Animated WebP Support #25608
related issues #24855#22569
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V4l default image size #25500
Added ability to set default image width and height for V4L capture. This is required for cameras that does not support 640x480 resolution because otherwise V4L capture cannot be opened and failed with "Pixel format of incoming image is unsupported by OpenCV" and then with "can't open camera by index" message. Because of the videoio architecture it is not possible to insert actions between CvCaptureCAM_V4L::CvCaptureCAM_V4L and CvCaptureCAM_V4L::open so the only way I found is to use environment variables to preselect the resolution.
Related bug report is [#25499](https://github.com/opencv/opencv/issues/25499)
Maybe (but not confirmed) this is also related to [#24551](https://github.com/opencv/opencv/issues/24551)
This fix was made and verified in my local environment: capture board AVMATRIX VC42, Ubuntu 20, NVidia Jetson Orin.
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FastCV-based HAL for OpenCV acceleration 2ndpost-2 #26619
### Detailed description:
- Add support for multiply 8u, 16s and 32f
- Add support for cv_hal_pyrdown 8u
- Add support for cv_hal_cvtBGRtoHSV and cv_hal_cvtBGRtoYUVApprox 8u
Requires binary from [opencv/opencv_3rdparty#90](https://github.com/opencv/opencv_3rdparty/pull/90)
Depends on: [opencv/opencv#26617](https://github.com/opencv/opencv/pull/26617)
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Fix VideoCapture fails to read single image with digits in name #26637
### Pull Request Readiness Checklist
Fix#26457
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js: fix generation of "const const" in code #26640
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Add RISC-V HAL implementation for meanStdDev #26624
`meanStdDev` benefits from the Universal Intrinsic backend of RVV, but we also found that the performance on the `8UC4` type is worse than the scalar version when there is a mask, and there is no optimization implementation on `32FC1`.
This patch implements `meanStdDev` function in RVV_HAL using native intrinsic, significantly optimizing the performance for `8UC1`, `8UC4` and `32FC1`.
This patch is tested on BPI-F3 for both gcc 14.2 and clang 19.1.
```
$ opencv_test_core --gtest_filter="*MeanStdDev*"
$ opencv_perf_core --gtest_filter="Size_MatType_meanStdDev*
```
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Android camera feature enhancements #26627
Closes https://github.com/opencv/opencv/issues/24687
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FastCV-based HAL for OpenCV acceleration 2ndpost-1 #26617
### Detailed description:
- Add parallel support for cv_hal_sobel
- Add cv_hal_gaussianBlurBinomial and parallel support.
- Add cv_hal_addWeighted8u and parallel support
- Add cv_hal_warpPerspective and parallel support
Requires binary from [opencv/opencv_3rdparty#90](https://github.com/opencv/opencv_3rdparty/pull/90)
Related patch to opencv_contrib: [opencv/opencv_contrib#3844](https://github.com/opencv/opencv_contrib/pull/3844)
### Pull Request Readiness Checklist
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More convenient GpuMatND constructor #26472Closes#26471
For convenience, GpuMatND can now accept a step.size() equal to size.size(), as long as the last step is equal to elemSize()
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Switch calibration.cpp to C++ #26490
The CvLevMarq code has to be kept in order to keep the same accuracy (the C++ solver is not as good).
There are two ways to review this PR: by comparing to the old code, or by checking what is different from the 5.x version (which is the first commit).
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Support C++20 standard #26590
Close https://github.com/opencv/opencv/issues/26589
Related https://github.com/opencv/opencv_contrib/pull/3842
Related: https://github.com/opencv/opencv/issues/20269
- do not arithmetic enums and ( different enums or floating numeric)
- remove unused variable
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Previously, the yoloPostProcessing function assumed that the number of classes (nc) was fixed at 80. This caused incorrect behavior when a different number of classes was specified, leading to mismatched output shapes.
This update modifies the code to use the provided `nc` value dynamically, ensuring that the output shapes are correctly calculated based on the specified number of classes. This prevents issues when `nc` is not equal to 80 and allows for greater flexibility in model configurations.
This branch and commit address an issue in the YOLO example (samples/dnn/yolo_detector.cpp) where the mean and scale parameters only affected the first channel (B) due to single-value input. The modification updates these parameters to accept multi-channel values, ensuring consistent preprocessing across all image channels.
