samples: add compatibility note for Mask R-CNN in OpenCV 5.0 #28053
### 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
### Summary
Updates documentation in `samples/dnn/mask_rcnn.py` to clarify a compatibility issue with OpenCV 5.0.
### Details
As verified in issue #27240, OpenCV 5.0 introduces stricter graph optimization. The default `.pbtxt` used in this sample treats `detection_out_final` as an intermediate layer (it is not listed in `getUnconnectedOutLayersNames`).
While OpenCV 4.x implicitly allows retrieving this layer, OpenCV 5.0 throws an error when requesting it:
> "the number of requested and actual outputs must be the same"
This PR adds:
1. A warning note in the file header explaining the strict output requirement.
2. An inline comment near `net.forward()` to guide users debugging this error.
Relates to issue: #27240
docs(js): Fix Mat.clone() documentation to use mat_clone() for deep copy #27985
- Update code example to use ```mat_clone()``` instead of ```clone()```
- Add explanatory note about shallow copy issue due to Emscripten embind
Problem
- OpenCV.js documentation shows ```Mat.clone()``` usage, but this method performs shallow copy instead of deep copy due to Emscripten embind limitations, causing unexpected behavior where modifications to cloned matrices affect the original.
Related Issues and PRs
- Fixes documentation aspect of issue #27572
- Related to PR #26643 (js_clone_fix)
### 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
Visualization script for one or more cameras calibration with calibration.cpp #27627
* It reads one or more YAML files produced by `calibration.cpp`.
* displays an interactive 3D scene of the calibration setup with camera frustums and board meshes.
* Supports both **camera-centric** and **board-centric** views.
* exports the entire scene as a `.obj/.mtl` for use in meshlab.
* also includes an error bar plot and frustum highlighting for single-camera setups.
calibration results generated by `calibration.cpp`: https://drive.google.com/drive/folders/13NvZHaxTFdAB-bzlNve8kKSMIg9gAu6K
Results Uploaded at: https://drive.google.com/drive/folders/1fkFyoBRGcNY4UCQhGgadO6uKlAkrzjTe
### 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
2025-10-14T05:53:31.5387050Z C:\GHA-OCV-1\_work\ci-gha-workflow\ci-gha-workflow\opencv\modules\imgcodecs\src\bitstrm.cpp(156,57): warning C4244: 'argument': conversion from 'int64_t' to 'ptrdiff_t', possible loss of data [C:\GHA-OCV-1\_work\ci-gha-workflow\ci-gha-workflow\build\modules\imgcodecs\opencv_imgcodecs.vcxproj]
### Pull Request Readiness Checklist
Optional Known Foreground Mask for Background Subtractors #27810
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
### Description
This adds an optional foreground input mask parameter to the MOG2 and KNN background subtractors, in line with issue https://github.com/opencv/opencv/issues/26476
4 tests are added under test_bgfg2.cpp:
2 for each subtractor type (1 with shadow detection and 1 without)
A demo shows the feature with only 3 parameters and with a 4th optional foreground mask for both core subtractor types.
Note: To patch contrib inheritance of the background subtraction class, empty apply method which throws a not implemented error is added to contrib subclasses. This is done to keep the overloaded apply function as pure virtual. Contrib PR to be made and linked shortly.
Contrib Repo Paired Pull Request: https://github.com/opencv/opencv_contrib/pull/4017
3D object pose estimation tutorial update #27874
1. Use cv::loadMesh instead of custom csv-based reader.
2. Use code snippets
### 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
- [ ] 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
Added image super-resolution samples using seemoredetails model #27592
Based on "See More Details: Efficient Image Super-Resolution by Experts Mining" (ICML 2024)
### 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
This pull request adds a new sample named seemore_superres under samples/dnn/, implemented in both Python and C++.
The sample demonstrates image super-resolution using the Seemoredetails model with OpenCV’s DNN module.
### Files Added:
- samples/dnn/seemore_superres.cpp
- samples/dnn/seemore_superres.py
- Updated samples/dnn/models.yml
### Functionality:
- Performs image upscaling(4x) using a specified Seemoredetails ONNX model.
- Accepts image path and ONNX model path as command-line arguments.
- Outputs the original and super-resolved images side by side for visual comparison.
### Sample Usage:
*C++*
./seemore_superres --input=path/to/image.jpg
`
*Python*
python seemore_superres.py --input=path/to/image.jpg
`
Add Alpha matting samples (C++ and Python) #27593
### 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
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](https://github.com/FFmpeg/FFmpeg/blob/ee1f79b0fa4c82da9c19328b049b593c71611402/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.
### 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
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.
Adding color correction module to photo module from opencv_contrib #27051
This PR moved color correction module from opencv_contrib to main repo inside photo module.
### 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
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.
Added DNN based deblurring samples #27349
Corresponding pull request adding quantized onnx model to opencv_zoo: https://github.com/opencv/opencv_zoo/pull/295
Model size: 88MB
### 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
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 https://github.com/opencv/opencv/commit/833f8d16fab5e57c5e800a55fa0fb08c7a31c3b1), which was moved to opencv_contrib a long time ago in this commit: https://github.com/opencv/opencv/commit/742f22c09bd0c27b450f141bc984f280c8cde98e. 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:
### 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
Fix hard dependency of dnn for mcc module. #27246
Currently building objdetect module without dnn fails due to mcc module. This PR makes the dependency optional, by checking if DNN is available in mcc module.
### 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
TonyCY24
Alexander Smorkalov
happy-capybara-man
Alexander Smorkalov
Skreg
Dave Merchant
D00E
MykhailoTrushch
Naresh M L
Harii Sankar S
Yan Wen
ClaudioMartino
krikera
leopardracer
Maxim Evtush
kilavvy
Gursimar Singh
Abhishek Gola
Kavyansh Tyagi
Ken Shirriff
nina16448
