OpenCVForUnity.Calib3dModule.Calib3d Class Reference

Static Public Member Functions
static double	calibrateCamera (List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs)
static double	calibrateCamera (List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags)
static double	calibrateCamera (List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags, in Vec3d criteria)
static double	calibrateCamera (List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs)
static double	calibrateCamera (List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags)
static double	calibrateCamera (List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags, in(double type, double maxCount, double epsilon) criteria)
static double	calibrateCamera (List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs)
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, int flags, TermCriteria criteria)
static double	calibrateCameraExtended (List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d 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.
static double	calibrateCameraExtended (List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d 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.
static double	calibrateCameraExtended (List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags, in Vec3d criteria)
	Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
static double	calibrateCameraExtended (List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) 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.
static double	calibrateCameraExtended (List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) 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.
static double	calibrateCameraExtended (List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags, in(double type, double maxCount, double epsilon) criteria)
	Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
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.
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.
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.
static double	calibrateCameraRO (List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints)
static double	calibrateCameraRO (List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d 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, in Vec2d imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints, int flags, in Vec3d criteria)
static double	calibrateCameraRO (List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints)
static double	calibrateCameraRO (List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) 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, in(double width, double height) imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints, int flags, in(double type, double maxCount, double epsilon) 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)
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, int flags, TermCriteria criteria)
static double	calibrateCameraROExtended (List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d 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.
static double	calibrateCameraROExtended (List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d 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.
static double	calibrateCameraROExtended (List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d 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, in Vec3d criteria)
	Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
static double	calibrateCameraROExtended (List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) 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.
static double	calibrateCameraROExtended (List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) 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.
static double	calibrateCameraROExtended (List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) 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, in(double type, double maxCount, double epsilon) criteria)
	Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
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.
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.
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.
static void	calibrateHandEye (List< Mat > R_gripper2base, List< Mat > t_gripper2base, List< Mat > R_target2cam, List< Mat > t_target2cam, Mat R_cam2gripper, Mat t_cam2gripper)
	Computes Hand-Eye calibration: \(_{}^{g}\textrm{T}_c\).
static void	calibrateHandEye (List< Mat > R_gripper2base, List< Mat > t_gripper2base, List< Mat > R_target2cam, List< Mat > t_target2cam, Mat R_cam2gripper, Mat t_cam2gripper, int method)
	Computes Hand-Eye calibration: \(_{}^{g}\textrm{T}_c\).
static void	calibrateRobotWorldHandEye (List< Mat > R_world2cam, List< Mat > t_world2cam, List< Mat > R_base2gripper, List< Mat > t_base2gripper, Mat R_base2world, Mat t_base2world, Mat R_gripper2cam, Mat t_gripper2cam)
	Computes Robot-World/Hand-Eye calibration: \(_{}^{w}\textrm{T}_b\) and \(_{}^{c}\textrm{T}_g\).
static void	calibrateRobotWorldHandEye (List< Mat > R_world2cam, List< Mat > t_world2cam, List< Mat > R_base2gripper, List< Mat > t_base2gripper, Mat R_base2world, Mat t_base2world, Mat R_gripper2cam, Mat t_gripper2cam, int method)
	Computes Robot-World/Hand-Eye calibration: \(_{}^{w}\textrm{T}_b\) and \(_{}^{c}\textrm{T}_g\).
static void	calibrationMatrixValues (Mat cameraMatrix, in Vec2d imageSize, double apertureWidth, double apertureHeight, double[] fovx, double[] fovy, double[] focalLength, out Vec2d principalPoint, double[] aspectRatio)
	Computes useful camera characteristics from the camera intrinsic matrix.
static void	calibrationMatrixValues (Mat cameraMatrix, in(double width, double height) imageSize, double apertureWidth, double apertureHeight, double[] fovx, double[] fovy, double[] focalLength, out(double x, double y) principalPoint, double[] aspectRatio)
	Computes useful camera characteristics from the camera intrinsic matrix.
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.
static bool	checkChessboard (Mat img, in Vec2d size)
static bool	checkChessboard (Mat img, in(double width, double height) size)
static bool	checkChessboard (Mat img, Size size)
static void	composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3)
	Combines two rotation-and-shift transformations.
static void	composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1)
	Combines two rotation-and-shift transformations.
static void	composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1)
	Combines two rotation-and-shift transformations.
static void	composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2)
	Combines two rotation-and-shift transformations.
static void	composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2)
	Combines two rotation-and-shift transformations.
static void	composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1)
	Combines two rotation-and-shift transformations.
static void	composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1, Mat dt3dt1)
	Combines two rotation-and-shift transformations.
static void	composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1, Mat dt3dt1, Mat dt3dr2)
	Combines two rotation-and-shift transformations.
static void	composeRT (Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1, Mat dt3dt1, Mat dt3dr2, Mat dt3dt2)
	Combines two rotation-and-shift transformations.
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.
static void	convertPointsFromHomogeneous (Mat src, Mat dst)
	Converts points from homogeneous to Euclidean space.
static void	convertPointsToHomogeneous (Mat src, Mat dst)
	Converts points from Euclidean to homogeneous space.
static void	correctMatches (Mat F, Mat points1, Mat points2, Mat newPoints1, Mat newPoints2)
	Refines coordinates of corresponding points.
static void	decomposeEssentialMat (Mat E, Mat R1, Mat R2, Mat t)
	Decompose an essential matrix to possible rotations and translation.
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).
static void	decomposeProjectionMatrix (Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect)
	Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
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.
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.
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.
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.
static void	drawChessboardCorners (Mat image, in Vec2d patternSize, MatOfPoint2f corners, bool patternWasFound)
	Renders the detected chessboard corners.
static void	drawChessboardCorners (Mat image, in(double width, double height) patternSize, MatOfPoint2f corners, bool patternWasFound)
	Renders the detected chessboard corners.
static void	drawChessboardCorners (Mat image, Size patternSize, MatOfPoint2f corners, bool patternWasFound)
	Renders the detected chessboard corners.
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.
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.
static Mat	estimateAffine2D (Mat from, Mat to)
	Computes an optimal affine transformation between two 2D point sets.
static Mat	estimateAffine2D (Mat from, Mat to, Mat inliers)
	Computes an optimal affine transformation between two 2D point sets.
static Mat	estimateAffine2D (Mat from, Mat to, Mat inliers, int method)
	Computes an optimal affine transformation between two 2D point sets.
static Mat	estimateAffine2D (Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold)
	Computes an optimal affine transformation between two 2D point sets.
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.
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.
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.
static Mat	estimateAffine2D (Mat pts1, Mat pts2, Mat inliers, UsacParams _params)
static Mat	estimateAffine3D (Mat src, Mat dst)
	Computes an optimal affine transformation between two 3D point sets.
static Mat	estimateAffine3D (Mat src, Mat dst, double[] scale)
	Computes an optimal affine transformation between two 3D point sets.
static Mat	estimateAffine3D (Mat src, Mat dst, double[] scale, bool force_rotation)
	Computes an optimal affine transformation between two 3D point sets.
static int	estimateAffine3D (Mat src, Mat dst, Mat _out, Mat inliers)
	Computes an optimal affine transformation between two 3D point sets.
static int	estimateAffine3D (Mat src, Mat dst, Mat _out, Mat inliers, double ransacThreshold)
	Computes an optimal affine transformation between two 3D point sets.
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.
static Mat	estimateAffinePartial2D (Mat from, Mat to)
	Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
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.
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.
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.
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.
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.
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.
static Scalar	estimateChessboardSharpness (Mat image, Size patternSize, Mat corners)
	Estimates the sharpness of a detected chessboard.
static Scalar	estimateChessboardSharpness (Mat image, Size patternSize, Mat corners, float rise_distance)
	Estimates the sharpness of a detected chessboard.
static Scalar	estimateChessboardSharpness (Mat image, Size patternSize, Mat corners, float rise_distance, bool vertical)
	Estimates the sharpness of a detected chessboard.
static Scalar	estimateChessboardSharpness (Mat image, Size patternSize, Mat corners, float rise_distance, bool vertical, Mat sharpness)
	Estimates the sharpness of a detected chessboard.
static double double double double v3	estimateChessboardSharpnessAsValueTuple (Mat image, in(double width, double height) patternSize, Mat corners)
static double double double double v3	estimateChessboardSharpnessAsValueTuple (Mat image, in(double width, double height) patternSize, Mat corners, float rise_distance)
static double double double double v3	estimateChessboardSharpnessAsValueTuple (Mat image, in(double width, double height) patternSize, Mat corners, float rise_distance, bool vertical)
static double double double double v3	estimateChessboardSharpnessAsValueTuple (Mat image, in(double width, double height) patternSize, Mat corners, float rise_distance, bool vertical, Mat sharpness)
static Vec4d	estimateChessboardSharpnessAsVec4d (Mat image, in Vec2d patternSize, Mat corners)
	Estimates the sharpness of a detected chessboard.
static Vec4d	estimateChessboardSharpnessAsVec4d (Mat image, in Vec2d patternSize, Mat corners, float rise_distance)
	Estimates the sharpness of a detected chessboard.
static Vec4d	estimateChessboardSharpnessAsVec4d (Mat image, in Vec2d patternSize, Mat corners, float rise_distance, bool vertical)
	Estimates the sharpness of a detected chessboard.
static Vec4d	estimateChessboardSharpnessAsVec4d (Mat image, in Vec2d patternSize, Mat corners, float rise_distance, bool vertical, Mat sharpness)
	Estimates the sharpness of a detected chessboard.
static int	estimateTranslation3D (Mat src, Mat dst, Mat _out, Mat inliers)
	Computes an optimal translation between two 3D point sets.
static int	estimateTranslation3D (Mat src, Mat dst, Mat _out, Mat inliers, double ransacThreshold)
	Computes an optimal translation between two 3D point sets.
static int	estimateTranslation3D (Mat src, Mat dst, Mat _out, Mat inliers, double ransacThreshold, double confidence)
	Computes an optimal translation between two 3D point sets.
static void	filterHomographyDecompByVisibleRefpoints (List< Mat > rotations, List< Mat > normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions)
	Filters homography decompositions based on additional information.
static void	filterHomographyDecompByVisibleRefpoints (List< Mat > rotations, List< Mat > normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions, Mat pointsMask)
	Filters homography decompositions based on additional information.
static void	filterSpeckles (Mat img, double newVal, int maxSpeckleSize, double maxDiff)
	Filters off small noise blobs (speckles) in the disparity map.
static void	filterSpeckles (Mat img, double newVal, int maxSpeckleSize, double maxDiff, Mat buf)
	Filters off small noise blobs (speckles) in the disparity map.
static bool	find4QuadCornerSubpix (Mat img, Mat corners, in Vec2d region_size)
static bool	find4QuadCornerSubpix (Mat img, Mat corners, in(double width, double height) region_size)
static bool	find4QuadCornerSubpix (Mat img, Mat corners, Size region_size)
static bool	findChessboardCorners (Mat image, in Vec2d patternSize, MatOfPoint2f corners)
	Finds the positions of internal corners of the chessboard.
static bool	findChessboardCorners (Mat image, in Vec2d patternSize, MatOfPoint2f corners, int flags)
	Finds the positions of internal corners of the chessboard.
static bool	findChessboardCorners (Mat image, in(double width, double height) patternSize, MatOfPoint2f corners)
	Finds the positions of internal corners of the chessboard.
static bool	findChessboardCorners (Mat image, in(double width, double height) patternSize, MatOfPoint2f corners, int flags)
	Finds the positions of internal corners of the chessboard.
static bool	findChessboardCorners (Mat image, Size patternSize, MatOfPoint2f corners)
	Finds the positions of internal corners of the chessboard.
static bool	findChessboardCorners (Mat image, Size patternSize, MatOfPoint2f corners, int flags)
	Finds the positions of internal corners of the chessboard.
static bool	findChessboardCornersSB (Mat image, in Vec2d patternSize, Mat corners)
static bool	findChessboardCornersSB (Mat image, in Vec2d patternSize, Mat corners, int flags)
static bool	findChessboardCornersSB (Mat image, in(double width, double height) patternSize, Mat corners)
static bool	findChessboardCornersSB (Mat image, in(double width, double height) patternSize, Mat corners, int flags)
static bool	findChessboardCornersSB (Mat image, Size patternSize, Mat corners)
static bool	findChessboardCornersSB (Mat image, Size patternSize, Mat corners, int flags)
static bool	findChessboardCornersSBWithMeta (Mat image, in Vec2d patternSize, Mat corners, int flags, Mat meta)
	Finds the positions of internal corners of the chessboard using a sector based approach.
static bool	findChessboardCornersSBWithMeta (Mat image, in(double width, double height) patternSize, Mat corners, int flags, Mat meta)
	Finds the positions of internal corners of the chessboard using a sector based approach.
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.
static bool	findCirclesGrid (Mat image, in Vec2d patternSize, Mat centers)
static bool	findCirclesGrid (Mat image, in Vec2d patternSize, Mat centers, int flags)
static bool	findCirclesGrid (Mat image, in(double width, double height) patternSize, Mat centers)
static bool	findCirclesGrid (Mat image, in(double width, double height) patternSize, Mat centers, int flags)
static bool	findCirclesGrid (Mat image, Size patternSize, Mat centers)
static bool	findCirclesGrid (Mat image, Size patternSize, Mat centers, int flags)
static Mat	findEssentialMat (Mat points1, Mat points2)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal, in Vec2d pp)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal, in Vec2d pp, int method)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal, in Vec2d pp, int method, double prob)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal, in Vec2d pp, int method, double prob, double threshold)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal, in Vec2d pp, int method, double prob, double threshold, int maxIters)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal, in Vec2d pp, int method, double prob, double threshold, int maxIters, Mat mask)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal, in(double x, double y) pp)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal, in(double x, double y) pp, int method)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal, in(double x, double y) pp, int method, double prob)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal, in(double x, double y) pp, int method, double prob, double threshold)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal, in(double x, double y) pp, int method, double prob, double threshold, int maxIters)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal, in(double x, double y) pp, int method, double prob, double threshold, int maxIters, Mat mask)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal, Point pp)
static Mat	findEssentialMat (Mat points1, Mat points2, double focal, Point pp, int method)
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, double prob, double threshold)
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, int maxIters, Mat mask)
static Mat	findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix)
	Calculates an essential matrix from the corresponding points in two images.
static Mat	findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix, int method)
	Calculates an essential matrix from the corresponding points in two images.
static Mat	findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix, int method, double prob)
	Calculates an essential matrix from the corresponding points in two images.
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.
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.
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.
static Mat	findEssentialMat (Mat points1, Mat points2, Mat cameraMatrix1, Mat cameraMatrix2, Mat dist_coeff1, Mat dist_coeff2, Mat mask, UsacParams _params)
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.
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.
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.
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.
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.
static Mat	findFundamentalMat (MatOfPoint2f points1, MatOfPoint2f points2)
static Mat	findFundamentalMat (MatOfPoint2f points1, MatOfPoint2f points2, int method)
static Mat	findFundamentalMat (MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold)
static Mat	findFundamentalMat (MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence)
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.
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.
static Mat	findFundamentalMat (MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, Mat mask)
static Mat	findFundamentalMat (MatOfPoint2f points1, MatOfPoint2f points2, Mat mask, UsacParams _params)
static Mat	findHomography (MatOfPoint2f srcPoints, MatOfPoint2f dstPoints)
	Finds a perspective transformation between two planes.
static Mat	findHomography (MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method)
	Finds a perspective transformation between two planes.
static Mat	findHomography (MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold)
	Finds a perspective transformation between two planes.
static Mat	findHomography (MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask)
	Finds a perspective transformation between two planes.
static Mat	findHomography (MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters)
	Finds a perspective transformation between two planes.
static Mat	findHomography (MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters, double confidence)
	Finds a perspective transformation between two planes.
static Mat	findHomography (MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, Mat mask, UsacParams _params)
static double	fisheye_calibrate (List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs)
	Performs camera calibration.
static double	fisheye_calibrate (List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs, int flags)
	Performs camera calibration.
static double	fisheye_calibrate (List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs, int flags, in Vec3d criteria)
	Performs camera calibration.
static double	fisheye_calibrate (List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs)
	Performs camera calibration.
static double	fisheye_calibrate (List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs, int flags)
	Performs camera calibration.
static double	fisheye_calibrate (List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs, int flags, in(double type, double maxCount, double epsilon) criteria)
	Performs camera calibration.
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.
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.
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.
static void	fisheye_distortPoints (Mat undistorted, Mat distorted, Mat K, Mat D)
	Distorts 2D points using fisheye model.
static void	fisheye_distortPoints (Mat undistorted, Mat distorted, Mat K, Mat D, double alpha)
	Distorts 2D points using fisheye model.
static void	fisheye_estimateNewCameraMatrixForUndistortRectify (Mat K, Mat D, in Vec2d image_size, Mat R, Mat P)
	Estimates new camera intrinsic matrix for undistortion or rectification.
static void	fisheye_estimateNewCameraMatrixForUndistortRectify (Mat K, Mat D, in Vec2d image_size, Mat R, Mat P, double balance)
	Estimates new camera intrinsic matrix for undistortion or rectification.
static void	fisheye_estimateNewCameraMatrixForUndistortRectify (Mat K, Mat D, in Vec2d image_size, Mat R, Mat P, double balance, in Vec2d new_size)
	Estimates new camera intrinsic matrix for undistortion or rectification.
static void	fisheye_estimateNewCameraMatrixForUndistortRectify (Mat K, Mat D, in Vec2d image_size, Mat R, Mat P, double balance, in Vec2d new_size, double fov_scale)
	Estimates new camera intrinsic matrix for undistortion or rectification.
static void	fisheye_estimateNewCameraMatrixForUndistortRectify (Mat K, Mat D, in(double width, double height) image_size, Mat R, Mat P)
	Estimates new camera intrinsic matrix for undistortion or rectification.
static void	fisheye_estimateNewCameraMatrixForUndistortRectify (Mat K, Mat D, in(double width, double height) image_size, Mat R, Mat P, double balance)
	Estimates new camera intrinsic matrix for undistortion or rectification.
static void	fisheye_estimateNewCameraMatrixForUndistortRectify (Mat K, Mat D, in(double width, double height) image_size, Mat R, Mat P, double balance, in(double width, double height) new_size)
	Estimates new camera intrinsic matrix for undistortion or rectification.
static void	fisheye_estimateNewCameraMatrixForUndistortRectify (Mat K, Mat D, in(double width, double height) image_size, Mat R, Mat P, double balance, in(double width, double height) new_size, double fov_scale)
	Estimates new camera intrinsic matrix for undistortion or rectification.
static void	fisheye_estimateNewCameraMatrixForUndistortRectify (Mat K, Mat D, Size image_size, Mat R, Mat P)
	Estimates new camera intrinsic matrix for undistortion or rectification.
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.
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.
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.
static void	fisheye_initUndistortRectifyMap (Mat K, Mat D, Mat R, Mat P, in Vec2d 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.
static void	fisheye_initUndistortRectifyMap (Mat K, Mat D, Mat R, Mat P, in(double width, double height) 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.
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.
static void	fisheye_projectPoints (Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D)
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, double alpha, Mat jacobian)
static bool	fisheye_solvePnP (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec)
	Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
static bool	fisheye_solvePnP (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess)
	Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
static bool	fisheye_solvePnP (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int flags)
	Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
static bool	fisheye_solvePnP (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int flags, in Vec3d criteria)
	Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
static bool	fisheye_solvePnP (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int flags, in(double type, double maxCount, double epsilon) criteria)
	Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
static bool	fisheye_solvePnP (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int flags, TermCriteria criteria)
	Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
static double	fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat T)
static double	fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d 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, in Vec2d imageSize, Mat R, Mat T, int flags, in Vec3d criteria)
static double	fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs)
	Performs stereo calibration.
static double	fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs, int flags)
	Performs stereo calibration.
static double	fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs, int flags, in Vec3d criteria)
	Performs stereo calibration.
static double	fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat T)
static double	fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) 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, in(double width, double height) imageSize, Mat R, Mat T, int flags, in(double type, double maxCount, double epsilon) criteria)
static double	fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs)
	Performs stereo calibration.
static double	fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs, int flags)
	Performs stereo calibration.
static double	fisheye_stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs, int flags, in(double type, double maxCount, double epsilon) criteria)
	Performs stereo calibration.
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 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, 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, List< Mat > rvecs, List< Mat > tvecs)
	Performs stereo calibration.
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.
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.
static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags)
	Stereo rectification for fisheye camera model.
static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in Vec2d newImageSize)
	Stereo rectification for fisheye camera model.
static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in Vec2d newImageSize, double balance)
	Stereo rectification for fisheye camera model.
static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in Vec2d newImageSize, double balance, double fov_scale)
	Stereo rectification for fisheye camera model.
static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags)
	Stereo rectification for fisheye camera model.
static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in(double width, double height) newImageSize)
	Stereo rectification for fisheye camera model.
static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in(double width, double height) newImageSize, double balance)
	Stereo rectification for fisheye camera model.
static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in(double width, double height) newImageSize, double balance, double fov_scale)
	Stereo rectification for fisheye camera model.
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.
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.
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.
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.
static void	fisheye_undistortImage (Mat distorted, Mat undistorted, Mat K, Mat D)
	Transforms an image to compensate for fisheye lens distortion.
static void	fisheye_undistortImage (Mat distorted, Mat undistorted, Mat K, Mat D, Mat Knew)
	Transforms an image to compensate for fisheye lens distortion.
static void	fisheye_undistortImage (Mat distorted, Mat undistorted, Mat K, Mat D, Mat Knew, in Vec2d new_size)
	Transforms an image to compensate for fisheye lens distortion.
static void	fisheye_undistortImage (Mat distorted, Mat undistorted, Mat K, Mat D, Mat Knew, in(double width, double height) new_size)
	Transforms an image to compensate for fisheye lens distortion.
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.
static void	fisheye_undistortPoints (Mat distorted, Mat undistorted, Mat K, Mat D)
	Undistorts 2D points using fisheye model.
static void	fisheye_undistortPoints (Mat distorted, Mat undistorted, Mat K, Mat D, Mat R)
	Undistorts 2D points using fisheye model.
static void	fisheye_undistortPoints (Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P)
	Undistorts 2D points using fisheye model.
static void	fisheye_undistortPoints (Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P, in Vec3d criteria)
	Undistorts 2D points using fisheye model.
static void	fisheye_undistortPoints (Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P, in(double type, double maxCount, double epsilon) criteria)
	Undistorts 2D points using fisheye model.
static void	fisheye_undistortPoints (Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P, TermCriteria criteria)
	Undistorts 2D points using fisheye model.
static Mat	getDefaultNewCameraMatrix (Mat cameraMatrix)
	Returns the default new camera matrix.
static Mat	getDefaultNewCameraMatrix (Mat cameraMatrix, in Vec2d imgsize)
	Returns the default new camera matrix.
static Mat	getDefaultNewCameraMatrix (Mat cameraMatrix, in Vec2d imgsize, bool centerPrincipalPoint)
	Returns the default new camera matrix.
static Mat	getDefaultNewCameraMatrix (Mat cameraMatrix, in(double width, double height) imgsize)
	Returns the default new camera matrix.
static Mat	getDefaultNewCameraMatrix (Mat cameraMatrix, in(double width, double height) imgsize, bool centerPrincipalPoint)
	Returns the default new camera matrix.
static Mat	getDefaultNewCameraMatrix (Mat cameraMatrix, Size imgsize)
	Returns the default new camera matrix.
static Mat	getDefaultNewCameraMatrix (Mat cameraMatrix, Size imgsize, bool centerPrincipalPoint)
	Returns the default new camera matrix.
static Mat	getOptimalNewCameraMatrix (Mat cameraMatrix, Mat distCoeffs, in Vec2d imageSize, double alpha)
	Returns the new camera intrinsic matrix based on the free scaling parameter.
static Mat	getOptimalNewCameraMatrix (Mat cameraMatrix, Mat distCoeffs, in Vec2d imageSize, double alpha, in Vec2d newImgSize)
	Returns the new camera intrinsic matrix based on the free scaling parameter.
static Mat	getOptimalNewCameraMatrix (Mat cameraMatrix, Mat distCoeffs, in Vec2d imageSize, double alpha, in Vec2d newImgSize, out Vec4i validPixROI)
	Returns the new camera intrinsic matrix based on the free scaling parameter.
static Mat	getOptimalNewCameraMatrix (Mat cameraMatrix, Mat distCoeffs, in Vec2d imageSize, double alpha, in Vec2d newImgSize, out Vec4i validPixROI, bool centerPrincipalPoint)
	Returns the new camera intrinsic matrix based on the free scaling parameter.
static Mat	getOptimalNewCameraMatrix (Mat cameraMatrix, Mat distCoeffs, in(double width, double height) imageSize, double alpha)
	Returns the new camera intrinsic matrix based on the free scaling parameter.
static Mat	getOptimalNewCameraMatrix (Mat cameraMatrix, Mat distCoeffs, in(double width, double height) imageSize, double alpha, in(double width, double height) newImgSize)
	Returns the new camera intrinsic matrix based on the free scaling parameter.
static Mat	getOptimalNewCameraMatrix (Mat cameraMatrix, Mat distCoeffs, in(double width, double height) imageSize, double alpha, in(double width, double height) newImgSize, out(int x, int y, int width, int height) validPixROI)
	Returns the new camera intrinsic matrix based on the free scaling parameter.
static Mat	getOptimalNewCameraMatrix (Mat cameraMatrix, Mat distCoeffs, in(double width, double height) imageSize, double alpha, in(double width, double height) newImgSize, out(int x, int y, int width, int height) validPixROI, bool centerPrincipalPoint)
	Returns the new camera intrinsic matrix based on the free scaling parameter.
static Mat	getOptimalNewCameraMatrix (Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha)
	Returns the new camera intrinsic matrix based on the free scaling parameter.
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.
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.
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.
static Rect	getValidDisparityROI (Rect roi1, Rect roi2, int minDisparity, int numberOfDisparities, int blockSize)
static int int int int height	getValidDisparityROIAsValueTuple (in(int x, int y, int width, int height) roi1,(int x, int y, int width, int height) roi2, int minDisparity, int numberOfDisparities, int blockSize)
static Vec4i	getValidDisparityROIAsVec4i (in Vec4i roi1, Vec4i roi2, int minDisparity, int numberOfDisparities, int blockSize)
static Mat	initCameraMatrix2D (List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, in Vec2d imageSize)
	Finds an initial camera intrinsic matrix from 3D-2D point correspondences.
static Mat	initCameraMatrix2D (List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, in Vec2d imageSize, double aspectRatio)
	Finds an initial camera intrinsic matrix from 3D-2D point correspondences.
static Mat	initCameraMatrix2D (List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, in(double width, double height) imageSize)
	Finds an initial camera intrinsic matrix from 3D-2D point correspondences.
static Mat	initCameraMatrix2D (List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, in(double width, double height) imageSize, double aspectRatio)
	Finds an initial camera intrinsic matrix from 3D-2D point correspondences.
static Mat	initCameraMatrix2D (List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, Size imageSize)
	Finds an initial camera intrinsic matrix from 3D-2D point correspondences.
static Mat	initCameraMatrix2D (List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, Size imageSize, double aspectRatio)
	Finds an initial camera intrinsic matrix from 3D-2D point correspondences.
static void	initInverseRectificationMap (Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, in Vec2d size, int m1type, Mat map1, Mat map2)
	Computes the projection and inverse-rectification transformation map. In essense, this is the inverse of initUndistortRectifyMap to accomodate stereo-rectification of projectors ('inverse-cameras') in projector-camera pairs.
static void	initInverseRectificationMap (Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, in(double width, double height) size, int m1type, Mat map1, Mat map2)
	Computes the projection and inverse-rectification transformation map. In essense, this is the inverse of initUndistortRectifyMap to accomodate stereo-rectification of projectors ('inverse-cameras') in projector-camera pairs.
static void	initInverseRectificationMap (Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, Size size, int m1type, Mat map1, Mat map2)
	Computes the projection and inverse-rectification transformation map. In essense, this is the inverse of initUndistortRectifyMap to accomodate stereo-rectification of projectors ('inverse-cameras') in projector-camera pairs.
static void	initUndistortRectifyMap (Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, in Vec2d size, int m1type, Mat map1, Mat map2)
	Computes the undistortion and rectification transformation map.
static void	initUndistortRectifyMap (Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, in(double width, double height) size, int m1type, Mat map1, Mat map2)
	Computes the undistortion and rectification transformation map.
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.
static void	matMulDeriv (Mat A, Mat B, Mat dABdA, Mat dABdB)
	Computes partial derivatives of the matrix product for each multiplied matrix.
static void	projectPoints (MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints)
	Projects 3D points to an image plane.
static void	projectPoints (MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian)
	Projects 3D points to an image plane.
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.
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.
static int	recoverPose (Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh)
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, Mat mask, Mat triangulatedPoints)
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.
static int	recoverPose (Mat E, Mat points1, Mat points2, Mat R, Mat t)
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, double focal, in Vec2d pp)
static int	recoverPose (Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, in Vec2d pp, Mat mask)
static int	recoverPose (Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, in(double x, double y) pp)
static int	recoverPose (Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, in(double x, double y) 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, Point pp, Mat mask)
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.
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.
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.
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.
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.
static float	rectify3Collinear (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat cameraMatrix3, Mat distCoeffs3, List< Mat > imgpt1, List< Mat > imgpt3, in Vec2d 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, in Vec2d newImgSize, out Vec4i roi1, out Vec4i roi2, int flags)
static float	rectify3Collinear (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat cameraMatrix3, Mat distCoeffs3, List< Mat > imgpt1, List< Mat > imgpt3, in(double width, double height) 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, in(double width, double height) newImgSize, out(int x, int y, int width, int height) roi1, out(int x, int y, int width, int height) roi2, int flags)
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 void	reprojectImageTo3D (Mat disparity, Mat _3dImage, Mat Q)
	Reprojects a disparity image to 3D space.
static void	reprojectImageTo3D (Mat disparity, Mat _3dImage, Mat Q, bool handleMissingValues)
	Reprojects a disparity image to 3D space.
static void	reprojectImageTo3D (Mat disparity, Mat _3dImage, Mat Q, bool handleMissingValues, int ddepth)
	Reprojects a disparity image to 3D space.
static void	Rodrigues (Mat src, Mat dst)
	Converts a rotation matrix to a rotation vector or vice versa.
static void	Rodrigues (Mat src, Mat dst, Mat jacobian)
	Converts a rotation matrix to a rotation vector or vice versa.
static double[]	RQDecomp3x3 (Mat src, Mat mtxR, Mat mtxQ)
	Computes an RQ decomposition of 3x3 matrices.
static double[]	RQDecomp3x3 (Mat src, Mat mtxR, Mat mtxQ, Mat Qx)
	Computes an RQ decomposition of 3x3 matrices.
static double[]	RQDecomp3x3 (Mat src, Mat mtxR, Mat mtxQ, Mat Qx, Mat Qy)
	Computes an RQ decomposition of 3x3 matrices.
static double[]	RQDecomp3x3 (Mat src, Mat mtxR, Mat mtxQ, Mat Qx, Mat Qy, Mat Qz)
	Computes an RQ decomposition of 3x3 matrices.
static double	sampsonDistance (Mat pt1, Mat pt2, Mat F)
	Calculates the Sampson Distance between two points.
static int	solveP3P (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags)
	Finds an object pose from 3 3D-2D point correspondences.
static bool	solvePnP (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec)
	Finds an object pose from 3D-2D point correspondences.
static bool	solvePnP (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess)
	Finds an object pose from 3D-2D point correspondences.
static bool	solvePnP (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int flags)
	Finds an object pose from 3D-2D point correspondences.
static int	solvePnPGeneric (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs)
	Finds an object pose from 3D-2D point correspondences.
static int	solvePnPGeneric (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess)
	Finds an object pose from 3D-2D point correspondences.
static int	solvePnPGeneric (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess, int flags)
	Finds an object pose from 3D-2D point correspondences.
static int	solvePnPGeneric (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess, int flags, Mat rvec)
	Finds an object pose from 3D-2D point correspondences.
static int	solvePnPGeneric (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess, int flags, Mat rvec, Mat tvec)
	Finds an object pose from 3D-2D point correspondences.
static int	solvePnPGeneric (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess, int flags, Mat rvec, Mat tvec, Mat reprojectionError)
	Finds an object pose from 3D-2D point correspondences.
static bool	solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec)
	Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
static bool	solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess)
	Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
static bool	solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount)
	Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
static bool	solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError)
	Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
static bool	solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence)
	Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
static bool	solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, Mat inliers)
	Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
static bool	solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, Mat inliers, int flags)
	Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
static bool	solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers)
static bool	solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers, UsacParams _params)
static void	solvePnPRefineLM (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec)
	Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
static void	solvePnPRefineLM (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, in Vec3d criteria)
	Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
static void	solvePnPRefineLM (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, in(double type, double maxCount, double epsilon) criteria)
	Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
static void	solvePnPRefineLM (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria)
	Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
static void	solvePnPRefineVVS (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec)
	Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
static void	solvePnPRefineVVS (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, in Vec3d criteria)
	Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
static void	solvePnPRefineVVS (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, in Vec3d criteria, double VVSlambda)
	Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
static void	solvePnPRefineVVS (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, in(double type, double maxCount, double epsilon) criteria)
	Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
static void	solvePnPRefineVVS (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, in(double type, double maxCount, double epsilon) criteria, double VVSlambda)
	Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
static void	solvePnPRefineVVS (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria)
	Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
static void	solvePnPRefineVVS (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria, double VVSlambda)
	Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
static double	stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d 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, in Vec2d 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, in Vec2d imageSize, Mat R, Mat T, Mat E, Mat F, int flags, in Vec3d criteria)
static double	stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors)
static double	stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d 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, in Vec2d imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors, int flags, in Vec3d criteria)
static double	stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) 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, in(double width, double height) 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, in(double width, double height) imageSize, Mat R, Mat T, Mat E, Mat F, int flags, in(double type, double maxCount, double epsilon) criteria)
static double	stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors)
static double	stereoCalibrate (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) 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, in(double width, double height) imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors, int flags, in(double type, double maxCount, double epsilon) 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)
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, 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)
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, int flags, TermCriteria criteria)
static double	stereoCalibrateExtended (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d 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.
static double	stereoCalibrateExtended (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d 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.
static double	stereoCalibrateExtended (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat E, Mat F, List< Mat > rvecs, List< Mat > tvecs, Mat perViewErrors, int flags, in Vec3d 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.
static double	stereoCalibrateExtended (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) 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.
static double	stereoCalibrateExtended (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) 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.
static double	stereoCalibrateExtended (List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat E, Mat F, List< Mat > rvecs, List< Mat > tvecs, Mat perViewErrors, int flags, in(double type, double maxCount, double epsilon) 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.
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.
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.
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.
static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d 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.
static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d 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.
static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d 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.
static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in Vec2d newImageSize)
	Computes rectification transforms for each head of a calibrated stereo camera.
static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in Vec2d newImageSize, out Vec4i validPixROI1)
	Computes rectification transforms for each head of a calibrated stereo camera.
static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in Vec2d newImageSize, out Vec4i validPixROI1, out Vec4i validPixROI2)
	Computes rectification transforms for each head of a calibrated stereo camera.
static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) 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.
static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) 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.
static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) 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.
static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in(double width, double height) newImageSize)
	Computes rectification transforms for each head of a calibrated stereo camera.
static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in(double width, double height) newImageSize, out(int x, int y, int width, int height) validPixROI1)
	Computes rectification transforms for each head of a calibrated stereo camera.
static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in(double width, double height) newImageSize, out(int x, int y, int width, int height) validPixROI1, out(int x, int y, int width, int height) validPixROI2)
	Computes rectification transforms for each head of a calibrated stereo camera.
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.
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.
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.
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.
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.
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.
static bool	stereoRectifyUncalibrated (Mat points1, Mat points2, Mat F, in Vec2d imgSize, Mat H1, Mat H2)
	Computes a rectification transform for an uncalibrated stereo camera.
static bool	stereoRectifyUncalibrated (Mat points1, Mat points2, Mat F, in Vec2d imgSize, Mat H1, Mat H2, double threshold)
	Computes a rectification transform for an uncalibrated stereo camera.
static bool	stereoRectifyUncalibrated (Mat points1, Mat points2, Mat F, in(double width, double height) imgSize, Mat H1, Mat H2)
	Computes a rectification transform for an uncalibrated stereo camera.
static bool	stereoRectifyUncalibrated (Mat points1, Mat points2, Mat F, in(double width, double height) imgSize, Mat H1, Mat H2, double threshold)
	Computes a rectification transform for an uncalibrated stereo camera.
static bool	stereoRectifyUncalibrated (Mat points1, Mat points2, Mat F, Size imgSize, Mat H1, Mat H2)
	Computes a rectification transform for an uncalibrated stereo camera.
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.
static void	triangulatePoints (Mat projMatr1, Mat projMatr2, Mat projPoints1, Mat projPoints2, Mat points4D)
	This function reconstructs 3-dimensional points (in homogeneous coordinates) by using their observations with a stereo camera.
static void	undistort (Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs)
	Transforms an image to compensate for lens distortion.
static void	undistort (Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat newCameraMatrix)
	Transforms an image to compensate for lens distortion.
static void	undistortImagePoints (Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs)
	Compute undistorted image points position.
static void	undistortImagePoints (Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, in Vec3d arg1)
	Compute undistorted image points position.
static void	undistortImagePoints (Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, in(double type, double maxCount, double epsilon) arg1)
	Compute undistorted image points position.
static void	undistortImagePoints (Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, TermCriteria arg1)
	Compute undistorted image points position.
static void	undistortPoints (MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs)
	Computes the ideal point coordinates from the observed point coordinates.
static void	undistortPoints (MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R)
	Computes the ideal point coordinates from the observed point coordinates.
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.
static void	undistortPointsIter (Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, in Vec3d criteria)
static void	undistortPointsIter (Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, in(double type, double maxCount, double epsilon) criteria)
static void	undistortPointsIter (Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, TermCriteria criteria)
static void	validateDisparity (Mat disparity, Mat cost, int minDisparity, int numberOfDisparities)
static void	validateDisparity (Mat disparity, Mat cost, int minDisparity, int numberOfDisparities, int disp12MaxDisp)

Static Public Attributes
const int	CALIB_CB_ACCURACY = 32
const int	CALIB_CB_ADAPTIVE_THRESH = 1
const int	CALIB_CB_ASYMMETRIC_GRID = 2
const int	CALIB_CB_CLUSTERING = 4
const int	CALIB_CB_EXHAUSTIVE = 16
const int	CALIB_CB_FAST_CHECK = 8
const int	CALIB_CB_FILTER_QUADS = 4
const int	CALIB_CB_LARGER = 64
const int	CALIB_CB_MARKER = 128
const int	CALIB_CB_NORMALIZE_IMAGE = 2
const int	CALIB_CB_PLAIN = 256
const int	CALIB_CB_SYMMETRIC_GRID = 1
const int	CALIB_FIX_ASPECT_RATIO = 0x00002
const int	CALIB_FIX_FOCAL_LENGTH = 0x00010
const int	CALIB_FIX_INTRINSIC = 0x00100
const int	CALIB_FIX_K1 = 0x00020
const int	CALIB_FIX_K2 = 0x00040
const int	CALIB_FIX_K3 = 0x00080
const int	CALIB_FIX_K4 = 0x00800
const int	CALIB_FIX_K5 = 0x01000
const int	CALIB_FIX_K6 = 0x02000
const int	CALIB_FIX_PRINCIPAL_POINT = 0x00004
const int	CALIB_FIX_S1_S2_S3_S4 = 0x10000
const int	CALIB_FIX_TANGENT_DIST = 0x200000
const int	CALIB_FIX_TAUX_TAUY = 0x80000
const int	CALIB_HAND_EYE_ANDREFF = 3
const int	CALIB_HAND_EYE_DANIILIDIS = 4
const int	CALIB_HAND_EYE_HORAUD = 2
const int	CALIB_HAND_EYE_PARK = 1
const int	CALIB_HAND_EYE_TSAI = 0
const int	CALIB_NINTRINSIC = 18
const int	CALIB_RATIONAL_MODEL = 0x04000
const int	CALIB_ROBOT_WORLD_HAND_EYE_LI = 1
const int	CALIB_ROBOT_WORLD_HAND_EYE_SHAH = 0
const int	CALIB_SAME_FOCAL_LENGTH = 0x00200
const int	CALIB_THIN_PRISM_MODEL = 0x08000
const int	CALIB_TILTED_MODEL = 0x40000
const int	CALIB_USE_EXTRINSIC_GUESS = (1 << 22)
const int	CALIB_USE_INTRINSIC_GUESS = 0x00001
const int	CALIB_USE_LU = (1 << 17)
const int	CALIB_USE_QR = 0x100000
const int	CALIB_ZERO_DISPARITY = 0x00400
const int	CALIB_ZERO_TANGENT_DIST = 0x00008
const int	CirclesGridFinderParameters_ASYMMETRIC_GRID = 1
const int	CirclesGridFinderParameters_SYMMETRIC_GRID = 0
const int	COV_POLISHER = 3
const int	CV_DLS = 3
const int	CV_EPNP = 1
const int	CV_ITERATIVE = 0
const int	CV_P3P = 2
const int	CvLevMarq_CALC_J = 2
const int	CvLevMarq_CHECK_ERR = 3
const int	CvLevMarq_DONE = 0
const int	CvLevMarq_STARTED = 1
const int	fisheye_CALIB_CHECK_COND = 1 << 2
const int	fisheye_CALIB_FIX_FOCAL_LENGTH = 1 << 11
const int	fisheye_CALIB_FIX_INTRINSIC = 1 << 8
const int	fisheye_CALIB_FIX_K1 = 1 << 4
const int	fisheye_CALIB_FIX_K2 = 1 << 5
const int	fisheye_CALIB_FIX_K3 = 1 << 6
const int	fisheye_CALIB_FIX_K4 = 1 << 7
const int	fisheye_CALIB_FIX_PRINCIPAL_POINT = 1 << 9
const int	fisheye_CALIB_FIX_SKEW = 1 << 3
const int	fisheye_CALIB_RECOMPUTE_EXTRINSIC = 1 << 1
const int	fisheye_CALIB_USE_INTRINSIC_GUESS = 1 << 0
const int	fisheye_CALIB_ZERO_DISPARITY = 1 << 10
const int	FM_7POINT = 1
const int	FM_8POINT = 2
const int	FM_LMEDS = 4
const int	FM_RANSAC = 8
const int	LMEDS = 4
const int	LOCAL_OPTIM_GC = 3
const int	LOCAL_OPTIM_INNER_AND_ITER_LO = 2
const int	LOCAL_OPTIM_INNER_LO = 1
const int	LOCAL_OPTIM_NULL = 0
const int	LOCAL_OPTIM_SIGMA = 4
const int	LSQ_POLISHER = 1
const int	MAGSAC = 2
const int	NEIGH_FLANN_KNN = 0
const int	NEIGH_FLANN_RADIUS = 2
const int	NEIGH_GRID = 1
const int	NONE_POLISHER = 0
const int	PROJ_SPHERICAL_EQRECT = 1
const int	PROJ_SPHERICAL_ORTHO = 0
const int	RANSAC = 8
const int	RHO = 16
const int	SAMPLING_NAPSAC = 2
const int	SAMPLING_PROGRESSIVE_NAPSAC = 1
const int	SAMPLING_PROSAC = 3
const int	SAMPLING_UNIFORM = 0
const int	SCORE_METHOD_LMEDS = 3
const int	SCORE_METHOD_MAGSAC = 2
const int	SCORE_METHOD_MSAC = 1
const int	SCORE_METHOD_RANSAC = 0
const int	SOLVEPNP_AP3P = 5
const int	SOLVEPNP_DLS = 3
const int	SOLVEPNP_EPNP = 1
const int	SOLVEPNP_IPPE = 6
const int	SOLVEPNP_IPPE_SQUARE = 7
const int	SOLVEPNP_ITERATIVE = 0
const int	SOLVEPNP_MAX_COUNT = 8 + 1
const int	SOLVEPNP_P3P = 2
const int	SOLVEPNP_SQPNP = 8
const int	SOLVEPNP_UPNP = 4
const int	USAC_ACCURATE = 36
const int	USAC_DEFAULT = 32
const int	USAC_FAST = 35
const int	USAC_FM_8PTS = 34
const int	USAC_MAGSAC = 38
const int	USAC_PARALLEL = 33
const int	USAC_PROSAC = 37
static double	v0
	Estimates the sharpness of a detected chessboard.
static double double	v1
static double double double	v2
static int int int	width
static int	x
static int int	y

Member Function Documentation

calibrateCamera() [1/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCamera ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs )

static

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

calibrateCamera() [2/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCamera ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags )

static

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

calibrateCamera() [3/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCamera ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags, in Vec3d criteria )

static

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

calibrateCamera() [4/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCamera ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs )

static

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

calibrateCamera() [5/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCamera ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags )

static

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

calibrateCamera() [6/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCamera ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags, in(double type, double maxCount, double epsilon) criteria )

static

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

calibrateCamera() [7/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCamera ( List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs )

static

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

calibrateCamera() [8/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCamera ( List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags )

static

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

calibrateCamera() [9/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCamera ( List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags, TermCriteria criteria )

static

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

calibrateCameraExtended() [1/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraExtended ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

Parameters

objectPoints	In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer vector contains as many elements as the number of pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns or even different patterns in different views. Then, the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. In the old interface all the vectors of object points from different views are concatenated together.
imagePoints	In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, respectively. In the old interface all the vectors of object points from different views are concatenated together.
imageSize	Size of the image used only to initialize the camera intrinsic matrix.
cameraMatrix	Input/output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If CALIB_USE_INTRINSIC_GUESS and/or CALIB_FIX_ASPECT_RATIO, CALIB_FIX_PRINCIPAL_POINT or CALIB_FIX_FOCAL_LENGTH are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
distCoeffs	Input/output vector of distortion coefficients \(\distcoeffs\).
rvecs	Output vector of rotation vectors (Rodrigues ) estimated for each pattern view (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space. Due to its duality, this tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate space.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter describtion above.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Use solvePnP instead. CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix . When CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further. CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \((p_1, p_2)\) are set to zeros and stay zero. CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if CALIB_USE_INTRINSIC_GUESS is set. CALIB_FIX_K1,..., CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients or more. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients or more. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000] and [BouguetMCT] . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.

The algorithm performs the following steps:

• Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
• Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using solvePnP .
• Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See projectPoints for details.

Note

If you use a non-square (i.e. non-N-by-N) grid and findChessboardCorners for calibration, and calibrateCamera returns bad values (zero distortion coefficients, \(c_x\) and \(c_y\) very far from the image center, and/or large differences between \(f_x\) and \(f_y\) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in findChessboardCorners.

The function may throw exceptions, if unsupported combination of parameters is provided or the system is underconstrained.

See also

calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraExtended() [2/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraExtended ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

Parameters

objectPoints	In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer vector contains as many elements as the number of pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns or even different patterns in different views. Then, the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. In the old interface all the vectors of object points from different views are concatenated together.
imagePoints	In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, respectively. In the old interface all the vectors of object points from different views are concatenated together.
imageSize	Size of the image used only to initialize the camera intrinsic matrix.
cameraMatrix	Input/output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If CALIB_USE_INTRINSIC_GUESS and/or CALIB_FIX_ASPECT_RATIO, CALIB_FIX_PRINCIPAL_POINT or CALIB_FIX_FOCAL_LENGTH are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
distCoeffs	Input/output vector of distortion coefficients \(\distcoeffs\).
rvecs	Output vector of rotation vectors (Rodrigues ) estimated for each pattern view (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space. Due to its duality, this tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate space.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter describtion above.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Use solvePnP instead. CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix . When CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further. CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \((p_1, p_2)\) are set to zeros and stay zero. CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if CALIB_USE_INTRINSIC_GUESS is set. CALIB_FIX_K1,..., CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients or more. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients or more. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000] and [BouguetMCT] . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.

The algorithm performs the following steps:

• Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
• Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using solvePnP .
• Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See projectPoints for details.

Note

If you use a non-square (i.e. non-N-by-N) grid and findChessboardCorners for calibration, and calibrateCamera returns bad values (zero distortion coefficients, \(c_x\) and \(c_y\) very far from the image center, and/or large differences between \(f_x\) and \(f_y\) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in findChessboardCorners.

The function may throw exceptions, if unsupported combination of parameters is provided or the system is underconstrained.

See also

calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraExtended() [3/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraExtended ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags, in Vec3d criteria )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

Parameters

objectPoints	In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer vector contains as many elements as the number of pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns or even different patterns in different views. Then, the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. In the old interface all the vectors of object points from different views are concatenated together.
imagePoints	In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, respectively. In the old interface all the vectors of object points from different views are concatenated together.
imageSize	Size of the image used only to initialize the camera intrinsic matrix.
cameraMatrix	Input/output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If CALIB_USE_INTRINSIC_GUESS and/or CALIB_FIX_ASPECT_RATIO, CALIB_FIX_PRINCIPAL_POINT or CALIB_FIX_FOCAL_LENGTH are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
distCoeffs	Input/output vector of distortion coefficients \(\distcoeffs\).
rvecs	Output vector of rotation vectors (Rodrigues ) estimated for each pattern view (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space. Due to its duality, this tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate space.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter describtion above.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Use solvePnP instead. CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix . When CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further. CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \((p_1, p_2)\) are set to zeros and stay zero. CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if CALIB_USE_INTRINSIC_GUESS is set. CALIB_FIX_K1,..., CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients or more. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients or more. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000] and [BouguetMCT] . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.

The algorithm performs the following steps:

• Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
• Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using solvePnP .
• Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See projectPoints for details.

Note

If you use a non-square (i.e. non-N-by-N) grid and findChessboardCorners for calibration, and calibrateCamera returns bad values (zero distortion coefficients, \(c_x\) and \(c_y\) very far from the image center, and/or large differences between \(f_x\) and \(f_y\) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in findChessboardCorners.

The function may throw exceptions, if unsupported combination of parameters is provided or the system is underconstrained.

See also

calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraExtended() [4/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraExtended ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

Parameters

objectPoints	In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer vector contains as many elements as the number of pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns or even different patterns in different views. Then, the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. In the old interface all the vectors of object points from different views are concatenated together.
imagePoints	In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, respectively. In the old interface all the vectors of object points from different views are concatenated together.
imageSize	Size of the image used only to initialize the camera intrinsic matrix.
cameraMatrix	Input/output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If CALIB_USE_INTRINSIC_GUESS and/or CALIB_FIX_ASPECT_RATIO, CALIB_FIX_PRINCIPAL_POINT or CALIB_FIX_FOCAL_LENGTH are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
distCoeffs	Input/output vector of distortion coefficients \(\distcoeffs\).
rvecs	Output vector of rotation vectors (Rodrigues ) estimated for each pattern view (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space. Due to its duality, this tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate space.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter describtion above.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Use solvePnP instead. CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix . When CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further. CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \((p_1, p_2)\) are set to zeros and stay zero. CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if CALIB_USE_INTRINSIC_GUESS is set. CALIB_FIX_K1,..., CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients or more. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients or more. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000] and [BouguetMCT] . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.

The algorithm performs the following steps:

• Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
• Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using solvePnP .
• Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See projectPoints for details.

Note

If you use a non-square (i.e. non-N-by-N) grid and findChessboardCorners for calibration, and calibrateCamera returns bad values (zero distortion coefficients, \(c_x\) and \(c_y\) very far from the image center, and/or large differences between \(f_x\) and \(f_y\) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in findChessboardCorners.

The function may throw exceptions, if unsupported combination of parameters is provided or the system is underconstrained.

See also

calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraExtended() [5/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraExtended ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

Parameters

objectPoints	In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer vector contains as many elements as the number of pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns or even different patterns in different views. Then, the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. In the old interface all the vectors of object points from different views are concatenated together.
imagePoints	In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, respectively. In the old interface all the vectors of object points from different views are concatenated together.
imageSize	Size of the image used only to initialize the camera intrinsic matrix.
cameraMatrix	Input/output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If CALIB_USE_INTRINSIC_GUESS and/or CALIB_FIX_ASPECT_RATIO, CALIB_FIX_PRINCIPAL_POINT or CALIB_FIX_FOCAL_LENGTH are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
distCoeffs	Input/output vector of distortion coefficients \(\distcoeffs\).
rvecs	Output vector of rotation vectors (Rodrigues ) estimated for each pattern view (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space. Due to its duality, this tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate space.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter describtion above.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Use solvePnP instead. CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix . When CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further. CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \((p_1, p_2)\) are set to zeros and stay zero. CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if CALIB_USE_INTRINSIC_GUESS is set. CALIB_FIX_K1,..., CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients or more. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients or more. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000] and [BouguetMCT] . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.

The algorithm performs the following steps:

• Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
• Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using solvePnP .
• Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See projectPoints for details.

Note

If you use a non-square (i.e. non-N-by-N) grid and findChessboardCorners for calibration, and calibrateCamera returns bad values (zero distortion coefficients, \(c_x\) and \(c_y\) very far from the image center, and/or large differences between \(f_x\) and \(f_y\) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in findChessboardCorners.

The function may throw exceptions, if unsupported combination of parameters is provided or the system is underconstrained.

See also

calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraExtended() [6/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraExtended ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags, in(double type, double maxCount, double epsilon) criteria )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

Parameters

objectPoints	In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer vector contains as many elements as the number of pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns or even different patterns in different views. Then, the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. In the old interface all the vectors of object points from different views are concatenated together.
imagePoints	In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, respectively. In the old interface all the vectors of object points from different views are concatenated together.
imageSize	Size of the image used only to initialize the camera intrinsic matrix.
cameraMatrix	Input/output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If CALIB_USE_INTRINSIC_GUESS and/or CALIB_FIX_ASPECT_RATIO, CALIB_FIX_PRINCIPAL_POINT or CALIB_FIX_FOCAL_LENGTH are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
distCoeffs	Input/output vector of distortion coefficients \(\distcoeffs\).
rvecs	Output vector of rotation vectors (Rodrigues ) estimated for each pattern view (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space. Due to its duality, this tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate space.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter describtion above.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Use solvePnP instead. CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix . When CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further. CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \((p_1, p_2)\) are set to zeros and stay zero. CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if CALIB_USE_INTRINSIC_GUESS is set. CALIB_FIX_K1,..., CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients or more. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients or more. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000] and [BouguetMCT] . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.

The algorithm performs the following steps:

• Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
• Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using solvePnP .
• Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See projectPoints for details.

Note

If you use a non-square (i.e. non-N-by-N) grid and findChessboardCorners for calibration, and calibrateCamera returns bad values (zero distortion coefficients, \(c_x\) and \(c_y\) very far from the image center, and/or large differences between \(f_x\) and \(f_y\) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in findChessboardCorners.

The function may throw exceptions, if unsupported combination of parameters is provided or the system is underconstrained.

See also

calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraExtended() [7/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

Parameters

objectPoints	In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer vector contains as many elements as the number of pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns or even different patterns in different views. Then, the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. In the old interface all the vectors of object points from different views are concatenated together.
imagePoints	In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, respectively. In the old interface all the vectors of object points from different views are concatenated together.
imageSize	Size of the image used only to initialize the camera intrinsic matrix.
cameraMatrix	Input/output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If CALIB_USE_INTRINSIC_GUESS and/or CALIB_FIX_ASPECT_RATIO, CALIB_FIX_PRINCIPAL_POINT or CALIB_FIX_FOCAL_LENGTH are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
distCoeffs	Input/output vector of distortion coefficients \(\distcoeffs\).
rvecs	Output vector of rotation vectors (Rodrigues ) estimated for each pattern view (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space. Due to its duality, this tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate space.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter describtion above.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Use solvePnP instead. CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix . When CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further. CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \((p_1, p_2)\) are set to zeros and stay zero. CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if CALIB_USE_INTRINSIC_GUESS is set. CALIB_FIX_K1,..., CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients or more. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients or more. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000] and [BouguetMCT] . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.

The algorithm performs the following steps:

• Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
• Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using solvePnP .
• Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See projectPoints for details.

Note

If you use a non-square (i.e. non-N-by-N) grid and findChessboardCorners for calibration, and calibrateCamera returns bad values (zero distortion coefficients, \(c_x\) and \(c_y\) very far from the image center, and/or large differences between \(f_x\) and \(f_y\) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in findChessboardCorners.

The function may throw exceptions, if unsupported combination of parameters is provided or the system is underconstrained.

See also

calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraExtended() [8/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

Parameters

objectPoints	In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer vector contains as many elements as the number of pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns or even different patterns in different views. Then, the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. In the old interface all the vectors of object points from different views are concatenated together.
imagePoints	In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, respectively. In the old interface all the vectors of object points from different views are concatenated together.
imageSize	Size of the image used only to initialize the camera intrinsic matrix.
cameraMatrix	Input/output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If CALIB_USE_INTRINSIC_GUESS and/or CALIB_FIX_ASPECT_RATIO, CALIB_FIX_PRINCIPAL_POINT or CALIB_FIX_FOCAL_LENGTH are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
distCoeffs	Input/output vector of distortion coefficients \(\distcoeffs\).
rvecs	Output vector of rotation vectors (Rodrigues ) estimated for each pattern view (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space. Due to its duality, this tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate space.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter describtion above.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Use solvePnP instead. CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix . When CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further. CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \((p_1, p_2)\) are set to zeros and stay zero. CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if CALIB_USE_INTRINSIC_GUESS is set. CALIB_FIX_K1,..., CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients or more. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients or more. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000] and [BouguetMCT] . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.

The algorithm performs the following steps:

• Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
• Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using solvePnP .
• Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See projectPoints for details.

Note

If you use a non-square (i.e. non-N-by-N) grid and findChessboardCorners for calibration, and calibrateCamera returns bad values (zero distortion coefficients, \(c_x\) and \(c_y\) very far from the image center, and/or large differences between \(f_x\) and \(f_y\) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in findChessboardCorners.

The function may throw exceptions, if unsupported combination of parameters is provided or the system is underconstrained.

See also

calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraExtended() [9/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

Parameters

objectPoints	In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer vector contains as many elements as the number of pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns or even different patterns in different views. Then, the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. In the old interface all the vectors of object points from different views are concatenated together.
imagePoints	In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, respectively. In the old interface all the vectors of object points from different views are concatenated together.
imageSize	Size of the image used only to initialize the camera intrinsic matrix.
cameraMatrix	Input/output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If CALIB_USE_INTRINSIC_GUESS and/or CALIB_FIX_ASPECT_RATIO, CALIB_FIX_PRINCIPAL_POINT or CALIB_FIX_FOCAL_LENGTH are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
distCoeffs	Input/output vector of distortion coefficients \(\distcoeffs\).
rvecs	Output vector of rotation vectors (Rodrigues ) estimated for each pattern view (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space. Due to its duality, this tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate space.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter describtion above.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Use solvePnP instead. CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix . When CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further. CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \((p_1, p_2)\) are set to zeros and stay zero. CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if CALIB_USE_INTRINSIC_GUESS is set. CALIB_FIX_K1,..., CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients or more. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients or more. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000] and [BouguetMCT] . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.

The algorithm performs the following steps:

• Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
• Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using solvePnP .
• Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See projectPoints for details.

Note

If you use a non-square (i.e. non-N-by-N) grid and findChessboardCorners for calibration, and calibrateCamera returns bad values (zero distortion coefficients, \(c_x\) and \(c_y\) very far from the image center, and/or large differences between \(f_x\) and \(f_y\) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in findChessboardCorners.

The function may throw exceptions, if unsupported combination of parameters is provided or the system is underconstrained.

See also

calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraRO() [1/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraRO ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints )

static

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

calibrateCameraRO() [2/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraRO ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints, int flags )

static

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

calibrateCameraRO() [3/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraRO ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints, int flags, in Vec3d criteria )

static

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

calibrateCameraRO() [4/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraRO ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints )

static

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

calibrateCameraRO() [5/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraRO ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints, int flags )

static

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

calibrateCameraRO() [6/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraRO ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints, int flags, in(double type, double maxCount, double epsilon) criteria )

static

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

calibrateCameraRO() [7/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraRO ( List< Mat > objectPoints, List< Mat > imagePoints, Size imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints )

static

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

calibrateCameraRO() [8/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.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

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

calibrateCameraRO() [9/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.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

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

calibrateCameraROExtended() [1/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraROExtended ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat stdDeviationsObjPoints, Mat perViewErrors )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

This function is an extension of calibrateCamera with the method of releasing object which was proposed in [strobl2011iccv]. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, calibrateCamera is a wrapper for this function.

Parameters

objectPoints	Vector of vectors of calibration pattern points in the calibration pattern coordinate space. See calibrateCamera for details. If the method of releasing object to be used, the identical calibration board must be used in each view and it must be fully visible, and all objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration target has to be rigid, or at least static if the camera (rather than the calibration target) is shifted for grabbing images.
imagePoints	Vector of vectors of the projections of calibration pattern points. See calibrateCamera for details.
imageSize	Size of the image used only to initialize the intrinsic camera matrix.
iFixedPoint	The index of the 3D object point in objectPoints[0] to be fixed. It also acts as a switch for calibration method selection. If object-releasing method to be used, pass in the parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will make standard calibration method selected. Usually the top-right corner point of the calibration board grid is recommended to be fixed when object-releasing method being utilized. According to [strobl2011iccv], two other points are also fixed. In this implementation, objectPoints[0].front and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
cameraMatrix	Output 3x3 floating-point camera matrix. See calibrateCamera for details.
distCoeffs	Output vector of distortion coefficients. See calibrateCamera for details.
rvecs	Output vector of rotation vectors estimated for each pattern view. See calibrateCamera for details.
tvecs	Output vector of translation vectors estimated for each pattern view.
newObjPoints	The updated output vector of calibration pattern points. The coordinates might be scaled based on three fixed points. The returned coordinates are accurate only if the above mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter is ignored with standard calibration method.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. See calibrateCamera for details.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. See calibrateCamera for details.
stdDeviationsObjPoints	Output vector of standard deviations estimated for refined coordinates of calibration pattern points. It has the same size and order as objectPoints[0] vector. This parameter is ignored with standard calibration method.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of some predefined values. See calibrateCamera for details. If the method of releasing object is used, the calibration time may be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially less precise and less stable in some rare cases.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000], [BouguetMCT] and [strobl2011iccv]. See calibrateCamera for other detailed explanations.

See also

calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraROExtended() [2/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraROExtended ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d 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 )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

This function is an extension of calibrateCamera with the method of releasing object which was proposed in [strobl2011iccv]. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, calibrateCamera is a wrapper for this function.

Parameters

objectPoints	Vector of vectors of calibration pattern points in the calibration pattern coordinate space. See calibrateCamera for details. If the method of releasing object to be used, the identical calibration board must be used in each view and it must be fully visible, and all objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration target has to be rigid, or at least static if the camera (rather than the calibration target) is shifted for grabbing images.
imagePoints	Vector of vectors of the projections of calibration pattern points. See calibrateCamera for details.
imageSize	Size of the image used only to initialize the intrinsic camera matrix.
iFixedPoint	The index of the 3D object point in objectPoints[0] to be fixed. It also acts as a switch for calibration method selection. If object-releasing method to be used, pass in the parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will make standard calibration method selected. Usually the top-right corner point of the calibration board grid is recommended to be fixed when object-releasing method being utilized. According to [strobl2011iccv], two other points are also fixed. In this implementation, objectPoints[0].front and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
cameraMatrix	Output 3x3 floating-point camera matrix. See calibrateCamera for details.
distCoeffs	Output vector of distortion coefficients. See calibrateCamera for details.
rvecs	Output vector of rotation vectors estimated for each pattern view. See calibrateCamera for details.
tvecs	Output vector of translation vectors estimated for each pattern view.
newObjPoints	The updated output vector of calibration pattern points. The coordinates might be scaled based on three fixed points. The returned coordinates are accurate only if the above mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter is ignored with standard calibration method.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. See calibrateCamera for details.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. See calibrateCamera for details.
stdDeviationsObjPoints	Output vector of standard deviations estimated for refined coordinates of calibration pattern points. It has the same size and order as objectPoints[0] vector. This parameter is ignored with standard calibration method.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of some predefined values. See calibrateCamera for details. If the method of releasing object is used, the calibration time may be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially less precise and less stable in some rare cases.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000], [BouguetMCT] and [strobl2011iccv]. See calibrateCamera for other detailed explanations.

See also

calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraROExtended() [3/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraROExtended ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d 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, in Vec3d criteria )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

This function is an extension of calibrateCamera with the method of releasing object which was proposed in [strobl2011iccv]. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, calibrateCamera is a wrapper for this function.

Parameters

objectPoints	Vector of vectors of calibration pattern points in the calibration pattern coordinate space. See calibrateCamera for details. If the method of releasing object to be used, the identical calibration board must be used in each view and it must be fully visible, and all objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration target has to be rigid, or at least static if the camera (rather than the calibration target) is shifted for grabbing images.
imagePoints	Vector of vectors of the projections of calibration pattern points. See calibrateCamera for details.
imageSize	Size of the image used only to initialize the intrinsic camera matrix.
iFixedPoint	The index of the 3D object point in objectPoints[0] to be fixed. It also acts as a switch for calibration method selection. If object-releasing method to be used, pass in the parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will make standard calibration method selected. Usually the top-right corner point of the calibration board grid is recommended to be fixed when object-releasing method being utilized. According to [strobl2011iccv], two other points are also fixed. In this implementation, objectPoints[0].front and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
cameraMatrix	Output 3x3 floating-point camera matrix. See calibrateCamera for details.
distCoeffs	Output vector of distortion coefficients. See calibrateCamera for details.
rvecs	Output vector of rotation vectors estimated for each pattern view. See calibrateCamera for details.
tvecs	Output vector of translation vectors estimated for each pattern view.
newObjPoints	The updated output vector of calibration pattern points. The coordinates might be scaled based on three fixed points. The returned coordinates are accurate only if the above mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter is ignored with standard calibration method.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. See calibrateCamera for details.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. See calibrateCamera for details.
stdDeviationsObjPoints	Output vector of standard deviations estimated for refined coordinates of calibration pattern points. It has the same size and order as objectPoints[0] vector. This parameter is ignored with standard calibration method.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of some predefined values. See calibrateCamera for details. If the method of releasing object is used, the calibration time may be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially less precise and less stable in some rare cases.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000], [BouguetMCT] and [strobl2011iccv]. See calibrateCamera for other detailed explanations.

See also

calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraROExtended() [4/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraROExtended ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, Mat newObjPoints, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat stdDeviationsObjPoints, Mat perViewErrors )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

This function is an extension of calibrateCamera with the method of releasing object which was proposed in [strobl2011iccv]. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, calibrateCamera is a wrapper for this function.

Parameters

objectPoints	Vector of vectors of calibration pattern points in the calibration pattern coordinate space. See calibrateCamera for details. If the method of releasing object to be used, the identical calibration board must be used in each view and it must be fully visible, and all objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration target has to be rigid, or at least static if the camera (rather than the calibration target) is shifted for grabbing images.
imagePoints	Vector of vectors of the projections of calibration pattern points. See calibrateCamera for details.
imageSize	Size of the image used only to initialize the intrinsic camera matrix.
iFixedPoint	The index of the 3D object point in objectPoints[0] to be fixed. It also acts as a switch for calibration method selection. If object-releasing method to be used, pass in the parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will make standard calibration method selected. Usually the top-right corner point of the calibration board grid is recommended to be fixed when object-releasing method being utilized. According to [strobl2011iccv], two other points are also fixed. In this implementation, objectPoints[0].front and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
cameraMatrix	Output 3x3 floating-point camera matrix. See calibrateCamera for details.
distCoeffs	Output vector of distortion coefficients. See calibrateCamera for details.
rvecs	Output vector of rotation vectors estimated for each pattern view. See calibrateCamera for details.
tvecs	Output vector of translation vectors estimated for each pattern view.
newObjPoints	The updated output vector of calibration pattern points. The coordinates might be scaled based on three fixed points. The returned coordinates are accurate only if the above mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter is ignored with standard calibration method.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. See calibrateCamera for details.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. See calibrateCamera for details.
stdDeviationsObjPoints	Output vector of standard deviations estimated for refined coordinates of calibration pattern points. It has the same size and order as objectPoints[0] vector. This parameter is ignored with standard calibration method.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of some predefined values. See calibrateCamera for details. If the method of releasing object is used, the calibration time may be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially less precise and less stable in some rare cases.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000], [BouguetMCT] and [strobl2011iccv]. See calibrateCamera for other detailed explanations.

See also

calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraROExtended() [5/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraROExtended ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) 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 )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

This function is an extension of calibrateCamera with the method of releasing object which was proposed in [strobl2011iccv]. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, calibrateCamera is a wrapper for this function.

Parameters

objectPoints	Vector of vectors of calibration pattern points in the calibration pattern coordinate space. See calibrateCamera for details. If the method of releasing object to be used, the identical calibration board must be used in each view and it must be fully visible, and all objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration target has to be rigid, or at least static if the camera (rather than the calibration target) is shifted for grabbing images.
imagePoints	Vector of vectors of the projections of calibration pattern points. See calibrateCamera for details.
imageSize	Size of the image used only to initialize the intrinsic camera matrix.
iFixedPoint	The index of the 3D object point in objectPoints[0] to be fixed. It also acts as a switch for calibration method selection. If object-releasing method to be used, pass in the parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will make standard calibration method selected. Usually the top-right corner point of the calibration board grid is recommended to be fixed when object-releasing method being utilized. According to [strobl2011iccv], two other points are also fixed. In this implementation, objectPoints[0].front and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
cameraMatrix	Output 3x3 floating-point camera matrix. See calibrateCamera for details.
distCoeffs	Output vector of distortion coefficients. See calibrateCamera for details.
rvecs	Output vector of rotation vectors estimated for each pattern view. See calibrateCamera for details.
tvecs	Output vector of translation vectors estimated for each pattern view.
newObjPoints	The updated output vector of calibration pattern points. The coordinates might be scaled based on three fixed points. The returned coordinates are accurate only if the above mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter is ignored with standard calibration method.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. See calibrateCamera for details.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. See calibrateCamera for details.
stdDeviationsObjPoints	Output vector of standard deviations estimated for refined coordinates of calibration pattern points. It has the same size and order as objectPoints[0] vector. This parameter is ignored with standard calibration method.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of some predefined values. See calibrateCamera for details. If the method of releasing object is used, the calibration time may be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially less precise and less stable in some rare cases.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000], [BouguetMCT] and [strobl2011iccv]. See calibrateCamera for other detailed explanations.

See also

calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraROExtended() [6/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.calibrateCameraROExtended ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) 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, in(double type, double maxCount, double epsilon) criteria )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

This function is an extension of calibrateCamera with the method of releasing object which was proposed in [strobl2011iccv]. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, calibrateCamera is a wrapper for this function.

Parameters

objectPoints	Vector of vectors of calibration pattern points in the calibration pattern coordinate space. See calibrateCamera for details. If the method of releasing object to be used, the identical calibration board must be used in each view and it must be fully visible, and all objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration target has to be rigid, or at least static if the camera (rather than the calibration target) is shifted for grabbing images.
imagePoints	Vector of vectors of the projections of calibration pattern points. See calibrateCamera for details.
imageSize	Size of the image used only to initialize the intrinsic camera matrix.
iFixedPoint	The index of the 3D object point in objectPoints[0] to be fixed. It also acts as a switch for calibration method selection. If object-releasing method to be used, pass in the parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will make standard calibration method selected. Usually the top-right corner point of the calibration board grid is recommended to be fixed when object-releasing method being utilized. According to [strobl2011iccv], two other points are also fixed. In this implementation, objectPoints[0].front and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
cameraMatrix	Output 3x3 floating-point camera matrix. See calibrateCamera for details.
distCoeffs	Output vector of distortion coefficients. See calibrateCamera for details.
rvecs	Output vector of rotation vectors estimated for each pattern view. See calibrateCamera for details.
tvecs	Output vector of translation vectors estimated for each pattern view.
newObjPoints	The updated output vector of calibration pattern points. The coordinates might be scaled based on three fixed points. The returned coordinates are accurate only if the above mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter is ignored with standard calibration method.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. See calibrateCamera for details.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. See calibrateCamera for details.
stdDeviationsObjPoints	Output vector of standard deviations estimated for refined coordinates of calibration pattern points. It has the same size and order as objectPoints[0] vector. This parameter is ignored with standard calibration method.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of some predefined values. See calibrateCamera for details. If the method of releasing object is used, the calibration time may be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially less precise and less stable in some rare cases.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000], [BouguetMCT] and [strobl2011iccv]. See calibrateCamera for other detailed explanations.

See also

calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraROExtended() [7/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

This function is an extension of calibrateCamera with the method of releasing object which was proposed in [strobl2011iccv]. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, calibrateCamera is a wrapper for this function.

Parameters

objectPoints	Vector of vectors of calibration pattern points in the calibration pattern coordinate space. See calibrateCamera for details. If the method of releasing object to be used, the identical calibration board must be used in each view and it must be fully visible, and all objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration target has to be rigid, or at least static if the camera (rather than the calibration target) is shifted for grabbing images.
imagePoints	Vector of vectors of the projections of calibration pattern points. See calibrateCamera for details.
imageSize	Size of the image used only to initialize the intrinsic camera matrix.
iFixedPoint	The index of the 3D object point in objectPoints[0] to be fixed. It also acts as a switch for calibration method selection. If object-releasing method to be used, pass in the parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will make standard calibration method selected. Usually the top-right corner point of the calibration board grid is recommended to be fixed when object-releasing method being utilized. According to [strobl2011iccv], two other points are also fixed. In this implementation, objectPoints[0].front and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
cameraMatrix	Output 3x3 floating-point camera matrix. See calibrateCamera for details.
distCoeffs	Output vector of distortion coefficients. See calibrateCamera for details.
rvecs	Output vector of rotation vectors estimated for each pattern view. See calibrateCamera for details.
tvecs	Output vector of translation vectors estimated for each pattern view.
newObjPoints	The updated output vector of calibration pattern points. The coordinates might be scaled based on three fixed points. The returned coordinates are accurate only if the above mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter is ignored with standard calibration method.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. See calibrateCamera for details.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. See calibrateCamera for details.
stdDeviationsObjPoints	Output vector of standard deviations estimated for refined coordinates of calibration pattern points. It has the same size and order as objectPoints[0] vector. This parameter is ignored with standard calibration method.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of some predefined values. See calibrateCamera for details. If the method of releasing object is used, the calibration time may be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially less precise and less stable in some rare cases.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000], [BouguetMCT] and [strobl2011iccv]. See calibrateCamera for other detailed explanations.

See also

calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraROExtended() [8/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

This function is an extension of calibrateCamera with the method of releasing object which was proposed in [strobl2011iccv]. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, calibrateCamera is a wrapper for this function.

Parameters

objectPoints	Vector of vectors of calibration pattern points in the calibration pattern coordinate space. See calibrateCamera for details. If the method of releasing object to be used, the identical calibration board must be used in each view and it must be fully visible, and all objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration target has to be rigid, or at least static if the camera (rather than the calibration target) is shifted for grabbing images.
imagePoints	Vector of vectors of the projections of calibration pattern points. See calibrateCamera for details.
imageSize	Size of the image used only to initialize the intrinsic camera matrix.
iFixedPoint	The index of the 3D object point in objectPoints[0] to be fixed. It also acts as a switch for calibration method selection. If object-releasing method to be used, pass in the parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will make standard calibration method selected. Usually the top-right corner point of the calibration board grid is recommended to be fixed when object-releasing method being utilized. According to [strobl2011iccv], two other points are also fixed. In this implementation, objectPoints[0].front and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
cameraMatrix	Output 3x3 floating-point camera matrix. See calibrateCamera for details.
distCoeffs	Output vector of distortion coefficients. See calibrateCamera for details.
rvecs	Output vector of rotation vectors estimated for each pattern view. See calibrateCamera for details.
tvecs	Output vector of translation vectors estimated for each pattern view.
newObjPoints	The updated output vector of calibration pattern points. The coordinates might be scaled based on three fixed points. The returned coordinates are accurate only if the above mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter is ignored with standard calibration method.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. See calibrateCamera for details.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. See calibrateCamera for details.
stdDeviationsObjPoints	Output vector of standard deviations estimated for refined coordinates of calibration pattern points. It has the same size and order as objectPoints[0] vector. This parameter is ignored with standard calibration method.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of some predefined values. See calibrateCamera for details. If the method of releasing object is used, the calibration time may be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially less precise and less stable in some rare cases.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000], [BouguetMCT] and [strobl2011iccv]. See calibrateCamera for other detailed explanations.

See also

calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateCameraROExtended() [9/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

This function is an extension of calibrateCamera with the method of releasing object which was proposed in [strobl2011iccv]. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, calibrateCamera is a wrapper for this function.

Parameters

objectPoints	Vector of vectors of calibration pattern points in the calibration pattern coordinate space. See calibrateCamera for details. If the method of releasing object to be used, the identical calibration board must be used in each view and it must be fully visible, and all objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration target has to be rigid, or at least static if the camera (rather than the calibration target) is shifted for grabbing images.
imagePoints	Vector of vectors of the projections of calibration pattern points. See calibrateCamera for details.
imageSize	Size of the image used only to initialize the intrinsic camera matrix.
iFixedPoint	The index of the 3D object point in objectPoints[0] to be fixed. It also acts as a switch for calibration method selection. If object-releasing method to be used, pass in the parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will make standard calibration method selected. Usually the top-right corner point of the calibration board grid is recommended to be fixed when object-releasing method being utilized. According to [strobl2011iccv], two other points are also fixed. In this implementation, objectPoints[0].front and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
cameraMatrix	Output 3x3 floating-point camera matrix. See calibrateCamera for details.
distCoeffs	Output vector of distortion coefficients. See calibrateCamera for details.
rvecs	Output vector of rotation vectors estimated for each pattern view. See calibrateCamera for details.
tvecs	Output vector of translation vectors estimated for each pattern view.
newObjPoints	The updated output vector of calibration pattern points. The coordinates might be scaled based on three fixed points. The returned coordinates are accurate only if the above mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter is ignored with standard calibration method.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. See calibrateCamera for details.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. See calibrateCamera for details.
stdDeviationsObjPoints	Output vector of standard deviations estimated for refined coordinates of calibration pattern points. It has the same size and order as objectPoints[0] vector. This parameter is ignored with standard calibration method.
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of some predefined values. See calibrateCamera for details. If the method of releasing object is used, the calibration time may be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially less precise and less stable in some rare cases.
criteria	Termination criteria for the iterative optimization algorithm.

Returns

the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000], [BouguetMCT] and [strobl2011iccv]. See calibrateCamera for other detailed explanations.

See also

calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

calibrateHandEye() [1/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.calibrateHandEye ( List< Mat > R_gripper2base, List< Mat > t_gripper2base, List< Mat > R_target2cam, List< Mat > t_target2cam, Mat R_cam2gripper, Mat t_cam2gripper )

static

Computes Hand-Eye calibration: \(_{}^{g}\textrm{T}_c\).

The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions) and the following methods are implemented:

• R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/EyeCalibration [Tsai89]
• F. Park, B. Martin Robot Sensor Calibration: Solving AX = XB on the Euclidean Group [Park94]
• R. Horaud, F. Dornaika Hand-Eye Calibration [Horaud95]

Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented methods:

• N. Andreff, R. Horaud, B. Espiau On-line Hand-Eye Calibration [Andreff99]
• K. Daniilidis Hand-Eye Calibration Using Dual Quaternions [Daniilidis98]

The following picture describes the Hand-Eye calibration problem where the transformation between a camera ("eye") mounted on a robot gripper ("hand") has to be estimated. This configuration is called eye-in-hand.

The eye-to-hand configuration consists in a static camera observing a calibration pattern mounted on the robot end-effector. The transformation from the camera to the robot base frame can then be estimated by inputting the suitable transformations to the function, see below.

The calibration procedure is the following:

• a static calibration pattern is used to estimate the transformation between the target frame and the camera frame
• the robot gripper is moved in order to acquire several poses
• for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for instance the robot kinematics

  \[ \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{b}\textrm{R}_g & _{}^{b}\textrm{t}_g \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} \]

• for each pose, the homogeneous transformation between the calibration target frame and the camera frame is recorded using for instance a pose estimation method (PnP) from 2D-3D point correspondences

  \[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_t & _{}^{c}\textrm{t}_t \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_t\\ Y_t\\ Z_t\\ 1 \end{bmatrix} \]

The Hand-Eye calibration procedure returns the following homogeneous transformation

\[ \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{g}\textrm{R}_c & _{}^{g}\textrm{t}_c \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} \]

This problem is also known as solving the \(\mathbf{A}\mathbf{X}=\mathbf{X}\mathbf{B}\) equation:

• for an eye-in-hand configuration

  \[ \begin{align*} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{b}{\textrm{T}_g}^{(2)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{b}{\textrm{T}_g}^{(2)})^{-1} \hspace{0.2em} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c &= \hspace{0.1em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*} \]

• for an eye-to-hand configuration

  \[ \begin{align*} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{g}{\textrm{T}_b}^{(2)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{g}{\textrm{T}_b}^{(2)})^{-1} \hspace{0.2em} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c &= \hspace{0.1em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*} \]

Note

Additional information can be found on this website.

A minimum of 2 motions with non parallel rotation axes are necessary to determine the hand-eye transformation. So at least 3 different poses are required, but it is strongly recommended to use many more poses.

calibrateHandEye() [2/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Computes Hand-Eye calibration: \(_{}^{g}\textrm{T}_c\).

The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions) and the following methods are implemented:

• R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/EyeCalibration [Tsai89]
• F. Park, B. Martin Robot Sensor Calibration: Solving AX = XB on the Euclidean Group [Park94]
• R. Horaud, F. Dornaika Hand-Eye Calibration [Horaud95]

Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented methods:

• N. Andreff, R. Horaud, B. Espiau On-line Hand-Eye Calibration [Andreff99]
• K. Daniilidis Hand-Eye Calibration Using Dual Quaternions [Daniilidis98]

The following picture describes the Hand-Eye calibration problem where the transformation between a camera ("eye") mounted on a robot gripper ("hand") has to be estimated. This configuration is called eye-in-hand.

The eye-to-hand configuration consists in a static camera observing a calibration pattern mounted on the robot end-effector. The transformation from the camera to the robot base frame can then be estimated by inputting the suitable transformations to the function, see below.

The calibration procedure is the following:

• a static calibration pattern is used to estimate the transformation between the target frame and the camera frame
• the robot gripper is moved in order to acquire several poses
• for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for instance the robot kinematics

  \[ \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{b}\textrm{R}_g & _{}^{b}\textrm{t}_g \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} \]

• for each pose, the homogeneous transformation between the calibration target frame and the camera frame is recorded using for instance a pose estimation method (PnP) from 2D-3D point correspondences

  \[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_t & _{}^{c}\textrm{t}_t \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_t\\ Y_t\\ Z_t\\ 1 \end{bmatrix} \]

The Hand-Eye calibration procedure returns the following homogeneous transformation

\[ \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{g}\textrm{R}_c & _{}^{g}\textrm{t}_c \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} \]

This problem is also known as solving the \(\mathbf{A}\mathbf{X}=\mathbf{X}\mathbf{B}\) equation:

• for an eye-in-hand configuration

  \[ \begin{align*} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{b}{\textrm{T}_g}^{(2)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{b}{\textrm{T}_g}^{(2)})^{-1} \hspace{0.2em} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c &= \hspace{0.1em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*} \]

• for an eye-to-hand configuration

  \[ \begin{align*} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{g}{\textrm{T}_b}^{(2)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{g}{\textrm{T}_b}^{(2)})^{-1} \hspace{0.2em} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c &= \hspace{0.1em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*} \]

Note

Additional information can be found on this website.

A minimum of 2 motions with non parallel rotation axes are necessary to determine the hand-eye transformation. So at least 3 different poses are required, but it is strongly recommended to use many more poses.

calibrateRobotWorldHandEye() [1/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Computes Robot-World/Hand-Eye calibration: \(_{}^{w}\textrm{T}_b\) and \(_{}^{c}\textrm{T}_g\).

The function performs the Robot-World/Hand-Eye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions):

• M. Shah, Solving the robot-world/hand-eye calibration problem using the kronecker product [Shah2013SolvingTR]

Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented method:

• A. Li, L. Wang, and D. Wu, Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product [Li2010SimultaneousRA]

The following picture describes the Robot-World/Hand-Eye calibration problem where the transformations between a robot and a world frame and between a robot gripper ("hand") and a camera ("eye") mounted at the robot end-effector have to be estimated.

The calibration procedure is the following:

• a static calibration pattern is used to estimate the transformation between the target frame and the camera frame
• the robot gripper is moved in order to acquire several poses
• for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for instance the robot kinematics

  \[ \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{g}\textrm{R}_b & _{}^{g}\textrm{t}_b \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} \]

• for each pose, the homogeneous transformation between the calibration target frame (the world frame) and the camera frame is recorded using for instance a pose estimation method (PnP) from 2D-3D point correspondences

  \[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_w & _{}^{c}\textrm{t}_w \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_w\\ Y_w\\ Z_w\\ 1 \end{bmatrix} \]

The Robot-World/Hand-Eye calibration procedure returns the following homogeneous transformations

\[ \begin{bmatrix} X_w\\ Y_w\\ Z_w\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{w}\textrm{R}_b & _{}^{w}\textrm{t}_b \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} \]

\[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_g & _{}^{c}\textrm{t}_g \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} \]

This problem is also known as solving the \(\mathbf{A}\mathbf{X}=\mathbf{Z}\mathbf{B}\) equation, with:

• \(\mathbf{A} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_w\)
• \(\mathbf{X} \Leftrightarrow \hspace{0.1em} _{}^{w}\textrm{T}_b\)
• \(\mathbf{Z} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_g\)
• \(\mathbf{B} \Leftrightarrow \hspace{0.1em} _{}^{g}\textrm{T}_b\)

Note

At least 3 measurements are required (input vectors size must be greater or equal to 3).

calibrateRobotWorldHandEye() [2/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Computes Robot-World/Hand-Eye calibration: \(_{}^{w}\textrm{T}_b\) and \(_{}^{c}\textrm{T}_g\).

The function performs the Robot-World/Hand-Eye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions):

• M. Shah, Solving the robot-world/hand-eye calibration problem using the kronecker product [Shah2013SolvingTR]

Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented method:

• A. Li, L. Wang, and D. Wu, Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product [Li2010SimultaneousRA]

The following picture describes the Robot-World/Hand-Eye calibration problem where the transformations between a robot and a world frame and between a robot gripper ("hand") and a camera ("eye") mounted at the robot end-effector have to be estimated.

The calibration procedure is the following:

• a static calibration pattern is used to estimate the transformation between the target frame and the camera frame
• the robot gripper is moved in order to acquire several poses
• for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for instance the robot kinematics

  \[ \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{g}\textrm{R}_b & _{}^{g}\textrm{t}_b \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} \]

• for each pose, the homogeneous transformation between the calibration target frame (the world frame) and the camera frame is recorded using for instance a pose estimation method (PnP) from 2D-3D point correspondences

  \[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_w & _{}^{c}\textrm{t}_w \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_w\\ Y_w\\ Z_w\\ 1 \end{bmatrix} \]

The Robot-World/Hand-Eye calibration procedure returns the following homogeneous transformations

\[ \begin{bmatrix} X_w\\ Y_w\\ Z_w\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{w}\textrm{R}_b & _{}^{w}\textrm{t}_b \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} \]

\[ \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_g & _{}^{c}\textrm{t}_g \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} \]

This problem is also known as solving the \(\mathbf{A}\mathbf{X}=\mathbf{Z}\mathbf{B}\) equation, with:

• \(\mathbf{A} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_w\)
• \(\mathbf{X} \Leftrightarrow \hspace{0.1em} _{}^{w}\textrm{T}_b\)
• \(\mathbf{Z} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_g\)
• \(\mathbf{B} \Leftrightarrow \hspace{0.1em} _{}^{g}\textrm{T}_b\)

Note

At least 3 measurements are required (input vectors size must be greater or equal to 3).

calibrationMatrixValues() [1/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.calibrationMatrixValues ( Mat cameraMatrix, in Vec2d imageSize, double apertureWidth, double apertureHeight, double[] fovx, double[] fovy, double[] focalLength, out Vec2d principalPoint, double[] aspectRatio )

static

Computes useful camera characteristics from the camera intrinsic matrix.

Parameters

cameraMatrix	Input camera intrinsic matrix that can be estimated by calibrateCamera or stereoCalibrate .
imageSize	Input image size in pixels.
apertureWidth	Physical width in mm of the sensor.
apertureHeight	Physical height in mm of the sensor.
fovx	Output field of view in degrees along the horizontal sensor axis.
fovy	Output field of view in degrees along the vertical sensor axis.
focalLength	Focal length of the lens in mm.
principalPoint	Principal point in mm.
aspectRatio	\(f_y/f_x\)

The function computes various useful camera characteristics from the previously estimated camera matrix.

Note

Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for the chessboard pitch (it can thus be any value).

calibrationMatrixValues() [2/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.calibrationMatrixValues ( Mat cameraMatrix, in(double width, double height) imageSize, double apertureWidth, double apertureHeight, double[] fovx, double[] fovy, double[] focalLength, out(double x, double y) principalPoint, double[] aspectRatio )

static

Computes useful camera characteristics from the camera intrinsic matrix.

Parameters

cameraMatrix	Input camera intrinsic matrix that can be estimated by calibrateCamera or stereoCalibrate .
imageSize	Input image size in pixels.
apertureWidth	Physical width in mm of the sensor.
apertureHeight	Physical height in mm of the sensor.
fovx	Output field of view in degrees along the horizontal sensor axis.
fovy	Output field of view in degrees along the vertical sensor axis.
focalLength	Focal length of the lens in mm.
principalPoint	Principal point in mm.
aspectRatio	\(f_y/f_x\)

The function computes various useful camera characteristics from the previously estimated camera matrix.

Note

Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for the chessboard pitch (it can thus be any value).

calibrationMatrixValues() [3/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.calibrationMatrixValues ( Mat cameraMatrix, Size imageSize, double apertureWidth, double apertureHeight, double[] fovx, double[] fovy, double[] focalLength, Point principalPoint, double[] aspectRatio )

static

Computes useful camera characteristics from the camera intrinsic matrix.

Parameters

cameraMatrix	Input camera intrinsic matrix that can be estimated by calibrateCamera or stereoCalibrate .
imageSize	Input image size in pixels.
apertureWidth	Physical width in mm of the sensor.
apertureHeight	Physical height in mm of the sensor.
fovx	Output field of view in degrees along the horizontal sensor axis.
fovy	Output field of view in degrees along the vertical sensor axis.
focalLength	Focal length of the lens in mm.
principalPoint	Principal point in mm.
aspectRatio	\(f_y/f_x\)

The function computes various useful camera characteristics from the previously estimated camera matrix.

Note

Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for the chessboard pitch (it can thus be any value).

checkChessboard() [1/3]

static bool OpenCVForUnity.Calib3dModule.Calib3d.checkChessboard ( Mat img, in Vec2d size )

static

checkChessboard() [2/3]

static bool OpenCVForUnity.Calib3dModule.Calib3d.checkChessboard ( Mat img, in(double width, double height) size )

static

checkChessboard() [3/3]

static bool OpenCVForUnity.Calib3dModule.Calib3d.checkChessboard ( Mat img, Size size )

static

composeRT() [1/9]

static void OpenCVForUnity.Calib3dModule.Calib3d.composeRT ( Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3 )

static

Combines two rotation-and-shift transformations.

Parameters

rvec1	First rotation vector.
tvec1	First translation vector.
rvec2	Second rotation vector.
tvec2	Second translation vector.
rvec3	Output rotation vector of the superposition.
tvec3	Output translation vector of the superposition.
dr3dr1	Optional output derivative of rvec3 with regard to rvec1
dr3dt1	Optional output derivative of rvec3 with regard to tvec1
dr3dr2	Optional output derivative of rvec3 with regard to rvec2
dr3dt2	Optional output derivative of rvec3 with regard to tvec2
dt3dr1	Optional output derivative of tvec3 with regard to rvec1
dt3dt1	Optional output derivative of tvec3 with regard to tvec1
dt3dr2	Optional output derivative of tvec3 with regard to rvec2
dt3dt2	Optional output derivative of tvec3 with regard to tvec2

The functions compute:

\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]

where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.

Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.

composeRT() [2/9]

static void OpenCVForUnity.Calib3dModule.Calib3d.composeRT ( Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1 )

static

Combines two rotation-and-shift transformations.

Parameters

rvec1	First rotation vector.
tvec1	First translation vector.
rvec2	Second rotation vector.
tvec2	Second translation vector.
rvec3	Output rotation vector of the superposition.
tvec3	Output translation vector of the superposition.
dr3dr1	Optional output derivative of rvec3 with regard to rvec1
dr3dt1	Optional output derivative of rvec3 with regard to tvec1
dr3dr2	Optional output derivative of rvec3 with regard to rvec2
dr3dt2	Optional output derivative of rvec3 with regard to tvec2
dt3dr1	Optional output derivative of tvec3 with regard to rvec1
dt3dt1	Optional output derivative of tvec3 with regard to tvec1
dt3dr2	Optional output derivative of tvec3 with regard to rvec2
dt3dt2	Optional output derivative of tvec3 with regard to tvec2

The functions compute:

\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]

where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.

Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.

composeRT() [3/9]

static void OpenCVForUnity.Calib3dModule.Calib3d.composeRT ( Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1 )

static

Combines two rotation-and-shift transformations.

Parameters

rvec1	First rotation vector.
tvec1	First translation vector.
rvec2	Second rotation vector.
tvec2	Second translation vector.
rvec3	Output rotation vector of the superposition.
tvec3	Output translation vector of the superposition.
dr3dr1	Optional output derivative of rvec3 with regard to rvec1
dr3dt1	Optional output derivative of rvec3 with regard to tvec1
dr3dr2	Optional output derivative of rvec3 with regard to rvec2
dr3dt2	Optional output derivative of rvec3 with regard to tvec2
dt3dr1	Optional output derivative of tvec3 with regard to rvec1
dt3dt1	Optional output derivative of tvec3 with regard to tvec1
dt3dr2	Optional output derivative of tvec3 with regard to rvec2
dt3dt2	Optional output derivative of tvec3 with regard to tvec2

The functions compute:

\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]

where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.

Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.

composeRT() [4/9]

static void OpenCVForUnity.Calib3dModule.Calib3d.composeRT ( Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2 )

static

Combines two rotation-and-shift transformations.

Parameters

rvec1	First rotation vector.
tvec1	First translation vector.
rvec2	Second rotation vector.
tvec2	Second translation vector.
rvec3	Output rotation vector of the superposition.
tvec3	Output translation vector of the superposition.
dr3dr1	Optional output derivative of rvec3 with regard to rvec1
dr3dt1	Optional output derivative of rvec3 with regard to tvec1
dr3dr2	Optional output derivative of rvec3 with regard to rvec2
dr3dt2	Optional output derivative of rvec3 with regard to tvec2
dt3dr1	Optional output derivative of tvec3 with regard to rvec1
dt3dt1	Optional output derivative of tvec3 with regard to tvec1
dt3dr2	Optional output derivative of tvec3 with regard to rvec2
dt3dt2	Optional output derivative of tvec3 with regard to tvec2

The functions compute:

\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]

where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.

Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.

composeRT() [5/9]

static void OpenCVForUnity.Calib3dModule.Calib3d.composeRT ( Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2 )

static

Combines two rotation-and-shift transformations.

Parameters

rvec1	First rotation vector.
tvec1	First translation vector.
rvec2	Second rotation vector.
tvec2	Second translation vector.
rvec3	Output rotation vector of the superposition.
tvec3	Output translation vector of the superposition.
dr3dr1	Optional output derivative of rvec3 with regard to rvec1
dr3dt1	Optional output derivative of rvec3 with regard to tvec1
dr3dr2	Optional output derivative of rvec3 with regard to rvec2
dr3dt2	Optional output derivative of rvec3 with regard to tvec2
dt3dr1	Optional output derivative of tvec3 with regard to rvec1
dt3dt1	Optional output derivative of tvec3 with regard to tvec1
dt3dr2	Optional output derivative of tvec3 with regard to rvec2
dt3dt2	Optional output derivative of tvec3 with regard to tvec2

The functions compute:

\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]

where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.

Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.

composeRT() [6/9]

static void OpenCVForUnity.Calib3dModule.Calib3d.composeRT ( Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1 )

static

Combines two rotation-and-shift transformations.

Parameters

rvec1	First rotation vector.
tvec1	First translation vector.
rvec2	Second rotation vector.
tvec2	Second translation vector.
rvec3	Output rotation vector of the superposition.
tvec3	Output translation vector of the superposition.
dr3dr1	Optional output derivative of rvec3 with regard to rvec1
dr3dt1	Optional output derivative of rvec3 with regard to tvec1
dr3dr2	Optional output derivative of rvec3 with regard to rvec2
dr3dt2	Optional output derivative of rvec3 with regard to tvec2
dt3dr1	Optional output derivative of tvec3 with regard to rvec1
dt3dt1	Optional output derivative of tvec3 with regard to tvec1
dt3dr2	Optional output derivative of tvec3 with regard to rvec2
dt3dt2	Optional output derivative of tvec3 with regard to tvec2

The functions compute:

\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]

where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.

Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.

composeRT() [7/9]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Combines two rotation-and-shift transformations.

Parameters

rvec1	First rotation vector.
tvec1	First translation vector.
rvec2	Second rotation vector.
tvec2	Second translation vector.
rvec3	Output rotation vector of the superposition.
tvec3	Output translation vector of the superposition.
dr3dr1	Optional output derivative of rvec3 with regard to rvec1
dr3dt1	Optional output derivative of rvec3 with regard to tvec1
dr3dr2	Optional output derivative of rvec3 with regard to rvec2
dr3dt2	Optional output derivative of rvec3 with regard to tvec2
dt3dr1	Optional output derivative of tvec3 with regard to rvec1
dt3dt1	Optional output derivative of tvec3 with regard to tvec1
dt3dr2	Optional output derivative of tvec3 with regard to rvec2
dt3dt2	Optional output derivative of tvec3 with regard to tvec2

The functions compute:

\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]

where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.

Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.

composeRT() [8/9]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Combines two rotation-and-shift transformations.

Parameters

rvec1	First rotation vector.
tvec1	First translation vector.
rvec2	Second rotation vector.
tvec2	Second translation vector.
rvec3	Output rotation vector of the superposition.
tvec3	Output translation vector of the superposition.
dr3dr1	Optional output derivative of rvec3 with regard to rvec1
dr3dt1	Optional output derivative of rvec3 with regard to tvec1
dr3dr2	Optional output derivative of rvec3 with regard to rvec2
dr3dt2	Optional output derivative of rvec3 with regard to tvec2
dt3dr1	Optional output derivative of tvec3 with regard to rvec1
dt3dt1	Optional output derivative of tvec3 with regard to tvec1
dt3dr2	Optional output derivative of tvec3 with regard to rvec2
dt3dt2	Optional output derivative of tvec3 with regard to tvec2

The functions compute:

\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]

where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.

Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.

composeRT() [9/9]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Combines two rotation-and-shift transformations.

Parameters

rvec1	First rotation vector.
tvec1	First translation vector.
rvec2	Second rotation vector.
tvec2	Second translation vector.
rvec3	Output rotation vector of the superposition.
tvec3	Output translation vector of the superposition.
dr3dr1	Optional output derivative of rvec3 with regard to rvec1
dr3dt1	Optional output derivative of rvec3 with regard to tvec1
dr3dr2	Optional output derivative of rvec3 with regard to rvec2
dr3dt2	Optional output derivative of rvec3 with regard to tvec2
dt3dr1	Optional output derivative of tvec3 with regard to rvec1
dt3dt1	Optional output derivative of tvec3 with regard to tvec1
dt3dr2	Optional output derivative of tvec3 with regard to rvec2
dt3dt2	Optional output derivative of tvec3 with regard to tvec2

The functions compute:

\[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\]

where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See Rodrigues for details.

Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.

computeCorrespondEpilines()

static void OpenCVForUnity.Calib3dModule.Calib3d.computeCorrespondEpilines ( Mat points, int whichImage, Mat F, Mat lines )

static

For points in an image of a stereo pair, computes the corresponding epilines in the other image.

Parameters

points	Input points. \(N \times 1\) or \(1 \times N\) matrix of type CV_32FC2 or vector<Point2f> .
whichImage	Index of the image (1 or 2) that contains the points .
F	Fundamental matrix that can be estimated using findFundamentalMat or stereoRectify .
lines	Output vector of the epipolar lines corresponding to the points in the other image. Each line \(ax + by + c=0\) is encoded by 3 numbers \((a, b, c)\) .

For every point in one of the two images of a stereo pair, the function finds the equation of the corresponding epipolar line in the other image.

From the fundamental matrix definition (see findFundamentalMat ), line \(l^{(2)}_i\) in the second image for the point \(p^{(1)}_i\) in the first image (when whichImage=1 ) is computed as:

\[l^{(2)}_i = F p^{(1)}_i\]

And vice versa, when whichImage=2, \(l^{(1)}_i\) is computed from \(p^{(2)}_i\) as:

\[l^{(1)}_i = F^T p^{(2)}_i\]

Line coefficients are defined up to a scale. They are normalized so that \(a_i^2+b_i^2=1\) .

convertPointsFromHomogeneous()

static void OpenCVForUnity.Calib3dModule.Calib3d.convertPointsFromHomogeneous ( Mat src, Mat dst )

static

Converts points from homogeneous to Euclidean space.

Parameters

src	Input vector of N-dimensional points.
dst	Output vector of N-1-dimensional points.

The function converts points homogeneous to Euclidean space using perspective projection. That is, each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the output point coordinates will be (0,0,0,...).

convertPointsToHomogeneous()

static void OpenCVForUnity.Calib3dModule.Calib3d.convertPointsToHomogeneous ( Mat src, Mat dst )

static

Converts points from Euclidean to homogeneous space.

Parameters

src	Input vector of N-dimensional points.
dst	Output vector of N+1-dimensional points.

The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1).

correctMatches()

static void OpenCVForUnity.Calib3dModule.Calib3d.correctMatches ( Mat F, Mat points1, Mat points2, Mat newPoints1, Mat newPoints2 )

static

Refines coordinates of corresponding points.

Parameters

F	3x3 fundamental matrix.
points1	1xN array containing the first set of points.
points2	1xN array containing the second set of points.
newPoints1	The optimized points1.
newPoints2	The optimized points2.

The function implements the Optimal Triangulation Method (see Multiple View Geometry [HartleyZ00] for details). For each given point correspondence points1[i] <-> points2[i], and a fundamental matrix F, it computes the corrected correspondences newPoints1[i] <-> newPoints2[i] that minimize the geometric error \(d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\) (where \(d(a,b)\) is the geometric distance between points \(a\) and \(b\) ) subject to the epipolar constraint \(newPoints2^T \cdot F \cdot newPoints1 = 0\) .

decomposeEssentialMat()

static void OpenCVForUnity.Calib3dModule.Calib3d.decomposeEssentialMat ( Mat E, Mat R1, Mat R2, Mat t )

static

Decompose an essential matrix to possible rotations and translation.

Parameters

E	The input essential matrix.
R1	One possible rotation matrix.
R2	Another possible rotation matrix.
t	One possible translation.

This function decomposes the essential matrix E using svd decomposition [HartleyZ00]. In general, four possible poses exist for the decomposition of E. They are \([R_1, t]\), \([R_1, -t]\), \([R_2, t]\), \([R_2, -t]\).

If E gives the epipolar constraint \([p_2; 1]^T A^{-T} E A^{-1} [p_1; 1] = 0\) between the image points \(p_1\) in the first image and \(p_2\) in second image, then any of the tuples \([R_1, t]\), \([R_1, -t]\), \([R_2, t]\), \([R_2, -t]\) is a change of basis from the first camera's coordinate system to the second camera's coordinate system. However, by decomposing E, one can only get the direction of the translation. For this reason, the translation t is returned with unit length.

decomposeHomographyMat()

static int OpenCVForUnity.Calib3dModule.Calib3d.decomposeHomographyMat ( Mat H, Mat K, List< Mat > rotations, List< Mat > translations, List< Mat > normals )

static

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

Parameters

H	The input homography matrix between two images.
K	The input camera intrinsic matrix.
rotations	Array of rotation matrices.
translations	Array of translation matrices.
normals	Array of plane normal matrices.

This function extracts relative camera motion between two views of a planar object and returns up to four mathematical solution tuples of rotation, translation, and plane normal. The decomposition of the homography matrix H is described in detail in [Malis2007].

If the homography H, induced by the plane, gives the constraint

\[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\]

on the source image points \(p_i\) and the destination image points \(p'_i\), then the tuple of rotations[k] and translations[k] is a change of basis from the source camera's coordinate system to the destination camera's coordinate system. However, by decomposing H, one can only get the translation normalized by the (typically unknown) depth of the scene, i.e. its direction but with normalized length.

If point correspondences are available, at least two solutions may further be invalidated, by applying positive depth constraint, i.e. all points must be in front of the camera.

decomposeProjectionMatrix() [1/5]

static void OpenCVForUnity.Calib3dModule.Calib3d.decomposeProjectionMatrix ( Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect )

static

Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.

Parameters

projMatrix	3x4 input projection matrix P.
cameraMatrix	Output 3x3 camera intrinsic matrix \(\cameramatrix{A}\).
rotMatrix	Output 3x3 external rotation matrix R.
transVect	Output 4x1 translation vector T.
rotMatrixX	Optional 3x3 rotation matrix around x-axis.
rotMatrixY	Optional 3x3 rotation matrix around y-axis.
rotMatrixZ	Optional 3x3 rotation matrix around z-axis.
eulerAngles	Optional three-element vector containing three Euler angles of rotation in degrees.

The function computes a decomposition of a projection matrix into a calibration and a rotation matrix and the position of a camera.

It optionally returns three rotation matrices, one for each axis, and three Euler angles that could be used in OpenGL. Note, there is always more than one sequence of rotations about the three principal axes that results in the same orientation of an object, e.g. see [Slabaugh] . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.

The function is based on RQDecomp3x3 .

decomposeProjectionMatrix() [2/5]

static void OpenCVForUnity.Calib3dModule.Calib3d.decomposeProjectionMatrix ( Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX )

static

Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.

Parameters

projMatrix	3x4 input projection matrix P.
cameraMatrix	Output 3x3 camera intrinsic matrix \(\cameramatrix{A}\).
rotMatrix	Output 3x3 external rotation matrix R.
transVect	Output 4x1 translation vector T.
rotMatrixX	Optional 3x3 rotation matrix around x-axis.
rotMatrixY	Optional 3x3 rotation matrix around y-axis.
rotMatrixZ	Optional 3x3 rotation matrix around z-axis.
eulerAngles	Optional three-element vector containing three Euler angles of rotation in degrees.

The function computes a decomposition of a projection matrix into a calibration and a rotation matrix and the position of a camera.

It optionally returns three rotation matrices, one for each axis, and three Euler angles that could be used in OpenGL. Note, there is always more than one sequence of rotations about the three principal axes that results in the same orientation of an object, e.g. see [Slabaugh] . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.

The function is based on RQDecomp3x3 .

decomposeProjectionMatrix() [3/5]

static void OpenCVForUnity.Calib3dModule.Calib3d.decomposeProjectionMatrix ( Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX, Mat rotMatrixY )

static

Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.

Parameters

projMatrix	3x4 input projection matrix P.
cameraMatrix	Output 3x3 camera intrinsic matrix \(\cameramatrix{A}\).
rotMatrix	Output 3x3 external rotation matrix R.
transVect	Output 4x1 translation vector T.
rotMatrixX	Optional 3x3 rotation matrix around x-axis.
rotMatrixY	Optional 3x3 rotation matrix around y-axis.
rotMatrixZ	Optional 3x3 rotation matrix around z-axis.
eulerAngles	Optional three-element vector containing three Euler angles of rotation in degrees.

The function computes a decomposition of a projection matrix into a calibration and a rotation matrix and the position of a camera.

It optionally returns three rotation matrices, one for each axis, and three Euler angles that could be used in OpenGL. Note, there is always more than one sequence of rotations about the three principal axes that results in the same orientation of an object, e.g. see [Slabaugh] . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.

The function is based on RQDecomp3x3 .

decomposeProjectionMatrix() [4/5]

static void OpenCVForUnity.Calib3dModule.Calib3d.decomposeProjectionMatrix ( Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX, Mat rotMatrixY, Mat rotMatrixZ )

static

Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.

Parameters

projMatrix	3x4 input projection matrix P.
cameraMatrix	Output 3x3 camera intrinsic matrix \(\cameramatrix{A}\).
rotMatrix	Output 3x3 external rotation matrix R.
transVect	Output 4x1 translation vector T.
rotMatrixX	Optional 3x3 rotation matrix around x-axis.
rotMatrixY	Optional 3x3 rotation matrix around y-axis.
rotMatrixZ	Optional 3x3 rotation matrix around z-axis.
eulerAngles	Optional three-element vector containing three Euler angles of rotation in degrees.

The function computes a decomposition of a projection matrix into a calibration and a rotation matrix and the position of a camera.

It optionally returns three rotation matrices, one for each axis, and three Euler angles that could be used in OpenGL. Note, there is always more than one sequence of rotations about the three principal axes that results in the same orientation of an object, e.g. see [Slabaugh] . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.

The function is based on RQDecomp3x3 .

decomposeProjectionMatrix() [5/5]

static void OpenCVForUnity.Calib3dModule.Calib3d.decomposeProjectionMatrix ( Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX, Mat rotMatrixY, Mat rotMatrixZ, Mat eulerAngles )

static

Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.

Parameters

projMatrix	3x4 input projection matrix P.
cameraMatrix	Output 3x3 camera intrinsic matrix \(\cameramatrix{A}\).
rotMatrix	Output 3x3 external rotation matrix R.
transVect	Output 4x1 translation vector T.
rotMatrixX	Optional 3x3 rotation matrix around x-axis.
rotMatrixY	Optional 3x3 rotation matrix around y-axis.
rotMatrixZ	Optional 3x3 rotation matrix around z-axis.
eulerAngles	Optional three-element vector containing three Euler angles of rotation in degrees.

The function computes a decomposition of a projection matrix into a calibration and a rotation matrix and the position of a camera.

It optionally returns three rotation matrices, one for each axis, and three Euler angles that could be used in OpenGL. Note, there is always more than one sequence of rotations about the three principal axes that results in the same orientation of an object, e.g. see [Slabaugh] . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.

The function is based on RQDecomp3x3 .

drawChessboardCorners() [1/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.drawChessboardCorners ( Mat image, in Vec2d patternSize, MatOfPoint2f corners, bool patternWasFound )

static

Renders the detected chessboard corners.

Parameters

image	Destination image. It must be an 8-bit color image.
patternSize	Number of inner corners per a chessboard row and column (patternSize = cv::Size(points_per_row,points_per_column)).
corners	Array of detected corners, the output of findChessboardCorners.
patternWasFound	Parameter indicating whether the complete board was found or not. The return value of findChessboardCorners should be passed here.

The function draws individual chessboard corners detected either as red circles if the board was not found, or as colored corners connected with lines if the board was found.

drawChessboardCorners() [2/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.drawChessboardCorners ( Mat image, in(double width, double height) patternSize, MatOfPoint2f corners, bool patternWasFound )

static

Renders the detected chessboard corners.

Parameters

image	Destination image. It must be an 8-bit color image.
patternSize	Number of inner corners per a chessboard row and column (patternSize = cv::Size(points_per_row,points_per_column)).
corners	Array of detected corners, the output of findChessboardCorners.
patternWasFound	Parameter indicating whether the complete board was found or not. The return value of findChessboardCorners should be passed here.

The function draws individual chessboard corners detected either as red circles if the board was not found, or as colored corners connected with lines if the board was found.

drawChessboardCorners() [3/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.drawChessboardCorners ( Mat image, Size patternSize, MatOfPoint2f corners, bool patternWasFound )

static

Renders the detected chessboard corners.

Parameters

image	Destination image. It must be an 8-bit color image.
patternSize	Number of inner corners per a chessboard row and column (patternSize = cv::Size(points_per_row,points_per_column)).
corners	Array of detected corners, the output of findChessboardCorners.
patternWasFound	Parameter indicating whether the complete board was found or not. The return value of findChessboardCorners should be passed here.

The function draws individual chessboard corners detected either as red circles if the board was not found, or as colored corners connected with lines if the board was found.

drawFrameAxes() [1/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.drawFrameAxes ( Mat image, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, float length )

static

Draw axes of the world/object coordinate system from pose estimation.

See also

solvePnP

Parameters

image	Input/output image. It must have 1 or 3 channels. The number of channels is not altered.
cameraMatrix	Input 3x3 floating-point matrix of camera intrinsic parameters. \(\cameramatrix{A}\)
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is empty, the zero distortion coefficients are assumed.
rvec	Rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Translation vector.
length	Length of the painted axes in the same unit than tvec (usually in meters).
thickness	Line thickness of the painted axes.

This function draws the axes of the world/object coordinate system w.r.t. to the camera frame. OX is drawn in red, OY in green and OZ in blue.

drawFrameAxes() [2/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.drawFrameAxes ( Mat image, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, float length, int thickness )

static

Draw axes of the world/object coordinate system from pose estimation.

See also

solvePnP

Parameters

image	Input/output image. It must have 1 or 3 channels. The number of channels is not altered.
cameraMatrix	Input 3x3 floating-point matrix of camera intrinsic parameters. \(\cameramatrix{A}\)
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is empty, the zero distortion coefficients are assumed.
rvec	Rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Translation vector.
length	Length of the painted axes in the same unit than tvec (usually in meters).
thickness	Line thickness of the painted axes.

This function draws the axes of the world/object coordinate system w.r.t. to the camera frame. OX is drawn in red, OY in green and OZ in blue.

estimateAffine2D() [1/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffine2D ( Mat from, Mat to )

static

Computes an optimal affine transformation between two 2D point sets.

It computes

\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]

Parameters

from	First input 2D point set containing \((X,Y)\).
to	Second input 2D point set containing \((x,y)\).
inliers	Output vector indicating which points are inliers (1-inlier, 0-outlier).
method	Robust method used to compute transformation. The following methods are possible: RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RANSAC is the default method.
ransacReprojThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC.
maxIters	The maximum number of robust method iterations.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters	Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.

Returns

Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation could not be estimated. The returned matrix has the following form:

\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]

The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Note

The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also

estimateAffinePartial2D, getAffineTransform

estimateAffine2D() [2/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffine2D ( Mat from, Mat to, Mat inliers )

static

Computes an optimal affine transformation between two 2D point sets.

It computes

\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]

Parameters

from	First input 2D point set containing \((X,Y)\).
to	Second input 2D point set containing \((x,y)\).
inliers	Output vector indicating which points are inliers (1-inlier, 0-outlier).
method	Robust method used to compute transformation. The following methods are possible: RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RANSAC is the default method.
ransacReprojThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC.
maxIters	The maximum number of robust method iterations.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters	Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.

Returns

Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation could not be estimated. The returned matrix has the following form:

\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]

The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Note

The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also

estimateAffinePartial2D, getAffineTransform

estimateAffine2D() [3/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffine2D ( Mat from, Mat to, Mat inliers, int method )

static

Computes an optimal affine transformation between two 2D point sets.

It computes

\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]

Parameters

from	First input 2D point set containing \((X,Y)\).
to	Second input 2D point set containing \((x,y)\).
inliers	Output vector indicating which points are inliers (1-inlier, 0-outlier).
method	Robust method used to compute transformation. The following methods are possible: RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RANSAC is the default method.
ransacReprojThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC.
maxIters	The maximum number of robust method iterations.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters	Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.

Returns

Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation could not be estimated. The returned matrix has the following form:

\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]

The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Note

The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also

estimateAffinePartial2D, getAffineTransform

estimateAffine2D() [4/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffine2D ( Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold )

static

Computes an optimal affine transformation between two 2D point sets.

It computes

\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]

Parameters

from	First input 2D point set containing \((X,Y)\).
to	Second input 2D point set containing \((x,y)\).
inliers	Output vector indicating which points are inliers (1-inlier, 0-outlier).
method	Robust method used to compute transformation. The following methods are possible: RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RANSAC is the default method.
ransacReprojThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC.
maxIters	The maximum number of robust method iterations.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters	Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.

Returns

Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation could not be estimated. The returned matrix has the following form:

\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]

The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Note

The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also

estimateAffinePartial2D, getAffineTransform

estimateAffine2D() [5/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffine2D ( Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters )

static

Computes an optimal affine transformation between two 2D point sets.

It computes

\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]

Parameters

from	First input 2D point set containing \((X,Y)\).
to	Second input 2D point set containing \((x,y)\).
inliers	Output vector indicating which points are inliers (1-inlier, 0-outlier).
method	Robust method used to compute transformation. The following methods are possible: RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RANSAC is the default method.
ransacReprojThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC.
maxIters	The maximum number of robust method iterations.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters	Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.

Returns

Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation could not be estimated. The returned matrix has the following form:

\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]

The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Note

The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also

estimateAffinePartial2D, getAffineTransform

estimateAffine2D() [6/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffine2D ( Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence )

static

Computes an optimal affine transformation between two 2D point sets.

It computes

\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]

Parameters

from	First input 2D point set containing \((X,Y)\).
to	Second input 2D point set containing \((x,y)\).
inliers	Output vector indicating which points are inliers (1-inlier, 0-outlier).
method	Robust method used to compute transformation. The following methods are possible: RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RANSAC is the default method.
ransacReprojThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC.
maxIters	The maximum number of robust method iterations.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters	Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.

Returns

Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation could not be estimated. The returned matrix has the following form:

\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]

The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Note

The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also

estimateAffinePartial2D, getAffineTransform

estimateAffine2D() [7/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffine2D ( Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters )

static

Computes an optimal affine transformation between two 2D point sets.

It computes

\[ \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \]

Parameters

from	First input 2D point set containing \((X,Y)\).
to	Second input 2D point set containing \((x,y)\).
inliers	Output vector indicating which points are inliers (1-inlier, 0-outlier).
method	Robust method used to compute transformation. The following methods are possible: RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RANSAC is the default method.
ransacReprojThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC.
maxIters	The maximum number of robust method iterations.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters	Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.

Returns

Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation could not be estimated. The returned matrix has the following form:

\[ \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \]

The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Note

The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also

estimateAffinePartial2D, getAffineTransform

estimateAffine2D() [8/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffine2D ( Mat pts1, Mat pts2, Mat inliers, UsacParams _params )

static

estimateAffine3D() [1/6]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffine3D ( Mat src, Mat dst )

static

Computes an optimal affine transformation between two 3D point sets.

It computes \(R,s,t\) minimizing \(\sum{i} dst_i - c \cdot R \cdot src_i \) where \(R\) is a 3x3 rotation matrix, \(t\) is a 3x1 translation vector and \(s\) is a scalar size value. This is an implementation of the algorithm by Umeyama [umeyama1991least] . The estimated affine transform has a homogeneous scale which is a subclass of affine transformations with 7 degrees of freedom. The paired point sets need to comprise at least 3 points each.

Parameters

src	First input 3D point set.
dst	Second input 3D point set.
scale	If null is passed, the scale parameter c will be assumed to be 1.0. Else the pointed-to variable will be set to the optimal scale.
force_rotation	If true, the returned rotation will never be a reflection. This might be unwanted, e.g. when optimizing a transform between a right- and a left-handed coordinate system.

Returns

3D affine transformation matrix \(3 \times 4\) of the form

\[T = \begin{bmatrix} R & t\\ \end{bmatrix} \]

estimateAffine3D() [2/6]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffine3D ( Mat src, Mat dst, double[] scale )

static

Computes an optimal affine transformation between two 3D point sets.

It computes \(R,s,t\) minimizing \(\sum{i} dst_i - c \cdot R \cdot src_i \) where \(R\) is a 3x3 rotation matrix, \(t\) is a 3x1 translation vector and \(s\) is a scalar size value. This is an implementation of the algorithm by Umeyama [umeyama1991least] . The estimated affine transform has a homogeneous scale which is a subclass of affine transformations with 7 degrees of freedom. The paired point sets need to comprise at least 3 points each.

Parameters

src	First input 3D point set.
dst	Second input 3D point set.
scale	If null is passed, the scale parameter c will be assumed to be 1.0. Else the pointed-to variable will be set to the optimal scale.
force_rotation	If true, the returned rotation will never be a reflection. This might be unwanted, e.g. when optimizing a transform between a right- and a left-handed coordinate system.

Returns

3D affine transformation matrix \(3 \times 4\) of the form

\[T = \begin{bmatrix} R & t\\ \end{bmatrix} \]

estimateAffine3D() [3/6]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffine3D ( Mat src, Mat dst, double[] scale, bool force_rotation )

static

Computes an optimal affine transformation between two 3D point sets.

It computes \(R,s,t\) minimizing \(\sum{i} dst_i - c \cdot R \cdot src_i \) where \(R\) is a 3x3 rotation matrix, \(t\) is a 3x1 translation vector and \(s\) is a scalar size value. This is an implementation of the algorithm by Umeyama [umeyama1991least] . The estimated affine transform has a homogeneous scale which is a subclass of affine transformations with 7 degrees of freedom. The paired point sets need to comprise at least 3 points each.

Parameters

src	First input 3D point set.
dst	Second input 3D point set.
scale	If null is passed, the scale parameter c will be assumed to be 1.0. Else the pointed-to variable will be set to the optimal scale.
force_rotation	If true, the returned rotation will never be a reflection. This might be unwanted, e.g. when optimizing a transform between a right- and a left-handed coordinate system.

Returns

3D affine transformation matrix \(3 \times 4\) of the form

\[T = \begin{bmatrix} R & t\\ \end{bmatrix} \]

estimateAffine3D() [4/6]

static int OpenCVForUnity.Calib3dModule.Calib3d.estimateAffine3D ( Mat src, Mat dst, Mat _out, Mat inliers )

static

Computes an optimal affine transformation between two 3D point sets.

It computes

\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12} & a_{13}\\ a_{21} & a_{22} & a_{23}\\ a_{31} & a_{32} & a_{33}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]

Parameters

src	First input 3D point set containing \((X,Y,Z)\).
dst	Second input 3D point set containing \((x,y,z)\).
out	Output 3D affine transformation matrix \(3 \times 4\) of the form \[ \begin{bmatrix} a_{11} & a_{12} & a_{13} & b_1\\ a_{21} & a_{22} & a_{23} & b_2\\ a_{31} & a_{32} & a_{33} & b_3\\ \end{bmatrix} \]
inliers	Output vector indicating which points are inliers (1-inlier, 0-outlier).
ransacThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.

The function estimates an optimal 3D affine transformation between two 3D point sets using the RANSAC algorithm.

estimateAffine3D() [5/6]

static int OpenCVForUnity.Calib3dModule.Calib3d.estimateAffine3D ( Mat src, Mat dst, Mat _out, Mat inliers, double ransacThreshold )

static

Computes an optimal affine transformation between two 3D point sets.

It computes

\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12} & a_{13}\\ a_{21} & a_{22} & a_{23}\\ a_{31} & a_{32} & a_{33}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]

Parameters

src	First input 3D point set containing \((X,Y,Z)\).
dst	Second input 3D point set containing \((x,y,z)\).
out	Output 3D affine transformation matrix \(3 \times 4\) of the form \[ \begin{bmatrix} a_{11} & a_{12} & a_{13} & b_1\\ a_{21} & a_{22} & a_{23} & b_2\\ a_{31} & a_{32} & a_{33} & b_3\\ \end{bmatrix} \]
inliers	Output vector indicating which points are inliers (1-inlier, 0-outlier).
ransacThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.

The function estimates an optimal 3D affine transformation between two 3D point sets using the RANSAC algorithm.

estimateAffine3D() [6/6]

static int OpenCVForUnity.Calib3dModule.Calib3d.estimateAffine3D ( Mat src, Mat dst, Mat _out, Mat inliers, double ransacThreshold, double confidence )

static

Computes an optimal affine transformation between two 3D point sets.

It computes

\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12} & a_{13}\\ a_{21} & a_{22} & a_{23}\\ a_{31} & a_{32} & a_{33}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]

Parameters

src	First input 3D point set containing \((X,Y,Z)\).
dst	Second input 3D point set containing \((x,y,z)\).
out	Output 3D affine transformation matrix \(3 \times 4\) of the form \[ \begin{bmatrix} a_{11} & a_{12} & a_{13} & b_1\\ a_{21} & a_{22} & a_{23} & b_2\\ a_{31} & a_{32} & a_{33} & b_3\\ \end{bmatrix} \]
inliers	Output vector indicating which points are inliers (1-inlier, 0-outlier).
ransacThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.

The function estimates an optimal 3D affine transformation between two 3D point sets using the RANSAC algorithm.

estimateAffinePartial2D() [1/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffinePartial2D ( Mat from, Mat to )

static

Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.

Parameters

from	First input 2D point set.
to	Second input 2D point set.
inliers	Output vector indicating which points are inliers.
method	Robust method used to compute transformation. The following methods are possible: RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RANSAC is the default method.
ransacReprojThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC.
maxIters	The maximum number of robust method iterations.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters	Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.

Returns

Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) or empty matrix if transformation could not be estimated.

The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Estimated transformation matrix is:

\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \]

Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.

Note

The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also

estimateAffine2D, getAffineTransform

estimateAffinePartial2D() [2/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffinePartial2D ( Mat from, Mat to, Mat inliers )

static

Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.

Parameters

from	First input 2D point set.
to	Second input 2D point set.
inliers	Output vector indicating which points are inliers.
method	Robust method used to compute transformation. The following methods are possible: RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RANSAC is the default method.
ransacReprojThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC.
maxIters	The maximum number of robust method iterations.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters	Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.

Returns

Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) or empty matrix if transformation could not be estimated.

The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Estimated transformation matrix is:

\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \]

Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.

Note

The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also

estimateAffine2D, getAffineTransform

estimateAffinePartial2D() [3/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffinePartial2D ( Mat from, Mat to, Mat inliers, int method )

static

Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.

Parameters

from	First input 2D point set.
to	Second input 2D point set.
inliers	Output vector indicating which points are inliers.
method	Robust method used to compute transformation. The following methods are possible: RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RANSAC is the default method.
ransacReprojThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC.
maxIters	The maximum number of robust method iterations.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters	Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.

Returns

Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) or empty matrix if transformation could not be estimated.

The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Estimated transformation matrix is:

\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \]

Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.

Note

The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also

estimateAffine2D, getAffineTransform

estimateAffinePartial2D() [4/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffinePartial2D ( Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold )

static

Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.

Parameters

from	First input 2D point set.
to	Second input 2D point set.
inliers	Output vector indicating which points are inliers.
method	Robust method used to compute transformation. The following methods are possible: RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RANSAC is the default method.
ransacReprojThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC.
maxIters	The maximum number of robust method iterations.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters	Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.

Returns

Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) or empty matrix if transformation could not be estimated.

The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Estimated transformation matrix is:

\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \]

Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.

Note

The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also

estimateAffine2D, getAffineTransform

estimateAffinePartial2D() [5/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffinePartial2D ( Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters )

static

Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.

Parameters

from	First input 2D point set.
to	Second input 2D point set.
inliers	Output vector indicating which points are inliers.
method	Robust method used to compute transformation. The following methods are possible: RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RANSAC is the default method.
ransacReprojThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC.
maxIters	The maximum number of robust method iterations.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters	Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.

Returns

Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) or empty matrix if transformation could not be estimated.

The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Estimated transformation matrix is:

\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \]

Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.

Note

The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also

estimateAffine2D, getAffineTransform

estimateAffinePartial2D() [6/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffinePartial2D ( Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence )

static

Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.

Parameters

from	First input 2D point set.
to	Second input 2D point set.
inliers	Output vector indicating which points are inliers.
method	Robust method used to compute transformation. The following methods are possible: RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RANSAC is the default method.
ransacReprojThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC.
maxIters	The maximum number of robust method iterations.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters	Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.

Returns

Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) or empty matrix if transformation could not be estimated.

The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Estimated transformation matrix is:

\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \]

Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.

Note

The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also

estimateAffine2D, getAffineTransform

estimateAffinePartial2D() [7/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.estimateAffinePartial2D ( Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters )

static

Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.

Parameters

from	First input 2D point set.
to	Second input 2D point set.
inliers	Output vector indicating which points are inliers.
method	Robust method used to compute transformation. The following methods are possible: RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RANSAC is the default method.
ransacReprojThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC.
maxIters	The maximum number of robust method iterations.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters	Maximum number of iterations of refining algorithm (Levenberg-Marquardt). Passing 0 will disable refining, so the output matrix will be output of robust method.

Returns

Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) or empty matrix if transformation could not be estimated.

The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Estimated transformation matrix is:

\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \]

Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.

Note

The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also

estimateAffine2D, getAffineTransform

estimateChessboardSharpness() [1/4]

static Scalar OpenCVForUnity.Calib3dModule.Calib3d.estimateChessboardSharpness ( Mat image, Size patternSize, Mat corners )

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.

Parameters

image	Gray image used to find chessboard corners
patternSize	Size of a found chessboard pattern
corners	Corners found by findChessboardCornersSB
rise_distance	Rise distance 0.8 means 10% ... 90% of the final signal strength
vertical	By default edge responses for horizontal lines are calculated
sharpness	Optional output array with a sharpness value for calculated edge responses (see description)

The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness)

Returns

Scalar(average sharpness, average min brightness, average max brightness,0)

estimateChessboardSharpness() [2/4]

static Scalar OpenCVForUnity.Calib3dModule.Calib3d.estimateChessboardSharpness ( Mat image, Size patternSize, Mat corners, float rise_distance )

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.

Parameters

image	Gray image used to find chessboard corners
patternSize	Size of a found chessboard pattern
corners	Corners found by findChessboardCornersSB
rise_distance	Rise distance 0.8 means 10% ... 90% of the final signal strength
vertical	By default edge responses for horizontal lines are calculated
sharpness	Optional output array with a sharpness value for calculated edge responses (see description)

The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness)

Returns

Scalar(average sharpness, average min brightness, average max brightness,0)

estimateChessboardSharpness() [3/4]

static Scalar OpenCVForUnity.Calib3dModule.Calib3d.estimateChessboardSharpness ( Mat image, Size patternSize, Mat corners, float rise_distance, bool vertical )

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.

Parameters

image	Gray image used to find chessboard corners
patternSize	Size of a found chessboard pattern
corners	Corners found by findChessboardCornersSB
rise_distance	Rise distance 0.8 means 10% ... 90% of the final signal strength
vertical	By default edge responses for horizontal lines are calculated
sharpness	Optional output array with a sharpness value for calculated edge responses (see description)

The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness)

Returns

Scalar(average sharpness, average min brightness, average max brightness,0)

estimateChessboardSharpness() [4/4]

static Scalar OpenCVForUnity.Calib3dModule.Calib3d.estimateChessboardSharpness ( Mat image, Size patternSize, Mat corners, float rise_distance, bool vertical, Mat sharpness )

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.

Parameters

image	Gray image used to find chessboard corners
patternSize	Size of a found chessboard pattern
corners	Corners found by findChessboardCornersSB
rise_distance	Rise distance 0.8 means 10% ... 90% of the final signal strength
vertical	By default edge responses for horizontal lines are calculated
sharpness	Optional output array with a sharpness value for calculated edge responses (see description)

The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness)

Returns

Scalar(average sharpness, average min brightness, average max brightness,0)

estimateChessboardSharpnessAsValueTuple() [1/4]

static double double double double v3 OpenCVForUnity.Calib3dModule.Calib3d.estimateChessboardSharpnessAsValueTuple ( Mat image, in(double width, double height) patternSize, Mat corners )

static

estimateChessboardSharpnessAsValueTuple() [2/4]

static double double double double v3 OpenCVForUnity.Calib3dModule.Calib3d.estimateChessboardSharpnessAsValueTuple ( Mat image, in(double width, double height) patternSize, Mat corners, float rise_distance )

static

estimateChessboardSharpnessAsValueTuple() [3/4]

static double double double double v3 OpenCVForUnity.Calib3dModule.Calib3d.estimateChessboardSharpnessAsValueTuple ( Mat image, in(double width, double height) patternSize, Mat corners, float rise_distance, bool vertical )

static

estimateChessboardSharpnessAsValueTuple() [4/4]

static double double double double v3 OpenCVForUnity.Calib3dModule.Calib3d.estimateChessboardSharpnessAsValueTuple ( Mat image, in(double width, double height) patternSize, Mat corners, float rise_distance, bool vertical, Mat sharpness )

static

estimateChessboardSharpnessAsVec4d() [1/4]

static Vec4d OpenCVForUnity.Calib3dModule.Calib3d.estimateChessboardSharpnessAsVec4d ( Mat image, in Vec2d patternSize, Mat corners )

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.

Parameters

image	Gray image used to find chessboard corners
patternSize	Size of a found chessboard pattern
corners	Corners found by findChessboardCornersSB
rise_distance	Rise distance 0.8 means 10% ... 90% of the final signal strength
vertical	By default edge responses for horizontal lines are calculated
sharpness	Optional output array with a sharpness value for calculated edge responses (see description)

The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness)

Returns

Scalar(average sharpness, average min brightness, average max brightness,0)

estimateChessboardSharpnessAsVec4d() [2/4]

static Vec4d OpenCVForUnity.Calib3dModule.Calib3d.estimateChessboardSharpnessAsVec4d ( Mat image, in Vec2d patternSize, Mat corners, float rise_distance )

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.

Parameters

image	Gray image used to find chessboard corners
patternSize	Size of a found chessboard pattern
corners	Corners found by findChessboardCornersSB
rise_distance	Rise distance 0.8 means 10% ... 90% of the final signal strength
vertical	By default edge responses for horizontal lines are calculated
sharpness	Optional output array with a sharpness value for calculated edge responses (see description)

The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness)

Returns

Scalar(average sharpness, average min brightness, average max brightness,0)

estimateChessboardSharpnessAsVec4d() [3/4]

static Vec4d OpenCVForUnity.Calib3dModule.Calib3d.estimateChessboardSharpnessAsVec4d ( Mat image, in Vec2d patternSize, Mat corners, float rise_distance, bool vertical )

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.

Parameters

image	Gray image used to find chessboard corners
patternSize	Size of a found chessboard pattern
corners	Corners found by findChessboardCornersSB
rise_distance	Rise distance 0.8 means 10% ... 90% of the final signal strength
vertical	By default edge responses for horizontal lines are calculated
sharpness	Optional output array with a sharpness value for calculated edge responses (see description)

The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness)

Returns

Scalar(average sharpness, average min brightness, average max brightness,0)

estimateChessboardSharpnessAsVec4d() [4/4]

static Vec4d OpenCVForUnity.Calib3dModule.Calib3d.estimateChessboardSharpnessAsVec4d ( Mat image, in Vec2d patternSize, Mat corners, float rise_distance, bool vertical, Mat sharpness )

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.

Parameters

image	Gray image used to find chessboard corners
patternSize	Size of a found chessboard pattern
corners	Corners found by findChessboardCornersSB
rise_distance	Rise distance 0.8 means 10% ... 90% of the final signal strength
vertical	By default edge responses for horizontal lines are calculated
sharpness	Optional output array with a sharpness value for calculated edge responses (see description)

The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness)

Returns

Scalar(average sharpness, average min brightness, average max brightness,0)

estimateTranslation3D() [1/3]

static int OpenCVForUnity.Calib3dModule.Calib3d.estimateTranslation3D ( Mat src, Mat dst, Mat _out, Mat inliers )

static

Computes an optimal translation between two 3D point sets.

It computes

\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]

Parameters

src	First input 3D point set containing \((X,Y,Z)\).
dst	Second input 3D point set containing \((x,y,z)\).
out	Output 3D translation vector \(3 \times 1\) of the form \[ \begin{bmatrix} b_1 \\ b_2 \\ b_3 \\ \end{bmatrix} \]
inliers	Output vector indicating which points are inliers (1-inlier, 0-outlier).
ransacThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.

The function estimates an optimal 3D translation between two 3D point sets using the RANSAC algorithm.

estimateTranslation3D() [2/3]

static int OpenCVForUnity.Calib3dModule.Calib3d.estimateTranslation3D ( Mat src, Mat dst, Mat _out, Mat inliers, double ransacThreshold )

static

Computes an optimal translation between two 3D point sets.

It computes

\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]

Parameters

src	First input 3D point set containing \((X,Y,Z)\).
dst	Second input 3D point set containing \((x,y,z)\).
out	Output 3D translation vector \(3 \times 1\) of the form \[ \begin{bmatrix} b_1 \\ b_2 \\ b_3 \\ \end{bmatrix} \]
inliers	Output vector indicating which points are inliers (1-inlier, 0-outlier).
ransacThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.

The function estimates an optimal 3D translation between two 3D point sets using the RANSAC algorithm.

estimateTranslation3D() [3/3]

static int OpenCVForUnity.Calib3dModule.Calib3d.estimateTranslation3D ( Mat src, Mat dst, Mat _out, Mat inliers, double ransacThreshold, double confidence )

static

Computes an optimal translation between two 3D point sets.

It computes

\[ \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \]

Parameters

src	First input 3D point set containing \((X,Y,Z)\).
dst	Second input 3D point set containing \((x,y,z)\).
out	Output 3D translation vector \(3 \times 1\) of the form \[ \begin{bmatrix} b_1 \\ b_2 \\ b_3 \\ \end{bmatrix} \]
inliers	Output vector indicating which points are inliers (1-inlier, 0-outlier).
ransacThreshold	Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier.
confidence	Confidence level, between 0 and 1, for the estimated transformation. Anything between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.

The function estimates an optimal 3D translation between two 3D point sets using the RANSAC algorithm.

filterHomographyDecompByVisibleRefpoints() [1/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.filterHomographyDecompByVisibleRefpoints ( List< Mat > rotations, List< Mat > normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions )

static

Filters homography decompositions based on additional information.

Parameters

rotations	Vector of rotation matrices.
normals	Vector of plane normal matrices.
beforePoints	Vector of (rectified) visible reference points before the homography is applied
afterPoints	Vector of (rectified) visible reference points after the homography is applied
possibleSolutions	Vector of int indices representing the viable solution set after filtering
pointsMask	optional Mat/Vector of 8u type representing the mask for the inliers as given by the findHomography function

This function is intended to filter the output of the decomposeHomographyMat based on additional information as described in [Malis2007] . The summary of the method: the decomposeHomographyMat function returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the sets of points visible in the camera frame before and after the homography transformation is applied, we can determine which are the true potential solutions and which are the opposites by verifying which homographies are consistent with all visible reference points being in front of the camera. The inputs are left unchanged; the filtered solution set is returned as indices into the existing one.

filterHomographyDecompByVisibleRefpoints() [2/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.filterHomographyDecompByVisibleRefpoints ( List< Mat > rotations, List< Mat > normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions, Mat pointsMask )

static

Filters homography decompositions based on additional information.

Parameters

rotations	Vector of rotation matrices.
normals	Vector of plane normal matrices.
beforePoints	Vector of (rectified) visible reference points before the homography is applied
afterPoints	Vector of (rectified) visible reference points after the homography is applied
possibleSolutions	Vector of int indices representing the viable solution set after filtering
pointsMask	optional Mat/Vector of 8u type representing the mask for the inliers as given by the findHomography function

This function is intended to filter the output of the decomposeHomographyMat based on additional information as described in [Malis2007] . The summary of the method: the decomposeHomographyMat function returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the sets of points visible in the camera frame before and after the homography transformation is applied, we can determine which are the true potential solutions and which are the opposites by verifying which homographies are consistent with all visible reference points being in front of the camera. The inputs are left unchanged; the filtered solution set is returned as indices into the existing one.

filterSpeckles() [1/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.filterSpeckles ( Mat img, double newVal, int maxSpeckleSize, double maxDiff )

static

Filters off small noise blobs (speckles) in the disparity map.

Parameters

img	The input 16-bit signed disparity image
newVal	The disparity value used to paint-off the speckles
maxSpeckleSize	The maximum speckle size to consider it a speckle. Larger blobs are not affected by the algorithm
maxDiff	Maximum difference between neighbor disparity pixels to put them into the same blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point disparity map, where disparity values are multiplied by 16, this scale factor should be taken into account when specifying this parameter value.
buf	The optional temporary buffer to avoid memory allocation within the function.

filterSpeckles() [2/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.filterSpeckles ( Mat img, double newVal, int maxSpeckleSize, double maxDiff, Mat buf )

static

Filters off small noise blobs (speckles) in the disparity map.

Parameters

img	The input 16-bit signed disparity image
newVal	The disparity value used to paint-off the speckles
maxSpeckleSize	The maximum speckle size to consider it a speckle. Larger blobs are not affected by the algorithm
maxDiff	Maximum difference between neighbor disparity pixels to put them into the same blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point disparity map, where disparity values are multiplied by 16, this scale factor should be taken into account when specifying this parameter value.
buf	The optional temporary buffer to avoid memory allocation within the function.

find4QuadCornerSubpix() [1/3]

static bool OpenCVForUnity.Calib3dModule.Calib3d.find4QuadCornerSubpix ( Mat img, Mat corners, in Vec2d region_size )

static

find4QuadCornerSubpix() [2/3]

static bool OpenCVForUnity.Calib3dModule.Calib3d.find4QuadCornerSubpix ( Mat img, Mat corners, in(double width, double height) region_size )

static

find4QuadCornerSubpix() [3/3]

static bool OpenCVForUnity.Calib3dModule.Calib3d.find4QuadCornerSubpix ( Mat img, Mat corners, Size region_size )

static

findChessboardCorners() [1/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCorners ( Mat image, in Vec2d patternSize, MatOfPoint2f corners )

static

Finds the positions of internal corners of the chessboard.

Parameters

image	Source chessboard view. It must be an 8-bit grayscale or color image.
patternSize	Number of inner corners per a chessboard row and column ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
corners	Output array of detected corners.
flags	Various operation flags that can be zero or a combination of the following values: CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black and white, rather than a fixed threshold level (computed from the average image brightness). CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with #equalizeHist before applying fixed or adaptive thresholding. CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter, square-like shape) to filter out false quads extracted at the contour retrieval stage. CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners, and shortcut the call if none is found. This can drastically speed up the call in the degenerate condition when no chessboard is observed. CALIB_CB_PLAIN All other flags are ignored. The input image is taken as is. No image processing is done to improve to find the checkerboard. This has the effect of speeding up the execution of the function but could lead to not recognizing the checkerboard if the image is not previously binarized in the appropriate manner.

The function attempts to determine whether the input image is a view of the chessboard pattern and locate the internal chessboard corners. The function returns a non-zero value if all of the corners are found and they are placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black squares touch each other. The detected coordinates are approximate, and to determine their positions more accurately, the function calls #cornerSubPix. You also may use the function #cornerSubPix with different parameters if returned coordinates are not accurate enough.

Sample usage of detecting and drawing chessboard corners: :

Size patternsize(8,6); //interior number of corners
Mat gray = ....; //source image
vector<Point2f> corners; //this will be filled by the detected corners
 
//CALIB_CB_FAST_CHECK saves a lot of time on images
//that do not contain any chessboard corners
bool patternfound = findChessboardCorners(gray, patternsize, corners,
        CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
        + CALIB_CB_FAST_CHECK);
 
if(patternfound)
  cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
    TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
 
drawChessboardCorners(img, patternsize, Mat(corners), patternfound);

Note

The function requires white space (like a square-thick border, the wider the better) around the board to make the detection more robust in various environments. Otherwise, if there is no border and the background is dark, the outer black squares cannot be segmented properly and so the square grouping and ordering algorithm fails.

Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.

findChessboardCorners() [2/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCorners ( Mat image, in Vec2d patternSize, MatOfPoint2f corners, int flags )

static

Finds the positions of internal corners of the chessboard.

Parameters

image	Source chessboard view. It must be an 8-bit grayscale or color image.
patternSize	Number of inner corners per a chessboard row and column ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
corners	Output array of detected corners.
flags	Various operation flags that can be zero or a combination of the following values: CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black and white, rather than a fixed threshold level (computed from the average image brightness). CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with #equalizeHist before applying fixed or adaptive thresholding. CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter, square-like shape) to filter out false quads extracted at the contour retrieval stage. CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners, and shortcut the call if none is found. This can drastically speed up the call in the degenerate condition when no chessboard is observed. CALIB_CB_PLAIN All other flags are ignored. The input image is taken as is. No image processing is done to improve to find the checkerboard. This has the effect of speeding up the execution of the function but could lead to not recognizing the checkerboard if the image is not previously binarized in the appropriate manner.

The function attempts to determine whether the input image is a view of the chessboard pattern and locate the internal chessboard corners. The function returns a non-zero value if all of the corners are found and they are placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black squares touch each other. The detected coordinates are approximate, and to determine their positions more accurately, the function calls #cornerSubPix. You also may use the function #cornerSubPix with different parameters if returned coordinates are not accurate enough.

Sample usage of detecting and drawing chessboard corners: :

Size patternsize(8,6); //interior number of corners
Mat gray = ....; //source image
vector<Point2f> corners; //this will be filled by the detected corners
 
//CALIB_CB_FAST_CHECK saves a lot of time on images
//that do not contain any chessboard corners
bool patternfound = findChessboardCorners(gray, patternsize, corners,
        CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
        + CALIB_CB_FAST_CHECK);
 
if(patternfound)
  cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
    TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
 
drawChessboardCorners(img, patternsize, Mat(corners), patternfound);

Note

The function requires white space (like a square-thick border, the wider the better) around the board to make the detection more robust in various environments. Otherwise, if there is no border and the background is dark, the outer black squares cannot be segmented properly and so the square grouping and ordering algorithm fails.

Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.

findChessboardCorners() [3/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCorners ( Mat image, in(double width, double height) patternSize, MatOfPoint2f corners )

static

Finds the positions of internal corners of the chessboard.

Parameters

image	Source chessboard view. It must be an 8-bit grayscale or color image.
patternSize	Number of inner corners per a chessboard row and column ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
corners	Output array of detected corners.
flags	Various operation flags that can be zero or a combination of the following values: CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black and white, rather than a fixed threshold level (computed from the average image brightness). CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with #equalizeHist before applying fixed or adaptive thresholding. CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter, square-like shape) to filter out false quads extracted at the contour retrieval stage. CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners, and shortcut the call if none is found. This can drastically speed up the call in the degenerate condition when no chessboard is observed. CALIB_CB_PLAIN All other flags are ignored. The input image is taken as is. No image processing is done to improve to find the checkerboard. This has the effect of speeding up the execution of the function but could lead to not recognizing the checkerboard if the image is not previously binarized in the appropriate manner.

The function attempts to determine whether the input image is a view of the chessboard pattern and locate the internal chessboard corners. The function returns a non-zero value if all of the corners are found and they are placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black squares touch each other. The detected coordinates are approximate, and to determine their positions more accurately, the function calls #cornerSubPix. You also may use the function #cornerSubPix with different parameters if returned coordinates are not accurate enough.

Sample usage of detecting and drawing chessboard corners: :

Size patternsize(8,6); //interior number of corners
Mat gray = ....; //source image
vector<Point2f> corners; //this will be filled by the detected corners
 
//CALIB_CB_FAST_CHECK saves a lot of time on images
//that do not contain any chessboard corners
bool patternfound = findChessboardCorners(gray, patternsize, corners,
        CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
        + CALIB_CB_FAST_CHECK);
 
if(patternfound)
  cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
    TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
 
drawChessboardCorners(img, patternsize, Mat(corners), patternfound);

Note

The function requires white space (like a square-thick border, the wider the better) around the board to make the detection more robust in various environments. Otherwise, if there is no border and the background is dark, the outer black squares cannot be segmented properly and so the square grouping and ordering algorithm fails.

Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.

findChessboardCorners() [4/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCorners ( Mat image, in(double width, double height) patternSize, MatOfPoint2f corners, int flags )

static

Finds the positions of internal corners of the chessboard.

Parameters

image	Source chessboard view. It must be an 8-bit grayscale or color image.
patternSize	Number of inner corners per a chessboard row and column ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
corners	Output array of detected corners.
flags	Various operation flags that can be zero or a combination of the following values: CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black and white, rather than a fixed threshold level (computed from the average image brightness). CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with #equalizeHist before applying fixed or adaptive thresholding. CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter, square-like shape) to filter out false quads extracted at the contour retrieval stage. CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners, and shortcut the call if none is found. This can drastically speed up the call in the degenerate condition when no chessboard is observed. CALIB_CB_PLAIN All other flags are ignored. The input image is taken as is. No image processing is done to improve to find the checkerboard. This has the effect of speeding up the execution of the function but could lead to not recognizing the checkerboard if the image is not previously binarized in the appropriate manner.

The function attempts to determine whether the input image is a view of the chessboard pattern and locate the internal chessboard corners. The function returns a non-zero value if all of the corners are found and they are placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black squares touch each other. The detected coordinates are approximate, and to determine their positions more accurately, the function calls #cornerSubPix. You also may use the function #cornerSubPix with different parameters if returned coordinates are not accurate enough.

Sample usage of detecting and drawing chessboard corners: :

Size patternsize(8,6); //interior number of corners
Mat gray = ....; //source image
vector<Point2f> corners; //this will be filled by the detected corners
 
//CALIB_CB_FAST_CHECK saves a lot of time on images
//that do not contain any chessboard corners
bool patternfound = findChessboardCorners(gray, patternsize, corners,
        CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
        + CALIB_CB_FAST_CHECK);
 
if(patternfound)
  cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
    TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
 
drawChessboardCorners(img, patternsize, Mat(corners), patternfound);

Note

The function requires white space (like a square-thick border, the wider the better) around the board to make the detection more robust in various environments. Otherwise, if there is no border and the background is dark, the outer black squares cannot be segmented properly and so the square grouping and ordering algorithm fails.

Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.

findChessboardCorners() [5/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCorners ( Mat image, Size patternSize, MatOfPoint2f corners )

static

Finds the positions of internal corners of the chessboard.

Parameters

image	Source chessboard view. It must be an 8-bit grayscale or color image.
patternSize	Number of inner corners per a chessboard row and column ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
corners	Output array of detected corners.
flags	Various operation flags that can be zero or a combination of the following values: CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black and white, rather than a fixed threshold level (computed from the average image brightness). CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with #equalizeHist before applying fixed or adaptive thresholding. CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter, square-like shape) to filter out false quads extracted at the contour retrieval stage. CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners, and shortcut the call if none is found. This can drastically speed up the call in the degenerate condition when no chessboard is observed. CALIB_CB_PLAIN All other flags are ignored. The input image is taken as is. No image processing is done to improve to find the checkerboard. This has the effect of speeding up the execution of the function but could lead to not recognizing the checkerboard if the image is not previously binarized in the appropriate manner.

The function attempts to determine whether the input image is a view of the chessboard pattern and locate the internal chessboard corners. The function returns a non-zero value if all of the corners are found and they are placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black squares touch each other. The detected coordinates are approximate, and to determine their positions more accurately, the function calls #cornerSubPix. You also may use the function #cornerSubPix with different parameters if returned coordinates are not accurate enough.

Sample usage of detecting and drawing chessboard corners: :

Size patternsize(8,6); //interior number of corners
Mat gray = ....; //source image
vector<Point2f> corners; //this will be filled by the detected corners
 
//CALIB_CB_FAST_CHECK saves a lot of time on images
//that do not contain any chessboard corners
bool patternfound = findChessboardCorners(gray, patternsize, corners,
        CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
        + CALIB_CB_FAST_CHECK);
 
if(patternfound)
  cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
    TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
 
drawChessboardCorners(img, patternsize, Mat(corners), patternfound);

Note

The function requires white space (like a square-thick border, the wider the better) around the board to make the detection more robust in various environments. Otherwise, if there is no border and the background is dark, the outer black squares cannot be segmented properly and so the square grouping and ordering algorithm fails.

Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.

findChessboardCorners() [6/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCorners ( Mat image, Size patternSize, MatOfPoint2f corners, int flags )

static

Finds the positions of internal corners of the chessboard.

Parameters

image	Source chessboard view. It must be an 8-bit grayscale or color image.
patternSize	Number of inner corners per a chessboard row and column ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
corners	Output array of detected corners.
flags	Various operation flags that can be zero or a combination of the following values: CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black and white, rather than a fixed threshold level (computed from the average image brightness). CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with #equalizeHist before applying fixed or adaptive thresholding. CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter, square-like shape) to filter out false quads extracted at the contour retrieval stage. CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners, and shortcut the call if none is found. This can drastically speed up the call in the degenerate condition when no chessboard is observed. CALIB_CB_PLAIN All other flags are ignored. The input image is taken as is. No image processing is done to improve to find the checkerboard. This has the effect of speeding up the execution of the function but could lead to not recognizing the checkerboard if the image is not previously binarized in the appropriate manner.

The function attempts to determine whether the input image is a view of the chessboard pattern and locate the internal chessboard corners. The function returns a non-zero value if all of the corners are found and they are placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black squares touch each other. The detected coordinates are approximate, and to determine their positions more accurately, the function calls #cornerSubPix. You also may use the function #cornerSubPix with different parameters if returned coordinates are not accurate enough.

Sample usage of detecting and drawing chessboard corners: :

Size patternsize(8,6); //interior number of corners
Mat gray = ....; //source image
vector<Point2f> corners; //this will be filled by the detected corners
 
//CALIB_CB_FAST_CHECK saves a lot of time on images
//that do not contain any chessboard corners
bool patternfound = findChessboardCorners(gray, patternsize, corners,
        CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
        + CALIB_CB_FAST_CHECK);
 
if(patternfound)
  cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
    TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
 
drawChessboardCorners(img, patternsize, Mat(corners), patternfound);

Note

The function requires white space (like a square-thick border, the wider the better) around the board to make the detection more robust in various environments. Otherwise, if there is no border and the background is dark, the outer black squares cannot be segmented properly and so the square grouping and ordering algorithm fails.

Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.

findChessboardCornersSB() [1/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCornersSB ( Mat image, in Vec2d patternSize, Mat corners )

static

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

findChessboardCornersSB() [2/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCornersSB ( Mat image, in Vec2d patternSize, Mat corners, int flags )

static

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

findChessboardCornersSB() [3/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCornersSB ( Mat image, in(double width, double height) patternSize, Mat corners )

static

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

findChessboardCornersSB() [4/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCornersSB ( Mat image, in(double width, double height) patternSize, Mat corners, int flags )

static

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

findChessboardCornersSB() [5/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCornersSB ( Mat image, Size patternSize, Mat corners )

static

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

findChessboardCornersSB() [6/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCornersSB ( Mat image, Size patternSize, Mat corners, int flags )

static

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

findChessboardCornersSBWithMeta() [1/3]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCornersSBWithMeta ( Mat image, in Vec2d patternSize, Mat corners, int flags, Mat meta )

static

Finds the positions of internal corners of the chessboard using a sector based approach.

Parameters

image	Source chessboard view. It must be an 8-bit grayscale or color image.
patternSize	Number of inner corners per a chessboard row and column ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
corners	Output array of detected corners.
flags	Various operation flags that can be zero or a combination of the following values: CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with equalizeHist before detection. CALIB_CB_EXHAUSTIVE Run an exhaustive search to improve detection rate. CALIB_CB_ACCURACY Up sample input image to improve sub-pixel accuracy due to aliasing effects. CALIB_CB_LARGER The detected pattern is allowed to be larger than patternSize (see description). CALIB_CB_MARKER The detected pattern must have a marker (see description). This should be used if an accurate camera calibration is required.
meta	Optional output arrray of detected corners (CV_8UC1 and size = cv::Size(columns,rows)). Each entry stands for one corner of the pattern and can have one of the following values: 0 = no meta data attached 1 = left-top corner of a black cell 2 = left-top corner of a white cell 3 = left-top corner of a black cell with a white marker dot 4 = left-top corner of a white cell with a black marker dot (pattern origin in case of markers otherwise first corner)

The function is analog to findChessboardCorners but uses a localized radon transformation approximated by box filters being more robust to all sort of noise, faster on larger images and is able to directly return the sub-pixel position of the internal chessboard corners. The Method is based on the paper [duda2018] "Accurate Detection and Localization of Checkerboard Corners for Calibration" demonstrating that the returned sub-pixel positions are more accurate than the one returned by cornerSubPix allowing a precise camera calibration for demanding applications.

In the case, the flags CALIB_CB_LARGER or CALIB_CB_MARKER are given, the result can be recovered from the optional meta array. Both flags are helpful to use calibration patterns exceeding the field of view of the camera. These oversized patterns allow more accurate calibrations as corners can be utilized, which are as close as possible to the image borders. For a consistent coordinate system across all images, the optional marker (see image below) can be used to move the origin of the board to the location where the black circle is located.

Note

The function requires a white boarder with roughly the same width as one of the checkerboard fields around the whole board to improve the detection in various environments. In addition, because of the localized radon transformation it is beneficial to use round corners for the field corners which are located on the outside of the board. The following figure illustrates a sample checkerboard optimized for the detection. However, any other checkerboard can be used as well.

Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.

findChessboardCornersSBWithMeta() [2/3]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCornersSBWithMeta ( Mat image, in(double width, double height) patternSize, Mat corners, int flags, Mat meta )

static

Finds the positions of internal corners of the chessboard using a sector based approach.

Parameters

image	Source chessboard view. It must be an 8-bit grayscale or color image.
patternSize	Number of inner corners per a chessboard row and column ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
corners	Output array of detected corners.
flags	Various operation flags that can be zero or a combination of the following values: CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with equalizeHist before detection. CALIB_CB_EXHAUSTIVE Run an exhaustive search to improve detection rate. CALIB_CB_ACCURACY Up sample input image to improve sub-pixel accuracy due to aliasing effects. CALIB_CB_LARGER The detected pattern is allowed to be larger than patternSize (see description). CALIB_CB_MARKER The detected pattern must have a marker (see description). This should be used if an accurate camera calibration is required.
meta	Optional output arrray of detected corners (CV_8UC1 and size = cv::Size(columns,rows)). Each entry stands for one corner of the pattern and can have one of the following values: 0 = no meta data attached 1 = left-top corner of a black cell 2 = left-top corner of a white cell 3 = left-top corner of a black cell with a white marker dot 4 = left-top corner of a white cell with a black marker dot (pattern origin in case of markers otherwise first corner)

The function is analog to findChessboardCorners but uses a localized radon transformation approximated by box filters being more robust to all sort of noise, faster on larger images and is able to directly return the sub-pixel position of the internal chessboard corners. The Method is based on the paper [duda2018] "Accurate Detection and Localization of Checkerboard Corners for Calibration" demonstrating that the returned sub-pixel positions are more accurate than the one returned by cornerSubPix allowing a precise camera calibration for demanding applications.

In the case, the flags CALIB_CB_LARGER or CALIB_CB_MARKER are given, the result can be recovered from the optional meta array. Both flags are helpful to use calibration patterns exceeding the field of view of the camera. These oversized patterns allow more accurate calibrations as corners can be utilized, which are as close as possible to the image borders. For a consistent coordinate system across all images, the optional marker (see image below) can be used to move the origin of the board to the location where the black circle is located.

Note

The function requires a white boarder with roughly the same width as one of the checkerboard fields around the whole board to improve the detection in various environments. In addition, because of the localized radon transformation it is beneficial to use round corners for the field corners which are located on the outside of the board. The following figure illustrates a sample checkerboard optimized for the detection. However, any other checkerboard can be used as well.

Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.

findChessboardCornersSBWithMeta() [3/3]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findChessboardCornersSBWithMeta ( Mat image, Size patternSize, Mat corners, int flags, Mat meta )

static

Finds the positions of internal corners of the chessboard using a sector based approach.

Parameters

image	Source chessboard view. It must be an 8-bit grayscale or color image.
patternSize	Number of inner corners per a chessboard row and column ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
corners	Output array of detected corners.
flags	Various operation flags that can be zero or a combination of the following values: CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with equalizeHist before detection. CALIB_CB_EXHAUSTIVE Run an exhaustive search to improve detection rate. CALIB_CB_ACCURACY Up sample input image to improve sub-pixel accuracy due to aliasing effects. CALIB_CB_LARGER The detected pattern is allowed to be larger than patternSize (see description). CALIB_CB_MARKER The detected pattern must have a marker (see description). This should be used if an accurate camera calibration is required.
meta	Optional output arrray of detected corners (CV_8UC1 and size = cv::Size(columns,rows)). Each entry stands for one corner of the pattern and can have one of the following values: 0 = no meta data attached 1 = left-top corner of a black cell 2 = left-top corner of a white cell 3 = left-top corner of a black cell with a white marker dot 4 = left-top corner of a white cell with a black marker dot (pattern origin in case of markers otherwise first corner)

The function is analog to findChessboardCorners but uses a localized radon transformation approximated by box filters being more robust to all sort of noise, faster on larger images and is able to directly return the sub-pixel position of the internal chessboard corners. The Method is based on the paper [duda2018] "Accurate Detection and Localization of Checkerboard Corners for Calibration" demonstrating that the returned sub-pixel positions are more accurate than the one returned by cornerSubPix allowing a precise camera calibration for demanding applications.

In the case, the flags CALIB_CB_LARGER or CALIB_CB_MARKER are given, the result can be recovered from the optional meta array. Both flags are helpful to use calibration patterns exceeding the field of view of the camera. These oversized patterns allow more accurate calibrations as corners can be utilized, which are as close as possible to the image borders. For a consistent coordinate system across all images, the optional marker (see image below) can be used to move the origin of the board to the location where the black circle is located.

Note

The function requires a white boarder with roughly the same width as one of the checkerboard fields around the whole board to improve the detection in various environments. In addition, because of the localized radon transformation it is beneficial to use round corners for the field corners which are located on the outside of the board. The following figure illustrates a sample checkerboard optimized for the detection. However, any other checkerboard can be used as well.

Use gen_pattern.py (tutorial_camera_calibration_pattern) to create checkerboard.

findCirclesGrid() [1/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findCirclesGrid ( Mat image, in Vec2d patternSize, Mat centers )

static

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

findCirclesGrid() [2/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findCirclesGrid ( Mat image, in Vec2d patternSize, Mat centers, int flags )

static

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

findCirclesGrid() [3/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findCirclesGrid ( Mat image, in(double width, double height) patternSize, Mat centers )

static

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

findCirclesGrid() [4/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findCirclesGrid ( Mat image, in(double width, double height) patternSize, Mat centers, int flags )

static

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

findCirclesGrid() [5/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findCirclesGrid ( Mat image, Size patternSize, Mat centers )

static

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

findCirclesGrid() [6/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.findCirclesGrid ( Mat image, Size patternSize, Mat centers, int flags )

static

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

findEssentialMat() [1/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2 )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [2/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [3/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, in Vec2d pp )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [4/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, in Vec2d pp, int method )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [5/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, in Vec2d pp, int method, double prob )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [6/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, in Vec2d pp, int method, double prob, double threshold )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [7/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, in Vec2d pp, int method, double prob, double threshold, int maxIters )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [8/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, in Vec2d pp, int method, double prob, double threshold, int maxIters, Mat mask )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [9/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, in(double x, double y) pp )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [10/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, in(double x, double y) pp, int method )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [11/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, in(double x, double y) pp, int method, double prob )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [12/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, in(double x, double y) pp, int method, double prob, double threshold )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [13/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, in(double x, double y) pp, int method, double prob, double threshold, int maxIters )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [14/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, in(double x, double y) pp, int method, double prob, double threshold, int maxIters, Mat mask )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [15/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, Point pp )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [16/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, Point pp, int method )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [17/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, Point pp, int method, double prob )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [18/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [19/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold, int maxIters )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [20/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold, int maxIters, Mat mask )

static

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

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
focal	focal length of the camera. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point.
pp	principal point of the camera.
method	Method for computing a fundamental matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

findEssentialMat() [21/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, Mat cameraMatrix )

static

Calculates an essential matrix from the corresponding points in two images.

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1.
cameraMatrix	Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix. If this assumption does not hold for your use case, use another function overload or undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:

\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]

where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.

findEssentialMat() [22/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, Mat cameraMatrix, int method )

static

Calculates an essential matrix from the corresponding points in two images.

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1.
cameraMatrix	Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix. If this assumption does not hold for your use case, use another function overload or undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:

\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]

where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.

findEssentialMat() [23/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, Mat cameraMatrix, int method, double prob )

static

Calculates an essential matrix from the corresponding points in two images.

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1.
cameraMatrix	Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix. If this assumption does not hold for your use case, use another function overload or undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:

\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]

where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.

findEssentialMat() [24/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold )

static

Calculates an essential matrix from the corresponding points in two images.

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1.
cameraMatrix	Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix. If this assumption does not hold for your use case, use another function overload or undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:

\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]

where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.

findEssentialMat() [25/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold, int maxIters )

static

Calculates an essential matrix from the corresponding points in two images.

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1.
cameraMatrix	Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix. If this assumption does not hold for your use case, use another function overload or undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:

\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]

where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.

findEssentialMat() [26/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold, int maxIters, Mat mask )

static

Calculates an essential matrix from the corresponding points in two images.

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1.
cameraMatrix	Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix. If this assumption does not hold for your use case, use another function overload or undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters	The maximum number of robust method iterations.

This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:

\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]

where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.

findEssentialMat() [27/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, Mat cameraMatrix1, Mat cameraMatrix2, Mat dist_coeff1, Mat dist_coeff2, Mat mask, UsacParams _params )

static

findEssentialMat() [28/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2 )

static

Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1.
cameraMatrix1	Camera matrix for the first camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
cameraMatrix2	Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs1	Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
distCoeffs2	Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.

This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:

\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]

where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.

findEssentialMat() [29/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method )

static

Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1.
cameraMatrix1	Camera matrix for the first camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
cameraMatrix2	Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs1	Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
distCoeffs2	Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.

This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:

\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]

where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.

findEssentialMat() [30/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method, double prob )

static

Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1.
cameraMatrix1	Camera matrix for the first camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
cameraMatrix2	Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs1	Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
distCoeffs2	Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.

This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:

\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]

where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.

findEssentialMat() [31/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method, double prob, double threshold )

static

Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1.
cameraMatrix1	Camera matrix for the first camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
cameraMatrix2	Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs1	Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
distCoeffs2	Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.

This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:

\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]

where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.

findEssentialMat() [32/32]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findEssentialMat ( Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method, double prob, double threshold, Mat mask )

static

Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.

Parameters

points1	Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1.
cameraMatrix1	Camera matrix for the first camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
cameraMatrix2	Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs1	Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
distCoeffs2	Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.

This function estimates essential matrix based on the five-point algorithm solver in [Nister03] . [SteweniusCFS] is also a related. The epipolar geometry is described by the following equation:

\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]

where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decomposeEssentialMat or recoverPose to recover the relative pose between cameras.

findFundamentalMat() [1/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findFundamentalMat ( MatOfPoint2f points1, MatOfPoint2f points2 )

static

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

findFundamentalMat() [2/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findFundamentalMat ( MatOfPoint2f points1, MatOfPoint2f points2, int method )

static

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

findFundamentalMat() [3/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findFundamentalMat ( MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold )

static

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

findFundamentalMat() [4/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findFundamentalMat ( MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence )

static

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

findFundamentalMat() [5/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findFundamentalMat ( MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, int maxIters )

static

Calculates a fundamental matrix from the corresponding points in two images.

Parameters

points1	Array of N points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
method	Method for computing a fundamental matrix. FM_7POINT for a 7-point algorithm. \(N = 7\) FM_8POINT for an 8-point algorithm. \(N \ge 8\) FM_RANSAC for the RANSAC algorithm. \(N \ge 8\) FM_LMEDS for the LMedS algorithm. \(N \ge 8\)
ransacReprojThreshold	Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
confidence	Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
maxIters	The maximum number of robust method iterations.

The epipolar geometry is described by the following equation:

\[[p_2; 1]^T F [p_1; 1] = 0\]

where \(F\) is a fundamental matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively.

The function calculates the fundamental matrix using one of four methods listed above and returns the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point algorithm, the function may return up to 3 solutions ( \(9 \times 3\) matrix that stores all 3 matrices sequentially).

The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the epipolar lines corresponding to the specified points. It can also be passed to stereoRectifyUncalibrated to compute the rectification transformation. :

// Example. Estimation of fundamental matrix using the RANSAC algorithm
int point_count = 100;
vector<Point2f> points1(point_count);
vector<Point2f> points2(point_count);
 
// initialize the points here ...
for( int i = 0; i < point_count; i++ )
{
    points1[i] = ...;
    points2[i] = ...;
}
 
Mat fundamental_matrix =
 findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);

findFundamentalMat() [6/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findFundamentalMat ( MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, int maxIters, Mat mask )

static

Calculates a fundamental matrix from the corresponding points in two images.

Parameters

points1	Array of N points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
method	Method for computing a fundamental matrix. FM_7POINT for a 7-point algorithm. \(N = 7\) FM_8POINT for an 8-point algorithm. \(N \ge 8\) FM_RANSAC for the RANSAC algorithm. \(N \ge 8\) FM_LMEDS for the LMedS algorithm. \(N \ge 8\)
ransacReprojThreshold	Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
confidence	Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
maxIters	The maximum number of robust method iterations.

The epipolar geometry is described by the following equation:

\[[p_2; 1]^T F [p_1; 1] = 0\]

where \(F\) is a fundamental matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively.

The function calculates the fundamental matrix using one of four methods listed above and returns the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point algorithm, the function may return up to 3 solutions ( \(9 \times 3\) matrix that stores all 3 matrices sequentially).

The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the epipolar lines corresponding to the specified points. It can also be passed to stereoRectifyUncalibrated to compute the rectification transformation. :

// Example. Estimation of fundamental matrix using the RANSAC algorithm
int point_count = 100;
vector<Point2f> points1(point_count);
vector<Point2f> points2(point_count);
 
// initialize the points here ...
for( int i = 0; i < point_count; i++ )
{
    points1[i] = ...;
    points2[i] = ...;
}
 
Mat fundamental_matrix =
 findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);

findFundamentalMat() [7/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findFundamentalMat ( MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, Mat mask )

static

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

findFundamentalMat() [8/8]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findFundamentalMat ( MatOfPoint2f points1, MatOfPoint2f points2, Mat mask, UsacParams _params )

static

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

findHomography() [1/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findHomography ( MatOfPoint2f srcPoints, MatOfPoint2f dstPoints )

static

Finds a perspective transformation between two planes.

Parameters

srcPoints	Coordinates of the points in the original plane, a matrix of the type CV_32FC2 or vector<Point2f> .
dstPoints	Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or a vector<Point2f> .
method	Method used to compute a homography matrix. The following methods are possible: 0 - a regular method using all the points, i.e., the least squares method RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RHO - PROSAC-based robust method
ransacReprojThreshold	Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC and RHO methods only). That is, if \[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} \cdot \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\] then the point \(i\) is considered as an outlier. If srcPoints and dstPoints are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.
mask	Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input mask values are ignored.
maxIters	The maximum number of RANSAC iterations.
confidence	Confidence level, between 0 and 1.

The function finds and returns the perspective transformation \(H\) between the source and the destination planes:

\[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\]

so that the back-projection error

\[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\]

is minimized. If the parameter method is set to the default value 0, the function uses all the point pairs to compute an initial homography estimate with a simple least-squares scheme.

However, if not all of the point pairs ( \(srcPoints_i\), \(dstPoints_i\) ) fit the rigid perspective transformation (that is, there are some outliers), this initial estimate will be poor. In this case, you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median re-projection error for LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.

Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the re-projection error even more.

The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the noise is rather small, use the default method (method=0).

The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is determined up to a scale. If \(h_{33}\) is non-zero, the matrix is normalized so that \(h_{33}=1\).

Note

Whenever an \(H\) matrix cannot be estimated, an empty one will be returned.

See also

getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform

findHomography() [2/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findHomography ( MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method )

static

Finds a perspective transformation between two planes.

Parameters

srcPoints	Coordinates of the points in the original plane, a matrix of the type CV_32FC2 or vector<Point2f> .
dstPoints	Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or a vector<Point2f> .
method	Method used to compute a homography matrix. The following methods are possible: 0 - a regular method using all the points, i.e., the least squares method RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RHO - PROSAC-based robust method
ransacReprojThreshold	Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC and RHO methods only). That is, if \[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} \cdot \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\] then the point \(i\) is considered as an outlier. If srcPoints and dstPoints are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.
mask	Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input mask values are ignored.
maxIters	The maximum number of RANSAC iterations.
confidence	Confidence level, between 0 and 1.

The function finds and returns the perspective transformation \(H\) between the source and the destination planes:

\[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\]

so that the back-projection error

\[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\]

is minimized. If the parameter method is set to the default value 0, the function uses all the point pairs to compute an initial homography estimate with a simple least-squares scheme.

However, if not all of the point pairs ( \(srcPoints_i\), \(dstPoints_i\) ) fit the rigid perspective transformation (that is, there are some outliers), this initial estimate will be poor. In this case, you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median re-projection error for LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.

Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the re-projection error even more.

The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the noise is rather small, use the default method (method=0).

The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is determined up to a scale. If \(h_{33}\) is non-zero, the matrix is normalized so that \(h_{33}=1\).

Note

Whenever an \(H\) matrix cannot be estimated, an empty one will be returned.

See also

getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform

findHomography() [3/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findHomography ( MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold )

static

Finds a perspective transformation between two planes.

Parameters

srcPoints	Coordinates of the points in the original plane, a matrix of the type CV_32FC2 or vector<Point2f> .
dstPoints	Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or a vector<Point2f> .
method	Method used to compute a homography matrix. The following methods are possible: 0 - a regular method using all the points, i.e., the least squares method RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RHO - PROSAC-based robust method
ransacReprojThreshold	Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC and RHO methods only). That is, if \[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} \cdot \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\] then the point \(i\) is considered as an outlier. If srcPoints and dstPoints are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.
mask	Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input mask values are ignored.
maxIters	The maximum number of RANSAC iterations.
confidence	Confidence level, between 0 and 1.

The function finds and returns the perspective transformation \(H\) between the source and the destination planes:

\[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\]

so that the back-projection error

\[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\]

is minimized. If the parameter method is set to the default value 0, the function uses all the point pairs to compute an initial homography estimate with a simple least-squares scheme.

However, if not all of the point pairs ( \(srcPoints_i\), \(dstPoints_i\) ) fit the rigid perspective transformation (that is, there are some outliers), this initial estimate will be poor. In this case, you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median re-projection error for LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.

Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the re-projection error even more.

The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the noise is rather small, use the default method (method=0).

The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is determined up to a scale. If \(h_{33}\) is non-zero, the matrix is normalized so that \(h_{33}=1\).

Note

Whenever an \(H\) matrix cannot be estimated, an empty one will be returned.

See also

getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform

findHomography() [4/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findHomography ( MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask )

static

Finds a perspective transformation between two planes.

Parameters

srcPoints	Coordinates of the points in the original plane, a matrix of the type CV_32FC2 or vector<Point2f> .
dstPoints	Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or a vector<Point2f> .
method	Method used to compute a homography matrix. The following methods are possible: 0 - a regular method using all the points, i.e., the least squares method RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RHO - PROSAC-based robust method
ransacReprojThreshold	Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC and RHO methods only). That is, if \[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} \cdot \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\] then the point \(i\) is considered as an outlier. If srcPoints and dstPoints are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.
mask	Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input mask values are ignored.
maxIters	The maximum number of RANSAC iterations.
confidence	Confidence level, between 0 and 1.

The function finds and returns the perspective transformation \(H\) between the source and the destination planes:

\[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\]

so that the back-projection error

\[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\]

is minimized. If the parameter method is set to the default value 0, the function uses all the point pairs to compute an initial homography estimate with a simple least-squares scheme.

However, if not all of the point pairs ( \(srcPoints_i\), \(dstPoints_i\) ) fit the rigid perspective transformation (that is, there are some outliers), this initial estimate will be poor. In this case, you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median re-projection error for LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.

Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the re-projection error even more.

The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the noise is rather small, use the default method (method=0).

The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is determined up to a scale. If \(h_{33}\) is non-zero, the matrix is normalized so that \(h_{33}=1\).

Note

Whenever an \(H\) matrix cannot be estimated, an empty one will be returned.

See also

getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform

findHomography() [5/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findHomography ( MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters )

static

Finds a perspective transformation between two planes.

Parameters

srcPoints	Coordinates of the points in the original plane, a matrix of the type CV_32FC2 or vector<Point2f> .
dstPoints	Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or a vector<Point2f> .
method	Method used to compute a homography matrix. The following methods are possible: 0 - a regular method using all the points, i.e., the least squares method RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RHO - PROSAC-based robust method
ransacReprojThreshold	Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC and RHO methods only). That is, if \[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} \cdot \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\] then the point \(i\) is considered as an outlier. If srcPoints and dstPoints are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.
mask	Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input mask values are ignored.
maxIters	The maximum number of RANSAC iterations.
confidence	Confidence level, between 0 and 1.

The function finds and returns the perspective transformation \(H\) between the source and the destination planes:

\[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\]

so that the back-projection error

\[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\]

is minimized. If the parameter method is set to the default value 0, the function uses all the point pairs to compute an initial homography estimate with a simple least-squares scheme.

However, if not all of the point pairs ( \(srcPoints_i\), \(dstPoints_i\) ) fit the rigid perspective transformation (that is, there are some outliers), this initial estimate will be poor. In this case, you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median re-projection error for LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.

Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the re-projection error even more.

The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the noise is rather small, use the default method (method=0).

The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is determined up to a scale. If \(h_{33}\) is non-zero, the matrix is normalized so that \(h_{33}=1\).

Note

Whenever an \(H\) matrix cannot be estimated, an empty one will be returned.

See also

getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform

findHomography() [6/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findHomography ( MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters, double confidence )

static

Finds a perspective transformation between two planes.

Parameters

srcPoints	Coordinates of the points in the original plane, a matrix of the type CV_32FC2 or vector<Point2f> .
dstPoints	Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or a vector<Point2f> .
method	Method used to compute a homography matrix. The following methods are possible: 0 - a regular method using all the points, i.e., the least squares method RANSAC - RANSAC-based robust method LMEDS - Least-Median robust method RHO - PROSAC-based robust method
ransacReprojThreshold	Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC and RHO methods only). That is, if \[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} \cdot \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\] then the point \(i\) is considered as an outlier. If srcPoints and dstPoints are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.
mask	Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input mask values are ignored.
maxIters	The maximum number of RANSAC iterations.
confidence	Confidence level, between 0 and 1.

The function finds and returns the perspective transformation \(H\) between the source and the destination planes:

\[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\]

so that the back-projection error

\[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\]

is minimized. If the parameter method is set to the default value 0, the function uses all the point pairs to compute an initial homography estimate with a simple least-squares scheme.

However, if not all of the point pairs ( \(srcPoints_i\), \(dstPoints_i\) ) fit the rigid perspective transformation (that is, there are some outliers), this initial estimate will be poor. In this case, you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median re-projection error for LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.

Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the re-projection error even more.

The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the noise is rather small, use the default method (method=0).

The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is determined up to a scale. If \(h_{33}\) is non-zero, the matrix is normalized so that \(h_{33}=1\).

Note

Whenever an \(H\) matrix cannot be estimated, an empty one will be returned.

See also

getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform

findHomography() [7/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.findHomography ( MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, Mat mask, UsacParams _params )

static

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

fisheye_calibrate() [1/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_calibrate ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs )

static

Performs camera calibration.

Parameters

objectPoints	vector of vectors of calibration pattern points in the calibration pattern coordinate space.
imagePoints	vector of vectors of the projections of calibration pattern points. imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i.
image_size	Size of the image used only to initialize the camera intrinsic matrix.
K	Output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
D	Output vector of distortion coefficients \(\distcoeffsfisheye\).
rvecs	Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. M -1).
tvecs	Output vector of translation vectors estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero. fisheye::CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when fisheye::CALIB_USE_INTRINSIC_GUESS is set too. fisheye::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization. It is the \(max(width,height)/\pi\) or the provided \(f_x\), \(f_y\) when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_calibrate() [2/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_calibrate ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs, int flags )

static

Performs camera calibration.

Parameters

objectPoints	vector of vectors of calibration pattern points in the calibration pattern coordinate space.
imagePoints	vector of vectors of the projections of calibration pattern points. imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i.
image_size	Size of the image used only to initialize the camera intrinsic matrix.
K	Output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
D	Output vector of distortion coefficients \(\distcoeffsfisheye\).
rvecs	Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. M -1).
tvecs	Output vector of translation vectors estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero. fisheye::CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when fisheye::CALIB_USE_INTRINSIC_GUESS is set too. fisheye::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization. It is the \(max(width,height)/\pi\) or the provided \(f_x\), \(f_y\) when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_calibrate() [3/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_calibrate ( List< Mat > objectPoints, List< Mat > imagePoints, in Vec2d image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs, int flags, in Vec3d criteria )

static

Performs camera calibration.

Parameters

objectPoints	vector of vectors of calibration pattern points in the calibration pattern coordinate space.
imagePoints	vector of vectors of the projections of calibration pattern points. imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i.
image_size	Size of the image used only to initialize the camera intrinsic matrix.
K	Output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
D	Output vector of distortion coefficients \(\distcoeffsfisheye\).
rvecs	Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. M -1).
tvecs	Output vector of translation vectors estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero. fisheye::CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when fisheye::CALIB_USE_INTRINSIC_GUESS is set too. fisheye::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization. It is the \(max(width,height)/\pi\) or the provided \(f_x\), \(f_y\) when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_calibrate() [4/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_calibrate ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs )

static

Performs camera calibration.

Parameters

objectPoints	vector of vectors of calibration pattern points in the calibration pattern coordinate space.
imagePoints	vector of vectors of the projections of calibration pattern points. imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i.
image_size	Size of the image used only to initialize the camera intrinsic matrix.
K	Output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
D	Output vector of distortion coefficients \(\distcoeffsfisheye\).
rvecs	Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. M -1).
tvecs	Output vector of translation vectors estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero. fisheye::CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when fisheye::CALIB_USE_INTRINSIC_GUESS is set too. fisheye::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization. It is the \(max(width,height)/\pi\) or the provided \(f_x\), \(f_y\) when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_calibrate() [5/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_calibrate ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs, int flags )

static

Performs camera calibration.

Parameters

objectPoints	vector of vectors of calibration pattern points in the calibration pattern coordinate space.
imagePoints	vector of vectors of the projections of calibration pattern points. imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i.
image_size	Size of the image used only to initialize the camera intrinsic matrix.
K	Output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
D	Output vector of distortion coefficients \(\distcoeffsfisheye\).
rvecs	Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. M -1).
tvecs	Output vector of translation vectors estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero. fisheye::CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when fisheye::CALIB_USE_INTRINSIC_GUESS is set too. fisheye::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization. It is the \(max(width,height)/\pi\) or the provided \(f_x\), \(f_y\) when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_calibrate() [6/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_calibrate ( List< Mat > objectPoints, List< Mat > imagePoints, in(double width, double height) image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs, int flags, in(double type, double maxCount, double epsilon) criteria )

static

Performs camera calibration.

Parameters

objectPoints	vector of vectors of calibration pattern points in the calibration pattern coordinate space.
imagePoints	vector of vectors of the projections of calibration pattern points. imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i.
image_size	Size of the image used only to initialize the camera intrinsic matrix.
K	Output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
D	Output vector of distortion coefficients \(\distcoeffsfisheye\).
rvecs	Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. M -1).
tvecs	Output vector of translation vectors estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero. fisheye::CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when fisheye::CALIB_USE_INTRINSIC_GUESS is set too. fisheye::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization. It is the \(max(width,height)/\pi\) or the provided \(f_x\), \(f_y\) when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_calibrate() [7/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_calibrate ( List< Mat > objectPoints, List< Mat > imagePoints, Size image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs )

static

Performs camera calibration.

Parameters

objectPoints	vector of vectors of calibration pattern points in the calibration pattern coordinate space.
imagePoints	vector of vectors of the projections of calibration pattern points. imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i.
image_size	Size of the image used only to initialize the camera intrinsic matrix.
K	Output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
D	Output vector of distortion coefficients \(\distcoeffsfisheye\).
rvecs	Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. M -1).
tvecs	Output vector of translation vectors estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero. fisheye::CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when fisheye::CALIB_USE_INTRINSIC_GUESS is set too. fisheye::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization. It is the \(max(width,height)/\pi\) or the provided \(f_x\), \(f_y\) when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_calibrate() [8/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_calibrate ( List< Mat > objectPoints, List< Mat > imagePoints, Size image_size, Mat K, Mat D, List< Mat > rvecs, List< Mat > tvecs, int flags )

static

Performs camera calibration.

Parameters

objectPoints	vector of vectors of calibration pattern points in the calibration pattern coordinate space.
imagePoints	vector of vectors of the projections of calibration pattern points. imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i.
image_size	Size of the image used only to initialize the camera intrinsic matrix.
K	Output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
D	Output vector of distortion coefficients \(\distcoeffsfisheye\).
rvecs	Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. M -1).
tvecs	Output vector of translation vectors estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero. fisheye::CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when fisheye::CALIB_USE_INTRINSIC_GUESS is set too. fisheye::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization. It is the \(max(width,height)/\pi\) or the provided \(f_x\), \(f_y\) when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_calibrate() [9/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Performs camera calibration.

Parameters

objectPoints	vector of vectors of calibration pattern points in the calibration pattern coordinate space.
imagePoints	vector of vectors of the projections of calibration pattern points. imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i.
image_size	Size of the image used only to initialize the camera intrinsic matrix.
K	Output 3x3 floating-point camera intrinsic matrix \(\cameramatrix{A}\) . If fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
D	Output vector of distortion coefficients \(\distcoeffsfisheye\).
rvecs	Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. M -1).
tvecs	Output vector of translation vectors estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero. fisheye::CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when fisheye::CALIB_USE_INTRINSIC_GUESS is set too. fisheye::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization. It is the \(max(width,height)/\pi\) or the provided \(f_x\), \(f_y\) when fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_distortPoints() [1/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_distortPoints ( Mat undistorted, Mat distorted, Mat K, Mat D )

static

Distorts 2D points using fisheye model.

Parameters

undistorted	Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
alpha	The skew coefficient.
distorted	Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .

Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity. This means if you want to distort image points you have to multiply them with \(K^{-1}\).

fisheye_distortPoints() [2/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_distortPoints ( Mat undistorted, Mat distorted, Mat K, Mat D, double alpha )

static

Distorts 2D points using fisheye model.

Parameters

undistorted	Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
alpha	The skew coefficient.
distorted	Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .

Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity. This means if you want to distort image points you have to multiply them with \(K^{-1}\).

fisheye_estimateNewCameraMatrixForUndistortRectify() [1/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_estimateNewCameraMatrixForUndistortRectify ( Mat K, Mat D, in Vec2d image_size, Mat R, Mat P )

static

Estimates new camera intrinsic matrix for undistortion or rectification.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
image_size	Size of the image
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
new_size	the new size
fov_scale	Divisor for new focal length.

fisheye_estimateNewCameraMatrixForUndistortRectify() [2/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_estimateNewCameraMatrixForUndistortRectify ( Mat K, Mat D, in Vec2d image_size, Mat R, Mat P, double balance )

static

Estimates new camera intrinsic matrix for undistortion or rectification.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
image_size	Size of the image
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
new_size	the new size
fov_scale	Divisor for new focal length.

fisheye_estimateNewCameraMatrixForUndistortRectify() [3/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_estimateNewCameraMatrixForUndistortRectify ( Mat K, Mat D, in Vec2d image_size, Mat R, Mat P, double balance, in Vec2d new_size )

static

Estimates new camera intrinsic matrix for undistortion or rectification.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
image_size	Size of the image
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
new_size	the new size
fov_scale	Divisor for new focal length.

fisheye_estimateNewCameraMatrixForUndistortRectify() [4/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_estimateNewCameraMatrixForUndistortRectify ( Mat K, Mat D, in Vec2d image_size, Mat R, Mat P, double balance, in Vec2d new_size, double fov_scale )

static

Estimates new camera intrinsic matrix for undistortion or rectification.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
image_size	Size of the image
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
new_size	the new size
fov_scale	Divisor for new focal length.

fisheye_estimateNewCameraMatrixForUndistortRectify() [5/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_estimateNewCameraMatrixForUndistortRectify ( Mat K, Mat D, in(double width, double height) image_size, Mat R, Mat P )

static

Estimates new camera intrinsic matrix for undistortion or rectification.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
image_size	Size of the image
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
new_size	the new size
fov_scale	Divisor for new focal length.

fisheye_estimateNewCameraMatrixForUndistortRectify() [6/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_estimateNewCameraMatrixForUndistortRectify ( Mat K, Mat D, in(double width, double height) image_size, Mat R, Mat P, double balance )

static

Estimates new camera intrinsic matrix for undistortion or rectification.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
image_size	Size of the image
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
new_size	the new size
fov_scale	Divisor for new focal length.

fisheye_estimateNewCameraMatrixForUndistortRectify() [7/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_estimateNewCameraMatrixForUndistortRectify ( Mat K, Mat D, in(double width, double height) image_size, Mat R, Mat P, double balance, in(double width, double height) new_size )

static

Estimates new camera intrinsic matrix for undistortion or rectification.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
image_size	Size of the image
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
new_size	the new size
fov_scale	Divisor for new focal length.

fisheye_estimateNewCameraMatrixForUndistortRectify() [8/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_estimateNewCameraMatrixForUndistortRectify ( Mat K, Mat D, in(double width, double height) image_size, Mat R, Mat P, double balance, in(double width, double height) new_size, double fov_scale )

static

Estimates new camera intrinsic matrix for undistortion or rectification.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
image_size	Size of the image
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
new_size	the new size
fov_scale	Divisor for new focal length.

fisheye_estimateNewCameraMatrixForUndistortRectify() [9/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_estimateNewCameraMatrixForUndistortRectify ( Mat K, Mat D, Size image_size, Mat R, Mat P )

static

Estimates new camera intrinsic matrix for undistortion or rectification.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
image_size	Size of the image
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
new_size	the new size
fov_scale	Divisor for new focal length.

fisheye_estimateNewCameraMatrixForUndistortRectify() [10/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_estimateNewCameraMatrixForUndistortRectify ( Mat K, Mat D, Size image_size, Mat R, Mat P, double balance )

static

Estimates new camera intrinsic matrix for undistortion or rectification.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
image_size	Size of the image
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
new_size	the new size
fov_scale	Divisor for new focal length.

fisheye_estimateNewCameraMatrixForUndistortRectify() [11/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_estimateNewCameraMatrixForUndistortRectify ( Mat K, Mat D, Size image_size, Mat R, Mat P, double balance, Size new_size )

static

Estimates new camera intrinsic matrix for undistortion or rectification.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
image_size	Size of the image
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
new_size	the new size
fov_scale	Divisor for new focal length.

fisheye_estimateNewCameraMatrixForUndistortRectify() [12/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_estimateNewCameraMatrixForUndistortRectify ( Mat K, Mat D, Size image_size, Mat R, Mat P, double balance, Size new_size, double fov_scale )

static

Estimates new camera intrinsic matrix for undistortion or rectification.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
image_size	Size of the image
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
new_size	the new size
fov_scale	Divisor for new focal length.

fisheye_initUndistortRectifyMap() [1/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_initUndistortRectifyMap ( Mat K, Mat D, Mat R, Mat P, in Vec2d size, int m1type, Mat map1, Mat map2 )

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.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
size	Undistorted image size.
m1type	Type of the first output map that can be CV_32FC1 or CV_16SC2 . See #convertMaps for details.
map1	The first output map.
map2	The second output map.

fisheye_initUndistortRectifyMap() [2/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_initUndistortRectifyMap ( Mat K, Mat D, Mat R, Mat P, in(double width, double height) size, int m1type, Mat map1, Mat map2 )

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.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
size	Undistorted image size.
m1type	Type of the first output map that can be CV_32FC1 or CV_16SC2 . See #convertMaps for details.
map1	The first output map.
map2	The second output map.

fisheye_initUndistortRectifyMap() [3/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_initUndistortRectifyMap ( Mat K, Mat D, Mat R, Mat P, Size size, int m1type, Mat map1, Mat map2 )

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.

Parameters

K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
size	Undistorted image size.
m1type	Type of the first output map that can be CV_32FC1 or CV_16SC2 . See #convertMaps for details.
map1	The first output map.
map2	The second output map.

fisheye_projectPoints() [1/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_projectPoints ( Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D )

static

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

fisheye_projectPoints() [2/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_projectPoints ( Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D, double alpha )

static

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

fisheye_projectPoints() [3/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_projectPoints ( Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D, double alpha, Mat jacobian )

static

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

fisheye_solvePnP() [1/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.fisheye_solvePnP ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec )

static

Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients (4x1/1x4).
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods: P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): need 4 input points to return a unique solution. SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order: point 0: [-squareLength / 2, squareLength / 2, 0] point 1: [ squareLength / 2, squareLength / 2, 0] point 2: [ squareLength / 2, -squareLength / 2, 0] point 3: [-squareLength / 2, -squareLength / 2, 0] for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. Check there and Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_solvePnP() [2/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.fisheye_solvePnP ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess )

static

Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients (4x1/1x4).
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods: P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): need 4 input points to return a unique solution. SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order: point 0: [-squareLength / 2, squareLength / 2, 0] point 1: [ squareLength / 2, squareLength / 2, 0] point 2: [ squareLength / 2, -squareLength / 2, 0] point 3: [-squareLength / 2, -squareLength / 2, 0] for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. Check there and Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_solvePnP() [3/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.fisheye_solvePnP ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int flags )

static

Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients (4x1/1x4).
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods: P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): need 4 input points to return a unique solution. SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order: point 0: [-squareLength / 2, squareLength / 2, 0] point 1: [ squareLength / 2, squareLength / 2, 0] point 2: [ squareLength / 2, -squareLength / 2, 0] point 3: [-squareLength / 2, -squareLength / 2, 0] for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. Check there and Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_solvePnP() [4/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.fisheye_solvePnP ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int flags, in Vec3d criteria )

static

Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients (4x1/1x4).
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods: P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): need 4 input points to return a unique solution. SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order: point 0: [-squareLength / 2, squareLength / 2, 0] point 1: [ squareLength / 2, squareLength / 2, 0] point 2: [ squareLength / 2, -squareLength / 2, 0] point 3: [-squareLength / 2, -squareLength / 2, 0] for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. Check there and Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_solvePnP() [5/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.fisheye_solvePnP ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int flags, in(double type, double maxCount, double epsilon) criteria )

static

Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients (4x1/1x4).
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods: P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): need 4 input points to return a unique solution. SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order: point 0: [-squareLength / 2, squareLength / 2, 0] point 1: [ squareLength / 2, squareLength / 2, 0] point 2: [ squareLength / 2, -squareLength / 2, 0] point 3: [-squareLength / 2, -squareLength / 2, 0] for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. Check there and Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_solvePnP() [6/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.fisheye_solvePnP ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int flags, TermCriteria criteria )

static

Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients (4x1/1x4).
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods: P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): need 4 input points to return a unique solution. SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order: point 0: [-squareLength / 2, squareLength / 2, 0] point 1: [ squareLength / 2, squareLength / 2, 0] point 2: [ squareLength / 2, -squareLength / 2, 0] point 3: [-squareLength / 2, -squareLength / 2, 0] for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. Check there and Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_stereoCalibrate() [1/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat T )

static

fisheye_stereoCalibrate() [2/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat T, int flags )

static

fisheye_stereoCalibrate() [3/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat T, int flags, in Vec3d criteria )

static

fisheye_stereoCalibrate() [4/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs )

static

Performs stereo calibration.

Parameters

objectPoints	Vector of vectors of the calibration pattern points.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera.
K1	Input/output first camera intrinsic matrix: \(\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\) , \(j = 0,\, 1\) . If any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, some or all of the matrix components must be initialized.
D1	Input/output vector of distortion coefficients \(\distcoeffsfisheye\) of 4 elements.
K2	Input/output second camera intrinsic matrix. The parameter is similar to K1 .
D2	Input/output lens distortion coefficients for the second camera. The parameter is similar to D1 .
imageSize	Size of the image used only to initialize camera intrinsic matrix.
R	Output rotation matrix between the 1st and the 2nd camera coordinate systems.
T	Output translation vector between the coordinate systems of the cameras.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices are estimated. fisheye::CALIB_USE_INTRINSIC_GUESS K1, K2 contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center (imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_stereoCalibrate() [5/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs, int flags )

static

Performs stereo calibration.

Parameters

objectPoints	Vector of vectors of the calibration pattern points.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera.
K1	Input/output first camera intrinsic matrix: \(\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\) , \(j = 0,\, 1\) . If any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, some or all of the matrix components must be initialized.
D1	Input/output vector of distortion coefficients \(\distcoeffsfisheye\) of 4 elements.
K2	Input/output second camera intrinsic matrix. The parameter is similar to K1 .
D2	Input/output lens distortion coefficients for the second camera. The parameter is similar to D1 .
imageSize	Size of the image used only to initialize camera intrinsic matrix.
R	Output rotation matrix between the 1st and the 2nd camera coordinate systems.
T	Output translation vector between the coordinate systems of the cameras.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices are estimated. fisheye::CALIB_USE_INTRINSIC_GUESS K1, K2 contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center (imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_stereoCalibrate() [6/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs, int flags, in Vec3d criteria )

static

Performs stereo calibration.

Parameters

objectPoints	Vector of vectors of the calibration pattern points.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera.
K1	Input/output first camera intrinsic matrix: \(\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\) , \(j = 0,\, 1\) . If any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, some or all of the matrix components must be initialized.
D1	Input/output vector of distortion coefficients \(\distcoeffsfisheye\) of 4 elements.
K2	Input/output second camera intrinsic matrix. The parameter is similar to K1 .
D2	Input/output lens distortion coefficients for the second camera. The parameter is similar to D1 .
imageSize	Size of the image used only to initialize camera intrinsic matrix.
R	Output rotation matrix between the 1st and the 2nd camera coordinate systems.
T	Output translation vector between the coordinate systems of the cameras.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices are estimated. fisheye::CALIB_USE_INTRINSIC_GUESS K1, K2 contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center (imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_stereoCalibrate() [7/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat T )

static

fisheye_stereoCalibrate() [8/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat T, int flags )

static

fisheye_stereoCalibrate() [9/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat T, int flags, in(double type, double maxCount, double epsilon) criteria )

static

fisheye_stereoCalibrate() [10/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs )

static

Performs stereo calibration.

Parameters

objectPoints	Vector of vectors of the calibration pattern points.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera.
K1	Input/output first camera intrinsic matrix: \(\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\) , \(j = 0,\, 1\) . If any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, some or all of the matrix components must be initialized.
D1	Input/output vector of distortion coefficients \(\distcoeffsfisheye\) of 4 elements.
K2	Input/output second camera intrinsic matrix. The parameter is similar to K1 .
D2	Input/output lens distortion coefficients for the second camera. The parameter is similar to D1 .
imageSize	Size of the image used only to initialize camera intrinsic matrix.
R	Output rotation matrix between the 1st and the 2nd camera coordinate systems.
T	Output translation vector between the coordinate systems of the cameras.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices are estimated. fisheye::CALIB_USE_INTRINSIC_GUESS K1, K2 contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center (imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_stereoCalibrate() [11/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs, int flags )

static

Performs stereo calibration.

Parameters

objectPoints	Vector of vectors of the calibration pattern points.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera.
K1	Input/output first camera intrinsic matrix: \(\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\) , \(j = 0,\, 1\) . If any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, some or all of the matrix components must be initialized.
D1	Input/output vector of distortion coefficients \(\distcoeffsfisheye\) of 4 elements.
K2	Input/output second camera intrinsic matrix. The parameter is similar to K1 .
D2	Input/output lens distortion coefficients for the second camera. The parameter is similar to D1 .
imageSize	Size of the image used only to initialize camera intrinsic matrix.
R	Output rotation matrix between the 1st and the 2nd camera coordinate systems.
T	Output translation vector between the coordinate systems of the cameras.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices are estimated. fisheye::CALIB_USE_INTRINSIC_GUESS K1, K2 contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center (imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_stereoCalibrate() [12/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat T, List< Mat > rvecs, List< Mat > tvecs, int flags, in(double type, double maxCount, double epsilon) criteria )

static

Performs stereo calibration.

Parameters

objectPoints	Vector of vectors of the calibration pattern points.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera.
K1	Input/output first camera intrinsic matrix: \(\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\) , \(j = 0,\, 1\) . If any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, some or all of the matrix components must be initialized.
D1	Input/output vector of distortion coefficients \(\distcoeffsfisheye\) of 4 elements.
K2	Input/output second camera intrinsic matrix. The parameter is similar to K1 .
D2	Input/output lens distortion coefficients for the second camera. The parameter is similar to D1 .
imageSize	Size of the image used only to initialize camera intrinsic matrix.
R	Output rotation matrix between the 1st and the 2nd camera coordinate systems.
T	Output translation vector between the coordinate systems of the cameras.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices are estimated. fisheye::CALIB_USE_INTRINSIC_GUESS K1, K2 contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center (imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_stereoCalibrate() [13/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.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

fisheye_stereoCalibrate() [14/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.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

fisheye_stereoCalibrate() [15/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.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

fisheye_stereoCalibrate() [16/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Performs stereo calibration.

Parameters

objectPoints	Vector of vectors of the calibration pattern points.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera.
K1	Input/output first camera intrinsic matrix: \(\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\) , \(j = 0,\, 1\) . If any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, some or all of the matrix components must be initialized.
D1	Input/output vector of distortion coefficients \(\distcoeffsfisheye\) of 4 elements.
K2	Input/output second camera intrinsic matrix. The parameter is similar to K1 .
D2	Input/output lens distortion coefficients for the second camera. The parameter is similar to D1 .
imageSize	Size of the image used only to initialize camera intrinsic matrix.
R	Output rotation matrix between the 1st and the 2nd camera coordinate systems.
T	Output translation vector between the coordinate systems of the cameras.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices are estimated. fisheye::CALIB_USE_INTRINSIC_GUESS K1, K2 contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center (imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_stereoCalibrate() [17/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Performs stereo calibration.

Parameters

objectPoints	Vector of vectors of the calibration pattern points.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera.
K1	Input/output first camera intrinsic matrix: \(\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\) , \(j = 0,\, 1\) . If any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, some or all of the matrix components must be initialized.
D1	Input/output vector of distortion coefficients \(\distcoeffsfisheye\) of 4 elements.
K2	Input/output second camera intrinsic matrix. The parameter is similar to K1 .
D2	Input/output lens distortion coefficients for the second camera. The parameter is similar to D1 .
imageSize	Size of the image used only to initialize camera intrinsic matrix.
R	Output rotation matrix between the 1st and the 2nd camera coordinate systems.
T	Output translation vector between the coordinate systems of the cameras.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices are estimated. fisheye::CALIB_USE_INTRINSIC_GUESS K1, K2 contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center (imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_stereoCalibrate() [18/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Performs stereo calibration.

Parameters

objectPoints	Vector of vectors of the calibration pattern points.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera.
K1	Input/output first camera intrinsic matrix: \(\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\) , \(j = 0,\, 1\) . If any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, some or all of the matrix components must be initialized.
D1	Input/output vector of distortion coefficients \(\distcoeffsfisheye\) of 4 elements.
K2	Input/output second camera intrinsic matrix. The parameter is similar to K1 .
D2	Input/output lens distortion coefficients for the second camera. The parameter is similar to D1 .
imageSize	Size of the image used only to initialize camera intrinsic matrix.
R	Output rotation matrix between the 1st and the 2nd camera coordinate systems.
T	Output translation vector between the coordinate systems of the cameras.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
flags	Different flags that may be zero or a combination of the following values: fisheye::CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices are estimated. fisheye::CALIB_USE_INTRINSIC_GUESS K1, K2 contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center (imageSize is used), and focal distances are computed in a least-squares fashion. fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration of intrinsic optimization. fisheye::CALIB_CHECK_COND The functions will check validity of condition number. fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. fisheye::CALIB_FIX_K1,..., fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay zero.
criteria	Termination criteria for the iterative optimization algorithm.

fisheye_stereoRectify() [1/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoRectify ( Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags )

static

Stereo rectification for fisheye camera model.

Parameters

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 \(4 \times 4\) disparity-to-depth 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.

fisheye_stereoRectify() [2/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoRectify ( Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in Vec2d newImageSize )

static

Stereo rectification for fisheye camera model.

Parameters

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 \(4 \times 4\) disparity-to-depth 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.

fisheye_stereoRectify() [3/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoRectify ( Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in Vec2d newImageSize, double balance )

static

Stereo rectification for fisheye camera model.

Parameters

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 \(4 \times 4\) disparity-to-depth 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.

fisheye_stereoRectify() [4/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoRectify ( Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in Vec2d newImageSize, double balance, double fov_scale )

static

Stereo rectification for fisheye camera model.

Parameters

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 \(4 \times 4\) disparity-to-depth 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.

fisheye_stereoRectify() [5/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoRectify ( Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags )

static

Stereo rectification for fisheye camera model.

Parameters

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 \(4 \times 4\) disparity-to-depth 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.

fisheye_stereoRectify() [6/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoRectify ( Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in(double width, double height) newImageSize )

static

Stereo rectification for fisheye camera model.

Parameters

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 \(4 \times 4\) disparity-to-depth 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.

fisheye_stereoRectify() [7/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoRectify ( Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in(double width, double height) newImageSize, double balance )

static

Stereo rectification for fisheye camera model.

Parameters

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 \(4 \times 4\) disparity-to-depth 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.

fisheye_stereoRectify() [8/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_stereoRectify ( Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in(double width, double height) newImageSize, double balance, double fov_scale )

static

Stereo rectification for fisheye camera model.

Parameters

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 \(4 \times 4\) disparity-to-depth 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.

fisheye_stereoRectify() [9/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Stereo rectification for fisheye camera model.

Parameters

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 \(4 \times 4\) disparity-to-depth 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.

fisheye_stereoRectify() [10/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Stereo rectification for fisheye camera model.

Parameters

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 \(4 \times 4\) disparity-to-depth 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.

fisheye_stereoRectify() [11/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Stereo rectification for fisheye camera model.

Parameters

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 \(4 \times 4\) disparity-to-depth 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.

fisheye_stereoRectify() [12/12]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Stereo rectification for fisheye camera model.

Parameters

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 \(4 \times 4\) disparity-to-depth 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.

fisheye_undistortImage() [1/5]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_undistortImage ( Mat distorted, Mat undistorted, Mat K, Mat D )

static

Transforms an image to compensate for fisheye lens distortion.

Parameters

distorted	image with fisheye lens distortion.
undistorted	Output image with compensated fisheye lens distortion.
K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
Knew	Camera intrinsic matrix of the distorted image. By default, it is the identity matrix but you may additionally scale and shift the result by using a different matrix.
new_size	the new size

The function transforms an image to compensate radial and tangential lens distortion.

The function is simply a combination of #fisheye::initUndistortRectifyMap (with unity R ) and #remap (with bilinear interpolation). See the former function for details of the transformation being performed.

See below the results of undistortImage.

• a) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, k_4, k_5, k_6) of distortion were optimized under calibration)
  • b) result of #fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, k_3, k_4) of fisheye distortion were optimized under calibration)
  • c) original image was captured with fisheye lens

Pictures a) and b) almost the same. But if we consider points of image located far from the center of image, we can notice that on image a) these points are distorted.

fisheye_undistortImage() [2/5]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_undistortImage ( Mat distorted, Mat undistorted, Mat K, Mat D, Mat Knew )

static

Transforms an image to compensate for fisheye lens distortion.

Parameters

distorted	image with fisheye lens distortion.
undistorted	Output image with compensated fisheye lens distortion.
K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
Knew	Camera intrinsic matrix of the distorted image. By default, it is the identity matrix but you may additionally scale and shift the result by using a different matrix.
new_size	the new size

The function transforms an image to compensate radial and tangential lens distortion.

The function is simply a combination of #fisheye::initUndistortRectifyMap (with unity R ) and #remap (with bilinear interpolation). See the former function for details of the transformation being performed.

See below the results of undistortImage.

• a) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, k_4, k_5, k_6) of distortion were optimized under calibration)
  • b) result of #fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, k_3, k_4) of fisheye distortion were optimized under calibration)
  • c) original image was captured with fisheye lens

Pictures a) and b) almost the same. But if we consider points of image located far from the center of image, we can notice that on image a) these points are distorted.

fisheye_undistortImage() [3/5]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_undistortImage ( Mat distorted, Mat undistorted, Mat K, Mat D, Mat Knew, in Vec2d new_size )

static

Transforms an image to compensate for fisheye lens distortion.

Parameters

distorted	image with fisheye lens distortion.
undistorted	Output image with compensated fisheye lens distortion.
K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
Knew	Camera intrinsic matrix of the distorted image. By default, it is the identity matrix but you may additionally scale and shift the result by using a different matrix.
new_size	the new size

The function transforms an image to compensate radial and tangential lens distortion.

The function is simply a combination of #fisheye::initUndistortRectifyMap (with unity R ) and #remap (with bilinear interpolation). See the former function for details of the transformation being performed.

See below the results of undistortImage.

• a) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, k_4, k_5, k_6) of distortion were optimized under calibration)
  • b) result of #fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, k_3, k_4) of fisheye distortion were optimized under calibration)
  • c) original image was captured with fisheye lens

Pictures a) and b) almost the same. But if we consider points of image located far from the center of image, we can notice that on image a) these points are distorted.

fisheye_undistortImage() [4/5]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_undistortImage ( Mat distorted, Mat undistorted, Mat K, Mat D, Mat Knew, in(double width, double height) new_size )

static

Transforms an image to compensate for fisheye lens distortion.

Parameters

distorted	image with fisheye lens distortion.
undistorted	Output image with compensated fisheye lens distortion.
K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
Knew	Camera intrinsic matrix of the distorted image. By default, it is the identity matrix but you may additionally scale and shift the result by using a different matrix.
new_size	the new size

The function transforms an image to compensate radial and tangential lens distortion.

The function is simply a combination of #fisheye::initUndistortRectifyMap (with unity R ) and #remap (with bilinear interpolation). See the former function for details of the transformation being performed.

See below the results of undistortImage.

• a) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, k_4, k_5, k_6) of distortion were optimized under calibration)
  • b) result of #fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, k_3, k_4) of fisheye distortion were optimized under calibration)
  • c) original image was captured with fisheye lens

Pictures a) and b) almost the same. But if we consider points of image located far from the center of image, we can notice that on image a) these points are distorted.

fisheye_undistortImage() [5/5]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_undistortImage ( Mat distorted, Mat undistorted, Mat K, Mat D, Mat Knew, Size new_size )

static

Transforms an image to compensate for fisheye lens distortion.

Parameters

distorted	image with fisheye lens distortion.
undistorted	Output image with compensated fisheye lens distortion.
K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
Knew	Camera intrinsic matrix of the distorted image. By default, it is the identity matrix but you may additionally scale and shift the result by using a different matrix.
new_size	the new size

The function transforms an image to compensate radial and tangential lens distortion.

The function is simply a combination of #fisheye::initUndistortRectifyMap (with unity R ) and #remap (with bilinear interpolation). See the former function for details of the transformation being performed.

See below the results of undistortImage.

• a) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, k_4, k_5, k_6) of distortion were optimized under calibration)
  • b) result of #fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, k_3, k_4) of fisheye distortion were optimized under calibration)
  • c) original image was captured with fisheye lens

Pictures a) and b) almost the same. But if we consider points of image located far from the center of image, we can notice that on image a) these points are distorted.

fisheye_undistortPoints() [1/6]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_undistortPoints ( Mat distorted, Mat undistorted, Mat K, Mat D )

static

Undistorts 2D points using fisheye model.

Parameters

distorted	Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
criteria	Termination criteria
undistorted	Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .

fisheye_undistortPoints() [2/6]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_undistortPoints ( Mat distorted, Mat undistorted, Mat K, Mat D, Mat R )

static

Undistorts 2D points using fisheye model.

Parameters

distorted	Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
criteria	Termination criteria
undistorted	Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .

fisheye_undistortPoints() [3/6]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_undistortPoints ( Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P )

static

Undistorts 2D points using fisheye model.

Parameters

distorted	Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
criteria	Termination criteria
undistorted	Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .

fisheye_undistortPoints() [4/6]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_undistortPoints ( Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P, in Vec3d criteria )

static

Undistorts 2D points using fisheye model.

Parameters

distorted	Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
criteria	Termination criteria
undistorted	Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .

fisheye_undistortPoints() [5/6]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_undistortPoints ( Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P, in(double type, double maxCount, double epsilon) criteria )

static

Undistorts 2D points using fisheye model.

Parameters

distorted	Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
criteria	Termination criteria
undistorted	Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .

fisheye_undistortPoints() [6/6]

static void OpenCVForUnity.Calib3dModule.Calib3d.fisheye_undistortPoints ( Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P, TermCriteria criteria )

static

Undistorts 2D points using fisheye model.

Parameters

distorted	Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
K	Camera intrinsic matrix \(cameramatrix{K}\).
D	Input vector of distortion coefficients \(\distcoeffsfisheye\).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4)
criteria	Termination criteria
undistorted	Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .

getDefaultNewCameraMatrix() [1/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getDefaultNewCameraMatrix ( Mat cameraMatrix )

static

Returns the default new camera matrix.

The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).

In the latter case, the new camera matrix will be:

\[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\]

where \(f_x\) and \(f_y\) are \((0,0)\) and \((1,1)\) elements of cameraMatrix, respectively.

By default, the undistortion functions in OpenCV (see initUndistortRectifyMap, undistort) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.

Parameters

cameraMatrix	Input camera matrix.
imgsize	Camera view image size in pixels.
centerPrincipalPoint	Location of the principal point in the new camera matrix. The parameter indicates whether this location should be at the image center or not.

getDefaultNewCameraMatrix() [2/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getDefaultNewCameraMatrix ( Mat cameraMatrix, in Vec2d imgsize )

static

Returns the default new camera matrix.

The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).

In the latter case, the new camera matrix will be:

\[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\]

where \(f_x\) and \(f_y\) are \((0,0)\) and \((1,1)\) elements of cameraMatrix, respectively.

By default, the undistortion functions in OpenCV (see initUndistortRectifyMap, undistort) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.

Parameters

cameraMatrix	Input camera matrix.
imgsize	Camera view image size in pixels.
centerPrincipalPoint	Location of the principal point in the new camera matrix. The parameter indicates whether this location should be at the image center or not.

getDefaultNewCameraMatrix() [3/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getDefaultNewCameraMatrix ( Mat cameraMatrix, in Vec2d imgsize, bool centerPrincipalPoint )

static

Returns the default new camera matrix.

The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).

In the latter case, the new camera matrix will be:

\[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\]

where \(f_x\) and \(f_y\) are \((0,0)\) and \((1,1)\) elements of cameraMatrix, respectively.

By default, the undistortion functions in OpenCV (see initUndistortRectifyMap, undistort) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.

Parameters

cameraMatrix	Input camera matrix.
imgsize	Camera view image size in pixels.
centerPrincipalPoint	Location of the principal point in the new camera matrix. The parameter indicates whether this location should be at the image center or not.

getDefaultNewCameraMatrix() [4/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getDefaultNewCameraMatrix ( Mat cameraMatrix, in(double width, double height) imgsize )

static

Returns the default new camera matrix.

The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).

In the latter case, the new camera matrix will be:

\[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\]

where \(f_x\) and \(f_y\) are \((0,0)\) and \((1,1)\) elements of cameraMatrix, respectively.

By default, the undistortion functions in OpenCV (see initUndistortRectifyMap, undistort) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.

Parameters

cameraMatrix	Input camera matrix.
imgsize	Camera view image size in pixels.
centerPrincipalPoint	Location of the principal point in the new camera matrix. The parameter indicates whether this location should be at the image center or not.

getDefaultNewCameraMatrix() [5/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getDefaultNewCameraMatrix ( Mat cameraMatrix, in(double width, double height) imgsize, bool centerPrincipalPoint )

static

Returns the default new camera matrix.

The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).

In the latter case, the new camera matrix will be:

\[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\]

where \(f_x\) and \(f_y\) are \((0,0)\) and \((1,1)\) elements of cameraMatrix, respectively.

By default, the undistortion functions in OpenCV (see initUndistortRectifyMap, undistort) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.

Parameters

cameraMatrix	Input camera matrix.
imgsize	Camera view image size in pixels.
centerPrincipalPoint	Location of the principal point in the new camera matrix. The parameter indicates whether this location should be at the image center or not.

getDefaultNewCameraMatrix() [6/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getDefaultNewCameraMatrix ( Mat cameraMatrix, Size imgsize )

static

Returns the default new camera matrix.

The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).

In the latter case, the new camera matrix will be:

\[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\]

where \(f_x\) and \(f_y\) are \((0,0)\) and \((1,1)\) elements of cameraMatrix, respectively.

By default, the undistortion functions in OpenCV (see initUndistortRectifyMap, undistort) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.

Parameters

cameraMatrix	Input camera matrix.
imgsize	Camera view image size in pixels.
centerPrincipalPoint	Location of the principal point in the new camera matrix. The parameter indicates whether this location should be at the image center or not.

getDefaultNewCameraMatrix() [7/7]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getDefaultNewCameraMatrix ( Mat cameraMatrix, Size imgsize, bool centerPrincipalPoint )

static

Returns the default new camera matrix.

The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).

In the latter case, the new camera matrix will be:

\[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\]

where \(f_x\) and \(f_y\) are \((0,0)\) and \((1,1)\) elements of cameraMatrix, respectively.

By default, the undistortion functions in OpenCV (see initUndistortRectifyMap, undistort) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.

Parameters

cameraMatrix	Input camera matrix.
imgsize	Camera view image size in pixels.
centerPrincipalPoint	Location of the principal point in the new camera matrix. The parameter indicates whether this location should be at the image center or not.

getOptimalNewCameraMatrix() [1/12]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getOptimalNewCameraMatrix ( Mat cameraMatrix, Mat distCoeffs, in Vec2d imageSize, double alpha )

static

Returns the new camera intrinsic matrix based on the free scaling parameter.

Parameters

cameraMatrix	Input camera intrinsic matrix.
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
imageSize	Original image size.
alpha	Free scaling parameter between 0 (when all the pixels in the undistorted image are valid) and 1 (when all the source image pixels are retained in the undistorted image). See stereoRectify for details.
newImgSize	Image size after rectification. By default, it is set to imageSize .
validPixROI	Optional output rectangle that outlines all-good-pixels region in the undistorted image. See roi1, roi2 description in stereoRectify .
centerPrincipalPoint	Optional flag that indicates whether in the new camera intrinsic matrix the principal point should be at the image center or not. By default, the principal point is chosen to best fit a subset of the source image (determined by alpha) to the corrected image.

Returns

new_camera_matrix Output new camera intrinsic matrix.

The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. When alpha>0 , the undistorted result is likely to have some black pixels corresponding to "virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to initUndistortRectifyMap to produce the maps for #remap .

getOptimalNewCameraMatrix() [2/12]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getOptimalNewCameraMatrix ( Mat cameraMatrix, Mat distCoeffs, in Vec2d imageSize, double alpha, in Vec2d newImgSize )

static

Returns the new camera intrinsic matrix based on the free scaling parameter.

Parameters

cameraMatrix	Input camera intrinsic matrix.
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
imageSize	Original image size.
alpha	Free scaling parameter between 0 (when all the pixels in the undistorted image are valid) and 1 (when all the source image pixels are retained in the undistorted image). See stereoRectify for details.
newImgSize	Image size after rectification. By default, it is set to imageSize .
validPixROI	Optional output rectangle that outlines all-good-pixels region in the undistorted image. See roi1, roi2 description in stereoRectify .
centerPrincipalPoint	Optional flag that indicates whether in the new camera intrinsic matrix the principal point should be at the image center or not. By default, the principal point is chosen to best fit a subset of the source image (determined by alpha) to the corrected image.

Returns

new_camera_matrix Output new camera intrinsic matrix.

The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. When alpha>0 , the undistorted result is likely to have some black pixels corresponding to "virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to initUndistortRectifyMap to produce the maps for #remap .

getOptimalNewCameraMatrix() [3/12]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getOptimalNewCameraMatrix ( Mat cameraMatrix, Mat distCoeffs, in Vec2d imageSize, double alpha, in Vec2d newImgSize, out Vec4i validPixROI )

static

Returns the new camera intrinsic matrix based on the free scaling parameter.

Parameters

cameraMatrix	Input camera intrinsic matrix.
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
imageSize	Original image size.
alpha	Free scaling parameter between 0 (when all the pixels in the undistorted image are valid) and 1 (when all the source image pixels are retained in the undistorted image). See stereoRectify for details.
newImgSize	Image size after rectification. By default, it is set to imageSize .
validPixROI	Optional output rectangle that outlines all-good-pixels region in the undistorted image. See roi1, roi2 description in stereoRectify .
centerPrincipalPoint	Optional flag that indicates whether in the new camera intrinsic matrix the principal point should be at the image center or not. By default, the principal point is chosen to best fit a subset of the source image (determined by alpha) to the corrected image.

Returns

new_camera_matrix Output new camera intrinsic matrix.

The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. When alpha>0 , the undistorted result is likely to have some black pixels corresponding to "virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to initUndistortRectifyMap to produce the maps for #remap .

getOptimalNewCameraMatrix() [4/12]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getOptimalNewCameraMatrix ( Mat cameraMatrix, Mat distCoeffs, in Vec2d imageSize, double alpha, in Vec2d newImgSize, out Vec4i validPixROI, bool centerPrincipalPoint )

static

Returns the new camera intrinsic matrix based on the free scaling parameter.

Parameters

cameraMatrix	Input camera intrinsic matrix.
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
imageSize	Original image size.
alpha	Free scaling parameter between 0 (when all the pixels in the undistorted image are valid) and 1 (when all the source image pixels are retained in the undistorted image). See stereoRectify for details.
newImgSize	Image size after rectification. By default, it is set to imageSize .
validPixROI	Optional output rectangle that outlines all-good-pixels region in the undistorted image. See roi1, roi2 description in stereoRectify .
centerPrincipalPoint	Optional flag that indicates whether in the new camera intrinsic matrix the principal point should be at the image center or not. By default, the principal point is chosen to best fit a subset of the source image (determined by alpha) to the corrected image.

Returns

new_camera_matrix Output new camera intrinsic matrix.

The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. When alpha>0 , the undistorted result is likely to have some black pixels corresponding to "virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to initUndistortRectifyMap to produce the maps for #remap .

getOptimalNewCameraMatrix() [5/12]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getOptimalNewCameraMatrix ( Mat cameraMatrix, Mat distCoeffs, in(double width, double height) imageSize, double alpha )

static

Returns the new camera intrinsic matrix based on the free scaling parameter.

Parameters

cameraMatrix	Input camera intrinsic matrix.
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
imageSize	Original image size.
alpha	Free scaling parameter between 0 (when all the pixels in the undistorted image are valid) and 1 (when all the source image pixels are retained in the undistorted image). See stereoRectify for details.
newImgSize	Image size after rectification. By default, it is set to imageSize .
validPixROI	Optional output rectangle that outlines all-good-pixels region in the undistorted image. See roi1, roi2 description in stereoRectify .
centerPrincipalPoint	Optional flag that indicates whether in the new camera intrinsic matrix the principal point should be at the image center or not. By default, the principal point is chosen to best fit a subset of the source image (determined by alpha) to the corrected image.

Returns

new_camera_matrix Output new camera intrinsic matrix.

The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. When alpha>0 , the undistorted result is likely to have some black pixels corresponding to "virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to initUndistortRectifyMap to produce the maps for #remap .

getOptimalNewCameraMatrix() [6/12]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getOptimalNewCameraMatrix ( Mat cameraMatrix, Mat distCoeffs, in(double width, double height) imageSize, double alpha, in(double width, double height) newImgSize )

static

Returns the new camera intrinsic matrix based on the free scaling parameter.

Parameters

cameraMatrix	Input camera intrinsic matrix.
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
imageSize	Original image size.
alpha	Free scaling parameter between 0 (when all the pixels in the undistorted image are valid) and 1 (when all the source image pixels are retained in the undistorted image). See stereoRectify for details.
newImgSize	Image size after rectification. By default, it is set to imageSize .
validPixROI	Optional output rectangle that outlines all-good-pixels region in the undistorted image. See roi1, roi2 description in stereoRectify .
centerPrincipalPoint	Optional flag that indicates whether in the new camera intrinsic matrix the principal point should be at the image center or not. By default, the principal point is chosen to best fit a subset of the source image (determined by alpha) to the corrected image.

Returns

new_camera_matrix Output new camera intrinsic matrix.

The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. When alpha>0 , the undistorted result is likely to have some black pixels corresponding to "virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to initUndistortRectifyMap to produce the maps for #remap .

getOptimalNewCameraMatrix() [7/12]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getOptimalNewCameraMatrix ( Mat cameraMatrix, Mat distCoeffs, in(double width, double height) imageSize, double alpha, in(double width, double height) newImgSize, out(int x, int y, int width, int height) validPixROI )

static

Returns the new camera intrinsic matrix based on the free scaling parameter.

Parameters

cameraMatrix	Input camera intrinsic matrix.
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
imageSize	Original image size.
alpha	Free scaling parameter between 0 (when all the pixels in the undistorted image are valid) and 1 (when all the source image pixels are retained in the undistorted image). See stereoRectify for details.
newImgSize	Image size after rectification. By default, it is set to imageSize .
validPixROI	Optional output rectangle that outlines all-good-pixels region in the undistorted image. See roi1, roi2 description in stereoRectify .
centerPrincipalPoint	Optional flag that indicates whether in the new camera intrinsic matrix the principal point should be at the image center or not. By default, the principal point is chosen to best fit a subset of the source image (determined by alpha) to the corrected image.

Returns

new_camera_matrix Output new camera intrinsic matrix.

The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. When alpha>0 , the undistorted result is likely to have some black pixels corresponding to "virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to initUndistortRectifyMap to produce the maps for #remap .

getOptimalNewCameraMatrix() [8/12]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getOptimalNewCameraMatrix ( Mat cameraMatrix, Mat distCoeffs, in(double width, double height) imageSize, double alpha, in(double width, double height) newImgSize, out(int x, int y, int width, int height) validPixROI, bool centerPrincipalPoint )

static

Returns the new camera intrinsic matrix based on the free scaling parameter.

Parameters

cameraMatrix	Input camera intrinsic matrix.
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
imageSize	Original image size.
alpha	Free scaling parameter between 0 (when all the pixels in the undistorted image are valid) and 1 (when all the source image pixels are retained in the undistorted image). See stereoRectify for details.
newImgSize	Image size after rectification. By default, it is set to imageSize .
validPixROI	Optional output rectangle that outlines all-good-pixels region in the undistorted image. See roi1, roi2 description in stereoRectify .
centerPrincipalPoint	Optional flag that indicates whether in the new camera intrinsic matrix the principal point should be at the image center or not. By default, the principal point is chosen to best fit a subset of the source image (determined by alpha) to the corrected image.

Returns

new_camera_matrix Output new camera intrinsic matrix.

The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. When alpha>0 , the undistorted result is likely to have some black pixels corresponding to "virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to initUndistortRectifyMap to produce the maps for #remap .

getOptimalNewCameraMatrix() [9/12]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getOptimalNewCameraMatrix ( Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha )

static

Returns the new camera intrinsic matrix based on the free scaling parameter.

Parameters

cameraMatrix	Input camera intrinsic matrix.
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
imageSize	Original image size.
alpha	Free scaling parameter between 0 (when all the pixels in the undistorted image are valid) and 1 (when all the source image pixels are retained in the undistorted image). See stereoRectify for details.
newImgSize	Image size after rectification. By default, it is set to imageSize .
validPixROI	Optional output rectangle that outlines all-good-pixels region in the undistorted image. See roi1, roi2 description in stereoRectify .
centerPrincipalPoint	Optional flag that indicates whether in the new camera intrinsic matrix the principal point should be at the image center or not. By default, the principal point is chosen to best fit a subset of the source image (determined by alpha) to the corrected image.

Returns

new_camera_matrix Output new camera intrinsic matrix.

The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. When alpha>0 , the undistorted result is likely to have some black pixels corresponding to "virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to initUndistortRectifyMap to produce the maps for #remap .

getOptimalNewCameraMatrix() [10/12]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getOptimalNewCameraMatrix ( Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize )

static

Returns the new camera intrinsic matrix based on the free scaling parameter.

Parameters

cameraMatrix	Input camera intrinsic matrix.
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
imageSize	Original image size.
alpha	Free scaling parameter between 0 (when all the pixels in the undistorted image are valid) and 1 (when all the source image pixels are retained in the undistorted image). See stereoRectify for details.
newImgSize	Image size after rectification. By default, it is set to imageSize .
validPixROI	Optional output rectangle that outlines all-good-pixels region in the undistorted image. See roi1, roi2 description in stereoRectify .
centerPrincipalPoint	Optional flag that indicates whether in the new camera intrinsic matrix the principal point should be at the image center or not. By default, the principal point is chosen to best fit a subset of the source image (determined by alpha) to the corrected image.

Returns

new_camera_matrix Output new camera intrinsic matrix.

The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. When alpha>0 , the undistorted result is likely to have some black pixels corresponding to "virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to initUndistortRectifyMap to produce the maps for #remap .

getOptimalNewCameraMatrix() [11/12]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getOptimalNewCameraMatrix ( Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize, Rect validPixROI )

static

Returns the new camera intrinsic matrix based on the free scaling parameter.

Parameters

cameraMatrix	Input camera intrinsic matrix.
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
imageSize	Original image size.
alpha	Free scaling parameter between 0 (when all the pixels in the undistorted image are valid) and 1 (when all the source image pixels are retained in the undistorted image). See stereoRectify for details.
newImgSize	Image size after rectification. By default, it is set to imageSize .
validPixROI	Optional output rectangle that outlines all-good-pixels region in the undistorted image. See roi1, roi2 description in stereoRectify .
centerPrincipalPoint	Optional flag that indicates whether in the new camera intrinsic matrix the principal point should be at the image center or not. By default, the principal point is chosen to best fit a subset of the source image (determined by alpha) to the corrected image.

Returns

new_camera_matrix Output new camera intrinsic matrix.

The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. When alpha>0 , the undistorted result is likely to have some black pixels corresponding to "virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to initUndistortRectifyMap to produce the maps for #remap .

getOptimalNewCameraMatrix() [12/12]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.getOptimalNewCameraMatrix ( Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize, Rect validPixROI, bool centerPrincipalPoint )

static

Returns the new camera intrinsic matrix based on the free scaling parameter.

Parameters

cameraMatrix	Input camera intrinsic matrix.
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
imageSize	Original image size.
alpha	Free scaling parameter between 0 (when all the pixels in the undistorted image are valid) and 1 (when all the source image pixels are retained in the undistorted image). See stereoRectify for details.
newImgSize	Image size after rectification. By default, it is set to imageSize .
validPixROI	Optional output rectangle that outlines all-good-pixels region in the undistorted image. See roi1, roi2 description in stereoRectify .
centerPrincipalPoint	Optional flag that indicates whether in the new camera intrinsic matrix the principal point should be at the image center or not. By default, the principal point is chosen to best fit a subset of the source image (determined by alpha) to the corrected image.

Returns

new_camera_matrix Output new camera intrinsic matrix.

The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. When alpha>0 , the undistorted result is likely to have some black pixels corresponding to "virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to initUndistortRectifyMap to produce the maps for #remap .

getValidDisparityROI()

static Rect OpenCVForUnity.Calib3dModule.Calib3d.getValidDisparityROI ( Rect roi1, Rect roi2, int minDisparity, int numberOfDisparities, int blockSize )

static

getValidDisparityROIAsValueTuple()

static int int int int height OpenCVForUnity.Calib3dModule.Calib3d.getValidDisparityROIAsValueTuple ( in(int x, int y, int width, int height) roi1, (int x, int y, int width, int height) roi2, int minDisparity, int numberOfDisparities, int blockSize )

static

getValidDisparityROIAsVec4i()

static Vec4i OpenCVForUnity.Calib3dModule.Calib3d.getValidDisparityROIAsVec4i ( in Vec4i roi1, Vec4i roi2, int minDisparity, int numberOfDisparities, int blockSize )

static

initCameraMatrix2D() [1/6]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.initCameraMatrix2D ( List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, in Vec2d imageSize )

static

Finds an initial camera intrinsic matrix from 3D-2D point correspondences.

Parameters

objectPoints	Vector of vectors of the calibration pattern points in the calibration pattern coordinate space. In the old interface all the per-view vectors are concatenated. See calibrateCamera for details.
imagePoints	Vector of vectors of the projections of the calibration pattern points. In the old interface all the per-view vectors are concatenated.
imageSize	Image size in pixels used to initialize the principal point.
aspectRatio	If it is zero or negative, both \(f_x\) and \(f_y\) are estimated independently. Otherwise, \(f_x = f_y \cdot \texttt{aspectRatio}\) .

The function estimates and returns an initial camera intrinsic matrix for the camera calibration process. Currently, the function only supports planar calibration patterns, which are patterns where each object point has z-coordinate =0.

initCameraMatrix2D() [2/6]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.initCameraMatrix2D ( List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, in Vec2d imageSize, double aspectRatio )

static

Finds an initial camera intrinsic matrix from 3D-2D point correspondences.

Parameters

objectPoints	Vector of vectors of the calibration pattern points in the calibration pattern coordinate space. In the old interface all the per-view vectors are concatenated. See calibrateCamera for details.
imagePoints	Vector of vectors of the projections of the calibration pattern points. In the old interface all the per-view vectors are concatenated.
imageSize	Image size in pixels used to initialize the principal point.
aspectRatio	If it is zero or negative, both \(f_x\) and \(f_y\) are estimated independently. Otherwise, \(f_x = f_y \cdot \texttt{aspectRatio}\) .

The function estimates and returns an initial camera intrinsic matrix for the camera calibration process. Currently, the function only supports planar calibration patterns, which are patterns where each object point has z-coordinate =0.

initCameraMatrix2D() [3/6]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.initCameraMatrix2D ( List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, in(double width, double height) imageSize )

static

Finds an initial camera intrinsic matrix from 3D-2D point correspondences.

Parameters

objectPoints	Vector of vectors of the calibration pattern points in the calibration pattern coordinate space. In the old interface all the per-view vectors are concatenated. See calibrateCamera for details.
imagePoints	Vector of vectors of the projections of the calibration pattern points. In the old interface all the per-view vectors are concatenated.
imageSize	Image size in pixels used to initialize the principal point.
aspectRatio	If it is zero or negative, both \(f_x\) and \(f_y\) are estimated independently. Otherwise, \(f_x = f_y \cdot \texttt{aspectRatio}\) .

The function estimates and returns an initial camera intrinsic matrix for the camera calibration process. Currently, the function only supports planar calibration patterns, which are patterns where each object point has z-coordinate =0.

initCameraMatrix2D() [4/6]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.initCameraMatrix2D ( List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, in(double width, double height) imageSize, double aspectRatio )

static

Finds an initial camera intrinsic matrix from 3D-2D point correspondences.

Parameters

objectPoints	Vector of vectors of the calibration pattern points in the calibration pattern coordinate space. In the old interface all the per-view vectors are concatenated. See calibrateCamera for details.
imagePoints	Vector of vectors of the projections of the calibration pattern points. In the old interface all the per-view vectors are concatenated.
imageSize	Image size in pixels used to initialize the principal point.
aspectRatio	If it is zero or negative, both \(f_x\) and \(f_y\) are estimated independently. Otherwise, \(f_x = f_y \cdot \texttt{aspectRatio}\) .

The function estimates and returns an initial camera intrinsic matrix for the camera calibration process. Currently, the function only supports planar calibration patterns, which are patterns where each object point has z-coordinate =0.

initCameraMatrix2D() [5/6]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.initCameraMatrix2D ( List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, Size imageSize )

static

Finds an initial camera intrinsic matrix from 3D-2D point correspondences.

Parameters

objectPoints	Vector of vectors of the calibration pattern points in the calibration pattern coordinate space. In the old interface all the per-view vectors are concatenated. See calibrateCamera for details.
imagePoints	Vector of vectors of the projections of the calibration pattern points. In the old interface all the per-view vectors are concatenated.
imageSize	Image size in pixels used to initialize the principal point.
aspectRatio	If it is zero or negative, both \(f_x\) and \(f_y\) are estimated independently. Otherwise, \(f_x = f_y \cdot \texttt{aspectRatio}\) .

The function estimates and returns an initial camera intrinsic matrix for the camera calibration process. Currently, the function only supports planar calibration patterns, which are patterns where each object point has z-coordinate =0.

initCameraMatrix2D() [6/6]

static Mat OpenCVForUnity.Calib3dModule.Calib3d.initCameraMatrix2D ( List< MatOfPoint3f > objectPoints, List< MatOfPoint2f > imagePoints, Size imageSize, double aspectRatio )

static

Finds an initial camera intrinsic matrix from 3D-2D point correspondences.

Parameters

objectPoints	Vector of vectors of the calibration pattern points in the calibration pattern coordinate space. In the old interface all the per-view vectors are concatenated. See calibrateCamera for details.
imagePoints	Vector of vectors of the projections of the calibration pattern points. In the old interface all the per-view vectors are concatenated.
imageSize	Image size in pixels used to initialize the principal point.
aspectRatio	If it is zero or negative, both \(f_x\) and \(f_y\) are estimated independently. Otherwise, \(f_x = f_y \cdot \texttt{aspectRatio}\) .

The function estimates and returns an initial camera intrinsic matrix for the camera calibration process. Currently, the function only supports planar calibration patterns, which are patterns where each object point has z-coordinate =0.

initInverseRectificationMap() [1/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.initInverseRectificationMap ( Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, in Vec2d size, int m1type, Mat map1, Mat map2 )

static

Computes the projection and inverse-rectification transformation map. In essense, this is the inverse of initUndistortRectifyMap to accomodate stereo-rectification of projectors ('inverse-cameras') in projector-camera pairs.

The function computes the joint projection and inverse rectification transformation and represents the result in the form of maps for #remap. The projected image looks like a distorted version of the original which, once projected by a projector, should visually match the original. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by getOptimalNewCameraMatrix for a better control over scaling. In case of a projector-camera pair, newCameraMatrix is normally set to P1 or P2 computed by stereoRectify .

The projector is oriented differently in the coordinate space, according to R. In case of projector-camera pairs, this helps align the projector (in the same manner as initUndistortRectifyMap for the camera) to create a stereo-rectified pair. This allows epipolar lines on both images to become horizontal and have the same y-coordinate (in case of a horizontally aligned projector-camera pair).

The function builds the maps for the inverse mapping algorithm that is used by #remap. That is, for each pixel \((u, v)\) in the destination (projected and inverse-rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original digital image). The following process is applied:

\[ \begin{array}{l} \text{newCameraMatrix}\\ x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\ \\\text{Undistortion} \\\scriptsize{\textit{though equation shown is for radial undistortion, function implements cv::undistortPoints()}}\\ r^2 \leftarrow x^2 + y^2 \\ \theta \leftarrow \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}\\ x' \leftarrow \frac{x}{\theta} \\ y' \leftarrow \frac{y}{\theta} \\ \\\text{Rectification}\\ {[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ x'' \leftarrow X/W \\ y'' \leftarrow Y/W \\ \\\text{cameraMatrix}\\ map_x(u,v) \leftarrow x'' f_x + c_x \\ map_y(u,v) \leftarrow y'' f_y + c_y \end{array} \]

where \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) are the distortion coefficients vector distCoeffs.

In case of a stereo-rectified projector-camera pair, this function is called for the projector while initUndistortRectifyMap is called for the camera head. This is done after stereoRectify, which in turn is called after stereoCalibrate. If the projector-camera pair is not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using stereoRectifyUncalibrated. For the projector and camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as

\[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\]

where cameraMatrix can be chosen arbitrarily.

Parameters

cameraMatrix	Input camera matrix \(A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	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.
R	Optional rectification transformation in the object space (3x3 matrix). R1 or R2, computed by stereoRectify can be passed here. If the matrix is empty, the identity transformation is assumed.
newCameraMatrix	New camera matrix \(A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\).
size	Distorted image size.
m1type	Type of the first output map. Can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
map1	The first output map for #remap.
map2	The second output map for #remap.

initInverseRectificationMap() [2/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.initInverseRectificationMap ( Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, in(double width, double height) size, int m1type, Mat map1, Mat map2 )

static

Computes the projection and inverse-rectification transformation map. In essense, this is the inverse of initUndistortRectifyMap to accomodate stereo-rectification of projectors ('inverse-cameras') in projector-camera pairs.

The function computes the joint projection and inverse rectification transformation and represents the result in the form of maps for #remap. The projected image looks like a distorted version of the original which, once projected by a projector, should visually match the original. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by getOptimalNewCameraMatrix for a better control over scaling. In case of a projector-camera pair, newCameraMatrix is normally set to P1 or P2 computed by stereoRectify .

The projector is oriented differently in the coordinate space, according to R. In case of projector-camera pairs, this helps align the projector (in the same manner as initUndistortRectifyMap for the camera) to create a stereo-rectified pair. This allows epipolar lines on both images to become horizontal and have the same y-coordinate (in case of a horizontally aligned projector-camera pair).

The function builds the maps for the inverse mapping algorithm that is used by #remap. That is, for each pixel \((u, v)\) in the destination (projected and inverse-rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original digital image). The following process is applied:

\[ \begin{array}{l} \text{newCameraMatrix}\\ x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\ \\\text{Undistortion} \\\scriptsize{\textit{though equation shown is for radial undistortion, function implements cv::undistortPoints()}}\\ r^2 \leftarrow x^2 + y^2 \\ \theta \leftarrow \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}\\ x' \leftarrow \frac{x}{\theta} \\ y' \leftarrow \frac{y}{\theta} \\ \\\text{Rectification}\\ {[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ x'' \leftarrow X/W \\ y'' \leftarrow Y/W \\ \\\text{cameraMatrix}\\ map_x(u,v) \leftarrow x'' f_x + c_x \\ map_y(u,v) \leftarrow y'' f_y + c_y \end{array} \]

where \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) are the distortion coefficients vector distCoeffs.

In case of a stereo-rectified projector-camera pair, this function is called for the projector while initUndistortRectifyMap is called for the camera head. This is done after stereoRectify, which in turn is called after stereoCalibrate. If the projector-camera pair is not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using stereoRectifyUncalibrated. For the projector and camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as

\[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\]

where cameraMatrix can be chosen arbitrarily.

Parameters

cameraMatrix	Input camera matrix \(A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	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.
R	Optional rectification transformation in the object space (3x3 matrix). R1 or R2, computed by stereoRectify can be passed here. If the matrix is empty, the identity transformation is assumed.
newCameraMatrix	New camera matrix \(A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\).
size	Distorted image size.
m1type	Type of the first output map. Can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
map1	The first output map for #remap.
map2	The second output map for #remap.

initInverseRectificationMap() [3/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.initInverseRectificationMap ( Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, Size size, int m1type, Mat map1, Mat map2 )

static

Computes the projection and inverse-rectification transformation map. In essense, this is the inverse of initUndistortRectifyMap to accomodate stereo-rectification of projectors ('inverse-cameras') in projector-camera pairs.

The function computes the joint projection and inverse rectification transformation and represents the result in the form of maps for #remap. The projected image looks like a distorted version of the original which, once projected by a projector, should visually match the original. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by getOptimalNewCameraMatrix for a better control over scaling. In case of a projector-camera pair, newCameraMatrix is normally set to P1 or P2 computed by stereoRectify .

The projector is oriented differently in the coordinate space, according to R. In case of projector-camera pairs, this helps align the projector (in the same manner as initUndistortRectifyMap for the camera) to create a stereo-rectified pair. This allows epipolar lines on both images to become horizontal and have the same y-coordinate (in case of a horizontally aligned projector-camera pair).

The function builds the maps for the inverse mapping algorithm that is used by #remap. That is, for each pixel \((u, v)\) in the destination (projected and inverse-rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original digital image). The following process is applied:

\[ \begin{array}{l} \text{newCameraMatrix}\\ x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\ \\\text{Undistortion} \\\scriptsize{\textit{though equation shown is for radial undistortion, function implements cv::undistortPoints()}}\\ r^2 \leftarrow x^2 + y^2 \\ \theta \leftarrow \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}\\ x' \leftarrow \frac{x}{\theta} \\ y' \leftarrow \frac{y}{\theta} \\ \\\text{Rectification}\\ {[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ x'' \leftarrow X/W \\ y'' \leftarrow Y/W \\ \\\text{cameraMatrix}\\ map_x(u,v) \leftarrow x'' f_x + c_x \\ map_y(u,v) \leftarrow y'' f_y + c_y \end{array} \]

where \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) are the distortion coefficients vector distCoeffs.

In case of a stereo-rectified projector-camera pair, this function is called for the projector while initUndistortRectifyMap is called for the camera head. This is done after stereoRectify, which in turn is called after stereoCalibrate. If the projector-camera pair is not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using stereoRectifyUncalibrated. For the projector and camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as

\[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\]

where cameraMatrix can be chosen arbitrarily.

Parameters

cameraMatrix	Input camera matrix \(A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	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.
R	Optional rectification transformation in the object space (3x3 matrix). R1 or R2, computed by stereoRectify can be passed here. If the matrix is empty, the identity transformation is assumed.
newCameraMatrix	New camera matrix \(A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\).
size	Distorted image size.
m1type	Type of the first output map. Can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
map1	The first output map for #remap.
map2	The second output map for #remap.

initUndistortRectifyMap() [1/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.initUndistortRectifyMap ( Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, in Vec2d size, int m1type, Mat map1, Mat map2 )

static

Computes the undistortion and rectification transformation map.

The function computes the joint undistortion and rectification transformation and represents the result in the form of maps for #remap. The undistorted image looks like original, as if it is captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, newCameraMatrix is normally set to P1 or P2 computed by stereoRectify .

Also, this new camera is oriented differently in the coordinate space, according to R. That, for example, helps to align two heads of a stereo camera so that the epipolar lines on both images become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).

The function actually builds the maps for the inverse mapping algorithm that is used by #remap. That is, for each pixel \((u, v)\) in the destination (corrected and rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original image from camera). The following process is applied:

\[ \begin{array}{l} x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\ {[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ x' \leftarrow X/W \\ y' \leftarrow Y/W \\ r^2 \leftarrow x'^2 + y'^2 \\ x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\ y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ s\vecthree{x'''}{y'''}{1} = \vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} {0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} {0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ map_x(u,v) \leftarrow x''' f_x + c_x \\ map_y(u,v) \leftarrow y''' f_y + c_y \end{array} \]

where \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) are the distortion coefficients.

In case of a stereo camera, this function is called twice: once for each camera head, after stereoRectify, which in its turn is called after stereoCalibrate. But if the stereo camera was not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using stereoRectifyUncalibrated. For each camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as

\[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\]

where cameraMatrix can be chosen arbitrarily.

Parameters

cameraMatrix	Input camera matrix \(A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	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.
R	Optional rectification transformation in the object space (3x3 matrix). R1 or R2 , computed by stereoRectify can be passed here. If the matrix is empty, the identity transformation is assumed. In initUndistortRectifyMap R assumed to be an identity matrix.
newCameraMatrix	New camera matrix \(A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\).
size	Undistorted image size.
m1type	Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
map1	The first output map.
map2	The second output map.

initUndistortRectifyMap() [2/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.initUndistortRectifyMap ( Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, in(double width, double height) size, int m1type, Mat map1, Mat map2 )

static

Computes the undistortion and rectification transformation map.

The function computes the joint undistortion and rectification transformation and represents the result in the form of maps for #remap. The undistorted image looks like original, as if it is captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, newCameraMatrix is normally set to P1 or P2 computed by stereoRectify .

Also, this new camera is oriented differently in the coordinate space, according to R. That, for example, helps to align two heads of a stereo camera so that the epipolar lines on both images become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).

The function actually builds the maps for the inverse mapping algorithm that is used by #remap. That is, for each pixel \((u, v)\) in the destination (corrected and rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original image from camera). The following process is applied:

\[ \begin{array}{l} x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\ {[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ x' \leftarrow X/W \\ y' \leftarrow Y/W \\ r^2 \leftarrow x'^2 + y'^2 \\ x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\ y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ s\vecthree{x'''}{y'''}{1} = \vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} {0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} {0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ map_x(u,v) \leftarrow x''' f_x + c_x \\ map_y(u,v) \leftarrow y''' f_y + c_y \end{array} \]

where \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) are the distortion coefficients.

In case of a stereo camera, this function is called twice: once for each camera head, after stereoRectify, which in its turn is called after stereoCalibrate. But if the stereo camera was not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using stereoRectifyUncalibrated. For each camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as

\[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\]

where cameraMatrix can be chosen arbitrarily.

Parameters

cameraMatrix	Input camera matrix \(A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	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.
R	Optional rectification transformation in the object space (3x3 matrix). R1 or R2 , computed by stereoRectify can be passed here. If the matrix is empty, the identity transformation is assumed. In initUndistortRectifyMap R assumed to be an identity matrix.
newCameraMatrix	New camera matrix \(A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\).
size	Undistorted image size.
m1type	Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
map1	The first output map.
map2	The second output map.

initUndistortRectifyMap() [3/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.initUndistortRectifyMap ( Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, Size size, int m1type, Mat map1, Mat map2 )

static

Computes the undistortion and rectification transformation map.

The function computes the joint undistortion and rectification transformation and represents the result in the form of maps for #remap. The undistorted image looks like original, as if it is captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, newCameraMatrix is normally set to P1 or P2 computed by stereoRectify .

Also, this new camera is oriented differently in the coordinate space, according to R. That, for example, helps to align two heads of a stereo camera so that the epipolar lines on both images become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).

The function actually builds the maps for the inverse mapping algorithm that is used by #remap. That is, for each pixel \((u, v)\) in the destination (corrected and rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original image from camera). The following process is applied:

\[ \begin{array}{l} x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\ {[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ x' \leftarrow X/W \\ y' \leftarrow Y/W \\ r^2 \leftarrow x'^2 + y'^2 \\ x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\ y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ s\vecthree{x'''}{y'''}{1} = \vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} {0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} {0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ map_x(u,v) \leftarrow x''' f_x + c_x \\ map_y(u,v) \leftarrow y''' f_y + c_y \end{array} \]

where \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) are the distortion coefficients.

In case of a stereo camera, this function is called twice: once for each camera head, after stereoRectify, which in its turn is called after stereoCalibrate. But if the stereo camera was not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using stereoRectifyUncalibrated. For each camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as

\[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\]

where cameraMatrix can be chosen arbitrarily.

Parameters

cameraMatrix	Input camera matrix \(A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	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.
R	Optional rectification transformation in the object space (3x3 matrix). R1 or R2 , computed by stereoRectify can be passed here. If the matrix is empty, the identity transformation is assumed. In initUndistortRectifyMap R assumed to be an identity matrix.
newCameraMatrix	New camera matrix \(A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\).
size	Undistorted image size.
m1type	Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
map1	The first output map.
map2	The second output map.

matMulDeriv()

static void OpenCVForUnity.Calib3dModule.Calib3d.matMulDeriv ( Mat A, Mat B, Mat dABdA, Mat dABdB )

static

Computes partial derivatives of the matrix product for each multiplied matrix.

Parameters

A	First multiplied matrix.
B	Second multiplied matrix.
dABdA	First output derivative matrix d(A*B)/dA of size \(\texttt{A.rows*B.cols} \times {A.rows*A.cols}\) .
dABdB	Second output derivative matrix d(A*B)/dB of size \(\texttt{A.rows*B.cols} \times {B.rows*B.cols}\) .

The function computes partial derivatives of the elements of the matrix product \(A*B\) with regard to the elements of each of the two input matrices. The function is used to compute the Jacobian matrices in stereoCalibrate but can also be used in any other similar optimization function.

projectPoints() [1/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.projectPoints ( MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints )

static

Projects 3D points to an image plane.

Parameters

objectPoints	Array of object points expressed wrt. the world coordinate frame. A 3xN/Nx3 1-channel or 1xN/Nx1 3-channel (or vector<Point3f> ), where N is the number of points in the view.
rvec	The rotation vector (Rodrigues) that, together with tvec, performs a change of basis from world to camera coordinate system, see calibrateCamera for details.
tvec	The translation vector, see parameter description above.
cameraMatrix	Camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\) . If the vector is empty, the zero distortion coefficients are assumed.
imagePoints	Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .
jacobian	Optional output 2Nx(10+<numDistCoeffs>) jacobian matrix of derivatives of image points with respect to components of the rotation vector, translation vector, focal lengths, coordinates of the principal point and the distortion coefficients. In the old interface different components of the jacobian are returned via different output parameters.
aspectRatio	Optional "fixed aspect ratio" parameter. If the parameter is not 0, the function assumes that the aspect ratio ( \(f_x / f_y\)) is fixed and correspondingly adjusts the jacobian matrix.

The function computes the 2D projections of 3D points to the image plane, given intrinsic and extrinsic camera parameters. Optionally, the function computes Jacobians -matrices of partial derivatives of image points coordinates (as functions of all the input parameters) with respect to the particular parameters, intrinsic and/or extrinsic. The Jacobians are used during the global optimization in calibrateCamera, solvePnP, and stereoCalibrate. The function itself can also be used to compute a re-projection error, given the current intrinsic and extrinsic parameters.

Note

By setting rvec = tvec = \([0, 0, 0]\), or by setting cameraMatrix to a 3x3 identity matrix, or by passing zero distortion coefficients, one can get various useful partial cases of the function. This means, one can compute the distorted coordinates for a sparse set of points or apply a perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup.

projectPoints() [2/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.projectPoints ( MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian )

static

Projects 3D points to an image plane.

Parameters

objectPoints	Array of object points expressed wrt. the world coordinate frame. A 3xN/Nx3 1-channel or 1xN/Nx1 3-channel (or vector<Point3f> ), where N is the number of points in the view.
rvec	The rotation vector (Rodrigues) that, together with tvec, performs a change of basis from world to camera coordinate system, see calibrateCamera for details.
tvec	The translation vector, see parameter description above.
cameraMatrix	Camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\) . If the vector is empty, the zero distortion coefficients are assumed.
imagePoints	Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .
jacobian	Optional output 2Nx(10+<numDistCoeffs>) jacobian matrix of derivatives of image points with respect to components of the rotation vector, translation vector, focal lengths, coordinates of the principal point and the distortion coefficients. In the old interface different components of the jacobian are returned via different output parameters.
aspectRatio	Optional "fixed aspect ratio" parameter. If the parameter is not 0, the function assumes that the aspect ratio ( \(f_x / f_y\)) is fixed and correspondingly adjusts the jacobian matrix.

The function computes the 2D projections of 3D points to the image plane, given intrinsic and extrinsic camera parameters. Optionally, the function computes Jacobians -matrices of partial derivatives of image points coordinates (as functions of all the input parameters) with respect to the particular parameters, intrinsic and/or extrinsic. The Jacobians are used during the global optimization in calibrateCamera, solvePnP, and stereoCalibrate. The function itself can also be used to compute a re-projection error, given the current intrinsic and extrinsic parameters.

Note

By setting rvec = tvec = \([0, 0, 0]\), or by setting cameraMatrix to a 3x3 identity matrix, or by passing zero distortion coefficients, one can get various useful partial cases of the function. This means, one can compute the distorted coordinates for a sparse set of points or apply a perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup.

projectPoints() [3/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.projectPoints ( MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian, double aspectRatio )

static

Projects 3D points to an image plane.

Parameters

objectPoints	Array of object points expressed wrt. the world coordinate frame. A 3xN/Nx3 1-channel or 1xN/Nx1 3-channel (or vector<Point3f> ), where N is the number of points in the view.
rvec	The rotation vector (Rodrigues) that, together with tvec, performs a change of basis from world to camera coordinate system, see calibrateCamera for details.
tvec	The translation vector, see parameter description above.
cameraMatrix	Camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\) . If the vector is empty, the zero distortion coefficients are assumed.
imagePoints	Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .
jacobian	Optional output 2Nx(10+<numDistCoeffs>) jacobian matrix of derivatives of image points with respect to components of the rotation vector, translation vector, focal lengths, coordinates of the principal point and the distortion coefficients. In the old interface different components of the jacobian are returned via different output parameters.
aspectRatio	Optional "fixed aspect ratio" parameter. If the parameter is not 0, the function assumes that the aspect ratio ( \(f_x / f_y\)) is fixed and correspondingly adjusts the jacobian matrix.

The function computes the 2D projections of 3D points to the image plane, given intrinsic and extrinsic camera parameters. Optionally, the function computes Jacobians -matrices of partial derivatives of image points coordinates (as functions of all the input parameters) with respect to the particular parameters, intrinsic and/or extrinsic. The Jacobians are used during the global optimization in calibrateCamera, solvePnP, and stereoCalibrate. The function itself can also be used to compute a re-projection error, given the current intrinsic and extrinsic parameters.

Note

By setting rvec = tvec = \([0, 0, 0]\), or by setting cameraMatrix to a 3x3 identity matrix, or by passing zero distortion coefficients, one can get various useful partial cases of the function. This means, one can compute the distorted coordinates for a sparse set of points or apply a perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup.

recoverPose() [1/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t )

static

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.

Parameters

E	The input essential matrix.
points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
cameraMatrix	Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix.
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter described below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.

This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible pose hypotheses by doing chirality check. The chirality check means that the triangulated 3D points should have positive depth. Some details can be found in [Nister03].

This function can be used to process the output E and mask from findEssentialMat. In this scenario, points1 and points2 are the same input for findEssentialMat :

// Example. Estimation of fundamental matrix using the RANSAC algorithm
int point_count = 100;
vector<Point2f> points1(point_count);
vector<Point2f> points2(point_count);
 
// initialize the points here ...
for( int i = 0; i < point_count; i++ )
{
    points1[i] = ...;
    points2[i] = ...;
}
 
// cametra matrix with both focal lengths = 1, and principal point = (0, 0)
Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
 
Mat E, R, t, mask;
 
E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
recoverPose(E, points1, points2, cameraMatrix, R, t, mask);

recoverPose() [2/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh )

static

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

Parameters

E	The input essential matrix.
points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1.
cameraMatrix	Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix.
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter description below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
distanceThresh	threshold distance which is used to filter out far away points (i.e. infinite points).
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.
triangulatedPoints	3D points which were reconstructed by triangulation.

This function differs from the one above that it outputs the triangulated 3D point that are used for the chirality check.

recoverPose() [3/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh, Mat mask )

static

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

Parameters

E	The input essential matrix.
points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1.
cameraMatrix	Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix.
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter description below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
distanceThresh	threshold distance which is used to filter out far away points (i.e. infinite points).
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.
triangulatedPoints	3D points which were reconstructed by triangulation.

This function differs from the one above that it outputs the triangulated 3D point that are used for the chirality check.

recoverPose() [4/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh, Mat mask, Mat triangulatedPoints )

static

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

Parameters

E	The input essential matrix.
points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1.
cameraMatrix	Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix.
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter description below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
distanceThresh	threshold distance which is used to filter out far away points (i.e. infinite points).
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.
triangulatedPoints	3D points which were reconstructed by triangulation.

This function differs from the one above that it outputs the triangulated 3D point that are used for the chirality check.

recoverPose() [5/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, Mat mask )

static

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.

Parameters

E	The input essential matrix.
points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
cameraMatrix	Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix.
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter described below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.

This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible pose hypotheses by doing chirality check. The chirality check means that the triangulated 3D points should have positive depth. Some details can be found in [Nister03].

This function can be used to process the output E and mask from findEssentialMat. In this scenario, points1 and points2 are the same input for findEssentialMat :

// Example. Estimation of fundamental matrix using the RANSAC algorithm
int point_count = 100;
vector<Point2f> points1(point_count);
vector<Point2f> points2(point_count);
 
// initialize the points here ...
for( int i = 0; i < point_count; i++ )
{
    points1[i] = ...;
    points2[i] = ...;
}
 
// cametra matrix with both focal lengths = 1, and principal point = (0, 0)
Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
 
Mat E, R, t, mask;
 
E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
recoverPose(E, points1, points2, cameraMatrix, R, t, mask);

recoverPose() [6/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat E, Mat points1, Mat points2, 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.

Parameters

E	The input essential matrix.
points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter description below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
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.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

recoverPose() [7/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal )

static

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

Parameters

E	The input essential matrix.
points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter description below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
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.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

recoverPose() [8/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, in Vec2d pp )

static

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

Parameters

E	The input essential matrix.
points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter description below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
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.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

recoverPose() [9/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, in Vec2d pp, Mat mask )

static

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

Parameters

E	The input essential matrix.
points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter description below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
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.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

recoverPose() [10/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, in(double x, double y) pp )

static

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

Parameters

E	The input essential matrix.
points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter description below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
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.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

recoverPose() [11/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, in(double x, double y) pp, Mat mask )

static

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

Parameters

E	The input essential matrix.
points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter description below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
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.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

recoverPose() [12/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, Point pp )

static

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

Parameters

E	The input essential matrix.
points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter description below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
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.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

recoverPose() [13/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, Point pp, Mat mask )

static

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

Parameters

E	The input essential matrix.
points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter description below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
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.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]

recoverPose() [14/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat E, Mat R, Mat t )

static

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.

Parameters

points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
cameraMatrix1	Input/output camera matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1	Input/output vector of distortion coefficients, the same as in calibrateCamera.
cameraMatrix2	Input/output camera matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs2	Input/output vector of distortion coefficients, the same as in calibrateCamera.
E	The output essential matrix.
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter described below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the cheirality check.

This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible pose hypotheses by doing cheirality check. The cheirality check means that the triangulated 3D points should have positive depth. Some details can be found in [Nister03].

This function can be used to process the output E and mask from findEssentialMat. In this scenario, points1 and points2 are the same input for findEssentialMat.:

// Example. Estimation of fundamental matrix using the RANSAC algorithm
int point_count = 100;
vector<Point2f> points1(point_count);
vector<Point2f> points2(point_count);
 
// initialize the points here ...
for( int i = 0; i < point_count; i++ )
{
    points1[i] = ...;
    points2[i] = ...;
}
 
// Input: camera calibration of both cameras, for example using intrinsic chessboard calibration.
Mat cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2;
 
// Output: Essential matrix, relative rotation and relative translation.
Mat E, R, t, mask;
 
recoverPose(points1, points2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, E, R, t, mask);

recoverPose() [15/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat E, Mat R, Mat t, int method )

static

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.

Parameters

points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
cameraMatrix1	Input/output camera matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1	Input/output vector of distortion coefficients, the same as in calibrateCamera.
cameraMatrix2	Input/output camera matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs2	Input/output vector of distortion coefficients, the same as in calibrateCamera.
E	The output essential matrix.
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter described below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the cheirality check.

This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible pose hypotheses by doing cheirality check. The cheirality check means that the triangulated 3D points should have positive depth. Some details can be found in [Nister03].

This function can be used to process the output E and mask from findEssentialMat. In this scenario, points1 and points2 are the same input for findEssentialMat.:

// Example. Estimation of fundamental matrix using the RANSAC algorithm
int point_count = 100;
vector<Point2f> points1(point_count);
vector<Point2f> points2(point_count);
 
// initialize the points here ...
for( int i = 0; i < point_count; i++ )
{
    points1[i] = ...;
    points2[i] = ...;
}
 
// Input: camera calibration of both cameras, for example using intrinsic chessboard calibration.
Mat cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2;
 
// Output: Essential matrix, relative rotation and relative translation.
Mat E, R, t, mask;
 
recoverPose(points1, points2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, E, R, t, mask);

recoverPose() [16/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.recoverPose ( Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat E, Mat R, Mat t, int method, double prob )

static

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.

Parameters

points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
cameraMatrix1	Input/output camera matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1	Input/output vector of distortion coefficients, the same as in calibrateCamera.
cameraMatrix2	Input/output camera matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs2	Input/output vector of distortion coefficients, the same as in calibrateCamera.
E	The output essential matrix.
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter described below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the cheirality check.

This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible pose hypotheses by doing cheirality check. The cheirality check means that the triangulated 3D points should have positive depth. Some details can be found in [Nister03].

This function can be used to process the output E and mask from findEssentialMat. In this scenario, points1 and points2 are the same input for findEssentialMat.:

// Example. Estimation of fundamental matrix using the RANSAC algorithm
int point_count = 100;
vector<Point2f> points1(point_count);
vector<Point2f> points2(point_count);
 
// initialize the points here ...
for( int i = 0; i < point_count; i++ )
{
    points1[i] = ...;
    points2[i] = ...;
}
 
// Input: camera calibration of both cameras, for example using intrinsic chessboard calibration.
Mat cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2;
 
// Output: Essential matrix, relative rotation and relative translation.
Mat E, R, t, mask;
 
recoverPose(points1, points2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, E, R, t, mask);

recoverPose() [17/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

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.

Parameters

points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
cameraMatrix1	Input/output camera matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1	Input/output vector of distortion coefficients, the same as in calibrateCamera.
cameraMatrix2	Input/output camera matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs2	Input/output vector of distortion coefficients, the same as in calibrateCamera.
E	The output essential matrix.
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter described below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the cheirality check.

This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible pose hypotheses by doing cheirality check. The cheirality check means that the triangulated 3D points should have positive depth. Some details can be found in [Nister03].

This function can be used to process the output E and mask from findEssentialMat. In this scenario, points1 and points2 are the same input for findEssentialMat.:

// Example. Estimation of fundamental matrix using the RANSAC algorithm
int point_count = 100;
vector<Point2f> points1(point_count);
vector<Point2f> points2(point_count);
 
// initialize the points here ...
for( int i = 0; i < point_count; i++ )
{
    points1[i] = ...;
    points2[i] = ...;
}
 
// Input: camera calibration of both cameras, for example using intrinsic chessboard calibration.
Mat cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2;
 
// Output: Essential matrix, relative rotation and relative translation.
Mat E, R, t, mask;
 
recoverPose(points1, points2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, E, R, t, mask);

recoverPose() [18/18]

static int OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

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.

Parameters

points1	Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
points2	Array of the second image points of the same size and format as points1 .
cameraMatrix1	Input/output camera matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1	Input/output vector of distortion coefficients, the same as in calibrateCamera.
cameraMatrix2	Input/output camera matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs2	Input/output vector of distortion coefficients, the same as in calibrateCamera.
E	The output essential matrix.
R	Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter described below.
t	Output translation vector. This vector is obtained by decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
method	Method for computing an essential matrix. RANSAC for the RANSAC algorithm. LMEDS for the LMedS algorithm.
prob	Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
threshold	Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask	Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the cheirality check.

This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible pose hypotheses by doing cheirality check. The cheirality check means that the triangulated 3D points should have positive depth. Some details can be found in [Nister03].

This function can be used to process the output E and mask from findEssentialMat. In this scenario, points1 and points2 are the same input for findEssentialMat.:

// Example. Estimation of fundamental matrix using the RANSAC algorithm
int point_count = 100;
vector<Point2f> points1(point_count);
vector<Point2f> points2(point_count);
 
// initialize the points here ...
for( int i = 0; i < point_count; i++ )
{
    points1[i] = ...;
    points2[i] = ...;
}
 
// Input: camera calibration of both cameras, for example using intrinsic chessboard calibration.
Mat cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2;
 
// Output: Essential matrix, relative rotation and relative translation.
Mat E, R, t, mask;
 
recoverPose(points1, points2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, E, R, t, mask);

rectify3Collinear() [1/3]

static float OpenCVForUnity.Calib3dModule.Calib3d.rectify3Collinear ( Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat cameraMatrix3, Mat distCoeffs3, List< Mat > imgpt1, List< Mat > imgpt3, in Vec2d 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, in Vec2d newImgSize, out Vec4i roi1, out Vec4i roi2, int flags )

static

rectify3Collinear() [2/3]

static float OpenCVForUnity.Calib3dModule.Calib3d.rectify3Collinear ( Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat cameraMatrix3, Mat distCoeffs3, List< Mat > imgpt1, List< Mat > imgpt3, in(double width, double height) 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, in(double width, double height) newImgSize, out(int x, int y, int width, int height) roi1, out(int x, int y, int width, int height) roi2, int flags )

static

rectify3Collinear() [3/3]

static float OpenCVForUnity.Calib3dModule.Calib3d.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

reprojectImageTo3D() [1/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.reprojectImageTo3D ( Mat disparity, Mat _3dImage, Mat Q )

static

Reprojects a disparity image to 3D space.

Parameters

disparity	Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit floating-point disparity image. The values of 8-bit / 16-bit signed formats are assumed to have no fractional bits. If the disparity is 16-bit signed format, as computed by StereoBM or StereoSGBM and maybe other algorithms, it should be divided by 16 (and scaled to float) before being used here.
_3dImage	Output 3-channel floating-point image of the same size as disparity. Each element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity map. If one uses Q obtained by stereoRectify, then the returned points are represented in the first camera's rectified coordinate system.
Q	\(4 \times 4\) perspective transformation matrix that can be obtained with stereoRectify.
handleMissingValues	Indicates, whether the function should handle missing values (i.e. points where the disparity was not computed). If handleMissingValues=true, then pixels with the minimal disparity that corresponds to the outliers (see StereoMatcher.compute ) are transformed to 3D points with a very large Z value (currently set to 10000).
ddepth	The optional output array depth. If it is -1, the output image will have CV_32F depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.

The function transforms a single-channel disparity map to a 3-channel image representing a 3D surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it computes:

\[\begin{bmatrix} X \\ Y \\ Z \\ W \end{bmatrix} = Q \begin{bmatrix} x \\ y \\ \texttt{disparity} (x,y) \\ 1 \end{bmatrix}.\]

See also

To reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform.

reprojectImageTo3D() [2/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.reprojectImageTo3D ( Mat disparity, Mat _3dImage, Mat Q, bool handleMissingValues )

static

Reprojects a disparity image to 3D space.

Parameters

disparity	Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit floating-point disparity image. The values of 8-bit / 16-bit signed formats are assumed to have no fractional bits. If the disparity is 16-bit signed format, as computed by StereoBM or StereoSGBM and maybe other algorithms, it should be divided by 16 (and scaled to float) before being used here.
_3dImage	Output 3-channel floating-point image of the same size as disparity. Each element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity map. If one uses Q obtained by stereoRectify, then the returned points are represented in the first camera's rectified coordinate system.
Q	\(4 \times 4\) perspective transformation matrix that can be obtained with stereoRectify.
handleMissingValues	Indicates, whether the function should handle missing values (i.e. points where the disparity was not computed). If handleMissingValues=true, then pixels with the minimal disparity that corresponds to the outliers (see StereoMatcher.compute ) are transformed to 3D points with a very large Z value (currently set to 10000).
ddepth	The optional output array depth. If it is -1, the output image will have CV_32F depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.

The function transforms a single-channel disparity map to a 3-channel image representing a 3D surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it computes:

\[\begin{bmatrix} X \\ Y \\ Z \\ W \end{bmatrix} = Q \begin{bmatrix} x \\ y \\ \texttt{disparity} (x,y) \\ 1 \end{bmatrix}.\]

See also

To reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform.

reprojectImageTo3D() [3/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.reprojectImageTo3D ( Mat disparity, Mat _3dImage, Mat Q, bool handleMissingValues, int ddepth )

static

Reprojects a disparity image to 3D space.

Parameters

disparity	Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit floating-point disparity image. The values of 8-bit / 16-bit signed formats are assumed to have no fractional bits. If the disparity is 16-bit signed format, as computed by StereoBM or StereoSGBM and maybe other algorithms, it should be divided by 16 (and scaled to float) before being used here.
_3dImage	Output 3-channel floating-point image of the same size as disparity. Each element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity map. If one uses Q obtained by stereoRectify, then the returned points are represented in the first camera's rectified coordinate system.
Q	\(4 \times 4\) perspective transformation matrix that can be obtained with stereoRectify.
handleMissingValues	Indicates, whether the function should handle missing values (i.e. points where the disparity was not computed). If handleMissingValues=true, then pixels with the minimal disparity that corresponds to the outliers (see StereoMatcher.compute ) are transformed to 3D points with a very large Z value (currently set to 10000).
ddepth	The optional output array depth. If it is -1, the output image will have CV_32F depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.

The function transforms a single-channel disparity map to a 3-channel image representing a 3D surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it computes:

\[\begin{bmatrix} X \\ Y \\ Z \\ W \end{bmatrix} = Q \begin{bmatrix} x \\ y \\ \texttt{disparity} (x,y) \\ 1 \end{bmatrix}.\]

See also

To reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform.

Rodrigues() [1/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.Rodrigues ( Mat src, Mat dst )

static

Converts a rotation matrix to a rotation vector or vice versa.

Parameters

src	Input rotation vector (3x1 or 1x3) or rotation matrix (3x3).
dst	Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively.
jacobian	Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial derivatives of the output array components with respect to the input array components.

\[\begin{array}{l} \theta \leftarrow norm(r) \\ r \leftarrow r/ \theta \\ R = \cos(\theta) I + (1- \cos{\theta} ) r r^T + \sin(\theta) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} \end{array}\]

Inverse transformation can be also done easily, since

\[\sin ( \theta ) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} = \frac{R - R^T}{2}\]

A rotation vector is a convenient and most compact representation of a rotation matrix (since any rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP .

Note

More information about the computation of the derivative of a 3D rotation matrix with respect to its exponential coordinate can be found in:

• A Compact Formula for the Derivative of a 3-D Rotation in Exponential Coordinates, Guillermo Gallego, Anthony J. Yezzi [Gallego2014ACF]

Useful information on SE(3) and Lie Groups can be found in:

• A tutorial on SE(3) transformation parameterizations and on-manifold optimization, Jose-Luis Blanco [blanco2010tutorial]
• Lie Groups for 2D and 3D Transformation, Ethan Eade [Eade17]
• A micro Lie theory for state estimation in robotics, Joan Solà, Jérémie Deray, Dinesh Atchuthan [Sol2018AML]

Rodrigues() [2/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.Rodrigues ( Mat src, Mat dst, Mat jacobian )

static

Converts a rotation matrix to a rotation vector or vice versa.

Parameters

src	Input rotation vector (3x1 or 1x3) or rotation matrix (3x3).
dst	Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively.
jacobian	Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial derivatives of the output array components with respect to the input array components.

\[\begin{array}{l} \theta \leftarrow norm(r) \\ r \leftarrow r/ \theta \\ R = \cos(\theta) I + (1- \cos{\theta} ) r r^T + \sin(\theta) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} \end{array}\]

Inverse transformation can be also done easily, since

\[\sin ( \theta ) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} = \frac{R - R^T}{2}\]

A rotation vector is a convenient and most compact representation of a rotation matrix (since any rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP .

Note

More information about the computation of the derivative of a 3D rotation matrix with respect to its exponential coordinate can be found in:

• A Compact Formula for the Derivative of a 3-D Rotation in Exponential Coordinates, Guillermo Gallego, Anthony J. Yezzi [Gallego2014ACF]

Useful information on SE(3) and Lie Groups can be found in:

• A tutorial on SE(3) transformation parameterizations and on-manifold optimization, Jose-Luis Blanco [blanco2010tutorial]
• Lie Groups for 2D and 3D Transformation, Ethan Eade [Eade17]
• A micro Lie theory for state estimation in robotics, Joan Solà, Jérémie Deray, Dinesh Atchuthan [Sol2018AML]

RQDecomp3x3() [1/4]

static double[] OpenCVForUnity.Calib3dModule.Calib3d.RQDecomp3x3 ( Mat src, Mat mtxR, Mat mtxQ )

static

Computes an RQ decomposition of 3x3 matrices.

Parameters

src	3x3 input matrix.
mtxR	Output 3x3 upper-triangular matrix.
mtxQ	Output 3x3 orthogonal matrix.
Qx	Optional output 3x3 rotation matrix around x-axis.
Qy	Optional output 3x3 rotation matrix around y-axis.
Qz	Optional output 3x3 rotation matrix around z-axis.

The function computes a RQ decomposition using the given rotations. This function is used in decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera and a rotation matrix.

It optionally returns three rotation matrices, one for each axis, and the three Euler angles in degrees (as the return value) 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.

RQDecomp3x3() [2/4]

static double[] OpenCVForUnity.Calib3dModule.Calib3d.RQDecomp3x3 ( Mat src, Mat mtxR, Mat mtxQ, Mat Qx )

static

Computes an RQ decomposition of 3x3 matrices.

Parameters

src	3x3 input matrix.
mtxR	Output 3x3 upper-triangular matrix.
mtxQ	Output 3x3 orthogonal matrix.
Qx	Optional output 3x3 rotation matrix around x-axis.
Qy	Optional output 3x3 rotation matrix around y-axis.
Qz	Optional output 3x3 rotation matrix around z-axis.

The function computes a RQ decomposition using the given rotations. This function is used in decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera and a rotation matrix.

It optionally returns three rotation matrices, one for each axis, and the three Euler angles in degrees (as the return value) 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.

RQDecomp3x3() [3/4]

static double[] OpenCVForUnity.Calib3dModule.Calib3d.RQDecomp3x3 ( Mat src, Mat mtxR, Mat mtxQ, Mat Qx, Mat Qy )

static

Computes an RQ decomposition of 3x3 matrices.

Parameters

src	3x3 input matrix.
mtxR	Output 3x3 upper-triangular matrix.
mtxQ	Output 3x3 orthogonal matrix.
Qx	Optional output 3x3 rotation matrix around x-axis.
Qy	Optional output 3x3 rotation matrix around y-axis.
Qz	Optional output 3x3 rotation matrix around z-axis.

The function computes a RQ decomposition using the given rotations. This function is used in decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera and a rotation matrix.

It optionally returns three rotation matrices, one for each axis, and the three Euler angles in degrees (as the return value) 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.

RQDecomp3x3() [4/4]

static double[] OpenCVForUnity.Calib3dModule.Calib3d.RQDecomp3x3 ( Mat src, Mat mtxR, Mat mtxQ, Mat Qx, Mat Qy, Mat Qz )

static

Computes an RQ decomposition of 3x3 matrices.

Parameters

src	3x3 input matrix.
mtxR	Output 3x3 upper-triangular matrix.
mtxQ	Output 3x3 orthogonal matrix.
Qx	Optional output 3x3 rotation matrix around x-axis.
Qy	Optional output 3x3 rotation matrix around y-axis.
Qz	Optional output 3x3 rotation matrix around z-axis.

The function computes a RQ decomposition using the given rotations. This function is used in decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera and a rotation matrix.

It optionally returns three rotation matrices, one for each axis, and the three Euler angles in degrees (as the return value) 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.

sampsonDistance()

static double OpenCVForUnity.Calib3dModule.Calib3d.sampsonDistance ( Mat pt1, Mat pt2, Mat F )

static

Calculates the Sampson Distance between two points.

The function cv::sampsonDistance calculates and returns the first order approximation of the geometric error as:

\[ sd( \texttt{pt1} , \texttt{pt2} )= \frac{(\texttt{pt2}^t \cdot \texttt{F} \cdot \texttt{pt1})^2} {((\texttt{F} \cdot \texttt{pt1})(0))^2 + ((\texttt{F} \cdot \texttt{pt1})(1))^2 + ((\texttt{F}^t \cdot \texttt{pt2})(0))^2 + ((\texttt{F}^t \cdot \texttt{pt2})(1))^2} \]

The fundamental matrix may be calculated using the findFundamentalMat function. See [HartleyZ00] 11.4.3 for details.

Parameters

pt1	first homogeneous 2d point
pt2	second homogeneous 2d point
F	fundamental matrix

Returns

The computed Sampson distance.

solveP3P()

static int OpenCVForUnity.Calib3dModule.Calib3d.solveP3P ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags )

static

Finds an object pose from 3 3D-2D point correspondences.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, 3x3 1-channel or 1x3/3x1 3-channel. vector<Point3f> can be also passed here.
imagePoints	Array of corresponding image points, 3x2 1-channel or 1x3/3x1 2-channel. vector<Point2f> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvecs	Output rotation vectors (see Rodrigues ) that, together with tvecs, brings points from the model coordinate system to the camera coordinate system. A P3P problem has up to 4 solutions.
tvecs	Output translation vectors.
flags	Method for solving a P3P problem: SOLVEPNP_P3P Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang "Complete Solution Classification for the Perspective-Three-Point Problem" ([gao2003complete]). SOLVEPNP_AP3P Method is based on the paper of T. Ke and S. Roumeliotis. "An Efficient Algebraic Solution to the Perspective-Three-Point Problem" ([Ke17]).

The function estimates the object pose given 3 object points, their corresponding image projections, as well as the camera intrinsic matrix and the distortion coefficients.

Note

The solutions are sorted by reprojection errors (lowest to highest).

solvePnP() [1/3]

static bool OpenCVForUnity.Calib3dModule.Calib3d.solvePnP ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec )

static

Finds an object pose from 3D-2D point correspondences.

See also

calib3d_solvePnP

This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods:

• P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): need 4 input points to return a unique solution.
• SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
• SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]
• for all the other flags, number of input points must be >= 4 and object points can be in any configuration.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags

More information about Perspective-n-Points is described in calib3d_solvePnP

Note

• An example of how to use solvePnP for planar augmented reality can be found at opencv_source_code/samples/python/plane_ar.py
• If you are using Python:
  • Numpy array slices won't work as input because solvePnP requires contiguous arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
  • The P3P algorithm requires image points to be in an array of shape (N,1,2) due to its calling of undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) which requires 2-channel information.
  • Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
• The methods SOLVEPNP_DLS and SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, SOLVEPNP_EPNP method will be used instead.
• The minimum number of points is 4 in the general case. In the case of SOLVEPNP_P3P and SOLVEPNP_AP3P methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
• With SOLVEPNP_ITERATIVE method and useExtrinsicGuess=true, the minimum number of points is 3 (3 points are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the global solution to converge.
• With SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
• With SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]

• With SOLVEPNP_SQPNP input points must be >= 3

solvePnP() [2/3]

static bool OpenCVForUnity.Calib3dModule.Calib3d.solvePnP ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess )

static

Finds an object pose from 3D-2D point correspondences.

See also

calib3d_solvePnP

This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods:

• P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): need 4 input points to return a unique solution.
• SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
• SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]
• for all the other flags, number of input points must be >= 4 and object points can be in any configuration.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags

More information about Perspective-n-Points is described in calib3d_solvePnP

Note

• An example of how to use solvePnP for planar augmented reality can be found at opencv_source_code/samples/python/plane_ar.py
• If you are using Python:
  • Numpy array slices won't work as input because solvePnP requires contiguous arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
  • The P3P algorithm requires image points to be in an array of shape (N,1,2) due to its calling of undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) which requires 2-channel information.
  • Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
• The methods SOLVEPNP_DLS and SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, SOLVEPNP_EPNP method will be used instead.
• The minimum number of points is 4 in the general case. In the case of SOLVEPNP_P3P and SOLVEPNP_AP3P methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
• With SOLVEPNP_ITERATIVE method and useExtrinsicGuess=true, the minimum number of points is 3 (3 points are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the global solution to converge.
• With SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
• With SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]

• With SOLVEPNP_SQPNP input points must be >= 3

solvePnP() [3/3]

static bool OpenCVForUnity.Calib3dModule.Calib3d.solvePnP ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int flags )

static

Finds an object pose from 3D-2D point correspondences.

See also

calib3d_solvePnP

This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods:

• P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): need 4 input points to return a unique solution.
• SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
• SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]
• for all the other flags, number of input points must be >= 4 and object points can be in any configuration.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags

More information about Perspective-n-Points is described in calib3d_solvePnP

Note

• An example of how to use solvePnP for planar augmented reality can be found at opencv_source_code/samples/python/plane_ar.py
• If you are using Python:
  • Numpy array slices won't work as input because solvePnP requires contiguous arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
  • The P3P algorithm requires image points to be in an array of shape (N,1,2) due to its calling of undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) which requires 2-channel information.
  • Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
• The methods SOLVEPNP_DLS and SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, SOLVEPNP_EPNP method will be used instead.
• The minimum number of points is 4 in the general case. In the case of SOLVEPNP_P3P and SOLVEPNP_AP3P methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
• With SOLVEPNP_ITERATIVE method and useExtrinsicGuess=true, the minimum number of points is 3 (3 points are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the global solution to converge.
• With SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
• With SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]

• With SOLVEPNP_SQPNP input points must be >= 3

solvePnPGeneric() [1/6]

static int OpenCVForUnity.Calib3dModule.Calib3d.solvePnPGeneric ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs )

static

Finds an object pose from 3D-2D point correspondences.

See also

calib3d_solvePnP

This function returns a list of all the possible solutions (a solution is a <rotation vector, translation vector> couple), depending on the number of input points and the chosen method:

• P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
• SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
• SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4 and 2 solutions are returned. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]
• for all the other flags, number of input points must be >= 4 and object points can be in any configuration. Only 1 solution is returned.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvecs	Vector of output rotation vectors (see Rodrigues ) that, together with tvecs, brings points from the model coordinate system to the camera coordinate system.
tvecs	Vector of output translation vectors.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags
rvec	Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true.
tvec	Translation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true.
reprojectionError	Optional vector of reprojection error, that is the RMS error ( \( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points and the 3D object points projected with the estimated pose.

More information is described in calib3d_solvePnP

Note

• An example of how to use solvePnP for planar augmented reality can be found at opencv_source_code/samples/python/plane_ar.py
• If you are using Python:
  • Numpy array slices won't work as input because solvePnP requires contiguous arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
  • The P3P algorithm requires image points to be in an array of shape (N,1,2) due to its calling of undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) which requires 2-channel information.
  • Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
• The methods SOLVEPNP_DLS and SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, SOLVEPNP_EPNP method will be used instead.
• The minimum number of points is 4 in the general case. In the case of SOLVEPNP_P3P and SOLVEPNP_AP3P methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
• With SOLVEPNP_ITERATIVE method and useExtrinsicGuess=true, the minimum number of points is 3 (3 points are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the global solution to converge.
• With SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
• With SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]

solvePnPGeneric() [2/6]

static int OpenCVForUnity.Calib3dModule.Calib3d.solvePnPGeneric ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess )

static

Finds an object pose from 3D-2D point correspondences.

See also

calib3d_solvePnP

This function returns a list of all the possible solutions (a solution is a <rotation vector, translation vector> couple), depending on the number of input points and the chosen method:

• P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
• SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
• SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4 and 2 solutions are returned. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]
• for all the other flags, number of input points must be >= 4 and object points can be in any configuration. Only 1 solution is returned.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvecs	Vector of output rotation vectors (see Rodrigues ) that, together with tvecs, brings points from the model coordinate system to the camera coordinate system.
tvecs	Vector of output translation vectors.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags
rvec	Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true.
tvec	Translation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true.
reprojectionError	Optional vector of reprojection error, that is the RMS error ( \( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points and the 3D object points projected with the estimated pose.

More information is described in calib3d_solvePnP

Note

• An example of how to use solvePnP for planar augmented reality can be found at opencv_source_code/samples/python/plane_ar.py
• If you are using Python:
  • Numpy array slices won't work as input because solvePnP requires contiguous arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
  • The P3P algorithm requires image points to be in an array of shape (N,1,2) due to its calling of undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) which requires 2-channel information.
  • Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
• The methods SOLVEPNP_DLS and SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, SOLVEPNP_EPNP method will be used instead.
• The minimum number of points is 4 in the general case. In the case of SOLVEPNP_P3P and SOLVEPNP_AP3P methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
• With SOLVEPNP_ITERATIVE method and useExtrinsicGuess=true, the minimum number of points is 3 (3 points are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the global solution to converge.
• With SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
• With SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]

solvePnPGeneric() [3/6]

static int OpenCVForUnity.Calib3dModule.Calib3d.solvePnPGeneric ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess, int flags )

static

Finds an object pose from 3D-2D point correspondences.

See also

calib3d_solvePnP

This function returns a list of all the possible solutions (a solution is a <rotation vector, translation vector> couple), depending on the number of input points and the chosen method:

• P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
• SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
• SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4 and 2 solutions are returned. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]
• for all the other flags, number of input points must be >= 4 and object points can be in any configuration. Only 1 solution is returned.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvecs	Vector of output rotation vectors (see Rodrigues ) that, together with tvecs, brings points from the model coordinate system to the camera coordinate system.
tvecs	Vector of output translation vectors.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags
rvec	Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true.
tvec	Translation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true.
reprojectionError	Optional vector of reprojection error, that is the RMS error ( \( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points and the 3D object points projected with the estimated pose.

More information is described in calib3d_solvePnP

Note

• An example of how to use solvePnP for planar augmented reality can be found at opencv_source_code/samples/python/plane_ar.py
• If you are using Python:
  • Numpy array slices won't work as input because solvePnP requires contiguous arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
  • The P3P algorithm requires image points to be in an array of shape (N,1,2) due to its calling of undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) which requires 2-channel information.
  • Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
• The methods SOLVEPNP_DLS and SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, SOLVEPNP_EPNP method will be used instead.
• The minimum number of points is 4 in the general case. In the case of SOLVEPNP_P3P and SOLVEPNP_AP3P methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
• With SOLVEPNP_ITERATIVE method and useExtrinsicGuess=true, the minimum number of points is 3 (3 points are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the global solution to converge.
• With SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
• With SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]

solvePnPGeneric() [4/6]

static int OpenCVForUnity.Calib3dModule.Calib3d.solvePnPGeneric ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess, int flags, Mat rvec )

static

Finds an object pose from 3D-2D point correspondences.

See also

calib3d_solvePnP

This function returns a list of all the possible solutions (a solution is a <rotation vector, translation vector> couple), depending on the number of input points and the chosen method:

• P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
• SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
• SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4 and 2 solutions are returned. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]
• for all the other flags, number of input points must be >= 4 and object points can be in any configuration. Only 1 solution is returned.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvecs	Vector of output rotation vectors (see Rodrigues ) that, together with tvecs, brings points from the model coordinate system to the camera coordinate system.
tvecs	Vector of output translation vectors.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags
rvec	Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true.
tvec	Translation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true.
reprojectionError	Optional vector of reprojection error, that is the RMS error ( \( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points and the 3D object points projected with the estimated pose.

More information is described in calib3d_solvePnP

Note

• An example of how to use solvePnP for planar augmented reality can be found at opencv_source_code/samples/python/plane_ar.py
• If you are using Python:
  • Numpy array slices won't work as input because solvePnP requires contiguous arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
  • The P3P algorithm requires image points to be in an array of shape (N,1,2) due to its calling of undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) which requires 2-channel information.
  • Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
• The methods SOLVEPNP_DLS and SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, SOLVEPNP_EPNP method will be used instead.
• The minimum number of points is 4 in the general case. In the case of SOLVEPNP_P3P and SOLVEPNP_AP3P methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
• With SOLVEPNP_ITERATIVE method and useExtrinsicGuess=true, the minimum number of points is 3 (3 points are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the global solution to converge.
• With SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
• With SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]

solvePnPGeneric() [5/6]

static int OpenCVForUnity.Calib3dModule.Calib3d.solvePnPGeneric ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess, int flags, Mat rvec, Mat tvec )

static

Finds an object pose from 3D-2D point correspondences.

See also

calib3d_solvePnP

This function returns a list of all the possible solutions (a solution is a <rotation vector, translation vector> couple), depending on the number of input points and the chosen method:

• P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
• SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
• SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4 and 2 solutions are returned. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]
• for all the other flags, number of input points must be >= 4 and object points can be in any configuration. Only 1 solution is returned.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvecs	Vector of output rotation vectors (see Rodrigues ) that, together with tvecs, brings points from the model coordinate system to the camera coordinate system.
tvecs	Vector of output translation vectors.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags
rvec	Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true.
tvec	Translation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true.
reprojectionError	Optional vector of reprojection error, that is the RMS error ( \( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points and the 3D object points projected with the estimated pose.

More information is described in calib3d_solvePnP

Note

• An example of how to use solvePnP for planar augmented reality can be found at opencv_source_code/samples/python/plane_ar.py
• If you are using Python:
  • Numpy array slices won't work as input because solvePnP requires contiguous arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
  • The P3P algorithm requires image points to be in an array of shape (N,1,2) due to its calling of undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) which requires 2-channel information.
  • Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
• The methods SOLVEPNP_DLS and SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, SOLVEPNP_EPNP method will be used instead.
• The minimum number of points is 4 in the general case. In the case of SOLVEPNP_P3P and SOLVEPNP_AP3P methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
• With SOLVEPNP_ITERATIVE method and useExtrinsicGuess=true, the minimum number of points is 3 (3 points are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the global solution to converge.
• With SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
• With SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]

solvePnPGeneric() [6/6]

static int OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Finds an object pose from 3D-2D point correspondences.

See also

calib3d_solvePnP

This function returns a list of all the possible solutions (a solution is a <rotation vector, translation vector> couple), depending on the number of input points and the chosen method:

• P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
• SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
• SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4 and 2 solutions are returned. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]
• for all the other flags, number of input points must be >= 4 and object points can be in any configuration. Only 1 solution is returned.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvecs	Vector of output rotation vectors (see Rodrigues ) that, together with tvecs, brings points from the model coordinate system to the camera coordinate system.
tvecs	Vector of output translation vectors.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: see calib3d_solvePnP_flags
rvec	Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true.
tvec	Translation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true.
reprojectionError	Optional vector of reprojection error, that is the RMS error ( \( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points and the 3D object points projected with the estimated pose.

More information is described in calib3d_solvePnP

Note

• An example of how to use solvePnP for planar augmented reality can be found at opencv_source_code/samples/python/plane_ar.py
• If you are using Python:
  • Numpy array slices won't work as input because solvePnP requires contiguous arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
  • The P3P algorithm requires image points to be in an array of shape (N,1,2) due to its calling of undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) which requires 2-channel information.
  • Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
• The methods SOLVEPNP_DLS and SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, SOLVEPNP_EPNP method will be used instead.
• The minimum number of points is 4 in the general case. In the case of SOLVEPNP_P3P and SOLVEPNP_AP3P methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
• With SOLVEPNP_ITERATIVE method and useExtrinsicGuess=true, the minimum number of points is 3 (3 points are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the global solution to converge.
• With SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
• With SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]

solvePnPRansac() [1/9]

static bool OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRansac ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec )

static

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
iterationsCount	Number of iterations.
reprojectionError	Inlier threshold value used by the RANSAC procedure. The parameter value is the maximum allowed distance between the observed and computed point projections to consider it an inlier.
confidence	The probability that the algorithm produces a useful result.
inliers	Output vector that contains indices of inliers in objectPoints and imagePoints .
flags	Method for solving a PnP problem (see solvePnP ).

The function estimates an object pose given a set of object points, their corresponding image projections, as well as the camera intrinsic matrix and the distortion coefficients. This function finds such a pose that minimizes reprojection error, that is, the sum of squared distances between the observed projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC makes the function resistant to outliers.

Note

• An example of how to use solvePNPRansac for object detection can be found at opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/
• The default method used to estimate the camera pose for the Minimal Sample Sets step is SOLVEPNP_EPNP. Exceptions are:
  • if you choose SOLVEPNP_P3P or SOLVEPNP_AP3P, these methods will be used.
  • if the number of input points is equal to 4, SOLVEPNP_P3P is used.
• The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to SOLVEPNP_P3P or SOLVEPNP_AP3P. In this case, the method SOLVEPNP_EPNP will be used instead.

solvePnPRansac() [2/9]

static bool OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRansac ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess )

static

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
iterationsCount	Number of iterations.
reprojectionError	Inlier threshold value used by the RANSAC procedure. The parameter value is the maximum allowed distance between the observed and computed point projections to consider it an inlier.
confidence	The probability that the algorithm produces a useful result.
inliers	Output vector that contains indices of inliers in objectPoints and imagePoints .
flags	Method for solving a PnP problem (see solvePnP ).

The function estimates an object pose given a set of object points, their corresponding image projections, as well as the camera intrinsic matrix and the distortion coefficients. This function finds such a pose that minimizes reprojection error, that is, the sum of squared distances between the observed projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC makes the function resistant to outliers.

Note

• An example of how to use solvePNPRansac for object detection can be found at opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/
• The default method used to estimate the camera pose for the Minimal Sample Sets step is SOLVEPNP_EPNP. Exceptions are:
  • if you choose SOLVEPNP_P3P or SOLVEPNP_AP3P, these methods will be used.
  • if the number of input points is equal to 4, SOLVEPNP_P3P is used.
• The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to SOLVEPNP_P3P or SOLVEPNP_AP3P. In this case, the method SOLVEPNP_EPNP will be used instead.

solvePnPRansac() [3/9]

static bool OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRansac ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount )

static

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
iterationsCount	Number of iterations.
reprojectionError	Inlier threshold value used by the RANSAC procedure. The parameter value is the maximum allowed distance between the observed and computed point projections to consider it an inlier.
confidence	The probability that the algorithm produces a useful result.
inliers	Output vector that contains indices of inliers in objectPoints and imagePoints .
flags	Method for solving a PnP problem (see solvePnP ).

The function estimates an object pose given a set of object points, their corresponding image projections, as well as the camera intrinsic matrix and the distortion coefficients. This function finds such a pose that minimizes reprojection error, that is, the sum of squared distances between the observed projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC makes the function resistant to outliers.

Note

• An example of how to use solvePNPRansac for object detection can be found at opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/
• The default method used to estimate the camera pose for the Minimal Sample Sets step is SOLVEPNP_EPNP. Exceptions are:
  • if you choose SOLVEPNP_P3P or SOLVEPNP_AP3P, these methods will be used.
  • if the number of input points is equal to 4, SOLVEPNP_P3P is used.
• The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to SOLVEPNP_P3P or SOLVEPNP_AP3P. In this case, the method SOLVEPNP_EPNP will be used instead.

solvePnPRansac() [4/9]

static bool OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRansac ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError )

static

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
iterationsCount	Number of iterations.
reprojectionError	Inlier threshold value used by the RANSAC procedure. The parameter value is the maximum allowed distance between the observed and computed point projections to consider it an inlier.
confidence	The probability that the algorithm produces a useful result.
inliers	Output vector that contains indices of inliers in objectPoints and imagePoints .
flags	Method for solving a PnP problem (see solvePnP ).

The function estimates an object pose given a set of object points, their corresponding image projections, as well as the camera intrinsic matrix and the distortion coefficients. This function finds such a pose that minimizes reprojection error, that is, the sum of squared distances between the observed projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC makes the function resistant to outliers.

Note

• An example of how to use solvePNPRansac for object detection can be found at opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/
• The default method used to estimate the camera pose for the Minimal Sample Sets step is SOLVEPNP_EPNP. Exceptions are:
  • if you choose SOLVEPNP_P3P or SOLVEPNP_AP3P, these methods will be used.
  • if the number of input points is equal to 4, SOLVEPNP_P3P is used.
• The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to SOLVEPNP_P3P or SOLVEPNP_AP3P. In this case, the method SOLVEPNP_EPNP will be used instead.

solvePnPRansac() [5/9]

static bool OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRansac ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence )

static

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
iterationsCount	Number of iterations.
reprojectionError	Inlier threshold value used by the RANSAC procedure. The parameter value is the maximum allowed distance between the observed and computed point projections to consider it an inlier.
confidence	The probability that the algorithm produces a useful result.
inliers	Output vector that contains indices of inliers in objectPoints and imagePoints .
flags	Method for solving a PnP problem (see solvePnP ).

The function estimates an object pose given a set of object points, their corresponding image projections, as well as the camera intrinsic matrix and the distortion coefficients. This function finds such a pose that minimizes reprojection error, that is, the sum of squared distances between the observed projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC makes the function resistant to outliers.

Note

• An example of how to use solvePNPRansac for object detection can be found at opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/
• The default method used to estimate the camera pose for the Minimal Sample Sets step is SOLVEPNP_EPNP. Exceptions are:
  • if you choose SOLVEPNP_P3P or SOLVEPNP_AP3P, these methods will be used.
  • if the number of input points is equal to 4, SOLVEPNP_P3P is used.
• The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to SOLVEPNP_P3P or SOLVEPNP_AP3P. In this case, the method SOLVEPNP_EPNP will be used instead.

solvePnPRansac() [6/9]

static bool OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRansac ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, Mat inliers )

static

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
iterationsCount	Number of iterations.
reprojectionError	Inlier threshold value used by the RANSAC procedure. The parameter value is the maximum allowed distance between the observed and computed point projections to consider it an inlier.
confidence	The probability that the algorithm produces a useful result.
inliers	Output vector that contains indices of inliers in objectPoints and imagePoints .
flags	Method for solving a PnP problem (see solvePnP ).

The function estimates an object pose given a set of object points, their corresponding image projections, as well as the camera intrinsic matrix and the distortion coefficients. This function finds such a pose that minimizes reprojection error, that is, the sum of squared distances between the observed projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC makes the function resistant to outliers.

Note

• An example of how to use solvePNPRansac for object detection can be found at opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/
• The default method used to estimate the camera pose for the Minimal Sample Sets step is SOLVEPNP_EPNP. Exceptions are:
  • if you choose SOLVEPNP_P3P or SOLVEPNP_AP3P, these methods will be used.
  • if the number of input points is equal to 4, SOLVEPNP_P3P is used.
• The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to SOLVEPNP_P3P or SOLVEPNP_AP3P. In this case, the method SOLVEPNP_EPNP will be used instead.

solvePnPRansac() [7/9]

static bool OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
iterationsCount	Number of iterations.
reprojectionError	Inlier threshold value used by the RANSAC procedure. The parameter value is the maximum allowed distance between the observed and computed point projections to consider it an inlier.
confidence	The probability that the algorithm produces a useful result.
inliers	Output vector that contains indices of inliers in objectPoints and imagePoints .
flags	Method for solving a PnP problem (see solvePnP ).

The function estimates an object pose given a set of object points, their corresponding image projections, as well as the camera intrinsic matrix and the distortion coefficients. This function finds such a pose that minimizes reprojection error, that is, the sum of squared distances between the observed projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC makes the function resistant to outliers.

Note

• An example of how to use solvePNPRansac for object detection can be found at opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/
• The default method used to estimate the camera pose for the Minimal Sample Sets step is SOLVEPNP_EPNP. Exceptions are:
  • if you choose SOLVEPNP_P3P or SOLVEPNP_AP3P, these methods will be used.
  • if the number of input points is equal to 4, SOLVEPNP_P3P is used.
• The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to SOLVEPNP_P3P or SOLVEPNP_AP3P. In this case, the method SOLVEPNP_EPNP will be used instead.

solvePnPRansac() [8/9]

static bool OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRansac ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers )

static

solvePnPRansac() [9/9]

static bool OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRansac ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers, UsacParams _params )

static

solvePnPRefineLM() [1/4]

static void OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRefineLM ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec )

static

Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can also be passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can also be passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Input/Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
tvec	Input/Output translation vector. Input values are used as an initial solution.
criteria	Criteria when to stop the Levenberg-Marquard iterative algorithm.

The function refines the object pose given at least 3 object points, their corresponding image projections, an initial solution for the rotation and translation vector, as well as the camera intrinsic matrix and the distortion coefficients. The function minimizes the projection error with respect to the rotation and the translation vectors, according to a Levenberg-Marquardt iterative minimization [Madsen04] [Eade13] process.

solvePnPRefineLM() [2/4]

static void OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRefineLM ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, in Vec3d criteria )

static

Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can also be passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can also be passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Input/Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
tvec	Input/Output translation vector. Input values are used as an initial solution.
criteria	Criteria when to stop the Levenberg-Marquard iterative algorithm.

The function refines the object pose given at least 3 object points, their corresponding image projections, an initial solution for the rotation and translation vector, as well as the camera intrinsic matrix and the distortion coefficients. The function minimizes the projection error with respect to the rotation and the translation vectors, according to a Levenberg-Marquardt iterative minimization [Madsen04] [Eade13] process.

solvePnPRefineLM() [3/4]

static void OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRefineLM ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, in(double type, double maxCount, double epsilon) criteria )

static

Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can also be passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can also be passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Input/Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
tvec	Input/Output translation vector. Input values are used as an initial solution.
criteria	Criteria when to stop the Levenberg-Marquard iterative algorithm.

The function refines the object pose given at least 3 object points, their corresponding image projections, an initial solution for the rotation and translation vector, as well as the camera intrinsic matrix and the distortion coefficients. The function minimizes the projection error with respect to the rotation and the translation vectors, according to a Levenberg-Marquardt iterative minimization [Madsen04] [Eade13] process.

solvePnPRefineLM() [4/4]

static void OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRefineLM ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria )

static

Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can also be passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can also be passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Input/Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
tvec	Input/Output translation vector. Input values are used as an initial solution.
criteria	Criteria when to stop the Levenberg-Marquard iterative algorithm.

The function refines the object pose given at least 3 object points, their corresponding image projections, an initial solution for the rotation and translation vector, as well as the camera intrinsic matrix and the distortion coefficients. The function minimizes the projection error with respect to the rotation and the translation vectors, according to a Levenberg-Marquardt iterative minimization [Madsen04] [Eade13] process.

solvePnPRefineVVS() [1/7]

static void OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRefineVVS ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec )

static

Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can also be passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can also be passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Input/Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
tvec	Input/Output translation vector. Input values are used as an initial solution.
criteria	Criteria when to stop the Levenberg-Marquard iterative algorithm.
VVSlambda	Gain for the virtual visual servoing control law, equivalent to the \(\alpha\) gain in the Damped Gauss-Newton formulation.

The function refines the object pose given at least 3 object points, their corresponding image projections, an initial solution for the rotation and translation vector, as well as the camera intrinsic matrix and the distortion coefficients. The function minimizes the projection error with respect to the rotation and the translation vectors, using a virtual visual servoing (VVS) [Chaumette06] [Marchand16] scheme.

solvePnPRefineVVS() [2/7]

static void OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRefineVVS ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, in Vec3d criteria )

static

Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can also be passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can also be passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Input/Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
tvec	Input/Output translation vector. Input values are used as an initial solution.
criteria	Criteria when to stop the Levenberg-Marquard iterative algorithm.
VVSlambda	Gain for the virtual visual servoing control law, equivalent to the \(\alpha\) gain in the Damped Gauss-Newton formulation.

The function refines the object pose given at least 3 object points, their corresponding image projections, an initial solution for the rotation and translation vector, as well as the camera intrinsic matrix and the distortion coefficients. The function minimizes the projection error with respect to the rotation and the translation vectors, using a virtual visual servoing (VVS) [Chaumette06] [Marchand16] scheme.

solvePnPRefineVVS() [3/7]

static void OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRefineVVS ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, in Vec3d criteria, double VVSlambda )

static

Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can also be passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can also be passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Input/Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
tvec	Input/Output translation vector. Input values are used as an initial solution.
criteria	Criteria when to stop the Levenberg-Marquard iterative algorithm.
VVSlambda	Gain for the virtual visual servoing control law, equivalent to the \(\alpha\) gain in the Damped Gauss-Newton formulation.

The function refines the object pose given at least 3 object points, their corresponding image projections, an initial solution for the rotation and translation vector, as well as the camera intrinsic matrix and the distortion coefficients. The function minimizes the projection error with respect to the rotation and the translation vectors, using a virtual visual servoing (VVS) [Chaumette06] [Marchand16] scheme.

solvePnPRefineVVS() [4/7]

static void OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRefineVVS ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, in(double type, double maxCount, double epsilon) criteria )

static

Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can also be passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can also be passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Input/Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
tvec	Input/Output translation vector. Input values are used as an initial solution.
criteria	Criteria when to stop the Levenberg-Marquard iterative algorithm.
VVSlambda	Gain for the virtual visual servoing control law, equivalent to the \(\alpha\) gain in the Damped Gauss-Newton formulation.

The function refines the object pose given at least 3 object points, their corresponding image projections, an initial solution for the rotation and translation vector, as well as the camera intrinsic matrix and the distortion coefficients. The function minimizes the projection error with respect to the rotation and the translation vectors, using a virtual visual servoing (VVS) [Chaumette06] [Marchand16] scheme.

solvePnPRefineVVS() [5/7]

static void OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRefineVVS ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, in(double type, double maxCount, double epsilon) criteria, double VVSlambda )

static

Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can also be passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can also be passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Input/Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
tvec	Input/Output translation vector. Input values are used as an initial solution.
criteria	Criteria when to stop the Levenberg-Marquard iterative algorithm.
VVSlambda	Gain for the virtual visual servoing control law, equivalent to the \(\alpha\) gain in the Damped Gauss-Newton formulation.

The function refines the object pose given at least 3 object points, their corresponding image projections, an initial solution for the rotation and translation vector, as well as the camera intrinsic matrix and the distortion coefficients. The function minimizes the projection error with respect to the rotation and the translation vectors, using a virtual visual servoing (VVS) [Chaumette06] [Marchand16] scheme.

solvePnPRefineVVS() [6/7]

static void OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRefineVVS ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria )

static

Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can also be passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can also be passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Input/Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
tvec	Input/Output translation vector. Input values are used as an initial solution.
criteria	Criteria when to stop the Levenberg-Marquard iterative algorithm.
VVSlambda	Gain for the virtual visual servoing control law, equivalent to the \(\alpha\) gain in the Damped Gauss-Newton formulation.

The function refines the object pose given at least 3 object points, their corresponding image projections, an initial solution for the rotation and translation vector, as well as the camera intrinsic matrix and the distortion coefficients. The function minimizes the projection error with respect to the rotation and the translation vectors, using a virtual visual servoing (VVS) [Chaumette06] [Marchand16] scheme.

solvePnPRefineVVS() [7/7]

static void OpenCVForUnity.Calib3dModule.Calib3d.solvePnPRefineVVS ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria, double VVSlambda )

static

Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.

See also

calib3d_solvePnP

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can also be passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can also be passed here.
cameraMatrix	Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs	Input vector of distortion coefficients \(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Input/Output rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
tvec	Input/Output translation vector. Input values are used as an initial solution.
criteria	Criteria when to stop the Levenberg-Marquard iterative algorithm.
VVSlambda	Gain for the virtual visual servoing control law, equivalent to the \(\alpha\) gain in the Damped Gauss-Newton formulation.

The function refines the object pose given at least 3 object points, their corresponding image projections, an initial solution for the rotation and translation vector, as well as the camera intrinsic matrix and the distortion coefficients. The function minimizes the projection error with respect to the rotation and the translation vectors, using a virtual visual servoing (VVS) [Chaumette06] [Marchand16] scheme.

stereoCalibrate() [1/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat E, Mat F )

static

stereoCalibrate() [2/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat E, Mat F, int flags )

static

stereoCalibrate() [3/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat E, Mat F, int flags, in Vec3d criteria )

static

stereoCalibrate() [4/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors )

static

stereoCalibrate() [5/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors, int flags )

static

stereoCalibrate() [6/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors, int flags, in Vec3d criteria )

static

stereoCalibrate() [7/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat E, Mat F )

static

stereoCalibrate() [8/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat E, Mat F, int flags )

static

stereoCalibrate() [9/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat E, Mat F, int flags, in(double type, double maxCount, double epsilon) criteria )

static

stereoCalibrate() [10/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors )

static

stereoCalibrate() [11/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors, int flags )

static

stereoCalibrate() [12/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrate ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors, int flags, in(double type, double maxCount, double epsilon) criteria )

static

stereoCalibrate() [13/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.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

stereoCalibrate() [14/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.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

stereoCalibrate() [15/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.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

stereoCalibrate() [16/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.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

stereoCalibrate() [17/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.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

stereoCalibrate() [18/18]

static double OpenCVForUnity.Calib3dModule.Calib3d.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

stereoCalibrateExtended() [1/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrateExtended ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat E, Mat F, List< Mat > rvecs, List< Mat > tvecs, Mat perViewErrors )

static

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.

Parameters

objectPoints	Vector of vectors of the calibration pattern points. The same structure as in calibrateCamera. For each pattern view, both cameras need to see the same object points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to be equal for each i.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera. The same structure as in calibrateCamera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera. The same structure as in calibrateCamera.
cameraMatrix1	Input/output camera intrinsic matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1	Input/output vector of distortion coefficients, the same as in calibrateCamera.
cameraMatrix2	Input/output second camera intrinsic matrix for the second camera. See description for cameraMatrix1.
distCoeffs2	Input/output lens distortion coefficients for the second camera. See description for distCoeffs1.
imageSize	Size of the image used only to initialize the camera intrinsic matrices.
R	Output rotation matrix. Together with the translation vector T, this matrix brings points given in the first camera's coordinate system to points in the second camera's coordinate system. In more technical terms, the tuple of R and T performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Due to its duality, this tuple is equivalent to the position of the first camera with respect to the second camera coordinate system.
T	Output translation vector, see description above.
E	Output essential matrix.
F	Output fundamental matrix.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_FIX_INTRINSIC Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F matrices are estimated. CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters according to the specified flags. Initial values are provided by the user. CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further. Otherwise R and T are initialized to the median value of the pattern views (each dimension separately). CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization. CALIB_FIX_FOCAL_LENGTH Fix \(f^{(j)}_x\) and \(f^{(j)}_y\) . CALIB_FIX_ASPECT_RATIO Optimize \(f^{(j)}_y\) . Fix the ratio \(f^{(j)}_x/f^{(j)}_y\) CALIB_SAME_FOCAL_LENGTH Enforce \(f^{(0)}_x=f^{(1)}_x\) and \(f^{(0)}_y=f^{(1)}_y\) . CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to zeros and fix there. CALIB_FIX_K1,..., CALIB_FIX_K6 Do not change the corresponding radial distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Enable coefficients k4, k5, and k6. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

The function estimates the transformation between two cameras making a stereo pair. If one computes the poses of an object relative to the first camera and to the second camera, ( \(R_1\), \(T_1\) ) and ( \(R_2\), \(T_2\)), respectively, for a stereo camera where the relative position and orientation between the two cameras are fixed, then those poses definitely relate to each other. This means, if the relative position and orientation ( \(R\), \(T\)) of the two cameras is known, it is possible to compute ( \(R_2\), \(T_2\)) when ( \(R_1\), \(T_1\)) is given. This is what the described function does. It computes ( \(R\), \(T\)) such that:

\[R_2=R R_1\]

\[T_2=R T_1 + T.\]

Therefore, one can compute the coordinate representation of a 3D point for the second camera's coordinate system when given the point's coordinate representation in the first camera's coordinate system:

\[\begin{bmatrix} X_2 \\ Y_2 \\ Z_2 \\ 1 \end{bmatrix} = \begin{bmatrix} R & T \\ 0 & 1 \end{bmatrix} \begin{bmatrix} X_1 \\ Y_1 \\ Z_1 \\ 1 \end{bmatrix}.\]

Optionally, it computes the essential matrix E:

\[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} R\]

where \(T_i\) are components of the translation vector \(T\) : \(T=[T_0, T_1, T_2]^T\) . And the function can also compute the fundamental matrix F:

\[F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\]

Besides the stereo-related information, the function can also perform a full calibration of each of the two cameras. However, due to the high dimensionality of the parameter space and noise in the input data, the function can diverge from the correct solution. If the intrinsic parameters can be estimated with high accuracy for each of the cameras individually (for example, using calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the function along with the computed intrinsic parameters. Otherwise, if all the parameters are estimated at once, it makes sense to restrict some parameters, for example, pass CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a reasonable assumption.

Similarly to calibrateCamera, the function minimizes the total re-projection error for all the points in all the available views from both cameras. The function returns the final value of the re-projection error.

stereoCalibrateExtended() [2/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrateExtended ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat E, Mat F, List< Mat > rvecs, List< Mat > tvecs, Mat perViewErrors, int flags )

static

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.

Parameters

objectPoints	Vector of vectors of the calibration pattern points. The same structure as in calibrateCamera. For each pattern view, both cameras need to see the same object points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to be equal for each i.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera. The same structure as in calibrateCamera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera. The same structure as in calibrateCamera.
cameraMatrix1	Input/output camera intrinsic matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1	Input/output vector of distortion coefficients, the same as in calibrateCamera.
cameraMatrix2	Input/output second camera intrinsic matrix for the second camera. See description for cameraMatrix1.
distCoeffs2	Input/output lens distortion coefficients for the second camera. See description for distCoeffs1.
imageSize	Size of the image used only to initialize the camera intrinsic matrices.
R	Output rotation matrix. Together with the translation vector T, this matrix brings points given in the first camera's coordinate system to points in the second camera's coordinate system. In more technical terms, the tuple of R and T performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Due to its duality, this tuple is equivalent to the position of the first camera with respect to the second camera coordinate system.
T	Output translation vector, see description above.
E	Output essential matrix.
F	Output fundamental matrix.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_FIX_INTRINSIC Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F matrices are estimated. CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters according to the specified flags. Initial values are provided by the user. CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further. Otherwise R and T are initialized to the median value of the pattern views (each dimension separately). CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization. CALIB_FIX_FOCAL_LENGTH Fix \(f^{(j)}_x\) and \(f^{(j)}_y\) . CALIB_FIX_ASPECT_RATIO Optimize \(f^{(j)}_y\) . Fix the ratio \(f^{(j)}_x/f^{(j)}_y\) CALIB_SAME_FOCAL_LENGTH Enforce \(f^{(0)}_x=f^{(1)}_x\) and \(f^{(0)}_y=f^{(1)}_y\) . CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to zeros and fix there. CALIB_FIX_K1,..., CALIB_FIX_K6 Do not change the corresponding radial distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Enable coefficients k4, k5, and k6. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

The function estimates the transformation between two cameras making a stereo pair. If one computes the poses of an object relative to the first camera and to the second camera, ( \(R_1\), \(T_1\) ) and ( \(R_2\), \(T_2\)), respectively, for a stereo camera where the relative position and orientation between the two cameras are fixed, then those poses definitely relate to each other. This means, if the relative position and orientation ( \(R\), \(T\)) of the two cameras is known, it is possible to compute ( \(R_2\), \(T_2\)) when ( \(R_1\), \(T_1\)) is given. This is what the described function does. It computes ( \(R\), \(T\)) such that:

\[R_2=R R_1\]

\[T_2=R T_1 + T.\]

Therefore, one can compute the coordinate representation of a 3D point for the second camera's coordinate system when given the point's coordinate representation in the first camera's coordinate system:

\[\begin{bmatrix} X_2 \\ Y_2 \\ Z_2 \\ 1 \end{bmatrix} = \begin{bmatrix} R & T \\ 0 & 1 \end{bmatrix} \begin{bmatrix} X_1 \\ Y_1 \\ Z_1 \\ 1 \end{bmatrix}.\]

Optionally, it computes the essential matrix E:

\[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} R\]

where \(T_i\) are components of the translation vector \(T\) : \(T=[T_0, T_1, T_2]^T\) . And the function can also compute the fundamental matrix F:

\[F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\]

Besides the stereo-related information, the function can also perform a full calibration of each of the two cameras. However, due to the high dimensionality of the parameter space and noise in the input data, the function can diverge from the correct solution. If the intrinsic parameters can be estimated with high accuracy for each of the cameras individually (for example, using calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the function along with the computed intrinsic parameters. Otherwise, if all the parameters are estimated at once, it makes sense to restrict some parameters, for example, pass CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a reasonable assumption.

Similarly to calibrateCamera, the function minimizes the total re-projection error for all the points in all the available views from both cameras. The function returns the final value of the re-projection error.

stereoCalibrateExtended() [3/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrateExtended ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat E, Mat F, List< Mat > rvecs, List< Mat > tvecs, Mat perViewErrors, int flags, in Vec3d criteria )

static

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.

Parameters

objectPoints	Vector of vectors of the calibration pattern points. The same structure as in calibrateCamera. For each pattern view, both cameras need to see the same object points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to be equal for each i.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera. The same structure as in calibrateCamera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera. The same structure as in calibrateCamera.
cameraMatrix1	Input/output camera intrinsic matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1	Input/output vector of distortion coefficients, the same as in calibrateCamera.
cameraMatrix2	Input/output second camera intrinsic matrix for the second camera. See description for cameraMatrix1.
distCoeffs2	Input/output lens distortion coefficients for the second camera. See description for distCoeffs1.
imageSize	Size of the image used only to initialize the camera intrinsic matrices.
R	Output rotation matrix. Together with the translation vector T, this matrix brings points given in the first camera's coordinate system to points in the second camera's coordinate system. In more technical terms, the tuple of R and T performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Due to its duality, this tuple is equivalent to the position of the first camera with respect to the second camera coordinate system.
T	Output translation vector, see description above.
E	Output essential matrix.
F	Output fundamental matrix.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_FIX_INTRINSIC Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F matrices are estimated. CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters according to the specified flags. Initial values are provided by the user. CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further. Otherwise R and T are initialized to the median value of the pattern views (each dimension separately). CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization. CALIB_FIX_FOCAL_LENGTH Fix \(f^{(j)}_x\) and \(f^{(j)}_y\) . CALIB_FIX_ASPECT_RATIO Optimize \(f^{(j)}_y\) . Fix the ratio \(f^{(j)}_x/f^{(j)}_y\) CALIB_SAME_FOCAL_LENGTH Enforce \(f^{(0)}_x=f^{(1)}_x\) and \(f^{(0)}_y=f^{(1)}_y\) . CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to zeros and fix there. CALIB_FIX_K1,..., CALIB_FIX_K6 Do not change the corresponding radial distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Enable coefficients k4, k5, and k6. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

The function estimates the transformation between two cameras making a stereo pair. If one computes the poses of an object relative to the first camera and to the second camera, ( \(R_1\), \(T_1\) ) and ( \(R_2\), \(T_2\)), respectively, for a stereo camera where the relative position and orientation between the two cameras are fixed, then those poses definitely relate to each other. This means, if the relative position and orientation ( \(R\), \(T\)) of the two cameras is known, it is possible to compute ( \(R_2\), \(T_2\)) when ( \(R_1\), \(T_1\)) is given. This is what the described function does. It computes ( \(R\), \(T\)) such that:

\[R_2=R R_1\]

\[T_2=R T_1 + T.\]

Therefore, one can compute the coordinate representation of a 3D point for the second camera's coordinate system when given the point's coordinate representation in the first camera's coordinate system:

\[\begin{bmatrix} X_2 \\ Y_2 \\ Z_2 \\ 1 \end{bmatrix} = \begin{bmatrix} R & T \\ 0 & 1 \end{bmatrix} \begin{bmatrix} X_1 \\ Y_1 \\ Z_1 \\ 1 \end{bmatrix}.\]

Optionally, it computes the essential matrix E:

\[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} R\]

where \(T_i\) are components of the translation vector \(T\) : \(T=[T_0, T_1, T_2]^T\) . And the function can also compute the fundamental matrix F:

\[F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\]

Besides the stereo-related information, the function can also perform a full calibration of each of the two cameras. However, due to the high dimensionality of the parameter space and noise in the input data, the function can diverge from the correct solution. If the intrinsic parameters can be estimated with high accuracy for each of the cameras individually (for example, using calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the function along with the computed intrinsic parameters. Otherwise, if all the parameters are estimated at once, it makes sense to restrict some parameters, for example, pass CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a reasonable assumption.

Similarly to calibrateCamera, the function minimizes the total re-projection error for all the points in all the available views from both cameras. The function returns the final value of the re-projection error.

stereoCalibrateExtended() [4/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrateExtended ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat E, Mat F, List< Mat > rvecs, List< Mat > tvecs, Mat perViewErrors )

static

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.

Parameters

objectPoints	Vector of vectors of the calibration pattern points. The same structure as in calibrateCamera. For each pattern view, both cameras need to see the same object points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to be equal for each i.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera. The same structure as in calibrateCamera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera. The same structure as in calibrateCamera.
cameraMatrix1	Input/output camera intrinsic matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1	Input/output vector of distortion coefficients, the same as in calibrateCamera.
cameraMatrix2	Input/output second camera intrinsic matrix for the second camera. See description for cameraMatrix1.
distCoeffs2	Input/output lens distortion coefficients for the second camera. See description for distCoeffs1.
imageSize	Size of the image used only to initialize the camera intrinsic matrices.
R	Output rotation matrix. Together with the translation vector T, this matrix brings points given in the first camera's coordinate system to points in the second camera's coordinate system. In more technical terms, the tuple of R and T performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Due to its duality, this tuple is equivalent to the position of the first camera with respect to the second camera coordinate system.
T	Output translation vector, see description above.
E	Output essential matrix.
F	Output fundamental matrix.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_FIX_INTRINSIC Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F matrices are estimated. CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters according to the specified flags. Initial values are provided by the user. CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further. Otherwise R and T are initialized to the median value of the pattern views (each dimension separately). CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization. CALIB_FIX_FOCAL_LENGTH Fix \(f^{(j)}_x\) and \(f^{(j)}_y\) . CALIB_FIX_ASPECT_RATIO Optimize \(f^{(j)}_y\) . Fix the ratio \(f^{(j)}_x/f^{(j)}_y\) CALIB_SAME_FOCAL_LENGTH Enforce \(f^{(0)}_x=f^{(1)}_x\) and \(f^{(0)}_y=f^{(1)}_y\) . CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to zeros and fix there. CALIB_FIX_K1,..., CALIB_FIX_K6 Do not change the corresponding radial distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Enable coefficients k4, k5, and k6. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

The function estimates the transformation between two cameras making a stereo pair. If one computes the poses of an object relative to the first camera and to the second camera, ( \(R_1\), \(T_1\) ) and ( \(R_2\), \(T_2\)), respectively, for a stereo camera where the relative position and orientation between the two cameras are fixed, then those poses definitely relate to each other. This means, if the relative position and orientation ( \(R\), \(T\)) of the two cameras is known, it is possible to compute ( \(R_2\), \(T_2\)) when ( \(R_1\), \(T_1\)) is given. This is what the described function does. It computes ( \(R\), \(T\)) such that:

\[R_2=R R_1\]

\[T_2=R T_1 + T.\]

Therefore, one can compute the coordinate representation of a 3D point for the second camera's coordinate system when given the point's coordinate representation in the first camera's coordinate system:

\[\begin{bmatrix} X_2 \\ Y_2 \\ Z_2 \\ 1 \end{bmatrix} = \begin{bmatrix} R & T \\ 0 & 1 \end{bmatrix} \begin{bmatrix} X_1 \\ Y_1 \\ Z_1 \\ 1 \end{bmatrix}.\]

Optionally, it computes the essential matrix E:

\[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} R\]

where \(T_i\) are components of the translation vector \(T\) : \(T=[T_0, T_1, T_2]^T\) . And the function can also compute the fundamental matrix F:

\[F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\]

Besides the stereo-related information, the function can also perform a full calibration of each of the two cameras. However, due to the high dimensionality of the parameter space and noise in the input data, the function can diverge from the correct solution. If the intrinsic parameters can be estimated with high accuracy for each of the cameras individually (for example, using calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the function along with the computed intrinsic parameters. Otherwise, if all the parameters are estimated at once, it makes sense to restrict some parameters, for example, pass CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a reasonable assumption.

Similarly to calibrateCamera, the function minimizes the total re-projection error for all the points in all the available views from both cameras. The function returns the final value of the re-projection error.

stereoCalibrateExtended() [5/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrateExtended ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat E, Mat F, List< Mat > rvecs, List< Mat > tvecs, Mat perViewErrors, int flags )

static

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.

Parameters

objectPoints	Vector of vectors of the calibration pattern points. The same structure as in calibrateCamera. For each pattern view, both cameras need to see the same object points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to be equal for each i.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera. The same structure as in calibrateCamera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera. The same structure as in calibrateCamera.
cameraMatrix1	Input/output camera intrinsic matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1	Input/output vector of distortion coefficients, the same as in calibrateCamera.
cameraMatrix2	Input/output second camera intrinsic matrix for the second camera. See description for cameraMatrix1.
distCoeffs2	Input/output lens distortion coefficients for the second camera. See description for distCoeffs1.
imageSize	Size of the image used only to initialize the camera intrinsic matrices.
R	Output rotation matrix. Together with the translation vector T, this matrix brings points given in the first camera's coordinate system to points in the second camera's coordinate system. In more technical terms, the tuple of R and T performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Due to its duality, this tuple is equivalent to the position of the first camera with respect to the second camera coordinate system.
T	Output translation vector, see description above.
E	Output essential matrix.
F	Output fundamental matrix.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_FIX_INTRINSIC Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F matrices are estimated. CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters according to the specified flags. Initial values are provided by the user. CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further. Otherwise R and T are initialized to the median value of the pattern views (each dimension separately). CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization. CALIB_FIX_FOCAL_LENGTH Fix \(f^{(j)}_x\) and \(f^{(j)}_y\) . CALIB_FIX_ASPECT_RATIO Optimize \(f^{(j)}_y\) . Fix the ratio \(f^{(j)}_x/f^{(j)}_y\) CALIB_SAME_FOCAL_LENGTH Enforce \(f^{(0)}_x=f^{(1)}_x\) and \(f^{(0)}_y=f^{(1)}_y\) . CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to zeros and fix there. CALIB_FIX_K1,..., CALIB_FIX_K6 Do not change the corresponding radial distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Enable coefficients k4, k5, and k6. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

The function estimates the transformation between two cameras making a stereo pair. If one computes the poses of an object relative to the first camera and to the second camera, ( \(R_1\), \(T_1\) ) and ( \(R_2\), \(T_2\)), respectively, for a stereo camera where the relative position and orientation between the two cameras are fixed, then those poses definitely relate to each other. This means, if the relative position and orientation ( \(R\), \(T\)) of the two cameras is known, it is possible to compute ( \(R_2\), \(T_2\)) when ( \(R_1\), \(T_1\)) is given. This is what the described function does. It computes ( \(R\), \(T\)) such that:

\[R_2=R R_1\]

\[T_2=R T_1 + T.\]

Therefore, one can compute the coordinate representation of a 3D point for the second camera's coordinate system when given the point's coordinate representation in the first camera's coordinate system:

\[\begin{bmatrix} X_2 \\ Y_2 \\ Z_2 \\ 1 \end{bmatrix} = \begin{bmatrix} R & T \\ 0 & 1 \end{bmatrix} \begin{bmatrix} X_1 \\ Y_1 \\ Z_1 \\ 1 \end{bmatrix}.\]

Optionally, it computes the essential matrix E:

\[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} R\]

where \(T_i\) are components of the translation vector \(T\) : \(T=[T_0, T_1, T_2]^T\) . And the function can also compute the fundamental matrix F:

\[F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\]

Besides the stereo-related information, the function can also perform a full calibration of each of the two cameras. However, due to the high dimensionality of the parameter space and noise in the input data, the function can diverge from the correct solution. If the intrinsic parameters can be estimated with high accuracy for each of the cameras individually (for example, using calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the function along with the computed intrinsic parameters. Otherwise, if all the parameters are estimated at once, it makes sense to restrict some parameters, for example, pass CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a reasonable assumption.

Similarly to calibrateCamera, the function minimizes the total re-projection error for all the points in all the available views from both cameras. The function returns the final value of the re-projection error.

stereoCalibrateExtended() [6/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.stereoCalibrateExtended ( List< Mat > objectPoints, List< Mat > imagePoints1, List< Mat > imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat E, Mat F, List< Mat > rvecs, List< Mat > tvecs, Mat perViewErrors, int flags, in(double type, double maxCount, double epsilon) criteria )

static

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.

Parameters

objectPoints	Vector of vectors of the calibration pattern points. The same structure as in calibrateCamera. For each pattern view, both cameras need to see the same object points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to be equal for each i.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera. The same structure as in calibrateCamera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera. The same structure as in calibrateCamera.
cameraMatrix1	Input/output camera intrinsic matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1	Input/output vector of distortion coefficients, the same as in calibrateCamera.
cameraMatrix2	Input/output second camera intrinsic matrix for the second camera. See description for cameraMatrix1.
distCoeffs2	Input/output lens distortion coefficients for the second camera. See description for distCoeffs1.
imageSize	Size of the image used only to initialize the camera intrinsic matrices.
R	Output rotation matrix. Together with the translation vector T, this matrix brings points given in the first camera's coordinate system to points in the second camera's coordinate system. In more technical terms, the tuple of R and T performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Due to its duality, this tuple is equivalent to the position of the first camera with respect to the second camera coordinate system.
T	Output translation vector, see description above.
E	Output essential matrix.
F	Output fundamental matrix.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_FIX_INTRINSIC Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F matrices are estimated. CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters according to the specified flags. Initial values are provided by the user. CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further. Otherwise R and T are initialized to the median value of the pattern views (each dimension separately). CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization. CALIB_FIX_FOCAL_LENGTH Fix \(f^{(j)}_x\) and \(f^{(j)}_y\) . CALIB_FIX_ASPECT_RATIO Optimize \(f^{(j)}_y\) . Fix the ratio \(f^{(j)}_x/f^{(j)}_y\) CALIB_SAME_FOCAL_LENGTH Enforce \(f^{(0)}_x=f^{(1)}_x\) and \(f^{(0)}_y=f^{(1)}_y\) . CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to zeros and fix there. CALIB_FIX_K1,..., CALIB_FIX_K6 Do not change the corresponding radial distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Enable coefficients k4, k5, and k6. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

The function estimates the transformation between two cameras making a stereo pair. If one computes the poses of an object relative to the first camera and to the second camera, ( \(R_1\), \(T_1\) ) and ( \(R_2\), \(T_2\)), respectively, for a stereo camera where the relative position and orientation between the two cameras are fixed, then those poses definitely relate to each other. This means, if the relative position and orientation ( \(R\), \(T\)) of the two cameras is known, it is possible to compute ( \(R_2\), \(T_2\)) when ( \(R_1\), \(T_1\)) is given. This is what the described function does. It computes ( \(R\), \(T\)) such that:

\[R_2=R R_1\]

\[T_2=R T_1 + T.\]

Therefore, one can compute the coordinate representation of a 3D point for the second camera's coordinate system when given the point's coordinate representation in the first camera's coordinate system:

\[\begin{bmatrix} X_2 \\ Y_2 \\ Z_2 \\ 1 \end{bmatrix} = \begin{bmatrix} R & T \\ 0 & 1 \end{bmatrix} \begin{bmatrix} X_1 \\ Y_1 \\ Z_1 \\ 1 \end{bmatrix}.\]

Optionally, it computes the essential matrix E:

\[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} R\]

where \(T_i\) are components of the translation vector \(T\) : \(T=[T_0, T_1, T_2]^T\) . And the function can also compute the fundamental matrix F:

\[F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\]

Besides the stereo-related information, the function can also perform a full calibration of each of the two cameras. However, due to the high dimensionality of the parameter space and noise in the input data, the function can diverge from the correct solution. If the intrinsic parameters can be estimated with high accuracy for each of the cameras individually (for example, using calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the function along with the computed intrinsic parameters. Otherwise, if all the parameters are estimated at once, it makes sense to restrict some parameters, for example, pass CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a reasonable assumption.

Similarly to calibrateCamera, the function minimizes the total re-projection error for all the points in all the available views from both cameras. The function returns the final value of the re-projection error.

stereoCalibrateExtended() [7/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

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.

Parameters

objectPoints	Vector of vectors of the calibration pattern points. The same structure as in calibrateCamera. For each pattern view, both cameras need to see the same object points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to be equal for each i.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera. The same structure as in calibrateCamera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera. The same structure as in calibrateCamera.
cameraMatrix1	Input/output camera intrinsic matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1	Input/output vector of distortion coefficients, the same as in calibrateCamera.
cameraMatrix2	Input/output second camera intrinsic matrix for the second camera. See description for cameraMatrix1.
distCoeffs2	Input/output lens distortion coefficients for the second camera. See description for distCoeffs1.
imageSize	Size of the image used only to initialize the camera intrinsic matrices.
R	Output rotation matrix. Together with the translation vector T, this matrix brings points given in the first camera's coordinate system to points in the second camera's coordinate system. In more technical terms, the tuple of R and T performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Due to its duality, this tuple is equivalent to the position of the first camera with respect to the second camera coordinate system.
T	Output translation vector, see description above.
E	Output essential matrix.
F	Output fundamental matrix.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_FIX_INTRINSIC Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F matrices are estimated. CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters according to the specified flags. Initial values are provided by the user. CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further. Otherwise R and T are initialized to the median value of the pattern views (each dimension separately). CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization. CALIB_FIX_FOCAL_LENGTH Fix \(f^{(j)}_x\) and \(f^{(j)}_y\) . CALIB_FIX_ASPECT_RATIO Optimize \(f^{(j)}_y\) . Fix the ratio \(f^{(j)}_x/f^{(j)}_y\) CALIB_SAME_FOCAL_LENGTH Enforce \(f^{(0)}_x=f^{(1)}_x\) and \(f^{(0)}_y=f^{(1)}_y\) . CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to zeros and fix there. CALIB_FIX_K1,..., CALIB_FIX_K6 Do not change the corresponding radial distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Enable coefficients k4, k5, and k6. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

The function estimates the transformation between two cameras making a stereo pair. If one computes the poses of an object relative to the first camera and to the second camera, ( \(R_1\), \(T_1\) ) and ( \(R_2\), \(T_2\)), respectively, for a stereo camera where the relative position and orientation between the two cameras are fixed, then those poses definitely relate to each other. This means, if the relative position and orientation ( \(R\), \(T\)) of the two cameras is known, it is possible to compute ( \(R_2\), \(T_2\)) when ( \(R_1\), \(T_1\)) is given. This is what the described function does. It computes ( \(R\), \(T\)) such that:

\[R_2=R R_1\]

\[T_2=R T_1 + T.\]

Therefore, one can compute the coordinate representation of a 3D point for the second camera's coordinate system when given the point's coordinate representation in the first camera's coordinate system:

\[\begin{bmatrix} X_2 \\ Y_2 \\ Z_2 \\ 1 \end{bmatrix} = \begin{bmatrix} R & T \\ 0 & 1 \end{bmatrix} \begin{bmatrix} X_1 \\ Y_1 \\ Z_1 \\ 1 \end{bmatrix}.\]

Optionally, it computes the essential matrix E:

\[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} R\]

where \(T_i\) are components of the translation vector \(T\) : \(T=[T_0, T_1, T_2]^T\) . And the function can also compute the fundamental matrix F:

\[F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\]

Besides the stereo-related information, the function can also perform a full calibration of each of the two cameras. However, due to the high dimensionality of the parameter space and noise in the input data, the function can diverge from the correct solution. If the intrinsic parameters can be estimated with high accuracy for each of the cameras individually (for example, using calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the function along with the computed intrinsic parameters. Otherwise, if all the parameters are estimated at once, it makes sense to restrict some parameters, for example, pass CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a reasonable assumption.

Similarly to calibrateCamera, the function minimizes the total re-projection error for all the points in all the available views from both cameras. The function returns the final value of the re-projection error.

stereoCalibrateExtended() [8/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

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.

Parameters

objectPoints	Vector of vectors of the calibration pattern points. The same structure as in calibrateCamera. For each pattern view, both cameras need to see the same object points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to be equal for each i.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera. The same structure as in calibrateCamera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera. The same structure as in calibrateCamera.
cameraMatrix1	Input/output camera intrinsic matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1	Input/output vector of distortion coefficients, the same as in calibrateCamera.
cameraMatrix2	Input/output second camera intrinsic matrix for the second camera. See description for cameraMatrix1.
distCoeffs2	Input/output lens distortion coefficients for the second camera. See description for distCoeffs1.
imageSize	Size of the image used only to initialize the camera intrinsic matrices.
R	Output rotation matrix. Together with the translation vector T, this matrix brings points given in the first camera's coordinate system to points in the second camera's coordinate system. In more technical terms, the tuple of R and T performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Due to its duality, this tuple is equivalent to the position of the first camera with respect to the second camera coordinate system.
T	Output translation vector, see description above.
E	Output essential matrix.
F	Output fundamental matrix.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_FIX_INTRINSIC Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F matrices are estimated. CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters according to the specified flags. Initial values are provided by the user. CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further. Otherwise R and T are initialized to the median value of the pattern views (each dimension separately). CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization. CALIB_FIX_FOCAL_LENGTH Fix \(f^{(j)}_x\) and \(f^{(j)}_y\) . CALIB_FIX_ASPECT_RATIO Optimize \(f^{(j)}_y\) . Fix the ratio \(f^{(j)}_x/f^{(j)}_y\) CALIB_SAME_FOCAL_LENGTH Enforce \(f^{(0)}_x=f^{(1)}_x\) and \(f^{(0)}_y=f^{(1)}_y\) . CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to zeros and fix there. CALIB_FIX_K1,..., CALIB_FIX_K6 Do not change the corresponding radial distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Enable coefficients k4, k5, and k6. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

The function estimates the transformation between two cameras making a stereo pair. If one computes the poses of an object relative to the first camera and to the second camera, ( \(R_1\), \(T_1\) ) and ( \(R_2\), \(T_2\)), respectively, for a stereo camera where the relative position and orientation between the two cameras are fixed, then those poses definitely relate to each other. This means, if the relative position and orientation ( \(R\), \(T\)) of the two cameras is known, it is possible to compute ( \(R_2\), \(T_2\)) when ( \(R_1\), \(T_1\)) is given. This is what the described function does. It computes ( \(R\), \(T\)) such that:

\[R_2=R R_1\]

\[T_2=R T_1 + T.\]

Therefore, one can compute the coordinate representation of a 3D point for the second camera's coordinate system when given the point's coordinate representation in the first camera's coordinate system:

\[\begin{bmatrix} X_2 \\ Y_2 \\ Z_2 \\ 1 \end{bmatrix} = \begin{bmatrix} R & T \\ 0 & 1 \end{bmatrix} \begin{bmatrix} X_1 \\ Y_1 \\ Z_1 \\ 1 \end{bmatrix}.\]

Optionally, it computes the essential matrix E:

\[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} R\]

where \(T_i\) are components of the translation vector \(T\) : \(T=[T_0, T_1, T_2]^T\) . And the function can also compute the fundamental matrix F:

\[F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\]

Besides the stereo-related information, the function can also perform a full calibration of each of the two cameras. However, due to the high dimensionality of the parameter space and noise in the input data, the function can diverge from the correct solution. If the intrinsic parameters can be estimated with high accuracy for each of the cameras individually (for example, using calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the function along with the computed intrinsic parameters. Otherwise, if all the parameters are estimated at once, it makes sense to restrict some parameters, for example, pass CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a reasonable assumption.

Similarly to calibrateCamera, the function minimizes the total re-projection error for all the points in all the available views from both cameras. The function returns the final value of the re-projection error.

stereoCalibrateExtended() [9/9]

static double OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

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.

Parameters

objectPoints	Vector of vectors of the calibration pattern points. The same structure as in calibrateCamera. For each pattern view, both cameras need to see the same object points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to be equal for each i.
imagePoints1	Vector of vectors of the projections of the calibration pattern points, observed by the first camera. The same structure as in calibrateCamera.
imagePoints2	Vector of vectors of the projections of the calibration pattern points, observed by the second camera. The same structure as in calibrateCamera.
cameraMatrix1	Input/output camera intrinsic matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1	Input/output vector of distortion coefficients, the same as in calibrateCamera.
cameraMatrix2	Input/output second camera intrinsic matrix for the second camera. See description for cameraMatrix1.
distCoeffs2	Input/output lens distortion coefficients for the second camera. See description for distCoeffs1.
imageSize	Size of the image used only to initialize the camera intrinsic matrices.
R	Output rotation matrix. Together with the translation vector T, this matrix brings points given in the first camera's coordinate system to points in the second camera's coordinate system. In more technical terms, the tuple of R and T performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Due to its duality, this tuple is equivalent to the position of the first camera with respect to the second camera coordinate system.
T	Output translation vector, see description above.
E	Output essential matrix.
F	Output fundamental matrix.
rvecs	Output vector of rotation vectors ( Rodrigues ) estimated for each pattern view in the coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs	Output vector of translation vectors estimated for each pattern view, see parameter description of previous output parameter ( rvecs ).
perViewErrors	Output vector of the RMS re-projection error estimated for each pattern view.
flags	Different flags that may be zero or a combination of the following values: CALIB_FIX_INTRINSIC Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F matrices are estimated. CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters according to the specified flags. Initial values are provided by the user. CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further. Otherwise R and T are initialized to the median value of the pattern views (each dimension separately). CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization. CALIB_FIX_FOCAL_LENGTH Fix \(f^{(j)}_x\) and \(f^{(j)}_y\) . CALIB_FIX_ASPECT_RATIO Optimize \(f^{(j)}_y\) . Fix the ratio \(f^{(j)}_x/f^{(j)}_y\) CALIB_SAME_FOCAL_LENGTH Enforce \(f^{(0)}_x=f^{(1)}_x\) and \(f^{(0)}_y=f^{(1)}_y\) . CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to zeros and fix there. CALIB_FIX_K1,..., CALIB_FIX_K6 Do not change the corresponding radial distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Enable coefficients k4, k5, and k6. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
criteria	Termination criteria for the iterative optimization algorithm.

The function estimates the transformation between two cameras making a stereo pair. If one computes the poses of an object relative to the first camera and to the second camera, ( \(R_1\), \(T_1\) ) and ( \(R_2\), \(T_2\)), respectively, for a stereo camera where the relative position and orientation between the two cameras are fixed, then those poses definitely relate to each other. This means, if the relative position and orientation ( \(R\), \(T\)) of the two cameras is known, it is possible to compute ( \(R_2\), \(T_2\)) when ( \(R_1\), \(T_1\)) is given. This is what the described function does. It computes ( \(R\), \(T\)) such that:

\[R_2=R R_1\]

\[T_2=R T_1 + T.\]

Therefore, one can compute the coordinate representation of a 3D point for the second camera's coordinate system when given the point's coordinate representation in the first camera's coordinate system:

\[\begin{bmatrix} X_2 \\ Y_2 \\ Z_2 \\ 1 \end{bmatrix} = \begin{bmatrix} R & T \\ 0 & 1 \end{bmatrix} \begin{bmatrix} X_1 \\ Y_1 \\ Z_1 \\ 1 \end{bmatrix}.\]

Optionally, it computes the essential matrix E:

\[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} R\]

where \(T_i\) are components of the translation vector \(T\) : \(T=[T_0, T_1, T_2]^T\) . And the function can also compute the fundamental matrix F:

\[F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\]

Besides the stereo-related information, the function can also perform a full calibration of each of the two cameras. However, due to the high dimensionality of the parameter space and noise in the input data, the function can diverge from the correct solution. If the intrinsic parameters can be estimated with high accuracy for each of the cameras individually (for example, using calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the function along with the computed intrinsic parameters. Otherwise, if all the parameters are estimated at once, it makes sense to restrict some parameters, for example, pass CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a reasonable assumption.

Similarly to calibrateCamera, the function minimizes the total re-projection error for all the points in all the available views from both cameras. The function returns the final value of the re-projection error.

stereoRectify() [1/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.stereoRectify ( Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [2/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.stereoRectify ( Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [3/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.stereoRectify ( Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [4/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.stereoRectify ( Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in Vec2d newImageSize )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [5/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.stereoRectify ( Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in Vec2d newImageSize, out Vec4i validPixROI1 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [6/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.stereoRectify ( Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in Vec2d newImageSize, out Vec4i validPixROI1, out Vec4i validPixROI2 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [7/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.stereoRectify ( Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [8/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.stereoRectify ( Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [9/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.stereoRectify ( Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [10/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.stereoRectify ( Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in(double width, double height) newImageSize )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [11/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.stereoRectify ( Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in(double width, double height) newImageSize, out(int x, int y, int width, int height) validPixROI1 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [12/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.stereoRectify ( Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in(double width, double height) newImageSize, out(int x, int y, int width, int height) validPixROI1, out(int x, int y, int width, int height) validPixROI2 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [13/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [14/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [15/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [16/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [17/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectify() [18/18]

static void OpenCVForUnity.Calib3dModule.Calib3d.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 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or 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.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
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 a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

• Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if CALIB_ZERO_DISPARITY is set.

• Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

stereoRectifyUncalibrated() [1/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.stereoRectifyUncalibrated ( Mat points1, Mat points2, Mat F, in Vec2d imgSize, Mat H1, Mat H2 )

static

Computes a rectification transform for an uncalibrated stereo camera.

Parameters

points1	Array of feature points in the first image.
points2	The corresponding points in the second image. The same formats as in findFundamentalMat are supported.
F	Input fundamental matrix. It can be computed from the same set of point pairs using findFundamentalMat .
imgSize	Size of the image.
H1	Output rectification homography matrix for the first image.
H2	Output rectification homography matrix for the second image.
threshold	Optional threshold used to filter out the outliers. If the parameter is greater than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which \(|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}|>\texttt{threshold}\) ) are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.

The function computes the rectification transformations without knowing intrinsic parameters of the cameras and their relative position in the space, which explains the suffix "uncalibrated". Another related difference from stereoRectify is that the function outputs not the rectification transformations in the object (3D) space, but the planar perspective transformations encoded by the homography matrices H1 and H2 . The function implements the algorithm [Hartley99] .

Note

While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, it would be better to correct it before computing the fundamental matrix and calling this function. For example, distortion coefficients can be estimated for each head of stereo camera separately by using calibrateCamera . Then, the images can be corrected using undistort , or just the point coordinates can be corrected with undistortPoints .

stereoRectifyUncalibrated() [2/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.stereoRectifyUncalibrated ( Mat points1, Mat points2, Mat F, in Vec2d imgSize, Mat H1, Mat H2, double threshold )

static

Computes a rectification transform for an uncalibrated stereo camera.

Parameters

points1	Array of feature points in the first image.
points2	The corresponding points in the second image. The same formats as in findFundamentalMat are supported.
F	Input fundamental matrix. It can be computed from the same set of point pairs using findFundamentalMat .
imgSize	Size of the image.
H1	Output rectification homography matrix for the first image.
H2	Output rectification homography matrix for the second image.
threshold	Optional threshold used to filter out the outliers. If the parameter is greater than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which \(|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}|>\texttt{threshold}\) ) are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.

The function computes the rectification transformations without knowing intrinsic parameters of the cameras and their relative position in the space, which explains the suffix "uncalibrated". Another related difference from stereoRectify is that the function outputs not the rectification transformations in the object (3D) space, but the planar perspective transformations encoded by the homography matrices H1 and H2 . The function implements the algorithm [Hartley99] .

Note

While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, it would be better to correct it before computing the fundamental matrix and calling this function. For example, distortion coefficients can be estimated for each head of stereo camera separately by using calibrateCamera . Then, the images can be corrected using undistort , or just the point coordinates can be corrected with undistortPoints .

stereoRectifyUncalibrated() [3/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.stereoRectifyUncalibrated ( Mat points1, Mat points2, Mat F, in(double width, double height) imgSize, Mat H1, Mat H2 )

static

Computes a rectification transform for an uncalibrated stereo camera.

Parameters

points1	Array of feature points in the first image.
points2	The corresponding points in the second image. The same formats as in findFundamentalMat are supported.
F	Input fundamental matrix. It can be computed from the same set of point pairs using findFundamentalMat .
imgSize	Size of the image.
H1	Output rectification homography matrix for the first image.
H2	Output rectification homography matrix for the second image.
threshold	Optional threshold used to filter out the outliers. If the parameter is greater than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which \(|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}|>\texttt{threshold}\) ) are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.

The function computes the rectification transformations without knowing intrinsic parameters of the cameras and their relative position in the space, which explains the suffix "uncalibrated". Another related difference from stereoRectify is that the function outputs not the rectification transformations in the object (3D) space, but the planar perspective transformations encoded by the homography matrices H1 and H2 . The function implements the algorithm [Hartley99] .

Note

While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, it would be better to correct it before computing the fundamental matrix and calling this function. For example, distortion coefficients can be estimated for each head of stereo camera separately by using calibrateCamera . Then, the images can be corrected using undistort , or just the point coordinates can be corrected with undistortPoints .

stereoRectifyUncalibrated() [4/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.stereoRectifyUncalibrated ( Mat points1, Mat points2, Mat F, in(double width, double height) imgSize, Mat H1, Mat H2, double threshold )

static

Computes a rectification transform for an uncalibrated stereo camera.

Parameters

points1	Array of feature points in the first image.
points2	The corresponding points in the second image. The same formats as in findFundamentalMat are supported.
F	Input fundamental matrix. It can be computed from the same set of point pairs using findFundamentalMat .
imgSize	Size of the image.
H1	Output rectification homography matrix for the first image.
H2	Output rectification homography matrix for the second image.
threshold	Optional threshold used to filter out the outliers. If the parameter is greater than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which \(|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}|>\texttt{threshold}\) ) are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.

The function computes the rectification transformations without knowing intrinsic parameters of the cameras and their relative position in the space, which explains the suffix "uncalibrated". Another related difference from stereoRectify is that the function outputs not the rectification transformations in the object (3D) space, but the planar perspective transformations encoded by the homography matrices H1 and H2 . The function implements the algorithm [Hartley99] .

Note

While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, it would be better to correct it before computing the fundamental matrix and calling this function. For example, distortion coefficients can be estimated for each head of stereo camera separately by using calibrateCamera . Then, the images can be corrected using undistort , or just the point coordinates can be corrected with undistortPoints .

stereoRectifyUncalibrated() [5/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.stereoRectifyUncalibrated ( Mat points1, Mat points2, Mat F, Size imgSize, Mat H1, Mat H2 )

static

Computes a rectification transform for an uncalibrated stereo camera.

Parameters

points1	Array of feature points in the first image.
points2	The corresponding points in the second image. The same formats as in findFundamentalMat are supported.
F	Input fundamental matrix. It can be computed from the same set of point pairs using findFundamentalMat .
imgSize	Size of the image.
H1	Output rectification homography matrix for the first image.
H2	Output rectification homography matrix for the second image.
threshold	Optional threshold used to filter out the outliers. If the parameter is greater than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which \(|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}|>\texttt{threshold}\) ) are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.

The function computes the rectification transformations without knowing intrinsic parameters of the cameras and their relative position in the space, which explains the suffix "uncalibrated". Another related difference from stereoRectify is that the function outputs not the rectification transformations in the object (3D) space, but the planar perspective transformations encoded by the homography matrices H1 and H2 . The function implements the algorithm [Hartley99] .

Note

While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, it would be better to correct it before computing the fundamental matrix and calling this function. For example, distortion coefficients can be estimated for each head of stereo camera separately by using calibrateCamera . Then, the images can be corrected using undistort , or just the point coordinates can be corrected with undistortPoints .

stereoRectifyUncalibrated() [6/6]

static bool OpenCVForUnity.Calib3dModule.Calib3d.stereoRectifyUncalibrated ( Mat points1, Mat points2, Mat F, Size imgSize, Mat H1, Mat H2, double threshold )

static

Computes a rectification transform for an uncalibrated stereo camera.

Parameters

points1	Array of feature points in the first image.
points2	The corresponding points in the second image. The same formats as in findFundamentalMat are supported.
F	Input fundamental matrix. It can be computed from the same set of point pairs using findFundamentalMat .
imgSize	Size of the image.
H1	Output rectification homography matrix for the first image.
H2	Output rectification homography matrix for the second image.
threshold	Optional threshold used to filter out the outliers. If the parameter is greater than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which \(|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}|>\texttt{threshold}\) ) are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.

The function computes the rectification transformations without knowing intrinsic parameters of the cameras and their relative position in the space, which explains the suffix "uncalibrated". Another related difference from stereoRectify is that the function outputs not the rectification transformations in the object (3D) space, but the planar perspective transformations encoded by the homography matrices H1 and H2 . The function implements the algorithm [Hartley99] .

Note

While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, it would be better to correct it before computing the fundamental matrix and calling this function. For example, distortion coefficients can be estimated for each head of stereo camera separately by using calibrateCamera . Then, the images can be corrected using undistort , or just the point coordinates can be corrected with undistortPoints .

triangulatePoints()

static void OpenCVForUnity.Calib3dModule.Calib3d.triangulatePoints ( Mat projMatr1, Mat projMatr2, Mat projPoints1, Mat projPoints2, Mat points4D )

static

This function reconstructs 3-dimensional points (in homogeneous coordinates) by using their observations with a stereo camera.

Parameters

projMatr1	3x4 projection matrix of the first camera, i.e. this matrix projects 3D points given in the world's coordinate system into the first image.
projMatr2	3x4 projection matrix of the second camera, i.e. this matrix projects 3D points given in the world's coordinate system into the second image.
projPoints1	2xN array of feature points in the first image. In the case of the c++ version, it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1.
projPoints2	2xN array of corresponding points in the second image. In the case of the c++ version, it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1.
points4D	4xN array of reconstructed points in homogeneous coordinates. These points are returned in the world's coordinate system.

Note

Keep in mind that all input data should be of float type in order for this function to work.

If the projection matrices from stereoRectify are used, then the returned points are represented in the first camera's rectified coordinate system.

See also

reprojectImageTo3D

undistort() [1/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.undistort ( Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs )

static

Transforms an image to compensate for lens distortion.

The function transforms an image to compensate radial and tangential lens distortion.

The function is simply a combination of initUndistortRectifyMap (with unity R ) and #remap (with bilinear interpolation). See the former function for details of the transformation being performed.

Those pixels in the destination image, for which there is no correspondent pixels in the source image, are filled with zeros (black color).

A particular subset of the source image that will be visible in the corrected image can be regulated by newCameraMatrix. You can use getOptimalNewCameraMatrix to compute the appropriate newCameraMatrix depending on your requirements.

The camera matrix and the distortion parameters can be determined using calibrateCamera. If the resolution of images is different from the resolution used at the calibration stage, \(f_x, f_y, c_x\) and \(c_y\) need to be scaled accordingly, while the distortion coefficients remain the same.

Parameters

src	Input (distorted) image.
dst	Output (corrected) image that has the same size and type as src .
cameraMatrix	Input camera matrix \(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	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.
newCameraMatrix	Camera matrix of the distorted image. By default, it is the same as cameraMatrix but you may additionally scale and shift the result by using a different matrix.

undistort() [2/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.undistort ( Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat newCameraMatrix )

static

Transforms an image to compensate for lens distortion.

The function transforms an image to compensate radial and tangential lens distortion.

The function is simply a combination of initUndistortRectifyMap (with unity R ) and #remap (with bilinear interpolation). See the former function for details of the transformation being performed.

Those pixels in the destination image, for which there is no correspondent pixels in the source image, are filled with zeros (black color).

A particular subset of the source image that will be visible in the corrected image can be regulated by newCameraMatrix. You can use getOptimalNewCameraMatrix to compute the appropriate newCameraMatrix depending on your requirements.

The camera matrix and the distortion parameters can be determined using calibrateCamera. If the resolution of images is different from the resolution used at the calibration stage, \(f_x, f_y, c_x\) and \(c_y\) need to be scaled accordingly, while the distortion coefficients remain the same.

Parameters

src	Input (distorted) image.
dst	Output (corrected) image that has the same size and type as src .
cameraMatrix	Input camera matrix \(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	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.
newCameraMatrix	Camera matrix of the distorted image. By default, it is the same as cameraMatrix but you may additionally scale and shift the result by using a different matrix.

undistortImagePoints() [1/4]

static void OpenCVForUnity.Calib3dModule.Calib3d.undistortImagePoints ( Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs )

static

Compute undistorted image points position.

Parameters

src	Observed points position, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or vector<Point2f> ).
dst	Output undistorted points position (1xN/Nx1 2-channel or vector<Point2f> ).
cameraMatrix	Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	Distortion coefficients

undistortImagePoints() [2/4]

static void OpenCVForUnity.Calib3dModule.Calib3d.undistortImagePoints ( Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, in Vec3d arg1 )

static

Compute undistorted image points position.

Parameters

src	Observed points position, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or vector<Point2f> ).
dst	Output undistorted points position (1xN/Nx1 2-channel or vector<Point2f> ).
cameraMatrix	Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	Distortion coefficients

undistortImagePoints() [3/4]

static void OpenCVForUnity.Calib3dModule.Calib3d.undistortImagePoints ( Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, in(double type, double maxCount, double epsilon) arg1 )

static

Compute undistorted image points position.

Parameters

src	Observed points position, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or vector<Point2f> ).
dst	Output undistorted points position (1xN/Nx1 2-channel or vector<Point2f> ).
cameraMatrix	Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	Distortion coefficients

undistortImagePoints() [4/4]

static void OpenCVForUnity.Calib3dModule.Calib3d.undistortImagePoints ( Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, TermCriteria arg1 )

static

Compute undistorted image points position.

Parameters

src	Observed points position, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or vector<Point2f> ).
dst	Output undistorted points position (1xN/Nx1 2-channel or vector<Point2f> ).
cameraMatrix	Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	Distortion coefficients

undistortPoints() [1/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.undistortPoints ( MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs )

static

Computes the ideal point coordinates from the observed point coordinates.

The function is similar to undistort and initUndistortRectifyMap but it operates on a sparse set of points instead of a raster image. Also the function performs a reverse transformation to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a planar object, it does, up to a translation vector, if the proper R is specified.

For each observed point coordinate \((u, v)\) the function computes:

\[ \begin{array}{l} x^{"} \leftarrow (u - c_x)/f_x \\ y^{"} \leftarrow (v - c_y)/f_y \\ (x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\ {[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ x \leftarrow X/W \\ y \leftarrow Y/W \\ \text{only performed if P is specified:} \\ u' \leftarrow x {f'}_x + {c'}_x \\ v' \leftarrow y {f'}_y + {c'}_y \end{array} \]

where undistort is an approximate iterative algorithm that estimates the normalized original point coordinates out of the normalized distorted point coordinates ("normalized" means that the coordinates do not depend on the camera matrix).

The function can be used for both a stereo camera head or a monocular camera (when R is empty).

Parameters

src	Observed point coordinates, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or vector<Point2f> ).
dst	Output ideal point coordinates (1xN/Nx1 2-channel or vector<Point2f> ) after undistortion and reverse perspective transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
cameraMatrix	Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	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.
R	Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by stereoRectify can be passed here. If the matrix is empty, the identity transformation is used.
P	New camera matrix (3x3) or new projection matrix (3x4) \(\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\). P1 or P2 computed by stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.

undistortPoints() [2/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.undistortPoints ( MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R )

static

Computes the ideal point coordinates from the observed point coordinates.

The function is similar to undistort and initUndistortRectifyMap but it operates on a sparse set of points instead of a raster image. Also the function performs a reverse transformation to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a planar object, it does, up to a translation vector, if the proper R is specified.

For each observed point coordinate \((u, v)\) the function computes:

\[ \begin{array}{l} x^{"} \leftarrow (u - c_x)/f_x \\ y^{"} \leftarrow (v - c_y)/f_y \\ (x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\ {[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ x \leftarrow X/W \\ y \leftarrow Y/W \\ \text{only performed if P is specified:} \\ u' \leftarrow x {f'}_x + {c'}_x \\ v' \leftarrow y {f'}_y + {c'}_y \end{array} \]

where undistort is an approximate iterative algorithm that estimates the normalized original point coordinates out of the normalized distorted point coordinates ("normalized" means that the coordinates do not depend on the camera matrix).

The function can be used for both a stereo camera head or a monocular camera (when R is empty).

Parameters

src	Observed point coordinates, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or vector<Point2f> ).
dst	Output ideal point coordinates (1xN/Nx1 2-channel or vector<Point2f> ) after undistortion and reverse perspective transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
cameraMatrix	Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	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.
R	Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by stereoRectify can be passed here. If the matrix is empty, the identity transformation is used.
P	New camera matrix (3x3) or new projection matrix (3x4) \(\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\). P1 or P2 computed by stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.

undistortPoints() [3/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.undistortPoints ( MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P )

static

Computes the ideal point coordinates from the observed point coordinates.

The function is similar to undistort and initUndistortRectifyMap but it operates on a sparse set of points instead of a raster image. Also the function performs a reverse transformation to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a planar object, it does, up to a translation vector, if the proper R is specified.

For each observed point coordinate \((u, v)\) the function computes:

\[ \begin{array}{l} x^{"} \leftarrow (u - c_x)/f_x \\ y^{"} \leftarrow (v - c_y)/f_y \\ (x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\ {[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ x \leftarrow X/W \\ y \leftarrow Y/W \\ \text{only performed if P is specified:} \\ u' \leftarrow x {f'}_x + {c'}_x \\ v' \leftarrow y {f'}_y + {c'}_y \end{array} \]

where undistort is an approximate iterative algorithm that estimates the normalized original point coordinates out of the normalized distorted point coordinates ("normalized" means that the coordinates do not depend on the camera matrix).

The function can be used for both a stereo camera head or a monocular camera (when R is empty).

Parameters

src	Observed point coordinates, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or vector<Point2f> ).
dst	Output ideal point coordinates (1xN/Nx1 2-channel or vector<Point2f> ) after undistortion and reverse perspective transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
cameraMatrix	Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs	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.
R	Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by stereoRectify can be passed here. If the matrix is empty, the identity transformation is used.
P	New camera matrix (3x3) or new projection matrix (3x4) \(\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\). P1 or P2 computed by stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.

undistortPointsIter() [1/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.undistortPointsIter ( Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, in Vec3d criteria )

static

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

Note

Default version of undistortPoints does 5 iterations to compute undistorted points.

undistortPointsIter() [2/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.undistortPointsIter ( Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, in(double type, double maxCount, double epsilon) criteria )

static

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

Note

Default version of undistortPoints does 5 iterations to compute undistorted points.

undistortPointsIter() [3/3]

static void OpenCVForUnity.Calib3dModule.Calib3d.undistortPointsIter ( Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, TermCriteria criteria )

static

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

Note

Default version of undistortPoints does 5 iterations to compute undistorted points.

validateDisparity() [1/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.validateDisparity ( Mat disparity, Mat cost, int minDisparity, int numberOfDisparities )

static

validateDisparity() [2/2]

static void OpenCVForUnity.Calib3dModule.Calib3d.validateDisparity ( Mat disparity, Mat cost, int minDisparity, int numberOfDisparities, int disp12MaxDisp )

static

Member Data Documentation

CALIB_CB_ACCURACY

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_CB_ACCURACY = 32

static

CALIB_CB_ADAPTIVE_THRESH

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_CB_ADAPTIVE_THRESH = 1

static

CALIB_CB_ASYMMETRIC_GRID

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_CB_ASYMMETRIC_GRID = 2

static

CALIB_CB_CLUSTERING

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_CB_CLUSTERING = 4

static

CALIB_CB_EXHAUSTIVE

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_CB_EXHAUSTIVE = 16

static

CALIB_CB_FAST_CHECK

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_CB_FAST_CHECK = 8

static

CALIB_CB_FILTER_QUADS

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_CB_FILTER_QUADS = 4

static

CALIB_CB_LARGER

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_CB_LARGER = 64

static

CALIB_CB_MARKER

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_CB_MARKER = 128

static

CALIB_CB_NORMALIZE_IMAGE

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_CB_NORMALIZE_IMAGE = 2

static

CALIB_CB_PLAIN

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_CB_PLAIN = 256

static

CALIB_CB_SYMMETRIC_GRID

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_CB_SYMMETRIC_GRID = 1

static

CALIB_FIX_ASPECT_RATIO

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_FIX_ASPECT_RATIO = 0x00002

static

CALIB_FIX_FOCAL_LENGTH

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_FIX_FOCAL_LENGTH = 0x00010

static

CALIB_FIX_INTRINSIC

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_FIX_INTRINSIC = 0x00100

static

CALIB_FIX_K1

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_FIX_K1 = 0x00020

static

CALIB_FIX_K2

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_FIX_K2 = 0x00040

static

CALIB_FIX_K3

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_FIX_K3 = 0x00080

static

CALIB_FIX_K4

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_FIX_K4 = 0x00800

static

CALIB_FIX_K5

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_FIX_K5 = 0x01000

static

CALIB_FIX_K6

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_FIX_K6 = 0x02000

static

CALIB_FIX_PRINCIPAL_POINT

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_FIX_PRINCIPAL_POINT = 0x00004

static

CALIB_FIX_S1_S2_S3_S4

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_FIX_S1_S2_S3_S4 = 0x10000

static

CALIB_FIX_TANGENT_DIST

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_FIX_TANGENT_DIST = 0x200000

static

CALIB_FIX_TAUX_TAUY

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_FIX_TAUX_TAUY = 0x80000

static

CALIB_HAND_EYE_ANDREFF

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_HAND_EYE_ANDREFF = 3

static

CALIB_HAND_EYE_DANIILIDIS

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_HAND_EYE_DANIILIDIS = 4

static

CALIB_HAND_EYE_HORAUD

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_HAND_EYE_HORAUD = 2

static

CALIB_HAND_EYE_PARK

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_HAND_EYE_PARK = 1

static

CALIB_HAND_EYE_TSAI

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_HAND_EYE_TSAI = 0

static

CALIB_NINTRINSIC

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_NINTRINSIC = 18

static

CALIB_RATIONAL_MODEL

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_RATIONAL_MODEL = 0x04000

static

CALIB_ROBOT_WORLD_HAND_EYE_LI

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_ROBOT_WORLD_HAND_EYE_LI = 1

static

CALIB_ROBOT_WORLD_HAND_EYE_SHAH

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_ROBOT_WORLD_HAND_EYE_SHAH = 0

static

CALIB_SAME_FOCAL_LENGTH

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_SAME_FOCAL_LENGTH = 0x00200

static

CALIB_THIN_PRISM_MODEL

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_THIN_PRISM_MODEL = 0x08000

static

CALIB_TILTED_MODEL

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_TILTED_MODEL = 0x40000

static

CALIB_USE_EXTRINSIC_GUESS

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_USE_EXTRINSIC_GUESS = (1 << 22)

static

CALIB_USE_INTRINSIC_GUESS

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_USE_INTRINSIC_GUESS = 0x00001

static

CALIB_USE_LU

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_USE_LU = (1 << 17)

static

CALIB_USE_QR

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_USE_QR = 0x100000

static

CALIB_ZERO_DISPARITY

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_ZERO_DISPARITY = 0x00400

static

CALIB_ZERO_TANGENT_DIST

const int OpenCVForUnity.Calib3dModule.Calib3d.CALIB_ZERO_TANGENT_DIST = 0x00008

static

CirclesGridFinderParameters_ASYMMETRIC_GRID

const int OpenCVForUnity.Calib3dModule.Calib3d.CirclesGridFinderParameters_ASYMMETRIC_GRID = 1

static

CirclesGridFinderParameters_SYMMETRIC_GRID

const int OpenCVForUnity.Calib3dModule.Calib3d.CirclesGridFinderParameters_SYMMETRIC_GRID = 0

static

COV_POLISHER

const int OpenCVForUnity.Calib3dModule.Calib3d.COV_POLISHER = 3

static

CV_DLS

const int OpenCVForUnity.Calib3dModule.Calib3d.CV_DLS = 3

static

CV_EPNP

const int OpenCVForUnity.Calib3dModule.Calib3d.CV_EPNP = 1

static

CV_ITERATIVE

const int OpenCVForUnity.Calib3dModule.Calib3d.CV_ITERATIVE = 0

static

CV_P3P

const int OpenCVForUnity.Calib3dModule.Calib3d.CV_P3P = 2

static

CvLevMarq_CALC_J

const int OpenCVForUnity.Calib3dModule.Calib3d.CvLevMarq_CALC_J = 2

static

CvLevMarq_CHECK_ERR

const int OpenCVForUnity.Calib3dModule.Calib3d.CvLevMarq_CHECK_ERR = 3

static

CvLevMarq_DONE

const int OpenCVForUnity.Calib3dModule.Calib3d.CvLevMarq_DONE = 0

static

CvLevMarq_STARTED

const int OpenCVForUnity.Calib3dModule.Calib3d.CvLevMarq_STARTED = 1

static

fisheye_CALIB_CHECK_COND

const int OpenCVForUnity.Calib3dModule.Calib3d.fisheye_CALIB_CHECK_COND = 1 << 2

static

fisheye_CALIB_FIX_FOCAL_LENGTH

const int OpenCVForUnity.Calib3dModule.Calib3d.fisheye_CALIB_FIX_FOCAL_LENGTH = 1 << 11

static

fisheye_CALIB_FIX_INTRINSIC

const int OpenCVForUnity.Calib3dModule.Calib3d.fisheye_CALIB_FIX_INTRINSIC = 1 << 8

static

fisheye_CALIB_FIX_K1

const int OpenCVForUnity.Calib3dModule.Calib3d.fisheye_CALIB_FIX_K1 = 1 << 4

static

fisheye_CALIB_FIX_K2

const int OpenCVForUnity.Calib3dModule.Calib3d.fisheye_CALIB_FIX_K2 = 1 << 5

static

fisheye_CALIB_FIX_K3

const int OpenCVForUnity.Calib3dModule.Calib3d.fisheye_CALIB_FIX_K3 = 1 << 6

static

fisheye_CALIB_FIX_K4

const int OpenCVForUnity.Calib3dModule.Calib3d.fisheye_CALIB_FIX_K4 = 1 << 7

static

fisheye_CALIB_FIX_PRINCIPAL_POINT

const int OpenCVForUnity.Calib3dModule.Calib3d.fisheye_CALIB_FIX_PRINCIPAL_POINT = 1 << 9

static

fisheye_CALIB_FIX_SKEW

const int OpenCVForUnity.Calib3dModule.Calib3d.fisheye_CALIB_FIX_SKEW = 1 << 3

static

fisheye_CALIB_RECOMPUTE_EXTRINSIC

const int OpenCVForUnity.Calib3dModule.Calib3d.fisheye_CALIB_RECOMPUTE_EXTRINSIC = 1 << 1

static

fisheye_CALIB_USE_INTRINSIC_GUESS

const int OpenCVForUnity.Calib3dModule.Calib3d.fisheye_CALIB_USE_INTRINSIC_GUESS = 1 << 0

static

fisheye_CALIB_ZERO_DISPARITY

const int OpenCVForUnity.Calib3dModule.Calib3d.fisheye_CALIB_ZERO_DISPARITY = 1 << 10

static

FM_7POINT

const int OpenCVForUnity.Calib3dModule.Calib3d.FM_7POINT = 1

static

FM_8POINT

const int OpenCVForUnity.Calib3dModule.Calib3d.FM_8POINT = 2

static

FM_LMEDS

const int OpenCVForUnity.Calib3dModule.Calib3d.FM_LMEDS = 4

static

FM_RANSAC

const int OpenCVForUnity.Calib3dModule.Calib3d.FM_RANSAC = 8

static

LMEDS

const int OpenCVForUnity.Calib3dModule.Calib3d.LMEDS = 4

static

LOCAL_OPTIM_GC

const int OpenCVForUnity.Calib3dModule.Calib3d.LOCAL_OPTIM_GC = 3

static

LOCAL_OPTIM_INNER_AND_ITER_LO

const int OpenCVForUnity.Calib3dModule.Calib3d.LOCAL_OPTIM_INNER_AND_ITER_LO = 2

static

LOCAL_OPTIM_INNER_LO

const int OpenCVForUnity.Calib3dModule.Calib3d.LOCAL_OPTIM_INNER_LO = 1

static

LOCAL_OPTIM_NULL

const int OpenCVForUnity.Calib3dModule.Calib3d.LOCAL_OPTIM_NULL = 0

static

LOCAL_OPTIM_SIGMA

const int OpenCVForUnity.Calib3dModule.Calib3d.LOCAL_OPTIM_SIGMA = 4

static

LSQ_POLISHER

const int OpenCVForUnity.Calib3dModule.Calib3d.LSQ_POLISHER = 1

static

MAGSAC

const int OpenCVForUnity.Calib3dModule.Calib3d.MAGSAC = 2

static

NEIGH_FLANN_KNN

const int OpenCVForUnity.Calib3dModule.Calib3d.NEIGH_FLANN_KNN = 0

static

NEIGH_FLANN_RADIUS

const int OpenCVForUnity.Calib3dModule.Calib3d.NEIGH_FLANN_RADIUS = 2

static

NEIGH_GRID

const int OpenCVForUnity.Calib3dModule.Calib3d.NEIGH_GRID = 1

static

NONE_POLISHER

const int OpenCVForUnity.Calib3dModule.Calib3d.NONE_POLISHER = 0

static

PROJ_SPHERICAL_EQRECT

const int OpenCVForUnity.Calib3dModule.Calib3d.PROJ_SPHERICAL_EQRECT = 1

static

PROJ_SPHERICAL_ORTHO

const int OpenCVForUnity.Calib3dModule.Calib3d.PROJ_SPHERICAL_ORTHO = 0

static

RANSAC

const int OpenCVForUnity.Calib3dModule.Calib3d.RANSAC = 8

static

RHO

const int OpenCVForUnity.Calib3dModule.Calib3d.RHO = 16

static

SAMPLING_NAPSAC

const int OpenCVForUnity.Calib3dModule.Calib3d.SAMPLING_NAPSAC = 2

static

SAMPLING_PROGRESSIVE_NAPSAC

const int OpenCVForUnity.Calib3dModule.Calib3d.SAMPLING_PROGRESSIVE_NAPSAC = 1

static

SAMPLING_PROSAC

const int OpenCVForUnity.Calib3dModule.Calib3d.SAMPLING_PROSAC = 3

static

SAMPLING_UNIFORM

const int OpenCVForUnity.Calib3dModule.Calib3d.SAMPLING_UNIFORM = 0

static

SCORE_METHOD_LMEDS

const int OpenCVForUnity.Calib3dModule.Calib3d.SCORE_METHOD_LMEDS = 3

static

SCORE_METHOD_MAGSAC

const int OpenCVForUnity.Calib3dModule.Calib3d.SCORE_METHOD_MAGSAC = 2

static

SCORE_METHOD_MSAC

const int OpenCVForUnity.Calib3dModule.Calib3d.SCORE_METHOD_MSAC = 1

static

SCORE_METHOD_RANSAC

const int OpenCVForUnity.Calib3dModule.Calib3d.SCORE_METHOD_RANSAC = 0

static

SOLVEPNP_AP3P

const int OpenCVForUnity.Calib3dModule.Calib3d.SOLVEPNP_AP3P = 5

static

SOLVEPNP_DLS

const int OpenCVForUnity.Calib3dModule.Calib3d.SOLVEPNP_DLS = 3

static

SOLVEPNP_EPNP

const int OpenCVForUnity.Calib3dModule.Calib3d.SOLVEPNP_EPNP = 1

static

SOLVEPNP_IPPE

const int OpenCVForUnity.Calib3dModule.Calib3d.SOLVEPNP_IPPE = 6

static

SOLVEPNP_IPPE_SQUARE

const int OpenCVForUnity.Calib3dModule.Calib3d.SOLVEPNP_IPPE_SQUARE = 7

static

SOLVEPNP_ITERATIVE

const int OpenCVForUnity.Calib3dModule.Calib3d.SOLVEPNP_ITERATIVE = 0

static

SOLVEPNP_MAX_COUNT

const int OpenCVForUnity.Calib3dModule.Calib3d.SOLVEPNP_MAX_COUNT = 8 + 1

static

SOLVEPNP_P3P

const int OpenCVForUnity.Calib3dModule.Calib3d.SOLVEPNP_P3P = 2

static

SOLVEPNP_SQPNP

const int OpenCVForUnity.Calib3dModule.Calib3d.SOLVEPNP_SQPNP = 8

static

SOLVEPNP_UPNP

const int OpenCVForUnity.Calib3dModule.Calib3d.SOLVEPNP_UPNP = 4

static

USAC_ACCURATE

const int OpenCVForUnity.Calib3dModule.Calib3d.USAC_ACCURATE = 36

static

USAC_DEFAULT

const int OpenCVForUnity.Calib3dModule.Calib3d.USAC_DEFAULT = 32

static

USAC_FAST

const int OpenCVForUnity.Calib3dModule.Calib3d.USAC_FAST = 35

static

USAC_FM_8PTS

const int OpenCVForUnity.Calib3dModule.Calib3d.USAC_FM_8PTS = 34

static

USAC_MAGSAC

const int OpenCVForUnity.Calib3dModule.Calib3d.USAC_MAGSAC = 38

static

USAC_PARALLEL

const int OpenCVForUnity.Calib3dModule.Calib3d.USAC_PARALLEL = 33

static

USAC_PROSAC

const int OpenCVForUnity.Calib3dModule.Calib3d.USAC_PROSAC = 37

static

v0

static double OpenCVForUnity.Calib3dModule.Calib3d.v0

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.

Parameters

image	Gray image used to find chessboard corners
patternSize	Size of a found chessboard pattern
corners	Corners found by findChessboardCornersSB
rise_distance	Rise distance 0.8 means 10% ... 90% of the final signal strength
vertical	By default edge responses for horizontal lines are calculated
sharpness	Optional output array with a sharpness value for calculated edge responses (see description)

The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness)

Returns

Scalar(average sharpness, average min brightness, average max brightness,0)

v1

static double double OpenCVForUnity.Calib3dModule.Calib3d.v1

static

v2

static double double double OpenCVForUnity.Calib3dModule.Calib3d.v2

static

width

int int int OpenCVForUnity.Calib3dModule.Calib3d.width

static

x

int OpenCVForUnity.Calib3dModule.Calib3d.x

static

y

int int OpenCVForUnity.Calib3dModule.Calib3d.y

static

---

The documentation for this class was generated from the following files:

• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/calib3d/Calib3d.cs
• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/calib3d/Calib3d_Struct.cs
• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/calib3d/Calib3d_ValueTuple.cs