Class Geometry
java.lang.Object
org.opencv.geometry.Geometry
-
Field Summary
FieldsModifier and TypeFieldDescriptionstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final int -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionstatic voidapproxPolyDP(MatOfPoint2f curve, MatOfPoint2f approxCurve, double epsilon, boolean closed) Approximates a polygonal curve(s) with the specified precision.static voidapproxPolyN(Mat curve, Mat approxCurve, int nsides) Approximates a polygon with a convex hull with a specified accuracy and number of sides.static voidapproxPolyN(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 voidapproxPolyN(Mat curve, Mat approxCurve, int nsides, float epsilon_percentage, boolean ensure_convex) Approximates a polygon with a convex hull with a specified accuracy and number of sides.static doublearcLength(MatOfPoint2f curve, boolean closed) Calculates a contour perimeter or a curve length.static RectboundingRect(Mat array) Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.static voidboxPoints(RotatedRect box, Mat points) Finds the four vertices of a rotated rect.static voidcalibrationMatrixValues(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 voidCombines two rotation-and-shift transformations.static voidCombines two rotation-and-shift transformations.static voidCombines two rotation-and-shift transformations.static voidcomposeRT(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 voidcomposeRT(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 voidcomposeRT(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 voidcomposeRT(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 voidcomposeRT(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 voidcomposeRT(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 voidcomputeCorrespondEpilines(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 doublecontourArea(Mat contour) Calculates a contour area.static doublecontourArea(Mat contour, boolean oriented) Calculates a contour area.static voidconvertPointsFromHomogeneous(Mat src, Mat dst) Converts points from homogeneous to Euclidean space.static voidconvertPointsFromHomogeneous(Mat src, Mat dst, int dtype) Converts points from homogeneous to Euclidean space.static voidconvertPointsToHomogeneous(Mat src, Mat dst) Converts points from Euclidean to homogeneous space.static voidconvertPointsToHomogeneous(Mat src, Mat dst, int dtype) Converts points from Euclidean to homogeneous space.static voidconvexHull(MatOfPoint points, MatOfInt hull) Finds the convex hull of a point set.static voidconvexHull(MatOfPoint points, MatOfInt hull, boolean clockwise) Finds the convex hull of a point set.static voidconvexityDefects(MatOfPoint contour, MatOfInt convexhull, MatOfInt4 convexityDefects) Finds the convexity defects of a contour.static voidRefines coordinates of corresponding points.static voiddecomposeEssentialMat(Mat E, Mat R1, Mat R2, Mat t) Decompose an essential matrix to possible rotations and translation.static intdecomposeHomographyMat(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 voiddecomposeProjectionMatrix(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect) Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.static voiddecomposeProjectionMatrix(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX) Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.static voiddecomposeProjectionMatrix(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 voiddecomposeProjectionMatrix(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 voiddecomposeProjectionMatrix(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 MatestimateAffine2D(Mat from, Mat to) Computes an optimal affine transformation between two 2D point sets.static MatestimateAffine2D(Mat from, Mat to, Mat inliers) Computes an optimal affine transformation between two 2D point sets.static MatestimateAffine2D(Mat from, Mat to, Mat inliers, int method) Computes an optimal affine transformation between two 2D point sets.static MatestimateAffine2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold) Computes an optimal affine transformation between two 2D point sets.static MatestimateAffine2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters) Computes an optimal affine transformation between two 2D point sets.static MatestimateAffine2D(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 MatestimateAffine2D(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 MatestimateAffine2D(Mat pts1, Mat pts2, Mat inliers, UsacParams params) static MatestimateAffine3D(Mat src, Mat dst) Computes an optimal affine transformation between two 3D point sets.static MatestimateAffine3D(Mat src, Mat dst, double[] scale) Computes an optimal affine transformation between two 3D point sets.static MatestimateAffine3D(Mat src, Mat dst, double[] scale, boolean force_rotation) Computes an optimal affine transformation between two 3D point sets.static booleanestimateAffine3D(Mat src, Mat dst, Mat out, Mat inliers) Computes an optimal affine transformation between two 3D point sets.static booleanestimateAffine3D(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold) Computes an optimal affine transformation between two 3D point sets.static booleanestimateAffine3D(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold, double confidence) Computes an optimal affine transformation between two 3D point sets.static MatestimateAffinePartial2D(Mat from, Mat to) Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.static MatestimateAffinePartial2D(Mat from, Mat to, Mat inliers) Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.static MatestimateAffinePartial2D(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 MatestimateAffinePartial2D(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 MatestimateAffinePartial2D(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 MatestimateAffinePartial2D(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 MatestimateAffinePartial2D(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 booleanestimateTranslation3D(Mat src, Mat dst, Mat out, Mat inliers) Computes an optimal translation between two 3D point sets.static booleanestimateTranslation3D(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold) Computes an optimal translation between two 3D point sets.static booleanestimateTranslation3D(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold, double confidence) Computes an optimal translation between two 3D point sets.static voidfilterHomographyDecompByVisibleRefpoints(List<Mat> rotations, List<Mat> normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions) Filters homography decompositions based on additional information.static voidfilterHomographyDecompByVisibleRefpoints(List<Mat> rotations, List<Mat> normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions, Mat pointsMask) Filters homography decompositions based on additional information.static MatfindEssentialMat(Mat points1, Mat points2) static MatfindEssentialMat(Mat points1, Mat points2, double focal) static MatfindEssentialMat(Mat points1, Mat points2, double focal, Point pp) static MatfindEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method) static MatfindEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method, double prob) static MatfindEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold) static MatfindEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold, int maxIters) static MatfindEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold, int maxIters, Mat mask) static MatfindEssentialMat(Mat points1, Mat points2, Mat cameraMatrix) Calculates an essential matrix from the corresponding points in two images.static MatfindEssentialMat(Mat points1, Mat points2, Mat cameraMatrix, int method) Calculates an essential matrix from the corresponding points in two images.static MatfindEssentialMat(Mat points1, Mat points2, Mat cameraMatrix, int method, double prob) Calculates an essential matrix from the corresponding points in two images.static MatfindEssentialMat(Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold) Calculates an essential matrix from the corresponding points in two images.static MatfindEssentialMat(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 MatfindEssentialMat(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 MatfindEssentialMat(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 MatfindEssentialMat(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 MatfindEssentialMat(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 MatfindEssentialMat(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 MatfindEssentialMat(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 MatfindEssentialMat(Mat points1, Mat points2, Mat cameraMatrix1, Mat cameraMatrix2, Mat dist_coeff1, Mat dist_coeff2, Mat mask, UsacParams params) static MatfindFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2) static MatfindFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method) static MatfindFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold) static MatfindFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence) static MatfindFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, int maxIters) Calculates a fundamental matrix from the corresponding points in two images.static MatfindFundamentalMat(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 MatfindFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, Mat mask) static MatfindFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, Mat mask, UsacParams params) static MatfindHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints) Finds a perspective transformation between two planes.static MatfindHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method) Finds a perspective transformation between two planes.static MatfindHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold) Finds a perspective transformation between two planes.static MatfindHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask) Finds a perspective transformation between two planes.static MatfindHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters) Finds a perspective transformation between two planes.static MatfindHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters, double confidence) Finds a perspective transformation between two planes.static MatfindHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, Mat mask, UsacParams params) static voidfisheye_distortPoints(Mat undistorted, Mat distorted, Mat K, Mat D) Distorts 2D points using fisheye model.static voidfisheye_distortPoints(Mat undistorted, Mat distorted, Mat K, Mat D, double alpha) Distorts 2D points using fisheye model.static voidOverload of distortPoints function to handle cases when undistorted points are got with non-identity camera matrix, e.g.static voidOverload of distortPoints function to handle cases when undistorted points are got with non-identity camera matrix, e.g.static voidEstimates new camera intrinsic matrix for undistortion or rectification.static voidfisheye_estimateNewCameraMatrixForUndistortRectify(Mat K, Mat D, Size image_size, Mat R, Mat P, double balance) Estimates new camera intrinsic matrix for undistortion or rectification.static voidfisheye_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 voidfisheye_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 voidstatic voidfisheye_projectPoints(Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D, double alpha) static voidfisheye_projectPoints(Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D, double alpha, Mat jacobian) static booleanfisheye_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 booleanfisheye_solvePnP(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess) Finds an object pose from 3D-2D point correspondences for fisheye camera model.static booleanfisheye_solvePnP(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int flags) Finds an object pose from 3D-2D point correspondences for fisheye camera model.static booleanfisheye_solvePnP(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int flags, TermCriteria criteria) Finds an object pose from 3D-2D point correspondences for fisheye camera model.static booleanfisheye_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 booleanfisheye_solvePnPRansac(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess) Finds an object pose from 3D-2D point correspondences using the RANSAC scheme for fisheye camera moodel.static booleanfisheye_solvePnPRansac(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int iterationsCount) Finds an object pose from 3D-2D point correspondences using the RANSAC scheme for fisheye camera moodel.static booleanfisheye_solvePnPRansac(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int iterationsCount, float reprojectionError) Finds an object pose from 3D-2D point correspondences using the RANSAC scheme for fisheye camera moodel.static booleanfisheye_solvePnPRansac(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean 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 booleanfisheye_solvePnPRansac(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean 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 booleanfisheye_solvePnPRansac(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean 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 booleanfisheye_solvePnPRansac(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean 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 voidfisheye_undistortPoints(Mat distorted, Mat undistorted, Mat K, Mat D) 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.static voidUndistorts 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.static voidUndistorts 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.static voidfisheye_undistortPoints(Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P, TermCriteria criteria) 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.static RotatedRectfitEllipse(MatOfPoint2f points) Fits an ellipse around a set of 2D points.static RotatedRectfitEllipseAMS(Mat points) Fits an ellipse around a set of 2D points.static RotatedRectfitEllipseDirect(Mat points) Fits an ellipse around a set of 2D points.static voidFits a line to a 2D or 3D point set.static MatgetAffineTransform(MatOfPoint2f src, MatOfPoint2f dst) static voidgetClosestEllipsePoints(RotatedRect ellipse_params, Mat points, Mat closest_pts) Compute for each 2d point the nearest 2d point located on a given ellipse.static MatgetDefaultNewCameraMatrix(Mat cameraMatrix) Returns the default new camera matrix.static MatgetDefaultNewCameraMatrix(Mat cameraMatrix, Size imgsize) Returns the default new camera matrix.static MatgetDefaultNewCameraMatrix(Mat cameraMatrix, Size imgsize, boolean centerPrincipalPoint) Returns the default new camera matrix.static MatgetOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha) Returns the new camera intrinsic matrix based on the free scaling parameter.static MatgetOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize) Returns the new camera intrinsic matrix based on the free scaling parameter.static MatgetOptimalNewCameraMatrix(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 MatgetOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize, Rect validPixROI, boolean centerPrincipalPoint) Returns the new camera intrinsic matrix based on the free scaling parameter.static MatgetPerspectiveTransform(Mat src, Mat dst) Calculates a perspective transform from four pairs of the corresponding points.static MatgetPerspectiveTransform(Mat src, Mat dst, int solveMethod) Calculates a perspective transform from four pairs of the corresponding points.static MatgetRotationMatrix2D(Point center, double angle, double scale) Calculates an affine matrix of 2D rotation.static voidstatic floatintersectConvexConvex(Mat p1, Mat p2, Mat p12) Finds intersection of two convex polygonsstatic floatintersectConvexConvex(Mat p1, Mat p2, Mat p12, boolean handleNested) Finds intersection of two convex polygonsstatic voidinvertAffineTransform(Mat M, Mat iM) Inverts an affine transformation.static booleanisContourConvex(MatOfPoint contour) Tests a contour convexity.static doublematchShapes(Mat contour1, Mat contour2, int method, double parameter) Compares two shapes.static voidmatMulDeriv(Mat A, Mat B, Mat dABdA, Mat dABdB) Computes partial derivatives of the matrix product for each multiplied matrix.static RotatedRectminAreaRect(MatOfPoint2f points) Finds a rotated rectangle of the minimum area enclosing the input 2D point set.static voidminEnclosingCircle(MatOfPoint2f points, Point center, float[] radius) Finds a circle of the minimum area enclosing a 2D point set.static doubleminEnclosingConvexPolygon(Mat points, Mat polygon, int k) Finds a convex polygon of minimum area enclosing a 2D point set and returns its area.static doubleminEnclosingTriangle(Mat points, Mat triangle) Finds a triangle of minimum area enclosing a 2D point set and returns its area.static MomentsCalculates all of the moments up to the third order of a polygon or rasterized shape.static MomentsCalculates all of the moments up to the third order of a polygon or rasterized shape.static doublepointPolygonTest(MatOfPoint2f contour, Point pt, boolean measureDist) Performs a point-in-contour test.static voidprojectPoints(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints) Projects 3D points to an image plane.static voidprojectPoints(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian) Projects 3D points to an image plane.static voidprojectPoints(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian, double aspectRatio) Projects 3D points to an image plane.static voidprojectPointsSepJ(Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt) static voidprojectPointsSepJ(Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf) static voidprojectPointsSepJ(Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf, Mat dpdc) static voidprojectPointsSepJ(Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf, Mat dpdc, Mat dpdk) static voidprojectPointsSepJ(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 voidprojectPointsSepJ(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 intstatic intstatic intstatic intstatic intRecovers the relative camera rotation and the translation from an estimated essential matrix and the corresponding points in two images, using chirality check.static intstatic intrecoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh, Mat mask) static intrecoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh, Mat mask, Mat triangulatedPoints) static intRecovers the relative camera rotation and the translation from an estimated essential matrix and the corresponding points in two images, using chirality check.static intrecoverPose(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.static intrecoverPose(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.static intrecoverPose(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.static intrecoverPose(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.static intrecoverPose(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.static voidConverts a rotation matrix to a rotation vector or vice versa.static voidConverts a rotation matrix to a rotation vector or vice versa.static introtatedRectangleIntersection(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[]Computes an RQ decomposition of 3x3 matrices.static double[]Computes an RQ decomposition of 3x3 matrices.static doublesampsonDistance(Mat pt1, Mat pt2, Mat F) Calculates the Sampson Distance between two points.static intsolveP3P(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 booleansolvePnP(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: { width=50% } SEE: REF: 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): need 4 input points to return a unique solution.static booleansolvePnP(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences: { width=50% } SEE: REF: 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): need 4 input points to return a unique solution.static booleansolvePnP(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int flags) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences: { width=50% } SEE: REF: 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): need 4 input points to return a unique solution.static intsolvePnPGeneric(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 intsolvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences.static intsolvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess, int flags) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences.static intsolvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess, int flags, Mat rvec) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences.static intsolvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess, int flags, Mat rvec, Mat tvec) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences.static intsolvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess, int flags, Mat rvec, Mat tvec, Mat reprojectionError) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences.static booleansolvePnPRansac(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 booleansolvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.static booleansolvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean 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 booleansolvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean 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 booleansolvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean 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 booleansolvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean 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 booleansolvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean 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 booleansolvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers) static booleansolvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers, UsacParams params) static voidsolvePnPRefineLM(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 voidsolvePnPRefineLM(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 voidsolvePnPRefineVVS(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 voidsolvePnPRefineVVS(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 voidsolvePnPRefineVVS(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 voidThis function reconstructs 3-dimensional points (in homogeneous coordinates) by using their observations with a stereo camera.static voidundistortImagePoints(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs) Compute undistorted image points positionstatic voidundistortImagePoints(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, TermCriteria arg1) Compute undistorted image points positionstatic voidundistortPoints(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs) Computes the ideal point coordinates from the observed point coordinates.static voidundistortPoints(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R) Computes the ideal point coordinates from the observed point coordinates.static voidundistortPoints(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P) Computes the ideal point coordinates from the observed point coordinates.static voidundistortPoints(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, TermCriteria criteria) Computes the ideal point coordinates from the observed point coordinates.
-
Field Details
-
LMEDS
public static final int LMEDS- See Also:
-
RANSAC
public static final int RANSAC- See Also:
-
RHO
public static final int RHO- See Also:
-
USAC_DEFAULT
public static final int USAC_DEFAULT- See Also:
-
USAC_PARALLEL
public static final int USAC_PARALLEL- See Also:
-
USAC_FM_8PTS
public static final int USAC_FM_8PTS- See Also:
-
USAC_FAST
public static final int USAC_FAST- See Also:
-
USAC_ACCURATE
public static final int USAC_ACCURATE- See Also:
-
USAC_PROSAC
public static final int USAC_PROSAC- See Also:
-
USAC_MAGSAC
public static final int USAC_MAGSAC- See Also:
-
FM_7POINT
public static final int FM_7POINT- See Also:
-
FM_8POINT
public static final int FM_8POINT- See Also:
-
FM_LMEDS
public static final int FM_LMEDS- See Also:
-
FM_RANSAC
public static final int FM_RANSAC- See Also:
-
DIST_USER
public static final int DIST_USER- See Also:
-
DIST_L1
public static final int DIST_L1- See Also:
-
DIST_L2
public static final int DIST_L2- See Also:
-
DIST_C
public static final int DIST_C- See Also:
-
DIST_L12
public static final int DIST_L12- See Also:
-
DIST_FAIR
public static final int DIST_FAIR- See Also:
-
DIST_WELSCH
public static final int DIST_WELSCH- See Also:
-
DIST_HUBER
public static final int DIST_HUBER- See Also:
-
LOCAL_OPTIM_NULL
public static final int LOCAL_OPTIM_NULL- See Also:
-
LOCAL_OPTIM_INNER_LO
public static final int LOCAL_OPTIM_INNER_LO- See Also:
-
LOCAL_OPTIM_INNER_AND_ITER_LO
public static final int LOCAL_OPTIM_INNER_AND_ITER_LO- See Also:
-
LOCAL_OPTIM_GC
public static final int LOCAL_OPTIM_GC- See Also:
-
LOCAL_OPTIM_SIGMA
public static final int LOCAL_OPTIM_SIGMA- See Also:
-
MST_PRIM
public static final int MST_PRIM- See Also:
-
MST_KRUSKAL
public static final int MST_KRUSKAL- See Also:
-
MatrixType_AUTO
public static final int MatrixType_AUTO- See Also:
-
MatrixType_DENSE
public static final int MatrixType_DENSE- See Also:
-
MatrixType_SPARSE
public static final int MatrixType_SPARSE- See Also:
-
NEIGH_FLANN_KNN
public static final int NEIGH_FLANN_KNN- See Also:
-
NEIGH_GRID
public static final int NEIGH_GRID- See Also:
-
NEIGH_FLANN_RADIUS
public static final int NEIGH_FLANN_RADIUS- See Also:
-
NONE_POLISHER
public static final int NONE_POLISHER- See Also:
-
LSQ_POLISHER
public static final int LSQ_POLISHER- See Also:
-
MAGSAC
public static final int MAGSAC- See Also:
-
COV_POLISHER
public static final int COV_POLISHER- See Also:
-
INTERSECT_NONE
public static final int INTERSECT_NONE- See Also:
-
INTERSECT_PARTIAL
public static final int INTERSECT_PARTIAL- See Also:
-
INTERSECT_FULL
public static final int INTERSECT_FULL- See Also:
-
SAC_METHOD_RANSAC
public static final int SAC_METHOD_RANSAC- See Also:
-
SAC_MODEL_PLANE
public static final int SAC_MODEL_PLANE- See Also:
-
SAC_MODEL_SPHERE
public static final int SAC_MODEL_SPHERE- See Also:
-
SAMPLING_UNIFORM
public static final int SAMPLING_UNIFORM- See Also:
-
SAMPLING_PROGRESSIVE_NAPSAC
public static final int SAMPLING_PROGRESSIVE_NAPSAC- See Also:
-
SAMPLING_NAPSAC
public static final int SAMPLING_NAPSAC- See Also:
-
SAMPLING_PROSAC
public static final int SAMPLING_PROSAC- See Also:
-
SCORE_METHOD_RANSAC
public static final int SCORE_METHOD_RANSAC- See Also:
-
SCORE_METHOD_MSAC
public static final int SCORE_METHOD_MSAC- See Also:
-
SCORE_METHOD_MAGSAC
public static final int SCORE_METHOD_MAGSAC- See Also:
-
SCORE_METHOD_LMEDS
public static final int SCORE_METHOD_LMEDS- See Also:
-
SOLVEPNP_ITERATIVE
public static final int SOLVEPNP_ITERATIVE- See Also:
-
SOLVEPNP_EPNP
public static final int SOLVEPNP_EPNP- See Also:
-
SOLVEPNP_P3P
public static final int SOLVEPNP_P3P- See Also:
-
SOLVEPNP_AP3P
public static final int SOLVEPNP_AP3P- See Also:
-
SOLVEPNP_IPPE
public static final int SOLVEPNP_IPPE- See Also:
-
SOLVEPNP_IPPE_SQUARE
public static final int SOLVEPNP_IPPE_SQUARE- See Also:
-
SOLVEPNP_SQPNP
public static final int SOLVEPNP_SQPNP- See Also:
-
SOLVEPNP_MAX_COUNT
public static final int SOLVEPNP_MAX_COUNT- See Also:
-
VariableType_LINEAR
public static final int VariableType_LINEAR- See Also:
-
VariableType_SO3
public static final int VariableType_SO3- See Also:
-
VariableType_SE3
public static final int VariableType_SE3- See Also:
-
-
Constructor Details
-
Geometry
public Geometry()
-
-
Method Details
-
Rodrigues
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 REF: calibrateCamera, REF: stereoCalibrate, or REF: 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 CITE: Gallego2014ACF
- A tutorial on SE(3) transformation parameterizations and on-manifold optimization, Jose-Luis Blanco CITE: blanco2010tutorial
- Lie Groups for 2D and 3D Transformation, Ethan Eade CITE: Eade17
- A micro Lie theory for state estimation in robotics, Joan Solà, Jérémie Deray, Dinesh Atchuthan CITE: Sol2018AML
-
Rodrigues
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. 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 REF: calibrateCamera, REF: stereoCalibrate, or REF: 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 CITE: Gallego2014ACF
- A tutorial on SE(3) transformation parameterizations and on-manifold optimization, Jose-Luis Blanco CITE: blanco2010tutorial
- Lie Groups for 2D and 3D Transformation, Ethan Eade CITE: Eade17
- A micro Lie theory for state estimation in robotics, Joan Solà, Jérémie Deray, Dinesh Atchuthan CITE: Sol2018AML
-
findHomography
public static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters, double confidence) 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
- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method
- REF: 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: getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform- Returns:
- automatically generated
-
findHomography
public static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters) 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
- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method
- REF: 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. 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: getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform- Returns:
- automatically generated
-
findHomography
public static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask) 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
- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method
- REF: 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. 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: getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform- Returns:
- automatically generated
-
findHomography
public static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold) 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
- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method
- REF: 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 values are ignored. 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: getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform- Returns:
- automatically generated
-
findHomography
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
- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method
- REF: RHO - PROSAC-based robust method
- Returns:
- automatically generated
-
findHomography
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> .- 0 - a regular method using all the points, i.e., the least squares method
- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method
- REF: RHO - PROSAC-based robust method
- Returns:
- automatically generated
-
findHomography
public static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, Mat mask, UsacParams params) -
RQDecomp3x3
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 CITE: Slabaugh . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.- Returns:
- automatically generated
-
RQDecomp3x3
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. 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 CITE: Slabaugh . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.- Returns:
- automatically generated
-
RQDecomp3x3
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. 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 CITE: Slabaugh . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.- Returns:
- automatically generated
-
RQDecomp3x3
Computes an RQ decomposition of 3x3 matrices.- Parameters:
src- 3x3 input matrix.mtxR- Output 3x3 upper-triangular matrix.mtxQ- Output 3x3 orthogonal matrix. 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 CITE: Slabaugh . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.- Returns:
- automatically generated
-
decomposeProjectionMatrix
public 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.- 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 CITE: Slabaugh . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions. The function is based on #RQDecomp3x3 .
-
decomposeProjectionMatrix
public 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.- 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. 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 CITE: Slabaugh . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions. The function is based on #RQDecomp3x3 .
-
decomposeProjectionMatrix
public 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.- 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. 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 CITE: Slabaugh . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions. The function is based on #RQDecomp3x3 .
-
decomposeProjectionMatrix
public 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.- 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. 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 CITE: Slabaugh . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions. The function is based on #RQDecomp3x3 .
-
decomposeProjectionMatrix
public static void decomposeProjectionMatrix(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect) 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. 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 CITE: Slabaugh . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions. The function is based on #RQDecomp3x3 .
-
matMulDeriv
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.
-
composeRT
public 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.- 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 rvec1dr3dt1- Optional output derivative of rvec3 with regard to tvec1dr3dr2- Optional output derivative of rvec3 with regard to rvec2dr3dt2- Optional output derivative of rvec3 with regard to tvec2dt3dr1- Optional output derivative of tvec3 with regard to rvec1dt3dt1- Optional output derivative of tvec3 with regard to tvec1dt3dr2- Optional output derivative of tvec3 with regard to rvec2dt3dt2- 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
public 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.- 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 rvec1dr3dt1- Optional output derivative of rvec3 with regard to tvec1dr3dr2- Optional output derivative of rvec3 with regard to rvec2dr3dt2- Optional output derivative of rvec3 with regard to tvec2dt3dr1- Optional output derivative of tvec3 with regard to rvec1dt3dt1- Optional output derivative of tvec3 with regard to tvec1dt3dr2- Optional output derivative of tvec3 with regard to rvec2 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
public 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.- 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 rvec1dr3dt1- Optional output derivative of rvec3 with regard to tvec1dr3dr2- Optional output derivative of rvec3 with regard to rvec2dr3dt2- Optional output derivative of rvec3 with regard to tvec2dt3dr1- Optional output derivative of tvec3 with regard to rvec1dt3dt1- Optional output derivative of tvec3 with regard to tvec1 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
public 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.- 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 rvec1dr3dt1- Optional output derivative of rvec3 with regard to tvec1dr3dr2- Optional output derivative of rvec3 with regard to rvec2dr3dt2- Optional output derivative of rvec3 with regard to tvec2dt3dr1- Optional output derivative of tvec3 with regard to rvec1 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
public 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.- 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 rvec1dr3dt1- Optional output derivative of rvec3 with regard to tvec1dr3dr2- Optional output derivative of rvec3 with regard to rvec2dr3dt2- Optional output derivative of rvec3 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
public 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.- 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 rvec1dr3dt1- Optional output derivative of rvec3 with regard to tvec1dr3dr2- Optional output derivative of rvec3 with regard to rvec2 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
public 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.- 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 rvec1dr3dt1- Optional output derivative of rvec3 with regard to tvec1 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
public static void composeRT(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1) 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 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
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. 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.
-
projectPoints
public 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. 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 REF: calibrateCamera, REF: solvePnP, and REF: 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.
- 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 (REF: Rodrigues) that, together with tvec, performs a change of basis from world to camera coordinate system, see REF: 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.
-
Input (
-
projectPoints
public static void projectPoints(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian) 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 REF: calibrateCamera, REF: solvePnP, and REF: 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.
- 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 (REF: Rodrigues) that, together with tvec, performs a change of basis from world to camera coordinate system, see REF: 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. 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.
-
Input (
-
projectPoints
public static void projectPoints(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints) 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 REF: calibrateCamera, REF: solvePnP, and REF: 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.
- 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 (REF: Rodrigues) that, together with tvec, performs a change of basis from world to camera coordinate system, see REF: 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> . 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. 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.
-
Input (
-
projectPointsSepJ
-
projectPointsSepJ
-
projectPointsSepJ
-
projectPointsSepJ
-
projectPointsSepJ
-
projectPointsSepJ
-
solvePnP
public static boolean solvePnP(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int flags) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences: { width=50% } SEE: REF: 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): need 4 input points to return a unique solution.
- REF: SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
-
REF: 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 REF: 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 REF: calib3d_solvePnP_flags More information about Perspective-n-Points is described in REF: 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 REF: SOLVEPNP_P3P and REF: 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 REF: 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 REF: SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
-
With REF: 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 REF: SOLVEPNP_SQPNP input points must be >= 3
- Returns:
- automatically generated
-
solvePnP
public static boolean solvePnP(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences: { width=50% } SEE: REF: 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): need 4 input points to return a unique solution.
- REF: SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
-
REF: 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 REF: 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. More information about Perspective-n-Points is described in REF: 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 REF: SOLVEPNP_P3P and REF: 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 REF: 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 REF: SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
-
With REF: 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 REF: SOLVEPNP_SQPNP input points must be >= 3
- Returns:
- automatically generated
-
solvePnP
public static boolean 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: { width=50% } SEE: REF: 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): need 4 input points to return a unique solution.
- REF: SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
-
REF: 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 REF: Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.tvec- Output translation vector. the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them. More information about Perspective-n-Points is described in REF: 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 REF: SOLVEPNP_P3P and REF: 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 REF: 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 REF: SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
-
With REF: 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 REF: SOLVEPNP_SQPNP input points must be >= 3
- Returns:
- automatically generated
-
solvePnPRansac
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean 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. { width=50% } SEE: REF: 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 REF: 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 REF: 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 REF: 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 REF: 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 REF: 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.
- Returns:
- automatically generated
-
solvePnPRansac
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean 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. { width=50% } SEE: REF: 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 REF: 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 REF: 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 . 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 REF: 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 REF: 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.
- Returns:
- automatically generated
-
solvePnPRansac
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean 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. { width=50% } SEE: REF: 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 REF: 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 REF: 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. 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 REF: 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 REF: 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.
- Returns:
- automatically generated
-
solvePnPRansac
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean 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. { width=50% } SEE: REF: 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 REF: 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 REF: 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. 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 REF: 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 REF: 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.
- Returns:
- automatically generated
-
solvePnPRansac
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean 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. { width=50% } SEE: REF: 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 REF: 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 REF: 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. is the maximum allowed distance between the observed and computed point projections to consider it an inlier. 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 REF: 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 REF: 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.
- Returns:
- automatically generated
-
solvePnPRansac
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches. { width=50% } SEE: REF: 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 REF: 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 REF: 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. is the maximum allowed distance between the observed and computed point projections to consider it an inlier. 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 REF: 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 REF: 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.
- Returns:
- automatically generated
-
solvePnPRansac
public static boolean 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. { width=50% } SEE: REF: 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 REF: Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.tvec- Output translation vector. the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them. is the maximum allowed distance between the observed and computed point projections to consider it an inlier. 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 REF: 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 REF: 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.
- Returns:
- automatically generated
-
solvePnPRansac
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers, UsacParams params) -
solvePnPRansac
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers) -
solveP3P
public 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. { width=50% } SEE: REF: 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 REF: 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:- REF: SOLVEPNP_P3P Method is based on the paper of Ding, Y., Yang, J., Larsson, V., Olsson, C., & Åstrom, K. "Revisiting the P3P Problem" (CITE: ding2023revisiting).
- REF: SOLVEPNP_AP3P Method is based on the paper of T. Ke and S. Roumeliotis. "An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (CITE: Ke17).
- Returns:
- automatically generated
-
solvePnPRefineLM
public 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. SEE: REF: 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 REF: 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 CITE: Madsen04 CITE: Eade13 process.
-
solvePnPRefineLM
public 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. SEE: REF: 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 REF: 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. 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 CITE: Madsen04 CITE: Eade13 process.
-
solvePnPRefineVVS
public 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. SEE: REF: 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 REF: 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) CITE: Chaumette06 CITE: Marchand16 scheme.
-
solvePnPRefineVVS
public 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. SEE: REF: 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 REF: 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. 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) CITE: Chaumette06 CITE: Marchand16 scheme.
-
solvePnPRefineVVS
public 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. SEE: REF: 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 REF: 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. 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) CITE: Chaumette06 CITE: Marchand16 scheme.
-
solvePnPGeneric
public static int solvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess, int flags, Mat rvec, Mat tvec, Mat reprojectionError) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences. { width=50% } SEE: REF: 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
- REF: SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
-
REF: 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 REF: 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 REF: calib3d_solvePnP_flagsrvec- Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is REF: SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true.tvec- Translation vector used to initialize an iterative PnP refinement algorithm, when flag is REF: 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 REF: 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 REF: SOLVEPNP_P3P and REF: 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 REF: 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 REF: SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
-
With REF: 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 REF: SOLVEPNP_SQPNP input points must be >= 3
- Returns:
- automatically generated
-
solvePnPGeneric
public static int solvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess, int flags, Mat rvec, Mat tvec) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences. { width=50% } SEE: REF: 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
- REF: SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
-
REF: 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 REF: 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 REF: calib3d_solvePnP_flagsrvec- Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is REF: SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true.tvec- Translation vector used to initialize an iterative PnP refinement algorithm, when flag is REF: SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true. (\( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points and the 3D object points projected with the estimated pose. More information is described in REF: 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 REF: SOLVEPNP_P3P and REF: 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 REF: 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 REF: SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
-
With REF: 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 REF: SOLVEPNP_SQPNP input points must be >= 3
- Returns:
- automatically generated
-
solvePnPGeneric
public static int solvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess, int flags, Mat rvec) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences. { width=50% } SEE: REF: 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
- REF: SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
-
REF: 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 REF: 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 REF: calib3d_solvePnP_flagsrvec- Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is REF: SOLVEPNP_ITERATIVE and useExtrinsicGuess is set to true. and useExtrinsicGuess is set to true. (\( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points and the 3D object points projected with the estimated pose. More information is described in REF: 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 REF: SOLVEPNP_P3P and REF: 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 REF: 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 REF: SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
-
With REF: 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 REF: SOLVEPNP_SQPNP input points must be >= 3
- Returns:
- automatically generated
-
solvePnPGeneric
public static int solvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess, int flags) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences. { width=50% } SEE: REF: 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
- REF: SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
-
REF: 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 REF: 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 REF: calib3d_solvePnP_flags and useExtrinsicGuess is set to true. and useExtrinsicGuess is set to true. (\( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points and the 3D object points projected with the estimated pose. More information is described in REF: 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 REF: SOLVEPNP_P3P and REF: 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 REF: 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 REF: SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
-
With REF: 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 REF: SOLVEPNP_SQPNP input points must be >= 3
- Returns:
- automatically generated
-
solvePnPGeneric
public static int solvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess) Finds an object pose \( {}^{c}\mathbf{T}_o \) from 3D-2D point correspondences. { width=50% } SEE: REF: 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
- REF: SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
-
REF: 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 REF: 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. and useExtrinsicGuess is set to true. and useExtrinsicGuess is set to true. (\( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points and the 3D object points projected with the estimated pose. More information is described in REF: 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 REF: SOLVEPNP_P3P and REF: 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 REF: 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 REF: SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
-
With REF: 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 REF: SOLVEPNP_SQPNP input points must be >= 3
- Returns:
- automatically generated
-
solvePnPGeneric
public 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. { width=50% } SEE: REF: 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
- REF: SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
-
REF: 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 REF: Rodrigues ) that, together with tvecs, brings points from the model coordinate system to the camera coordinate system.tvecs- Vector of output translation vectors. the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them. and useExtrinsicGuess is set to true. and useExtrinsicGuess is set to true. (\( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points and the 3D object points projected with the estimated pose. More information is described in REF: 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 REF: SOLVEPNP_P3P and REF: 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 REF: 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 REF: SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
-
With REF: 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 REF: SOLVEPNP_SQPNP input points must be >= 3
- Returns:
- automatically generated
-
convertPointsToHomogeneous
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
Converts points from Euclidean to homogeneous space.- Parameters:
src- Input vector of N-dimensional points.dst- Output vector of N+1-dimensional points. 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).
-
convertPointsFromHomogeneous
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
Converts points from homogeneous to Euclidean space.- Parameters:
src- Input vector of N-dimensional points.dst- Output vector of N-1-dimensional points. 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,...).
-
findFundamentalMat
public 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.- 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.- REF: FM_7POINT for a 7-point algorithm. \(N = 7\)
- REF: FM_8POINT for an 8-point algorithm. \(N \ge 8\)
- REF: FM_RANSAC for the RANSAC algorithm. \(N \ge 8\)
- REF: FM_LMEDS for the LMedS algorithm. \(N \ge 8\)
ransacReprojThreshold- Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.confidence- Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.maxIters- The maximum number of robust method iterations. The epipolar geometry is described by the following equation: \([p_2; 1]^T F [p_1; 1] = 0\) where \(F\) is a fundamental matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The function calculates the fundamental matrix using one of four methods listed above and returns the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point algorithm, the function may return up to 3 solutions ( \(9 \times 3\) matrix that stores all 3 matrices sequentially). The calculated fundamental matrix may be passed further to #computeCorrespondEpilines that finds the epipolar lines corresponding to the specified points. It can also be passed to #stereoRectifyUncalibrated to compute the rectification transformation. :// Example. Estimation of fundamental matrix using the RANSAC algorithm int point_count = 100; vector<Point2f> points1(point_count); vector<Point2f> points2(point_count); // initialize the points here ... for( int i = 0; i < point_count; i++ ) { points1[i] = ...; points2[i] = ...; } Mat fundamental_matrix = findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);mask- optional output mask- Returns:
- automatically generated
-
findFundamentalMat
public 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.- 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.- REF: FM_7POINT for a 7-point algorithm. \(N = 7\)
- REF: FM_8POINT for an 8-point algorithm. \(N \ge 8\)
- REF: FM_RANSAC for the RANSAC algorithm. \(N \ge 8\)
- REF: FM_LMEDS for the LMedS algorithm. \(N \ge 8\)
ransacReprojThreshold- Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.confidence- Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.maxIters- The maximum number of robust method iterations. The epipolar geometry is described by the following equation: \([p_2; 1]^T F [p_1; 1] = 0\) where \(F\) is a fundamental matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The function calculates the fundamental matrix using one of four methods listed above and returns the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point algorithm, the function may return up to 3 solutions ( \(9 \times 3\) matrix that stores all 3 matrices sequentially). The calculated fundamental matrix may be passed further to #computeCorrespondEpilines that finds the epipolar lines corresponding to the specified points. It can also be passed to #stereoRectifyUncalibrated to compute the rectification transformation. :// Example. Estimation of fundamental matrix using the RANSAC algorithm int point_count = 100; vector<Point2f> points1(point_count); vector<Point2f> points2(point_count); // initialize the points here ... for( int i = 0; i < point_count; i++ ) { points1[i] = ...; points2[i] = ...; } Mat fundamental_matrix = findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);- Returns:
- automatically generated
-
findFundamentalMat
public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, Mat mask) -
findFundamentalMat
public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence) -
findFundamentalMat
public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold) -
findFundamentalMat
-
findFundamentalMat
-
findFundamentalMat
public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, Mat mask, UsacParams params) -
findEssentialMat
public 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.- 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 withP = 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.- REF: RANSAC for the RANSAC algorithm.
- REF: 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.maxIters- The maximum number of robust method iterations. This function estimates essential matrix based on the five-point algorithm solver in CITE: Nister03 . CITE: 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.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.- Returns:
- automatically generated
-
findEssentialMat
public 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.- 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 withP = 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.- REF: RANSAC for the RANSAC algorithm.
- REF: 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. 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 CITE: Nister03 . CITE: 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.- Returns:
- automatically generated
-
findEssentialMat
public 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.- 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 withP = 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.- REF: RANSAC for the RANSAC algorithm.
- REF: 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. 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 CITE: Nister03 . CITE: 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.- Returns:
- automatically generated
-
findEssentialMat
public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix, int method, double prob) 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 withP = 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.- REF: RANSAC for the RANSAC algorithm.
- REF: 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. 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. 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 CITE: Nister03 . CITE: 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.- Returns:
- automatically generated
-
findEssentialMat
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 withP = 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.- REF: RANSAC for the RANSAC algorithm.
- REF: LMEDS for the LMedS algorithm.
- Returns:
- automatically generated
-
findEssentialMat
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 withP = 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.- REF: RANSAC for the RANSAC algorithm.
- REF: LMEDS for the LMedS algorithm.
- Returns:
- automatically generated
-
findEssentialMat
public static Mat findEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold, int maxIters, Mat mask) - 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.- REF: RANSAC for the RANSAC algorithm.
- REF: 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.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: \(A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\)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.- Returns:
- automatically generated
-
findEssentialMat
public static Mat findEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold, int maxIters) - 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.- REF: RANSAC for the RANSAC algorithm.
- REF: 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. for the other points. The array is computed only in the RANSAC and LMedS methods.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.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: \(A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\)- Returns:
- automatically generated
-
findEssentialMat
public static Mat findEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold) - 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.- REF: RANSAC for the RANSAC algorithm.
- REF: 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. for the other points. The array is computed only in the RANSAC and LMedS methods. This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point: \(A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\)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.- Returns:
- automatically generated
-
findEssentialMat
public static Mat findEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method, double prob) - 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.- REF: RANSAC for the RANSAC algorithm.
- REF: 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. for the other points. The array is computed only in the RANSAC and LMedS methods. This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point: \(A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\)- Returns:
- automatically generated
-
findEssentialMat
- 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.- REF: RANSAC for the RANSAC algorithm.
- REF: LMEDS for the LMedS algorithm.
- Returns:
- automatically generated
-
findEssentialMat
- 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.- REF: RANSAC for the RANSAC algorithm.
- REF: LMEDS for the LMedS algorithm.
- Returns:
- automatically generated
-
findEssentialMat
- 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.- REF: RANSAC for the RANSAC algorithm.
- REF: LMEDS for the LMedS algorithm.
- Returns:
- automatically generated
-
findEssentialMat
- 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 . are feature points from cameras with same focal length and principal point.- REF: RANSAC for the RANSAC algorithm.
- REF: LMEDS for the LMedS algorithm.
- Returns:
- automatically generated
-
findEssentialMat
public 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.- 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 \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .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.cameraMatrix2- Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .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.- REF: RANSAC for the RANSAC algorithm.
- REF: 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 CITE: Nister03 . CITE: 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.- Returns:
- automatically generated
-
findEssentialMat
public 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.- 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 \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .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.cameraMatrix2- Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .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.- REF: RANSAC for the RANSAC algorithm.
- REF: 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. 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 CITE: Nister03 . CITE: 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.- Returns:
- automatically generated
-
findEssentialMat
public 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.- 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 \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .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.cameraMatrix2- Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .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.- REF: RANSAC for the RANSAC algorithm.
- REF: 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. 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. 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 CITE: Nister03 . CITE: 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.- Returns:
- automatically generated
-
findEssentialMat
public 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.- 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 \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .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.cameraMatrix2- Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .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.- REF: RANSAC for the RANSAC algorithm.
- REF: LMEDS for the LMedS algorithm.
- Returns:
- automatically generated
-
findEssentialMat
public 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.- 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 \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .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.cameraMatrix2- Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .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.- REF: RANSAC for the RANSAC algorithm.
- REF: LMEDS for the LMedS algorithm.
- Returns:
- automatically generated
-
findEssentialMat
-
decomposeEssentialMat
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 CITE: 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.
-
recoverPose
public 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.- 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 REF: calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.distCoeffs1- Input/output vector of distortion coefficients, the same as in REF: calibrateCamera.cameraMatrix2- Input/output camera matrix for the first camera, the same as in REF: calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.distCoeffs2- Input/output vector of distortion coefficients, the same as in REF: 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 REF: 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.- REF: RANSAC for the RANSAC algorithm.
- REF: 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 REF: 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 CITE: Nister03. This function can be used to process the output E and mask from REF: 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);- Returns:
- automatically generated
-
recoverPose
public 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.- 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 REF: calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.distCoeffs1- Input/output vector of distortion coefficients, the same as in REF: calibrateCamera.cameraMatrix2- Input/output camera matrix for the first camera, the same as in REF: calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.distCoeffs2- Input/output vector of distortion coefficients, the same as in REF: 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 REF: 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.- REF: RANSAC for the RANSAC algorithm.
- REF: 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. 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 REF: 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 CITE: Nister03. This function can be used to process the output E and mask from REF: 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);- Returns:
- automatically generated
-
recoverPose
public 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.- 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 REF: calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.distCoeffs1- Input/output vector of distortion coefficients, the same as in REF: calibrateCamera.cameraMatrix2- Input/output camera matrix for the first camera, the same as in REF: calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.distCoeffs2- Input/output vector of distortion coefficients, the same as in REF: 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 REF: 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.- REF: RANSAC for the RANSAC algorithm.
- REF: 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. 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. 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 REF: 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 CITE: Nister03. This function can be used to process the output E and mask from REF: 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);- Returns:
- automatically generated
-
recoverPose
public 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.- 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 REF: calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.distCoeffs1- Input/output vector of distortion coefficients, the same as in REF: calibrateCamera.cameraMatrix2- Input/output camera matrix for the first camera, the same as in REF: calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.distCoeffs2- Input/output vector of distortion coefficients, the same as in REF: 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 REF: 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.- REF: RANSAC for the RANSAC algorithm.
- REF: LMEDS for the LMedS algorithm.
// 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);- Returns:
- automatically generated
-
recoverPose
public 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.- 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 REF: calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.distCoeffs1- Input/output vector of distortion coefficients, the same as in REF: calibrateCamera.cameraMatrix2- Input/output camera matrix for the first camera, the same as in REF: calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.distCoeffs2- Input/output vector of distortion coefficients, the same as in REF: 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 REF: decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.- REF: RANSAC for the RANSAC algorithm.
- REF: LMEDS for the LMedS algorithm.
// 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);- Returns:
- automatically generated
-
recoverPose
public 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.- 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 REF: 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 REF: 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 CITE: Nister03. This function can be used to process the output E and mask from REF: 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);- Returns:
- automatically generated
-
recoverPose
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 REF: decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length. 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 REF: 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 CITE: Nister03. This function can be used to process the output E and mask from REF: 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);- Returns:
- automatically generated
-
recoverPose
public static int recoverPose(Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, Point pp, Mat mask) - 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 REF: 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: \(A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\)- Returns:
- automatically generated
-
recoverPose
public static int recoverPose(Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, Point pp) - 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 REF: 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. 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: \(A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\)- Returns:
- automatically generated
-
recoverPose
- 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 REF: 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. 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: \(A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\)- Returns:
- automatically generated
-
recoverPose
- 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 REF: decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length. are feature points from cameras with same focal length and principal point. 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: \(A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\)- Returns:
- automatically generated
-
recoverPose
public static int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh, Mat mask, Mat triangulatedPoints) - 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 REF: 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.- Returns:
- automatically generated
-
recoverPose
public static int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh, Mat mask) - 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 REF: 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. This function differs from the one above that it outputs the triangulated 3D point that are used for the chirality check.- Returns:
- automatically generated
-
recoverPose
public static int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh) - 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 REF: 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). 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 outputs the triangulated 3D point that are used for the chirality check.- Returns:
- automatically generated
-
computeCorrespondEpilines
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 \(a_i^2+b_i^2=1\) .
-
triangulatePoints
public 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.- 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. Note: If the projection matrices from REF: stereoRectify are used, then the returned points are represented in the first camera's rectified coordinate system. SEE: reprojectImageTo3D
-
correctMatches
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 CITE: 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 \(newPoints2^T \cdot F \cdot newPoints1 = 0\) .
-
sampsonDistance
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 CITE: HartleyZ00 11.4.3 for details.- Parameters:
pt1- first homogeneous 2d pointpt2- second homogeneous 2d pointF- fundamental matrix- Returns:
- The computed Sampson distance.
-
estimateAffine3D
public static boolean estimateAffine3D(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold, double confidence) 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
public static boolean estimateAffine3D(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold) 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. 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
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). an inlier. 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
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 \cite 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
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 \cite 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. 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
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 \cite 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. Else the pointed-to variable will be set to the optimal scale. 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 \(T = \begin{bmatrix} R & t\\ \end{bmatrix} \)
-
estimateTranslation3D
public static boolean estimateTranslation3D(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold, double confidence) 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
public static boolean estimateTranslation3D(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold) 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. 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
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). an inlier. 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.
-
estimateAffine2D
public 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. 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:- REF: RANSAC - RANSAC-based robust method
- REF: 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: estimateAffinePartial2D, getAffineTransform
-
estimateAffine2D
public 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. 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:- REF: RANSAC - RANSAC-based robust method
- REF: 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. 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: estimateAffinePartial2D, getAffineTransform
-
estimateAffine2D
public 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. 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:- REF: RANSAC - RANSAC-based robust method
- REF: 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. 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. 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: estimateAffinePartial2D, getAffineTransform
-
estimateAffine2D
public static Mat estimateAffine2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold) 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:- REF: RANSAC - RANSAC-based robust method
- REF: 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. 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. 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: estimateAffinePartial2D, getAffineTransform
-
estimateAffine2D
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:- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method RANSAC is the default 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: estimateAffinePartial2D, getAffineTransform
-
estimateAffine2D
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).- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method RANSAC is the default 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: estimateAffinePartial2D, getAffineTransform
-
estimateAffine2D
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 \((x,y)\).- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method RANSAC is the default 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: estimateAffinePartial2D, getAffineTransform
-
estimateAffine2D
-
estimateAffinePartial2D
public 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.- 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:- REF: RANSAC - RANSAC-based robust method
- REF: 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: estimateAffine2D, getAffineTransform
-
estimateAffinePartial2D
public 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.- 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:- REF: RANSAC - RANSAC-based robust method
- REF: 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. 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: estimateAffine2D, getAffineTransform
-
estimateAffinePartial2D
public 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.- 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:- REF: RANSAC - RANSAC-based robust method
- REF: 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. 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. 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: estimateAffine2D, getAffineTransform
-
estimateAffinePartial2D
public 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.- 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:- REF: RANSAC - RANSAC-based robust method
- REF: 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. 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. 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: estimateAffine2D, getAffineTransform
-
estimateAffinePartial2D
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:- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method RANSAC is the default 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: estimateAffine2D, getAffineTransform
-
estimateAffinePartial2D
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.- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method RANSAC is the default 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: estimateAffine2D, getAffineTransform
-
estimateAffinePartial2D
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.- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method RANSAC is the default 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: estimateAffine2D, getAffineTransform
-
estimateTranslation2D
public 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. 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:- REF: RANSAC - RANSAC-based robust method
- REF: 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, ifinliersis provided, the mask is filled with zeros. \par 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: estimateAffine2D, estimateAffinePartial2D, getAffineTransform
-
estimateTranslation2D
public 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. 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:- REF: RANSAC - RANSAC-based robust method
- REF: 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. 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, ifinliersis provided, the mask is filled with zeros. \par 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: estimateAffine2D, estimateAffinePartial2D, getAffineTransform
-
estimateTranslation2D
public 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. 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:- REF: RANSAC - RANSAC-based robust method
- REF: 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. between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8–0.9 can result in an incorrectly estimated transformation. the 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, ifinliersis provided, the mask is filled with zeros. \par 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: estimateAffine2D, estimateAffinePartial2D, getAffineTransform
-
estimateTranslation2D
public static double[] estimateTranslation2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold) 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:- REF: RANSAC - RANSAC-based robust method
- REF: 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. between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8–0.9 can result in an incorrectly estimated transformation. the 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, ifinliersis provided, the mask is filled with zeros. \par 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: estimateAffine2D, estimateAffinePartial2D, getAffineTransform
-
estimateTranslation2D
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:- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method RANSAC is the default method.
- 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, ifinliersis provided, the mask is filled with zeros. \par 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: estimateAffine2D, estimateAffinePartial2D, getAffineTransform
-
estimateTranslation2D
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).- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method RANSAC is the default method.
- 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, ifinliersis provided, the mask is filled with zeros. \par 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: estimateAffine2D, estimateAffinePartial2D, getAffineTransform
-
estimateTranslation2D
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 \((x,y)\).- REF: RANSAC - RANSAC-based robust method
- REF: LMEDS - Least-Median robust method RANSAC is the default method.
- 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, ifinliersis provided, the mask is filled with zeros. \par 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: estimateAffine2D, estimateAffinePartial2D, getAffineTransform
-
decomposeHomographyMat
public 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).- 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 CITE: 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.- Returns:
- automatically generated
-
filterHomographyDecompByVisibleRefpoints
public static void filterHomographyDecompByVisibleRefpoints(List<Mat> rotations, List<Mat> normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions, Mat pointsMask) 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 appliedafterPoints- Vector of (rectified) visible reference points after the homography is appliedpossibleSolutions- Vector of int indices representing the viable solution set after filteringpointsMask- 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 CITE: 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
public static void filterHomographyDecompByVisibleRefpoints(List<Mat> rotations, List<Mat> normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions) 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 appliedafterPoints- Vector of (rectified) visible reference points after the homography is appliedpossibleSolutions- Vector of int indices representing the viable solution set after filtering as given by the #findHomography function This function is intended to filter the output of the #decomposeHomographyMat based on additional information as described in CITE: 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.
-
calibrationMatrixValues
public 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.- 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).
-
getDefaultNewCameraMatrix
public static Mat getDefaultNewCameraMatrix(Mat cameraMatrix, Size imgsize, boolean centerPrincipalPoint) 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.- Returns:
- automatically generated
-
getDefaultNewCameraMatrix
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. parameter indicates whether this location should be at the image center or not.- Returns:
- automatically generated
-
getDefaultNewCameraMatrix
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. parameter indicates whether this location should be at the image center or not.- Returns:
- automatically generated
-
getOptimalNewCameraMatrix
public static Mat getOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize, Rect validPixROI, boolean centerPrincipalPoint) 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
public 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.- 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 . 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
public 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.- 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 . undistorted image. See roi1, roi2 description in #stereoRectify . 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
public static Mat getOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha) 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. undistorted image. See roi1, roi2 description in #stereoRectify . 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 .
-
undistortPoints
public 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. 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 parameterP:-
If
Pis provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrixP. -
If
Pis 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.
-
If
- 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
-
Input (
-
undistortPoints
public 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. 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 parameterP:-
If
Pis provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrixP. -
If
Pis 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.
-
If
- 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.
-
Input (
-
undistortPoints
public static void undistortPoints(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R) 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 parameterP:-
If
Pis provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrixP. -
If
Pis 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.
-
If
- 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. #stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used and output will be in normalized coordinates.
-
Input (
-
undistortPoints
public static void undistortPoints(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs) 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 parameterP:-
If
Pis provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrixP. -
If
Pis 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.
-
If
- 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. #stereoRectify can be passed here. If the matrix is empty, the identity transformation is used. #stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used and output will be in normalized coordinates.
-
Input (
-
undistortImagePoints
public static void undistortImagePoints(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, TermCriteria arg1) 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 coefficientsarg1- automatically generated
-
undistortImagePoints
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
-
fisheye_projectPoints
-
fisheye_projectPoints
-
fisheye_projectPoints
-
fisheye_distortPoints
public static void fisheye_distortPoints(Mat undistorted, Mat distorted, Mat K, Mat D, double alpha) 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.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.K- Camera intrinsic matrix \(cameramatrix{K}\).D- Input vector of distortion coefficients \(\distcoeffsfisheye\).alpha- The skew coefficient.
-
fisheye_distortPoints
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.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.K- Camera intrinsic matrix \(cameramatrix{K}\).D- Input vector of distortion coefficients \(\distcoeffsfisheye\).
-
fisheye_distortPoints
public static void fisheye_distortPoints(Mat undistorted, Mat distorted, Mat Kundistorted, Mat K, Mat D, double alpha) 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.distorted- Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> . SEE: estimateNewCameraMatrixForUndistortRectifyKundistorted- 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.
-
fisheye_distortPoints
public static void fisheye_distortPoints(Mat undistorted, Mat distorted, Mat Kundistorted, Mat K, Mat D) 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.distorted- Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> . SEE: estimateNewCameraMatrixForUndistortRectifyKundistorted- Camera intrinsic matrix used as new camera matrix for undistortion.K- Camera intrinsic matrix \(cameramatrix{K}\).D- Input vector of distortion coefficients \(\distcoeffsfisheye\).
-
fisheye_undistortPoints
public 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 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 parameterP:-
If
Pis provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrixP. -
If
Pis 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.
-
If
- 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.undistorted- Output array of undistorted image points, 1xN/Nx1 2-channel, or vector<Point2f> . The coordinate system depends on parameter P (see above).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.
-
Input (
-
fisheye_undistortPoints
public static void fisheye_undistortPoints(Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P) 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 parameterP:-
If
Pis provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrixP. -
If
Pis 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.
-
If
- 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.undistorted- Output array of undistorted image points, 1xN/Nx1 2-channel, or vector<Point2f> . The coordinate system depends on parameter P (see above).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.
-
Input (
-
fisheye_undistortPoints
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 parameterP:-
If
Pis provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrixP. -
If
Pis 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.
-
If
- 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.undistorted- Output array of undistorted image points, 1xN/Nx1 2-channel, or vector<Point2f> . The coordinate system depends on parameter P (see above).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. output will be in normalized camera coordinates.
-
Input (
-
fisheye_undistortPoints
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 parameterP:-
If
Pis provided (not empty): Output points are in pixel coordinates of the rectified/undistorted image plane, using the camera matrixP. -
If
Pis 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.
-
If
- 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.undistorted- Output array of undistorted image points, 1xN/Nx1 2-channel, or vector<Point2f> . The coordinate system depends on parameter P (see above).K- Camera intrinsic matrix \(\cameramatrix{K}\) of the fisheye camera.D- Input vector of fisheye distortion coefficients \(\distcoeffsfisheye\) (must contain exactly 4 coefficients). 1-channel or 1x1 3-channel. If empty, the identity transformation is used. output will be in normalized camera coordinates.
-
Input (
-
fisheye_estimateNewCameraMatrixForUndistortRectify
public 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.- Parameters:
K- Camera intrinsic matrix \(cameramatrix{K}\).D- Input vector of distortion coefficients \(\distcoeffsfisheye\).image_size- Size of the imageR- Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channelP- 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 sizefov_scale- Divisor for new focal length.
-
fisheye_estimateNewCameraMatrixForUndistortRectify
public 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.- Parameters:
K- Camera intrinsic matrix \(cameramatrix{K}\).D- Input vector of distortion coefficients \(\distcoeffsfisheye\).image_size- Size of the imageR- Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channelP- 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
-
fisheye_estimateNewCameraMatrixForUndistortRectify
public 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.- Parameters:
K- Camera intrinsic matrix \(cameramatrix{K}\).D- Input vector of distortion coefficients \(\distcoeffsfisheye\).image_size- Size of the imageR- Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channelP- 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].
-
fisheye_estimateNewCameraMatrixForUndistortRectify
public static void fisheye_estimateNewCameraMatrixForUndistortRectify(Mat K, Mat D, Size image_size, Mat R, Mat P) Estimates new camera intrinsic matrix for undistortion or rectification.- Parameters:
K- Camera intrinsic matrix \(cameramatrix{K}\).D- Input vector of distortion coefficients \(\distcoeffsfisheye\).image_size- Size of the imageR- Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channelP- New camera intrinsic matrix (3x3) or new projection matrix (3x4) length. Balance is in range of [0, 1].
-
fisheye_solvePnP
public static boolean fisheye_solvePnP(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int flags, TermCriteria criteria) 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 REF: 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 REF: 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): need 4 input points to return a unique solution.
- REF: SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
- REF: 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 REF: undistortPoints and call REF: cv::solvePnP, thus the input are very similar. Check there and Perspective-n-Points is described in REF: calib3d_solvePnP for more information.- Returns:
- automatically generated
-
fisheye_solvePnP
public static boolean fisheye_solvePnP(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int flags) 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 REF: 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 REF: 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): need 4 input points to return a unique solution.
- REF: SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
- REF: 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.
- Returns:
- automatically generated
-
fisheye_solvePnP
public static boolean fisheye_solvePnP(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess) 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 REF: 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. 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): need 4 input points to return a unique solution.
- REF: SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
- REF: 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.
- Returns:
- automatically generated
-
fisheye_solvePnP
public static boolean 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.- 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 REF: Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.tvec- Output translation vector. the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them. 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 (REF: SOLVEPNP_P3P, REF: SOLVEPNP_AP3P): need 4 input points to return a unique solution.
- REF: SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
- REF: 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.
- Returns:
- automatically generated
-
fisheye_solvePnPRansac
public static boolean fisheye_solvePnPRansac(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean 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.- 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 REF: 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 REF: calib3d_solvePnP_flagscriteria- Termination criteria for internal undistortPoints call. The function interally undistorts points with REF: undistortPoints and call REF: cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in REF: calib3d_solvePnP for more information.- Returns:
- automatically generated
-
fisheye_solvePnPRansac
public static boolean fisheye_solvePnPRansac(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean 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.- 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 REF: 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 REF: calib3d_solvePnP_flags The function interally undistorts points with REF: undistortPoints and call REF: cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in REF: calib3d_solvePnP for more information.- Returns:
- automatically generated
-
fisheye_solvePnPRansac
public static boolean fisheye_solvePnPRansac(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean 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.- 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 REF: 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 . The function interally undistorts points with REF: undistortPoints and call REF: cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in REF: calib3d_solvePnP for more information.- Returns:
- automatically generated
-
fisheye_solvePnPRansac
public static boolean fisheye_solvePnPRansac(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence) 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 REF: 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. The function interally undistorts points with REF: undistortPoints and call REF: cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in REF: calib3d_solvePnP for more information.- Returns:
- automatically generated
-
fisheye_solvePnPRansac
public static boolean fisheye_solvePnPRansac(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int iterationsCount, float reprojectionError) 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 REF: 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. The function interally undistorts points with REF: undistortPoints and call REF: cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in REF: calib3d_solvePnP for more information.- Returns:
- automatically generated
-
fisheye_solvePnPRansac
public static boolean fisheye_solvePnPRansac(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int iterationsCount) 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 REF: 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. is the maximum allowed distance between the observed and computed point projections to consider it an inlier. The function interally undistorts points with REF: undistortPoints and call REF: cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in REF: calib3d_solvePnP for more information.- Returns:
- automatically generated
-
fisheye_solvePnPRansac
public static boolean fisheye_solvePnPRansac(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess) 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 REF: 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. is the maximum allowed distance between the observed and computed point projections to consider it an inlier. The function interally undistorts points with REF: undistortPoints and call REF: cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in REF: calib3d_solvePnP for more information.- Returns:
- automatically generated
-
fisheye_solvePnPRansac
public static boolean 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.- 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 REF: Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.tvec- Output translation vector. the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them. is the maximum allowed distance between the observed and computed point projections to consider it an inlier. The function interally undistorts points with REF: undistortPoints and call REF: cv::solvePnP, thus the input are very similar. More information about Perspective-n-Points is described in REF: calib3d_solvePnP for more information.- Returns:
- automatically generated
-
approxPolyDP
public static void approxPolyDP(MatOfPoint2f curve, MatOfPoint2f approxCurve, double epsilon, boolean closed) 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>- Parameters:
curve- Input vector of a 2D point stored in std::vector or MatapproxCurve- 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
public static void approxPolyN(Mat curve, Mat approxCurve, int nsides, float epsilon_percentage, boolean ensure_convex) 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 CITE: 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
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 CITE: 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.
-
approxPolyN
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 CITE: 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. 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.
-
minAreaRect
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- Returns:
- automatically generated
-
boxPoints
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 REF: tutorial_bounding_rotated_ellipses "tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information.- Parameters:
box- The input rotated rectangle. It may be the output of REF: minAreaRect.points- The output array of four vertices of rectangles.
-
minEnclosingCircle
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 Matcenter- Output center of the circle.radius- Output radius of the circle.
-
minEnclosingTriangle
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*.  The implementation of the algorithm is based on O'Rourke's CITE: ORourke86 and Klee and Laskowski's CITE: 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 Mattriangle- Output vector of three 2D points defining the vertices of the triangle. The depth of the OutputArray must be CV_32F.- Returns:
- automatically generated
-
minEnclosingConvexPolygon
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 CITE: 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 Matpolygon- Output vector of 2D points defining the vertices of the enclosing polygonk- Number of vertices of the output polygon- Returns:
- automatically generated
-
moments
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. Note: For contour-based moments, the zeroth-order moment \c 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 \c m00 == 0. In this case, the centroid coordinates (\c m10/m00, \c 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: contourArea, arcLength
-
moments
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). 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. Note: For contour-based moments, the zeroth-order moment \c 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 \c m00 == 0. In this case, the centroid coordinates (\c m10/m00, \c 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: contourArea, arcLength
-
HuMoments
-
matchShapes
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 #ShapeMatchModesparameter- Method-specific parameter (not supported now).- Returns:
- automatically generated
-
convexHull
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 CITE: 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. 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:pointsandhullshould be different arrays, inplace processing isn't supported. Check REF: tutorial_hull "the corresponding tutorial" for more details. useful links: https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/
-
convexHull
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 CITE: 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. Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing to the right, and its Y axis pointing upwards. 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:pointsandhullshould be different arrays, inplace processing isn't supported. Check REF: tutorial_hull "the corresponding tutorial" for more details. useful links: https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/
-
convexityDefects
public static void convexityDefects(MatOfPoint contour, MatOfInt convexhull, MatOfInt4 convexityDefects) 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.
-
isContourConvex
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- Returns:
- automatically generated
-
intersectConvexConvex
Finds intersection of two convex polygons- Parameters:
p1- First polygonp2- Second polygonp12- Output polygon describing the intersecting areahandleNested- 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
Finds intersection of two convex polygons- Parameters:
p1- First polygonp2- Second polygonp12- Output polygon describing the intersecting area 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.
-
fitEllipse
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 CITE: 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 REF: Point2i or REF: Point2f and at least 5 points are required. Note: REF: getClosestEllipsePoints function can be used to compute the ellipse fitting error.- Returns:
- automatically generated
-
fitEllipseAMS
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 CITE: 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: \(align*}{ D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} & D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} & D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\} \) The AMS method minimizes the cost function \(equation*}{ \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } \) The minimum cost is found by solving the generalized eigenvalue problem. \(equation*}{ D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A \)- Parameters:
points- Input 2D point set, stored in std::vector<> or Mat Note: Input point types are REF: Point2i or REF: Point2f and at least 5 points are required. Note: REF: getClosestEllipsePoints function can be used to compute the ellipse fitting error.- Returns:
- automatically generated
-
fitEllipseDirect
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 CITE: 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. \(equation*}{ \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix} 0 & 0 & 2 & 0 & 0 & 0 \\ 0 & -1 & 0 & 0 & 0 & 0 \\ 2 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \end{matrix} \right) \) The minimum cost is found by solving the generalized eigenvalue problem. \(equation*}{ D^T D A = \lambda \left( C\right) A \) 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 \(equation*}{ A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} \) 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 REF: Point2i or REF: Point2f and at least 5 points are required. Note: REF: getClosestEllipsePoints function can be used to compute the ellipse fitting error.- Returns:
- automatically generated
-
getClosestEllipsePoints
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 CITE: Chatfield2017 code. This function can be used to compute for instance the ellipse fitting error.- Parameters:
ellipse_params- Ellipse parameterspoints- Input 2d pointsclosest_pts- For each 2d point, their corresponding closest 2d point located on a given ellipse Note: Input point types are REF: Point2i or REF: Point2f SEE: fitEllipse, fitEllipseAMS, fitEllipseDirect
-
fitLine
public 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. 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> ) 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 #DistanceTypesparam- 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.
-
pointPolygonTest
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.- Returns:
- automatically generated
-
rotatedRectangleIntersection
public static int rotatedRectangleIntersection(RotatedRect rect1, RotatedRect rect2, Mat intersectingRegion) 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 rectanglerect2- Second rectangleintersectingRegion- 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
-
arcLength
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.- Returns:
- automatically generated
-
contourArea
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.- Returns:
- automatically generated
-
contourArea
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. 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.- Returns:
- automatically generated
-
boundingRect
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.- Returns:
- automatically generated
-
getRotationMatrix2D
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: getAffineTransform, warpAffine, transform- Returns:
- automatically generated
-
invertAffineTransform
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.
-
getPerspectiveTransform
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: findHomography, warpPerspective, perspectiveTransform- Returns:
- automatically generated
-
getPerspectiveTransform
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. SEE: findHomography, warpPerspective, perspectiveTransform- Returns:
- automatically generated
-
getAffineTransform
-