opencv/doc/tutorials/objdetect/aruco_calibration/aruco_calibration.markdown
Alexander Panov e2621f128e
Merge pull request #25378 from AleksandrPanov:move_charuco_tutorial
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


### 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
2024-04-16 12:14:33 +03:00

4.8 KiB

Calibration with ArUco and ChArUco

@prev_tutorial{tutorial_charuco_diamond_detection} @next_tutorial{tutorial_aruco_faq}

The ArUco module can also be used to calibrate a camera. Camera calibration consists in obtaining the camera intrinsic parameters and distortion coefficients. This parameters remain fixed unless the camera optic is modified, thus camera calibration only need to be done once.

Camera calibration is usually performed using the OpenCV cv::calibrateCamera() function. This function requires some correspondences between environment points and their projection in the camera image from different viewpoints. In general, these correspondences are obtained from the corners of chessboard patterns. See cv::calibrateCamera() function documentation or the OpenCV calibration tutorial for more detailed information.

Using the ArUco module, calibration can be performed based on ArUco markers corners or ChArUco corners. Calibrating using ArUco is much more versatile than using traditional chessboard patterns, since it allows occlusions or partial views.

As it can be stated, calibration can be done using both, marker corners or ChArUco corners. However, it is highly recommended using the ChArUco corners approach since the provided corners are much more accurate in comparison to the marker corners. Calibration using a standard Board should only be employed in those scenarios where the ChArUco boards cannot be employed because of any kind of restriction.

Calibration with ChArUco Boards

To calibrate using a ChArUco board, it is necessary to detect the board from different viewpoints, in the same way that the standard calibration does with the traditional chessboard pattern. However, due to the benefits of using ChArUco, occlusions and partial views are allowed, and not all the corners need to be visible in all the viewpoints.

ChArUco calibration viewpoints

The example of using cv::calibrateCamera() for cv::aruco::CharucoBoard:

@snippet samples/cpp/tutorial_code/objectDetection/calibrate_camera_charuco.cpp CalibrationWithCharucoBoard1 @snippet samples/cpp/tutorial_code/objectDetection/calibrate_camera_charuco.cpp CalibrationWithCharucoBoard2 @snippet samples/cpp/tutorial_code/objectDetection/calibrate_camera_charuco.cpp CalibrationWithCharucoBoard3

The ChArUco corners and ChArUco identifiers captured on each viewpoint are stored in the vectors allCharucoCorners and allCharucoIds, one element per viewpoint.

The calibrateCamera() function will fill the cameraMatrix and distCoeffs arrays with the camera calibration parameters. It will return the reprojection error obtained from the calibration. The elements in rvecs and tvecs will be filled with the estimated pose of the camera (respect to the ChArUco board) in each of the viewpoints.

Finally, the calibrationFlags parameter determines some of the options for the calibration.

A full working example is included in the calibrate_camera_charuco.cpp inside the samples/cpp/tutorial_code/objectDetection folder.

The samples now take input via commandline via the cv::CommandLineParser. For this file the example parameters will look like: @code{.cpp} "camera_calib.txt" -w=5 -h=7 -sl=0.04 -ml=0.02 -d=10 -v=path/img_%02d.jpg @endcode

The camera calibration parameters from opencv/samples/cpp/tutorial_code/objectDetection/tutorial_camera_charuco.yml were obtained by the img_00.jpg-img_03.jpg placed from this folder.

Calibration with ArUco Boards

As it has been stated, it is recommended the use of ChAruco boards instead of ArUco boards for camera calibration, since ChArUco corners are more accurate than marker corners. However, in some special cases it must be required to use calibration based on ArUco boards. As in the previous case, it requires the detections of an ArUco board from different viewpoints.

ArUco calibration viewpoints

The example of using cv::calibrateCamera() for cv::aruco::GridBoard:

@snippet samples/cpp/tutorial_code/objectDetection/calibrate_camera.cpp CalibrationWithArucoBoard1 @snippet samples/cpp/tutorial_code/objectDetection/calibrate_camera.cpp CalibrationWithArucoBoard2 @snippet samples/cpp/tutorial_code/objectDetection/calibrate_camera.cpp CalibrationWithArucoBoard3

A full working example is included in the calibrate_camera.cpp inside the samples/cpp/tutorial_code/objectDetection folder.

The samples now take input via commandline via the cv::CommandLineParser. For this file the example parameters will look like: @code{.cpp} "camera_calib.txt" -w=5 -h=7 -l=100 -s=10 -d=10 -v=path/aruco_videos_or_images @endcode