mirror of
https://github.com/opencv/opencv.git
synced 2026-07-21 19:33:03 +04:00
Jonas Perolini
2bca09a191
Include pixel-based confidence in ArUco marker detection #23190 The aim of this pull request is to compute a **pixel-based confidence** of the marker detection. The confidence [0;1] is defined as the percentage of correctly detected pixels, with 1 describing a pixel perfect detection. Currently it is possible to get the normalized Hamming distance between the detected marker and the dictionary ground truth [Dictionary::getDistanceToId()](https://github.com/opencv/opencv/blob/4.x/modules/objdetect/src/aruco/aruco_dictionary.cpp#L114) However, this distance is based on the extracted bits and we lose information in the [majority count step](https://github.com/opencv/opencv/blob/4.x/modules/objdetect/src/aruco/aruco_detector.cpp#L487). For example, even if each cell has 49% incorrect pixels, we still obtain a perfect Hamming distance. **Implementation tests**: Generate 36 synthetic images containing 4 markers each (with different ids) so a total of 144 markers. Invert a given percentage of pixels in each cell of the marker to simulate uncertain detection. Assuming a perfect detection, define the ground truth uncertainty as the percentage of inverted pixels. The test is passed if `abs(computedConfidece - groundTruthConfidence) < 0.05` where `0.05` accounts for minor detection inaccuracies. - Performed for both regular and inverted markers - Included perspective-distorted markers - Markers in all 4 possible rotations [0, 90, 180, 270] - Different set of detection params: - `perspectiveRemovePixelPerCell` - `perspectiveRemoveIgnoredMarginPerCell` - `markerBorderBits`  The code properly builds locally and `opencv_test_objdetect` and `opencv_test_core` passed. Please let me know if there are any further modifications needed. Thanks! I've also pushed minor unrelated improvement (let me know if you want a separate PR) in the [bit extraction method](https://github.com/opencv/opencv/blob/4.x/modules/objdetect/src/aruco/aruco_detector.cpp#L435). `CV_Assert(perspectiveRemoveIgnoredMarginPerCell <=1)` should be `< 0.5`. Since there are margins on both sides of the cell, the margins must be smaller than half of the cell. When setting `perspectiveRemoveIgnoredMarginPerCell >= 0.5`, `opencv_test_objdetect` fails. Note: 0.499 is ok because `int()` will floor the result, thus `cellMarginPixels = int(cellMarginRate * cellSize)` will be smaller than `cellSize / 2` ### Pull Request Readiness Checklist - [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 - [ ] 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 PR is proposed to the proper branch - [x] The feature is well documented and sample code can be built with the project CMake
OpenCV: Open Source Computer Vision Library
Resources
- Homepage: https://opencv.org
- Courses: https://opencv.org/courses
- Docs: https://docs.opencv.org/4.x/
- Q&A forum: https://forum.opencv.org
- previous forum (read only): http://answers.opencv.org
- Issue tracking: https://github.com/opencv/opencv/issues
- Additional OpenCV functionality: https://github.com/opencv/opencv_contrib
- Donate to OpenCV: https://opencv.org/support/
Contributing
Please read the contribution guidelines before starting work on a pull request.
Summary of the guidelines:
- One pull request per issue;
- Choose the right base branch;
- Include tests and documentation;
- Clean up "oops" commits before submitting;
- Follow the coding style guide.
Additional Resources
- Submit your OpenCV-based project for inclusion in Community Friday on opencv.org
- Subscribe to the OpenCV YouTube Channel featuring OpenCV Live, an hour-long streaming show
- Follow OpenCV on LinkedIn for daily posts showing the state-of-the-art in computer vision & AI
- Apply to be an OpenCV Volunteer to help organize events and online campaigns as well as amplify them
- Follow OpenCV on Mastodon in the Fediverse
- Follow OpenCV on Twitter
- OpenCV.ai: Computer Vision and AI development services from the OpenCV team.