Relates to #24603
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Tests added for mixed type arithmetic operations #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
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- [x] I agree to contribute to the project under Apache 2 License.
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Suppress build warnings for GCC14 #25686Close#25674
### Pull Request Readiness Checklist
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Fix Homography computation. #25665
The bug was introduced in https://github.com/opencv/opencv/pull/25308
I am sorry I do not have a proper test.
### Pull Request Readiness Checklist
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- [x] I agree to contribute to the project under Apache 2 License.
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Support Global_Pool_2D ops in .tflite model #25613
### Pull Request Readiness Checklist
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/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.
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Reverted contour approximation behavior #25680
Related issue #25663 - revert new function behavior despite it returning different result than the old one (reverts PR #25672).
Also added Coverity issue fix.
Port G-API ONNXRT backend into V2 API #25662
### Pull Request Readiness Checklist
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Slice layer parser fix to support empty input case #25660
This PR fixes Slice Layer's parser to handle empty input cases (cases with initializer)
It fixed the issue rased in #24838
### Pull Request Readiness Checklist
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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).
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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
Robert Lexmann
Pierre Chatelier
Maxim Smolskiy
Alexander Smorkalov
Dmitry Kurtaev
Maksim Shabunin
Maxim Milashchenko
Alexander Panov
Vadim Levin
Alexander Lyulkov
cudawarped
Alexander Smorkalov
Patrick Keane
Alexander Alekhin
Rostislav Vasilikhin
Kumataro
Vincent Rabaud
CNOCycle
fengyuentau
Ujjayant Kadian
Abduragim Shtanchaev
Junyan721113
Danial Javady
Suleyman TURKMEN
John Stechschulte