bump opencv to 4.10.0 #12
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core: Add cgroupsv2 support to parallel.cpp
imgcodecs: jpeg: (test) fix condition to compare rgb and cmyk jpeg
Optimize int8 layers in DNN modules by using RISC-V Vector intrinsic. opencv#25230 This patch optimize 3 functions in the int8 layer by using RVV Native Intrinsic. This patch was tested on QEMU using VLEN=128 and VLEN=256 on `./bin/opencv_test_dnn --gtest_filter="*Int8*"`; On the real device (k230, VLEN=128), `EfficientDet_int8` in `opencv_perf_dnn` showed a performance improvement of 1.46x. | Name of Test | Original | optimized | Speed-up | | ------------------------------------------ | -------- | ---------- | -------- | | EfficientDet_int8::DNNTestNetwork::OCV/CPU | 2843.467 | 1947.013 | 1.46 | ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [ ] I agree to contribute to the project under Apache 2 License. - [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [ ] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
fix download file hash value mismatch issue
…corners-in-ChessBoardDetector-findQuadNeighbors Use initial quads corners in ChessBoardDetector::findQuadNeighbors
Ownership check in TFLite importer opencv#25312 ### Pull Request Readiness Checklist resolves opencv#25310 See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
toBufferedImage Modifying the original array copying method here can double the speed.
…_SRGGB8, V4L2_PIX_FMT_SBGGR8, V4L2_PIX_FMT_SGBRG8, V4L2_PIX_FMT_SGRBG8 options. Update modules/videoio/test/test_v4l2.cpp test file.
[BugFix] dnn (ONNX): Foce dropping constant inputs in parseClip if they are shared opencv#25319 Resolves opencv#25278 Merge with opencv/opencv_extra#1165 In Gold-YOLO ,`Div` has a constant input `B=6` which is then parsed into a `Const` layer in the ONNX importer, but `Clip` also has the shared constant input `max=6` which is already a `Const` layer and then connected to `Elementwise` layer. This should not happen because in the `forward()` of `Elementwise` layer, the legacy code goes through and apply activation to each input. More details on opencv#25278 (comment). ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
Resolve valgrind issue at QRCode detector
Update highgui+HighGui.java
Export TIFF compression options as API and git rid of tiff.h.
…use_cvtColor Use cvtColor() for Bayer image color demosaicing with V4L2_PIX_FMT_SGRBG8
Added option to dump v4l2 test frame from virtual camera
Remove unnecessary floating point literal
Libjpeg-turbo update to version 3.0.3 opencv#25623 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
YUV codes for cvtColor: descriptions added opencv#25616 This PR contains descriptions for various RGB <-> YUV color conversion codes as well as detailed comments in the source code. ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
minor cosmetic changes
Prevent signed integer overflow in LshTable
imgcodecs: support IMWRITE_JPEG_LUMA/CHROMA_QUALITY with internal libjpeg-turbo opencv#25647 Close opencv#25646 - increase JPEG_LIB_VERSION for internal libjpeg-turbo from 62 to 70 - add log when using IMWRITE_JPEG_LUMA/CHROMA_QUALITY with JPEG_LIB_VERSION<70 - add document IMWRITE_JPEG_LUMA/CHROMA_QUALITY requests JPEG_LIB_VERSION >= 70 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
Refactor DNN module to build with cudnn 9 opencv#25412 A lot of APIs that are currently being used in the dnn module have been removed in cudnn 9. They were deprecated in 8. This PR updates said code accordingly to the newer API. Some key notes: 1) This is my first PR. I am new to openCV. 2) `opencv_test_core` tests pass 3) On a 3080, cuda 12.4(should be irrelevant since I didn't build the `opencv_modules`, gcc 11.4, WSL 2. 4) For brevity I will avoid including macro code that will allow for older versions of cudnn to build. I was unable to get the tests working for `opencv_test_dnn` and `opencv_perf_dnn`. The errors I get are of the following: ``` OpenCV tests: Can't find required data file: dnn/onnx/conformance/node/test_reduce_prod_default_axes_keepdims_example/model.onnx in function 'findData' " thrown in the test body. ``` So before I spend more time investigating I was hoping to get a maintainer to point me in the right direction here. I would like to run these tests and confirm things are working as intended. I may have missed some details. ### Pull Request Readiness Checklist relevant issue (opencv#24983 - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [ ] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
3rdparty: NDSRVP - A New 3rdparty Library with Optimizations Based on RISC-V P Extension v0.5.2 - Part 1: Basic Functions opencv#25167 # Summary ### Previous context From PR opencv#24556: >> * As you wrote, the P-extension differs from RVV thus can not be easily implemented via Universal Intrinsics mechanism, but there is another HAL mechanism for lower-level CPU optimizations which is used by the [Carotene](https://github.com/opencv/opencv/tree/4.x/3rdparty/carotene) library on ARM platforms. I suggest moving all non-dnn code to similar third-party component. For example, FAST algorithm should allow such optimization-shortcut: see https://github.com/opencv/opencv/blob/4.x/modules/features2d/src/hal_replacement.hpp >> Reference documentation is here: >> >> * https://docs.opencv.org/4.x/d1/d1b/group__core__hal__interface.html >> * https://docs.opencv.org/4.x/dd/d8b/group__imgproc__hal__interface.html >> * https://docs.opencv.org/4.x/db/d47/group__features2d__hal__interface.html >> * Carotene library is turned on here: https://github.com/opencv/opencv/blob/8bbf08f0de9c387c12afefdb05af7780d989e4c3/CMakeLists.txt#L906-L911 > As a test outside of this PR, A 3rdparty component called ndsrvp is created, containing one of the non-dnn code (integral_SIMD), and it works very well. > All the non-dnn code in this PR have been removed, currently this PR can be focused on dnn optinizations. > This HAL mechanism is quite suitable for rvp optimizations, all the non-dnn code is expected to be moved into ndsrvp soon. ### Progress #### Part 1 (This PR) - [Core](https://docs.opencv.org/4.x/d1/d1b/group__core__hal__interface.html) - [x] Element-wise add and subtract - [x] Element-wise minimum or maximum - [x] Element-wise absolute difference - [x] Bitwise logical operations - [x] Element-wise compare - [ImgProc](https://docs.opencv.org/4.x/dd/d8b/group__imgproc__hal__interface.html) - [x] Integral - [x] Threshold - [x] WarpAffine - [x] WarpPerspective - [Features2D](https://docs.opencv.org/4.x/db/d47/group__features2d__hal__interface.html) #### Part 2 (Next PR) **Rough Estimate. Todo List May Change.** - [Core](https://docs.opencv.org/4.x/d1/d1b/group__core__hal__interface.html) - [ImgProc](https://docs.opencv.org/4.x/dd/d8b/group__imgproc__hal__interface.html) - smaller remap HAL interface - AdaptiveThreshold - BoxFilter - Canny - Convert - Filter - GaussianBlur - MedianBlur - Morph - Pyrdown - Resize - Scharr - SepFilter - Sobel - [Features2D](https://docs.opencv.org/4.x/db/d47/group__features2d__hal__interface.html) - FAST ### Performance Tests The optimization does not contain floating point opreations. **Absolute Difference** Geometric mean (ms) |Name of Test|opencv perf core Absdiff|opencv perf core Absdiff|opencv perf core Absdiff vs opencv perf core Absdiff (x-factor)| |---|:-:|:-:|:-:| |Absdiff::OCL_AbsDiffFixture::(640x480, 8UC1)|23.104|5.972|3.87| |Absdiff::OCL_AbsDiffFixture::(640x480, 32FC1)|39.500|40.830|0.97| |Absdiff::OCL_AbsDiffFixture::(640x480, 8UC3)|69.155|15.051|4.59| |Absdiff::OCL_AbsDiffFixture::(640x480, 32FC3)|118.715|120.509|0.99| |Absdiff::OCL_AbsDiffFixture::(640x480, 8UC4)|93.001|19.770|4.70| |Absdiff::OCL_AbsDiffFixture::(640x480, 32FC4)|161.136|160.791|1.00| |Absdiff::OCL_AbsDiffFixture::(1280x720, 8UC1)|69.211|15.140|4.57| |Absdiff::OCL_AbsDiffFixture::(1280x720, 32FC1)|118.762|119.263|1.00| |Absdiff::OCL_AbsDiffFixture::(1280x720, 8UC3)|212.414|44.692|4.75| |Absdiff::OCL_AbsDiffFixture::(1280x720, 32FC3)|367.512|366.569|1.00| |Absdiff::OCL_AbsDiffFixture::(1280x720, 8UC4)|285.337|59.708|4.78| |Absdiff::OCL_AbsDiffFixture::(1280x720, 32FC4)|490.395|491.118|1.00| |Absdiff::OCL_AbsDiffFixture::(1920x1080, 8UC1)|158.827|33.462|4.75| |Absdiff::OCL_AbsDiffFixture::(1920x1080, 32FC1)|273.503|273.668|1.00| |Absdiff::OCL_AbsDiffFixture::(1920x1080, 8UC3)|484.175|100.520|4.82| |Absdiff::OCL_AbsDiffFixture::(1920x1080, 32FC3)|828.758|829.689|1.00| |Absdiff::OCL_AbsDiffFixture::(1920x1080, 8UC4)|648.592|137.195|4.73| |Absdiff::OCL_AbsDiffFixture::(1920x1080, 32FC4)|1116.755|1109.587|1.01| |Absdiff::OCL_AbsDiffFixture::(3840x2160, 8UC1)|648.715|134.875|4.81| |Absdiff::OCL_AbsDiffFixture::(3840x2160, 32FC1)|1115.939|1113.818|1.00| |Absdiff::OCL_AbsDiffFixture::(3840x2160, 8UC3)|1944.791|413.420|4.70| |Absdiff::OCL_AbsDiffFixture::(3840x2160, 32FC3)|3354.193|3324.672|1.01| |Absdiff::OCL_AbsDiffFixture::(3840x2160, 8UC4)|2594.585|553.486|4.69| |Absdiff::OCL_AbsDiffFixture::(3840x2160, 32FC4)|4473.543|4438.453|1.01| **Bitwise Operation** Geometric mean (ms) |Name of Test|opencv perf core Bit|opencv perf core Bit|opencv perf core Bit vs opencv perf core Bit (x-factor)| |---|:-:|:-:|:-:| |Bitwise_and::OCL_BitwiseAndFixture::(640x480, 8UC1)|22.542|4.971|4.53| |Bitwise_and::OCL_BitwiseAndFixture::(640x480, 32FC1)|90.210|19.917|4.53| |Bitwise_and::OCL_BitwiseAndFixture::(640x480, 8UC3)|68.429|15.037|4.55| |Bitwise_and::OCL_BitwiseAndFixture::(640x480, 32FC3)|280.168|59.239|4.73| |Bitwise_and::OCL_BitwiseAndFixture::(640x480, 8UC4)|90.565|19.735|4.59| |Bitwise_and::OCL_BitwiseAndFixture::(640x480, 32FC4)|374.695|79.257|4.73| |Bitwise_and::OCL_BitwiseAndFixture::(1280x720, 8UC1)|67.824|14.873|4.56| |Bitwise_and::OCL_BitwiseAndFixture::(1280x720, 32FC1)|279.514|59.232|4.72| |Bitwise_and::OCL_BitwiseAndFixture::(1280x720, 8UC3)|208.337|44.234|4.71| |Bitwise_and::OCL_BitwiseAndFixture::(1280x720, 32FC3)|851.211|182.522|4.66| |Bitwise_and::OCL_BitwiseAndFixture::(1280x720, 8UC4)|279.529|59.095|4.73| |Bitwise_and::OCL_BitwiseAndFixture::(1280x720, 32FC4)|1132.065|244.877|4.62| |Bitwise_and::OCL_BitwiseAndFixture::(1920x1080, 8UC1)|155.685|33.078|4.71| |Bitwise_and::OCL_BitwiseAndFixture::(1920x1080, 32FC1)|635.253|137.482|4.62| |Bitwise_and::OCL_BitwiseAndFixture::(1920x1080, 8UC3)|474.494|100.166|4.74| |Bitwise_and::OCL_BitwiseAndFixture::(1920x1080, 32FC3)|1907.340|412.841|4.62| |Bitwise_and::OCL_BitwiseAndFixture::(1920x1080, 8UC4)|635.538|134.544|4.72| |Bitwise_and::OCL_BitwiseAndFixture::(1920x1080, 32FC4)|2552.666|556.397|4.59| |Bitwise_and::OCL_BitwiseAndFixture::(3840x2160, 8UC1)|634.736|136.355|4.66| |Bitwise_and::OCL_BitwiseAndFixture::(3840x2160, 32FC1)|2548.283|561.827|4.54| |Bitwise_and::OCL_BitwiseAndFixture::(3840x2160, 8UC3)|1911.454|421.571|4.53| |Bitwise_and::OCL_BitwiseAndFixture::(3840x2160, 32FC3)|7663.803|1677.289|4.57| |Bitwise_and::OCL_BitwiseAndFixture::(3840x2160, 8UC4)|2543.983|562.780|4.52| |Bitwise_and::OCL_BitwiseAndFixture::(3840x2160, 32FC4)|10211.693|2237.393|4.56| |Bitwise_not::OCL_BitwiseNotFixture::(640x480, 8UC1)|22.341|4.811|4.64| |Bitwise_not::OCL_BitwiseNotFixture::(640x480, 32FC1)|89.975|19.288|4.66| |Bitwise_not::OCL_BitwiseNotFixture::(640x480, 8UC3)|67.237|14.643|4.59| |Bitwise_not::OCL_BitwiseNotFixture::(640x480, 32FC3)|276.324|58.609|4.71| |Bitwise_not::OCL_BitwiseNotFixture::(640x480, 8UC4)|89.587|19.554|4.58| |Bitwise_not::OCL_BitwiseNotFixture::(640x480, 32FC4)|370.986|77.136|4.81| |Bitwise_not::OCL_BitwiseNotFixture::(1280x720, 8UC1)|67.227|14.541|4.62| |Bitwise_not::OCL_BitwiseNotFixture::(1280x720, 32FC1)|276.357|58.076|4.76| |Bitwise_not::OCL_BitwiseNotFixture::(1280x720, 8UC3)|206.752|43.376|4.77| |Bitwise_not::OCL_BitwiseNotFixture::(1280x720, 32FC3)|841.638|177.787|4.73| |Bitwise_not::OCL_BitwiseNotFixture::(1280x720, 8UC4)|276.773|57.784|4.79| |Bitwise_not::OCL_BitwiseNotFixture::(1280x720, 32FC4)|1127.740|237.472|4.75| |Bitwise_not::OCL_BitwiseNotFixture::(1920x1080, 8UC1)|153.808|32.531|4.73| |Bitwise_not::OCL_BitwiseNotFixture::(1920x1080, 32FC1)|627.765|129.990|4.83| |Bitwise_not::OCL_BitwiseNotFixture::(1920x1080, 8UC3)|469.799|98.249|4.78| |Bitwise_not::OCL_BitwiseNotFixture::(1920x1080, 32FC3)|1893.591|403.694|4.69| |Bitwise_not::OCL_BitwiseNotFixture::(1920x1080, 8UC4)|627.724|129.962|4.83| |Bitwise_not::OCL_BitwiseNotFixture::(1920x1080, 32FC4)|2529.967|540.744|4.68| |Bitwise_not::OCL_BitwiseNotFixture::(3840x2160, 8UC1)|628.089|130.277|4.82| |Bitwise_not::OCL_BitwiseNotFixture::(3840x2160, 32FC1)|2521.817|540.146|4.67| |Bitwise_not::OCL_BitwiseNotFixture::(3840x2160, 8UC3)|1905.004|404.704|4.71| |Bitwise_not::OCL_BitwiseNotFixture::(3840x2160, 32FC3)|7567.971|1627.898|4.65| |Bitwise_not::OCL_BitwiseNotFixture::(3840x2160, 8UC4)|2531.476|540.181|4.69| |Bitwise_not::OCL_BitwiseNotFixture::(3840x2160, 32FC4)|10075.594|2181.654|4.62| |Bitwise_or::OCL_BitwiseOrFixture::(640x480, 8UC1)|22.566|5.076|4.45| |Bitwise_or::OCL_BitwiseOrFixture::(640x480, 32FC1)|90.391|19.928|4.54| |Bitwise_or::OCL_BitwiseOrFixture::(640x480, 8UC3)|67.758|14.740|4.60| |Bitwise_or::OCL_BitwiseOrFixture::(640x480, 32FC3)|279.253|59.844|4.67| |Bitwise_or::OCL_BitwiseOrFixture::(640x480, 8UC4)|90.296|19.802|4.56| |Bitwise_or::OCL_BitwiseOrFixture::(640x480, 32FC4)|373.972|79.815|4.69| |Bitwise_or::OCL_BitwiseOrFixture::(1280x720, 8UC1)|67.815|14.865|4.56| |Bitwise_or::OCL_BitwiseOrFixture::(1280x720, 32FC1)|279.398|60.054|4.65| |Bitwise_or::OCL_BitwiseOrFixture::(1280x720, 8UC3)|208.643|45.043|4.63| |Bitwise_or::OCL_BitwiseOrFixture::(1280x720, 32FC3)|850.042|180.985|4.70| |Bitwise_or::OCL_BitwiseOrFixture::(1280x720, 8UC4)|279.363|60.385|4.63| |Bitwise_or::OCL_BitwiseOrFixture::(1280x720, 32FC4)|1134.858|243.062|4.67| |Bitwise_or::OCL_BitwiseOrFixture::(1920x1080, 8UC1)|155.212|33.155|4.68| |Bitwise_or::OCL_BitwiseOrFixture::(1920x1080, 32FC1)|634.985|134.911|4.71| |Bitwise_or::OCL_BitwiseOrFixture::(1920x1080, 8UC3)|474.648|100.407|4.73| |Bitwise_or::OCL_BitwiseOrFixture::(1920x1080, 32FC3)|1912.049|414.184|4.62| |Bitwise_or::OCL_BitwiseOrFixture::(1920x1080, 8UC4)|635.252|132.587|4.79| |Bitwise_or::OCL_BitwiseOrFixture::(1920x1080, 32FC4)|2544.471|560.737|4.54| |Bitwise_or::OCL_BitwiseOrFixture::(3840x2160, 8UC1)|634.574|134.966|4.70| |Bitwise_or::OCL_BitwiseOrFixture::(3840x2160, 32FC1)|2545.129|561.498|4.53| |Bitwise_or::OCL_BitwiseOrFixture::(3840x2160, 8UC3)|1910.900|419.365|4.56| |Bitwise_or::OCL_BitwiseOrFixture::(3840x2160, 32FC3)|7662.603|1685.812|4.55| |Bitwise_or::OCL_BitwiseOrFixture::(3840x2160, 8UC4)|2548.971|560.787|4.55| |Bitwise_or::OCL_BitwiseOrFixture::(3840x2160, 32FC4)|10201.407|2237.552|4.56| |Bitwise_xor::OCL_BitwiseXorFixture::(640x480, 8UC1)|22.718|4.961|4.58| |Bitwise_xor::OCL_BitwiseXorFixture::(640x480, 32FC1)|91.496|19.831|4.61| |Bitwise_xor::OCL_BitwiseXorFixture::(640x480, 8UC3)|67.910|15.151|4.48| |Bitwise_xor::OCL_BitwiseXorFixture::(640x480, 32FC3)|279.612|59.792|4.68| |Bitwise_xor::OCL_BitwiseXorFixture::(640x480, 8UC4)|91.073|19.853|4.59| |Bitwise_xor::OCL_BitwiseXorFixture::(640x480, 32FC4)|374.641|79.155|4.73| |Bitwise_xor::OCL_BitwiseXorFixture::(1280x720, 8UC1)|67.704|15.008|4.51| |Bitwise_xor::OCL_BitwiseXorFixture::(1280x720, 32FC1)|279.229|60.088|4.65| |Bitwise_xor::OCL_BitwiseXorFixture::(1280x720, 8UC3)|208.156|44.426|4.69| |Bitwise_xor::OCL_BitwiseXorFixture::(1280x720, 32FC3)|849.501|180.848|4.70| |Bitwise_xor::OCL_BitwiseXorFixture::(1280x720, 8UC4)|279.642|59.728|4.68| |Bitwise_xor::OCL_BitwiseXorFixture::(1280x720, 32FC4)|1129.826|242.880|4.65| |Bitwise_xor::OCL_BitwiseXorFixture::(1920x1080, 8UC1)|155.585|33.354|4.66| |Bitwise_xor::OCL_BitwiseXorFixture::(1920x1080, 32FC1)|634.090|134.995|4.70| |Bitwise_xor::OCL_BitwiseXorFixture::(1920x1080, 8UC3)|474.931|99.598|4.77| |Bitwise_xor::OCL_BitwiseXorFixture::(1920x1080, 32FC3)|1910.519|413.138|4.62| |Bitwise_xor::OCL_BitwiseXorFixture::(1920x1080, 8UC4)|635.026|135.155|4.70| |Bitwise_xor::OCL_BitwiseXorFixture::(1920x1080, 32FC4)|2560.167|560.838|4.56| |Bitwise_xor::OCL_BitwiseXorFixture::(3840x2160, 8UC1)|634.893|134.883|4.71| |Bitwise_xor::OCL_BitwiseXorFixture::(3840x2160, 32FC1)|2548.166|560.831|4.54| |Bitwise_xor::OCL_BitwiseXorFixture::(3840x2160, 8UC3)|1911.392|419.816|4.55| |Bitwise_xor::OCL_BitwiseXorFixture::(3840x2160, 32FC3)|7646.634|1677.988|4.56| |Bitwise_xor::OCL_BitwiseXorFixture::(3840x2160, 8UC4)|2560.637|560.805|4.57| |Bitwise_xor::OCL_BitwiseXorFixture::(3840x2160, 32FC4)|10227.044|2249.458|4.55| ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
Have the findContours_legacy overload call findContours_legacy.
imgproc: fix contour approximation, added test
Added branch with variadic version of Trust tuple
…ck_ilp64 core: deployment compatibility for old mac after Accelerate New LAPACK fix opencv#25625 Attempt to fix opencv#24804 (comment) We may need to explicitly add build option `-DCMAKE_OSX_DEPLOYMENT_TARGET=12.0` or environment variable (`export MACOSX_DEPLOYMENT_TARGET=12.0`) for mac builds (python package most probably) on builders with new macOS (>= 13.3). ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
Fixed offset computation for ND case in MinMaxIdx HAL.
…-input Slice layer parser fix to support empty input case opencv#25660 This PR fixes Slice Layer's parser to handle empty input cases (cases with initializer) It fixed the issue rased in opencv#24838 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
…xrt-backend-into-v2-api Port G-API ONNXRT backend into V2 API opencv#25662 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [ ] I agree to contribute to the project under Apache 2 License. - [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [ ] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
…_markers fixed marker generation in charuco board opencv#25673 When generating Charuco board markers in `generateImage()`, a problem occurs as in opencv#24806, opencv#24873: In low resolution:  In medium resolution:  This PR fixed this problems:   The test checks the inner and outer borders of the Aruco markers. In the outer border of Aruco marker, all pixels should be white. In the inner border of Aruco marker, all pixels should be black.  ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
…alib3d calib3d: fix data type for ilp64 lapack
Reverted contour approximation behavior opencv#25680 Related issue opencv#25663 - revert new function behavior despite it returning different result than the old one (reverts PR opencv#25672). Also added Coverity issue fix.
Support Global_Pool_2D ops in .tflite model opencv#25613 ### Pull Request Readiness Checklist **Merge with extra**: opencv/opencv_extra#1180 This PR adds support for `GlobalAveragePooling2D` and `GlobalMaxPool2D` on the TFlite backend. When the k`eep_dims` option is enabled, the output is a 2D tensor, necessitating the inclusion of an additional flatten layer. Additionally, the names of these layers have been updated to match the output tensor names generated by `generate.py` from the opencv_extra repository. - [X] I agree to contribute to the project under Apache 2 License. - [X] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [X] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [X] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [X] The feature is well documented and sample code can be built with the project CMake
Fix Homography computation. opencv#25665 The bug was introduced in opencv#25308 I am sorry I do not have a proper test. ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
Suppress build warnings for GCC14 opencv#25686 Close opencv#25674 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
Tests added for mixed type arithmetic operations opencv#25671 ### Changes * added accuracy tests for mixed type arithmetic operations _Note: div-by-zero values are removed from checking since the result is implementation-defined in common case_ * added perf tests for the same cases * fixed a typo in `getMulExtTab()` function that lead to dead code ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
These are common with USB cameras.
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Draft PR since I totally goofed the merge history. This needs to be just pushed to a branch directly i think.