core: deployment compatibility for old mac after Accelerate New LAPACK fix#25625
Attempt to fix https://github.com/opencv/opencv/pull/24804#discussion_r1609957747
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
3rdparty: NDSRVP - A New 3rdparty Library with Optimizations Based on RISC-V P Extension v0.5.2 - Part 1: Basic Functions #25167
# Summary
### Previous context
From PR #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: 8bbf08f0de/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
Refactor DNN module to build with cudnn 9 #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
(https://github.com/opencv/opencv/issues/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
imgcodecs: support IMWRITE_JPEG_LUMA/CHROMA_QUALITY with internal libjpeg-turbo #25647Close#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
YUV codes for cvtColor: descriptions added #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
Libjpeg-turbo update to version 3.0.3 #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
Fix handeye #24897
Fixes to the hand-eye calibration methods, from #24871.
The Tsai method is sensitive to poses separated by small rotations, so I filter those out.
The Horaud and Daniilidis methods use quaternions (and dual quaternions), where $q$ and $-q$ represent the same transform.
However, these methods depend on the gripper motion and camera motion having the same sign for the real part.
The fix was simply to multiply the (dual) quaternions by -1 if their real part is negative.
### 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~~ N/A
Tests for cvSmooth -> tests for boxFilter #25634fixes#25448
### Motivation
The obsolete function `cvSmooth` has two modes in which it calls `cv::boxFilter()` inside with and without normalization.
This function is covered by tests exactly for that modes.
This means that by replacing `cvSmooth` call by `cv::boxFilter()` we will leave the coverage untouched (but more obvious) and remove that obsolete function from tests.
### 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
Tests for cv::rotate() added #25633fixes#25449
### Pull Request Readiness Checklist
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Fixed CMake Missing variable is: CMAKE_ASM_COMPILE_OBJECT in PNG build #25631
Error message with `-DBUILD_PNG=ON` on ARM64:
```
-- Configuring done
CMake Error: Error required internal CMake variable not set, cmake may not be built correctly.
Missing variable is:
CMAKE_ASM_COMPILE_OBJECT
-- Generating done
CMake Generate step failed. Build files cannot be regenerated correctly.
```
### Pull Request Readiness Checklist
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KleidiCV HAL update to version 0.1.0. #25618
Original integration PR: https://github.com/opencv/opencv/pull/25443
Force the library for testing with CI
### Pull Request Readiness Checklist
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3rdparty: update libpng 1.6.43 #25580
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core: try to solve warnings caused by Apple's new LAPACK interface #24804
Resolves https://github.com/opencv/opencv/issues/24660
Apple's BLAS documentation: https://developer.apple.com/documentation/accelerate/blas?language=objc
New interface since macOS >= 13.3, iOS >= 16.4.
Todo:
- [x] Detect macOS version.
- [x] ~Detect iOS versions (major and minor version).~ No calling of Accelerate New LAPACK on iOS.
- [x] Solve calling `cblas_cgemm` and `cblas_zgemm`.
### Pull Request Readiness Checklist
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Fix v_round and enable unit tests for scalable universal intrinsic 64F type. #25586
This may be a legacy issue from the previous PR #24325. I don't quite remember why the float 64 part of the unit test was not enabled at that time.
Whatever, this patch enables the unit tests for scalable 64F type , and makes the necessary modifications to the RVV backend to make the tests pass.
This patch is compiled by GCC 14 and LLVM 17 &18, and tested on QEMU and k230.
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Element-wise test for 1D #25116
This PR introduces 1D parametrized test for element wise layer. The means that the tests covers following layer:
`Clip`, `ReLU6`, `ReLU`,
`GeLU`, `GeluApprox`, `TanH`,
`Swish`, `Mish`, `Sigmoid`,
`ELULayer`, `Abs`, `BNLL`,
`Ceil`, `Floor`, `LogLayer`,
`Round`, `Sqrt`, `Acos`,
`Acosh`, `Asin`, `Asinh`,
`Atan`, `Atanh`, `Cos`,
`Sin`, `Sinh`, `Tan`, `Erf`,
`Reciprocal`, `Cosh`, `HardSwish`,
`Softplus`, `Softsign`, `Celu`,
`HardSigmid`, `Selu`, `ThresholdedRelu`,
`Power`, `Exp`, `Sign`, `Shrink`,
`ChannelsPReLU`
Not sure if this is best way to implement this test.
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Add support for scalar and matrix multiplication in einsum #25595
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