OpenCVForUnity.GeometryModule.Geometry Class Reference

Static Public Member Functions
static void	approxPolyDP (MatOfPoint2f curve, MatOfPoint2f approxCurve, double epsilon, bool closed)
	Approximates a polygonal curve(s) with the specified precision.
static void	approxPolyN (Mat curve, Mat approxCurve, int nsides)
	Approximates a polygon with a convex hull with a specified accuracy and number of sides.
static void	approxPolyN (Mat curve, Mat approxCurve, int nsides, float epsilon_percentage)
	Approximates a polygon with a convex hull with a specified accuracy and number of sides.
static void	approxPolyN (Mat curve, Mat approxCurve, int nsides, float epsilon_percentage, bool ensure_convex)
	Approximates a polygon with a convex hull with a specified accuracy and number of sides.
static double	arcLength (MatOfPoint2f curve, bool closed)
	Calculates a contour perimeter or a curve length.
static Rect	boundingRect (Mat array)
	Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.
static int int int int height	boundingRectAsValueTuple (Mat array)
static Vec4i	boundingRectAsVec4i (Mat array)
	Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.
static void	boxPoints (in Vec5d box, Mat points)
	Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
static void	boxPoints (in(double x, double y, double width, double height, double angle) box, Mat points)
	Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
static void	boxPoints (RotatedRect box, Mat points)
	Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
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 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 double	contourArea (Mat contour)
	Calculates a contour area.
static double	contourArea (Mat contour, bool oriented)
	Calculates a contour area.
static void	convertPointsFromHomogeneous (Mat src, Mat dst)
	Converts points from homogeneous to Euclidean space.
static void	convertPointsFromHomogeneous (Mat src, Mat dst, int dtype)
	Converts points from homogeneous to Euclidean space.
static void	convertPointsToHomogeneous (Mat src, Mat dst)
	Converts points from Euclidean to homogeneous space.
static void	convertPointsToHomogeneous (Mat src, Mat dst, int dtype)
	Converts points from Euclidean to homogeneous space.
static void	convexHull (MatOfPoint points, MatOfInt hull)
	Finds the convex hull of a point set.
static void	convexHull (MatOfPoint points, MatOfInt hull, bool clockwise)
	Finds the convex hull of a point set.
static void	convexityDefects (MatOfPoint contour, MatOfInt convexhull, MatOfInt4 convexityDefects)
	Finds the convexity defects of a contour.
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 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 bool	estimateAffine3D (Mat src, Mat dst, Mat _out, Mat inliers)
	Computes an optimal affine transformation between two 3D point sets.
static bool	estimateAffine3D (Mat src, Mat dst, Mat _out, Mat inliers, double ransacThreshold)
	Computes an optimal affine transformation between two 3D point sets.
static bool	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 double[]	estimateTranslation2D (Mat from, Mat to)
	Computes a pure 2D translation between two 2D point sets.
static double[]	estimateTranslation2D (Mat from, Mat to, Mat inliers)
	Computes a pure 2D translation between two 2D point sets.
static double[]	estimateTranslation2D (Mat from, Mat to, Mat inliers, int method)
	Computes a pure 2D translation between two 2D point sets.
static double[]	estimateTranslation2D (Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold)
	Computes a pure 2D translation between two 2D point sets.
static double[]	estimateTranslation2D (Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters)
	Computes a pure 2D translation between two 2D point sets.
static double[]	estimateTranslation2D (Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence)
	Computes a pure 2D translation between two 2D point sets.
static double[]	estimateTranslation2D (Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters)
	Computes a pure 2D translation between two 2D point sets.
static bool	estimateTranslation3D (Mat src, Mat dst, Mat _out, Mat inliers)
	Computes an optimal translation between two 3D point sets.
static bool	estimateTranslation3D (Mat src, Mat dst, Mat _out, Mat inliers, double ransacThreshold)
	Computes an optimal translation between two 3D point sets.
static bool	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 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 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_distortPoints (Mat undistorted, Mat distorted, Mat Kundistorted, Mat K, Mat D)
static void	fisheye_distortPoints (Mat undistorted, Mat distorted, Mat Kundistorted, Mat K, Mat D, double alpha)
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_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 model.
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 model.
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 model.
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 model.
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 model.
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 model.
static bool	fisheye_solvePnPRansac (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec)
	Finds an object pose from 3D-2D point correspondences using the RANSAC scheme for fisheye camera moodel.
static bool	fisheye_solvePnPRansac (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess)
	Finds an object pose from 3D-2D point correspondences using the RANSAC scheme for fisheye camera moodel.
static bool	fisheye_solvePnPRansac (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount)
	Finds an object pose from 3D-2D point correspondences using the RANSAC scheme for fisheye camera moodel.
static bool	fisheye_solvePnPRansac (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError)
	Finds an object pose from 3D-2D point correspondences using the RANSAC scheme for fisheye camera moodel.
static bool	fisheye_solvePnPRansac (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat 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 for fisheye camera moodel.
static bool	fisheye_solvePnPRansac (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat 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 for fisheye camera moodel.
static bool	fisheye_solvePnPRansac (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat 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 for fisheye camera moodel.
static bool	fisheye_solvePnPRansac (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, Mat inliers, int flags, in Vec3d criteria)
	Finds an object pose from 3D-2D point correspondences using the RANSAC scheme for fisheye camera moodel.
static bool	fisheye_solvePnPRansac (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, Mat inliers, int flags, in(double type, double maxCount, double epsilon) criteria)
	Finds an object pose from 3D-2D point correspondences using the RANSAC scheme for fisheye camera moodel.
static bool	fisheye_solvePnPRansac (Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, Mat inliers, int flags, TermCriteria criteria)
	Finds an object pose from 3D-2D point correspondences using the RANSAC scheme for fisheye camera moodel.
static void	fisheye_undistortPoints (Mat distorted, Mat undistorted, Mat K, Mat D)
	Undistorts 2D points using fisheye camera model.
static void	fisheye_undistortPoints (Mat distorted, Mat undistorted, Mat K, Mat D, Mat R)
	Undistorts 2D points using fisheye camera model.
static void	fisheye_undistortPoints (Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P)
	Undistorts 2D points using fisheye camera 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 camera 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 camera model.
static void	fisheye_undistortPoints (Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P, TermCriteria criteria)
	Undistorts 2D points using fisheye camera model.
static RotatedRect	fitEllipse (MatOfPoint2f points)
	Fits an ellipse around a set of 2D points.
static RotatedRect	fitEllipseAMS (Mat points)
	Fits an ellipse around a set of 2D points.
static double double double double double angle	fitEllipseAMSAsValueTuple (Mat points)
static Vec5d	fitEllipseAMSAsVec5d (Mat points)
	Fits an ellipse around a set of 2D points.
static double double double double double angle	fitEllipseAsValueTuple (MatOfPoint2f points)
static Vec5d	fitEllipseAsVec5d (MatOfPoint2f points)
	Fits an ellipse around a set of 2D points.
static RotatedRect	fitEllipseDirect (Mat points)
	Fits an ellipse around a set of 2D points.
static double double double double double angle	fitEllipseDirectAsValueTuple (Mat points)
static Vec5d	fitEllipseDirectAsVec5d (Mat points)
	Fits an ellipse around a set of 2D points.
static void	fitLine (Mat points, Mat line, int distType, double param, double reps, double aeps)
	Fits a line to a 2D or 3D point set.
static Mat	getAffineTransform (MatOfPoint2f src, MatOfPoint2f dst)
static void	getClosestEllipsePoints (in Vec5d ellipse_params, Mat points, Mat closest_pts)
	Compute for each 2d point the nearest 2d point located on a given ellipse.
static void	getClosestEllipsePoints (in(double x, double y, double width, double height, double angle) ellipse_params, Mat points, Mat closest_pts)
	Compute for each 2d point the nearest 2d point located on a given ellipse.
static void	getClosestEllipsePoints (RotatedRect ellipse_params, Mat points, Mat closest_pts)
	Compute for each 2d point the nearest 2d point located on a given ellipse.
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 Mat	getPerspectiveTransform (Mat src, Mat dst)
	Calculates a perspective transform from four pairs of the corresponding points.
static Mat	getPerspectiveTransform (Mat src, Mat dst, int solveMethod)
	Calculates a perspective transform from four pairs of the corresponding points.
static Mat	getRotationMatrix2D (in Vec2d center, double angle, double scale)
	Calculates an affine matrix of 2D rotation.
static Mat	getRotationMatrix2D (in(double x, double y) center, double angle, double scale)
	Calculates an affine matrix of 2D rotation.
static Mat	getRotationMatrix2D (Point center, double angle, double scale)
	Calculates an affine matrix of 2D rotation.
static void	HuMoments (in Vec10d m, Mat hu)
static void	HuMoments (in(double m00, double m10, double m01, double m20, double m11, double m02, double m30, double m21, double m12, double m03) m, Mat hu)
static void	HuMoments (Moments m, Mat hu)
static float	intersectConvexConvex (Mat p1, Mat p2, Mat p12)
	Finds intersection of two convex polygons.
static float	intersectConvexConvex (Mat p1, Mat p2, Mat p12, bool handleNested)
	Finds intersection of two convex polygons.
static void	invertAffineTransform (Mat M, Mat iM)
	Inverts an affine transformation.
static bool	isContourConvex (MatOfPoint contour)
	Tests a contour convexity.
static double	matchShapes (Mat contour1, Mat contour2, int method, double parameter)
	Compares two shapes.
static void	matMulDeriv (Mat A, Mat B, Mat dABdA, Mat dABdB)
	Computes partial derivatives of the matrix product for each multiplied matrix.
static RotatedRect	minAreaRect (MatOfPoint2f points)
	Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
static double double double double double angle	minAreaRectAsValueTuple (MatOfPoint2f points)
static Vec5d	minAreaRectAsVec5d (MatOfPoint2f points)
	Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
static void	minEnclosingCircle (MatOfPoint2f points, out Vec2d center, float[] radius)
	Finds a circle of the minimum area enclosing a 2D point set.
static void	minEnclosingCircle (MatOfPoint2f points, out(double x, double y) center, float[] radius)
	Finds a circle of the minimum area enclosing a 2D point set.
static void	minEnclosingCircle (MatOfPoint2f points, Point center, float[] radius)
	Finds a circle of the minimum area enclosing a 2D point set.
static double	minEnclosingConvexPolygon (Mat points, Mat polygon, int k)
	Finds a convex polygon of minimum area enclosing a 2D point set and returns its area.
static double	minEnclosingTriangle (Mat points, Mat triangle)
	Finds a triangle of minimum area enclosing a 2D point set and returns its area.
static Moments	moments (Mat array)
	Calculates all of the moments up to the third order of a polygon or rasterized shape.
static Moments	moments (Mat array, bool binaryImage)
	Calculates all of the moments up to the third order of a polygon or rasterized shape.
static double double double double double double double double double double m03	momentsAsValueTuple (Mat array)
static double double double double double double double double double double m03	momentsAsValueTuple (Mat array, bool binaryImage)
static Vec10d	momentsAsVec10d (Mat array)
	Calculates all of the moments up to the third order of a polygon or rasterized shape.
static Vec10d	momentsAsVec10d (Mat array, bool binaryImage)
	Calculates all of the moments up to the third order of a polygon or rasterized shape.
static double	pointPolygonTest (MatOfPoint2f contour, in Vec2d pt, bool measureDist)
	Performs a point-in-contour test.
static double	pointPolygonTest (MatOfPoint2f contour, in(double x, double y) pt, bool measureDist)
	Performs a point-in-contour test.
static double	pointPolygonTest (MatOfPoint2f contour, Point pt, bool measureDist)
	Performs a point-in-contour test.
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 void	projectPointsSepJ (Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt)
static void	projectPointsSepJ (Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf)
static void	projectPointsSepJ (Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf, Mat dpdc)
static void	projectPointsSepJ (Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf, Mat dpdc, Mat dpdk)
static void	projectPointsSepJ (Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf, Mat dpdc, Mat dpdk, Mat dpdo)
static void	projectPointsSepJ (Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf, Mat dpdc, Mat dpdk, Mat dpdo, double aspectRatio)
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 chirality 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 chirality 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 chirality 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 chirality 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 chirality check. Returns the number of inliers that pass the check.
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 int	rotatedRectangleIntersection (in Vec5d rect1, in Vec5d rect2, Mat intersectingRegion)
	Finds out if there is any intersection between two rotated rectangles.
static int	rotatedRectangleIntersection (in(double x, double y, double width, double height, double angle) rect1, in(double x, double y, double width, double height, double angle) rect2, Mat intersectingRegion)
	Finds out if there is any intersection between two rotated rectangles.
static int	rotatedRectangleIntersection (RotatedRect rect1, RotatedRect rect2, Mat intersectingRegion)
	Finds out if there is any intersection between two rotated rectangles.
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 \( {}^{c}\mathbf{T}_o \) 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 \( {}^{c}\mathbf{T}_o \) 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 \( {}^{c}\mathbf{T}_o \) 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 \( {}^{c}\mathbf{T}_o \) 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 \( {}^{c}\mathbf{T}_o \) 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 \( {}^{c}\mathbf{T}_o \) 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 \( {}^{c}\mathbf{T}_o \) 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 \( {}^{c}\mathbf{T}_o \) 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 \( {}^{c}\mathbf{T}_o \) 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 \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences.
static bool	solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec)
	Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.
static bool	solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess)
	Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.
static bool	solvePnPRansac (MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount)
	Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.
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 \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.
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 \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.
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 \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.
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 \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.
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 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	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	undistortPoints (MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, in Vec3d criteria)
	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, in(double type, double maxCount, double epsilon) criteria)
	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, TermCriteria criteria)
	Computes the ideal point coordinates from the observed point coordinates.

Static Public Attributes
const int	COV_POLISHER = 3
	C++: enum PolishingMethod (cv.PolishingMethod)
const int	DIST_C = 3
	C++: enum DistanceTypes (cv.DistanceTypes)
const int	DIST_FAIR = 5
	C++: enum DistanceTypes (cv.DistanceTypes)
const int	DIST_HUBER = 7
	C++: enum DistanceTypes (cv.DistanceTypes)
const int	DIST_L1 = 1
	C++: enum DistanceTypes (cv.DistanceTypes)
const int	DIST_L12 = 4
	C++: enum DistanceTypes (cv.DistanceTypes)
const int	DIST_L2 = 2
	C++: enum DistanceTypes (cv.DistanceTypes)
const int	DIST_USER = -1
	C++: enum DistanceTypes (cv.DistanceTypes)
const int	DIST_WELSCH = 6
	C++: enum DistanceTypes (cv.DistanceTypes)
const int	FM_7POINT = 1
	C++: enum <unnamed>
const int	FM_8POINT = 2
	C++: enum <unnamed>
const int	FM_LMEDS = 4
	C++: enum <unnamed>
const int	FM_RANSAC = 8
	C++: enum <unnamed>
static double double double double	height
const int	INTERSECT_FULL = 2
	C++: enum RectanglesIntersectTypes (cv.RectanglesIntersectTypes)
const int	INTERSECT_NONE = 0
	C++: enum RectanglesIntersectTypes (cv.RectanglesIntersectTypes)
const int	INTERSECT_PARTIAL = 1
	C++: enum RectanglesIntersectTypes (cv.RectanglesIntersectTypes)
const int	LMEDS = 4
	C++: enum <unnamed>
const int	LOCAL_OPTIM_GC = 3
	C++: enum LocalOptimMethod (cv.LocalOptimMethod)
const int	LOCAL_OPTIM_INNER_AND_ITER_LO = 2
	C++: enum LocalOptimMethod (cv.LocalOptimMethod)
const int	LOCAL_OPTIM_INNER_LO = 1
	C++: enum LocalOptimMethod (cv.LocalOptimMethod)
const int	LOCAL_OPTIM_NULL = 0
	C++: enum LocalOptimMethod (cv.LocalOptimMethod)
const int	LOCAL_OPTIM_SIGMA = 4
	C++: enum LocalOptimMethod (cv.LocalOptimMethod)
const int	LSQ_POLISHER = 1
	C++: enum PolishingMethod (cv.PolishingMethod)
static double	m00
	Calculates all of the moments up to the third order of a polygon or rasterized shape.
static double double double	m01
static double double double double double double	m02
static double double	m10
static double double double double double	m11
static double double double double double double double double double	m12
static double double double double	m20
static double double double double double double double double	m21
static double double double double double double double	m30
const int	MAGSAC = 2
	C++: enum PolishingMethod (cv.PolishingMethod)
const int	MatrixType_AUTO = 0
	C++: enum MatrixType (cv.MatrixType)
const int	MatrixType_DENSE = 1
	C++: enum MatrixType (cv.MatrixType)
const int	MatrixType_SPARSE = 2
	C++: enum MatrixType (cv.MatrixType)
const int	MST_KRUSKAL = 1
	C++: enum MSTAlgorithm (cv.MSTAlgorithm)
const int	MST_PRIM = 0
	C++: enum MSTAlgorithm (cv.MSTAlgorithm)
const int	NEIGH_FLANN_KNN = 0
	C++: enum NeighborSearchMethod (cv.NeighborSearchMethod)
const int	NEIGH_FLANN_RADIUS = 2
	C++: enum NeighborSearchMethod (cv.NeighborSearchMethod)
const int	NEIGH_GRID = 1
	C++: enum NeighborSearchMethod (cv.NeighborSearchMethod)
const int	NONE_POLISHER = 0
	C++: enum PolishingMethod (cv.PolishingMethod)
const int	RANSAC = 8
	C++: enum <unnamed>
const int	RHO = 16
	C++: enum <unnamed>
const int	SAC_METHOD_RANSAC = 0
	C++: enum SacMethod (cv.SacMethod)
const int	SAC_MODEL_PLANE = 0
	C++: enum SacModelType (cv.SacModelType)
const int	SAC_MODEL_SPHERE = 1
	C++: enum SacModelType (cv.SacModelType)
const int	SAMPLING_NAPSAC = 2
	C++: enum SamplingMethod (cv.SamplingMethod)
const int	SAMPLING_PROGRESSIVE_NAPSAC = 1
	C++: enum SamplingMethod (cv.SamplingMethod)
const int	SAMPLING_PROSAC = 3
	C++: enum SamplingMethod (cv.SamplingMethod)
const int	SAMPLING_UNIFORM = 0
	C++: enum SamplingMethod (cv.SamplingMethod)
const int	SCORE_METHOD_LMEDS = 3
	C++: enum ScoreMethod (cv.ScoreMethod)
const int	SCORE_METHOD_MAGSAC = 2
	C++: enum ScoreMethod (cv.ScoreMethod)
const int	SCORE_METHOD_MSAC = 1
	C++: enum ScoreMethod (cv.ScoreMethod)
const int	SCORE_METHOD_RANSAC = 0
	C++: enum ScoreMethod (cv.ScoreMethod)
const int	SOLVEPNP_AP3P = 3
	C++: enum SolvePnPMethod (cv.SolvePnPMethod)
const int	SOLVEPNP_EPNP = 1
	C++: enum SolvePnPMethod (cv.SolvePnPMethod)
const int	SOLVEPNP_IPPE = 4
	C++: enum SolvePnPMethod (cv.SolvePnPMethod)
const int	SOLVEPNP_IPPE_SQUARE = 5
	C++: enum SolvePnPMethod (cv.SolvePnPMethod)
const int	SOLVEPNP_ITERATIVE = 0
	C++: enum SolvePnPMethod (cv.SolvePnPMethod)
const int	SOLVEPNP_MAX_COUNT = 6 + 1
	C++: enum SolvePnPMethod (cv.SolvePnPMethod)
const int	SOLVEPNP_P3P = 2
	C++: enum SolvePnPMethod (cv.SolvePnPMethod)
const int	SOLVEPNP_SQPNP = 6
	C++: enum SolvePnPMethod (cv.SolvePnPMethod)
const int	USAC_ACCURATE = 36
	C++: enum <unnamed>
const int	USAC_DEFAULT = 32
	C++: enum <unnamed>
const int	USAC_FAST = 35
	C++: enum <unnamed>
const int	USAC_FM_8PTS = 34
	C++: enum <unnamed>
const int	USAC_MAGSAC = 38
	C++: enum <unnamed>
const int	USAC_PARALLEL = 33
	C++: enum <unnamed>
const int	USAC_PROSAC = 37
	C++: enum <unnamed>
const int	VariableType_LINEAR = 0
	C++: enum VariableType (cv.VariableType)
const int	VariableType_SE3 = 2
	C++: enum VariableType (cv.VariableType)
const int	VariableType_SO3 = 1
	C++: enum VariableType (cv.VariableType)
static double double double	width
static int int int	width
static double	x
	Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
static int	x
	Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.
static double double	y
static int int	y

Member Function Documentation

approxPolyDP()

static void OpenCVForUnity.GeometryModule.Geometry.approxPolyDP ( MatOfPoint2f curve, MatOfPoint2f approxCurve, double epsilon, bool closed )

static

Approximates a polygonal curve(s) with the specified precision.

T he function cv::approxPolyDP approximates a curve or a p*olygon with another curve/polygon with less vertices so that the distance between them is less or equal to the specified precision. It uses the Douglas-Peucker algorithm <https://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm&gt;

Parameters

curve	Input vector of a 2D point stored in std::vector or Mat
approxCurve	Result of the approximation. The type should match the type of the input curve.
epsilon	Parameter specifying the approximation accuracy. This is the maximum distance between the original curve and its approximation.
closed	If true, the approximated curve is closed (its first and last vertices are connected). Otherwise, it is not closed.

approxPolyN() [1/3]

static void OpenCVForUnity.GeometryModule.Geometry.approxPolyN ( Mat curve, Mat approxCurve, int nsides )

static

Approximates a polygon with a convex hull with a specified accuracy and number of sides.

T he cv::approxPolyN function approximates a polygon with *a convex hull so that the difference between the contour area of the original contour and the new polygon is minimal. It uses a greedy algorithm for contracting two vertices into one in such a way that the additional area is minimal. Straight lines formed by each edge of the convex contour are drawn and the areas of the resulting triangles are considered. Each vertex will lie either on the original contour or outside it.

The algorithm based on the paper [LowIlie2003] .

Parameters

curve	Input vector of a 2D points stored in std::vector or Mat, points must be float or integer.
approxCurve	Result of the approximation. The type is vector of a 2D point (Point2f or Point) in std::vector or Mat.
nsides	The parameter defines the number of sides of the result polygon.
epsilon_percentage	defines the percentage of the maximum of additional area. If it equals -1, it is not used. Otherwise algorithm stops if additional area is greater than contourArea(_curve) * percentage. If additional area exceeds the limit, algorithm returns as many vertices as there were at the moment the limit was exceeded.
ensure_convex	If it is true, algorithm creates a convex hull of input contour. Otherwise input vector should be convex.

approxPolyN() [2/3]

static void OpenCVForUnity.GeometryModule.Geometry.approxPolyN ( Mat curve, Mat approxCurve, int nsides, float epsilon_percentage )

static

Approximates a polygon with a convex hull with a specified accuracy and number of sides.

T he cv::approxPolyN function approximates a polygon with *a convex hull so that the difference between the contour area of the original contour and the new polygon is minimal. It uses a greedy algorithm for contracting two vertices into one in such a way that the additional area is minimal. Straight lines formed by each edge of the convex contour are drawn and the areas of the resulting triangles are considered. Each vertex will lie either on the original contour or outside it.

The algorithm based on the paper [LowIlie2003] .

Parameters

curve	Input vector of a 2D points stored in std::vector or Mat, points must be float or integer.
approxCurve	Result of the approximation. The type is vector of a 2D point (Point2f or Point) in std::vector or Mat.
nsides	The parameter defines the number of sides of the result polygon.
epsilon_percentage	defines the percentage of the maximum of additional area. If it equals -1, it is not used. Otherwise algorithm stops if additional area is greater than contourArea(_curve) * percentage. If additional area exceeds the limit, algorithm returns as many vertices as there were at the moment the limit was exceeded.
ensure_convex	If it is true, algorithm creates a convex hull of input contour. Otherwise input vector should be convex.

approxPolyN() [3/3]

static void OpenCVForUnity.GeometryModule.Geometry.approxPolyN ( Mat curve, Mat approxCurve, int nsides, float epsilon_percentage, bool ensure_convex )

static

Approximates a polygon with a convex hull with a specified accuracy and number of sides.

T he cv::approxPolyN function approximates a polygon with *a convex hull so that the difference between the contour area of the original contour and the new polygon is minimal. It uses a greedy algorithm for contracting two vertices into one in such a way that the additional area is minimal. Straight lines formed by each edge of the convex contour are drawn and the areas of the resulting triangles are considered. Each vertex will lie either on the original contour or outside it.

The algorithm based on the paper [LowIlie2003] .

Parameters

curve	Input vector of a 2D points stored in std::vector or Mat, points must be float or integer.
approxCurve	Result of the approximation. The type is vector of a 2D point (Point2f or Point) in std::vector or Mat.
nsides	The parameter defines the number of sides of the result polygon.
epsilon_percentage	defines the percentage of the maximum of additional area. If it equals -1, it is not used. Otherwise algorithm stops if additional area is greater than contourArea(_curve) * percentage. If additional area exceeds the limit, algorithm returns as many vertices as there were at the moment the limit was exceeded.
ensure_convex	If it is true, algorithm creates a convex hull of input contour. Otherwise input vector should be convex.

arcLength()

static double OpenCVForUnity.GeometryModule.Geometry.arcLength ( MatOfPoint2f curve, bool closed )

static

Calculates a contour perimeter or a curve length.

The function computes a curve length or a closed contour perimeter.

Parameters

curve	Input vector of 2D points, stored in std::vector or Mat.
closed	Flag indicating whether the curve is closed or not.

boundingRect()

static Rect OpenCVForUnity.GeometryModule.Geometry.boundingRect ( Mat array )

static

Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.

The function calculates and returns the minimal up-right bounding rectangle for the specified point set or non-zero pixels of gray-scale image.

Parameters

array

Input gray-scale image or 2D point set, stored in std::vector or Mat.

boundingRectAsValueTuple()

static int int int int height OpenCVForUnity.GeometryModule.Geometry.boundingRectAsValueTuple ( Mat array )

static

boundingRectAsVec4i()

static Vec4i OpenCVForUnity.GeometryModule.Geometry.boundingRectAsVec4i ( Mat array )

static

Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.

The function calculates and returns the minimal up-right bounding rectangle for the specified point set or non-zero pixels of gray-scale image.

Parameters

array

Input gray-scale image or 2D point set, stored in std::vector or Mat.

boxPoints() [1/3]

static void OpenCVForUnity.GeometryModule.Geometry.boxPoints ( in Vec5d box, Mat points )

static

Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.

The function finds the four vertices of a rotated rectangle. The four vertices are returned in clockwise order starting from the point with greatest \(y\). If two points have the same \(y\) coordinate the rightmost is the starting point. This function is useful to draw the rectangle. In C++, instead of using this function, you can directly use RotatedRect.points method. Please visit the tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information. </remarks> <param name="box"> The input rotated rectangle. It may be the output of @ref minAreaRect. </param> <param name="points"> The output array of four vertices of rectangles.

boxPoints() [2/3]

static void OpenCVForUnity.GeometryModule.Geometry.boxPoints ( in(double x, double y, double width, double height, double angle) box, Mat points )

static

Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.

The function finds the four vertices of a rotated rectangle. The four vertices are returned in clockwise order starting from the point with greatest \(y\). If two points have the same \(y\) coordinate the rightmost is the starting point. This function is useful to draw the rectangle. In C++, instead of using this function, you can directly use RotatedRect.points method. Please visit the tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information. </remarks> <param name="box"> The input rotated rectangle. It may be the output of @ref minAreaRect. </param> <param name="points"> The output array of four vertices of rectangles.

boxPoints() [3/3]

static void OpenCVForUnity.GeometryModule.Geometry.boxPoints ( RotatedRect box, Mat points )

static

Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.

The function finds the four vertices of a rotated rectangle. The four vertices are returned in clockwise order starting from the point with greatest \(y\). If two points have the same \(y\) coordinate the rightmost is the starting point. This function is useful to draw the rectangle. In C++, instead of using this function, you can directly use RotatedRect.points method. Please visit the tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information. </remarks> <param name="box"> The input rotated rectangle. It may be the output of @ref minAreaRect. </param> <param name="points"> The output array of four vertices of rectangles.

calibrationMatrixValues() [1/3]

static void OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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).

composeRT() [1/9]

static void OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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 \(a_i^2+b_i^2=1\) .

contourArea() [1/2]

static double OpenCVForUnity.GeometryModule.Geometry.contourArea ( Mat contour )

static

Calculates a contour area.

The function computes a contour area. Similarly to moments , the area is computed using the Green formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using #drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong results for contours with self-intersections.

Example:

vector<Point> contour;
contour.push_back(Point2f(0, 0));
contour.push_back(Point2f(10, 0));
contour.push_back(Point2f(10, 10));
contour.push_back(Point2f(5, 4));
 
double area0 = contourArea(contour);
vector<Point> approx;
approxPolyDP(contour, approx, 5, true);
double area1 = contourArea(approx);
 
cout << "area0 =" << area0 << endl <<
        "area1 =" << area1 << endl <<
        "approx poly vertices" << approx.size() << endl;

Parameters

contour	Input vector of 2D points (contour vertices), stored in std::vector or Mat.
oriented	Oriented area flag. If it is true, the function returns a signed area value, depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can determine orientation of a contour by taking the sign of an area. By default, the parameter is false, which means that the absolute value is returned.

contourArea() [2/2]

static double OpenCVForUnity.GeometryModule.Geometry.contourArea ( Mat contour, bool oriented )

static

Calculates a contour area.

The function computes a contour area. Similarly to moments , the area is computed using the Green formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using #drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong results for contours with self-intersections.

Example:

vector<Point> contour;
contour.push_back(Point2f(0, 0));
contour.push_back(Point2f(10, 0));
contour.push_back(Point2f(10, 10));
contour.push_back(Point2f(5, 4));
 
double area0 = contourArea(contour);
vector<Point> approx;
approxPolyDP(contour, approx, 5, true);
double area1 = contourArea(approx);
 
cout << "area0 =" << area0 << endl <<
        "area1 =" << area1 << endl <<
        "approx poly vertices" << approx.size() << endl;

Parameters

contour	Input vector of 2D points (contour vertices), stored in std::vector or Mat.
oriented	Oriented area flag. If it is true, the function returns a signed area value, depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can determine orientation of a contour by taking the sign of an area. By default, the parameter is false, which means that the absolute value is returned.

convertPointsFromHomogeneous() [1/2]

static void OpenCVForUnity.GeometryModule.Geometry.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.
dtype	The desired output array depth (either CV_32F or CV_64F are currently supported). If it's -1, then it's set automatically to CV_32F or CV_64F, depending on the input depth.

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,...).

convertPointsFromHomogeneous() [2/2]

static void OpenCVForUnity.GeometryModule.Geometry.convertPointsFromHomogeneous ( Mat src, Mat dst, int dtype )

static

Converts points from homogeneous to Euclidean space.

Parameters

src	Input vector of N-dimensional points.
dst	Output vector of N-1-dimensional points.
dtype	The desired output array depth (either CV_32F or CV_64F are currently supported). If it's -1, then it's set automatically to CV_32F or CV_64F, depending on the input depth.

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() [1/2]

static void OpenCVForUnity.GeometryModule.Geometry.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.
dtype	The desired output array depth (either CV_32F or CV_64F are currently supported). If it's -1, then it's set automatically to CV_32F or CV_64F, depending on the input depth.

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).

convertPointsToHomogeneous() [2/2]

static void OpenCVForUnity.GeometryModule.Geometry.convertPointsToHomogeneous ( Mat src, Mat dst, int dtype )

static

Converts points from Euclidean to homogeneous space.

Parameters

src	Input vector of N-dimensional points.
dst	Output vector of N+1-dimensional points.
dtype	The desired output array depth (either CV_32F or CV_64F are currently supported). If it's -1, then it's set automatically to CV_32F or CV_64F, depending on the input depth.

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).

convexHull() [1/2]

static void OpenCVForUnity.GeometryModule.Geometry.convexHull ( MatOfPoint points, MatOfInt hull )

static

Finds the convex hull of a point set.

The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm [Sklansky82] that has O(N logN) complexity in the current implementation.

Parameters

points	Input 2D point set, stored in std::vector or Mat.
hull	Output convex hull. It is either an integer vector of indices or vector of points. In the first case, the hull elements are 0-based indices of the convex hull points in the original array (since the set of convex hull points is a subset of the original point set). In the second case, hull elements are the convex hull points themselves.
clockwise	Orientation flag. If it is true, the output convex hull is oriented clockwise. Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing to the right, and its Y axis pointing upwards.
returnPoints	Operation flag. In case of a matrix, when the flag is true, the function returns convex hull points. Otherwise, it returns indices of the convex hull points. When the output array is std::vector, the flag is ignored, and the output depends on the type of the vector: std::vector<int> implies returnPoints=false, std::vector<Point> implies returnPoints=true.

Note

points and hull should be different arrays, inplace processing isn't supported.

Check the corresponding tutorial for more details.

useful links:

https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/

convexHull() [2/2]

static void OpenCVForUnity.GeometryModule.Geometry.convexHull ( MatOfPoint points, MatOfInt hull, bool clockwise )

static

Finds the convex hull of a point set.

The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm [Sklansky82] that has O(N logN) complexity in the current implementation.

Parameters

points	Input 2D point set, stored in std::vector or Mat.
hull	Output convex hull. It is either an integer vector of indices or vector of points. In the first case, the hull elements are 0-based indices of the convex hull points in the original array (since the set of convex hull points is a subset of the original point set). In the second case, hull elements are the convex hull points themselves.
clockwise	Orientation flag. If it is true, the output convex hull is oriented clockwise. Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing to the right, and its Y axis pointing upwards.
returnPoints	Operation flag. In case of a matrix, when the flag is true, the function returns convex hull points. Otherwise, it returns indices of the convex hull points. When the output array is std::vector, the flag is ignored, and the output depends on the type of the vector: std::vector<int> implies returnPoints=false, std::vector<Point> implies returnPoints=true.

Note

points and hull should be different arrays, inplace processing isn't supported.

Check the corresponding tutorial for more details.

useful links:

https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/

convexityDefects()

static void OpenCVForUnity.GeometryModule.Geometry.convexityDefects ( MatOfPoint contour, MatOfInt convexhull, MatOfInt4 convexityDefects )

static

Finds the convexity defects of a contour.

The figure below displays convexity defects of a hand contour:

Parameters

contour	Input contour.
convexhull	Convex hull obtained using convexHull that should contain indices of the contour points that make the hull.
convexityDefects	The output vector of convexity defects. In C++ and the new Python/Java interface each convexity defect is represented as 4-element integer vector (a.k.a. #Vec4i): (start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices in the original contour of the convexity defect beginning, end and the farthest point, and fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the farthest contour point and the hull. That is, to get the floating-point value of the depth will be fixpt_depth/256.0.

correctMatches()

static void OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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 .

estimateAffine2D() [1/8]

static Mat OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.estimateAffine2D ( Mat pts1, Mat pts2, Mat inliers, UsacParams _params )

static

estimateAffine3D() [1/6]

static Mat OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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 bool OpenCVForUnity.GeometryModule.Geometry.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.

Returns

Whether a solution was found.

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

estimateAffine3D() [5/6]

static bool OpenCVForUnity.GeometryModule.Geometry.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.

Returns

Whether a solution was found.

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

estimateAffine3D() [6/6]

static bool OpenCVForUnity.GeometryModule.Geometry.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.

Returns

Whether a solution was found.

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

estimateAffinePartial2D() [1/7]

static Mat OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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

estimateTranslation2D() [1/7]

static double[] OpenCVForUnity.GeometryModule.Geometry.estimateTranslation2D ( Mat from, Mat to )

static

Computes a pure 2D translation between two 2D point sets.

It computes

\[ \begin{bmatrix} x\\ y \end{bmatrix} = \begin{bmatrix} 1 & 0\\ 0 & 1 \end{bmatrix} \begin{bmatrix} X\\ Y \end{bmatrix} + \begin{bmatrix} t_x\\ t_y \end{bmatrix}. \]

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 the 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 the refining algorithm. For pure translation the least-squares solution on inliers is closed-form, so passing 0 is recommended (no additional refine).

Returns

A 2D translation vector \([t_x, t_y]^T\) as cv::Vec2d. If the translation could not be estimated, both components are set to NaN and, if inliers is provided, the mask is filled with zeros.

Converting to a 2x3 transformation matrix:

\[ \begin{bmatrix} 1 & 0 & t_x\\ 0 & 1 & t_y \end{bmatrix} \]

cv::Vec2d t = cv::estimateTranslation2D(from, to, inliers);
cv::Mat T = (cv::Mat_<double>(2,3) << 1,0,t[0], 0,1,t[1]);

The function estimates a pure 2D translation between two 2D point sets using the selected robust algorithm. Inliers are determined by the reprojection error threshold.

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 works correctly only when there are more than 50% inliers.

See also

estimateAffine2D, estimateAffinePartial2D, getAffineTransform

estimateTranslation2D() [2/7]

static double[] OpenCVForUnity.GeometryModule.Geometry.estimateTranslation2D ( Mat from, Mat to, Mat inliers )

static

Computes a pure 2D translation between two 2D point sets.

It computes

\[ \begin{bmatrix} x\\ y \end{bmatrix} = \begin{bmatrix} 1 & 0\\ 0 & 1 \end{bmatrix} \begin{bmatrix} X\\ Y \end{bmatrix} + \begin{bmatrix} t_x\\ t_y \end{bmatrix}. \]

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 the 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 the refining algorithm. For pure translation the least-squares solution on inliers is closed-form, so passing 0 is recommended (no additional refine).

Returns

A 2D translation vector \([t_x, t_y]^T\) as cv::Vec2d. If the translation could not be estimated, both components are set to NaN and, if inliers is provided, the mask is filled with zeros.

Converting to a 2x3 transformation matrix:

\[ \begin{bmatrix} 1 & 0 & t_x\\ 0 & 1 & t_y \end{bmatrix} \]

cv::Vec2d t = cv::estimateTranslation2D(from, to, inliers);
cv::Mat T = (cv::Mat_<double>(2,3) << 1,0,t[0], 0,1,t[1]);

The function estimates a pure 2D translation between two 2D point sets using the selected robust algorithm. Inliers are determined by the reprojection error threshold.

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 works correctly only when there are more than 50% inliers.

See also

estimateAffine2D, estimateAffinePartial2D, getAffineTransform

estimateTranslation2D() [3/7]

static double[] OpenCVForUnity.GeometryModule.Geometry.estimateTranslation2D ( Mat from, Mat to, Mat inliers, int method )

static

Computes a pure 2D translation between two 2D point sets.

It computes

\[ \begin{bmatrix} x\\ y \end{bmatrix} = \begin{bmatrix} 1 & 0\\ 0 & 1 \end{bmatrix} \begin{bmatrix} X\\ Y \end{bmatrix} + \begin{bmatrix} t_x\\ t_y \end{bmatrix}. \]

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 the 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 the refining algorithm. For pure translation the least-squares solution on inliers is closed-form, so passing 0 is recommended (no additional refine).

Returns

A 2D translation vector \([t_x, t_y]^T\) as cv::Vec2d. If the translation could not be estimated, both components are set to NaN and, if inliers is provided, the mask is filled with zeros.

Converting to a 2x3 transformation matrix:

\[ \begin{bmatrix} 1 & 0 & t_x\\ 0 & 1 & t_y \end{bmatrix} \]

cv::Vec2d t = cv::estimateTranslation2D(from, to, inliers);
cv::Mat T = (cv::Mat_<double>(2,3) << 1,0,t[0], 0,1,t[1]);

The function estimates a pure 2D translation between two 2D point sets using the selected robust algorithm. Inliers are determined by the reprojection error threshold.

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 works correctly only when there are more than 50% inliers.

See also

estimateAffine2D, estimateAffinePartial2D, getAffineTransform

estimateTranslation2D() [4/7]

static double[] OpenCVForUnity.GeometryModule.Geometry.estimateTranslation2D ( Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold )

static

Computes a pure 2D translation between two 2D point sets.

It computes

\[ \begin{bmatrix} x\\ y \end{bmatrix} = \begin{bmatrix} 1 & 0\\ 0 & 1 \end{bmatrix} \begin{bmatrix} X\\ Y \end{bmatrix} + \begin{bmatrix} t_x\\ t_y \end{bmatrix}. \]

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 the 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 the refining algorithm. For pure translation the least-squares solution on inliers is closed-form, so passing 0 is recommended (no additional refine).

Returns

A 2D translation vector \([t_x, t_y]^T\) as cv::Vec2d. If the translation could not be estimated, both components are set to NaN and, if inliers is provided, the mask is filled with zeros.

Converting to a 2x3 transformation matrix:

\[ \begin{bmatrix} 1 & 0 & t_x\\ 0 & 1 & t_y \end{bmatrix} \]

cv::Vec2d t = cv::estimateTranslation2D(from, to, inliers);
cv::Mat T = (cv::Mat_<double>(2,3) << 1,0,t[0], 0,1,t[1]);

The function estimates a pure 2D translation between two 2D point sets using the selected robust algorithm. Inliers are determined by the reprojection error threshold.

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 works correctly only when there are more than 50% inliers.

See also

estimateAffine2D, estimateAffinePartial2D, getAffineTransform

estimateTranslation2D() [5/7]

static double[] OpenCVForUnity.GeometryModule.Geometry.estimateTranslation2D ( Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters )

static

Computes a pure 2D translation between two 2D point sets.

It computes

\[ \begin{bmatrix} x\\ y \end{bmatrix} = \begin{bmatrix} 1 & 0\\ 0 & 1 \end{bmatrix} \begin{bmatrix} X\\ Y \end{bmatrix} + \begin{bmatrix} t_x\\ t_y \end{bmatrix}. \]

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 the 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 the refining algorithm. For pure translation the least-squares solution on inliers is closed-form, so passing 0 is recommended (no additional refine).

Returns

A 2D translation vector \([t_x, t_y]^T\) as cv::Vec2d. If the translation could not be estimated, both components are set to NaN and, if inliers is provided, the mask is filled with zeros.

Converting to a 2x3 transformation matrix:

\[ \begin{bmatrix} 1 & 0 & t_x\\ 0 & 1 & t_y \end{bmatrix} \]

cv::Vec2d t = cv::estimateTranslation2D(from, to, inliers);
cv::Mat T = (cv::Mat_<double>(2,3) << 1,0,t[0], 0,1,t[1]);

The function estimates a pure 2D translation between two 2D point sets using the selected robust algorithm. Inliers are determined by the reprojection error threshold.

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 works correctly only when there are more than 50% inliers.

See also

estimateAffine2D, estimateAffinePartial2D, getAffineTransform

estimateTranslation2D() [6/7]

static double[] OpenCVForUnity.GeometryModule.Geometry.estimateTranslation2D ( Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence )

static

Computes a pure 2D translation between two 2D point sets.

It computes

\[ \begin{bmatrix} x\\ y \end{bmatrix} = \begin{bmatrix} 1 & 0\\ 0 & 1 \end{bmatrix} \begin{bmatrix} X\\ Y \end{bmatrix} + \begin{bmatrix} t_x\\ t_y \end{bmatrix}. \]

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 the 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 the refining algorithm. For pure translation the least-squares solution on inliers is closed-form, so passing 0 is recommended (no additional refine).

Returns

A 2D translation vector \([t_x, t_y]^T\) as cv::Vec2d. If the translation could not be estimated, both components are set to NaN and, if inliers is provided, the mask is filled with zeros.

Converting to a 2x3 transformation matrix:

\[ \begin{bmatrix} 1 & 0 & t_x\\ 0 & 1 & t_y \end{bmatrix} \]

cv::Vec2d t = cv::estimateTranslation2D(from, to, inliers);
cv::Mat T = (cv::Mat_<double>(2,3) << 1,0,t[0], 0,1,t[1]);

The function estimates a pure 2D translation between two 2D point sets using the selected robust algorithm. Inliers are determined by the reprojection error threshold.

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 works correctly only when there are more than 50% inliers.

See also

estimateAffine2D, estimateAffinePartial2D, getAffineTransform

estimateTranslation2D() [7/7]

static double[] OpenCVForUnity.GeometryModule.Geometry.estimateTranslation2D ( Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters )

static

Computes a pure 2D translation between two 2D point sets.

It computes

\[ \begin{bmatrix} x\\ y \end{bmatrix} = \begin{bmatrix} 1 & 0\\ 0 & 1 \end{bmatrix} \begin{bmatrix} X\\ Y \end{bmatrix} + \begin{bmatrix} t_x\\ t_y \end{bmatrix}. \]

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 the 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 the refining algorithm. For pure translation the least-squares solution on inliers is closed-form, so passing 0 is recommended (no additional refine).

Returns

A 2D translation vector \([t_x, t_y]^T\) as cv::Vec2d. If the translation could not be estimated, both components are set to NaN and, if inliers is provided, the mask is filled with zeros.

Converting to a 2x3 transformation matrix:

\[ \begin{bmatrix} 1 & 0 & t_x\\ 0 & 1 & t_y \end{bmatrix} \]

cv::Vec2d t = cv::estimateTranslation2D(from, to, inliers);
cv::Mat T = (cv::Mat_<double>(2,3) << 1,0,t[0], 0,1,t[1]);

The function estimates a pure 2D translation between two 2D point sets using the selected robust algorithm. Inliers are determined by the reprojection error threshold.

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 works correctly only when there are more than 50% inliers.

See also

estimateAffine2D, estimateAffinePartial2D, getAffineTransform

estimateTranslation3D() [1/3]

static bool OpenCVForUnity.GeometryModule.Geometry.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.

Returns

Whether a translation was found.

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

estimateTranslation3D() [2/3]

static bool OpenCVForUnity.GeometryModule.Geometry.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.

Returns

Whether a translation was found.

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

estimateTranslation3D() [3/3]

static bool OpenCVForUnity.GeometryModule.Geometry.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.

Returns

Whether a translation was found.

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

filterHomographyDecompByVisibleRefpoints() [1/2]

static void OpenCVForUnity.GeometryModule.Geometry.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 CV_8U, CV_8S or CV_Bool 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.GeometryModule.Geometry.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 CV_8U, CV_8S or CV_Bool 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.

findEssentialMat() [1/32]

static Mat OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.
mask	optional output mask
maxIters	The maximum number of robust method iterations.

The epipolar geometry is described by the following equation:

\[[p_2; 1]^T F [p_1; 1] = 0\]

where \(F\) is a fundamental matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively.

The function calculates the fundamental matrix using one of four methods listed above and returns the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point algorithm, the function may return up to 3 solutions ( \(9 \times 3\) matrix that stores all 3 matrices sequentially).

The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the epipolar lines corresponding to the specified points. It can also be passed to #stereoRectifyUncalibrated to compute the rectification transformation. :

// 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.GeometryModule.Geometry.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.
mask	optional output mask
maxIters	The maximum number of robust method iterations.

The epipolar geometry is described by the following equation:

\[[p_2; 1]^T F [p_1; 1] = 0\]

where \(F\) is a fundamental matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively.

The function calculates the fundamental matrix using one of four methods listed above and returns the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point algorithm, the function may return up to 3 solutions ( \(9 \times 3\) matrix that stores all 3 matrices sequentially).

The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the epipolar lines corresponding to the specified points. It can also be passed to #stereoRectifyUncalibrated to compute the rectification transformation. :

// 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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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_distortPoints() [1/4]

static void OpenCVForUnity.GeometryModule.Geometry.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 \(K^{-1}\) or use another function overload.

fisheye_distortPoints() [2/4]

static void OpenCVForUnity.GeometryModule.Geometry.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 \(K^{-1}\) or use another function overload.

fisheye_distortPoints() [3/4]

static void OpenCVForUnity.GeometryModule.Geometry.fisheye_distortPoints ( Mat undistorted, Mat distorted, Mat Kundistorted, 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. Overload of distortPoints function to handle cases when undistorted points are got with non-identity camera matrix, e.g. output of #estimateNewCameraMatrixForUndistortRectify.

Parameters

undistorted	Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
Kundistorted	Camera intrinsic matrix used as new camera matrix for undistortion.
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> .

estimateNewCameraMatrixForUndistortRectify

fisheye_distortPoints() [4/4]

static void OpenCVForUnity.GeometryModule.Geometry.fisheye_distortPoints ( Mat undistorted, Mat distorted, Mat Kundistorted, 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. Overload of distortPoints function to handle cases when undistorted points are got with non-identity camera matrix, e.g. output of #estimateNewCameraMatrixForUndistortRectify.

Parameters

undistorted	Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
Kundistorted	Camera intrinsic matrix used as new camera matrix for undistortion.
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> .

estimateNewCameraMatrixForUndistortRectify

fisheye_estimateNewCameraMatrixForUndistortRectify() [1/12]

static void OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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_projectPoints() [1/3]

static void OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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 model.

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 (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 internally 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.GeometryModule.Geometry.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 model.

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 (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 internally 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.GeometryModule.Geometry.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 model.

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 (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 internally 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.GeometryModule.Geometry.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 model.

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 (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 internally 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.GeometryModule.Geometry.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 model.

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 (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 internally 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.GeometryModule.Geometry.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 model.

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 (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 internally 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_solvePnPRansac() [1/10]

static bool OpenCVForUnity.GeometryModule.Geometry.fisheye_solvePnPRansac ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec )

static

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme 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.
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 calib3d_solvePnP_flags
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_solvePnPRansac() [2/10]

static bool OpenCVForUnity.GeometryModule.Geometry.fisheye_solvePnPRansac ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess )

static

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme 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.
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 calib3d_solvePnP_flags
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_solvePnPRansac() [3/10]

static bool OpenCVForUnity.GeometryModule.Geometry.fisheye_solvePnPRansac ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount )

static

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme 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.
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 calib3d_solvePnP_flags
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_solvePnPRansac() [4/10]

static bool OpenCVForUnity.GeometryModule.Geometry.fisheye_solvePnPRansac ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat 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 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.
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 calib3d_solvePnP_flags
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_solvePnPRansac() [5/10]

static bool OpenCVForUnity.GeometryModule.Geometry.fisheye_solvePnPRansac ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat 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 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.
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 calib3d_solvePnP_flags
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_solvePnPRansac() [6/10]

static bool OpenCVForUnity.GeometryModule.Geometry.fisheye_solvePnPRansac ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat 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 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.
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 calib3d_solvePnP_flags
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_solvePnPRansac() [7/10]

static bool OpenCVForUnity.GeometryModule.Geometry.fisheye_solvePnPRansac ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat 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 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.
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 calib3d_solvePnP_flags
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_solvePnPRansac() [8/10]

static bool OpenCVForUnity.GeometryModule.Geometry.fisheye_solvePnPRansac ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, Mat inliers, int flags, in Vec3d criteria )

static

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme 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.
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 calib3d_solvePnP_flags
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_solvePnPRansac() [9/10]

static bool OpenCVForUnity.GeometryModule.Geometry.fisheye_solvePnPRansac ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, Mat inliers, int flags, in(double type, double maxCount, double epsilon) criteria )

static

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme 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.
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 calib3d_solvePnP_flags
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_solvePnPRansac() [10/10]

static bool OpenCVForUnity.GeometryModule.Geometry.fisheye_solvePnPRansac ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, Mat inliers, int flags, TermCriteria criteria )

static

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme 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.
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 calib3d_solvePnP_flags
criteria	Termination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in calib3d_solvePnP for more information.

fisheye_undistortPoints() [1/6]

static void OpenCVForUnity.GeometryModule.Geometry.fisheye_undistortPoints ( Mat distorted, Mat undistorted, Mat K, Mat D )

static

Undistorts 2D points using fisheye camera model.

This function performs undistortion for fisheye camera models, which use a different distortion model compared to the standard pinhole camera model used by undistortPoints. The fisheye model is suitable for wide-angle cameras.

The function transforms points from the distorted fisheye image to undistorted coordinates, optionally applying a rectification transformation (R) and projecting to a new image plane (P).

Note

Coordinate Systems:

• Input (distorted): Points are expected in pixel coordinates of the distorted fisheye image, i.e., coordinates measured in pixels from the top-left corner of the image.
• Output (undistorted): The coordinate system depends on parameter P:
  • If P is provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrix P.
  • If P is empty or identity: Output points are in normalized camera coordinates (normalized image coordinates), which are dimensionless coordinates in the camera's focal plane, independent of intrinsic parameters.

Fisheye vs. Standard Model: Use this function (#cv::fisheye::undistortPoints) for fisheye cameras (wide-angle lenses). For standard pinhole cameras, use undistortPoints instead. The fisheye model uses a different distortion parameterization (4 coefficients) compared to the standard model (4-14 coefficients).

Parameters

distorted	Array of distorted point coordinates in pixel coordinates of the fisheye image, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
K	Camera intrinsic matrix \(\cameramatrix{K}\) of the fisheye camera.
D	Input vector of fisheye distortion coefficients \(\distcoeffsfisheye\) (must contain exactly 4 coefficients).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel. If empty, the identity transformation is used.
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4). If empty or identity, output will be in normalized camera coordinates.
criteria	Termination criteria for the iterative undistortion algorithm.
undistorted	Output array of undistorted image points, 1xN/Nx1 2-channel, or vector<Point2f> . The coordinate system depends on parameter P (see above).

fisheye_undistortPoints() [2/6]

static void OpenCVForUnity.GeometryModule.Geometry.fisheye_undistortPoints ( Mat distorted, Mat undistorted, Mat K, Mat D, Mat R )

static

Undistorts 2D points using fisheye camera model.

This function performs undistortion for fisheye camera models, which use a different distortion model compared to the standard pinhole camera model used by undistortPoints. The fisheye model is suitable for wide-angle cameras.

The function transforms points from the distorted fisheye image to undistorted coordinates, optionally applying a rectification transformation (R) and projecting to a new image plane (P).

Note

Coordinate Systems:

• Input (distorted): Points are expected in pixel coordinates of the distorted fisheye image, i.e., coordinates measured in pixels from the top-left corner of the image.
• Output (undistorted): The coordinate system depends on parameter P:
  • If P is provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrix P.
  • If P is empty or identity: Output points are in normalized camera coordinates (normalized image coordinates), which are dimensionless coordinates in the camera's focal plane, independent of intrinsic parameters.

Fisheye vs. Standard Model: Use this function (#cv::fisheye::undistortPoints) for fisheye cameras (wide-angle lenses). For standard pinhole cameras, use undistortPoints instead. The fisheye model uses a different distortion parameterization (4 coefficients) compared to the standard model (4-14 coefficients).

Parameters

distorted	Array of distorted point coordinates in pixel coordinates of the fisheye image, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
K	Camera intrinsic matrix \(\cameramatrix{K}\) of the fisheye camera.
D	Input vector of fisheye distortion coefficients \(\distcoeffsfisheye\) (must contain exactly 4 coefficients).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel. If empty, the identity transformation is used.
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4). If empty or identity, output will be in normalized camera coordinates.
criteria	Termination criteria for the iterative undistortion algorithm.
undistorted	Output array of undistorted image points, 1xN/Nx1 2-channel, or vector<Point2f> . The coordinate system depends on parameter P (see above).

fisheye_undistortPoints() [3/6]

static void OpenCVForUnity.GeometryModule.Geometry.fisheye_undistortPoints ( Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P )

static

Undistorts 2D points using fisheye camera model.

This function performs undistortion for fisheye camera models, which use a different distortion model compared to the standard pinhole camera model used by undistortPoints. The fisheye model is suitable for wide-angle cameras.

The function transforms points from the distorted fisheye image to undistorted coordinates, optionally applying a rectification transformation (R) and projecting to a new image plane (P).

Note

Coordinate Systems:

• Input (distorted): Points are expected in pixel coordinates of the distorted fisheye image, i.e., coordinates measured in pixels from the top-left corner of the image.
• Output (undistorted): The coordinate system depends on parameter P:
  • If P is provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrix P.
  • If P is empty or identity: Output points are in normalized camera coordinates (normalized image coordinates), which are dimensionless coordinates in the camera's focal plane, independent of intrinsic parameters.

Fisheye vs. Standard Model: Use this function (#cv::fisheye::undistortPoints) for fisheye cameras (wide-angle lenses). For standard pinhole cameras, use undistortPoints instead. The fisheye model uses a different distortion parameterization (4 coefficients) compared to the standard model (4-14 coefficients).

Parameters

distorted	Array of distorted point coordinates in pixel coordinates of the fisheye image, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
K	Camera intrinsic matrix \(\cameramatrix{K}\) of the fisheye camera.
D	Input vector of fisheye distortion coefficients \(\distcoeffsfisheye\) (must contain exactly 4 coefficients).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel. If empty, the identity transformation is used.
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4). If empty or identity, output will be in normalized camera coordinates.
criteria	Termination criteria for the iterative undistortion algorithm.
undistorted	Output array of undistorted image points, 1xN/Nx1 2-channel, or vector<Point2f> . The coordinate system depends on parameter P (see above).

fisheye_undistortPoints() [4/6]

static void OpenCVForUnity.GeometryModule.Geometry.fisheye_undistortPoints ( Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P, in Vec3d criteria )

static

Undistorts 2D points using fisheye camera model.

This function performs undistortion for fisheye camera models, which use a different distortion model compared to the standard pinhole camera model used by undistortPoints. The fisheye model is suitable for wide-angle cameras.

The function transforms points from the distorted fisheye image to undistorted coordinates, optionally applying a rectification transformation (R) and projecting to a new image plane (P).

Note

Coordinate Systems:

• Input (distorted): Points are expected in pixel coordinates of the distorted fisheye image, i.e., coordinates measured in pixels from the top-left corner of the image.
• Output (undistorted): The coordinate system depends on parameter P:
  • If P is provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrix P.
  • If P is empty or identity: Output points are in normalized camera coordinates (normalized image coordinates), which are dimensionless coordinates in the camera's focal plane, independent of intrinsic parameters.

Fisheye vs. Standard Model: Use this function (#cv::fisheye::undistortPoints) for fisheye cameras (wide-angle lenses). For standard pinhole cameras, use undistortPoints instead. The fisheye model uses a different distortion parameterization (4 coefficients) compared to the standard model (4-14 coefficients).

Parameters

distorted	Array of distorted point coordinates in pixel coordinates of the fisheye image, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
K	Camera intrinsic matrix \(\cameramatrix{K}\) of the fisheye camera.
D	Input vector of fisheye distortion coefficients \(\distcoeffsfisheye\) (must contain exactly 4 coefficients).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel. If empty, the identity transformation is used.
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4). If empty or identity, output will be in normalized camera coordinates.
criteria	Termination criteria for the iterative undistortion algorithm.
undistorted	Output array of undistorted image points, 1xN/Nx1 2-channel, or vector<Point2f> . The coordinate system depends on parameter P (see above).

fisheye_undistortPoints() [5/6]

static void OpenCVForUnity.GeometryModule.Geometry.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 camera model.

This function performs undistortion for fisheye camera models, which use a different distortion model compared to the standard pinhole camera model used by undistortPoints. The fisheye model is suitable for wide-angle cameras.

The function transforms points from the distorted fisheye image to undistorted coordinates, optionally applying a rectification transformation (R) and projecting to a new image plane (P).

Note

Coordinate Systems:

• Input (distorted): Points are expected in pixel coordinates of the distorted fisheye image, i.e., coordinates measured in pixels from the top-left corner of the image.
• Output (undistorted): The coordinate system depends on parameter P:
  • If P is provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrix P.
  • If P is empty or identity: Output points are in normalized camera coordinates (normalized image coordinates), which are dimensionless coordinates in the camera's focal plane, independent of intrinsic parameters.

Fisheye vs. Standard Model: Use this function (#cv::fisheye::undistortPoints) for fisheye cameras (wide-angle lenses). For standard pinhole cameras, use undistortPoints instead. The fisheye model uses a different distortion parameterization (4 coefficients) compared to the standard model (4-14 coefficients).

Parameters

distorted	Array of distorted point coordinates in pixel coordinates of the fisheye image, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
K	Camera intrinsic matrix \(\cameramatrix{K}\) of the fisheye camera.
D	Input vector of fisheye distortion coefficients \(\distcoeffsfisheye\) (must contain exactly 4 coefficients).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel. If empty, the identity transformation is used.
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4). If empty or identity, output will be in normalized camera coordinates.
criteria	Termination criteria for the iterative undistortion algorithm.
undistorted	Output array of undistorted image points, 1xN/Nx1 2-channel, or vector<Point2f> . The coordinate system depends on parameter P (see above).

fisheye_undistortPoints() [6/6]

static void OpenCVForUnity.GeometryModule.Geometry.fisheye_undistortPoints ( Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P, TermCriteria criteria )

static

Undistorts 2D points using fisheye camera model.

This function performs undistortion for fisheye camera models, which use a different distortion model compared to the standard pinhole camera model used by undistortPoints. The fisheye model is suitable for wide-angle cameras.

The function transforms points from the distorted fisheye image to undistorted coordinates, optionally applying a rectification transformation (R) and projecting to a new image plane (P).

Note

Coordinate Systems:

• Input (distorted): Points are expected in pixel coordinates of the distorted fisheye image, i.e., coordinates measured in pixels from the top-left corner of the image.
• Output (undistorted): The coordinate system depends on parameter P:
  • If P is provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrix P.
  • If P is empty or identity: Output points are in normalized camera coordinates (normalized image coordinates), which are dimensionless coordinates in the camera's focal plane, independent of intrinsic parameters.

Fisheye vs. Standard Model: Use this function (#cv::fisheye::undistortPoints) for fisheye cameras (wide-angle lenses). For standard pinhole cameras, use undistortPoints instead. The fisheye model uses a different distortion parameterization (4 coefficients) compared to the standard model (4-14 coefficients).

Parameters

distorted	Array of distorted point coordinates in pixel coordinates of the fisheye image, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
K	Camera intrinsic matrix \(\cameramatrix{K}\) of the fisheye camera.
D	Input vector of fisheye distortion coefficients \(\distcoeffsfisheye\) (must contain exactly 4 coefficients).
R	Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel. If empty, the identity transformation is used.
P	New camera intrinsic matrix (3x3) or new projection matrix (3x4). If empty or identity, output will be in normalized camera coordinates.
criteria	Termination criteria for the iterative undistortion algorithm.
undistorted	Output array of undistorted image points, 1xN/Nx1 2-channel, or vector<Point2f> . The coordinate system depends on parameter P (see above).

fitEllipse()

static RotatedRect OpenCVForUnity.GeometryModule.Geometry.fitEllipse ( MatOfPoint2f points )

static

Fits an ellipse around a set of 2D points.

The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by [Fitzgibbon95] is used. Developer should keep in mind that it is possible that the returned ellipse/rotatedRect data contains negative indices, due to the data points being close to the border of the containing Mat element.

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

Note

Input point types are Point2i or Point2f and at least 5 points are required.

getClosestEllipsePoints function can be used to compute the ellipse fitting error.

fitEllipseAMS()

static RotatedRect OpenCVForUnity.GeometryModule.Geometry.fitEllipseAMS ( Mat points )

static

Fits an ellipse around a set of 2D points.

The function calculates the ellipse that fits a set of 2D points. It returns the rotated rectangle in which the ellipse is inscribed. The Approximate Mean Square (AMS) proposed by [Taubin1991] is used.

For an ellipse, this basis set is \( \chi= \left(x^2, x y, y^2, x, y, 1\right) \), which is a set of six free coefficients \( A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \). However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \( (a,b) \), the position \( (x_0,y_0) \), and the orientation \( \theta \). This is because the basis set includes lines, quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. If the fit is found to be a parabolic or hyperbolic function then the standard fitEllipse method is used. The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves by imposing the condition that \( A^T ( D_x^T D_x + D_y^T D_y) A = 1 \) where the matrices \( Dx \) and \( Dy \) are the partial derivatives of the design matrix \( D \) with respect to x and y. The matrices are formed row by row applying the following to each of the points in the set:

\begin{align*} D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} & D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} & D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\} \end{align*}

The AMS method minimizes the cost function

\begin{equation*} \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } \end{equation*}

The minimum cost is found by solving the generalized eigenvalue problem.

\begin{equation*} D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A \end{equation*}

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

Note

Input point types are Point2i or Point2f and at least 5 points are required.

getClosestEllipsePoints function can be used to compute the ellipse fitting error.

fitEllipseAMSAsValueTuple()

static double double double double double angle OpenCVForUnity.GeometryModule.Geometry.fitEllipseAMSAsValueTuple ( Mat points )

static

fitEllipseAMSAsVec5d()

static Vec5d OpenCVForUnity.GeometryModule.Geometry.fitEllipseAMSAsVec5d ( Mat points )

static

Fits an ellipse around a set of 2D points.

The function calculates the ellipse that fits a set of 2D points. It returns the rotated rectangle in which the ellipse is inscribed. The Approximate Mean Square (AMS) proposed by [Taubin1991] is used.

For an ellipse, this basis set is \( \chi= \left(x^2, x y, y^2, x, y, 1\right) \), which is a set of six free coefficients \( A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \). However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \( (a,b) \), the position \( (x_0,y_0) \), and the orientation \( \theta \). This is because the basis set includes lines, quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. If the fit is found to be a parabolic or hyperbolic function then the standard fitEllipse method is used. The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves by imposing the condition that \( A^T ( D_x^T D_x + D_y^T D_y) A = 1 \) where the matrices \( Dx \) and \( Dy \) are the partial derivatives of the design matrix \( D \) with respect to x and y. The matrices are formed row by row applying the following to each of the points in the set:

\begin{align*} D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} & D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} & D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\} \end{align*}

The AMS method minimizes the cost function

\begin{equation*} \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } \end{equation*}

The minimum cost is found by solving the generalized eigenvalue problem.

\begin{equation*} D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A \end{equation*}

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

Note

Input point types are Point2i or Point2f and at least 5 points are required.

getClosestEllipsePoints function can be used to compute the ellipse fitting error.

fitEllipseAsValueTuple()

static double double double double double angle OpenCVForUnity.GeometryModule.Geometry.fitEllipseAsValueTuple ( MatOfPoint2f points )

static

fitEllipseAsVec5d()

static Vec5d OpenCVForUnity.GeometryModule.Geometry.fitEllipseAsVec5d ( MatOfPoint2f points )

static

Fits an ellipse around a set of 2D points.

The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by [Fitzgibbon95] is used. Developer should keep in mind that it is possible that the returned ellipse/rotatedRect data contains negative indices, due to the data points being close to the border of the containing Mat element.

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

Note

Input point types are Point2i or Point2f and at least 5 points are required.

getClosestEllipsePoints function can be used to compute the ellipse fitting error.

fitEllipseDirect()

static RotatedRect OpenCVForUnity.GeometryModule.Geometry.fitEllipseDirect ( Mat points )

static

Fits an ellipse around a set of 2D points.

The function calculates the ellipse that fits a set of 2D points. It returns the rotated rectangle in which the ellipse is inscribed. The Direct least square (Direct) method by [oy1998NumericallySD] is used.

For an ellipse, this basis set is \( \chi= \left(x^2, x y, y^2, x, y, 1\right) \), which is a set of six free coefficients \( A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \). However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \( (a,b) \), the position \( (x_0,y_0) \), and the orientation \( \theta \). This is because the basis set includes lines, quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. The Direct method confines the fit to ellipses by ensuring that \( 4 A_{xx} A_{yy}- A_{xy}^2 > 0 \). The condition imposed is that \( 4 A_{xx} A_{yy}- A_{xy}^2=1 \) which satisfies the inequality and as the coefficients can be arbitrarily scaled is not overly restrictive.

\begin{equation*} \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix} 0 & 0 & 2 & 0 & 0 & 0 \\ 0 & -1 & 0 & 0 & 0 & 0 \\ 2 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \end{matrix} \right) \end{equation*}

The minimum cost is found by solving the generalized eigenvalue problem.

\begin{equation*} D^T D A = \lambda \left( C\right) A \end{equation*}

The system produces only one positive eigenvalue \( \lambda\) which is chosen as the solution with its eigenvector \(\mathbf{u}\). These are used to find the coefficients

\begin{equation*} A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} \end{equation*}

The scaling factor guarantees that \(A^T C A =1\).

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

Note

Input point types are Point2i or Point2f and at least 5 points are required.

getClosestEllipsePoints function can be used to compute the ellipse fitting error.

fitEllipseDirectAsValueTuple()

static double double double double double angle OpenCVForUnity.GeometryModule.Geometry.fitEllipseDirectAsValueTuple ( Mat points )

static

fitEllipseDirectAsVec5d()

static Vec5d OpenCVForUnity.GeometryModule.Geometry.fitEllipseDirectAsVec5d ( Mat points )

static

Fits an ellipse around a set of 2D points.

The function calculates the ellipse that fits a set of 2D points. It returns the rotated rectangle in which the ellipse is inscribed. The Direct least square (Direct) method by [oy1998NumericallySD] is used.

For an ellipse, this basis set is \( \chi= \left(x^2, x y, y^2, x, y, 1\right) \), which is a set of six free coefficients \( A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \). However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \( (a,b) \), the position \( (x_0,y_0) \), and the orientation \( \theta \). This is because the basis set includes lines, quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. The Direct method confines the fit to ellipses by ensuring that \( 4 A_{xx} A_{yy}- A_{xy}^2 > 0 \). The condition imposed is that \( 4 A_{xx} A_{yy}- A_{xy}^2=1 \) which satisfies the inequality and as the coefficients can be arbitrarily scaled is not overly restrictive.

\begin{equation*} \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix} 0 & 0 & 2 & 0 & 0 & 0 \\ 0 & -1 & 0 & 0 & 0 & 0 \\ 2 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \end{matrix} \right) \end{equation*}

The minimum cost is found by solving the generalized eigenvalue problem.

\begin{equation*} D^T D A = \lambda \left( C\right) A \end{equation*}

The system produces only one positive eigenvalue \( \lambda\) which is chosen as the solution with its eigenvector \(\mathbf{u}\). These are used to find the coefficients

\begin{equation*} A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} \end{equation*}

The scaling factor guarantees that \(A^T C A =1\).

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

Note

Input point types are Point2i or Point2f and at least 5 points are required.

getClosestEllipsePoints function can be used to compute the ellipse fitting error.

fitLine()

static void OpenCVForUnity.GeometryModule.Geometry.fitLine ( Mat points, Mat line, int distType, double param, double reps, double aeps )

static

Fits a line to a 2D or 3D point set.

The function fitLine fits a line to a 2D or 3D point set by minimizing \(\sum_i \rho(r_i)\) where \(r_i\) is a distance between the \(i^{th}\) point, the line and \(\rho(r)\) is a distance function, one of the following:

• DIST_L2

  \[\rho (r) = r^2/2 \quad \text{(the simplest and the fastest least-squares method)}\]

• DIST_L1

  \[\rho (r) = r\]

• DIST_L12

  \[\rho (r) = 2 \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\]

• DIST_FAIR

  \[\rho \left (r \right ) = C^2 \cdot \left ( \frac{r}{C} - \log{\left(1 + \frac{r}{C}\right)} \right ) \quad \text{where} \quad C=1.3998\]

• DIST_WELSCH

  \[\rho \left (r \right ) = \frac{C^2}{2} \cdot \left ( 1 - \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right ) \quad \text{where} \quad C=2.9846\]

• DIST_HUBER

  \[\rho (r) = \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\]

The algorithm is based on the M-estimator ( <https://en.wikipedia.org/wiki/M-estimator&gt; ) technique that iteratively fits the line using the weighted least-squares algorithm. After each iteration the weights \(w_i\) are adjusted to be inversely proportional to \(\rho(r_i)\) .

Parameters

points	Input vector of 2D or 3D points, stored in std::vector<> or Mat.
line	Output line parameters. In case of 2D fitting, it should be a vector of 4 elements (like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and (x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line and (x0, y0, z0) is a point on the line.
distType	Distance used by the M-estimator, see #DistanceTypes
param	Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value is chosen.
reps	Sufficient accuracy for the radius (distance between the coordinate origin and the line).
aeps	Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps.

getAffineTransform()

static Mat OpenCVForUnity.GeometryModule.Geometry.getAffineTransform ( MatOfPoint2f src, MatOfPoint2f dst )

static

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

getClosestEllipsePoints() [1/3]

static void OpenCVForUnity.GeometryModule.Geometry.getClosestEllipsePoints ( in Vec5d ellipse_params, Mat points, Mat closest_pts )

static

Compute for each 2d point the nearest 2d point located on a given ellipse.

The function computes the nearest 2d location on a given ellipse for a vector of 2d points and is based on [Chatfield2017] code. This function can be used to compute for instance the ellipse fitting error.

Parameters

ellipse_params	Ellipse parameters
points	Input 2d points
closest_pts	For each 2d point, their corresponding closest 2d point located on a given ellipse

Note

Input point types are Point2i or Point2f

See also

fitEllipse, fitEllipseAMS, fitEllipseDirect

getClosestEllipsePoints() [2/3]

static void OpenCVForUnity.GeometryModule.Geometry.getClosestEllipsePoints ( in(double x, double y, double width, double height, double angle) ellipse_params, Mat points, Mat closest_pts )

static

Compute for each 2d point the nearest 2d point located on a given ellipse.

The function computes the nearest 2d location on a given ellipse for a vector of 2d points and is based on [Chatfield2017] code. This function can be used to compute for instance the ellipse fitting error.

Parameters

ellipse_params	Ellipse parameters
points	Input 2d points
closest_pts	For each 2d point, their corresponding closest 2d point located on a given ellipse

Note

Input point types are Point2i or Point2f

See also

fitEllipse, fitEllipseAMS, fitEllipseDirect

getClosestEllipsePoints() [3/3]

static void OpenCVForUnity.GeometryModule.Geometry.getClosestEllipsePoints ( RotatedRect ellipse_params, Mat points, Mat closest_pts )

static

Compute for each 2d point the nearest 2d point located on a given ellipse.

The function computes the nearest 2d location on a given ellipse for a vector of 2d points and is based on [Chatfield2017] code. This function can be used to compute for instance the ellipse fitting error.

Parameters

ellipse_params	Ellipse parameters
points	Input 2d points
closest_pts	For each 2d point, their corresponding closest 2d point located on a given ellipse

Note

Input point types are Point2i or Point2f

See also

fitEllipse, fitEllipseAMS, fitEllipseDirect

getDefaultNewCameraMatrix() [1/7]

static Mat OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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 .

getPerspectiveTransform() [1/2]

static Mat OpenCVForUnity.GeometryModule.Geometry.getPerspectiveTransform ( Mat src, Mat dst )

static

Calculates a perspective transform from four pairs of the corresponding points.

The function calculates the \(3 \times 3\) matrix of a perspective transform so that:

\[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\]

where

\[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\]

Parameters

src	Coordinates of quadrangle vertices in the source image.
dst	Coordinates of the corresponding quadrangle vertices in the destination image.
solveMethod	method passed to cv::solve (#DecompTypes)

See also

findHomography, warpPerspective, perspectiveTransform

getPerspectiveTransform() [2/2]

static Mat OpenCVForUnity.GeometryModule.Geometry.getPerspectiveTransform ( Mat src, Mat dst, int solveMethod )

static

Calculates a perspective transform from four pairs of the corresponding points.

The function calculates the \(3 \times 3\) matrix of a perspective transform so that:

\[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\]

where

\[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\]

Parameters

src	Coordinates of quadrangle vertices in the source image.
dst	Coordinates of the corresponding quadrangle vertices in the destination image.
solveMethod	method passed to cv::solve (#DecompTypes)

See also

findHomography, warpPerspective, perspectiveTransform

getRotationMatrix2D() [1/3]

static Mat OpenCVForUnity.GeometryModule.Geometry.getRotationMatrix2D ( in Vec2d center, double angle, double scale )

static

Calculates an affine matrix of 2D rotation.

The function calculates the following matrix:

\[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\]

where

\[\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\]

The transformation maps the rotation center to itself. If this is not the target, adjust the shift.

Parameters

center	Center of the rotation in the source image.
angle	Rotation angle in degrees. Positive values mean counter-clockwise rotation (the coordinate origin is assumed to be the top-left corner).
scale	Isotropic scale factor.

See also

getAffineTransform, warpAffine, transform

getRotationMatrix2D() [2/3]

static Mat OpenCVForUnity.GeometryModule.Geometry.getRotationMatrix2D ( in(double x, double y) center, double angle, double scale )

static

Calculates an affine matrix of 2D rotation.

The function calculates the following matrix:

\[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\]

where

\[\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\]

The transformation maps the rotation center to itself. If this is not the target, adjust the shift.

Parameters

center	Center of the rotation in the source image.
angle	Rotation angle in degrees. Positive values mean counter-clockwise rotation (the coordinate origin is assumed to be the top-left corner).
scale	Isotropic scale factor.

See also

getAffineTransform, warpAffine, transform

getRotationMatrix2D() [3/3]

static Mat OpenCVForUnity.GeometryModule.Geometry.getRotationMatrix2D ( Point center, double angle, double scale )

static

Calculates an affine matrix of 2D rotation.

The function calculates the following matrix:

\[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\]

where

\[\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\]

The transformation maps the rotation center to itself. If this is not the target, adjust the shift.

Parameters

center	Center of the rotation in the source image.
angle	Rotation angle in degrees. Positive values mean counter-clockwise rotation (the coordinate origin is assumed to be the top-left corner).
scale	Isotropic scale factor.

See also

getAffineTransform, warpAffine, transform

HuMoments() [1/3]

static void OpenCVForUnity.GeometryModule.Geometry.HuMoments ( in Vec10d m, Mat hu )

static

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

HuMoments() [2/3]

static void OpenCVForUnity.GeometryModule.Geometry.HuMoments ( in(double m00, double m10, double m01, double m20, double m11, double m02, double m30, double m21, double m12, double m03) m, Mat hu )

static

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

HuMoments() [3/3]

static void OpenCVForUnity.GeometryModule.Geometry.HuMoments ( Moments m, Mat hu )

static

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

intersectConvexConvex() [1/2]

static float OpenCVForUnity.GeometryModule.Geometry.intersectConvexConvex ( Mat p1, Mat p2, Mat p12 )

static

Finds intersection of two convex polygons.

Parameters

p1	First polygon
p2	Second polygon
p12	Output polygon describing the intersecting area
handleNested	When true, an intersection is found if one of the polygons is fully enclosed in the other. When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested.

Returns

Area of intersecting polygon. May be negative, if algorithm has not converged, e.g. non-convex input.

Note

intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't.

intersectConvexConvex() [2/2]

static float OpenCVForUnity.GeometryModule.Geometry.intersectConvexConvex ( Mat p1, Mat p2, Mat p12, bool handleNested )

static

Finds intersection of two convex polygons.

Parameters

p1	First polygon
p2	Second polygon
p12	Output polygon describing the intersecting area
handleNested	When true, an intersection is found if one of the polygons is fully enclosed in the other. When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested.

Returns

Area of intersecting polygon. May be negative, if algorithm has not converged, e.g. non-convex input.

Note

intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't.

invertAffineTransform()

static void OpenCVForUnity.GeometryModule.Geometry.invertAffineTransform ( Mat M, Mat iM )

static

Inverts an affine transformation.

The function computes an inverse affine transformation represented by \(2 \times 3\) matrix M:

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

The result is also a \(2 \times 3\) matrix of the same type as M.

Parameters

M	Original affine transformation.
iM	Output reverse affine transformation.

isContourConvex()

static bool OpenCVForUnity.GeometryModule.Geometry.isContourConvex ( MatOfPoint contour )

static

Tests a contour convexity.

The function tests whether the input contour is convex or not. The contour must be simple, that is, without self-intersections. Otherwise, the function output is undefined.

Parameters

contour

Input vector of 2D points, stored in std::vector<> or Mat

matchShapes()

static double OpenCVForUnity.GeometryModule.Geometry.matchShapes ( Mat contour1, Mat contour2, int method, double parameter )

static

Compares two shapes.

The function compares two shapes. All three implemented methods use the Hu invariants (see HuMoments)

Parameters

contour1	First contour or grayscale image.
contour2	Second contour or grayscale image.
method	Comparison method, see #ShapeMatchModes
parameter	Method-specific parameter (not supported now).

matMulDeriv()

static void OpenCVForUnity.GeometryModule.Geometry.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.

minAreaRect()

static RotatedRect OpenCVForUnity.GeometryModule.Geometry.minAreaRect ( MatOfPoint2f points )

static

Finds a rotated rectangle of the minimum area enclosing the input 2D point set.

The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a specified point set. The angle of rotation represents the angle between the line connecting the starting and ending points (based on the clockwise order with greatest index for the corner with greatest \(y\)) and the horizontal axis. This angle always falls between \([-90, 0)\) because, if the object rotates more than a rect angle, the next edge is used to measure the angle. The starting and ending points change as the object rotates.Developer should keep in mind that the returned RotatedRect can contain negative indices when data is close to the containing Mat element boundary.

Parameters

points

Input vector of 2D points, stored in std::vector<> or Mat

minAreaRectAsValueTuple()

static double double double double double angle OpenCVForUnity.GeometryModule.Geometry.minAreaRectAsValueTuple ( MatOfPoint2f points )

static

minAreaRectAsVec5d()

static Vec5d OpenCVForUnity.GeometryModule.Geometry.minAreaRectAsVec5d ( MatOfPoint2f points )

static

Finds a rotated rectangle of the minimum area enclosing the input 2D point set.

The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a specified point set. The angle of rotation represents the angle between the line connecting the starting and ending points (based on the clockwise order with greatest index for the corner with greatest \(y\)) and the horizontal axis. This angle always falls between \([-90, 0)\) because, if the object rotates more than a rect angle, the next edge is used to measure the angle. The starting and ending points change as the object rotates.Developer should keep in mind that the returned RotatedRect can contain negative indices when data is close to the containing Mat element boundary.

Parameters

points

Input vector of 2D points, stored in std::vector<> or Mat

minEnclosingCircle() [1/3]

static void OpenCVForUnity.GeometryModule.Geometry.minEnclosingCircle ( MatOfPoint2f points, out Vec2d center, float[] radius )

static

Finds a circle of the minimum area enclosing a 2D point set.

The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm.

Parameters

points	Input vector of 2D points, stored in std::vector<> or Mat
center	Output center of the circle.
radius	Output radius of the circle.

minEnclosingCircle() [2/3]

static void OpenCVForUnity.GeometryModule.Geometry.minEnclosingCircle ( MatOfPoint2f points, out(double x, double y) center, float[] radius )

static

Finds a circle of the minimum area enclosing a 2D point set.

The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm.

Parameters

points	Input vector of 2D points, stored in std::vector<> or Mat
center	Output center of the circle.
radius	Output radius of the circle.

minEnclosingCircle() [3/3]

static void OpenCVForUnity.GeometryModule.Geometry.minEnclosingCircle ( MatOfPoint2f points, Point center, float[] radius )

static

Finds a circle of the minimum area enclosing a 2D point set.

The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm.

Parameters

points	Input vector of 2D points, stored in std::vector<> or Mat
center	Output center of the circle.
radius	Output radius of the circle.

minEnclosingConvexPolygon()

static double OpenCVForUnity.GeometryModule.Geometry.minEnclosingConvexPolygon ( Mat points, Mat polygon, int k )

static

Finds a convex polygon of minimum area enclosing a 2D point set and returns its area.

This function takes a given set of 2D points and finds the enclosing polygon with k vertices and minimal area. It takes the set of points and the parameter k as input and returns the area of the minimal enclosing polygon.

The Implementation is based on a paper by Aggarwal, Chang and Yap [Aggarwal1985]. They provide a \(\theta(n²log(n)log(k))\) algorithm for finding the minimal convex polygon with k vertices enclosing a 2D convex polygon with n vertices (k < n). Since the minEnclosingConvexPolygon function takes a 2D point set as input, an additional preprocessing step of computing the convex hull of the 2D point set is required. The complexity of the convexHull function is \(O(n log(n))\) which is lower than \(\theta(n²log(n)log(k))\). Thus the overall complexity of the function is \(O(n²log(n)log(k))\).

Parameters

points	Input vector of 2D points, stored in std::vector<> or Mat
polygon	Output vector of 2D points defining the vertices of the enclosing polygon
k	Number of vertices of the output polygon

minEnclosingTriangle()

static double OpenCVForUnity.GeometryModule.Geometry.minEnclosingTriangle ( Mat points, Mat triangle )

static

Finds a triangle of minimum area enclosing a 2D point set and returns its area.

The function finds a triangle of minimum area enclosing the given set of 2D points and returns its area. The output for a given 2D point set is shown in the image below. 2D points are depicted in red* and the enclosing triangle in yellow.

Sample output of the minimum enclosing triangle function

The implementation of the algorithm is based on O'Rourke's [ORourke86] and Klee and Laskowski's [KleeLaskowski85] papers. O'Rourke provides a \(\theta(n)\) algorithm for finding the minimal enclosing triangle of a 2D convex polygon with n vertices. Since the minEnclosingTriangle function takes a 2D point set as input an additional preprocessing step of computing the convex hull of the 2D point set is required. The complexity of the convexHull function is \(O(n log(n))\) which is higher than \(\theta(n)\). Thus the overall complexity of the function is \(O(n log(n))\).

Parameters

points	Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector<> or Mat
triangle	Output vector of three 2D points defining the vertices of the triangle. The depth of the OutputArray must be CV_32F.

moments() [1/2]

static Moments OpenCVForUnity.GeometryModule.Geometry.moments ( Mat array )

static

Calculates all of the moments up to the third order of a polygon or rasterized shape.

The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The results are returned in the structure cv::Moments.

Parameters

array	Single channel raster image (CV_8U, CV_16U, CV_16S, CV_32F, CV_64F) or an array ( \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f).
binaryImage	If it is true, all non-zero image pixels are treated as 1's. The parameter is used for images only.

Returns

moments.

Note

Only applicable to contour moments calculations from Python bindings: Note that the numpy type for the input array should be either np.int32 or np.float32.

For contour-based moments, the zeroth-order moment m00 represents the contour area.

If the input contour is degenerate (for example, a single point or all points are collinear), the area is zero and therefore m00 == 0.

In this case, the centroid coordinates (m10/m00, m01/m00) are undefined and must be handled explicitly by the caller.

A common workaround is to compute the center using cv::boundingRect() or by averaging the input points.

See also

contourArea, arcLength

moments() [2/2]

static Moments OpenCVForUnity.GeometryModule.Geometry.moments ( Mat array, bool binaryImage )

static

Calculates all of the moments up to the third order of a polygon or rasterized shape.

The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The results are returned in the structure cv::Moments.

Parameters

array	Single channel raster image (CV_8U, CV_16U, CV_16S, CV_32F, CV_64F) or an array ( \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f).
binaryImage	If it is true, all non-zero image pixels are treated as 1's. The parameter is used for images only.

Returns

moments.

Note

Only applicable to contour moments calculations from Python bindings: Note that the numpy type for the input array should be either np.int32 or np.float32.

For contour-based moments, the zeroth-order moment m00 represents the contour area.

If the input contour is degenerate (for example, a single point or all points are collinear), the area is zero and therefore m00 == 0.

In this case, the centroid coordinates (m10/m00, m01/m00) are undefined and must be handled explicitly by the caller.

A common workaround is to compute the center using cv::boundingRect() or by averaging the input points.

See also

contourArea, arcLength

momentsAsValueTuple() [1/2]

static double double double double double double double double double double m03 OpenCVForUnity.GeometryModule.Geometry.momentsAsValueTuple ( Mat array )

static

momentsAsValueTuple() [2/2]

static double double double double double double double double double double m03 OpenCVForUnity.GeometryModule.Geometry.momentsAsValueTuple ( Mat array, bool binaryImage )

static

momentsAsVec10d() [1/2]

static Vec10d OpenCVForUnity.GeometryModule.Geometry.momentsAsVec10d ( Mat array )

static

Calculates all of the moments up to the third order of a polygon or rasterized shape.

The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The results are returned in the structure cv::Moments.

Parameters

array	Single channel raster image (CV_8U, CV_16U, CV_16S, CV_32F, CV_64F) or an array ( \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f).
binaryImage	If it is true, all non-zero image pixels are treated as 1's. The parameter is used for images only.

Returns

moments.

Note

Only applicable to contour moments calculations from Python bindings: Note that the numpy type for the input array should be either np.int32 or np.float32.

For contour-based moments, the zeroth-order moment m00 represents the contour area.

If the input contour is degenerate (for example, a single point or all points are collinear), the area is zero and therefore m00 == 0.

In this case, the centroid coordinates (m10/m00, m01/m00) are undefined and must be handled explicitly by the caller.

A common workaround is to compute the center using cv::boundingRect() or by averaging the input points.

See also

contourArea, arcLength

momentsAsVec10d() [2/2]

static Vec10d OpenCVForUnity.GeometryModule.Geometry.momentsAsVec10d ( Mat array, bool binaryImage )

static

Calculates all of the moments up to the third order of a polygon or rasterized shape.

The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The results are returned in the structure cv::Moments.

Parameters

array	Single channel raster image (CV_8U, CV_16U, CV_16S, CV_32F, CV_64F) or an array ( \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f).
binaryImage	If it is true, all non-zero image pixels are treated as 1's. The parameter is used for images only.

Returns

moments.

Note

Only applicable to contour moments calculations from Python bindings: Note that the numpy type for the input array should be either np.int32 or np.float32.

For contour-based moments, the zeroth-order moment m00 represents the contour area.

If the input contour is degenerate (for example, a single point or all points are collinear), the area is zero and therefore m00 == 0.

In this case, the centroid coordinates (m10/m00, m01/m00) are undefined and must be handled explicitly by the caller.

A common workaround is to compute the center using cv::boundingRect() or by averaging the input points.

See also

contourArea, arcLength

pointPolygonTest() [1/3]

static double OpenCVForUnity.GeometryModule.Geometry.pointPolygonTest ( MatOfPoint2f contour, in Vec2d pt, bool measureDist )

static

Performs a point-in-contour test.

The function determines whether the point is inside a contour, outside, or lies on an edge (or coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. Otherwise, the return value is a signed distance between the point and the nearest contour edge.

See below a sample output of the function where each image pixel is tested against the contour:

Parameters

contour	Input contour.
pt	Point tested against the contour.
measureDist	If true, the function estimates the signed distance from the point to the nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.

pointPolygonTest() [2/3]

static double OpenCVForUnity.GeometryModule.Geometry.pointPolygonTest ( MatOfPoint2f contour, in(double x, double y) pt, bool measureDist )

static

Performs a point-in-contour test.

The function determines whether the point is inside a contour, outside, or lies on an edge (or coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. Otherwise, the return value is a signed distance between the point and the nearest contour edge.

See below a sample output of the function where each image pixel is tested against the contour:

Parameters

contour	Input contour.
pt	Point tested against the contour.
measureDist	If true, the function estimates the signed distance from the point to the nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.

pointPolygonTest() [3/3]

static double OpenCVForUnity.GeometryModule.Geometry.pointPolygonTest ( MatOfPoint2f contour, Point pt, bool measureDist )

static

Performs a point-in-contour test.

The function determines whether the point is inside a contour, outside, or lies on an edge (or coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. Otherwise, the return value is a signed distance between the point and the nearest contour edge.

See below a sample output of the function where each image pixel is tested against the contour:

Parameters

contour	Input contour.
pt	Point tested against the contour.
measureDist	If true, the function estimates the signed distance from the point to the nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.

projectPoints() [1/3]

static void OpenCVForUnity.GeometryModule.Geometry.projectPoints ( MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints )

static

Projects 3D points to an image plane.

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

Coordinate Systems:

• Input (objectPoints): 3D points in the world coordinate frame.
• Output (imagePoints): 2D projections in pixel coordinates of the image plane, with distortion applied. The coordinates \((u, v)\) are measured in pixels from the top-left corner of the image.

The transformation chain is: World coordinates → Camera coordinates (via rvec/tvec) → Normalized camera coordinates → Distortion applied → Pixel coordinates (via cameraMatrix).

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 in pixel coordinates, 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.

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.GeometryModule.Geometry.projectPoints ( MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian )

static

Projects 3D points to an image plane.

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

Coordinate Systems:

• Input (objectPoints): 3D points in the world coordinate frame.
• Output (imagePoints): 2D projections in pixel coordinates of the image plane, with distortion applied. The coordinates \((u, v)\) are measured in pixels from the top-left corner of the image.

The transformation chain is: World coordinates → Camera coordinates (via rvec/tvec) → Normalized camera coordinates → Distortion applied → Pixel coordinates (via cameraMatrix).

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 in pixel coordinates, 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.

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.GeometryModule.Geometry.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.

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

Coordinate Systems:

• Input (objectPoints): 3D points in the world coordinate frame.
• Output (imagePoints): 2D projections in pixel coordinates of the image plane, with distortion applied. The coordinates \((u, v)\) are measured in pixels from the top-left corner of the image.

The transformation chain is: World coordinates → Camera coordinates (via rvec/tvec) → Normalized camera coordinates → Distortion applied → Pixel coordinates (via cameraMatrix).

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 in pixel coordinates, 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.

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.

projectPointsSepJ() [1/6]

static void OpenCVForUnity.GeometryModule.Geometry.projectPointsSepJ ( Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt )

static

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

projectPointsSepJ() [2/6]

static void OpenCVForUnity.GeometryModule.Geometry.projectPointsSepJ ( Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf )

static

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

projectPointsSepJ() [3/6]

static void OpenCVForUnity.GeometryModule.Geometry.projectPointsSepJ ( Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf, Mat dpdc )

static

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

projectPointsSepJ() [4/6]

static void OpenCVForUnity.GeometryModule.Geometry.projectPointsSepJ ( Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf, Mat dpdc, Mat dpdk )

static

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

projectPointsSepJ() [5/6]

static void OpenCVForUnity.GeometryModule.Geometry.projectPointsSepJ ( Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf, Mat dpdc, Mat dpdk, Mat dpdo )

static

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

projectPointsSepJ() [6/6]

static void OpenCVForUnity.GeometryModule.Geometry.projectPointsSepJ ( Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf, Mat dpdc, Mat dpdk, Mat dpdo, double aspectRatio )

static

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

recoverPose() [1/18]

static int OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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 chirality 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 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] = ...;
}
 
// 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.GeometryModule.Geometry.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 chirality 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 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] = ...;
}
 
// 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.GeometryModule.Geometry.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 chirality 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 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] = ...;
}
 
// 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.GeometryModule.Geometry.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 chirality 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 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] = ...;
}
 
// 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.GeometryModule.Geometry.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 chirality 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 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] = ...;
}
 
// 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);

Rodrigues() [1/2]

static void OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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]

rotatedRectangleIntersection() [1/3]

static int OpenCVForUnity.GeometryModule.Geometry.rotatedRectangleIntersection ( in Vec5d rect1, in Vec5d rect2, Mat intersectingRegion )

static

Finds out if there is any intersection between two rotated rectangles.

If there is then the vertices of the intersecting region are returned as well.

Below are some examples of intersection configurations. The hatched pattern indicates the intersecting region and the red vertices are returned by the function.

Parameters

rect1	First rectangle
rect2	Second rectangle
intersectingRegion	The output array of the vertices of the intersecting region. It returns at most 8 vertices. Stored as std::vector<cv::Point2f> or cv::Mat as Mx1 of type CV_32FC2.

Returns

One of #RectanglesIntersectTypes

rotatedRectangleIntersection() [2/3]

static int OpenCVForUnity.GeometryModule.Geometry.rotatedRectangleIntersection ( in(double x, double y, double width, double height, double angle) rect1, in(double x, double y, double width, double height, double angle) rect2, Mat intersectingRegion )

static

Finds out if there is any intersection between two rotated rectangles.

If there is then the vertices of the intersecting region are returned as well.

Below are some examples of intersection configurations. The hatched pattern indicates the intersecting region and the red vertices are returned by the function.

Parameters

rect1	First rectangle
rect2	Second rectangle
intersectingRegion	The output array of the vertices of the intersecting region. It returns at most 8 vertices. Stored as std::vector<cv::Point2f> or cv::Mat as Mx1 of type CV_32FC2.

Returns

One of #RectanglesIntersectTypes

rotatedRectangleIntersection() [3/3]

static int OpenCVForUnity.GeometryModule.Geometry.rotatedRectangleIntersection ( RotatedRect rect1, RotatedRect rect2, Mat intersectingRegion )

static

Finds out if there is any intersection between two rotated rectangles.

If there is then the vertices of the intersecting region are returned as well.

Below are some examples of intersection configurations. The hatched pattern indicates the intersecting region and the red vertices are returned by the function.

Parameters

rect1	First rectangle
rect2	Second rectangle
intersectingRegion	The output array of the vertices of the intersecting region. It returns at most 8 vertices. Stored as std::vector<cv::Point2f> or cv::Mat as Mx1 of type CV_32FC2.

Returns

One of #RectanglesIntersectTypes

RQDecomp3x3() [1/4]

static double[] OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.solveP3P ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, int flags )

static

Finds an object pose \( {}^{c}\mathbf{T}_o \) 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 Ding, Y., Yang, J., Larsson, V., Olsson, C., & Åstrom, K. "Revisiting the P3P Problem" ([ding2023revisiting]). 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.GeometryModule.Geometry.solvePnP ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec )

static

Finds an object pose \( {}^{c}\mathbf{T}_o \) 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/3d/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/3d/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 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. The function returns true if some solution is found. User code is responsible for solution quality assessment.
• 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.GeometryModule.Geometry.solvePnP ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess )

static

Finds an object pose \( {}^{c}\mathbf{T}_o \) 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/3d/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/3d/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 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. The function returns true if some solution is found. User code is responsible for solution quality assessment.
• 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.GeometryModule.Geometry.solvePnP ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int flags )

static

Finds an object pose \( {}^{c}\mathbf{T}_o \) 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/3d/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/3d/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 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. The function returns true if some solution is found. User code is responsible for solution quality assessment.
• 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.GeometryModule.Geometry.solvePnPGeneric ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs )

static

Finds an object pose \( {}^{c}\mathbf{T}_o \) 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/3d/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/3d/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 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() [2/6]

static int OpenCVForUnity.GeometryModule.Geometry.solvePnPGeneric ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess )

static

Finds an object pose \( {}^{c}\mathbf{T}_o \) 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/3d/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/3d/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 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() [3/6]

static int OpenCVForUnity.GeometryModule.Geometry.solvePnPGeneric ( Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List< Mat > rvecs, List< Mat > tvecs, bool useExtrinsicGuess, int flags )

static

Finds an object pose \( {}^{c}\mathbf{T}_o \) 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/3d/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/3d/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 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() [4/6]

static int OpenCVForUnity.GeometryModule.Geometry.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 \( {}^{c}\mathbf{T}_o \) 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/3d/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/3d/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 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() [5/6]

static int OpenCVForUnity.GeometryModule.Geometry.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 \( {}^{c}\mathbf{T}_o \) 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/3d/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/3d/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 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() [6/6]

static int OpenCVForUnity.GeometryModule.Geometry.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 \( {}^{c}\mathbf{T}_o \) 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/3d/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/3d/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 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

solvePnPRansac() [1/9]

static bool OpenCVForUnity.GeometryModule.Geometry.solvePnPRansac ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec )

static

Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.

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 tutorial_real_time_pose
• 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.GeometryModule.Geometry.solvePnPRansac ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess )

static

Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.

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 tutorial_real_time_pose
• 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.GeometryModule.Geometry.solvePnPRansac ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount )

static

Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.

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 tutorial_real_time_pose
• 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.GeometryModule.Geometry.solvePnPRansac ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError )

static

Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.

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 tutorial_real_time_pose
• 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.GeometryModule.Geometry.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 \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.

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 tutorial_real_time_pose
• 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.GeometryModule.Geometry.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 \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.

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 tutorial_real_time_pose
• 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.GeometryModule.Geometry.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 \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.

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 tutorial_real_time_pose
• 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.GeometryModule.Geometry.solvePnPRansac ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers )

static

solvePnPRansac() [9/9]

static bool OpenCVForUnity.GeometryModule.Geometry.solvePnPRansac ( MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers, UsacParams _params )

static

solvePnPRefineLM() [1/4]

static void OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.

triangulatePoints()

static void OpenCVForUnity.GeometryModule.Geometry.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

undistortImagePoints() [1/4]

static void OpenCVForUnity.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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.GeometryModule.Geometry.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() [1/6]

static void OpenCVForUnity.GeometryModule.Geometry.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).

Note

Coordinate Systems:

• Input (src): Points are expected in pixel coordinates of the distorted image, i.e., coordinates \((u, v)\) measured in pixels from the top-left corner of the image.
• Output (dst): The coordinate system of output points depends on parameter P:
  • If P is provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrix P.
  • If P is empty or identity: Output points are in normalized camera coordinates (also called "normalized image coordinates"), which are dimensionless coordinates \((x, y)\) in the camera's focal plane, related to pixel coordinates by: \(x = (u - c_x) / f_x\) and \(y = (v - c_y) / f_y\). These normalized coordinates are independent of the camera's intrinsic parameters and are useful for 3D reconstruction or epipolar geometry.

Parameters

src	Observed point coordinates in pixel coordinates of the distorted image, 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 and output will be in normalized coordinates.
criteria	termination criteria for the iterative point undistortion algorithm

undistortPoints() [2/6]

static void OpenCVForUnity.GeometryModule.Geometry.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).

Note

Coordinate Systems:

• Input (src): Points are expected in pixel coordinates of the distorted image, i.e., coordinates \((u, v)\) measured in pixels from the top-left corner of the image.
• Output (dst): The coordinate system of output points depends on parameter P:
  • If P is provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrix P.
  • If P is empty or identity: Output points are in normalized camera coordinates (also called "normalized image coordinates"), which are dimensionless coordinates \((x, y)\) in the camera's focal plane, related to pixel coordinates by: \(x = (u - c_x) / f_x\) and \(y = (v - c_y) / f_y\). These normalized coordinates are independent of the camera's intrinsic parameters and are useful for 3D reconstruction or epipolar geometry.

Parameters

src	Observed point coordinates in pixel coordinates of the distorted image, 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 and output will be in normalized coordinates.
criteria	termination criteria for the iterative point undistortion algorithm

undistortPoints() [3/6]

static void OpenCVForUnity.GeometryModule.Geometry.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).

Note

Coordinate Systems:

• Input (src): Points are expected in pixel coordinates of the distorted image, i.e., coordinates \((u, v)\) measured in pixels from the top-left corner of the image.
• Output (dst): The coordinate system of output points depends on parameter P:
  • If P is provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrix P.
  • If P is empty or identity: Output points are in normalized camera coordinates (also called "normalized image coordinates"), which are dimensionless coordinates \((x, y)\) in the camera's focal plane, related to pixel coordinates by: \(x = (u - c_x) / f_x\) and \(y = (v - c_y) / f_y\). These normalized coordinates are independent of the camera's intrinsic parameters and are useful for 3D reconstruction or epipolar geometry.

Parameters

src	Observed point coordinates in pixel coordinates of the distorted image, 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 and output will be in normalized coordinates.
criteria	termination criteria for the iterative point undistortion algorithm

undistortPoints() [4/6]

static void OpenCVForUnity.GeometryModule.Geometry.undistortPoints ( MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, in Vec3d criteria )

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).

Note

Coordinate Systems:

• Input (src): Points are expected in pixel coordinates of the distorted image, i.e., coordinates \((u, v)\) measured in pixels from the top-left corner of the image.
• Output (dst): The coordinate system of output points depends on parameter P:
  • If P is provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrix P.
  • If P is empty or identity: Output points are in normalized camera coordinates (also called "normalized image coordinates"), which are dimensionless coordinates \((x, y)\) in the camera's focal plane, related to pixel coordinates by: \(x = (u - c_x) / f_x\) and \(y = (v - c_y) / f_y\). These normalized coordinates are independent of the camera's intrinsic parameters and are useful for 3D reconstruction or epipolar geometry.

Parameters

src	Observed point coordinates in pixel coordinates of the distorted image, 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 and output will be in normalized coordinates.
criteria	termination criteria for the iterative point undistortion algorithm

undistortPoints() [5/6]

static void OpenCVForUnity.GeometryModule.Geometry.undistortPoints ( MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, in(double type, double maxCount, double epsilon) criteria )

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).

Note

Coordinate Systems:

• Input (src): Points are expected in pixel coordinates of the distorted image, i.e., coordinates \((u, v)\) measured in pixels from the top-left corner of the image.
• Output (dst): The coordinate system of output points depends on parameter P:
  • If P is provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrix P.
  • If P is empty or identity: Output points are in normalized camera coordinates (also called "normalized image coordinates"), which are dimensionless coordinates \((x, y)\) in the camera's focal plane, related to pixel coordinates by: \(x = (u - c_x) / f_x\) and \(y = (v - c_y) / f_y\). These normalized coordinates are independent of the camera's intrinsic parameters and are useful for 3D reconstruction or epipolar geometry.

Parameters

src	Observed point coordinates in pixel coordinates of the distorted image, 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 and output will be in normalized coordinates.
criteria	termination criteria for the iterative point undistortion algorithm

undistortPoints() [6/6]

static void OpenCVForUnity.GeometryModule.Geometry.undistortPoints ( MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, TermCriteria criteria )

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).

Note

Coordinate Systems:

• Input (src): Points are expected in pixel coordinates of the distorted image, i.e., coordinates \((u, v)\) measured in pixels from the top-left corner of the image.
• Output (dst): The coordinate system of output points depends on parameter P:
  • If P is provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrix P.
  • If P is empty or identity: Output points are in normalized camera coordinates (also called "normalized image coordinates"), which are dimensionless coordinates \((x, y)\) in the camera's focal plane, related to pixel coordinates by: \(x = (u - c_x) / f_x\) and \(y = (v - c_y) / f_y\). These normalized coordinates are independent of the camera's intrinsic parameters and are useful for 3D reconstruction or epipolar geometry.

Parameters

src	Observed point coordinates in pixel coordinates of the distorted image, 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 and output will be in normalized coordinates.
criteria	termination criteria for the iterative point undistortion algorithm

Member Data Documentation

COV_POLISHER

const int OpenCVForUnity.GeometryModule.Geometry.COV_POLISHER = 3

static

C++: enum PolishingMethod (cv.PolishingMethod)

DIST_C

const int OpenCVForUnity.GeometryModule.Geometry.DIST_C = 3

static

C++: enum DistanceTypes (cv.DistanceTypes)

DIST_FAIR

const int OpenCVForUnity.GeometryModule.Geometry.DIST_FAIR = 5

static

C++: enum DistanceTypes (cv.DistanceTypes)

DIST_HUBER

const int OpenCVForUnity.GeometryModule.Geometry.DIST_HUBER = 7

static

C++: enum DistanceTypes (cv.DistanceTypes)

DIST_L1

const int OpenCVForUnity.GeometryModule.Geometry.DIST_L1 = 1

static

C++: enum DistanceTypes (cv.DistanceTypes)

DIST_L12

const int OpenCVForUnity.GeometryModule.Geometry.DIST_L12 = 4

static

C++: enum DistanceTypes (cv.DistanceTypes)

DIST_L2

const int OpenCVForUnity.GeometryModule.Geometry.DIST_L2 = 2

static

C++: enum DistanceTypes (cv.DistanceTypes)

DIST_USER

const int OpenCVForUnity.GeometryModule.Geometry.DIST_USER = -1

static

C++: enum DistanceTypes (cv.DistanceTypes)

DIST_WELSCH

const int OpenCVForUnity.GeometryModule.Geometry.DIST_WELSCH = 6

static

C++: enum DistanceTypes (cv.DistanceTypes)

FM_7POINT

const int OpenCVForUnity.GeometryModule.Geometry.FM_7POINT = 1

static

C++: enum <unnamed>

FM_8POINT

const int OpenCVForUnity.GeometryModule.Geometry.FM_8POINT = 2

static

C++: enum <unnamed>

FM_LMEDS

const int OpenCVForUnity.GeometryModule.Geometry.FM_LMEDS = 4

static

C++: enum <unnamed>

FM_RANSAC

const int OpenCVForUnity.GeometryModule.Geometry.FM_RANSAC = 8

static

C++: enum <unnamed>

height

static double double double double OpenCVForUnity.GeometryModule.Geometry.height

static

INTERSECT_FULL

const int OpenCVForUnity.GeometryModule.Geometry.INTERSECT_FULL = 2

static

C++: enum RectanglesIntersectTypes (cv.RectanglesIntersectTypes)

INTERSECT_NONE

const int OpenCVForUnity.GeometryModule.Geometry.INTERSECT_NONE = 0

static

C++: enum RectanglesIntersectTypes (cv.RectanglesIntersectTypes)

INTERSECT_PARTIAL

const int OpenCVForUnity.GeometryModule.Geometry.INTERSECT_PARTIAL = 1

static

C++: enum RectanglesIntersectTypes (cv.RectanglesIntersectTypes)

LMEDS

const int OpenCVForUnity.GeometryModule.Geometry.LMEDS = 4

static

C++: enum <unnamed>

LOCAL_OPTIM_GC

const int OpenCVForUnity.GeometryModule.Geometry.LOCAL_OPTIM_GC = 3

static

C++: enum LocalOptimMethod (cv.LocalOptimMethod)

LOCAL_OPTIM_INNER_AND_ITER_LO

const int OpenCVForUnity.GeometryModule.Geometry.LOCAL_OPTIM_INNER_AND_ITER_LO = 2

static

C++: enum LocalOptimMethod (cv.LocalOptimMethod)

LOCAL_OPTIM_INNER_LO

const int OpenCVForUnity.GeometryModule.Geometry.LOCAL_OPTIM_INNER_LO = 1

static

C++: enum LocalOptimMethod (cv.LocalOptimMethod)

LOCAL_OPTIM_NULL

const int OpenCVForUnity.GeometryModule.Geometry.LOCAL_OPTIM_NULL = 0

static

C++: enum LocalOptimMethod (cv.LocalOptimMethod)

LOCAL_OPTIM_SIGMA

const int OpenCVForUnity.GeometryModule.Geometry.LOCAL_OPTIM_SIGMA = 4

static

C++: enum LocalOptimMethod (cv.LocalOptimMethod)

LSQ_POLISHER

const int OpenCVForUnity.GeometryModule.Geometry.LSQ_POLISHER = 1

static

C++: enum PolishingMethod (cv.PolishingMethod)

m00

static double OpenCVForUnity.GeometryModule.Geometry.m00

static

Calculates all of the moments up to the third order of a polygon or rasterized shape.

The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The results are returned in the structure cv::Moments.

Parameters

array	Single channel raster image (CV_8U, CV_16U, CV_16S, CV_32F, CV_64F) or an array ( \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f).
binaryImage	If it is true, all non-zero image pixels are treated as 1's. The parameter is used for images only.

Returns

moments.

Note

Only applicable to contour moments calculations from Python bindings: Note that the numpy type for the input array should be either np.int32 or np.float32.

For contour-based moments, the zeroth-order moment m00 represents the contour area.

If the input contour is degenerate (for example, a single point or all points are collinear), the area is zero and therefore m00 == 0.

In this case, the centroid coordinates (m10/m00, m01/m00) are undefined and must be handled explicitly by the caller.

A common workaround is to compute the center using cv::boundingRect() or by averaging the input points.

See also

contourArea, arcLength

m01

static double double double OpenCVForUnity.GeometryModule.Geometry.m01

static

m02

static double double double double double double OpenCVForUnity.GeometryModule.Geometry.m02

static

m10

static double double OpenCVForUnity.GeometryModule.Geometry.m10

static

m11

static double double double double double OpenCVForUnity.GeometryModule.Geometry.m11

static

m12

static double double double double double double double double double OpenCVForUnity.GeometryModule.Geometry.m12

static

m20

static double double double double OpenCVForUnity.GeometryModule.Geometry.m20

static

m21

static double double double double double double double double OpenCVForUnity.GeometryModule.Geometry.m21

static

m30

static double double double double double double double OpenCVForUnity.GeometryModule.Geometry.m30

static

MAGSAC

const int OpenCVForUnity.GeometryModule.Geometry.MAGSAC = 2

static

C++: enum PolishingMethod (cv.PolishingMethod)

MatrixType_AUTO

const int OpenCVForUnity.GeometryModule.Geometry.MatrixType_AUTO = 0

static

C++: enum MatrixType (cv.MatrixType)

MatrixType_DENSE

const int OpenCVForUnity.GeometryModule.Geometry.MatrixType_DENSE = 1

static

C++: enum MatrixType (cv.MatrixType)

MatrixType_SPARSE

const int OpenCVForUnity.GeometryModule.Geometry.MatrixType_SPARSE = 2

static

C++: enum MatrixType (cv.MatrixType)

MST_KRUSKAL

const int OpenCVForUnity.GeometryModule.Geometry.MST_KRUSKAL = 1

static

C++: enum MSTAlgorithm (cv.MSTAlgorithm)

MST_PRIM

const int OpenCVForUnity.GeometryModule.Geometry.MST_PRIM = 0

static

C++: enum MSTAlgorithm (cv.MSTAlgorithm)

NEIGH_FLANN_KNN

const int OpenCVForUnity.GeometryModule.Geometry.NEIGH_FLANN_KNN = 0

static

C++: enum NeighborSearchMethod (cv.NeighborSearchMethod)

NEIGH_FLANN_RADIUS

const int OpenCVForUnity.GeometryModule.Geometry.NEIGH_FLANN_RADIUS = 2

static

C++: enum NeighborSearchMethod (cv.NeighborSearchMethod)

NEIGH_GRID

const int OpenCVForUnity.GeometryModule.Geometry.NEIGH_GRID = 1

static

C++: enum NeighborSearchMethod (cv.NeighborSearchMethod)

NONE_POLISHER

const int OpenCVForUnity.GeometryModule.Geometry.NONE_POLISHER = 0

static

C++: enum PolishingMethod (cv.PolishingMethod)

RANSAC

const int OpenCVForUnity.GeometryModule.Geometry.RANSAC = 8

static

C++: enum <unnamed>

RHO

const int OpenCVForUnity.GeometryModule.Geometry.RHO = 16

static

C++: enum <unnamed>

SAC_METHOD_RANSAC

const int OpenCVForUnity.GeometryModule.Geometry.SAC_METHOD_RANSAC = 0

static

C++: enum SacMethod (cv.SacMethod)

SAC_MODEL_PLANE

const int OpenCVForUnity.GeometryModule.Geometry.SAC_MODEL_PLANE = 0

static

C++: enum SacModelType (cv.SacModelType)

SAC_MODEL_SPHERE

const int OpenCVForUnity.GeometryModule.Geometry.SAC_MODEL_SPHERE = 1

static

C++: enum SacModelType (cv.SacModelType)

SAMPLING_NAPSAC

const int OpenCVForUnity.GeometryModule.Geometry.SAMPLING_NAPSAC = 2

static

C++: enum SamplingMethod (cv.SamplingMethod)

SAMPLING_PROGRESSIVE_NAPSAC

const int OpenCVForUnity.GeometryModule.Geometry.SAMPLING_PROGRESSIVE_NAPSAC = 1

static

C++: enum SamplingMethod (cv.SamplingMethod)

SAMPLING_PROSAC

const int OpenCVForUnity.GeometryModule.Geometry.SAMPLING_PROSAC = 3

static

C++: enum SamplingMethod (cv.SamplingMethod)

SAMPLING_UNIFORM

const int OpenCVForUnity.GeometryModule.Geometry.SAMPLING_UNIFORM = 0

static

C++: enum SamplingMethod (cv.SamplingMethod)

SCORE_METHOD_LMEDS

const int OpenCVForUnity.GeometryModule.Geometry.SCORE_METHOD_LMEDS = 3

static

C++: enum ScoreMethod (cv.ScoreMethod)

SCORE_METHOD_MAGSAC

const int OpenCVForUnity.GeometryModule.Geometry.SCORE_METHOD_MAGSAC = 2

static

C++: enum ScoreMethod (cv.ScoreMethod)

SCORE_METHOD_MSAC

const int OpenCVForUnity.GeometryModule.Geometry.SCORE_METHOD_MSAC = 1

static

C++: enum ScoreMethod (cv.ScoreMethod)

SCORE_METHOD_RANSAC

const int OpenCVForUnity.GeometryModule.Geometry.SCORE_METHOD_RANSAC = 0

static

C++: enum ScoreMethod (cv.ScoreMethod)

SOLVEPNP_AP3P

const int OpenCVForUnity.GeometryModule.Geometry.SOLVEPNP_AP3P = 3

static

C++: enum SolvePnPMethod (cv.SolvePnPMethod)

SOLVEPNP_EPNP

const int OpenCVForUnity.GeometryModule.Geometry.SOLVEPNP_EPNP = 1

static

C++: enum SolvePnPMethod (cv.SolvePnPMethod)

SOLVEPNP_IPPE

const int OpenCVForUnity.GeometryModule.Geometry.SOLVEPNP_IPPE = 4

static

C++: enum SolvePnPMethod (cv.SolvePnPMethod)

SOLVEPNP_IPPE_SQUARE

const int OpenCVForUnity.GeometryModule.Geometry.SOLVEPNP_IPPE_SQUARE = 5

static

C++: enum SolvePnPMethod (cv.SolvePnPMethod)

SOLVEPNP_ITERATIVE

const int OpenCVForUnity.GeometryModule.Geometry.SOLVEPNP_ITERATIVE = 0

static

C++: enum SolvePnPMethod (cv.SolvePnPMethod)

SOLVEPNP_MAX_COUNT

const int OpenCVForUnity.GeometryModule.Geometry.SOLVEPNP_MAX_COUNT = 6 + 1

static

C++: enum SolvePnPMethod (cv.SolvePnPMethod)

SOLVEPNP_P3P

const int OpenCVForUnity.GeometryModule.Geometry.SOLVEPNP_P3P = 2

static

C++: enum SolvePnPMethod (cv.SolvePnPMethod)

SOLVEPNP_SQPNP

const int OpenCVForUnity.GeometryModule.Geometry.SOLVEPNP_SQPNP = 6

static

C++: enum SolvePnPMethod (cv.SolvePnPMethod)

USAC_ACCURATE

const int OpenCVForUnity.GeometryModule.Geometry.USAC_ACCURATE = 36

static

C++: enum <unnamed>

USAC_DEFAULT

const int OpenCVForUnity.GeometryModule.Geometry.USAC_DEFAULT = 32

static

C++: enum <unnamed>

USAC_FAST

const int OpenCVForUnity.GeometryModule.Geometry.USAC_FAST = 35

static

C++: enum <unnamed>

USAC_FM_8PTS

const int OpenCVForUnity.GeometryModule.Geometry.USAC_FM_8PTS = 34

static

C++: enum <unnamed>

USAC_MAGSAC

const int OpenCVForUnity.GeometryModule.Geometry.USAC_MAGSAC = 38

static

C++: enum <unnamed>

USAC_PARALLEL

const int OpenCVForUnity.GeometryModule.Geometry.USAC_PARALLEL = 33

static

C++: enum <unnamed>

USAC_PROSAC

const int OpenCVForUnity.GeometryModule.Geometry.USAC_PROSAC = 37

static

C++: enum <unnamed>

VariableType_LINEAR

const int OpenCVForUnity.GeometryModule.Geometry.VariableType_LINEAR = 0

static

C++: enum VariableType (cv.VariableType)

VariableType_SE3

const int OpenCVForUnity.GeometryModule.Geometry.VariableType_SE3 = 2

static

C++: enum VariableType (cv.VariableType)

VariableType_SO3

const int OpenCVForUnity.GeometryModule.Geometry.VariableType_SO3 = 1

static

C++: enum VariableType (cv.VariableType)

width [1/2]

static double double double OpenCVForUnity.GeometryModule.Geometry.width

static

width [2/2]

int int int OpenCVForUnity.GeometryModule.Geometry.width

static

x [1/2]

static double OpenCVForUnity.GeometryModule.Geometry.x

static

Finds a rotated rectangle of the minimum area enclosing the input 2D point set.

Fits an ellipse around a set of 2D points.

The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a specified point set. The angle of rotation represents the angle between the line connecting the starting and ending points (based on the clockwise order with greatest index for the corner with greatest \(y\)) and the horizontal axis. This angle always falls between \([-90, 0)\) because, if the object rotates more than a rect angle, the next edge is used to measure the angle. The starting and ending points change as the object rotates.Developer should keep in mind that the returned RotatedRect can contain negative indices when data is close to the containing Mat element boundary.

Parameters

points

Input vector of 2D points, stored in std::vector<> or Mat

The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by [Fitzgibbon95] is used. Developer should keep in mind that it is possible that the returned ellipse/rotatedRect data contains negative indices, due to the data points being close to the border of the containing Mat element.

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

Note

Input point types are Point2i or Point2f and at least 5 points are required.

getClosestEllipsePoints function can be used to compute the ellipse fitting error.

The function calculates the ellipse that fits a set of 2D points. It returns the rotated rectangle in which the ellipse is inscribed. The Approximate Mean Square (AMS) proposed by [Taubin1991] is used.

For an ellipse, this basis set is \( \chi= \left(x^2, x y, y^2, x, y, 1\right) \), which is a set of six free coefficients \( A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \). However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \( (a,b) \), the position \( (x_0,y_0) \), and the orientation \( \theta \). This is because the basis set includes lines, quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. If the fit is found to be a parabolic or hyperbolic function then the standard fitEllipse method is used. The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves by imposing the condition that \( A^T ( D_x^T D_x + D_y^T D_y) A = 1 \) where the matrices \( Dx \) and \( Dy \) are the partial derivatives of the design matrix \( D \) with respect to x and y. The matrices are formed row by row applying the following to each of the points in the set:

\begin{align*} D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} & D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} & D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\} \end{align*}

The AMS method minimizes the cost function

\begin{equation*} \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } \end{equation*}

The minimum cost is found by solving the generalized eigenvalue problem.

\begin{equation*} D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A \end{equation*}

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

Note

Input point types are Point2i or Point2f and at least 5 points are required.

getClosestEllipsePoints function can be used to compute the ellipse fitting error.

The function calculates the ellipse that fits a set of 2D points. It returns the rotated rectangle in which the ellipse is inscribed. The Direct least square (Direct) method by [oy1998NumericallySD] is used.

For an ellipse, this basis set is \( \chi= \left(x^2, x y, y^2, x, y, 1\right) \), which is a set of six free coefficients \( A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \). However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \( (a,b) \), the position \( (x_0,y_0) \), and the orientation \( \theta \). This is because the basis set includes lines, quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. The Direct method confines the fit to ellipses by ensuring that \( 4 A_{xx} A_{yy}- A_{xy}^2 > 0 \). The condition imposed is that \( 4 A_{xx} A_{yy}- A_{xy}^2=1 \) which satisfies the inequality and as the coefficients can be arbitrarily scaled is not overly restrictive.

\begin{equation*} \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix} 0 & 0 & 2 & 0 & 0 & 0 \\ 0 & -1 & 0 & 0 & 0 & 0 \\ 2 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \end{matrix} \right) \end{equation*}

The minimum cost is found by solving the generalized eigenvalue problem.

\begin{equation*} D^T D A = \lambda \left( C\right) A \end{equation*}

The system produces only one positive eigenvalue \( \lambda\) which is chosen as the solution with its eigenvector \(\mathbf{u}\). These are used to find the coefficients

\begin{equation*} A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} \end{equation*}

The scaling factor guarantees that \(A^T C A =1\).

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

Note

Input point types are Point2i or Point2f and at least 5 points are required.

getClosestEllipsePoints function can be used to compute the ellipse fitting error.

x [2/2]

int OpenCVForUnity.GeometryModule.Geometry.x

static

Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.

The function calculates and returns the minimal up-right bounding rectangle for the specified point set or non-zero pixels of gray-scale image.

Parameters

array

Input gray-scale image or 2D point set, stored in std::vector or Mat.

y [1/2]

static double double OpenCVForUnity.GeometryModule.Geometry.y

static

y [2/2]

int int OpenCVForUnity.GeometryModule.Geometry.y

static

---

The documentation for this class was generated from the following files:

• OpenCVForUnity/Assets/OpenCVForUnity/Scripts/Core/org/opencv/geometry/Geometry.cs
• OpenCVForUnity/Assets/OpenCVForUnity/Scripts/Extensions/org/opencv/geometry/Geometry_Struct.cs
• OpenCVForUnity/Assets/OpenCVForUnity/Scripts/Extensions/org/opencv/geometry/Geometry_ValueTuple.cs