OpenCV for Unity
2.6.0
Enox Software / Please refer to OpenCV official document ( http://docs.opencv.org/4.9.0/index.html ) for the details of the argument of the method.

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 rotationandshift 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 rotationandshift 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 rotationandshift 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 rotationandshift 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 rotationandshift 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 rotationandshift transformations. More...  
static void  composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1) 
Combines two rotationandshift transformations. More...  
static void  composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1) 
Combines two rotationandshift transformations. More...  
static void  composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3) 
Combines two rotationandshift 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 3D2D 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 3D2D point correspondences. More...  
static bool  solvePnP (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec) 
Finds an object pose from 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D 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 3D2D point correspondences. More...  
static Mat  initCameraMatrix2D (List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, Size imageSize, double aspectRatio) 
Finds an initial camera intrinsic matrix from 3D2D point correspondences. More...  
static Mat  initCameraMatrix2D (List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, Size imageSize) 
Finds an initial camera intrinsic matrix from 3D2D 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 HandEye 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 HandEye 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 RobotWorld/HandEye 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 RobotWorld/HandEye 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 3dimensional 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 inverserectification transformation map. In essense, this is the inverse of initUndistortRectifyMap to accomodate stereorectification of projectors ('inversecameras') in projectorcamera 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 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
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 Zcoordinate), 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 floatingpoint 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 ith rotation vector together with the corresponding ith 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 ith 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 reprojection error estimated for each pattern view. 
flags  Different flags that may be zero or a combination of the following values:

criteria  Termination criteria for the iterative optimization algorithm. 
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 builtin 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 Zcoordinates 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:

static 
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
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 Zcoordinate), 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 floatingpoint 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 ith rotation vector together with the corresponding ith 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 ith 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 reprojection error estimated for each pattern view. 
flags  Different flags that may be zero or a combination of the following values:

criteria  Termination criteria for the iterative optimization algorithm. 
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 builtin 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 Zcoordinates 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:

static 
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
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 Zcoordinate), 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 floatingpoint 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 ith rotation vector together with the corresponding ith 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 ith 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 reprojection error estimated for each pattern view. 
flags  Different flags that may be zero or a combination of the following values:

criteria  Termination criteria for the iterative optimization algorithm. 
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 builtin 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 Zcoordinates 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:

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

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 objectreleasing 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.
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 objectreleasing 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 topright corner point of the calibration board grid is recommended to be fixed when objectreleasing 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 objectreleasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough. 
cameraMatrix  Output 3x3 floatingpoint 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 reprojection 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. 
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.

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 objectreleasing 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.
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 objectreleasing 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 topright corner point of the calibration board grid is recommended to be fixed when objectreleasing 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 objectreleasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough. 
cameraMatrix  Output 3x3 floatingpoint 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 reprojection 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. 
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.

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 objectreleasing 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.
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 objectreleasing 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 topright corner point of the calibration board grid is recommended to be fixed when objectreleasing 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 objectreleasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough. 
cameraMatrix  Output 3x3 floatingpoint 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 reprojection 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. 
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.

static 
Computes HandEye calibration: \(_{}^{g}\textrm{T}_c\).
[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 HandEye calibration method, see cv::HandEyeCalibrationMethod 
The function performs the HandEye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions) and the following methods are implemented:
Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented methods:
The following picture describes the HandEye calibration problem where the transformation between a camera ("eye") mounted on a robot gripper ("hand") has to be estimated. This configuration is called eyeinhand.
The eyetohand configuration consists in a static camera observing a calibration pattern mounted on the robot endeffector. 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:
\[ \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{b}\textrm{R}_g & _{}^{b}\textrm{t}_g \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} \]
\[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_t & _{}^{c}\textrm{t}_t \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_t\\ Y_t\\ Z_t\\ 1 \end{bmatrix} \]
The HandEye 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:
\[ \begin{align*} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{b}{\textrm{T}_g}^{(2)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{b}{\textrm{T}_g}^{(2)})^{1} \hspace{0.2em} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c &= \hspace{0.1em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*} \]
\[ \begin{align*} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{g}{\textrm{T}_b}^{(2)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{g}{\textrm{T}_b}^{(2)})^{1} \hspace{0.2em} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c &= \hspace{0.1em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*} \]

static 
Computes HandEye calibration: \(_{}^{g}\textrm{T}_c\).
[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 HandEye calibration method, see cv::HandEyeCalibrationMethod 
The function performs the HandEye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions) and the following methods are implemented:
Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented methods:
The following picture describes the HandEye calibration problem where the transformation between a camera ("eye") mounted on a robot gripper ("hand") has to be estimated. This configuration is called eyeinhand.
The eyetohand configuration consists in a static camera observing a calibration pattern mounted on the robot endeffector. 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:
\[ \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{b}\textrm{R}_g & _{}^{b}\textrm{t}_g \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} \]
\[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_t & _{}^{c}\textrm{t}_t \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_t\\ Y_t\\ Z_t\\ 1 \end{bmatrix} \]
The HandEye 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:
\[ \begin{align*} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{b}{\textrm{T}_g}^{(2)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{b}{\textrm{T}_g}^{(2)})^{1} \hspace{0.2em} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c &= \hspace{0.1em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*} \]
\[ \begin{align*} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{g}{\textrm{T}_b}^{(2)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{g}{\textrm{T}_b}^{(2)})^{1} \hspace{0.2em} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c &= \hspace{0.1em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*} \]

static 
Computes RobotWorld/HandEye calibration: \(_{}^{w}\textrm{T}_b\) and \(_{}^{c}\textrm{T}_g\).
[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 RobotWorld/HandEye calibration method, see cv::RobotWorldHandEyeCalibrationMethod 
The function performs the RobotWorld/HandEye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions):
Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented method:
The following picture describes the RobotWorld/HandEye 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 endeffector have to be estimated.
The calibration procedure is the following:
\[ \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{g}\textrm{R}_b & _{}^{g}\textrm{t}_b \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} \]
\[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_w & _{}^{c}\textrm{t}_w \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_w\\ Y_w\\ Z_w\\ 1 \end{bmatrix} \]
The RobotWorld/HandEye 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:

static 
Computes RobotWorld/HandEye calibration: \(_{}^{w}\textrm{T}_b\) and \(_{}^{c}\textrm{T}_g\).
[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 RobotWorld/HandEye calibration method, see cv::RobotWorldHandEyeCalibrationMethod 
The function performs the RobotWorld/HandEye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions):
Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented method:
The following picture describes the RobotWorld/HandEye 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 endeffector have to be estimated.
The calibration procedure is the following:
\[ \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{g}\textrm{R}_b & _{}^{g}\textrm{t}_b \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} \]
\[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_w & _{}^{c}\textrm{t}_w \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_w\\ Y_w\\ Z_w\\ 1 \end{bmatrix} \]
The RobotWorld/HandEye 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:

static 
Computes useful camera characteristics from the camera intrinsic matrix.
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.

static 
Combines two rotationandshift transformations.
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 LevenbergMarquardt or another gradientbased solver is used to optimize a function that contains a matrix multiplication.

static 
Combines two rotationandshift transformations.
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 LevenbergMarquardt or another gradientbased solver is used to optimize a function that contains a matrix multiplication.

static 
Combines two rotationandshift transformations.
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 LevenbergMarquardt or another gradientbased solver is used to optimize a function that contains a matrix multiplication.

static 
Combines two rotationandshift transformations.
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 LevenbergMarquardt or another gradientbased solver is used to optimize a function that contains a matrix multiplication.

static 
Combines two rotationandshift transformations.
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 LevenbergMarquardt or another gradientbased solver is used to optimize a function that contains a matrix multiplication.

static 
Combines two rotationandshift transformations.
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 LevenbergMarquardt or another gradientbased solver is used to optimize a function that contains a matrix multiplication.

static 
Combines two rotationandshift transformations.
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 LevenbergMarquardt or another gradientbased solver is used to optimize a function that contains a matrix multiplication.

static 
Combines two rotationandshift transformations.
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 LevenbergMarquardt or another gradientbased solver is used to optimize a function that contains a matrix multiplication.

static 
Combines two rotationandshift transformations.
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 LevenbergMarquardt or another gradientbased solver is used to optimize a function that contains a matrix multiplication.

static 
For points in an image of a stereo pair, computes the corresponding epilines in the other image.
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 \(a_i^2+b_i^2=1\) .

static 
Converts points from homogeneous to Euclidean space.
src  Input vector of Ndimensional points. 
dst  Output vector of N1dimensional points. 
The function converts points homogeneous to Euclidean space using perspective projection. That is, each point (x1, x2, ... x(n1), xn) is converted to (x1/xn, x2/xn, ..., x(n1)/xn). When xn=0, the output point coordinates will be (0,0,0,...).

static 
Converts points from Euclidean to homogeneous space.
src  Input vector of Ndimensional points. 
dst  Output vector of N+1dimensional 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).

static 
Refines coordinates of corresponding points.
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 \(newPoints2^T \cdot F \cdot newPoints1 = 0\) .

static 
Decompose an essential matrix to possible rotations and translation.
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.

static 
Decompose a homography matrix to rotation(s), translation(s) and plane normal(s).
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.

static 
Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
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 xaxis. 
rotMatrixY  Optional 3x3 rotation matrix around yaxis. 
rotMatrixZ  Optional 3x3 rotation matrix around zaxis. 
eulerAngles  Optional threeelement 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 .

static 
Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
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 xaxis. 
rotMatrixY  Optional 3x3 rotation matrix around yaxis. 
rotMatrixZ  Optional 3x3 rotation matrix around zaxis. 
eulerAngles  Optional threeelement 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 .

static 
Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
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 xaxis. 
rotMatrixY  Optional 3x3 rotation matrix around yaxis. 
rotMatrixZ  Optional 3x3 rotation matrix around zaxis. 
eulerAngles  Optional threeelement 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 .

static 
Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
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 xaxis. 
rotMatrixY  Optional 3x3 rotation matrix around yaxis. 
rotMatrixZ  Optional 3x3 rotation matrix around zaxis. 
eulerAngles  Optional threeelement 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 .

static 
Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
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 xaxis. 
rotMatrixY  Optional 3x3 rotation matrix around yaxis. 
rotMatrixZ  Optional 3x3 rotation matrix around zaxis. 
eulerAngles  Optional threeelement 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 .

static 
Renders the detected chessboard corners.
image  Destination image. It must be an 8bit 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.

static 
Draw axes of the world/object coordinate system from pose estimation.
image  Input/output image. It must have 1 or 3 channels. The number of channels is not altered. 
cameraMatrix  Input 3x3 floatingpoint 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.

static 
Draw axes of the world/object coordinate system from pose estimation.
image  Input/output image. It must have 1 or 3 channels. The number of channels is not altered. 
cameraMatrix  Input 3x3 floatingpoint 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.

static 
Computes an optimal affine transformation between two 2D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]
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 (1inlier, 0outlier). 
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 0.80.9 can result in an incorrectly estimated transformation. 
refineIters  Maximum number of iterations of refining algorithm (LevenbergMarquardt). Passing 0 will disable refining, so the output matrix will be output of robust method. 
\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]
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 LevenbergMarquardt method to reduce the reprojection error even more.

static 
Computes an optimal affine transformation between two 2D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]
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 (1inlier, 0outlier). 
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 0.80.9 can result in an incorrectly estimated transformation. 
refineIters  Maximum number of iterations of refining algorithm (LevenbergMarquardt). Passing 0 will disable refining, so the output matrix will be output of robust method. 
\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]
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 LevenbergMarquardt method to reduce the reprojection error even more.

static 
Computes an optimal affine transformation between two 2D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]
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 (1inlier, 0outlier). 
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 0.80.9 can result in an incorrectly estimated transformation. 
refineIters  Maximum number of iterations of refining algorithm (LevenbergMarquardt). Passing 0 will disable refining, so the output matrix will be output of robust method. 
\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]
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 LevenbergMarquardt method to reduce the reprojection error even more.

static 
Computes an optimal affine transformation between two 2D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]
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 (1inlier, 0outlier). 
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 0.80.9 can result in an incorrectly estimated transformation. 
refineIters  Maximum number of iterations of refining algorithm (LevenbergMarquardt). Passing 0 will disable refining, so the output matrix will be output of robust method. 
\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]
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 LevenbergMarquardt method to reduce the reprojection error even more.

static 
Computes an optimal affine transformation between two 2D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]
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 (1inlier, 0outlier). 
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 0.80.9 can result in an incorrectly estimated transformation. 
refineIters  Maximum number of iterations of refining algorithm (LevenbergMarquardt). Passing 0 will disable refining, so the output matrix will be output of robust method. 
\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]
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 LevenbergMarquardt method to reduce the reprojection error even more.

static 
Computes an optimal affine transformation between two 2D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]
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 (1inlier, 0outlier). 
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 0.80.9 can result in an incorrectly estimated transformation. 
refineIters  Maximum number of iterations of refining algorithm (LevenbergMarquardt). Passing 0 will disable refining, so the output matrix will be output of robust method. 
\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]
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 LevenbergMarquardt method to reduce the reprojection error even more.
Computes an optimal affine transformation between two 2D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]
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 (1inlier, 0outlier). 
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 0.80.9 can result in an incorrectly estimated transformation. 
refineIters  Maximum number of iterations of refining algorithm (LevenbergMarquardt). Passing 0 will disable refining, so the output matrix will be output of robust method. 
\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]
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 LevenbergMarquardt method to reduce the reprojection error even more.

static 

static 
Computes an optimal affine transformation between two 3D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12} & a_{13}\\ a_{21} & a_{22} & a_{23}\\ a_{31} & a_{32} & a_{33}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]
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 (1inlier, 0outlier). 
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 0.80.9 can result in an incorrectly estimated transformation. 
The function estimates an optimal 3D affine transformation between two 3D point sets using the RANSAC algorithm.

static 
Computes an optimal affine transformation between two 3D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12} & a_{13}\\ a_{21} & a_{22} & a_{23}\\ a_{31} & a_{32} & a_{33}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]
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 (1inlier, 0outlier). 
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 0.80.9 can result in an incorrectly estimated transformation. 
The function estimates an optimal 3D affine transformation between two 3D point sets using the RANSAC algorithm.

static 
Computes an optimal affine transformation between two 3D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12} & a_{13}\\ a_{21} & a_{22} & a_{23}\\ a_{31} & a_{32} & a_{33}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]
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 (1inlier, 0outlier). 
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 0.80.9 can result in an incorrectly estimated transformation. 
The function estimates an optimal 3D affine transformation between two 3D point sets using the RANSAC algorithm.

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.
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 pointedto 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 lefthanded coordinate system. 
\[T = \begin{bmatrix} R & t\\ \end{bmatrix} \]

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.
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 pointedto 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 lefthanded coordinate system. 
\[T = \begin{bmatrix} R & t\\ \end{bmatrix} \]
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.
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 pointedto 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 lefthanded coordinate system. 
\[T = \begin{bmatrix} R & t\\ \end{bmatrix} \]

static 
Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
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 0.80.9 can result in an incorrectly estimated transformation. 
refineIters  Maximum number of iterations of refining algorithm (LevenbergMarquardt). Passing 0 will disable refining, so the output matrix will be output of robust method. 
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 LevenbergMarquardt method to reduce the reprojection 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.

static 
Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
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 0.80.9 can result in an incorrectly estimated transformation. 
refineIters  Maximum number of iterations of refining algorithm (LevenbergMarquardt). Passing 0 will disable refining, so the output matrix will be output of robust method. 
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 LevenbergMarquardt method to reduce the reprojection 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.

static 
Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
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 0.80.9 can result in an incorrectly estimated transformation. 
refineIters  Maximum number of iterations of refining algorithm (LevenbergMarquardt). Passing 0 will disable refining, so the output matrix will be output of robust method. 
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 LevenbergMarquardt method to reduce the reprojection 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.

static 
Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
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 0.80.9 can result in an incorrectly estimated transformation. 
refineIters  Maximum number of iterations of refining algorithm (LevenbergMarquardt). Passing 0 will disable refining, so the output matrix will be output of robust method. 
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 LevenbergMarquardt method to reduce the reprojection 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.

static 
Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
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 0.80.9 can result in an incorrectly estimated transformation. 
refineIters  Maximum number of iterations of refining algorithm (LevenbergMarquardt). Passing 0 will disable refining, so the output matrix will be output of robust method. 
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 LevenbergMarquardt method to reduce the reprojection 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.

static 
Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
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 0.80.9 can result in an incorrectly estimated transformation. 
refineIters  Maximum number of iterations of refining algorithm (LevenbergMarquardt). Passing 0 will disable refining, so the output matrix will be output of robust method. 
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 LevenbergMarquardt method to reduce the reprojection 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.
Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
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 0.80.9 can result in an incorrectly estimated transformation. 
refineIters  Maximum number of iterations of refining algorithm (LevenbergMarquardt). Passing 0 will disable refining, so the output matrix will be output of robust method. 
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 LevenbergMarquardt method to reduce the reprojection 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.

static 
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.
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)

static 
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.
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)

static 
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.
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)

static 
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.
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)

static 
Computes an optimal translation between two 3D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]
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 (1inlier, 0outlier). 
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 0.80.9 can result in an incorrectly estimated transformation. 
The function estimates an optimal 3D translation between two 3D point sets using the RANSAC algorithm.

static 
Computes an optimal translation between two 3D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]
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 (1inlier, 0outlier). 
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 0.80.9 can result in an incorrectly estimated transformation. 
The function estimates an optimal 3D translation between two 3D point sets using the RANSAC algorithm.

static 
Computes an optimal translation between two 3D point sets.
It computes
\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]
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 (1inlier, 0outlier). 
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 0.80.9 can result in an incorrectly estimated transformation. 
The function estimates an optimal 3D translation between two 3D point sets using the RANSAC algorithm.

static 
Filters homography decompositions based on additional information.
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.

static 
Filters homography decompositions based on additional information.
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.

static 
Filters off small noise blobs (speckles) in the disparity map.
img  The input 16bit signed disparity image 
newVal  The disparity value used to paintoff 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 fixedpoint 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. 

static 
Filters off small noise blobs (speckles) in the disparity map.
img  The input 16bit signed disparity image 
newVal  The disparity value used to paintoff 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 fixedpoint 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. 

static 

static 
Finds the positions of internal corners of the chessboard.
image  Source chessboard view. It must be an 8bit 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:

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 nonzero 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: :
Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.

static 
Finds the positions of internal corners of the chessboard.
image  Source chessboard view. It must be an 8bit 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:

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 nonzero 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: :
Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
Finds the positions of internal corners of the chessboard using a sector based approach.
image  Source chessboard view. It must be an 8bit 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:

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:

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 subpixel 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 subpixel 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.
Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
Calculates an essential matrix from the corresponding points in two images.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (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 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 13, 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 fivepoint 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.

static 
Calculates an essential matrix from the corresponding points in two images.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (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 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 13, 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 fivepoint 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.

static 
Calculates an essential matrix from the corresponding points in two images.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (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 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 13, 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 fivepoint 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.

static 
Calculates an essential matrix from the corresponding points in two images.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (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 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 13, 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 fivepoint 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.

static 
Calculates an essential matrix from the corresponding points in two images.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (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 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 13, 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 fivepoint 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.

static 
Calculates an essential matrix from the corresponding points in two images.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (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 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 13, 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 fivepoint 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.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (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 13, 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:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (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 13, 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:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (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 13, 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:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (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 13, 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:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (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 13, 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:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (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 13, 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:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (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 13, 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:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (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 13, 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:
\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

static 
Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (single or double precision). 
points2  Array of the second image points of the same size and format as points1 . 
cameraMatrix1  Camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera matrix. If this assumption does not hold for your use case, use undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera matrix. When passing these coordinates, pass the identity matrix for this parameter. 
cameraMatrix2  Camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera matrix. If this assumption does not hold for your use case, use undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera matrix. When passing these coordinates, pass the identity matrix for this parameter. 
distCoeffs1  Input vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) 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 \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) 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 13, 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 fivepoint 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.

static 
Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (single or double precision). 
points2  Array of the second image points of the same size and format as points1 . 
cameraMatrix1  Camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera matrix. If this assumption does not hold for your use case, use undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera matrix. When passing these coordinates, pass the identity matrix for this parameter. 
cameraMatrix2  Camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera matrix. If this assumption does not hold for your use case, use undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera matrix. When passing these coordinates, pass the identity matrix for this parameter. 
distCoeffs1  Input vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) 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 \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) 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 13, 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 fivepoint 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.

static 
Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (single or double precision). 
points2  Array of the second image points of the same size and format as points1 . 
cameraMatrix1  Camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera matrix. If this assumption does not hold for your use case, use undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera matrix. When passing these coordinates, pass the identity matrix for this parameter. 
cameraMatrix2  Camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera matrix. If this assumption does not hold for your use case, use undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera matrix. When passing these coordinates, pass the identity matrix for this parameter. 
distCoeffs1  Input vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) 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 \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) 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 13, 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 fivepoint 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.

static 
Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (single or double precision). 
points2  Array of the second image points of the same size and format as points1 . 
cameraMatrix1  Camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera matrix. If this assumption does not hold for your use case, use undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera matrix. When passing these coordinates, pass the identity matrix for this parameter. 
cameraMatrix2  Camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera matrix. If this assumption does not hold for your use case, use undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera matrix. When passing these coordinates, pass the identity matrix for this parameter. 
distCoeffs1  Input vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) 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 \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) 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 13, 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 fivepoint 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.

static 
Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
points1  Array of N (N >= 5) 2D points from the first image. The point coordinates should be floatingpoint (single or double precision). 
points2  Array of the second image points of the same size and format as points1 . 
cameraMatrix1  Camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera matrix. If this assumption does not hold for your use case, use undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera matrix. When passing these coordinates, pass the identity matrix for this parameter. 
cameraMatrix2  Camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera matrix. If this assumption does not hold for your use case, use undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera matrix. When passing these coordinates, pass the identity matrix for this parameter. 
distCoeffs1  Input vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) 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 \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) 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 13, 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 fivepoint 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.

static 

static 
Calculates a fundamental matrix from the corresponding points in two images.
points1  Array of N points from the first image. The point coordinates should be floatingpoint (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 13, 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 7point 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. :

static 
Calculates a fundamental matrix from the corresponding points in two images.
points1  Array of N points from the first image. The point coordinates should be floatingpoint (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 13, 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 7point 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. :

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
Finds a perspective transformation between two planes.
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 backprojection 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 leastsquares 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 leastsquares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median reprojection 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 LevenbergMarquardt method to reduce the reprojection 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. Thus, it is normalized so that \(h_{33}=1\). Note that whenever an \(H\) matrix cannot be estimated, an empty one will be returned.

static 
Finds a perspective transformation between two planes.
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 backprojection 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 leastsquares 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 leastsquares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median reprojection 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 LevenbergMarquardt method to reduce the reprojection 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. Thus, it is normalized so that \(h_{33}=1\). Note that whenever an \(H\) matrix cannot be estimated, an empty one will be returned.

static 
Finds a perspective transformation between two planes.
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 backprojection 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 leastsquares 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 leastsquares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median reprojection 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 LevenbergMarquardt method to reduce the reprojection 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. Thus, it is normalized so that \(h_{33}=1\). Note that whenever an \(H\) matrix cannot be estimated, an empty one will be returned.

static 
Finds a perspective transformation between two planes.
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 backprojection 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 leastsquares 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 leastsquares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median reprojection 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 LevenbergMarquardt method to reduce the reprojection 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. Thus, it is normalized so that \(h_{33}=1\). Note that whenever an \(H\) matrix cannot be estimated, an empty one will be returned.

static 
Finds a perspective transformation between two planes.
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 backprojection 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 leastsquares 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 leastsquares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median reprojection 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 LevenbergMarquardt method to reduce the reprojection 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. Thus, it is normalized so that \(h_{33}=1\). Note that whenever an \(H\) matrix cannot be estimated, an empty one will be returned.

static 
Finds a perspective transformation between two planes.
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 backprojection 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 leastsquares 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 leastsquares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median reprojection 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 LevenbergMarquardt method to reduce the reprojection 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. Thus, it is normalized so that \(h_{33}=1\). Note that whenever an \(H\) matrix cannot be estimated, an empty one will be returned.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
Performs camera calibration.
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 floatingpoint 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 kth rotation vector together with the corresponding kth 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 kth 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:

criteria  Termination criteria for the iterative optimization algorithm. 

static 
Performs camera calibration.
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 floatingpoint 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 kth rotation vector together with the corresponding kth 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 kth 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:

criteria  Termination criteria for the iterative optimization algorithm. 

static 
Performs camera calibration.
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 floatingpoint 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 kth rotation vector together with the corresponding kth 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 kth 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:

criteria  Termination criteria for the iterative optimization algorithm. 

static 
Distorts 2D points using fisheye model.
undistorted  Array of object points, 1xN/Nx1 2channel (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 2channel, 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#160. 

static 
Distorts 2D points using fisheye model.
undistorted  Array of object points, 1xN/Nx1 2channel (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 2channel, 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#160. 

static 
Estimates new camera intrinsic matrix for undistortion or rectification.
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 1channel, or vector: 3x1/1x3 1channel or 1x1 3channel 
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. 

static 
Estimates new camera intrinsic matrix for undistortion or rectification.
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 1channel, or vector: 3x1/1x3 1channel or 1x1 3channel 
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. 

static 
Estimates new camera intrinsic matrix for undistortion or rectification.
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 1channel, or vector: 3x1/1x3 1channel or 1x1 3channel 
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. 

static 
Estimates new camera intrinsic matrix for undistortion or rectification.
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 1channel, or vector: 3x1/1x3 1channel or 1x1 3channel 
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. 

static 
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.
K  Camera intrinsic matrix \(cameramatrix{K}\). 
D  Input vector of distortion coefficients \(\distcoeffsfisheye\). 
R  Rectification transformation in the object space: 3x3 1channel, or vector: 3x1/1x3 1channel or 1x1 3channel 
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. 

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
Performs stereo calibration.
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 ith rotation vector together with the corresponding ith 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 ith 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:

criteria  Termination criteria for the iterative optimization algorithm. 

static 
Performs stereo calibration.
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 ith rotation vector together with the corresponding ith 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 ith 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:

criteria  Termination criteria for the iterative optimization algorithm. 

static 
Performs stereo calibration.
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 ith rotation vector together with the corresponding ith 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 ith 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:

criteria  Termination criteria for the iterative optimization algorithm. 

static 

static 

static 

static 
Stereo rectification for fisheye camera model.
K1  First camera intrinsic matrix. 
D1  First camera distortion parameters. 
K2  Second camera intrinsic matrix. 
D2  Second camera distortion parameters. 
imageSize  Size of the image used for stereo calibration. 
R  Rotation matrix between the coordinate systems of the first and the second cameras. 
tvec  Translation vector between coordinate systems of the cameras. 
R1  Output 3x3 rectification transform (rotation matrix) for the first camera. 
R2  Output 3x3 rectification transform (rotation matrix) for the second camera. 
P1  Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera. 
P2  Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera. 
Q  Output \(4 \times 4\) disparitytodepth mapping matrix (see reprojectImageTo3D ). 
flags  Operation flags that may be zero or fisheye::CALIB_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area. 
newImageSize  New image resolution after rectification. The same size should be passed to initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to larger value can help you preserve details in the original image, especially when there is a big radial distortion. 
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]. 
fov_scale  Divisor for new focal length. 