calib3d library

Classes

Fisheye

Functions

calibrateCamera(Contours3f objectPoints, Contours2f imagePoints, (int, int) imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, {Mat? rvecs, Mat? tvecs, int flags = 0, (int, int, double) criteria = (TERM_COUNT + TERM_EPS, 30, 1e-4)}) → (double, Mat, Mat, Mat, Mat)
calibrateCameraAsync(Contours3f objectPoints, Contours2f imagePoints, (int, int) imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, {Mat? rvecs, Mat? tvecs, int flags = 0, (int, int, double) criteria = (TERM_COUNT + TERM_EPS, 30, 1e-4)}) Future<(double, Mat, Mat, Mat, Mat)>
drawChessboardCorners(InputOutputArray image, (int, int) patternSize, VecPoint2f corners, bool patternWasFound) Mat
drawChessboardCornersAsync(InputOutputArray image, (int, int) patternSize, VecPoint2f corners, bool patternWasFound) Future<Mat>
estimateAffine2D(VecPoint2f from, VecPoint2f to, {int method = RANSAC, double ransacReprojThreshold = 3, int maxIters = 2000, double confidence = 0.99, int refineIters = 10, OutputArray? inliers}) → (Mat, Mat)
estimateAffine2DAsync(VecPoint2f from, VecPoint2f to, {int method = RANSAC, double ransacReprojThreshold = 3, int maxIters = 2000, double confidence = 0.99, int refineIters = 10, OutputArray? inliers}) Future<(Mat, Mat)>
estimateAffinePartial2D(VecPoint2f from, VecPoint2f to, {int method = RANSAC, double ransacReprojThreshold = 3, int maxIters = 2000, double confidence = 0.99, int refineIters = 10, OutputArray? inliers}) → (Mat, Mat)
estimateAffinePartial2DAsync(VecPoint2f from, VecPoint2f to, {int method = RANSAC, double ransacReprojThreshold = 3, int maxIters = 2000, double confidence = 0.99, int refineIters = 10, OutputArray? inliers}) Future<(Mat, Mat)>
findChessboardCorners(InputArray image, (int, int) patternSize, {VecPoint2f? corners, int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE}) → (bool, VecPoint2f)
findChessboardCornersAsync(InputArray image, (int, int) patternSize, {VecPoint2f? corners, int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE}) Future<(bool, VecPoint2f)>
findChessboardCornersSB(InputArray image, (int, int) patternSize, {int flags = 0, VecPoint2f? corners}) → (bool, VecPoint2f)
findChessboardCornersSBAsync(InputArray image, (int, int) patternSize, int flags, {VecPoint2f? corners}) Future<(bool, VecPoint2f)>
findChessboardCornersSBWithMeta(InputArray image, (int, int) patternSize, int flags, {VecPoint2f? corners, OutputArray? meta}) → (bool, VecPoint2f, Mat)
findChessboardCornersSBWithMetaAsync(InputArray image, (int, int) patternSize, int flags, {VecPoint2f? corners, OutputArray? meta}) Future<(bool, VecPoint2f, Mat)>
findHomography(InputArray srcPoints, InputArray dstPoints, {int method = 0, double ransacReprojThreshold = 3, OutputArray? mask, int maxIters = 2000, double confidence = 0.995}) Mat
FindHomography finds an optimal homography matrix using 4 or more point pairs (as opposed to GetPerspectiveTransform, which uses exactly 4)
findHomographyAsync(InputArray srcPoints, InputArray dstPoints, {int method = 0, double ransacReprojThreshold = 3, OutputArray? mask, int maxIters = 2000, double confidence = 0.995}) Future<(Mat, Mat)>
FindHomography finds an optimal homography matrix using 4 or more point pairs (as opposed to GetPerspectiveTransform, which uses exactly 4)
getOptimalNewCameraMatrix(InputArray cameraMatrix, InputArray distCoeffs, (int, int) imageSize, double alpha, {(int, int) newImgSize = (0, 0), bool centerPrincipalPoint = false}) → (Mat, Rect)
GetOptimalNewCameraMatrixWithParams computes and returns the optimal new camera matrix based on the free scaling parameter.
getOptimalNewCameraMatrixAsync(InputArray cameraMatrix, InputArray distCoeffs, (int, int) imageSize, double alpha, {(int, int) newImgSize = (0, 0), bool centerPrincipalPoint = false}) Future<(Mat, Rect)>
GetOptimalNewCameraMatrixWithParams computes and returns the optimal new camera matrix based on the free scaling parameter.
initUndistortRectifyMap(InputArray cameraMatrix, InputArray distCoeffs, InputArray R, InputArray newCameraMatrix, (int, int) size, int m1type, {OutputArray? map1, OutputArray? map2}) → (Mat, Mat)
InitUndistortRectifyMap computes the joint undistortion and rectification transformation and represents the result in the form of maps for remap
initUndistortRectifyMapAsync(InputArray cameraMatrix, InputArray distCoeffs, InputArray R, InputArray newCameraMatrix, (int, int) size, int m1type, {OutputArray? map1, OutputArray? map2}) Future<(Mat, Mat)>
InitUndistortRectifyMap computes the joint undistortion and rectification transformation and represents the result in the form of maps for remap
undistort(InputArray src, InputArray cameraMatrix, InputArray distCoeffs, {OutputArray? dst, InputArray? newCameraMatrix}) Mat
undistortAsync(InputArray src, InputArray cameraMatrix, InputArray distCoeffs, {OutputArray? dst, InputArray? newCameraMatrix}) Future<Mat>
undistortPoints(InputArray src, InputArray cameraMatrix, InputArray distCoeffs, {OutputArray? dst, InputArray? R, InputArray? P, (int, int, double) criteria = (TERM_COUNT + TERM_EPS, 30, 1e-4)}) Mat
undistortPointsAsync(InputArray src, InputArray cameraMatrix, InputArray distCoeffs, {OutputArray? dst, InputArray? R, InputArray? P, (int, int, double) criteria = (TERM_COUNT + TERM_EPS, 30, 1e-4)}) Future<Mat>