Static Public Member Functions | |
| static void | Rodrigues (Mat src, Mat dst, Mat jacobian) |
| Converts a rotation matrix to a rotation vector or vice versa. More... | |
| static void | Rodrigues (Mat src, Mat dst) |
| Converts a rotation matrix to a rotation vector or vice versa. More... | |
| static Mat | findHomography (MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters, double confidence) |
| Finds a perspective transformation between two planes. More... | |
| static Mat | findHomography (MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters) |
| Finds a perspective transformation between two planes. More... | |
| static Mat | findHomography (MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask) |
| Finds a perspective transformation between two planes. More... | |
| static Mat | findHomography (MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold) |
| Finds a perspective transformation between two planes. More... | |
| static Mat | findHomography (MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method) |
| Finds a perspective transformation between two planes. More... | |
| static Mat | findHomography (MatOfPoint2f srcPoints, MatOfPoint2f dstPoints) |
| Finds a perspective transformation between two planes. More... | |
| static Mat | findHomography (MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, Mat mask, UsacParams _params) |
| static double [] | RQDecomp3x3 (Mat src, Mat mtxR, Mat mtxQ, Mat Qx, Mat Qy, Mat Qz) |
| Computes an RQ decomposition of 3x3 matrices. More... | |
| static double [] | RQDecomp3x3 (Mat src, Mat mtxR, Mat mtxQ, Mat Qx, Mat Qy) |
| Computes an RQ decomposition of 3x3 matrices. More... | |
| static double [] | RQDecomp3x3 (Mat src, Mat mtxR, Mat mtxQ, Mat Qx) |
| Computes an RQ decomposition of 3x3 matrices. More... | |
| static double [] | RQDecomp3x3 (Mat src, Mat mtxR, Mat mtxQ) |
| Computes an RQ decomposition of 3x3 matrices. More... | |
| static void | decomposeProjectionMatrix (Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX, Mat rotMatrixY, Mat rotMatrixZ, Mat eulerAngles) |
| Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix. More... | |
| static void | decomposeProjectionMatrix (Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX, Mat rotMatrixY, Mat rotMatrixZ) |
| Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix. More... | |
| static void | decomposeProjectionMatrix (Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX, Mat rotMatrixY) |
| Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix. More... | |
| static void | decomposeProjectionMatrix (Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX) |
| Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix. More... | |
| static void | decomposeProjectionMatrix (Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect) |
| Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix. More... | |
| static void | matMulDeriv (Mat A, Mat B, Mat dABdA, Mat dABdB) |
| Computes partial derivatives of the matrix product for each multiplied matrix. More... | |
| static void | composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1, Mat dt3dt1, Mat dt3dr2, Mat dt3dt2) |
| Combines two rotation-and-shift transformations. More... | |
| static void | composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1, Mat dt3dt1, Mat dt3dr2) |
| Combines two rotation-and-shift transformations. More... | |
| static void | composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1, Mat dt3dt1) |
| Combines two rotation-and-shift transformations. More... | |
| static void | composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1) |
| Combines two rotation-and-shift transformations. More... | |
| static void | composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2) |
| Combines two rotation-and-shift transformations. More... | |
| static void | composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2) |
| Combines two rotation-and-shift transformations. More... | |
| static void | composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1) |
| Combines two rotation-and-shift transformations. More... | |
| static void | composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1) |
| Combines two rotation-and-shift transformations. More... | |
| static void | composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3) |
| Combines two rotation-and-shift transformations. More... | |
| static void | projectPoints (MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian, double aspectRatio) |
| Projects 3D points to an image plane. More... | |
| static void | projectPoints (MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian) |
| Projects 3D points to an image plane. More... | |
| static void | projectPoints (MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints) |
| Projects 3D points to an image plane. More... | |
| static bool | solvePnP (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int flags) |
| Finds an object pose from 3D-2D point correspondences. More... | |
| static bool | solvePnP (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess) |
| Finds an object pose from 3D-2D point correspondences. More... | |
| static bool | solvePnP (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec) |
| Finds an object pose from 3D-2D point correspondences. More... | |
| static bool | solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, Mat inliers, int flags) |
| Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. More... | |
| static bool | solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, Mat inliers) |
| Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. More... | |
| static bool | solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence) |
| Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. More... | |
| static bool | solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError) |
| Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. More... | |
| static bool | solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount) |
| Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. More... | |
| static bool | solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess) |
| Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. More... | |
| static bool | solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec) |
| Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. More... | |
| static bool | solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers, UsacParams _params) |
| static bool | solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers) |
| static int | solveP3P (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags) |
| Finds an object pose from 3 3D-2D point correspondences. More... | |
| static void | solvePnPRefineLM (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria) |
| Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution. More... | |
| static void | solvePnPRefineLM (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec) |
| Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution. More... | |
| static void | solvePnPRefineVVS (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria, double VVSlambda) |
| Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution. More... | |
| static void | solvePnPRefineVVS (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria) |
| Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution. More... | |
| static void | solvePnPRefineVVS (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec) |
| Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution. More... | |
| static int | solvePnPGeneric (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess, int flags, Mat rvec, Mat tvec, Mat reprojectionError) |
| Finds an object pose from 3D-2D point correspondences. More... | |
| static int | solvePnPGeneric (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess, int flags, Mat rvec, Mat tvec) |
| Finds an object pose from 3D-2D point correspondences. More... | |
| static int | solvePnPGeneric (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess, int flags, Mat rvec) |
| Finds an object pose from 3D-2D point correspondences. More... | |
| static int | solvePnPGeneric (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess, int flags) |
| Finds an object pose from 3D-2D point correspondences. More... | |
| static int | solvePnPGeneric (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess) |
| Finds an object pose from 3D-2D point correspondences. More... | |
| static int | solvePnPGeneric (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs) |
| Finds an object pose from 3D-2D point correspondences. More... | |
| static Mat | initCameraMatrix2D (List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, Size imageSize, double aspectRatio) |
| Finds an initial camera intrinsic matrix from 3D-2D point correspondences. More... | |
| static Mat | initCameraMatrix2D (List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, Size imageSize) |
| Finds an initial camera intrinsic matrix from 3D-2D point correspondences. More... | |
| static bool | findChessboardCorners (Mat image, Size patternSize, MatOfPoint2f corners, int flags) |
| Finds the positions of internal corners of the chessboard. More... | |
| static bool | findChessboardCorners (Mat image, Size patternSize, MatOfPoint2f corners) |
| Finds the positions of internal corners of the chessboard. More... | |
| static bool | checkChessboard (Mat img, Size size) |
| static bool | findChessboardCornersSBWithMeta (Mat image, Size patternSize, Mat corners, int flags, Mat meta) |
| Finds the positions of internal corners of the chessboard using a sector based approach. More... | |
| static bool | findChessboardCornersSB (Mat image, Size patternSize, Mat corners, int flags) |
| static bool | findChessboardCornersSB (Mat image, Size patternSize, Mat corners) |
| static Scalar | estimateChessboardSharpness (Mat image, Size patternSize, Mat corners, float rise_distance, bool vertical, Mat sharpness) |
| Estimates the sharpness of a detected chessboard. More... | |
| static Scalar | estimateChessboardSharpness (Mat image, Size patternSize, Mat corners, float rise_distance, bool vertical) |
| Estimates the sharpness of a detected chessboard. More... | |
| static Scalar | estimateChessboardSharpness (Mat image, Size patternSize, Mat corners, float rise_distance) |
| Estimates the sharpness of a detected chessboard. More... | |
| static Scalar | estimateChessboardSharpness (Mat image, Size patternSize, Mat corners) |
| Estimates the sharpness of a detected chessboard. More... | |
| static bool | find4QuadCornerSubpix (Mat img, Mat corners, Size region_size) |
| static void | drawChessboardCorners (Mat image, Size patternSize, MatOfPoint2f corners, bool patternWasFound) |
| Renders the detected chessboard corners. More... | |
| static void | drawFrameAxes (Mat image, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, float length, int thickness) |
| Draw axes of the world/object coordinate system from pose estimation. More... | |
| static void | drawFrameAxes (Mat image, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, float length) |
| Draw axes of the world/object coordinate system from pose estimation. More... | |
| static bool | findCirclesGrid (Mat image, Size patternSize, Mat centers, int flags) |
| static bool | findCirclesGrid (Mat image, Size patternSize, Mat centers) |
| static double | calibrateCameraExtended (List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags, TermCriteria criteria) |
| Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. More... | |
| static double | calibrateCameraExtended (List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags) |
| Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. More... | |
| static double | calibrateCameraExtended (List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors) |
| Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. More... | |
| static double | calibrateCamera (List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags, TermCriteria criteria) |
| static double | calibrateCamera (List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags) |
| static double | calibrateCamera (List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs) |
| static double | calibrateCameraROExtended (List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat stdDeviationsObjPoints, Mat perViewErrors, int flags, TermCriteria criteria) |
| Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. More... | |
| static double | calibrateCameraROExtended (List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat stdDeviationsObjPoints, Mat perViewErrors, int flags) |
| Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. More... | |
| static double | calibrateCameraROExtended (List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat stdDeviationsObjPoints, Mat perViewErrors) |
| Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. More... | |
| static double | calibrateCameraRO (List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints, int flags, TermCriteria criteria) |
| static double | calibrateCameraRO (List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints, int flags) |
| static double | calibrateCameraRO (List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints) |
| static void | calibrationMatrixValues (Mat cameraMatrix, Size imageSize, double apertureWidth, double apertureHeight, double[] fovx, double[] fovy, double[] focalLength, Point principalPoint, double[] aspectRatio) |
| Computes useful camera characteristics from the camera intrinsic matrix. More... | |
| static double | stereoCalibrateExtended (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, List< Mat > rvecs, List< Mat > tvecs, Mat perViewErrors, int flags, TermCriteria criteria) |
| Calibrates a stereo camera set up. This function finds the intrinsic parameters for each of the two cameras and the extrinsic parameters between the two cameras. More... | |
| static double | stereoCalibrateExtended (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, List< Mat > rvecs, List< Mat > tvecs, Mat perViewErrors, int flags) |
| Calibrates a stereo camera set up. This function finds the intrinsic parameters for each of the two cameras and the extrinsic parameters between the two cameras. More... | |
| static double | stereoCalibrateExtended (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, List< Mat > rvecs, List< Mat > tvecs, Mat perViewErrors) |
| Calibrates a stereo camera set up. This function finds the intrinsic parameters for each of the two cameras and the extrinsic parameters between the two cameras. More... | |
| static double | stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, int flags, TermCriteria criteria) |
| static double | stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, int flags) |
| static double | stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F) |
| static double | stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors, int flags, TermCriteria criteria) |
| static double | stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors, int flags) |
| static double | stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors) |
| static void | stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, Size newImageSize, Rect validPixROI1, Rect validPixROI2) |
| Computes rectification transforms for each head of a calibrated stereo camera. More... | |
| static void | stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, Size newImageSize, Rect validPixROI1) |
| Computes rectification transforms for each head of a calibrated stereo camera. More... | |
| static void | stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, Size newImageSize) |
| Computes rectification transforms for each head of a calibrated stereo camera. More... | |
| static void | stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha) |
| Computes rectification transforms for each head of a calibrated stereo camera. More... | |
| static void | stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags) |
| Computes rectification transforms for each head of a calibrated stereo camera. More... | |
| static void | stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q) |
| Computes rectification transforms for each head of a calibrated stereo camera. More... | |
| static bool | stereoRectifyUncalibrated (Mat points1, Mat points2, Mat F, Size imgSize, Mat H1, Mat H2, double threshold) |
| Computes a rectification transform for an uncalibrated stereo camera. More... | |
| static bool | stereoRectifyUncalibrated (Mat points1, Mat points2, Mat F, Size imgSize, Mat H1, Mat H2) |
| Computes a rectification transform for an uncalibrated stereo camera. More... | |
| static float | rectify3Collinear (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat cameraMatrix3, Mat distCoeffs3, List< Mat > imgpt1, List< Mat > imgpt3, Size imageSize, Mat R12, Mat T12, Mat R13, Mat T13, Mat R1, Mat R2, Mat R3, Mat P1, Mat P2, Mat P3, Mat Q, double alpha, Size newImgSize, Rect roi1, Rect roi2, int flags) |
| static Mat | getOptimalNewCameraMatrix (Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize, Rect validPixROI, bool centerPrincipalPoint) |
| Returns the new camera intrinsic matrix based on the free scaling parameter. More... | |
| static Mat | getOptimalNewCameraMatrix (Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize, Rect validPixROI) |
| Returns the new camera intrinsic matrix based on the free scaling parameter. More... | |
| static Mat | getOptimalNewCameraMatrix (Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize) |
| Returns the new camera intrinsic matrix based on the free scaling parameter. More... | |
| static Mat | getOptimalNewCameraMatrix (Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha) |
| Returns the new camera intrinsic matrix based on the free scaling parameter. More... | |
| static void | calibrateHandEye (List< Mat > R_gripper2base, List< Mat > t_gripper2base, List< Mat > R_target2cam, List< Mat > t_target2cam, Mat R_cam2gripper, Mat t_cam2gripper, int method) |
| Computes Hand-Eye calibration: \(_{}^{g}\textrm{T}_c\). More... | |
| static void | calibrateHandEye (List< Mat > R_gripper2base, List< Mat > t_gripper2base, List< Mat > R_target2cam, List< Mat > t_target2cam, Mat R_cam2gripper, Mat t_cam2gripper) |
| Computes Hand-Eye calibration: \(_{}^{g}\textrm{T}_c\). More... | |
| static void | calibrateRobotWorldHandEye (List< Mat > R_world2cam, List< Mat > t_world2cam, List< Mat > R_base2gripper, List< Mat > t_base2gripper, Mat R_base2world, Mat t_base2world, Mat R_gripper2cam, Mat t_gripper2cam, int method) |
| Computes Robot-World/Hand-Eye calibration: \(_{}^{w}\textrm{T}_b\) and \(_{}^{c}\textrm{T}_g\). More... | |
| static void | calibrateRobotWorldHandEye (List< Mat > R_world2cam, List< Mat > t_world2cam, List< Mat > R_base2gripper, List< Mat > t_base2gripper, Mat R_base2world, Mat t_base2world, Mat R_gripper2cam, Mat t_gripper2cam) |
| Computes Robot-World/Hand-Eye calibration: \(_{}^{w}\textrm{T}_b\) and \(_{}^{c}\textrm{T}_g\). More... | |
| static void | convertPointsToHomogeneous (Mat src, Mat dst) |
| Converts points from Euclidean to homogeneous space. More... | |
| static void | convertPointsFromHomogeneous (Mat src, Mat dst) |
| Converts points from homogeneous to Euclidean space. More... | |
| static Mat | findFundamentalMat (MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, int maxIters, Mat mask) |
| Calculates a fundamental matrix from the corresponding points in two images. More... | |
| static Mat | findFundamentalMat (MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, int maxIters) |
| Calculates a fundamental matrix from the corresponding points in two images. More... | |
| static Mat | findFundamentalMat (MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, Mat mask) |
| static Mat | findFundamentalMat (MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence) |
| static Mat | findFundamentalMat (MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold) |
| static Mat | findFundamentalMat (MatOfPoint2f points1, MatOfPoint2f points2, int method) |
| static Mat | findFundamentalMat (MatOfPoint2f points1, MatOfPoint2f points2) |
| static Mat | findFundamentalMat (MatOfPoint2f points1, MatOfPoint2f points2, Mat mask, UsacParams _params) |
| static Mat | findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold, int maxIters, Mat mask) |
| Calculates an essential matrix from the corresponding points in two images. More... | |
| static Mat | findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold, int maxIters) |
| Calculates an essential matrix from the corresponding points in two images. More... | |
| static Mat | findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold) |
| Calculates an essential matrix from the corresponding points in two images. More... | |
| static Mat | findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix, int method, double prob) |
| Calculates an essential matrix from the corresponding points in two images. More... | |
| static Mat | findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix, int method) |
| Calculates an essential matrix from the corresponding points in two images. More... | |
| static Mat | findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix) |
| Calculates an essential matrix from the corresponding points in two images. More... | |
| static Mat | findEssentialMat (Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold, int maxIters, Mat mask) |
| static Mat | findEssentialMat (Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold, int maxIters) |
| static Mat | findEssentialMat (Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold) |
| static Mat | findEssentialMat (Mat points1, Mat points2, double focal, Point pp, int method, double prob) |
| static Mat | findEssentialMat (Mat points1, Mat points2, double focal, Point pp, int method) |
| static Mat | findEssentialMat (Mat points1, Mat points2, double focal, Point pp) |
| static Mat | findEssentialMat (Mat points1, Mat points2, double focal) |
| static Mat | findEssentialMat (Mat points1, Mat points2) |
| static Mat | findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method, double prob, double threshold, Mat mask) |
| Calculates an essential matrix from the corresponding points in two images from potentially two different cameras. More... | |
| static Mat | findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method, double prob, double threshold) |
| Calculates an essential matrix from the corresponding points in two images from potentially two different cameras. More... | |
| static Mat | findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method, double prob) |
| Calculates an essential matrix from the corresponding points in two images from potentially two different cameras. More... | |
| static Mat | findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method) |
| Calculates an essential matrix from the corresponding points in two images from potentially two different cameras. More... | |
| static Mat | findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2) |
| Calculates an essential matrix from the corresponding points in two images from potentially two different cameras. More... | |
| static Mat | findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix1, Mat cameraMatrix2, Mat dist_coeff1, Mat dist_coeff2, Mat mask, UsacParams _params) |
| static void | decomposeEssentialMat (Mat E, Mat R1, Mat R2, Mat t) |
| Decompose an essential matrix to possible rotations and translation. More... | |
| static int | recoverPose (Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat E, Mat R, Mat t, int method, double prob, double threshold, Mat mask) |
| Recovers the relative camera rotation and the translation from corresponding points in two images from two different cameras, using cheirality check. Returns the number of inliers that pass the check. More... | |
| static int | recoverPose (Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat E, Mat R, Mat t, int method, double prob, double threshold) |
| Recovers the relative camera rotation and the translation from corresponding points in two images from two different cameras, using cheirality check. Returns the number of inliers that pass the check. More... | |
| static int | recoverPose (Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat E, Mat R, Mat t, int method, double prob) |
| Recovers the relative camera rotation and the translation from corresponding points in two images from two different cameras, using cheirality check. Returns the number of inliers that pass the check. More... | |
| static int | recoverPose (Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat E, Mat R, Mat t, int method) |
| Recovers the relative camera rotation and the translation from corresponding points in two images from two different cameras, using cheirality check. Returns the number of inliers that pass the check. More... | |
| static int | recoverPose (Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat E, Mat R, Mat t) |
| Recovers the relative camera rotation and the translation from corresponding points in two images from two different cameras, using cheirality check. Returns the number of inliers that pass the check. More... | |
| static int | recoverPose (Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, Mat mask) |
| Recovers the relative camera rotation and the translation from an estimated essential matrix and the corresponding points in two images, using chirality check. Returns the number of inliers that pass the check. More... | |
| static int | recoverPose (Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t) |
| Recovers the relative camera rotation and the translation from an estimated essential matrix and the corresponding points in two images, using chirality check. Returns the number of inliers that pass the check. More... | |
| static int | recoverPose (Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, Point pp, Mat mask) |
| static int | recoverPose (Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, Point pp) |
| static int | recoverPose (Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal) |
| static int | recoverPose (Mat E, Mat points1, Mat points2, Mat R, Mat t) |
| static int | recoverPose (Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh, Mat mask, Mat triangulatedPoints) |
| static int | recoverPose (Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh, Mat mask) |
| static int | recoverPose (Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh) |
| static void | computeCorrespondEpilines (Mat points, int whichImage, Mat F, Mat lines) |
| For points in an image of a stereo pair, computes the corresponding epilines in the other image. More... | |
| static void | triangulatePoints (Mat projMatr1, Mat projMatr2, Mat projPoints1, Mat projPoints2, Mat points4D) |
| This function reconstructs 3-dimensional points (in homogeneous coordinates) by using their observations with a stereo camera. More... | |
| static void | correctMatches (Mat F, Mat points1, Mat points2, Mat newPoints1, Mat newPoints2) |
| Refines coordinates of corresponding points. More... | |
| static void | filterSpeckles (Mat img, double newVal, int maxSpeckleSize, double maxDiff, Mat buf) |
| Filters off small noise blobs (speckles) in the disparity map. More... | |
| static void | filterSpeckles (Mat img, double newVal, int maxSpeckleSize, double maxDiff) |
| Filters off small noise blobs (speckles) in the disparity map. More... | |
| static Rect | getValidDisparityROI (Rect roi1, Rect roi2, int minDisparity, int numberOfDisparities, int blockSize) |
| static void | validateDisparity (Mat disparity, Mat cost, int minDisparity, int numberOfDisparities, int disp12MaxDisp) |
| static void | validateDisparity (Mat disparity, Mat cost, int minDisparity, int numberOfDisparities) |
| static void | reprojectImageTo3D (Mat disparity, Mat _3dImage, Mat Q, bool handleMissingValues, int ddepth) |
| Reprojects a disparity image to 3D space. More... | |
| static void | reprojectImageTo3D (Mat disparity, Mat _3dImage, Mat Q, bool handleMissingValues) |
| Reprojects a disparity image to 3D space. More... | |
| static void | reprojectImageTo3D (Mat disparity, Mat _3dImage, Mat Q) |
| Reprojects a disparity image to 3D space. More... | |
| static double | sampsonDistance (Mat pt1, Mat pt2, Mat F) |
| Calculates the Sampson Distance between two points. More... | |
| static int | estimateAffine3D (Mat src, Mat dst, Mat _out, Mat inliers, double ransacThreshold, double confidence) |
| Computes an optimal affine transformation between two 3D point sets. More... | |
| static int | estimateAffine3D (Mat src, Mat dst, Mat _out, Mat inliers, double ransacThreshold) |
| Computes an optimal affine transformation between two 3D point sets. More... | |
| static int | estimateAffine3D (Mat src, Mat dst, Mat _out, Mat inliers) |
| Computes an optimal affine transformation between two 3D point sets. More... | |
| static Mat | estimateAffine3D (Mat src, Mat dst, double[] scale, bool force_rotation) |
| Computes an optimal affine transformation between two 3D point sets. More... | |
| static Mat | estimateAffine3D (Mat src, Mat dst, double[] scale) |
| Computes an optimal affine transformation between two 3D point sets. More... | |
| static Mat | estimateAffine3D (Mat src, Mat dst) |
| Computes an optimal affine transformation between two 3D point sets. More... | |
| static int | estimateTranslation3D (Mat src, Mat dst, Mat _out, Mat inliers, double ransacThreshold, double confidence) |
| Computes an optimal translation between two 3D point sets. More... | |
| static int | estimateTranslation3D (Mat src, Mat dst, Mat _out, Mat inliers, double ransacThreshold) |
| Computes an optimal translation between two 3D point sets. More... | |
| static int | estimateTranslation3D (Mat src, Mat dst, Mat _out, Mat inliers) |
| Computes an optimal translation between two 3D point sets. More... | |
| static Mat | estimateAffine2D (Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters) |
| Computes an optimal affine transformation between two 2D point sets. More... | |
| static Mat | estimateAffine2D (Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence) |
| Computes an optimal affine transformation between two 2D point sets. More... | |
| static Mat | estimateAffine2D (Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters) |
| Computes an optimal affine transformation between two 2D point sets. More... | |
| static Mat | estimateAffine2D (Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold) |
| Computes an optimal affine transformation between two 2D point sets. More... | |
| static Mat | estimateAffine2D (Mat from, Mat to, Mat inliers, int method) |
| Computes an optimal affine transformation between two 2D point sets. More... | |
| static Mat | estimateAffine2D (Mat from, Mat to, Mat inliers) |
| Computes an optimal affine transformation between two 2D point sets. More... | |
| static Mat | estimateAffine2D (Mat from, Mat to) |
| Computes an optimal affine transformation between two 2D point sets. More... | |
| static Mat | estimateAffine2D (Mat pts1, Mat pts2, Mat inliers, UsacParams _params) |
| static Mat | estimateAffinePartial2D (Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters) |
| Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets. More... | |
| static Mat | estimateAffinePartial2D (Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence) |
| Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets. More... | |
| static Mat | estimateAffinePartial2D (Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters) |
| Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets. More... | |
| static Mat | estimateAffinePartial2D (Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold) |
| Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets. More... | |
| static Mat | estimateAffinePartial2D (Mat from, Mat to, Mat inliers, int method) |
| Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets. More... | |
| static Mat | estimateAffinePartial2D (Mat from, Mat to, Mat inliers) |
| Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets. More... | |
| static Mat | estimateAffinePartial2D (Mat from, Mat to) |
| Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets. More... | |
| static int | decomposeHomographyMat (Mat H, Mat K, List< Mat > rotations, List< Mat > translations, List< Mat > normals) |
| Decompose a homography matrix to rotation(s), translation(s) and plane normal(s). More... | |
| static void | filterHomographyDecompByVisibleRefpoints (List< Mat > rotations, List< Mat > normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions, Mat pointsMask) |
| Filters homography decompositions based on additional information. More... | |
| static void | filterHomographyDecompByVisibleRefpoints (List< Mat > rotations, List< Mat > normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions) |
| Filters homography decompositions based on additional information. More... | |
| static void | undistort (Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat newCameraMatrix) |
| Transforms an image to compensate for lens distortion. More... | |
| static void | undistort (Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs) |
| Transforms an image to compensate for lens distortion. More... | |
| static void | initUndistortRectifyMap (Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, Size size, int m1type, Mat map1, Mat map2) |
| Computes the undistortion and rectification transformation map. More... | |
| static void | initInverseRectificationMap (Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, Size size, int m1type, Mat map1, Mat map2) |
| Computes the projection and inverse-rectification transformation map. In essense, this is the inverse of initUndistortRectifyMap to accomodate stereo-rectification of projectors ('inverse-cameras') in projector-camera pairs. More... | |
| static Mat | getDefaultNewCameraMatrix (Mat cameraMatrix, Size imgsize, bool centerPrincipalPoint) |
| Returns the default new camera matrix. More... | |
| static Mat | getDefaultNewCameraMatrix (Mat cameraMatrix, Size imgsize) |
| Returns the default new camera matrix. More... | |
| static Mat | getDefaultNewCameraMatrix (Mat cameraMatrix) |
| Returns the default new camera matrix. More... | |
| static void | undistortPoints (MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P) |
| Computes the ideal point coordinates from the observed point coordinates. More... | |
| static void | undistortPoints (MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R) |
| Computes the ideal point coordinates from the observed point coordinates. More... | |
| static void | undistortPoints (MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs) |
| Computes the ideal point coordinates from the observed point coordinates. More... | |
| static void | undistortPointsIter (Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, TermCriteria criteria) |
| static void | undistortImagePoints (Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, TermCriteria arg1) |
| Compute undistorted image points position. More... | |
| static void | undistortImagePoints (Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs) |
| Compute undistorted image points position. More... | |
| static void | fisheye_projectPoints (Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D, double alpha, Mat jacobian) |
| static void | fisheye_projectPoints (Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D, double alpha) |
| static void | fisheye_projectPoints (Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D) |
| static void | fisheye_distortPoints (Mat undistorted, Mat distorted, Mat K, Mat D, double alpha) |
| Distorts 2D points using fisheye model. More... | |
| static void | fisheye_distortPoints (Mat undistorted, Mat distorted, Mat K, Mat D) |
| Distorts 2D points using fisheye model. More... | |
| static void | fisheye_undistortPoints (Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P, TermCriteria criteria) |
| Undistorts 2D points using fisheye model. More... | |
| static void | fisheye_undistortPoints (Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P) |
| Undistorts 2D points using fisheye model. More... | |
| static void | fisheye_undistortPoints (Mat distorted, Mat undistorted, Mat K, Mat D, Mat R) |
| Undistorts 2D points using fisheye model. More... | |
| static void | fisheye_undistortPoints (Mat distorted, Mat undistorted, Mat K, Mat D) |
| Undistorts 2D points using fisheye model. More... | |
| static void | fisheye_initUndistortRectifyMap (Mat K, Mat D, Mat R, Mat P, Size size, int m1type, Mat map1, Mat map2) |
| Computes undistortion and rectification maps for image transform by #remap. If D is empty zero distortion is used, if R or P is empty identity matrixes are used. More... | |
| static void | fisheye_undistortImage (Mat distorted, Mat undistorted, Mat K, Mat D, Mat Knew, Size new_size) |
| Transforms an image to compensate for fisheye lens distortion. More... | |
| static void | fisheye_undistortImage (Mat distorted, Mat undistorted, Mat K, Mat D, Mat Knew) |
| Transforms an image to compensate for fisheye lens distortion. More... | |
| static void | fisheye_undistortImage (Mat distorted, Mat undistorted, Mat K, Mat D) |
| Transforms an image to compensate for fisheye lens distortion. More... | |
| static void | fisheye_estimateNewCameraMatrixForUndistortRectify (Mat K, Mat D, Size image_size, Mat R, Mat P, double balance, Size new_size, double fov_scale) |
| Estimates new camera intrinsic matrix for undistortion or rectification. More... | |
| static void | fisheye_estimateNewCameraMatrixForUndistortRectify (Mat K, Mat D, Size image_size, Mat R, Mat P, double balance, Size new_size) |
| Estimates new camera intrinsic matrix for undistortion or rectification. More... | |
| static void | fisheye_estimateNewCameraMatrixForUndistortRectify (Mat K, Mat D, Size image_size, Mat R, Mat P, double balance) |
| Estimates new camera intrinsic matrix for undistortion or rectification. More... | |
| static void | fisheye_estimateNewCameraMatrixForUndistortRectify (Mat K, Mat D, Size image_size, Mat R, Mat P) |
| Estimates new camera intrinsic matrix for undistortion or rectification. More... | |
| static double | fisheye_calibrate (List< Mat > objectPoints, List< Mat > imagePoints, Size image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs, int flags, TermCriteria criteria) |
| Performs camera calibration. More... | |
| static double | fisheye_calibrate (List< Mat > objectPoints, List< Mat > imagePoints, Size image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs, int flags) |
| Performs camera calibration. More... | |
| static double | fisheye_calibrate (List< Mat > objectPoints, List< Mat > imagePoints, Size image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs) |
| Performs camera calibration. More... | |
| static void | fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, Size newImageSize, double balance, double fov_scale) |
| Stereo rectification for fisheye camera model. More... | |
| static void | fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, Size newImageSize, double balance) |
| Stereo rectification for fisheye camera model. More... | |
| static void | fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, Size newImageSize) |
| Stereo rectification for fisheye camera model. More... | |
| static void | fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags) |
| Stereo rectification for fisheye camera model. More... | |
| static double | fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs, int flags, TermCriteria criteria) |
| Performs stereo calibration. More... | |
| static double | fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs, int flags) |
| Performs stereo calibration. More... | |
| static double | fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs) |
| Performs stereo calibration. More... | |
| static double | fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T, int flags, TermCriteria criteria) |
| static double | fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T, int flags) |
| static double | fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T) |
| static bool | fisheye_solvePnP (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int flags, TermCriteria criteria) |
| Finds an object pose from 3D-2D point correspondences for fisheye camera moodel. More... | |
| static bool | fisheye_solvePnP (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int flags) |
| Finds an object pose from 3D-2D point correspondences for fisheye camera moodel. More... | |
| static bool | fisheye_solvePnP (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess) |
| Finds an object pose from 3D-2D point correspondences for fisheye camera moodel. More... | |
| static bool | fisheye_solvePnP (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec) |
| Finds an object pose from 3D-2D point correspondences for fisheye camera moodel. More... | |
Member Function Documentation
◆ calibrateCamera() [1/3]
|
static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ calibrateCamera() [2/3]
|
static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ calibrateCamera() [3/3]
|
static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ calibrateCameraExtended() [1/3]
|
static |
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
- Parameters
-
objectPoints In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer vector contains as many elements as the number of pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns or even different patterns in different views. Then, the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. In the old interface all the vectors of object points from different views are concatenated together. imagePoints In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, respectively. In the old interface all the vectors of object points from different views are concatenated together. imageSize Size of the image used only to initialize the camera intrinsic matrix. cameraMatrix Input/output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If CALIB_USE_INTRINSIC_GUESS and/or CALIB_FIX_ASPECT_RATIO, CALIB_FIX_PRINCIPAL_POINT or CALIB_FIX_FOCAL_LENGTH are specified, some or all of fx, fy, cx, cy must be initialized before calling the function. distCoeffs Input/output vector of distortion coefficients \(\distcoeffs\). rvecs Output vector of rotation vectors (Rodrigues ) estimated for each pattern view (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space. Due to its duality, this tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate space. tvecs Output vector of translation vectors estimated for each pattern view, see parameter describtion above. stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero. stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors. perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. flags Different flags that may be zero or a combination of the following values: - CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Use solvePnP instead.
- CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too.
- CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix . When CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further.
- CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \((p_1, p_2)\) are set to zeros and stay zero.
- CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if CALIB_USE_INTRINSIC_GUESS is set.
- CALIB_FIX_K1,..., CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
- CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients or more.
- CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients or more.
- CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
- CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients.
- CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria Termination criteria for the iterative optimization algorithm.
- Returns
- the overall RMS re-projection error.
The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000] and [BouguetMCT] . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.
The algorithm performs the following steps:
- Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
- Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using solvePnP .
- Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See projectPoints for details.
- Note
- If you use a non-square (i.e. non-N-by-N) grid and findChessboardCorners for calibration, and calibrateCamera returns bad values (zero distortion coefficients, \(c_x\) and \(c_y\) very far from the image center, and/or large differences between \(f_x\) and \(f_y\) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in findChessboardCorners.
- The function may throw exceptions, if unsupported combination of parameters is provided or the system is underconstrained.
◆ calibrateCameraExtended() [2/3]
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static |
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
- Parameters
-
objectPoints In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer vector contains as many elements as the number of pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns or even different patterns in different views. Then, the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. In the old interface all the vectors of object points from different views are concatenated together. imagePoints In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, respectively. In the old interface all the vectors of object points from different views are concatenated together. imageSize Size of the image used only to initialize the camera intrinsic matrix. cameraMatrix Input/output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If CALIB_USE_INTRINSIC_GUESS and/or CALIB_FIX_ASPECT_RATIO, CALIB_FIX_PRINCIPAL_POINT or CALIB_FIX_FOCAL_LENGTH are specified, some or all of fx, fy, cx, cy must be initialized before calling the function. distCoeffs Input/output vector of distortion coefficients \(\distcoeffs\). rvecs Output vector of rotation vectors (Rodrigues ) estimated for each pattern view (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space. Due to its duality, this tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate space. tvecs Output vector of translation vectors estimated for each pattern view, see parameter describtion above. stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero. stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors. perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. flags Different flags that may be zero or a combination of the following values: - CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Use solvePnP instead.
- CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too.
- CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix . When CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further.
- CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \((p_1, p_2)\) are set to zeros and stay zero.
- CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if CALIB_USE_INTRINSIC_GUESS is set.
- CALIB_FIX_K1,..., CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
- CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients or more.
- CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients or more.
- CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
- CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients.
- CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria Termination criteria for the iterative optimization algorithm.
- Returns
- the overall RMS re-projection error.
The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000] and [BouguetMCT] . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.
The algorithm performs the following steps:
- Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
- Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using solvePnP .
- Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See projectPoints for details.
- Note
- If you use a non-square (i.e. non-N-by-N) grid and findChessboardCorners for calibration, and calibrateCamera returns bad values (zero distortion coefficients, \(c_x\) and \(c_y\) very far from the image center, and/or large differences between \(f_x\) and \(f_y\) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in findChessboardCorners.
- The function may throw exceptions, if unsupported combination of parameters is provided or the system is underconstrained.
◆ calibrateCameraExtended() [3/3]
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static |
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
- Parameters
-
objectPoints In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer vector contains as many elements as the number of pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns or even different patterns in different views. Then, the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. In the old interface all the vectors of object points from different views are concatenated together. imagePoints In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, respectively. In the old interface all the vectors of object points from different views are concatenated together. imageSize Size of the image used only to initialize the camera intrinsic matrix. cameraMatrix Input/output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If CALIB_USE_INTRINSIC_GUESS and/or CALIB_FIX_ASPECT_RATIO, CALIB_FIX_PRINCIPAL_POINT or CALIB_FIX_FOCAL_LENGTH are specified, some or all of fx, fy, cx, cy must be initialized before calling the function. distCoeffs Input/output vector of distortion coefficients \(\distcoeffs\). rvecs Output vector of rotation vectors (Rodrigues ) estimated for each pattern view (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space. Due to its duality, this tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate space. tvecs Output vector of translation vectors estimated for each pattern view, see parameter describtion above. stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero. stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors. perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. flags Different flags that may be zero or a combination of the following values: - CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Use solvePnP instead.
- CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too.
- CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix . When CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further.
- CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \((p_1, p_2)\) are set to zeros and stay zero.
- CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if CALIB_USE_INTRINSIC_GUESS is set.
- CALIB_FIX_K1,..., CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
- CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients or more.
- CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients or more.
- CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
- CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients.
- CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria Termination criteria for the iterative optimization algorithm.
- Returns
- the overall RMS re-projection error.
The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000] and [BouguetMCT] . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.
The algorithm performs the following steps:
- Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
- Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using solvePnP .
- Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See projectPoints for details.
- Note
- If you use a non-square (i.e. non-N-by-N) grid and findChessboardCorners for calibration, and calibrateCamera returns bad values (zero distortion coefficients, \(c_x\) and \(c_y\) very far from the image center, and/or large differences between \(f_x\) and \(f_y\) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in findChessboardCorners.
- The function may throw exceptions, if unsupported combination of parameters is provided or the system is underconstrained.
◆ calibrateCameraRO() [1/3]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ calibrateCameraRO() [2/3]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ calibrateCameraRO() [3/3]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ calibrateCameraROExtended() [1/3]
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static |
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
This function is an extension of calibrateCamera with the method of releasing object which was proposed in [strobl2011iccv]. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, calibrateCamera is a wrapper for this function.
- Parameters
-
objectPoints Vector of vectors of calibration pattern points in the calibration pattern coordinate space. See calibrateCamera for details. If the method of releasing object to be used, the identical calibration board must be used in each view and it must be fully visible, and all objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration target has to be rigid, or at least static if the camera (rather than the calibration target) is shifted for grabbing images. imagePoints Vector of vectors of the projections of calibration pattern points. See calibrateCamera for details. imageSize Size of the image used only to initialize the intrinsic camera matrix. iFixedPoint The index of the 3D object point in objectPoints[0] to be fixed. It also acts as a switch for calibration method selection. If object-releasing method to be used, pass in the parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will make standard calibration method selected. Usually the top-right corner point of the calibration board grid is recommended to be fixed when object-releasing method being utilized. According to [strobl2011iccv], two other points are also fixed. In this implementation, objectPoints[0].front and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough. cameraMatrix Output 3x3 floating-point camera matrix. See calibrateCamera for details. distCoeffs Output vector of distortion coefficients. See calibrateCamera for details. rvecs Output vector of rotation vectors estimated for each pattern view. See calibrateCamera for details. tvecs Output vector of translation vectors estimated for each pattern view. newObjPoints The updated output vector of calibration pattern points. The coordinates might be scaled based on three fixed points. The returned coordinates are accurate only if the above mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter is ignored with standard calibration method. stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. See calibrateCamera for details. stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. See calibrateCamera for details. stdDeviationsObjPoints Output vector of standard deviations estimated for refined coordinates of calibration pattern points. It has the same size and order as objectPoints[0] vector. This parameter is ignored with standard calibration method. perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. flags Different flags that may be zero or a combination of some predefined values. See calibrateCamera for details. If the method of releasing object is used, the calibration time may be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially less precise and less stable in some rare cases. criteria Termination criteria for the iterative optimization algorithm.
- Returns
- the overall RMS re-projection error.
The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000], [BouguetMCT] and [strobl2011iccv]. See calibrateCamera for other detailed explanations.
◆ calibrateCameraROExtended() [2/3]
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static |
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
This function is an extension of calibrateCamera with the method of releasing object which was proposed in [strobl2011iccv]. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, calibrateCamera is a wrapper for this function.
- Parameters
-
objectPoints Vector of vectors of calibration pattern points in the calibration pattern coordinate space. See calibrateCamera for details. If the method of releasing object to be used, the identical calibration board must be used in each view and it must be fully visible, and all objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration target has to be rigid, or at least static if the camera (rather than the calibration target) is shifted for grabbing images. imagePoints Vector of vectors of the projections of calibration pattern points. See calibrateCamera for details. imageSize Size of the image used only to initialize the intrinsic camera matrix. iFixedPoint The index of the 3D object point in objectPoints[0] to be fixed. It also acts as a switch for calibration method selection. If object-releasing method to be used, pass in the parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will make standard calibration method selected. Usually the top-right corner point of the calibration board grid is recommended to be fixed when object-releasing method being utilized. According to [strobl2011iccv], two other points are also fixed. In this implementation, objectPoints[0].front and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough. cameraMatrix Output 3x3 floating-point camera matrix. See calibrateCamera for details. distCoeffs Output vector of distortion coefficients. See calibrateCamera for details. rvecs Output vector of rotation vectors estimated for each pattern view. See calibrateCamera for details. tvecs Output vector of translation vectors estimated for each pattern view. newObjPoints The updated output vector of calibration pattern points. The coordinates might be scaled based on three fixed points. The returned coordinates are accurate only if the above mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter is ignored with standard calibration method. stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. See calibrateCamera for details. stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. See calibrateCamera for details. stdDeviationsObjPoints Output vector of standard deviations estimated for refined coordinates of calibration pattern points. It has the same size and order as objectPoints[0] vector. This parameter is ignored with standard calibration method. perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. flags Different flags that may be zero or a combination of some predefined values. See calibrateCamera for details. If the method of releasing object is used, the calibration time may be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially less precise and less stable in some rare cases. criteria Termination criteria for the iterative optimization algorithm.
- Returns
- the overall RMS re-projection error.
The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000], [BouguetMCT] and [strobl2011iccv]. See calibrateCamera for other detailed explanations.
◆ calibrateCameraROExtended() [3/3]
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static |
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
This function is an extension of calibrateCamera with the method of releasing object which was proposed in [strobl2011iccv]. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, calibrateCamera is a wrapper for this function.
- Parameters
-
objectPoints Vector of vectors of calibration pattern points in the calibration pattern coordinate space. See calibrateCamera for details. If the method of releasing object to be used, the identical calibration board must be used in each view and it must be fully visible, and all objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration target has to be rigid, or at least static if the camera (rather than the calibration target) is shifted for grabbing images. imagePoints Vector of vectors of the projections of calibration pattern points. See calibrateCamera for details. imageSize Size of the image used only to initialize the intrinsic camera matrix. iFixedPoint The index of the 3D object point in objectPoints[0] to be fixed. It also acts as a switch for calibration method selection. If object-releasing method to be used, pass in the parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will make standard calibration method selected. Usually the top-right corner point of the calibration board grid is recommended to be fixed when object-releasing method being utilized. According to [strobl2011iccv], two other points are also fixed. In this implementation, objectPoints[0].front and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough. cameraMatrix Output 3x3 floating-point camera matrix. See calibrateCamera for details. distCoeffs Output vector of distortion coefficients. See calibrateCamera for details. rvecs Output vector of rotation vectors estimated for each pattern view. See calibrateCamera for details. tvecs Output vector of translation vectors estimated for each pattern view. newObjPoints The updated output vector of calibration pattern points. The coordinates might be scaled based on three fixed points. The returned coordinates are accurate only if the above mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter is ignored with standard calibration method. stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. See calibrateCamera for details. stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. See calibrateCamera for details. stdDeviationsObjPoints Output vector of standard deviations estimated for refined coordinates of calibration pattern points. It has the same size and order as objectPoints[0] vector. This parameter is ignored with standard calibration method. perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. flags Different flags that may be zero or a combination of some predefined values. See calibrateCamera for details. If the method of releasing object is used, the calibration time may be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially less precise and less stable in some rare cases. criteria Termination criteria for the iterative optimization algorithm.
- Returns
- the overall RMS re-projection error.
The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000], [BouguetMCT] and [strobl2011iccv]. See calibrateCamera for other detailed explanations.
◆ calibrateHandEye() [1/2]
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static |
Computes Hand-Eye calibration: \(_{}^{g}\textrm{T}_c\).
- Parameters
-
[in] R_gripper2base Rotation part extracted from the homogeneous matrix that transforms a point expressed in the gripper frame to the robot base frame ( \(_{}^{b}\textrm{T}_g\)). This is a vector ( vector<Mat>) that contains the rotation,(3x3)rotation matrices or(3x1)rotation vectors, for all the transformations from gripper frame to robot base frame.[in] t_gripper2base Translation part extracted from the homogeneous matrix that transforms a point expressed in the gripper frame to the robot base frame ( \(_{}^{b}\textrm{T}_g\)). This is a vector ( vector<Mat>) that contains the(3x1)translation vectors for all the transformations from gripper frame to robot base frame.[in] R_target2cam Rotation part extracted from the homogeneous matrix that transforms a point expressed in the target frame to the camera frame ( \(_{}^{c}\textrm{T}_t\)). This is a vector ( vector<Mat>) that contains the rotation,(3x3)rotation matrices or(3x1)rotation vectors, for all the transformations from calibration target frame to camera frame.[in] t_target2cam Rotation part extracted from the homogeneous matrix that transforms a point expressed in the target frame to the camera frame ( \(_{}^{c}\textrm{T}_t\)). This is a vector ( vector<Mat>) that contains the(3x1)translation vectors for all the transformations from calibration target frame to camera frame.[out] R_cam2gripper Estimated (3x3)rotation part extracted from the homogeneous matrix that transforms a point expressed in the camera frame to the gripper frame ( \(_{}^{g}\textrm{T}_c\)).[out] t_cam2gripper Estimated (3x1)translation part extracted from the homogeneous matrix that transforms a point expressed in the camera frame to the gripper frame ( \(_{}^{g}\textrm{T}_c\)).[in] method One of the implemented Hand-Eye calibration method, see cv::HandEyeCalibrationMethod
The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions) and the following methods are implemented:
- R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/EyeCalibration [Tsai89]
- F. Park, B. Martin Robot Sensor Calibration: Solving AX = XB on the Euclidean Group [Park94]
- R. Horaud, F. Dornaika Hand-Eye Calibration [Horaud95]
Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented methods:
- N. Andreff, R. Horaud, B. Espiau On-line Hand-Eye Calibration [Andreff99]
- K. Daniilidis Hand-Eye Calibration Using Dual Quaternions [Daniilidis98]
The following picture describes the Hand-Eye calibration problem where the transformation between a camera ("eye") mounted on a robot gripper ("hand") has to be estimated. This configuration is called eye-in-hand.
The eye-to-hand configuration consists in a static camera observing a calibration pattern mounted on the robot end-effector. The transformation from the camera to the robot base frame can then be estimated by inputting the suitable transformations to the function, see below.
The calibration procedure is the following:
- a static calibration pattern is used to estimate the transformation between the target frame and the camera frame
- the robot gripper is moved in order to acquire several poses
- for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for instance the robot kinematics
\[ \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{b}\textrm{R}_g & _{}^{b}\textrm{t}_g \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} \]
- for each pose, the homogeneous transformation between the calibration target frame and the camera frame is recorded using for instance a pose estimation method (PnP) from 2D-3D point correspondences
\[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_t & _{}^{c}\textrm{t}_t \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_t\\ Y_t\\ Z_t\\ 1 \end{bmatrix} \]
The Hand-Eye calibration procedure returns the following homogeneous transformation
\[ \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{g}\textrm{R}_c & _{}^{g}\textrm{t}_c \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} \]
This problem is also known as solving the \(\mathbf{A}\mathbf{X}=\mathbf{X}\mathbf{B}\) equation:
- for an eye-in-hand configuration
\[ \begin{align*} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{b}{\textrm{T}_g}^{(2)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{b}{\textrm{T}_g}^{(2)})^{-1} \hspace{0.2em} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c &= \hspace{0.1em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*} \]
- for an eye-to-hand configuration
\[ \begin{align*} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{g}{\textrm{T}_b}^{(2)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{g}{\textrm{T}_b}^{(2)})^{-1} \hspace{0.2em} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c &= \hspace{0.1em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*} \]
- Note
- Additional information can be found on this website.
- A minimum of 2 motions with non parallel rotation axes are necessary to determine the hand-eye transformation. So at least 3 different poses are required, but it is strongly recommended to use many more poses.
◆ calibrateHandEye() [2/2]
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static |
Computes Hand-Eye calibration: \(_{}^{g}\textrm{T}_c\).
- Parameters
-
[in] R_gripper2base Rotation part extracted from the homogeneous matrix that transforms a point expressed in the gripper frame to the robot base frame ( \(_{}^{b}\textrm{T}_g\)). This is a vector ( vector<Mat>) that contains the rotation,(3x3)rotation matrices or(3x1)rotation vectors, for all the transformations from gripper frame to robot base frame.[in] t_gripper2base Translation part extracted from the homogeneous matrix that transforms a point expressed in the gripper frame to the robot base frame ( \(_{}^{b}\textrm{T}_g\)). This is a vector ( vector<Mat>) that contains the(3x1)translation vectors for all the transformations from gripper frame to robot base frame.[in] R_target2cam Rotation part extracted from the homogeneous matrix that transforms a point expressed in the target frame to the camera frame ( \(_{}^{c}\textrm{T}_t\)). This is a vector ( vector<Mat>) that contains the rotation,(3x3)rotation matrices or(3x1)rotation vectors, for all the transformations from calibration target frame to camera frame.[in] t_target2cam Rotation part extracted from the homogeneous matrix that transforms a point expressed in the target frame to the camera frame ( \(_{}^{c}\textrm{T}_t\)). This is a vector ( vector<Mat>) that contains the(3x1)translation vectors for all the transformations from calibration target frame to camera frame.[out] R_cam2gripper Estimated (3x3)rotation part extracted from the homogeneous matrix that transforms a point expressed in the camera frame to the gripper frame ( \(_{}^{g}\textrm{T}_c\)).[out] t_cam2gripper Estimated (3x1)translation part extracted from the homogeneous matrix that transforms a point expressed in the camera frame to the gripper frame ( \(_{}^{g}\textrm{T}_c\)).[in] method One of the implemented Hand-Eye calibration method, see cv::HandEyeCalibrationMethod
The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions) and the following methods are implemented:
- R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/EyeCalibration [Tsai89]
- F. Park, B. Martin Robot Sensor Calibration: Solving AX = XB on the Euclidean Group [Park94]
- R. Horaud, F. Dornaika Hand-Eye Calibration [Horaud95]
Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented methods:
- N. Andreff, R. Horaud, B. Espiau On-line Hand-Eye Calibration [Andreff99]
- K. Daniilidis Hand-Eye Calibration Using Dual Quaternions [Daniilidis98]
The following picture describes the Hand-Eye calibration problem where the transformation between a camera ("eye") mounted on a robot gripper ("hand") has to be estimated. This configuration is called eye-in-hand.
The eye-to-hand configuration consists in a static camera observing a calibration pattern mounted on the robot end-effector. The transformation from the camera to the robot base frame can then be estimated by inputting the suitable transformations to the function, see below.
The calibration procedure is the following:
- a static calibration pattern is used to estimate the transformation between the target frame and the camera frame
- the robot gripper is moved in order to acquire several poses
- for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for instance the robot kinematics
\[ \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{b}\textrm{R}_g & _{}^{b}\textrm{t}_g \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} \]
- for each pose, the homogeneous transformation between the calibration target frame and the camera frame is recorded using for instance a pose estimation method (PnP) from 2D-3D point correspondences
\[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_t & _{}^{c}\textrm{t}_t \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_t\\ Y_t\\ Z_t\\ 1 \end{bmatrix} \]
The Hand-Eye calibration procedure returns the following homogeneous transformation
\[ \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{g}\textrm{R}_c & _{}^{g}\textrm{t}_c \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} \]
This problem is also known as solving the \(\mathbf{A}\mathbf{X}=\mathbf{X}\mathbf{B}\) equation:
- for an eye-in-hand configuration
\[ \begin{align*} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{b}{\textrm{T}_g}^{(2)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{b}{\textrm{T}_g}^{(2)})^{-1} \hspace{0.2em} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c &= \hspace{0.1em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*} \]
- for an eye-to-hand configuration
\[ \begin{align*} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{g}{\textrm{T}_b}^{(2)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{g}{\textrm{T}_b}^{(2)})^{-1} \hspace{0.2em} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c &= \hspace{0.1em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*} \]
- Note
- Additional information can be found on this website.
- A minimum of 2 motions with non parallel rotation axes are necessary to determine the hand-eye transformation. So at least 3 different poses are required, but it is strongly recommended to use many more poses.
◆ calibrateRobotWorldHandEye() [1/2]
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Computes Robot-World/Hand-Eye calibration: \(_{}^{w}\textrm{T}_b\) and \(_{}^{c}\textrm{T}_g\).
- Parameters
-
[in] R_world2cam Rotation part extracted from the homogeneous matrix that transforms a point expressed in the world frame to the camera frame ( \(_{}^{c}\textrm{T}_w\)). This is a vector ( vector<Mat>) that contains the rotation,(3x3)rotation matrices or(3x1)rotation vectors, for all the transformations from world frame to the camera frame.[in] t_world2cam Translation part extracted from the homogeneous matrix that transforms a point expressed in the world frame to the camera frame ( \(_{}^{c}\textrm{T}_w\)). This is a vector ( vector<Mat>) that contains the(3x1)translation vectors for all the transformations from world frame to the camera frame.[in] R_base2gripper Rotation part extracted from the homogeneous matrix that transforms a point expressed in the robot base frame to the gripper frame ( \(_{}^{g}\textrm{T}_b\)). This is a vector ( vector<Mat>) that contains the rotation,(3x3)rotation matrices or(3x1)rotation vectors, for all the transformations from robot base frame to the gripper frame.[in] t_base2gripper Rotation part extracted from the homogeneous matrix that transforms a point expressed in the robot base frame to the gripper frame ( \(_{}^{g}\textrm{T}_b\)). This is a vector ( vector<Mat>) that contains the(3x1)translation vectors for all the transformations from robot base frame to the gripper frame.[out] R_base2world Estimated (3x3)rotation part extracted from the homogeneous matrix that transforms a point expressed in the robot base frame to the world frame ( \(_{}^{w}\textrm{T}_b\)).[out] t_base2world Estimated (3x1)translation part extracted from the homogeneous matrix that transforms a point expressed in the robot base frame to the world frame ( \(_{}^{w}\textrm{T}_b\)).[out] R_gripper2cam Estimated (3x3)rotation part extracted from the homogeneous matrix that transforms a point expressed in the gripper frame to the camera frame ( \(_{}^{c}\textrm{T}_g\)).[out] t_gripper2cam Estimated (3x1)translation part extracted from the homogeneous matrix that transforms a point expressed in the gripper frame to the camera frame ( \(_{}^{c}\textrm{T}_g\)).[in] method One of the implemented Robot-World/Hand-Eye calibration method, see cv::RobotWorldHandEyeCalibrationMethod
The function performs the Robot-World/Hand-Eye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions):
- M. Shah, Solving the robot-world/hand-eye calibration problem using the kronecker product [Shah2013SolvingTR]
Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented method:
- A. Li, L. Wang, and D. Wu, Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product [Li2010SimultaneousRA]
The following picture describes the Robot-World/Hand-Eye calibration problem where the transformations between a robot and a world frame and between a robot gripper ("hand") and a camera ("eye") mounted at the robot end-effector have to be estimated.
The calibration procedure is the following:
- a static calibration pattern is used to estimate the transformation between the target frame and the camera frame
- the robot gripper is moved in order to acquire several poses
- for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for instance the robot kinematics
\[ \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{g}\textrm{R}_b & _{}^{g}\textrm{t}_b \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} \]
- for each pose, the homogeneous transformation between the calibration target frame (the world frame) and the camera frame is recorded using for instance a pose estimation method (PnP) from 2D-3D point correspondences
\[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_w & _{}^{c}\textrm{t}_w \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_w\\ Y_w\\ Z_w\\ 1 \end{bmatrix} \]
The Robot-World/Hand-Eye calibration procedure returns the following homogeneous transformations
\[ \begin{bmatrix} X_w\\ Y_w\\ Z_w\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{w}\textrm{R}_b & _{}^{w}\textrm{t}_b \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} \]
\[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_g & _{}^{c}\textrm{t}_g \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} \]
This problem is also known as solving the \(\mathbf{A}\mathbf{X}=\mathbf{Z}\mathbf{B}\) equation, with:
- \(\mathbf{A} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_w\)
- \(\mathbf{X} \Leftrightarrow \hspace{0.1em} _{}^{w}\textrm{T}_b\)
- \(\mathbf{Z} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_g\)
- \(\mathbf{B} \Leftrightarrow \hspace{0.1em} _{}^{g}\textrm{T}_b\)
- Note
- At least 3 measurements are required (input vectors size must be greater or equal to 3).
◆ calibrateRobotWorldHandEye() [2/2]
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static |
Computes Robot-World/Hand-Eye calibration: \(_{}^{w}\textrm{T}_b\) and \(_{}^{c}\textrm{T}_g\).
- Parameters
-
[in] R_world2cam Rotation part extracted from the homogeneous matrix that transforms a point expressed in the world frame to the camera frame ( \(_{}^{c}\textrm{T}_w\)). This is a vector ( vector<Mat>) that contains the rotation,(3x3)rotation matrices or(3x1)rotation vectors, for all the transformations from world frame to the camera frame.[in] t_world2cam Translation part extracted from the homogeneous matrix that transforms a point expressed in the world frame to the camera frame ( \(_{}^{c}\textrm{T}_w\)). This is a vector ( vector<Mat>) that contains the(3x1)translation vectors for all the transformations from world frame to the camera frame.[in] R_base2gripper Rotation part extracted from the homogeneous matrix that transforms a point expressed in the robot base frame to the gripper frame ( \(_{}^{g}\textrm{T}_b\)). This is a vector ( vector<Mat>) that contains the rotation,(3x3)rotation matrices or(3x1)rotation vectors, for all the transformations from robot base frame to the gripper frame.[in] t_base2gripper Rotation part extracted from the homogeneous matrix that transforms a point expressed in the robot base frame to the gripper frame ( \(_{}^{g}\textrm{T}_b\)). This is a vector ( vector<Mat>) that contains the(3x1)translation vectors for all the transformations from robot base frame to the gripper frame.[out] R_base2world Estimated (3x3)rotation part extracted from the homogeneous matrix that transforms a point expressed in the robot base frame to the world frame ( \(_{}^{w}\textrm{T}_b\)).[out] t_base2world Estimated (3x1)translation part extracted from the homogeneous matrix that transforms a point expressed in the robot base frame to the world frame ( \(_{}^{w}\textrm{T}_b\)).[out] R_gripper2cam Estimated (3x3)rotation part extracted from the homogeneous matrix that transforms a point expressed in the gripper frame to the camera frame ( \(_{}^{c}\textrm{T}_g\)).[out] t_gripper2cam Estimated (3x1)translation part extracted from the homogeneous matrix that transforms a point expressed in the gripper frame to the camera frame ( \(_{}^{c}\textrm{T}_g\)).[in] method One of the implemented Robot-World/Hand-Eye calibration method, see cv::RobotWorldHandEyeCalibrationMethod
The function performs the Robot-World/Hand-Eye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions):
- M. Shah, Solving the robot-world/hand-eye calibration problem using the kronecker product [Shah2013SolvingTR]
Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented method:
- A. Li, L. Wang, and D. Wu, Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product [Li2010SimultaneousRA]
The following picture describes the Robot-World/Hand-Eye calibration problem where the transformations between a robot and a world frame and between a robot gripper ("hand") and a camera ("eye") mounted at the robot end-effector have to be estimated.
The calibration procedure is the following:
- a static calibration pattern is used to estimate the transformation between the target frame and the camera frame
- the robot gripper is moved in order to acquire several poses
- for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for instance the robot kinematics
\[ \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{g}\textrm{R}_b & _{}^{g}\textrm{t}_b \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} \]
- for each pose, the homogeneous transformation between the calibration target frame (the world frame) and the camera frame is recorded using for instance a pose estimation method (PnP) from 2D-3D point correspondences
\[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_w & _{}^{c}\textrm{t}_w \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_w\\ Y_w\\ Z_w\\ 1 \end{bmatrix} \]
The Robot-World/Hand-Eye calibration procedure returns the following homogeneous transformations
\[ \begin{bmatrix} X_w\\ Y_w\\ Z_w\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{w}\textrm{R}_b & _{}^{w}\textrm{t}_b \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} \]
\[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_g & _{}^{c}\textrm{t}_g \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} \]
This problem is also known as solving the \(\mathbf{A}\mathbf{X}=\mathbf{Z}\mathbf{B}\) equation, with:
- \(\mathbf{A} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_w\)
- \(\mathbf{X} \Leftrightarrow \hspace{0.1em} _{}^{w}\textrm{T}_b\)
- \(\mathbf{Z} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_g\)
- \(\mathbf{B} \Leftrightarrow \hspace{0.1em} _{}^{g}\textrm{T}_b\)
- Note
- At least 3 measurements are required (input vectors size must be greater or equal to 3).
◆ calibrationMatrixValues()
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static |
Computes useful camera characteristics from the camera intrinsic matrix.
- Parameters
-
cameraMatrix Input camera intrinsic matrix that can be estimated by calibrateCamera or stereoCalibrate . imageSize Input image size in pixels. apertureWidth Physical width in mm of the sensor. apertureHeight Physical height in mm of the sensor. fovx Output field of view in degrees along the horizontal sensor axis. fovy Output field of view in degrees along the vertical sensor axis. focalLength Focal length of the lens in mm. principalPoint Principal point in mm. aspectRatio \(f_y/f_x\)
The function computes various useful camera characteristics from the previously estimated camera matrix.
- Note
- Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for the chessboard pitch (it can thus be any value).
◆ checkChessboard()
◆ composeRT() [1/9]
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static |
Combines two rotation-and-shift transformations.
- Parameters
-
rvec1 First rotation vector. tvec1 First translation vector. rvec2 Second rotation vector. tvec2 Second translation vector. rvec3 Output rotation vector of the superposition. tvec3 Output translation vector of the superposition. dr3dr1 Optional output derivative of rvec3 with regard to rvec1 dr3dt1 Optional output derivative of rvec3 with regard to tvec1 dr3dr2 Optional output derivative of rvec3 with regard to rvec2 dr3dt2 Optional output derivative of rvec3 with regard to tvec2 dt3dr1 Optional output derivative of tvec3 with regard to rvec1 dt3dt1 Optional output derivative of tvec3 with regard to tvec1 dt3dr2 Optional output derivative of tvec3 with regard to rvec2 dt3dt2 Optional output derivative of tvec3 with regard to tvec2
The functions compute:
\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.
◆ composeRT() [2/9]
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static |
Combines two rotation-and-shift transformations.
- Parameters
-
rvec1 First rotation vector. tvec1 First translation vector. rvec2 Second rotation vector. tvec2 Second translation vector. rvec3 Output rotation vector of the superposition. tvec3 Output translation vector of the superposition. dr3dr1 Optional output derivative of rvec3 with regard to rvec1 dr3dt1 Optional output derivative of rvec3 with regard to tvec1 dr3dr2 Optional output derivative of rvec3 with regard to rvec2 dr3dt2 Optional output derivative of rvec3 with regard to tvec2 dt3dr1 Optional output derivative of tvec3 with regard to rvec1 dt3dt1 Optional output derivative of tvec3 with regard to tvec1 dt3dr2 Optional output derivative of tvec3 with regard to rvec2 dt3dt2 Optional output derivative of tvec3 with regard to tvec2
The functions compute:
\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.
◆ composeRT() [3/9]
|
static |
Combines two rotation-and-shift transformations.
- Parameters
-
rvec1 First rotation vector. tvec1 First translation vector. rvec2 Second rotation vector. tvec2 Second translation vector. rvec3 Output rotation vector of the superposition. tvec3 Output translation vector of the superposition. dr3dr1 Optional output derivative of rvec3 with regard to rvec1 dr3dt1 Optional output derivative of rvec3 with regard to tvec1 dr3dr2 Optional output derivative of rvec3 with regard to rvec2 dr3dt2 Optional output derivative of rvec3 with regard to tvec2 dt3dr1 Optional output derivative of tvec3 with regard to rvec1 dt3dt1 Optional output derivative of tvec3 with regard to tvec1 dt3dr2 Optional output derivative of tvec3 with regard to rvec2 dt3dt2 Optional output derivative of tvec3 with regard to tvec2
The functions compute:
\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.
◆ composeRT() [4/9]
|
static |
Combines two rotation-and-shift transformations.
- Parameters
-
rvec1 First rotation vector. tvec1 First translation vector. rvec2 Second rotation vector. tvec2 Second translation vector. rvec3 Output rotation vector of the superposition. tvec3 Output translation vector of the superposition. dr3dr1 Optional output derivative of rvec3 with regard to rvec1 dr3dt1 Optional output derivative of rvec3 with regard to tvec1 dr3dr2 Optional output derivative of rvec3 with regard to rvec2 dr3dt2 Optional output derivative of rvec3 with regard to tvec2 dt3dr1 Optional output derivative of tvec3 with regard to rvec1 dt3dt1 Optional output derivative of tvec3 with regard to tvec1 dt3dr2 Optional output derivative of tvec3 with regard to rvec2 dt3dt2 Optional output derivative of tvec3 with regard to tvec2
The functions compute:
\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.
◆ composeRT() [5/9]
|
static |
Combines two rotation-and-shift transformations.
- Parameters
-
rvec1 First rotation vector. tvec1 First translation vector. rvec2 Second rotation vector. tvec2 Second translation vector. rvec3 Output rotation vector of the superposition. tvec3 Output translation vector of the superposition. dr3dr1 Optional output derivative of rvec3 with regard to rvec1 dr3dt1 Optional output derivative of rvec3 with regard to tvec1 dr3dr2 Optional output derivative of rvec3 with regard to rvec2 dr3dt2 Optional output derivative of rvec3 with regard to tvec2 dt3dr1 Optional output derivative of tvec3 with regard to rvec1 dt3dt1 Optional output derivative of tvec3 with regard to tvec1 dt3dr2 Optional output derivative of tvec3 with regard to rvec2 dt3dt2 Optional output derivative of tvec3 with regard to tvec2
The functions compute:
\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.
◆ composeRT() [6/9]
|
static |
Combines two rotation-and-shift transformations.
- Parameters
-
rvec1 First rotation vector. tvec1 First translation vector. rvec2 Second rotation vector. tvec2 Second translation vector. rvec3 Output rotation vector of the superposition. tvec3 Output translation vector of the superposition. dr3dr1 Optional output derivative of rvec3 with regard to rvec1 dr3dt1 Optional output derivative of rvec3 with regard to tvec1 dr3dr2 Optional output derivative of rvec3 with regard to rvec2 dr3dt2 Optional output derivative of rvec3 with regard to tvec2 dt3dr1 Optional output derivative of tvec3 with regard to rvec1 dt3dt1 Optional output derivative of tvec3 with regard to tvec1 dt3dr2 Optional output derivative of tvec3 with regard to rvec2 dt3dt2 Optional output derivative of tvec3 with regard to tvec2
The functions compute:
\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.
◆ composeRT() [7/9]
|
static |
Combines two rotation-and-shift transformations.
- Parameters
-
rvec1 First rotation vector. tvec1 First translation vector. rvec2 Second rotation vector. tvec2 Second translation vector. rvec3 Output rotation vector of the superposition. tvec3 Output translation vector of the superposition. dr3dr1 Optional output derivative of rvec3 with regard to rvec1 dr3dt1 Optional output derivative of rvec3 with regard to tvec1 dr3dr2 Optional output derivative of rvec3 with regard to rvec2 dr3dt2 Optional output derivative of rvec3 with regard to tvec2 dt3dr1 Optional output derivative of tvec3 with regard to rvec1 dt3dt1 Optional output derivative of tvec3 with regard to tvec1 dt3dr2 Optional output derivative of tvec3 with regard to rvec2 dt3dt2 Optional output derivative of tvec3 with regard to tvec2
The functions compute:
\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.
◆ composeRT() [8/9]
|
static |
Combines two rotation-and-shift transformations.
- Parameters
-
rvec1 First rotation vector. tvec1 First translation vector. rvec2 Second rotation vector. tvec2 Second translation vector. rvec3 Output rotation vector of the superposition. tvec3 Output translation vector of the superposition. dr3dr1 Optional output derivative of rvec3 with regard to rvec1 dr3dt1 Optional output derivative of rvec3 with regard to tvec1 dr3dr2 Optional output derivative of rvec3 with regard to rvec2 dr3dt2 Optional output derivative of rvec3 with regard to tvec2 dt3dr1 Optional output derivative of tvec3 with regard to rvec1 dt3dt1 Optional output derivative of tvec3 with regard to tvec1 dt3dr2 Optional output derivative of tvec3 with regard to rvec2 dt3dt2 Optional output derivative of tvec3 with regard to tvec2
The functions compute:
\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.
◆ composeRT() [9/9]
|
static |
Combines two rotation-and-shift transformations.
- Parameters
-
rvec1 First rotation vector. tvec1 First translation vector. rvec2 Second rotation vector. tvec2 Second translation vector. rvec3 Output rotation vector of the superposition. tvec3 Output translation vector of the superposition. dr3dr1 Optional output derivative of rvec3 with regard to rvec1 dr3dt1 Optional output derivative of rvec3 with regard to tvec1 dr3dr2 Optional output derivative of rvec3 with regard to rvec2 dr3dt2 Optional output derivative of rvec3 with regard to tvec2 dt3dr1 Optional output derivative of tvec3 with regard to rvec1 dt3dt1 Optional output derivative of tvec3 with regard to tvec1 dt3dr2 Optional output derivative of tvec3 with regard to rvec2 dt3dt2 Optional output derivative of tvec3 with regard to tvec2
The functions compute:
\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.
◆ computeCorrespondEpilines()
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static |
For points in an image of a stereo pair, computes the corresponding epilines in the other image.
- Parameters
-
points Input points. \(N \times 1\) or \(1 \times N\) matrix of type CV_32FC2 or vector<Point2f> . whichImage Index of the image (1 or 2) that contains the points . F Fundamental matrix that can be estimated using findFundamentalMat or stereoRectify . lines Output vector of the epipolar lines corresponding to the points in the other image. Each line \(ax + by + c=0\) is encoded by 3 numbers \((a, b, c)\) .
For every point in one of the two images of a stereo pair, the function finds the equation of the corresponding epipolar line in the other image.
From the fundamental matrix definition (see findFundamentalMat ), line \(l^{(2)}_i\) in the second image for the point \(p^{(1)}_i\) in the first image (when whichImage=1 ) is computed as:
\[l^{(2)}_i = F p^{(1)}_i\]
And vice versa, when whichImage=2, \(l^{(1)}_i\) is computed from \(p^{(2)}_i\) as:
\[l^{(1)}_i = F^T p^{(2)}_i\]
Line coefficients are defined up to a scale. They are normalized so that \(a_i^2+b_i^2=1\) .
◆ convertPointsFromHomogeneous()
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static |
Converts points from homogeneous to Euclidean space.
- Parameters
-
src Input vector of N-dimensional points. dst Output vector of N-1-dimensional points.
The function converts points homogeneous to Euclidean space using perspective projection. That is, each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the output point coordinates will be (0,0,0,...).
◆ convertPointsToHomogeneous()
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static |
Converts points from Euclidean to homogeneous space.
- Parameters
-
src Input vector of N-dimensional points. dst Output vector of N+1-dimensional points.
The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1).
◆ correctMatches()
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static |
Refines coordinates of corresponding points.
- Parameters
-
F 3x3 fundamental matrix. points1 1xN array containing the first set of points. points2 1xN array containing the second set of points. newPoints1 The optimized points1. newPoints2 The optimized points2.
The function implements the Optimal Triangulation Method (see Multiple View Geometry [HartleyZ00] for details). For each given point correspondence points1[i] <-> points2[i], and a fundamental matrix F, it computes the corrected correspondences newPoints1[i] <-> newPoints2[i] that minimize the geometric error \(d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\) (where \(d(a,b)\) is the geometric distance between points \(a\) and \(b\) ) subject to the epipolar constraint \(newPoints2^T \cdot F \cdot newPoints1 = 0\) .
◆ decomposeEssentialMat()
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static |
Decompose an essential matrix to possible rotations and translation.
- Parameters
-
E The input essential matrix. R1 One possible rotation matrix. R2 Another possible rotation matrix. t One possible translation.
This function decomposes the essential matrix E using svd decomposition [HartleyZ00]. In general, four possible poses exist for the decomposition of E. They are \([R_1, t]\), \([R_1, -t]\), \([R_2, t]\), \([R_2, -t]\).
If E gives the epipolar constraint \([p_2; 1]^T A^{-T} E A^{-1} [p_1; 1] = 0\) between the image points \(p_1\) in the first image and \(p_2\) in second image, then any of the tuples \([R_1, t]\), \([R_1, -t]\), \([R_2, t]\), \([R_2, -t]\) is a change of basis from the first camera's coordinate system to the second camera's coordinate system. However, by decomposing E, one can only get the direction of the translation. For this reason, the translation t is returned with unit length.
◆ decomposeHomographyMat()
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static |
Decompose a homography matrix to rotation(s), translation(s) and plane normal(s).
- Parameters
-
H The input homography matrix between two images. K The input camera intrinsic matrix. rotations Array of rotation matrices. translations Array of translation matrices. normals Array of plane normal matrices.
This function extracts relative camera motion between two views of a planar object and returns up to four mathematical solution tuples of rotation, translation, and plane normal. The decomposition of the homography matrix H is described in detail in [Malis2007].
If the homography H, induced by the plane, gives the constraint
\[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\]
on the source image points \(p_i\) and the destination image points \(p'_i\), then the tuple of rotations[k] and translations[k] is a change of basis from the source camera's coordinate system to the destination camera's coordinate system. However, by decomposing H, one can only get the translation normalized by the (typically unknown) depth of the scene, i.e. its direction but with normalized length.
If point correspondences are available, at least two solutions may further be invalidated, by applying positive depth constraint, i.e. all points must be in front of the camera.
◆ decomposeProjectionMatrix() [1/5]
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static |
Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
- Parameters
-
projMatrix 3x4 input projection matrix P. cameraMatrix Output 3x3 camera intrinsic matrix \(\cameramatrix{A}\). rotMatrix Output 3x3 external rotation matrix R. transVect Output 4x1 translation vector T. rotMatrixX Optional 3x3 rotation matrix around x-axis. rotMatrixY Optional 3x3 rotation matrix around y-axis. rotMatrixZ Optional 3x3 rotation matrix around z-axis. eulerAngles Optional three-element vector containing three Euler angles of rotation in degrees.
The function computes a decomposition of a projection matrix into a calibration and a rotation matrix and the position of a camera.
It optionally returns three rotation matrices, one for each axis, and three Euler angles that could be used in OpenGL. Note, there is always more than one sequence of rotations about the three principal axes that results in the same orientation of an object, e.g. see [Slabaugh] . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.
The function is based on RQDecomp3x3 .
◆ decomposeProjectionMatrix() [2/5]
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static |
Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
- Parameters
-
projMatrix 3x4 input projection matrix P. cameraMatrix Output 3x3 camera intrinsic matrix \(\cameramatrix{A}\). rotMatrix Output 3x3 external rotation matrix R. transVect Output 4x1 translation vector T. rotMatrixX Optional 3x3 rotation matrix around x-axis. rotMatrixY Optional 3x3 rotation matrix around y-axis. rotMatrixZ Optional 3x3 rotation matrix around z-axis. eulerAngles Optional three-element vector containing three Euler angles of rotation in degrees.
The function computes a decomposition of a projection matrix into a calibration and a rotation matrix and the position of a camera.
It optionally returns three rotation matrices, one for each axis, and three Euler angles that could be used in OpenGL. Note, there is always more than one sequence of rotations about the three principal axes that results in the same orientation of an object, e.g. see [Slabaugh] . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.
The function is based on RQDecomp3x3 .
◆ decomposeProjectionMatrix() [3/5]
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static |
Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
- Parameters
-
projMatrix 3x4 input projection matrix P. cameraMatrix Output 3x3 camera intrinsic matrix \(\cameramatrix{A}\). rotMatrix Output 3x3 external rotation matrix R. transVect Output 4x1 translation vector T. rotMatrixX Optional 3x3 rotation matrix around x-axis. rotMatrixY Optional 3x3 rotation matrix around y-axis. rotMatrixZ Optional 3x3 rotation matrix around z-axis. eulerAngles Optional three-element vector containing three Euler angles of rotation in degrees.
The function computes a decomposition of a projection matrix into a calibration and a rotation matrix and the position of a camera.
It optionally returns three rotation matrices, one for each axis, and three Euler angles that could be used in OpenGL. Note, there is always more than one sequence of rotations about the three principal axes that results in the same orientation of an object, e.g. see [Slabaugh] . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.
The function is based on RQDecomp3x3 .
◆ decomposeProjectionMatrix() [4/5]
|
static |
Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
- Parameters
-
projMatrix 3x4 input projection matrix P. cameraMatrix Output 3x3 camera intrinsic matrix \(\cameramatrix{A}\). rotMatrix Output 3x3 external rotation matrix R. transVect Output 4x1 translation vector T. rotMatrixX Optional 3x3 rotation matrix around x-axis. rotMatrixY Optional 3x3 rotation matrix around y-axis. rotMatrixZ Optional 3x3 rotation matrix around z-axis. eulerAngles Optional three-element vector containing three Euler angles of rotation in degrees.
The function computes a decomposition of a projection matrix into a calibration and a rotation matrix and the position of a camera.
It optionally returns three rotation matrices, one for each axis, and three Euler angles that could be used in OpenGL. Note, there is always more than one sequence of rotations about the three principal axes that results in the same orientation of an object, e.g. see [Slabaugh] . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.
The function is based on RQDecomp3x3 .
◆ decomposeProjectionMatrix() [5/5]
|
static |
Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
- Parameters
-
projMatrix 3x4 input projection matrix P. cameraMatrix Output 3x3 camera intrinsic matrix \(\cameramatrix{A}\). rotMatrix Output 3x3 external rotation matrix R. transVect Output 4x1 translation vector T. rotMatrixX Optional 3x3 rotation matrix around x-axis. rotMatrixY Optional 3x3 rotation matrix around y-axis. rotMatrixZ Optional 3x3 rotation matrix around z-axis. eulerAngles Optional three-element vector containing three Euler angles of rotation in degrees.
The function computes a decomposition of a projection matrix into a calibration and a rotation matrix and the position of a camera.
It optionally returns three rotation matrices, one for each axis, and three Euler angles that could be used in OpenGL. Note, there is always more than one sequence of rotations about the three principal axes that results in the same orientation of an object, e.g. see [Slabaugh] . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.
The function is based on RQDecomp3x3 .
◆ drawChessboardCorners()
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static |
Renders the detected chessboard corners.
- Parameters
-
image Destination image. It must be an 8-bit color image. patternSize Number of inner corners per a chessboard row and column (patternSize = cv::Size(points_per_row,points_per_column)). corners Array of detected corners, the output of findChessboardCorners. patternWasFound Parameter indicating whether the complete board was found or not. The return value of findChessboardCorners should be passed here.
The function draws individual chessboard corners detected either as red circles if the board was not found, or as colored corners connected with lines if the board was found.
◆ drawFrameAxes() [1/2]
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static |
Draw axes of the world/object coordinate system from pose estimation.
- See also
- solvePnP
- Parameters
-
image Input/output image. It must have 1 or 3 channels. The number of channels is not altered. cameraMatrix Input 3x3 floating-point matrix of camera intrinsic parameters. \(\cameramatrix{A}\) distCoeffs Input vector of distortion coefficients \(\distcoeffs\). If the vector is empty, the zero distortion coefficients are assumed. rvec Rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. tvec Translation vector. length Length of the painted axes in the same unit than tvec (usually in meters). thickness Line thickness of the painted axes.
This function draws the axes of the world/object coordinate system w.r.t. to the camera frame. OX is drawn in red, OY in green and OZ in blue.
◆ drawFrameAxes() [2/2]
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static |
Draw axes of the world/object coordinate system from pose estimation.
- See also
- solvePnP
- Parameters
-
image Input/output image. It must have 1 or 3 channels. The number of channels is not altered. cameraMatrix Input 3x3 floating-point matrix of camera intrinsic parameters. \(\cameramatrix{A}\) distCoeffs Input vector of distortion coefficients \(\distcoeffs\). If the vector is empty, the zero distortion coefficients are assumed. rvec Rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. tvec Translation vector. length Length of the painted axes in the same unit than tvec (usually in meters). thickness Line thickness of the painted axes.
This function draws the axes of the world/object coordinate system w.r.t. to the camera frame. OX is drawn in red, OY in green and OZ in blue.
◆ estimateAffine2D() [1/8]
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static |
Computes an optimal affine transformation between two 2D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]
- Parameters
-
from First input 2D point set containing \((X,Y)\). to Second input 2D point set containing \((x,y)\). inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). method Robust method used to compute transformation. The following methods are possible: ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. maxIters The maximum number of robust method iterations. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.
- Returns
- Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation could not be estimated. The returned matrix has the following form:
\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]
The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
- Note
- The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
- See also
- estimateAffinePartial2D, getAffineTransform
◆ estimateAffine2D() [2/8]
|
static |
Computes an optimal affine transformation between two 2D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]
- Parameters
-
from First input 2D point set containing \((X,Y)\). to Second input 2D point set containing \((x,y)\). inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). method Robust method used to compute transformation. The following methods are possible: ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. maxIters The maximum number of robust method iterations. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.
- Returns
- Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation could not be estimated. The returned matrix has the following form:
\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]
The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
- Note
- The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
- See also
- estimateAffinePartial2D, getAffineTransform
◆ estimateAffine2D() [3/8]
|
static |
Computes an optimal affine transformation between two 2D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]
- Parameters
-
from First input 2D point set containing \((X,Y)\). to Second input 2D point set containing \((x,y)\). inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). method Robust method used to compute transformation. The following methods are possible: ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. maxIters The maximum number of robust method iterations. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.
- Returns
- Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation could not be estimated. The returned matrix has the following form:
\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]
The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
- Note
- The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
- See also
- estimateAffinePartial2D, getAffineTransform
◆ estimateAffine2D() [4/8]
|
static |
Computes an optimal affine transformation between two 2D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]
- Parameters
-
from First input 2D point set containing \((X,Y)\). to Second input 2D point set containing \((x,y)\). inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). method Robust method used to compute transformation. The following methods are possible: ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. maxIters The maximum number of robust method iterations. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.
- Returns
- Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation could not be estimated. The returned matrix has the following form:
\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]
The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
- Note
- The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
- See also
- estimateAffinePartial2D, getAffineTransform
◆ estimateAffine2D() [5/8]
|
static |
Computes an optimal affine transformation between two 2D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]
- Parameters
-
from First input 2D point set containing \((X,Y)\). to Second input 2D point set containing \((x,y)\). inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). method Robust method used to compute transformation. The following methods are possible: ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. maxIters The maximum number of robust method iterations. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.
- Returns
- Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation could not be estimated. The returned matrix has the following form:
\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]
The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
- Note
- The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
- See also
- estimateAffinePartial2D, getAffineTransform
◆ estimateAffine2D() [6/8]
|
static |
Computes an optimal affine transformation between two 2D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]
- Parameters
-
from First input 2D point set containing \((X,Y)\). to Second input 2D point set containing \((x,y)\). inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). method Robust method used to compute transformation. The following methods are possible: ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. maxIters The maximum number of robust method iterations. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.
- Returns
- Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation could not be estimated. The returned matrix has the following form:
\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]
The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
- Note
- The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
- See also
- estimateAffinePartial2D, getAffineTransform
◆ estimateAffine2D() [7/8]
Computes an optimal affine transformation between two 2D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]
- Parameters
-
from First input 2D point set containing \((X,Y)\). to Second input 2D point set containing \((x,y)\). inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). method Robust method used to compute transformation. The following methods are possible: ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. maxIters The maximum number of robust method iterations. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.
- Returns
- Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation could not be estimated. The returned matrix has the following form:
\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]
The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
- Note
- The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
- See also
- estimateAffinePartial2D, getAffineTransform
◆ estimateAffine2D() [8/8]
|
static |
◆ estimateAffine3D() [1/6]
|
static |
Computes an optimal affine transformation between two 3D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12} & a_{13}\\ a_{21} & a_{22} & a_{23}\\ a_{31} & a_{32} & a_{33}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]
- Parameters
-
src First input 3D point set containing \((X,Y,Z)\). dst Second input 3D point set containing \((x,y,z)\). out Output 3D affine transformation matrix \(3 \times 4\) of the form \[ \begin{bmatrix} a_{11} & a_{12} & a_{13} & b_1\\ a_{21} & a_{22} & a_{23} & b_2\\ a_{31} & a_{32} & a_{33} & b_3\\ \end{bmatrix} \]
inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
The function estimates an optimal 3D affine transformation between two 3D point sets using the RANSAC algorithm.
◆ estimateAffine3D() [2/6]
|
static |
Computes an optimal affine transformation between two 3D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12} & a_{13}\\ a_{21} & a_{22} & a_{23}\\ a_{31} & a_{32} & a_{33}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]
- Parameters
-
src First input 3D point set containing \((X,Y,Z)\). dst Second input 3D point set containing \((x,y,z)\). out Output 3D affine transformation matrix \(3 \times 4\) of the form \[ \begin{bmatrix} a_{11} & a_{12} & a_{13} & b_1\\ a_{21} & a_{22} & a_{23} & b_2\\ a_{31} & a_{32} & a_{33} & b_3\\ \end{bmatrix} \]
inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
The function estimates an optimal 3D affine transformation between two 3D point sets using the RANSAC algorithm.
◆ estimateAffine3D() [3/6]
|
static |
Computes an optimal affine transformation between two 3D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12} & a_{13}\\ a_{21} & a_{22} & a_{23}\\ a_{31} & a_{32} & a_{33}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]
- Parameters
-
src First input 3D point set containing \((X,Y,Z)\). dst Second input 3D point set containing \((x,y,z)\). out Output 3D affine transformation matrix \(3 \times 4\) of the form \[ \begin{bmatrix} a_{11} & a_{12} & a_{13} & b_1\\ a_{21} & a_{22} & a_{23} & b_2\\ a_{31} & a_{32} & a_{33} & b_3\\ \end{bmatrix} \]
inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
The function estimates an optimal 3D affine transformation between two 3D point sets using the RANSAC algorithm.
◆ estimateAffine3D() [4/6]
|
static |
Computes an optimal affine transformation between two 3D point sets.
It computes \(R,s,t\) minimizing \(\sum{i} dst_i - c \cdot R \cdot src_i \) where \(R\) is a 3x3 rotation matrix, \(t\) is a 3x1 translation vector and \(s\) is a scalar size value. This is an implementation of the algorithm by Umeyama [umeyama1991least] . The estimated affine transform has a homogeneous scale which is a subclass of affine transformations with 7 degrees of freedom. The paired point sets need to comprise at least 3 points each.
- Parameters
-
src First input 3D point set. dst Second input 3D point set. scale If null is passed, the scale parameter c will be assumed to be 1.0. Else the pointed-to variable will be set to the optimal scale. force_rotation If true, the returned rotation will never be a reflection. This might be unwanted, e.g. when optimizing a transform between a right- and a left-handed coordinate system.
- Returns
- 3D affine transformation matrix \(3 \times 4\) of the form
\[T = \begin{bmatrix} R & t\\ \end{bmatrix} \]
◆ estimateAffine3D() [5/6]
|
static |
Computes an optimal affine transformation between two 3D point sets.
It computes \(R,s,t\) minimizing \(\sum{i} dst_i - c \cdot R \cdot src_i \) where \(R\) is a 3x3 rotation matrix, \(t\) is a 3x1 translation vector and \(s\) is a scalar size value. This is an implementation of the algorithm by Umeyama [umeyama1991least] . The estimated affine transform has a homogeneous scale which is a subclass of affine transformations with 7 degrees of freedom. The paired point sets need to comprise at least 3 points each.
- Parameters
-
src First input 3D point set. dst Second input 3D point set. scale If null is passed, the scale parameter c will be assumed to be 1.0. Else the pointed-to variable will be set to the optimal scale. force_rotation If true, the returned rotation will never be a reflection. This might be unwanted, e.g. when optimizing a transform between a right- and a left-handed coordinate system.
- Returns
- 3D affine transformation matrix \(3 \times 4\) of the form
\[T = \begin{bmatrix} R & t\\ \end{bmatrix} \]
◆ estimateAffine3D() [6/6]
Computes an optimal affine transformation between two 3D point sets.
It computes \(R,s,t\) minimizing \(\sum{i} dst_i - c \cdot R \cdot src_i \) where \(R\) is a 3x3 rotation matrix, \(t\) is a 3x1 translation vector and \(s\) is a scalar size value. This is an implementation of the algorithm by Umeyama [umeyama1991least] . The estimated affine transform has a homogeneous scale which is a subclass of affine transformations with 7 degrees of freedom. The paired point sets need to comprise at least 3 points each.
- Parameters
-
src First input 3D point set. dst Second input 3D point set. scale If null is passed, the scale parameter c will be assumed to be 1.0. Else the pointed-to variable will be set to the optimal scale. force_rotation If true, the returned rotation will never be a reflection. This might be unwanted, e.g. when optimizing a transform between a right- and a left-handed coordinate system.
- Returns
- 3D affine transformation matrix \(3 \times 4\) of the form
\[T = \begin{bmatrix} R & t\\ \end{bmatrix} \]
◆ estimateAffinePartial2D() [1/7]
|
static |
Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
- Parameters
-
from First input 2D point set. to Second input 2D point set. inliers Output vector indicating which points are inliers. method Robust method used to compute transformation. The following methods are possible: ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. maxIters The maximum number of robust method iterations. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.
- Returns
- Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) or empty matrix if transformation could not be estimated.
The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
Estimated transformation matrix is:
\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \]
Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.
- Note
- The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
- See also
- estimateAffine2D, getAffineTransform
◆ estimateAffinePartial2D() [2/7]
|
static |
Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
- Parameters
-
from First input 2D point set. to Second input 2D point set. inliers Output vector indicating which points are inliers. method Robust method used to compute transformation. The following methods are possible: ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. maxIters The maximum number of robust method iterations. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.
- Returns
- Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) or empty matrix if transformation could not be estimated.
The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
Estimated transformation matrix is:
\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \]
Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.
- Note
- The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
- See also
- estimateAffine2D, getAffineTransform
◆ estimateAffinePartial2D() [3/7]
|
static |
Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
- Parameters
-
from First input 2D point set. to Second input 2D point set. inliers Output vector indicating which points are inliers. method Robust method used to compute transformation. The following methods are possible: ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. maxIters The maximum number of robust method iterations. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.
- Returns
- Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) or empty matrix if transformation could not be estimated.
The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
Estimated transformation matrix is:
\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \]
Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.
- Note
- The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
- See also
- estimateAffine2D, getAffineTransform
◆ estimateAffinePartial2D() [4/7]
|
static |
Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
- Parameters
-
from First input 2D point set. to Second input 2D point set. inliers Output vector indicating which points are inliers. method Robust method used to compute transformation. The following methods are possible: ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. maxIters The maximum number of robust method iterations. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.
- Returns
- Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) or empty matrix if transformation could not be estimated.
The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
Estimated transformation matrix is:
\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \]
Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.
- Note
- The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
- See also
- estimateAffine2D, getAffineTransform
◆ estimateAffinePartial2D() [5/7]
|
static |
Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
- Parameters
-
from First input 2D point set. to Second input 2D point set. inliers Output vector indicating which points are inliers. method Robust method used to compute transformation. The following methods are possible: ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. maxIters The maximum number of robust method iterations. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.
- Returns
- Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) or empty matrix if transformation could not be estimated.
The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
Estimated transformation matrix is:
\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \]
Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.
- Note
- The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
- See also
- estimateAffine2D, getAffineTransform
◆ estimateAffinePartial2D() [6/7]
|
static |
Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
- Parameters
-
from First input 2D point set. to Second input 2D point set. inliers Output vector indicating which points are inliers. method Robust method used to compute transformation. The following methods are possible: ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. maxIters The maximum number of robust method iterations. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.
- Returns
- Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) or empty matrix if transformation could not be estimated.
The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
Estimated transformation matrix is:
\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \]
Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.
- Note
- The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
- See also
- estimateAffine2D, getAffineTransform
◆ estimateAffinePartial2D() [7/7]
Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
- Parameters
-
from First input 2D point set. to Second input 2D point set. inliers Output vector indicating which points are inliers. method Robust method used to compute transformation. The following methods are possible: ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. maxIters The maximum number of robust method iterations. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.
- Returns
- Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) or empty matrix if transformation could not be estimated.
The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
Estimated transformation matrix is:
\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \]
Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.
- Note
- The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
- See also
- estimateAffine2D, getAffineTransform
◆ estimateChessboardSharpness() [1/4]
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Estimates the sharpness of a detected chessboard.
Image sharpness, as well as brightness, are a critical parameter for accuracte camera calibration. For accessing these parameters for filtering out problematic calibraiton images, this method calculates edge profiles by traveling from black to white chessboard cell centers. Based on this, the number of pixels is calculated required to transit from black to white. This width of the transition area is a good indication of how sharp the chessboard is imaged and should be below ~3.0 pixels.
- Parameters
-
image Gray image used to find chessboard corners patternSize Size of a found chessboard pattern corners Corners found by findChessboardCornersSB rise_distance Rise distance 0.8 means 10% ... 90% of the final signal strength vertical By default edge responses for horizontal lines are calculated sharpness Optional output array with a sharpness value for calculated edge responses (see description)
The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness)
- Returns
- Scalar(average sharpness, average min brightness, average max brightness,0)
◆ estimateChessboardSharpness() [2/4]
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Estimates the sharpness of a detected chessboard.
Image sharpness, as well as brightness, are a critical parameter for accuracte camera calibration. For accessing these parameters for filtering out problematic calibraiton images, this method calculates edge profiles by traveling from black to white chessboard cell centers. Based on this, the number of pixels is calculated required to transit from black to white. This width of the transition area is a good indication of how sharp the chessboard is imaged and should be below ~3.0 pixels.
- Parameters
-
image Gray image used to find chessboard corners patternSize Size of a found chessboard pattern corners Corners found by findChessboardCornersSB rise_distance Rise distance 0.8 means 10% ... 90% of the final signal strength vertical By default edge responses for horizontal lines are calculated sharpness Optional output array with a sharpness value for calculated edge responses (see description)
The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness)
- Returns
- Scalar(average sharpness, average min brightness, average max brightness,0)
◆ estimateChessboardSharpness() [3/4]
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Estimates the sharpness of a detected chessboard.
Image sharpness, as well as brightness, are a critical parameter for accuracte camera calibration. For accessing these parameters for filtering out problematic calibraiton images, this method calculates edge profiles by traveling from black to white chessboard cell centers. Based on this, the number of pixels is calculated required to transit from black to white. This width of the transition area is a good indication of how sharp the chessboard is imaged and should be below ~3.0 pixels.
- Parameters
-
image Gray image used to find chessboard corners patternSize Size of a found chessboard pattern corners Corners found by findChessboardCornersSB rise_distance Rise distance 0.8 means 10% ... 90% of the final signal strength vertical By default edge responses for horizontal lines are calculated sharpness Optional output array with a sharpness value for calculated edge responses (see description)
The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness)
- Returns
- Scalar(average sharpness, average min brightness, average max brightness,0)
◆ estimateChessboardSharpness() [4/4]
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Estimates the sharpness of a detected chessboard.
Image sharpness, as well as brightness, are a critical parameter for accuracte camera calibration. For accessing these parameters for filtering out problematic calibraiton images, this method calculates edge profiles by traveling from black to white chessboard cell centers. Based on this, the number of pixels is calculated required to transit from black to white. This width of the transition area is a good indication of how sharp the chessboard is imaged and should be below ~3.0 pixels.
- Parameters
-
image Gray image used to find chessboard corners patternSize Size of a found chessboard pattern corners Corners found by findChessboardCornersSB rise_distance Rise distance 0.8 means 10% ... 90% of the final signal strength vertical By default edge responses for horizontal lines are calculated sharpness Optional output array with a sharpness value for calculated edge responses (see description)
The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness)
- Returns
- Scalar(average sharpness, average min brightness, average max brightness,0)
◆ estimateTranslation3D() [1/3]
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Computes an optimal translation between two 3D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]
- Parameters
-
src First input 3D point set containing \((X,Y,Z)\). dst Second input 3D point set containing \((x,y,z)\). out Output 3D translation vector \(3 \times 1\) of the form \[ \begin{bmatrix} b_1 \\ b_2 \\ b_3 \\ \end{bmatrix} \]
inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
The function estimates an optimal 3D translation between two 3D point sets using the RANSAC algorithm.
◆ estimateTranslation3D() [2/3]
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Computes an optimal translation between two 3D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]
- Parameters
-
src First input 3D point set containing \((X,Y,Z)\). dst Second input 3D point set containing \((x,y,z)\). out Output 3D translation vector \(3 \times 1\) of the form \[ \begin{bmatrix} b_1 \\ b_2 \\ b_3 \\ \end{bmatrix} \]
inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
The function estimates an optimal 3D translation between two 3D point sets using the RANSAC algorithm.
◆ estimateTranslation3D() [3/3]
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Computes an optimal translation between two 3D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]
- Parameters
-
src First input 3D point set containing \((X,Y,Z)\). dst Second input 3D point set containing \((x,y,z)\). out Output 3D translation vector \(3 \times 1\) of the form \[ \begin{bmatrix} b_1 \\ b_2 \\ b_3 \\ \end{bmatrix} \]
inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. confidence Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
The function estimates an optimal 3D translation between two 3D point sets using the RANSAC algorithm.
◆ filterHomographyDecompByVisibleRefpoints() [1/2]
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Filters homography decompositions based on additional information.
- Parameters
-
rotations Vector of rotation matrices. normals Vector of plane normal matrices. beforePoints Vector of (rectified) visible reference points before the homography is applied afterPoints Vector of (rectified) visible reference points after the homography is applied possibleSolutions Vector of int indices representing the viable solution set after filtering pointsMask optional Mat/Vector of 8u type representing the mask for the inliers as given by the findHomography function
This function is intended to filter the output of the decomposeHomographyMat based on additional information as described in [Malis2007] . The summary of the method: the decomposeHomographyMat function returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the sets of points visible in the camera frame before and after the homography transformation is applied, we can determine which are the true potential solutions and which are the opposites by verifying which homographies are consistent with all visible reference points being in front of the camera. The inputs are left unchanged; the filtered solution set is returned as indices into the existing one.
◆ filterHomographyDecompByVisibleRefpoints() [2/2]
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Filters homography decompositions based on additional information.
- Parameters
-
rotations Vector of rotation matrices. normals Vector of plane normal matrices. beforePoints Vector of (rectified) visible reference points before the homography is applied afterPoints Vector of (rectified) visible reference points after the homography is applied possibleSolutions Vector of int indices representing the viable solution set after filtering pointsMask optional Mat/Vector of 8u type representing the mask for the inliers as given by the findHomography function
This function is intended to filter the output of the decomposeHomographyMat based on additional information as described in [Malis2007] . The summary of the method: the decomposeHomographyMat function returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the sets of points visible in the camera frame before and after the homography transformation is applied, we can determine which are the true potential solutions and which are the opposites by verifying which homographies are consistent with all visible reference points being in front of the camera. The inputs are left unchanged; the filtered solution set is returned as indices into the existing one.
◆ filterSpeckles() [1/2]
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Filters off small noise blobs (speckles) in the disparity map.
- Parameters
-
img The input 16-bit signed disparity image newVal The disparity value used to paint-off the speckles maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not affected by the algorithm maxDiff Maximum difference between neighbor disparity pixels to put them into the same blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point disparity map, where disparity values are multiplied by 16, this scale factor should be taken into account when specifying this parameter value. buf The optional temporary buffer to avoid memory allocation within the function.
◆ filterSpeckles() [2/2]
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Filters off small noise blobs (speckles) in the disparity map.
- Parameters
-
img The input 16-bit signed disparity image newVal The disparity value used to paint-off the speckles maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not affected by the algorithm maxDiff Maximum difference between neighbor disparity pixels to put them into the same blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point disparity map, where disparity values are multiplied by 16, this scale factor should be taken into account when specifying this parameter value. buf The optional temporary buffer to avoid memory allocation within the function.
◆ find4QuadCornerSubpix()
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◆ findChessboardCorners() [1/2]
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Finds the positions of internal corners of the chessboard.
- Parameters
-
image Source chessboard view. It must be an 8-bit grayscale or color image. patternSize Number of inner corners per a chessboard row and column ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ). corners Output array of detected corners. flags Various operation flags that can be zero or a combination of the following values: - CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black and white, rather than a fixed threshold level (computed from the average image brightness).
- CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with #equalizeHist before applying fixed or adaptive thresholding.
- CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter, square-like shape) to filter out false quads extracted at the contour retrieval stage.
- CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners, and shortcut the call if none is found. This can drastically speed up the call in the degenerate condition when no chessboard is observed.
- CALIB_CB_PLAIN All other flags are ignored. The input image is taken as is. No image processing is done to improve to find the checkerboard. This has the effect of speeding up the execution of the function but could lead to not recognizing the checkerboard if the image is not previously binarized in the appropriate manner.
The function attempts to determine whether the input image is a view of the chessboard pattern and locate the internal chessboard corners. The function returns a non-zero value if all of the corners are found and they are placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black squares touch each other. The detected coordinates are approximate, and to determine their positions more accurately, the function calls #cornerSubPix. You also may use the function #cornerSubPix with different parameters if returned coordinates are not accurate enough.
Sample usage of detecting and drawing chessboard corners: :
- Note
- The function requires white space (like a square-thick border, the wider the better) around the board to make the detection more robust in various environments. Otherwise, if there is no border and the background is dark, the outer black squares cannot be segmented properly and so the square grouping and ordering algorithm fails.
Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.
◆ findChessboardCorners() [2/2]
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Finds the positions of internal corners of the chessboard.
- Parameters
-
image Source chessboard view. It must be an 8-bit grayscale or color image. patternSize Number of inner corners per a chessboard row and column ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ). corners Output array of detected corners. flags Various operation flags that can be zero or a combination of the following values: - CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black and white, rather than a fixed threshold level (computed from the average image brightness).
- CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with #equalizeHist before applying fixed or adaptive thresholding.
- CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter, square-like shape) to filter out false quads extracted at the contour retrieval stage.
- CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners, and shortcut the call if none is found. This can drastically speed up the call in the degenerate condition when no chessboard is observed.
- CALIB_CB_PLAIN All other flags are ignored. The input image is taken as is. No image processing is done to improve to find the checkerboard. This has the effect of speeding up the execution of the function but could lead to not recognizing the checkerboard if the image is not previously binarized in the appropriate manner.
The function attempts to determine whether the input image is a view of the chessboard pattern and locate the internal chessboard corners. The function returns a non-zero value if all of the corners are found and they are placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black squares touch each other. The detected coordinates are approximate, and to determine their positions more accurately, the function calls #cornerSubPix. You also may use the function #cornerSubPix with different parameters if returned coordinates are not accurate enough.
Sample usage of detecting and drawing chessboard corners: :
- Note
- The function requires white space (like a square-thick border, the wider the better) around the board to make the detection more robust in various environments. Otherwise, if there is no border and the background is dark, the outer black squares cannot be segmented properly and so the square grouping and ordering algorithm fails.
Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.
◆ findChessboardCornersSB() [1/2]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ findChessboardCornersSB() [2/2]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ findChessboardCornersSBWithMeta()
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Finds the positions of internal corners of the chessboard using a sector based approach.
- Parameters
-
image Source chessboard view. It must be an 8-bit grayscale or color image. patternSize Number of inner corners per a chessboard row and column ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ). corners Output array of detected corners. flags Various operation flags that can be zero or a combination of the following values: - CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with equalizeHist before detection.
- CALIB_CB_EXHAUSTIVE Run an exhaustive search to improve detection rate.
- CALIB_CB_ACCURACY Up sample input image to improve sub-pixel accuracy due to aliasing effects.
- CALIB_CB_LARGER The detected pattern is allowed to be larger than patternSize (see description).
- CALIB_CB_MARKER The detected pattern must have a marker (see description). This should be used if an accurate camera calibration is required.
meta Optional output arrray of detected corners (CV_8UC1 and size = cv::Size(columns,rows)). Each entry stands for one corner of the pattern and can have one of the following values: - 0 = no meta data attached
- 1 = left-top corner of a black cell
- 2 = left-top corner of a white cell
- 3 = left-top corner of a black cell with a white marker dot
- 4 = left-top corner of a white cell with a black marker dot (pattern origin in case of markers otherwise first corner)
The function is analog to findChessboardCorners but uses a localized radon transformation approximated by box filters being more robust to all sort of noise, faster on larger images and is able to directly return the sub-pixel position of the internal chessboard corners. The Method is based on the paper [duda2018] "Accurate Detection and Localization of Checkerboard Corners for Calibration" demonstrating that the returned sub-pixel positions are more accurate than the one returned by cornerSubPix allowing a precise camera calibration for demanding applications.
In the case, the flags CALIB_CB_LARGER or CALIB_CB_MARKER are given, the result can be recovered from the optional meta array. Both flags are helpful to use calibration patterns exceeding the field of view of the camera. These oversized patterns allow more accurate calibrations as corners can be utilized, which are as close as possible to the image borders. For a consistent coordinate system across all images, the optional marker (see image below) can be used to move the origin of the board to the location where the black circle is located.
- Note
- The function requires a white boarder with roughly the same width as one of the checkerboard fields around the whole board to improve the detection in various environments. In addition, because of the localized radon transformation it is beneficial to use round corners for the field corners which are located on the outside of the board. The following figure illustrates a sample checkerboard optimized for the detection. However, any other checkerboard can be used as well.
Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.
◆ findCirclesGrid() [1/2]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ findCirclesGrid() [2/2]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ findEssentialMat() [1/20]
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Calculates an essential matrix from the corresponding points in two images.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1. cameraMatrix Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix. If this assumption does not hold for your use case, use another function overload or undistortPoints with P = cv::NoArray()for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.method Method for computing an essential matrix. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods. maxIters The maximum number of robust method iterations.
This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:
\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]
where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
◆ findEssentialMat() [2/20]
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Calculates an essential matrix from the corresponding points in two images.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1. cameraMatrix Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix. If this assumption does not hold for your use case, use another function overload or undistortPoints with P = cv::NoArray()for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.method Method for computing an essential matrix. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods. maxIters The maximum number of robust method iterations.
This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:
\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]
where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
◆ findEssentialMat() [3/20]
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Calculates an essential matrix from the corresponding points in two images.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1. cameraMatrix Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix. If this assumption does not hold for your use case, use another function overload or undistortPoints with P = cv::NoArray()for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.method Method for computing an essential matrix. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods. maxIters The maximum number of robust method iterations.
This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:
\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]
where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
◆ findEssentialMat() [4/20]
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Calculates an essential matrix from the corresponding points in two images.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1. cameraMatrix Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix. If this assumption does not hold for your use case, use another function overload or undistortPoints with P = cv::NoArray()for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.method Method for computing an essential matrix. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods. maxIters The maximum number of robust method iterations.
This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:
\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]
where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
◆ findEssentialMat() [5/20]
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Calculates an essential matrix from the corresponding points in two images.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1. cameraMatrix Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix. If this assumption does not hold for your use case, use another function overload or undistortPoints with P = cv::NoArray()for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.method Method for computing an essential matrix. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods. maxIters The maximum number of robust method iterations.
This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:
\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]
where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
◆ findEssentialMat() [6/20]
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static |
Calculates an essential matrix from the corresponding points in two images.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1. cameraMatrix Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix. If this assumption does not hold for your use case, use another function overload or undistortPoints with P = cv::NoArray()for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.method Method for computing an essential matrix. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods. maxIters The maximum number of robust method iterations.
This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:
\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]
where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
◆ findEssentialMat() [7/20]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1 . focal focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point. pp principal point of the camera. method Method for computing a fundamental matrix. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods. maxIters The maximum number of robust method iterations.
This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]
◆ findEssentialMat() [8/20]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1 . focal focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point. pp principal point of the camera. method Method for computing a fundamental matrix. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods. maxIters The maximum number of robust method iterations.
This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]
◆ findEssentialMat() [9/20]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1 . focal focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point. pp principal point of the camera. method Method for computing a fundamental matrix. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods. maxIters The maximum number of robust method iterations.
This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]
◆ findEssentialMat() [10/20]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1 . focal focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point. pp principal point of the camera. method Method for computing a fundamental matrix. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods. maxIters The maximum number of robust method iterations.
This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]
◆ findEssentialMat() [11/20]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1 . focal focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point. pp principal point of the camera. method Method for computing a fundamental matrix. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods. maxIters The maximum number of robust method iterations.
This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]
◆ findEssentialMat() [12/20]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1 . focal focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point. pp principal point of the camera. method Method for computing a fundamental matrix. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods. maxIters The maximum number of robust method iterations.
This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]
◆ findEssentialMat() [13/20]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1 . focal focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point. pp principal point of the camera. method Method for computing a fundamental matrix. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods. maxIters The maximum number of robust method iterations.
This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]
◆ findEssentialMat() [14/20]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1 . focal focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point. pp principal point of the camera. method Method for computing a fundamental matrix. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods. maxIters The maximum number of robust method iterations.
This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]
◆ findEssentialMat() [15/20]
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static |
Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1. cameraMatrix1 Camera matrix for the first camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . cameraMatrix2 Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . distCoeffs1 Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. distCoeffs2 Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. method Method for computing an essential matrix. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:
\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]
where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
◆ findEssentialMat() [16/20]
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static |
Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1. cameraMatrix1 Camera matrix for the first camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . cameraMatrix2 Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . distCoeffs1 Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. distCoeffs2 Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. method Method for computing an essential matrix. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:
\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]
where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
◆ findEssentialMat() [17/20]
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static |
Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1. cameraMatrix1 Camera matrix for the first camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . cameraMatrix2 Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . distCoeffs1 Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. distCoeffs2 Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. method Method for computing an essential matrix. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:
\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]
where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
◆ findEssentialMat() [18/20]
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static |
Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1. cameraMatrix1 Camera matrix for the first camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . cameraMatrix2 Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . distCoeffs1 Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. distCoeffs2 Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. method Method for computing an essential matrix. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:
\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]
where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
◆ findEssentialMat() [19/20]
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static |
Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
- Parameters
-
points1 Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1. cameraMatrix1 Camera matrix for the first camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . cameraMatrix2 Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . distCoeffs1 Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. distCoeffs2 Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. method Method for computing an essential matrix. prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. mask Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:
\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]
where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
◆ findEssentialMat() [20/20]
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static |
◆ findFundamentalMat() [1/8]
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static |
Calculates a fundamental matrix from the corresponding points in two images.
- Parameters
-
points1 Array of N points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1 . method Method for computing a fundamental matrix. ransacReprojThreshold Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. confidence Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. [out] mask optional output mask maxIters The maximum number of robust method iterations.
The epipolar geometry is described by the following equation:
\[[p_2; 1]^T F [p_1; 1] = 0\]
where \(F\) is a fundamental matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively.
The function calculates the fundamental matrix using one of four methods listed above and returns the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point algorithm, the function may return up to 3 solutions ( \(9 \times 3\) matrix that stores all 3 matrices sequentially).
The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the epipolar lines corresponding to the specified points. It can also be passed to stereoRectifyUncalibrated to compute the rectification transformation. :
◆ findFundamentalMat() [2/8]
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static |
Calculates a fundamental matrix from the corresponding points in two images.
- Parameters
-
points1 Array of N points from the first image. The point coordinates should be floating-point (single or double precision). points2 Array of the second image points of the same size and format as points1 . method Method for computing a fundamental matrix. ransacReprojThreshold Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. confidence Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. [out] mask optional output mask maxIters The maximum number of robust method iterations.
The epipolar geometry is described by the following equation:
\[[p_2; 1]^T F [p_1; 1] = 0\]
where \(F\) is a fundamental matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively.
The function calculates the fundamental matrix using one of four methods listed above and returns the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point algorithm, the function may return up to 3 solutions ( \(9 \times 3\) matrix that stores all 3 matrices sequentially).
The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the epipolar lines corresponding to the specified points. It can also be passed to stereoRectifyUncalibrated to compute the rectification transformation. :
◆ findFundamentalMat() [3/8]
|
static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ findFundamentalMat() [4/8]
|
static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ findFundamentalMat() [5/8]
|
static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ findFundamentalMat() [6/8]
|
static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ findFundamentalMat() [7/8]
|
static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ findFundamentalMat() [8/8]
|
static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ findHomography() [1/7]
|
static |
Finds a perspective transformation between two planes.
- Parameters
-
srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2 or vector<Point2f> . dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or a vector<Point2f> . method Method used to compute a homography matrix. The following methods are possible: ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC and RHO methods only). That is, if \[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} \cdot \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\]
then the point \(i\) is considered as an outlier. If srcPoints and dstPoints are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.mask Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input mask values are ignored. maxIters The maximum number of RANSAC iterations. confidence Confidence level, between 0 and 1.
The function finds and returns the perspective transformation \(H\) between the source and the destination planes:
\[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\]
so that the back-projection error
\[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\]
is minimized. If the parameter method is set to the default value 0, the function uses all the point pairs to compute an initial homography estimate with a simple least-squares scheme.
However, if not all of the point pairs ( \(srcPoints_i\), \(dstPoints_i\) ) fit the rigid perspective transformation (that is, there are some outliers), this initial estimate will be poor. In this case, you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median re-projection error for LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.
Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the re-projection error even more.
The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the noise is rather small, use the default method (method=0).
The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is determined up to a scale. If \(h_{33}\) is non-zero, the matrix is normalized so that \(h_{33}=1\).
- Note
- Whenever an \(H\) matrix cannot be estimated, an empty one will be returned.
- See also
- getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform
◆ findHomography() [2/7]
|
static |
Finds a perspective transformation between two planes.
- Parameters
-
srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2 or vector<Point2f> . dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or a vector<Point2f> . method Method used to compute a homography matrix. The following methods are possible: ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC and RHO methods only). That is, if \[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} \cdot \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\]
then the point \(i\) is considered as an outlier. If srcPoints and dstPoints are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.mask Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input mask values are ignored. maxIters The maximum number of RANSAC iterations. confidence Confidence level, between 0 and 1.
The function finds and returns the perspective transformation \(H\) between the source and the destination planes:
\[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\]
so that the back-projection error
\[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\]
is minimized. If the parameter method is set to the default value 0, the function uses all the point pairs to compute an initial homography estimate with a simple least-squares scheme.
However, if not all of the point pairs ( \(srcPoints_i\), \(dstPoints_i\) ) fit the rigid perspective transformation (that is, there are some outliers), this initial estimate will be poor. In this case, you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median re-projection error for LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.
Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the re-projection error even more.
The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the noise is rather small, use the default method (method=0).
The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is determined up to a scale. If \(h_{33}\) is non-zero, the matrix is normalized so that \(h_{33}=1\).
- Note
- Whenever an \(H\) matrix cannot be estimated, an empty one will be returned.
- See also
- getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform
◆ findHomography() [3/7]
|
static |
Finds a perspective transformation between two planes.
- Parameters
-
srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2 or vector<Point2f> . dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or a vector<Point2f> . method Method used to compute a homography matrix. The following methods are possible: ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC and RHO methods only). That is, if \[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} \cdot \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\]
then the point \(i\) is considered as an outlier. If srcPoints and dstPoints are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.mask Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input mask values are ignored. maxIters The maximum number of RANSAC iterations. confidence Confidence level, between 0 and 1.
The function finds and returns the perspective transformation \(H\) between the source and the destination planes:
\[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\]
so that the back-projection error
\[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\]
is minimized. If the parameter method is set to the default value 0, the function uses all the point pairs to compute an initial homography estimate with a simple least-squares scheme.
However, if not all of the point pairs ( \(srcPoints_i\), \(dstPoints_i\) ) fit the rigid perspective transformation (that is, there are some outliers), this initial estimate will be poor. In this case, you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median re-projection error for LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.
Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the re-projection error even more.
The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the noise is rather small, use the default method (method=0).
The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is determined up to a scale. If \(h_{33}\) is non-zero, the matrix is normalized so that \(h_{33}=1\).
- Note
- Whenever an \(H\) matrix cannot be estimated, an empty one will be returned.
- See also
- getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform
◆ findHomography() [4/7]
|
static |
Finds a perspective transformation between two planes.
- Parameters
-
srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2 or vector<Point2f> . dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or a vector<Point2f> . method Method used to compute a homography matrix. The following methods are possible: ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC and RHO methods only). That is, if \[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} \cdot \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\]
then the point \(i\) is considered as an outlier. If srcPoints and dstPoints are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.mask Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input mask values are ignored. maxIters The maximum number of RANSAC iterations. confidence Confidence level, between 0 and 1.
The function finds and returns the perspective transformation \(H\) between the source and the destination planes:
\[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\]
so that the back-projection error
\[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\]
is minimized. If the parameter method is set to the default value 0, the function uses all the point pairs to compute an initial homography estimate with a simple least-squares scheme.
However, if not all of the point pairs ( \(srcPoints_i\), \(dstPoints_i\) ) fit the rigid perspective transformation (that is, there are some outliers), this initial estimate will be poor. In this case, you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median re-projection error for LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.
Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the re-projection error even more.
The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the noise is rather small, use the default method (method=0).
The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is determined up to a scale. If \(h_{33}\) is non-zero, the matrix is normalized so that \(h_{33}=1\).
- Note
- Whenever an \(H\) matrix cannot be estimated, an empty one will be returned.
- See also
- getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform
◆ findHomography() [5/7]
|
static |
Finds a perspective transformation between two planes.
- Parameters
-
srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2 or vector<Point2f> . dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or a vector<Point2f> . method Method used to compute a homography matrix. The following methods are possible: ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC and RHO methods only). That is, if \[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} \cdot \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\]
then the point \(i\) is considered as an outlier. If srcPoints and dstPoints are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.mask Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input mask values are ignored. maxIters The maximum number of RANSAC iterations. confidence Confidence level, between 0 and 1.
The function finds and returns the perspective transformation \(H\) between the source and the destination planes:
\[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\]
so that the back-projection error
\[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\]
is minimized. If the parameter method is set to the default value 0, the function uses all the point pairs to compute an initial homography estimate with a simple least-squares scheme.
However, if not all of the point pairs ( \(srcPoints_i\), \(dstPoints_i\) ) fit the rigid perspective transformation (that is, there are some outliers), this initial estimate will be poor. In this case, you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median re-projection error for LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.
Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the re-projection error even more.
The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the noise is rather small, use the default method (method=0).
The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is determined up to a scale. If \(h_{33}\) is non-zero, the matrix is normalized so that \(h_{33}=1\).
- Note
- Whenever an \(H\) matrix cannot be estimated, an empty one will be returned.
- See also
- getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform
◆ findHomography() [6/7]
|
static |
Finds a perspective transformation between two planes.
- Parameters
-
srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2 or vector<Point2f> . dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or a vector<Point2f> . method Method used to compute a homography matrix. The following methods are possible: ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC and RHO methods only). That is, if \[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} \cdot \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\]
then the point \(i\) is considered as an outlier. If srcPoints and dstPoints are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.mask Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input mask values are ignored. maxIters The maximum number of RANSAC iterations. confidence Confidence level, between 0 and 1.
The function finds and returns the perspective transformation \(H\) between the source and the destination planes:
\[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\]
so that the back-projection error
\[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\]
is minimized. If the parameter method is set to the default value 0, the function uses all the point pairs to compute an initial homography estimate with a simple least-squares scheme.
However, if not all of the point pairs ( \(srcPoints_i\), \(dstPoints_i\) ) fit the rigid perspective transformation (that is, there are some outliers), this initial estimate will be poor. In this case, you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median re-projection error for LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.
Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the re-projection error even more.
The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the noise is rather small, use the default method (method=0).
The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is determined up to a scale. If \(h_{33}\) is non-zero, the matrix is normalized so that \(h_{33}=1\).
- Note
- Whenever an \(H\) matrix cannot be estimated, an empty one will be returned.
- See also
- getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform
◆ findHomography() [7/7]
|
static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ fisheye_calibrate() [1/3]
|
static |
Performs camera calibration.
- Parameters
-
objectPoints vector of vectors of calibration pattern points in the calibration pattern coordinate space. imagePoints vector of vectors of the projections of calibration pattern points. imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i. image_size Size of the image used only to initialize the camera intrinsic matrix. K Output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be initialized before calling the function. D Output vector of distortion coefficients \(\distcoeffsfisheye\). rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. M -1). tvecs Output vector of translation vectors estimated for each pattern view. flags Different flags that may be zero or a combination of the following values: - fisheye::CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion.
- fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization.
- fisheye::CALIB_CHECK_COND The functions will check validity of condition number.
- fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero.
- fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero.
- fisheye::CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
- fisheye::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization. It is the \(max(width,height)/\pi\) or the provided \(f_x\), \(f_y\) when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
criteria Termination criteria for the iterative optimization algorithm.
◆ fisheye_calibrate() [2/3]
|
static |
Performs camera calibration.
- Parameters
-
objectPoints vector of vectors of calibration pattern points in the calibration pattern coordinate space. imagePoints vector of vectors of the projections of calibration pattern points. imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i. image_size Size of the image used only to initialize the camera intrinsic matrix. K Output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be initialized before calling the function. D Output vector of distortion coefficients \(\distcoeffsfisheye\). rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. M -1). tvecs Output vector of translation vectors estimated for each pattern view. flags Different flags that may be zero or a combination of the following values: - fisheye::CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion.
- fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization.
- fisheye::CALIB_CHECK_COND The functions will check validity of condition number.
- fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero.
- fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero.
- fisheye::CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
- fisheye::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization. It is the \(max(width,height)/\pi\) or the provided \(f_x\), \(f_y\) when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
criteria Termination criteria for the iterative optimization algorithm.
◆ fisheye_calibrate() [3/3]
|
static |
Performs camera calibration.
- Parameters
-
objectPoints vector of vectors of calibration pattern points in the calibration pattern coordinate space. imagePoints vector of vectors of the projections of calibration pattern points. imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i. image_size Size of the image used only to initialize the camera intrinsic matrix. K Output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be initialized before calling the function. D Output vector of distortion coefficients \(\distcoeffsfisheye\). rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. M -1). tvecs Output vector of translation vectors estimated for each pattern view. flags Different flags that may be zero or a combination of the following values: - fisheye::CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion.
- fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization.
- fisheye::CALIB_CHECK_COND The functions will check validity of condition number.
- fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero.
- fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero.
- fisheye::CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
- fisheye::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization. It is the \(max(width,height)/\pi\) or the provided \(f_x\), \(f_y\) when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
criteria Termination criteria for the iterative optimization algorithm.
◆ fisheye_distortPoints() [1/2]
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Distorts 2D points using fisheye model.
- Parameters
-
undistorted Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view. K Camera intrinsic matrix \(cameramatrix{K}\). D Input vector of distortion coefficients \(\distcoeffsfisheye\). alpha The skew coefficient. distorted Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> . Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity. This means if you want to distort image points you have to multiply them with \form#161.
◆ fisheye_distortPoints() [2/2]
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Distorts 2D points using fisheye model.
- Parameters
-
undistorted Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view. K Camera intrinsic matrix \(cameramatrix{K}\). D Input vector of distortion coefficients \(\distcoeffsfisheye\). alpha The skew coefficient. distorted Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> . Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity. This means if you want to distort image points you have to multiply them with \form#161.
◆ fisheye_estimateNewCameraMatrixForUndistortRectify() [1/4]
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Estimates new camera intrinsic matrix for undistortion or rectification.
- Parameters
-
K Camera intrinsic matrix \(cameramatrix{K}\). image_size Size of the image D Input vector of distortion coefficients \(\distcoeffsfisheye\). R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel P New camera intrinsic matrix (3x3) or new projection matrix (3x4) balance Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1]. new_size the new size fov_scale Divisor for new focal length.
◆ fisheye_estimateNewCameraMatrixForUndistortRectify() [2/4]
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Estimates new camera intrinsic matrix for undistortion or rectification.
- Parameters
-
K Camera intrinsic matrix \(cameramatrix{K}\). image_size Size of the image D Input vector of distortion coefficients \(\distcoeffsfisheye\). R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel P New camera intrinsic matrix (3x3) or new projection matrix (3x4) balance Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1]. new_size the new size fov_scale Divisor for new focal length.
◆ fisheye_estimateNewCameraMatrixForUndistortRectify() [3/4]
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Estimates new camera intrinsic matrix for undistortion or rectification.
- Parameters
-
K Camera intrinsic matrix \(cameramatrix{K}\). image_size Size of the image D Input vector of distortion coefficients \(\distcoeffsfisheye\). R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel P New camera intrinsic matrix (3x3) or new projection matrix (3x4) balance Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1]. new_size the new size fov_scale Divisor for new focal length.
◆ fisheye_estimateNewCameraMatrixForUndistortRectify() [4/4]
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Estimates new camera intrinsic matrix for undistortion or rectification.
- Parameters
-
K Camera intrinsic matrix \(cameramatrix{K}\). image_size Size of the image D Input vector of distortion coefficients \(\distcoeffsfisheye\). R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel P New camera intrinsic matrix (3x3) or new projection matrix (3x4) balance Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1]. new_size the new size fov_scale Divisor for new focal length.
◆ fisheye_initUndistortRectifyMap()
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Computes undistortion and rectification maps for image transform by #remap. If D is empty zero distortion is used, if R or P is empty identity matrixes are used.
- Parameters
-
K Camera intrinsic matrix \(cameramatrix{K}\). D Input vector of distortion coefficients \(\distcoeffsfisheye\). R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel P New camera intrinsic matrix (3x3) or new projection matrix (3x4) size Undistorted image size. m1type Type of the first output map that can be CV_32FC1 or CV_16SC2 . See #convertMaps for details. map1 The first output map. map2 The second output map.
◆ fisheye_projectPoints() [1/3]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ fisheye_projectPoints() [2/3]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ fisheye_projectPoints() [3/3]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ fisheye_solvePnP() [1/4]
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Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
- Parameters
-
objectPoints Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here. imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here. cameraMatrix Input camera intrinsic matrix \(\cameramatrix{A}\) . distCoeffs Input vector of distortion coefficients (4x1/1x4). rvec Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. tvec Output translation vector. useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them. flags Method for solving a PnP problem: see calib3d_solvePnP_flags This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods: - P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): need 4 input points to return a unique solution.
- SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
- SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
- point 0: [-squareLength / 2, squareLength / 2, 0]
- point 1: [ squareLength / 2, squareLength / 2, 0]
- point 2: [ squareLength / 2, -squareLength / 2, 0]
- point 3: [-squareLength / 2, -squareLength / 2, 0]
- for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
criteria Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. Check there and Perspective-n-Points is described in calib3d_solvePnP for more information.
◆ fisheye_solvePnP() [2/4]
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Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
- Parameters
-
objectPoints Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here. imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here. cameraMatrix Input camera intrinsic matrix \(\cameramatrix{A}\) . distCoeffs Input vector of distortion coefficients (4x1/1x4). rvec Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. tvec Output translation vector. useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them. flags Method for solving a PnP problem: see calib3d_solvePnP_flags This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods: - P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): need 4 input points to return a unique solution.
- SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
- SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
- point 0: [-squareLength / 2, squareLength / 2, 0]
- point 1: [ squareLength / 2, squareLength / 2, 0]
- point 2: [ squareLength / 2, -squareLength / 2, 0]
- point 3: [-squareLength / 2, -squareLength / 2, 0]
- for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
criteria Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. Check there and Perspective-n-Points is described in calib3d_solvePnP for more information.
◆ fisheye_solvePnP() [3/4]
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Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
- Parameters
-
objectPoints Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here. imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here. cameraMatrix Input camera intrinsic matrix \(\cameramatrix{A}\) . distCoeffs Input vector of distortion coefficients (4x1/1x4). rvec Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. tvec Output translation vector. useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them. flags Method for solving a PnP problem: see calib3d_solvePnP_flags This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods: - P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): need 4 input points to return a unique solution.
- SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
- SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
- point 0: [-squareLength / 2, squareLength / 2, 0]
- point 1: [ squareLength / 2, squareLength / 2, 0]
- point 2: [ squareLength / 2, -squareLength / 2, 0]
- point 3: [-squareLength / 2, -squareLength / 2, 0]
- for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
criteria Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. Check there and Perspective-n-Points is described in calib3d_solvePnP for more information.
◆ fisheye_solvePnP() [4/4]
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Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
- Parameters
-
objectPoints Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here. imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here. cameraMatrix Input camera intrinsic matrix \(\cameramatrix{A}\) . distCoeffs Input vector of distortion coefficients (4x1/1x4). rvec Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. tvec Output translation vector. useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them. flags Method for solving a PnP problem: see calib3d_solvePnP_flags This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods: - P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): need 4 input points to return a unique solution.
- SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
- SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
- point 0: [-squareLength / 2, squareLength / 2, 0]
- point 1: [ squareLength / 2, squareLength / 2, 0]
- point 2: [ squareLength / 2, -squareLength / 2, 0]
- point 3: [-squareLength / 2, -squareLength / 2, 0]
- for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
criteria Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. Check there and Perspective-n-Points is described in calib3d_solvePnP for more information.
◆ fisheye_stereoCalibrate() [1/6]
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Performs stereo calibration.
- Parameters
-
objectPoints Vector of vectors of the calibration pattern points. imagePoints1 Vector of vectors of the projections of the calibration pattern points, observed by the first camera. imagePoints2 Vector of vectors of the projections of the calibration pattern points, observed by the second camera. K1 Input/output first camera intrinsic matrix: \(\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\) , \(j = 0,\, 1\) . If any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, some or all of the matrix components must be initialized. D1 Input/output vector of distortion coefficients \(\distcoeffsfisheye\) of 4 elements. K2 Input/output second camera intrinsic matrix. The parameter is similar to K1 . D2 Input/output lens distortion coefficients for the second camera. The parameter is similar to D1 . imageSize Size of the image used only to initialize camera intrinsic matrix. R Output rotation matrix between the 1st and the 2nd camera coordinate systems. T Output translation vector between the coordinate systems of the cameras. rvecs Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair. tvecs Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ). flags Different flags that may be zero or a combination of the following values: - fisheye::CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices are estimated.
- fisheye::CALIB_USE_INTRINSIC_GUESS K1, K2 contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center (imageSize is used), and focal distances are computed in a least-squares fashion.
- fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization.
- fisheye::CALIB_CHECK_COND The functions will check validity of condition number.
- fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero.
- fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero.
criteria Termination criteria for the iterative optimization algorithm.
◆ fisheye_stereoCalibrate() [2/6]
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Performs stereo calibration.
- Parameters
-
objectPoints Vector of vectors of the calibration pattern points. imagePoints1 Vector of vectors of the projections of the calibration pattern points, observed by the first camera. imagePoints2 Vector of vectors of the projections of the calibration pattern points, observed by the second camera. K1 Input/output first camera intrinsic matrix: \(\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\) , \(j = 0,\, 1\) . If any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, some or all of the matrix components must be initialized. D1 Input/output vector of distortion coefficients \(\distcoeffsfisheye\) of 4 elements. K2 Input/output second camera intrinsic matrix. The parameter is similar to K1 . D2 Input/output lens distortion coefficients for the second camera. The parameter is similar to D1 . imageSize Size of the image used only to initialize camera intrinsic matrix. R Output rotation matrix between the 1st and the 2nd camera coordinate systems. T Output translation vector between the coordinate systems of the cameras. rvecs Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair. tvecs Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ). flags Different flags that may be zero or a combination of the following values: - fisheye::CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices are estimated.
- fisheye::CALIB_USE_INTRINSIC_GUESS K1, K2 contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center (imageSize is used), and focal distances are computed in a least-squares fashion.
- fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization.
- fisheye::CALIB_CHECK_COND The functions will check validity of condition number.
- fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero.
- fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero.
criteria Termination criteria for the iterative optimization algorithm.