Add Raspberry Pi 4 and 5 V4L2 Stateless HEVC Hardware Acceleration with FFmpeg #27453
This PR enables V4L2 stateless HEVC hardware acceleration for Raspberry Pi 5 within OpenCV's videoio module. It leverages FFmpeg's drm acceleration ([FFmpeg API changes](ee1f79b0fa/doc/APIchanges (L1529))), significantly improving HEVC decoding performance on RPi5 for robotics and embedded vision applications.
I have a working proof-of-concept with local benchmarks showing clear gains.
Checklist Status:
Ready: License, branch (4.x), FFmpeg reference, and (linked) related issue (#27452).
Seeking Guidance: Need help with formal C++ performance/accuracy tests, opencv_extra integration, and full documentation/examples.
As a Python developer, I welcome C++ best practice feedback and assistance with testing setup.
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The Kullback-Leibler divergence works with histogram that have integral = 1,
otherwise it can return negative values. The normalization of the histograms
have been changed accordingly, and all the six comparison methods have been
used in the histogram comparison tutorial.
Fix Typos in Comments and Documentation #27455
Description:
This pull request corrects minor typos in comments and documentation within the codebase:
- Replaces "representitive" with "representative" in kmeans.cpp.
- Replaces "indices" with the correct spelling in a comment in main.cu.
Fix Typos in Comments and Error Messages Across Multiple Files #27434
Description:
This pull request corrects several typographical errors in comments and error messages in the following files:
- `samples/directx/d3d11_interop.cpp`: Fixed typo in the error message ("betweem" → "between").
- `samples/dnn/yolo_detector.cpp`: Fixed typo in a comment ("elemets" → "elements").
- `samples/winrt/ImageManipulations/MediaExtensions/OcvTransform.cpp`: Fixed typo in a comment ("peferred" → "preferred").
These changes improve code readability and maintain consistency in documentation and error reporting. No functional code was modified.
Fix NaNs in HDR Triangle Weights and Tonemapping and Update LDR Ground Truth in tutorial #27396
The PR closes#27392
Updated the triangle weights to use a small epsilon value instead of zero to prevent NaN issues in HDR processing.
Also fixed a float-to-double division issue by explicitly casting double values to float, which was previously producing garbage values and leading to NaNs in tonemapping.
The current LDR ground truth image used in the tutorial [ldr.png](https://github.com/opencv/opencv/blob/4.x/doc/tutorials/others/images/ldr.png) was originally generated using TonemapDurand (check this commit 833f8d16fa), which was moved to opencv_contrib a long time ago in this commit: 742f22c09b. However, the current Tonemap implementation in OpenCV main only performs normalization and gamma correction, which produces noticeably different results. This PR updates the LDR grouth truth image in tutorial with the result of TonemapDrago, and tutorials to use TonemapDrago as Tonemap gives a darker image.
Tonemap output:
TonemapDrago output:
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Replaced sprintf with snprintf #26815
Fixes : #26814
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Feature: weighted Hough Transform #21407
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AndroidMediaNdkVideoWriter pixel format enhancement #26698
* videoio(Android): Add source pixel formats RGBA and GRAY to AndroidMediaNdkVideoWriter
Let AndroidMediaNdkVideoWriter::write() deduce source pixel format from matrix type:
CV_8UC3 -> BGR (as before)
CV_8UC4 -> RGBA (use in conjunction with CvCameraViewFrame)
CV_8UC1 -> GRAY
* samples/android/video-recorder: Send images to VideoWriter in RGBA format
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AndroidMediaNdkCapture pixel format enhancement #26656
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Animated WebP Support #25608
related issues #24855#22569
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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.
Updated trackbar callback function and improved documentation #26524
This Fixes#26467
Description:
This pull request improve the OpenCV documentation regarding the Trackbar functionality. The current documentation does not provide clear guidance on certain aspects, such as handling the value pointer deprecation and utilizing callback arguments in C. This update addresses those gaps and provides an updated example for better clarity.
Changes:
Updated Documentation:
Clarified the usage of the value pointer and explained how to pass an initial value, since the value pointer is deprecated.
Added more detailed explanations about callback arguments in C, ensuring that users understand how to access and use them in Trackbar callbacks.
Added a note on how to properly handle initial value passing without relying on the deprecated value pointer.
Updated Tutorial Example:
Renamed and used callback function parameters to make them more understandable.
Included a demonstration on how to utilize userdata in the callback function.
Additional Notes:
Removed reliance on the value pointer for updating trackbar values. Users are now encouraged to use other mechanisms as per the current implementation to avoid the runtime warning.
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Update Documentation #26260
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Added and tested yolov5l model. #26154
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Improved samples/python/tracker.py docstring #25959
This PR removed unused arguments and updated existing argument placeholders to be more descriptive of what they are.
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Support OpenGL GTK3 New API #25822Fixes#20001
GSoC2024 Project
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Add sample support of YOLOv9 and YOLOv10 in OpenCV #25794
This PR adds sample support of [`YOLOv9`](https://github.com/WongKinYiu/yolov9) and [`YOLOv10`](https://github.com/THU-MIG/yolov10/tree/main)) in OpenCV. Models for this test are located in this [PR](https://github.com/opencv/opencv_extra/pull/1186).
**Running YOLOv10 using OpenCV.**
1. In oder to run `YOLOv10` one needs to cut off postporcessing with dynamic shapes from torch and then convert it to ONNX. If someone is looking for ready solution, there is [this forked branch](https://github.com/Abdurrahheem/yolov10/tree/ash/opencv-export) from official YOLOv10. Particularty follow this proceduce.
```bash
git clone git@github.com:Abdurrahheem/yolov10.git
conda create -n yolov10 python=3.9
conda activate yolov10
pip install -r requirements.txt
python export_opencv.py --model=<model-name> --imgsz=<input-img-size>
```
By default `model="yolov10s"` and `imgsz=(480,640)`. This will generate file `yolov10s.onnx`, which can be use for inference in OpenCV
2. For inference part on OpenCV. one can use `yolo_detector.cpp` [sample](https://github.com/opencv/opencv/blob/4.x/samples/dnn/yolo_detector.cpp). If you have followed above exporting procedure, then you can use following command to run the model.
``` bash
build opencv from source
cd build
./bin/example_dnn_yolo_detector --model=<path-to-yolov10s.onnx-file> --yolo=yolov10 --width=640 --height=480 --input=<path-to-image> --scale=0.003921568627 --padvalue=114
```
If you do not specify `--input` argument, OpenCV will grab first camera that is avaliable on your platform.
For more deatils on how to run the `yolo_detector.cpp` file see this [guide](https://docs.opencv.org/4.x/da/d9d/tutorial_dnn_yolo.html#autotoc_md443)
**Running YOLOv9 using OpenCV**
1. Export model following [official guide](https://github.com/WongKinYiu/yolov9)of the YOLOv9 repository. Particularly you can do following for converting.
```bash
git clone https://github.com/WongKinYiu/yolov9.git
cd yolov9
conda create -n yolov9 python=3.9
conda activate yolov9
pip install -r requirements.txt
wget https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-t-converted.pt
python export.py --weights=./yolov9-t-converted.pt --include=onnx --img-size=(480,640)
```
This will generate <yolov9-t-converted.onnx> file.
2. Inference on OpenCV.
```bash
build opencv from source
cd build
./bin/example_dnn_yolo_detector --model=<path-to-yolov9-t-converted.onnx> --yolo=yolov9 --width=640 --height=480 --scale=0.003921568627 --padvalue=114 --path=<path-to-image>
```
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Update the tutorial of using Orbbec Astra cameras #25813
This PR is the backport of Orbbec OpenNI-based Astra camera related changes from #25410 to the 4.x branch, which includes updating the tutorial of Orbbec Astra cameras, renaming `orbbec_astra.cpp`.
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Add yolov8l.onnx to samples #25775
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Hello, I noticed that the /samples/dnn/models.yml said it should be used for all yolov8 models, but the YOLOv8l is not included in the file, so I added it to the file, thanks.
video: fix vittrack in the case where crop size grows until out-of-memory when the input is black #25771
Fixes https://github.com/opencv/opencv/issues/25760
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Move Charuco/Calib tutorials and samples to main repo #25378
Merge with https://github.com/opencv/opencv_contrib/pull/3708
Move Charuco/Calib tutorials and samples to main repo:
- [x] update/fix charuco_detection.markdown and samples
- [x] update/fix charuco_diamond_detection.markdown and samples
- [x] update/fix aruco_calibration.markdown and samples
- [x] update/fix aruco_faq.markdown
- [x] move tutorials, samples and tests to main repo
- [x] remove old tutorials, samples and tests from contrib
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Added and tested yolov8m model. #25357
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