Package org.opencv.cv3d

## Class Cv3d

- java.lang.Object
- - org.opencv.cv3d.Cv3d

-     public class Cv3d
      extends java.lang.Object

- - ### Field Summary

    Fields
    Modifier and Type | Field | Description
    --- | --- | ---
    `static int` | `COV_POLISHER` |
    `static int` | `FM_7POINT` |
    `static int` | `FM_8POINT` |
    `static int` | `FM_LMEDS` |
    `static int` | `FM_RANSAC` |
    `static int` | `LMEDS` |
    `static int` | `LOCAL_OPTIM_GC` |
    `static int` | `LOCAL_OPTIM_INNER_AND_ITER_LO` |
    `static int` | `LOCAL_OPTIM_INNER_LO` |
    `static int` | `LOCAL_OPTIM_NULL` |
    `static int` | `LOCAL_OPTIM_SIGMA` |
    `static int` | `LSQ_POLISHER` |
    `static int` | `MAGSAC` |
    `static int` | `MatrixType_AUTO` |
    `static int` | `MatrixType_DENSE` |
    `static int` | `MatrixType_SPARSE` |
    `static int` | `N_PYRAMIDS` |
    `static int` | `NEIGH_FLANN_KNN` |
    `static int` | `NEIGH_FLANN_RADIUS` |
    `static int` | `NEIGH_GRID` |
    `static int` | `NONE_POLISHER` |
    `static int` | `OdometryAlgoType_COMMON` |
    `static int` | `OdometryAlgoType_FAST` |
    `static int` | `OdometryType_DEPTH` |
    `static int` | `OdometryType_RGB` |
    `static int` | `OdometryType_RGB_DEPTH` |
    `static int` | `PROJ_SPHERICAL_EQRECT` |
    `static int` | `PROJ_SPHERICAL_ORTHO` |
    `static int` | `PYR_CLOUD` |
    `static int` | `PYR_DEPTH` |
    `static int` | `PYR_DIX` |
    `static int` | `PYR_DIY` |
    `static int` | `PYR_IMAGE` |
    `static int` | `PYR_MASK` |
    `static int` | `PYR_NORM` |
    `static int` | `PYR_NORMMASK` |
    `static int` | `PYR_TEXMASK` |
    `static int` | `RANSAC` |
    `static int` | `RASTERIZE_COMPAT_DISABLED` |
    `static int` | `RASTERIZE_COMPAT_INVDEPTH` |
    `static int` | `RASTERIZE_CULLING_CCW` |
    `static int` | `RASTERIZE_CULLING_CW` |
    `static int` | `RASTERIZE_CULLING_NONE` |
    `static int` | `RASTERIZE_SHADING_FLAT` |
    `static int` | `RASTERIZE_SHADING_SHADED` |
    `static int` | `RASTERIZE_SHADING_WHITE` |
    `static int` | `RGBD_PLANE_METHOD_DEFAULT` |
    `static int` | `RHO` |
    `static int` | `SAC_METHOD_RANSAC` |
    `static int` | `SAC_MODEL_PLANE` |
    `static int` | `SAC_MODEL_SPHERE` |
    `static int` | `SAMPLING_NAPSAC` |
    `static int` | `SAMPLING_PROGRESSIVE_NAPSAC` |
    `static int` | `SAMPLING_PROSAC` |
    `static int` | `SAMPLING_UNIFORM` |
    `static int` | `SCORE_METHOD_LMEDS` |
    `static int` | `SCORE_METHOD_MAGSAC` |
    `static int` | `SCORE_METHOD_MSAC` |
    `static int` | `SCORE_METHOD_RANSAC` |
    `static int` | `SOLVEPNP_AP3P` |
    `static int` | `SOLVEPNP_DLS` |
    `static int` | `SOLVEPNP_EPNP` |
    `static int` | `SOLVEPNP_IPPE` |
    `static int` | `SOLVEPNP_IPPE_SQUARE` |
    `static int` | `SOLVEPNP_ITERATIVE` |
    `static int` | `SOLVEPNP_MAX_COUNT` |
    `static int` | `SOLVEPNP_P3P` |
    `static int` | `SOLVEPNP_SQPNP` |
    `static int` | `SOLVEPNP_UPNP` |
    `static int` | `USAC_ACCURATE` |
    `static int` | `USAC_DEFAULT` |
    `static int` | `USAC_FAST` |
    `static int` | `USAC_FM_8PTS` |
    `static int` | `USAC_MAGSAC` |
    `static int` | `USAC_PARALLEL` |
    `static int` | `USAC_PROSAC` |
    `static int` | `VariableType_LINEAR` |
    `static int` | `VariableType_SE3` |
    `static int` | `VariableType_SO3` |
    `static int` | `VolumeType_ColorTSDF` |
    `static int` | `VolumeType_HashTSDF` |
    `static int` | `VolumeType_TSDF` |

  - ### Constructor Summary

    Constructors
    Constructor | Description
    --- | ---
    `Cv3d()` |

  - ### Method Summary

    All Methods Static Methods Concrete Methods
    Modifier and Type | Method | Description
    --- | --- | ---
    `static void` | `composeRT​(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3)` | Combines two rotation-and-shift transformations.
    `static void` | `composeRT​(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1)` | Combines two rotation-and-shift transformations.
    `static void` | `composeRT​(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1)` | Combines two rotation-and-shift transformations.
    `static void` | `composeRT​(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2)` | Combines two rotation-and-shift transformations.
    `static void` | `composeRT​(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2)` | Combines two rotation-and-shift transformations.
    `static void` | `composeRT​(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1)` | Combines two rotation-and-shift transformations.
    `static void` | `composeRT​(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1, Mat dt3dt1)` | Combines two rotation-and-shift transformations.
    `static void` | `composeRT​(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1, Mat dt3dt1, Mat dt3dr2)` | Combines two rotation-and-shift transformations.
    `static void` | `composeRT​(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1, Mat dt3dt1, Mat dt3dr2, Mat dt3dt2)` | Combines two rotation-and-shift transformations.
    `static void` | `computeCorrespondEpilines​(Mat points, int whichImage, Mat F, Mat lines)` | For points in an image of a stereo pair, computes the corresponding epilines in the other image.
    `static void` | `convertPointsFromHomogeneous​(Mat src, Mat dst)` | Converts points from homogeneous to Euclidean space.
    `static void` | `convertPointsFromHomogeneous​(Mat src, Mat dst, int dtype)` | Converts points from homogeneous to Euclidean space.
    `static void` | `convertPointsToHomogeneous​(Mat src, Mat dst)` | Converts points from Euclidean to homogeneous space.
    `static void` | `convertPointsToHomogeneous​(Mat src, Mat dst, int dtype)` | Converts points from Euclidean to homogeneous space.
    `static void` | `correctMatches​(Mat F, Mat points1, Mat points2, Mat newPoints1, Mat newPoints2)` | Refines coordinates of corresponding points.
    `static void` | `decomposeEssentialMat​(Mat E, Mat R1, Mat R2, Mat t)` | Decompose an essential matrix to possible rotations and translation.
    `static int` | `decomposeHomographyMat​(Mat H, Mat K, java.util.List<Mat> rotations, java.util.List<Mat> translations, java.util.List<Mat> normals)` | Decompose a homography matrix to rotation(s), translation(s) and plane normal(s).
    `static void` | `decomposeProjectionMatrix​(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect)` | Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
    `static void` | `decomposeProjectionMatrix​(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX)` | Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
    `static void` | `decomposeProjectionMatrix​(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX, Mat rotMatrixY)` | Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
    `static void` | `decomposeProjectionMatrix​(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX, Mat rotMatrixY, Mat rotMatrixZ)` | Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
    `static void` | `decomposeProjectionMatrix​(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX, Mat rotMatrixY, Mat rotMatrixZ, Mat eulerAngles)` | Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
    `static void` | `depthTo3d​(Mat depth, Mat K, Mat points3d)` | Converts a depth image to 3d points.
    `static void` | `depthTo3d​(Mat depth, Mat K, Mat points3d, Mat mask)` | Converts a depth image to 3d points.
    `static void` | `depthTo3dSparse​(Mat depth, Mat in_K, Mat in_points, Mat points3d)` |
    `static void` | `drawFrameAxes​(Mat image, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, float length)` | Draw axes of the world/object coordinate system from pose estimation.
    `static void` | `drawFrameAxes​(Mat image, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, float length, int thickness)` | Draw axes of the world/object coordinate system from pose estimation.
    `static Mat` | `estimateAffine2D​(Mat from, Mat to)` | Computes an optimal affine transformation between two 2D point sets.
    `static Mat` | `estimateAffine2D​(Mat from, Mat to, Mat inliers)` | Computes an optimal affine transformation between two 2D point sets.
    `static Mat` | `estimateAffine2D​(Mat from, Mat to, Mat inliers, int method)` | Computes an optimal affine transformation between two 2D point sets.
    `static Mat` | `estimateAffine2D​(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold)` | Computes an optimal affine transformation between two 2D point sets.
    `static Mat` | `estimateAffine2D​(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters)` | Computes an optimal affine transformation between two 2D point sets.
    `static Mat` | `estimateAffine2D​(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence)` | Computes an optimal affine transformation between two 2D point sets.
    `static Mat` | `estimateAffine2D​(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters)` | Computes an optimal affine transformation between two 2D point sets.
    `static Mat` | `estimateAffine2D​(Mat pts1, Mat pts2, Mat inliers, UsacParams params)` |
    `static Mat` | `estimateAffine3D​(Mat src, Mat dst)` | Computes an optimal affine transformation between two 3D point sets.
    `static Mat` | `estimateAffine3D​(Mat src, Mat dst, double[] scale)` | Computes an optimal affine transformation between two 3D point sets.
    `static Mat` | `estimateAffine3D​(Mat src, Mat dst, double[] scale, boolean force_rotation)` | Computes an optimal affine transformation between two 3D point sets.
    `static int` | `estimateAffine3D​(Mat src, Mat dst, Mat out, Mat inliers)` | Computes an optimal affine transformation between two 3D point sets.
    `static int` | `estimateAffine3D​(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold)` | Computes an optimal affine transformation between two 3D point sets.
    `static int` | `estimateAffine3D​(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold, double confidence)` | Computes an optimal affine transformation between two 3D point sets.
    `static Mat` | `estimateAffinePartial2D​(Mat from, Mat to)` | Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
    `static Mat` | `estimateAffinePartial2D​(Mat from, Mat to, Mat inliers)` | Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
    `static Mat` | `estimateAffinePartial2D​(Mat from, Mat to, Mat inliers, int method)` | Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
    `static Mat` | `estimateAffinePartial2D​(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold)` | Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
    `static Mat` | `estimateAffinePartial2D​(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters)` | Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
    `static Mat` | `estimateAffinePartial2D​(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence)` | Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
    `static Mat` | `estimateAffinePartial2D​(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters)` | Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets.
    `static int` | `estimateTranslation3D​(Mat src, Mat dst, Mat out, Mat inliers)` | Computes an optimal translation between two 3D point sets.
    `static int` | `estimateTranslation3D​(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold)` | Computes an optimal translation between two 3D point sets.
    `static int` | `estimateTranslation3D​(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold, double confidence)` | Computes an optimal translation between two 3D point sets.
    `static void` | `filterHomographyDecompByVisibleRefpoints​(java.util.List<Mat> rotations, java.util.List<Mat> normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions)` | Filters homography decompositions based on additional information.
    `static void` | `filterHomographyDecompByVisibleRefpoints​(java.util.List<Mat> rotations, java.util.List<Mat> normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions, Mat pointsMask)` | Filters homography decompositions based on additional information.
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2)` |
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, double focal)` |
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, double focal, Point pp)` |
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, double focal, Point pp, int method)` |
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, double focal, Point pp, int method, double prob)` |
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold)` |
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold, int maxIters)` |
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold, int maxIters, Mat mask)` |
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, Mat cameraMatrix)` | Calculates an essential matrix from the corresponding points in two images.
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, Mat cameraMatrix, int method)` | Calculates an essential matrix from the corresponding points in two images.
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, Mat cameraMatrix, int method, double prob)` | Calculates an essential matrix from the corresponding points in two images.
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold)` | Calculates an essential matrix from the corresponding points in two images.
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold, int maxIters)` | Calculates an essential matrix from the corresponding points in two images.
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold, int maxIters, Mat mask)` | Calculates an essential matrix from the corresponding points in two images.
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2)` | Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method)` | Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method, double prob)` | Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method, double prob, double threshold)` | Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method, double prob, double threshold, Mat mask)` | Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
    `static Mat` | `findEssentialMat​(Mat points1, Mat points2, Mat cameraMatrix1, Mat cameraMatrix2, Mat dist_coeff1, Mat dist_coeff2, Mat mask, UsacParams params)` |
    `static Mat` | `findFundamentalMat​(MatOfPoint2f points1, MatOfPoint2f points2)` |
    `static Mat` | `findFundamentalMat​(MatOfPoint2f points1, MatOfPoint2f points2, int method)` |
    `static Mat` | `findFundamentalMat​(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold)` |
    `static Mat` | `findFundamentalMat​(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence)` |
    `static Mat` | `findFundamentalMat​(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, int maxIters)` | Calculates a fundamental matrix from the corresponding points in two images.
    `static Mat` | `findFundamentalMat​(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, int maxIters, Mat mask)` | Calculates a fundamental matrix from the corresponding points in two images.
    `static Mat` | `findFundamentalMat​(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, Mat mask)` |
    `static Mat` | `findFundamentalMat​(MatOfPoint2f points1, MatOfPoint2f points2, Mat mask, UsacParams params)` |
    `static Mat` | `findHomography​(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints)` | Finds a perspective transformation between two planes.
    `static Mat` | `findHomography​(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method)` | Finds a perspective transformation between two planes.
    `static Mat` | `findHomography​(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold)` | Finds a perspective transformation between two planes.
    `static Mat` | `findHomography​(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask)` | Finds a perspective transformation between two planes.
    `static Mat` | `findHomography​(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters)` | Finds a perspective transformation between two planes.
    `static Mat` | `findHomography​(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters, double confidence)` | Finds a perspective transformation between two planes.
    `static Mat` | `findHomography​(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, Mat mask, UsacParams params)` |
    `static void` | `findPlanes​(Mat points3d, Mat normals, Mat mask, Mat plane_coefficients)` | Find the planes in a depth image
    `static void` | `findPlanes​(Mat points3d, Mat normals, Mat mask, Mat plane_coefficients, int block_size)` | Find the planes in a depth image
    `static void` | `findPlanes​(Mat points3d, Mat normals, Mat mask, Mat plane_coefficients, int block_size, int min_size)` | Find the planes in a depth image
    `static void` | `findPlanes​(Mat points3d, Mat normals, Mat mask, Mat plane_coefficients, int block_size, int min_size, double threshold)` | Find the planes in a depth image
    `static void` | `findPlanes​(Mat points3d, Mat normals, Mat mask, Mat plane_coefficients, int block_size, int min_size, double threshold, double sensor_error_a)` | Find the planes in a depth image
    `static void` | `findPlanes​(Mat points3d, Mat normals, Mat mask, Mat plane_coefficients, int block_size, int min_size, double threshold, double sensor_error_a, double sensor_error_b)` | Find the planes in a depth image
    `static void` | `findPlanes​(Mat points3d, Mat normals, Mat mask, Mat plane_coefficients, int block_size, int min_size, double threshold, double sensor_error_a, double sensor_error_b, double sensor_error_c)` | Find the planes in a depth image
    `static void` | `findPlanes​(Mat points3d, Mat normals, Mat mask, Mat plane_coefficients, int block_size, int min_size, double threshold, double sensor_error_a, double sensor_error_b, double sensor_error_c, int method)` | Find the planes in a depth image
    `static void` | `fisheye_distortPoints​(Mat undistorted, Mat distorted, Mat K, Mat D)` | Distorts 2D points using fisheye model.
    `static void` | `fisheye_distortPoints​(Mat undistorted, Mat distorted, Mat K, Mat D, double alpha)` | Distorts 2D points using fisheye model.
    `static void` | `fisheye_distortPoints​(Mat undistorted, Mat distorted, Mat Kundistorted, Mat K, Mat D)` | Overload of distortPoints function to handle cases when undistorted points are got with non-identity camera matrix, e.g.
    `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.
    `static void` | `fisheye_estimateNewCameraMatrixForUndistortRectify​(Mat K, Mat D, Size image_size, Mat R, Mat P)` | Estimates new camera intrinsic matrix for undistortion or rectification.
    `static void` | `fisheye_estimateNewCameraMatrixForUndistortRectify​(Mat K, Mat D, Size image_size, Mat R, Mat P, double balance)` | Estimates new camera intrinsic matrix for undistortion or rectification.
    `static void` | `fisheye_estimateNewCameraMatrixForUndistortRectify​(Mat K, Mat D, Size image_size, Mat R, Mat P, double balance, Size new_size)` | Estimates new camera intrinsic matrix for undistortion or rectification.
    `static void` | `fisheye_estimateNewCameraMatrixForUndistortRectify​(Mat K, Mat D, Size image_size, Mat R, Mat P, double balance, Size new_size, double fov_scale)` | Estimates new camera intrinsic matrix for undistortion or rectification.
    `static void` | `fisheye_initUndistortRectifyMap​(Mat K, Mat D, Mat R, Mat P, Size size, int m1type, Mat map1, Mat map2)` | Computes undistortion and rectification maps for image transform by cv::remap().
    `static void` | `fisheye_projectPoints​(Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D)` |
    `static void` | `fisheye_projectPoints​(Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D, double alpha)` |
    `static void` | `fisheye_projectPoints​(Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D, double alpha, Mat jacobian)` |
    `static 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 moodel.
    `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 moodel.
    `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 moodel.
    `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 moodel.
    `static void` | `fisheye_undistortImage​(Mat distorted, Mat undistorted, Mat K, Mat D)` | Transforms an image to compensate for fisheye lens distortion.
    `static void` | `fisheye_undistortImage​(Mat distorted, Mat undistorted, Mat K, Mat D, Mat Knew)` | Transforms an image to compensate for fisheye lens distortion.
    `static void` | `fisheye_undistortImage​(Mat distorted, Mat undistorted, Mat K, Mat D, Mat Knew, Size new_size)` | Transforms an image to compensate for fisheye lens distortion.
    `static void` | `fisheye_undistortPoints​(Mat distorted, Mat undistorted, Mat K, Mat D)` | Undistorts 2D points using fisheye model
    `static void` | `fisheye_undistortPoints​(Mat distorted, Mat undistorted, Mat K, Mat D, Mat R)` | Undistorts 2D points using fisheye model
    `static void` | `fisheye_undistortPoints​(Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P)` | Undistorts 2D points using fisheye model
    `static void` | `fisheye_undistortPoints​(Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P, TermCriteria criteria)` | Undistorts 2D points using fisheye model
    `static Mat` | `getDefaultNewCameraMatrix​(Mat cameraMatrix)` | Returns the default new camera matrix.
    `static Mat` | `getDefaultNewCameraMatrix​(Mat cameraMatrix, Size imgsize)` | Returns the default new camera matrix.
    `static Mat` | `getDefaultNewCameraMatrix​(Mat cameraMatrix, Size imgsize, boolean centerPrincipalPoint)` | Returns the default new camera matrix.
    `static Mat` | `getOptimalNewCameraMatrix​(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha)` | Returns the new camera intrinsic matrix based on the free scaling parameter.
    `static Mat` | `getOptimalNewCameraMatrix​(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize)` | Returns the new camera intrinsic matrix based on the free scaling parameter.
    `static Mat` | `getOptimalNewCameraMatrix​(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize, Rect validPixROI)` | Returns the new camera intrinsic matrix based on the free scaling parameter.
    `static Mat` | `getOptimalNewCameraMatrix​(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize, Rect validPixROI, boolean centerPrincipalPoint)` | Returns the new camera intrinsic matrix based on the free scaling parameter.
    `static void` | `initInverseRectificationMap​(Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, Size size, int m1type, Mat map1, Mat map2)` | Computes the projection and inverse-rectification transformation map.
    `static void` | `initUndistortRectifyMap​(Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, Size size, int m1type, Mat map1, Mat map2)` | Computes the undistortion and rectification transformation map.
    `static void` | `loadMesh​(java.lang.String filename, Mat vertices, java.util.List<Mat> indices)` | Loads a mesh from a file.
    `static void` | `loadMesh​(java.lang.String filename, Mat vertices, java.util.List<Mat> indices, Mat normals)` | Loads a mesh from a file.
    `static void` | `loadMesh​(java.lang.String filename, Mat vertices, java.util.List<Mat> indices, Mat normals, Mat colors)` | Loads a mesh from a file.
    `static void` | `loadMesh​(java.lang.String filename, Mat vertices, java.util.List<Mat> indices, Mat normals, Mat colors, Mat texCoords)` | Loads a mesh from a file.
    `static void` | `loadPointCloud​(java.lang.String filename, Mat vertices)` | Loads a point cloud from a file.
    `static void` | `loadPointCloud​(java.lang.String filename, Mat vertices, Mat normals)` | Loads a point cloud from a file.
    `static void` | `loadPointCloud​(java.lang.String filename, Mat vertices, Mat normals, Mat rgb)` | Loads a point cloud from a file.
    `static void` | `matMulDeriv​(Mat A, Mat B, Mat dABdA, Mat dABdB)` | Computes partial derivatives of the matrix product for each multiplied matrix.
    `static void` | `projectPoints​(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints)` | Projects 3D points to an image plane.
    `static void` | `projectPoints​(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian)` | Projects 3D points to an image plane.
    `static void` | `projectPoints​(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian, double aspectRatio)` | Projects 3D points to an image plane.
    `static void` | `projectPointsSepJ​(Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt)` |
    `static void` | `projectPointsSepJ​(Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf)` |
    `static void` | `projectPointsSepJ​(Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf, Mat dpdc)` |
    `static void` | `projectPointsSepJ​(Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf, Mat dpdc, Mat dpdk)` |
    `static void` | `projectPointsSepJ​(Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf, Mat dpdc, Mat dpdk, Mat dpdo)` |
    `static void` | `projectPointsSepJ​(Mat objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, Mat distCoeffs, Mat imagePoints, Mat dpdr, Mat dpdt, Mat dpdf, Mat dpdc, Mat dpdk, Mat dpdo, double aspectRatio)` |
    `static int` | `recoverPose​(Mat E, Mat points1, Mat points2, Mat R, Mat t)` |
    `static int` | `recoverPose​(Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal)` |
    `static int` | `recoverPose​(Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, Point pp)` |
    `static int` | `recoverPose​(Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, Point pp, Mat mask)` |
    `static int` | `recoverPose​(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t)` | Recovers the relative camera rotation and the translation from an estimated essential matrix and the corresponding points in two images, using chirality check.
    `static int` | `recoverPose​(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh)` |
    `static int` | `recoverPose​(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh, Mat mask)` |
    `static int` | `recoverPose​(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh, Mat mask, Mat triangulatedPoints)` |
    `static int` | `recoverPose​(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, Mat mask)` | Recovers the relative camera rotation and the translation from an estimated essential matrix and the corresponding points in two images, using chirality check.
    `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.
    `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.
    `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.
    `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.
    `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.
    `static void` | `registerDepth​(Mat unregisteredCameraMatrix, Mat registeredCameraMatrix, Mat registeredDistCoeffs, Mat Rt, Mat unregisteredDepth, Size outputImagePlaneSize, Mat registeredDepth)` | Registers depth data to an external camera Registration is performed by creating a depth cloud, transforming the cloud by the rigid body transformation between the cameras, and then projecting the transformed points into the RGB camera.
    `static void` | `registerDepth​(Mat unregisteredCameraMatrix, Mat registeredCameraMatrix, Mat registeredDistCoeffs, Mat Rt, Mat unregisteredDepth, Size outputImagePlaneSize, Mat registeredDepth, boolean depthDilation)` | Registers depth data to an external camera Registration is performed by creating a depth cloud, transforming the cloud by the rigid body transformation between the cameras, and then projecting the transformed points into the RGB camera.
    `static void` | `rescaleDepth​(Mat in, int type, Mat out)` | If the input image is of type CV_16UC1 (like the Kinect one), the image is converted to floats, divided by depth_factor to get a depth in meters, and the values 0 are converted to std::numeric_limits<float>::quiet_NaN() Otherwise, the image is simply converted to floats
    `static void` | `rescaleDepth​(Mat in, int type, Mat out, double depth_factor)` | If the input image is of type CV_16UC1 (like the Kinect one), the image is converted to floats, divided by depth_factor to get a depth in meters, and the values 0 are converted to std::numeric_limits<float>::quiet_NaN() Otherwise, the image is simply converted to floats
    `static void` | `Rodrigues​(Mat src, Mat dst)` | Converts a rotation matrix to a rotation vector or vice versa.
    `static void` | `Rodrigues​(Mat src, Mat dst, Mat jacobian)` | Converts a rotation matrix to a rotation vector or vice versa.
    `static double[]` | `RQDecomp3x3​(Mat src, Mat mtxR, Mat mtxQ)` | Computes an RQ decomposition of 3x3 matrices.
    `static double[]` | `RQDecomp3x3​(Mat src, Mat mtxR, Mat mtxQ, Mat Qx)` | Computes an RQ decomposition of 3x3 matrices.
    `static double[]` | `RQDecomp3x3​(Mat src, Mat mtxR, Mat mtxQ, Mat Qx, Mat Qy)` | Computes an RQ decomposition of 3x3 matrices.
    `static double[]` | `RQDecomp3x3​(Mat src, Mat mtxR, Mat mtxQ, Mat Qx, Mat Qy, Mat Qz)` | Computes an RQ decomposition of 3x3 matrices.
    `static double` | `sampsonDistance​(Mat pt1, Mat pt2, Mat F)` | Calculates the Sampson Distance between two points.
    `static void` | `saveMesh​(java.lang.String filename, Mat vertices, java.util.List<Mat> indices)` | Saves a mesh to a specified file.
    `static void` | `saveMesh​(java.lang.String filename, Mat vertices, java.util.List<Mat> indices, Mat normals)` | Saves a mesh to a specified file.
    `static void` | `saveMesh​(java.lang.String filename, Mat vertices, java.util.List<Mat> indices, Mat normals, Mat colors)` | Saves a mesh to a specified file.
    `static void` | `saveMesh​(java.lang.String filename, Mat vertices, java.util.List<Mat> indices, Mat normals, Mat colors, Mat texCoords)` | Saves a mesh to a specified file.
    `static void` | `savePointCloud​(java.lang.String filename, Mat vertices)` | Saves a point cloud to a specified file.
    `static void` | `savePointCloud​(java.lang.String filename, Mat vertices, Mat normals)` | Saves a point cloud to a specified file.
    `static void` | `savePointCloud​(java.lang.String filename, Mat vertices, Mat normals, Mat rgb)` | Saves a point cloud to a specified file.
    `static int` | `solveP3P​(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, java.util.List<Mat> rvecs, java.util.List<Mat> tvecs, int flags)` | Finds an object pose from 3 3D-2D point correspondences.
    `static boolean` | `solvePnP​(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec)` | Finds an object pose from 3D-2D point correspondences.
    `static boolean` | `solvePnP​(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess)` | Finds an object pose from 3D-2D point correspondences.
    `static boolean` | `solvePnP​(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int flags)` | Finds an object pose from 3D-2D point correspondences.
    `static int` | `solvePnPGeneric​(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, java.util.List<Mat> rvecs, java.util.List<Mat> tvecs)` | Finds an object pose from 3D-2D point correspondences.
    `static int` | `solvePnPGeneric​(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, java.util.List<Mat> rvecs, java.util.List<Mat> tvecs, boolean useExtrinsicGuess)` | Finds an object pose from 3D-2D point correspondences.
    `static int` | `solvePnPGeneric​(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, java.util.List<Mat> rvecs, java.util.List<Mat> tvecs, boolean useExtrinsicGuess, int flags)` | Finds an object pose from 3D-2D point correspondences.
    `static int` | `solvePnPGeneric​(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, java.util.List<Mat> rvecs, java.util.List<Mat> tvecs, boolean useExtrinsicGuess, int flags, Mat rvec)` | Finds an object pose from 3D-2D point correspondences.
    `static int` | `solvePnPGeneric​(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, java.util.List<Mat> rvecs, java.util.List<Mat> tvecs, boolean useExtrinsicGuess, int flags, Mat rvec, Mat tvec)` | Finds an object pose from 3D-2D point correspondences.
    `static int` | `solvePnPGeneric​(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, java.util.List<Mat> rvecs, java.util.List<Mat> tvecs, boolean useExtrinsicGuess, int flags, Mat rvec, Mat tvec, Mat reprojectionError)` | Finds an object pose from 3D-2D point correspondences.
    `static boolean` | `solvePnPRansac​(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec)` | Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
    `static boolean` | `solvePnPRansac​(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess)` | Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
    `static boolean` | `solvePnPRansac​(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int iterationsCount)` | Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
    `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 from 3D-2D point correspondences using the RANSAC scheme.
    `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 from 3D-2D point correspondences using the RANSAC scheme.
    `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 from 3D-2D point correspondences using the RANSAC scheme.
    `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 from 3D-2D point correspondences using the RANSAC scheme.
    `static boolean` | `solvePnPRansac​(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers)` |
    `static boolean` | `solvePnPRansac​(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers, UsacParams params)` |
    `static void` | `solvePnPRefineLM​(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec)` | Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
    `static void` | `solvePnPRefineLM​(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria)` | Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
    `static void` | `solvePnPRefineVVS​(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec)` | Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
    `static void` | `solvePnPRefineVVS​(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria)` | Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
    `static void` | `solvePnPRefineVVS​(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria, double VVSlambda)` | Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
    `static void` | `triangleRasterize​(Mat vertices, Mat indices, Mat colors, Mat colorBuf, Mat depthBuf, Mat world2cam, double fovY, double zNear, double zFar)` | Renders a set of triangles on a depth and color image Triangles can be drawn white (1.0, 1.0, 1.0), flat-shaded or with a color interpolation between vertices.
    `static void` | `triangleRasterize​(Mat vertices, Mat indices, Mat colors, Mat colorBuf, Mat depthBuf, Mat world2cam, double fovY, double zNear, double zFar, TriangleRasterizeSettings settings)` | Renders a set of triangles on a depth and color image Triangles can be drawn white (1.0, 1.0, 1.0), flat-shaded or with a color interpolation between vertices.
    `static void` | `triangleRasterizeColor​(Mat vertices, Mat indices, Mat colors, Mat colorBuf, Mat world2cam, double fovY, double zNear, double zFar)` | Overloaded version of triangleRasterize() with color-only rendering
    `static void` | `triangleRasterizeColor​(Mat vertices, Mat indices, Mat colors, Mat colorBuf, Mat world2cam, double fovY, double zNear, double zFar, TriangleRasterizeSettings settings)` | Overloaded version of triangleRasterize() with color-only rendering
    `static void` | `triangleRasterizeDepth​(Mat vertices, Mat indices, Mat depthBuf, Mat world2cam, double fovY, double zNear, double zFar)` | Overloaded version of triangleRasterize() with depth-only rendering
    `static void` | `triangleRasterizeDepth​(Mat vertices, Mat indices, Mat depthBuf, Mat world2cam, double fovY, double zNear, double zFar, TriangleRasterizeSettings settings)` | Overloaded version of triangleRasterize() with depth-only rendering
    `static void` | `triangulatePoints​(Mat projMatr1, Mat projMatr2, Mat projPoints1, Mat projPoints2, Mat points4D)` | This function reconstructs 3-dimensional points (in homogeneous coordinates) by using their observations with a stereo camera.
    `static void` | `undistort​(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs)` | Transforms an image to compensate for lens distortion.
    `static void` | `undistort​(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat newCameraMatrix)` | Transforms an image to compensate for lens distortion.
    `static void` | `undistortImagePoints​(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs)` | Compute undistorted image points position
    `static void` | `undistortImagePoints​(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, TermCriteria arg1)` | Compute undistorted image points position
    `static void` | `undistortPoints​(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs)` | Computes the ideal point coordinates from the observed point coordinates.
    `static void` | `undistortPoints​(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R)` | Computes the ideal point coordinates from the observed point coordinates.
    `static void` | `undistortPoints​(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P)` | Computes the ideal point coordinates from the observed point coordinates.
    `static void` | `undistortPoints​(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, TermCriteria criteria)` | Computes the ideal point coordinates from the observed point coordinates.
    `static void` | `warpFrame​(Mat depth, Mat image, Mat mask, Mat Rt, Mat cameraMatrix)` | Warps depth or RGB-D image by reprojecting it in 3d, applying Rt transformation and then projecting it back onto the image plane.
    `static void` | `warpFrame​(Mat depth, Mat image, Mat mask, Mat Rt, Mat cameraMatrix, Mat warpedDepth)` | Warps depth or RGB-D image by reprojecting it in 3d, applying Rt transformation and then projecting it back onto the image plane.
    `static void` | `warpFrame​(Mat depth, Mat image, Mat mask, Mat Rt, Mat cameraMatrix, Mat warpedDepth, Mat warpedImage)` | Warps depth or RGB-D image by reprojecting it in 3d, applying Rt transformation and then projecting it back onto the image plane.
    `static void` | `warpFrame​(Mat depth, Mat image, Mat mask, Mat Rt, Mat cameraMatrix, Mat warpedDepth, Mat warpedImage, Mat warpedMask)` | Warps depth or RGB-D image by reprojecting it in 3d, applying Rt transformation and then projecting it back onto the image plane.

    - ### Methods inherited from class java.lang.Object

      `clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- - ### Field Detail

    - #### LMEDS

          public static final int LMEDS

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        Constant Field Values

    - #### RANSAC

          public static final int RANSAC

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        Constant Field Values

    - #### RHO

          public static final int RHO

      See Also:
        Constant Field Values

    - #### USAC_DEFAULT

          public static final int USAC_DEFAULT

      See Also:
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    - #### USAC_PARALLEL

          public static final int USAC_PARALLEL

      See Also:
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    - #### USAC_FM_8PTS

          public static final int USAC_FM_8PTS

      See Also:
        Constant Field Values

    - #### USAC_FAST

          public static final int USAC_FAST

      See Also:
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    - #### USAC_ACCURATE

          public static final int USAC_ACCURATE

      See Also:
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    - #### USAC_PROSAC

          public static final int USAC_PROSAC

      See Also:
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    - #### USAC_MAGSAC

          public static final int USAC_MAGSAC

      See Also:
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    - #### FM_7POINT

          public static final int FM_7POINT

      See Also:
        Constant Field Values

    - #### FM_8POINT

          public static final int FM_8POINT

      See Also:
        Constant Field Values

    - #### FM_LMEDS

          public static final int FM_LMEDS

      See Also:
        Constant Field Values

    - #### FM_RANSAC

          public static final int FM_RANSAC

      See Also:
        Constant Field Values

    - #### LOCAL_OPTIM_NULL

          public static final int LOCAL_OPTIM_NULL

      See Also:
        Constant Field Values

    - #### LOCAL_OPTIM_INNER_LO

          public static final int LOCAL_OPTIM_INNER_LO

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    - #### LOCAL_OPTIM_INNER_AND_ITER_LO

          public static final int LOCAL_OPTIM_INNER_AND_ITER_LO

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    - #### LOCAL_OPTIM_GC

          public static final int LOCAL_OPTIM_GC

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    - #### LOCAL_OPTIM_SIGMA

          public static final int LOCAL_OPTIM_SIGMA

      See Also:
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    - #### MatrixType_AUTO

          public static final int MatrixType_AUTO

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    - #### MatrixType_DENSE

          public static final int MatrixType_DENSE

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    - #### MatrixType_SPARSE

          public static final int MatrixType_SPARSE

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    - #### NEIGH_FLANN_KNN

          public static final int NEIGH_FLANN_KNN

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    - #### NEIGH_GRID

          public static final int NEIGH_GRID

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    - #### NEIGH_FLANN_RADIUS

          public static final int NEIGH_FLANN_RADIUS

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    - #### OdometryAlgoType_COMMON

          public static final int OdometryAlgoType_COMMON

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        Constant Field Values

    - #### OdometryAlgoType_FAST

          public static final int OdometryAlgoType_FAST

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    - #### PYR_IMAGE

          public static final int PYR_IMAGE

      See Also:
        Constant Field Values

    - #### PYR_DEPTH

          public static final int PYR_DEPTH

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    - #### PYR_MASK

          public static final int PYR_MASK

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    - #### PYR_CLOUD

          public static final int PYR_CLOUD

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    - #### PYR_DIX

          public static final int PYR_DIX

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    - #### PYR_DIY

          public static final int PYR_DIY

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    - #### PYR_TEXMASK

          public static final int PYR_TEXMASK

      See Also:
        Constant Field Values

    - #### PYR_NORM

          public static final int PYR_NORM

      See Also:
        Constant Field Values

    - #### PYR_NORMMASK

          public static final int PYR_NORMMASK

      See Also:
        Constant Field Values

    - #### N_PYRAMIDS

          public static final int N_PYRAMIDS

      See Also:
        Constant Field Values

    - #### OdometryType_DEPTH

          public static final int OdometryType_DEPTH

      See Also:
        Constant Field Values

    - #### OdometryType_RGB

          public static final int OdometryType_RGB

      See Also:
        Constant Field Values

    - #### OdometryType_RGB_DEPTH

          public static final int OdometryType_RGB_DEPTH

      See Also:
        Constant Field Values

    - #### NONE_POLISHER

          public static final int NONE_POLISHER

      See Also:
        Constant Field Values

    - #### LSQ_POLISHER

          public static final int LSQ_POLISHER

      See Also:
        Constant Field Values

    - #### MAGSAC

          public static final int MAGSAC

      See Also:
        Constant Field Values

    - #### COV_POLISHER

          public static final int COV_POLISHER

      See Also:
        Constant Field Values

    - #### RGBD_PLANE_METHOD_DEFAULT

          public static final int RGBD_PLANE_METHOD_DEFAULT

      See Also:
        Constant Field Values

    - #### SAC_METHOD_RANSAC

          public static final int SAC_METHOD_RANSAC

      See Also:
        Constant Field Values

    - #### SAC_MODEL_PLANE

          public static final int SAC_MODEL_PLANE

      See Also:
        Constant Field Values

    - #### SAC_MODEL_SPHERE

          public static final int SAC_MODEL_SPHERE

      See Also:
        Constant Field Values

    - #### SAMPLING_UNIFORM

          public static final int SAMPLING_UNIFORM

      See Also:
        Constant Field Values

    - #### SAMPLING_PROGRESSIVE_NAPSAC

          public static final int SAMPLING_PROGRESSIVE_NAPSAC

      See Also:
        Constant Field Values

    - #### SAMPLING_NAPSAC

          public static final int SAMPLING_NAPSAC

      See Also:
        Constant Field Values

    - #### SAMPLING_PROSAC

          public static final int SAMPLING_PROSAC

      See Also:
        Constant Field Values

    - #### SCORE_METHOD_RANSAC

          public static final int SCORE_METHOD_RANSAC

      See Also:
        Constant Field Values

    - #### SCORE_METHOD_MSAC

          public static final int SCORE_METHOD_MSAC

      See Also:
        Constant Field Values

    - #### SCORE_METHOD_MAGSAC

          public static final int SCORE_METHOD_MAGSAC

      See Also:
        Constant Field Values

    - #### SCORE_METHOD_LMEDS

          public static final int SCORE_METHOD_LMEDS

      See Also:
        Constant Field Values

    - #### SOLVEPNP_ITERATIVE

          public static final int SOLVEPNP_ITERATIVE

      See Also:
        Constant Field Values

    - #### SOLVEPNP_EPNP

          public static final int SOLVEPNP_EPNP

      See Also:
        Constant Field Values

    - #### SOLVEPNP_P3P

          public static final int SOLVEPNP_P3P

      See Also:
        Constant Field Values

    - #### SOLVEPNP_DLS

          public static final int SOLVEPNP_DLS

      See Also:
        Constant Field Values

    - #### SOLVEPNP_UPNP

          public static final int SOLVEPNP_UPNP

      See Also:
        Constant Field Values

    - #### SOLVEPNP_AP3P

          public static final int SOLVEPNP_AP3P

      See Also:
        Constant Field Values

    - #### SOLVEPNP_IPPE

          public static final int SOLVEPNP_IPPE

      See Also:
        Constant Field Values

    - #### SOLVEPNP_IPPE_SQUARE

          public static final int SOLVEPNP_IPPE_SQUARE

      See Also:
        Constant Field Values

    - #### SOLVEPNP_SQPNP

          public static final int SOLVEPNP_SQPNP

      See Also:
        Constant Field Values

    - #### SOLVEPNP_MAX_COUNT

          public static final int SOLVEPNP_MAX_COUNT

      See Also:
        Constant Field Values

    - #### RASTERIZE_CULLING_NONE

          public static final int RASTERIZE_CULLING_NONE

      See Also:
        Constant Field Values

    - #### RASTERIZE_CULLING_CW

          public static final int RASTERIZE_CULLING_CW

      See Also:
        Constant Field Values

    - #### RASTERIZE_CULLING_CCW

          public static final int RASTERIZE_CULLING_CCW

      See Also:
        Constant Field Values

    - #### RASTERIZE_COMPAT_DISABLED

          public static final int RASTERIZE_COMPAT_DISABLED

      See Also:
        Constant Field Values

    - #### RASTERIZE_COMPAT_INVDEPTH

          public static final int RASTERIZE_COMPAT_INVDEPTH

      See Also:
        Constant Field Values

    - #### RASTERIZE_SHADING_WHITE

          public static final int RASTERIZE_SHADING_WHITE

      See Also:
        Constant Field Values

    - #### RASTERIZE_SHADING_FLAT

          public static final int RASTERIZE_SHADING_FLAT

      See Also:
        Constant Field Values

    - #### RASTERIZE_SHADING_SHADED

          public static final int RASTERIZE_SHADING_SHADED

      See Also:
        Constant Field Values

    - #### PROJ_SPHERICAL_ORTHO

          public static final int PROJ_SPHERICAL_ORTHO

      See Also:
        Constant Field Values

    - #### PROJ_SPHERICAL_EQRECT

          public static final int PROJ_SPHERICAL_EQRECT

      See Also:
        Constant Field Values

    - #### VariableType_LINEAR

          public static final int VariableType_LINEAR

      See Also:
        Constant Field Values

    - #### VariableType_SO3

          public static final int VariableType_SO3

      See Also:
        Constant Field Values

    - #### VariableType_SE3

          public static final int VariableType_SE3

      See Also:
        Constant Field Values

    - #### VolumeType_TSDF

          public static final int VolumeType_TSDF

      See Also:
        Constant Field Values

    - #### VolumeType_HashTSDF

          public static final int VolumeType_HashTSDF

      See Also:
        Constant Field Values

    - #### VolumeType_ColorTSDF

          public static final int VolumeType_ColorTSDF

      See Also:
        Constant Field Values

  - ### Constructor Detail

    - #### Cv3d

          public Cv3d()

  - ### Method Detail

    - #### Rodrigues

          public static void Rodrigues​(Mat src,
                                       Mat dst,
                                       Mat jacobian)

      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
        Note: Useful information on SE(3) and Lie Groups can be found in:
        - A tutorial on SE(3) transformation parameterizations and on-manifold optimization, Jose-Luis Blanco 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

          public static void Rodrigues​(Mat src,
                                       Mat dst)

      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
        Note: Useful information on SE(3) and Lie Groups can be found in:
        - A tutorial on SE(3) transformation parameterizations and on-manifold optimization, Jose-Luis Blanco 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

          public static Mat findHomography​(MatOfPoint2f srcPoints,
                                           MatOfPoint2f dstPoints,
                                           int method)

      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
        (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

          public static Mat findHomography​(MatOfPoint2f srcPoints,
                                           MatOfPoint2f dstPoints)

      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
        (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

          public static Mat findHomography​(MatOfPoint2f srcPoints,
                                           MatOfPoint2f dstPoints,
                                           Mat mask,
                                           UsacParams params)

    - #### RQDecomp3x3

          public static double[] RQDecomp3x3​(Mat src,
                                             Mat mtxR,
                                             Mat mtxQ,
                                             Mat Qx,
                                             Mat Qy,
                                             Mat Qz)

      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

          public static double[] RQDecomp3x3​(Mat src,
                                             Mat mtxR,
                                             Mat mtxQ,
                                             Mat Qx,
                                             Mat Qy)

      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

          public static double[] RQDecomp3x3​(Mat src,
                                             Mat mtxR,
                                             Mat mtxQ,
                                             Mat Qx)

      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

          public static double[] RQDecomp3x3​(Mat src,
                                             Mat mtxR,
                                             Mat mtxQ)

      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

          public static void matMulDeriv​(Mat A,
                                         Mat B,
                                         Mat dABdA,
                                         Mat dABdB)

      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 rvec1
        `dr3dt1` - Optional output derivative of rvec3 with regard to tvec1
        `dr3dr2` - Optional output derivative of rvec3 with regard to rvec2
        `dr3dt2` - Optional output derivative of rvec3 with regard to tvec2
        `dt3dr1` - Optional output derivative of tvec3 with regard to rvec1
        `dt3dt1` - Optional output derivative of tvec3 with regard to tvec1
        `dt3dr2` - Optional output derivative of tvec3 with regard to rvec2
        `dt3dt2` - Optional output derivative of tvec3 with regard to tvec2 The functions compute: \(\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\) where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See #Rodrigues for details. Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see #matMulDeriv ). The functions are used inside #stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.

    - #### composeRT

          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 rvec1
        `dr3dt1` - Optional output derivative of rvec3 with regard to tvec1
        `dr3dr2` - Optional output derivative of rvec3 with regard to rvec2
        `dr3dt2` - Optional output derivative of rvec3 with regard to tvec2
        `dt3dr1` - Optional output derivative of tvec3 with regard to rvec1
        `dt3dt1` - Optional output derivative of tvec3 with regard to tvec1
        `dt3dr2` - Optional output derivative of tvec3 with regard to rvec2 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 rvec1
        `dr3dt1` - Optional output derivative of rvec3 with regard to tvec1
        `dr3dr2` - Optional output derivative of rvec3 with regard to rvec2
        `dr3dt2` - Optional output derivative of rvec3 with regard to tvec2
        `dt3dr1` - Optional output derivative of tvec3 with regard to rvec1
        `dt3dt1` - Optional output derivative of tvec3 with regard to tvec1 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 rvec1
        `dr3dt1` - Optional output derivative of rvec3 with regard to tvec1
        `dr3dr2` - Optional output derivative of rvec3 with regard to rvec2
        `dr3dt2` - Optional output derivative of rvec3 with regard to tvec2
        `dt3dr1` - Optional output derivative of tvec3 with regard to rvec1 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 rvec1
        `dr3dt1` - Optional output derivative of rvec3 with regard to tvec1
        `dr3dr2` - Optional output derivative of rvec3 with regard to rvec2
        `dr3dt2` - Optional output derivative of rvec3 with regard to tvec2 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 rvec1
        `dr3dt1` - Optional output derivative of rvec3 with regard to tvec1
        `dr3dr2` - 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 rvec1
        `dr3dt1` - 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

          public static void composeRT​(Mat rvec1,
                                       Mat tvec1,
                                       Mat rvec2,
                                       Mat tvec2,
                                       Mat rvec3,
                                       Mat tvec3)

      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.
      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, 1xN/Nx1 2-channel, or vector<Point2f> .
        `jacobian` - Optional output 2Nx(10+<numDistCoeffs>) jacobian matrix of derivatives of image points with respect to components of the rotation vector, translation vector, focal lengths, coordinates of the principal point and the distortion coefficients. In the old interface different components of the jacobian are returned via different output parameters.
        `aspectRatio` - Optional "fixed aspect ratio" parameter. If the parameter is not 0, the function assumes that the aspect ratio (\(f_x / f_y\)) is fixed and correspondingly adjusts the jacobian matrix. The function computes the 2D projections of 3D points to the image plane, given intrinsic and extrinsic camera parameters. Optionally, the function computes Jacobians -matrices of partial derivatives of image points coordinates (as functions of all the input parameters) with respect to the particular parameters, intrinsic and/or extrinsic. The Jacobians are used during the global optimization in 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: By setting rvec = tvec = \([0, 0, 0]\), or by setting cameraMatrix to a 3x3 identity matrix, or by passing zero distortion coefficients, one can get various useful partial cases of the function. This means, one can compute the distorted coordinates for a sparse set of points or apply a perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup.

    - #### projectPoints

          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.
      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, 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. 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: By setting rvec = tvec = \([0, 0, 0]\), or by setting cameraMatrix to a 3x3 identity matrix, or by passing zero distortion coefficients, one can get various useful partial cases of the function. This means, one can compute the distorted coordinates for a sparse set of points or apply a perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup.

    - #### projectPoints

          public static void projectPoints​(MatOfPoint3f objectPoints,
                                           Mat rvec,
                                           Mat tvec,
                                           Mat cameraMatrix,
                                           MatOfDouble distCoeffs,
                                           MatOfPoint2f imagePoints)

      Projects 3D points to an image plane.
      Parameters:
        `objectPoints` - Array of object points expressed wrt. the world coordinate frame. A 3xN/Nx3 1-channel or 1xN/Nx1 3-channel (or vector<Point3f> ), where N is the number of points in the view.
        `rvec` - The rotation vector (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, 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. 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: By setting rvec = tvec = \([0, 0, 0]\), or by setting cameraMatrix to a 3x3 identity matrix, or by passing zero distortion coefficients, one can get various useful partial cases of the function. This means, one can compute the distorted coordinates for a sparse set of points or apply a perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup.

    - #### projectPointsSepJ

          public static void projectPointsSepJ​(Mat objectPoints,
                                               Mat rvec,
                                               Mat tvec,
                                               Mat cameraMatrix,
                                               Mat distCoeffs,
                                               Mat imagePoints,
                                               Mat dpdr,
                                               Mat dpdt,
                                               Mat dpdf,
                                               Mat dpdc,
                                               Mat dpdk,
                                               Mat dpdo,
                                               double aspectRatio)

    - #### projectPointsSepJ

          public static void projectPointsSepJ​(Mat objectPoints,
                                               Mat rvec,
                                               Mat tvec,
                                               Mat cameraMatrix,
                                               Mat distCoeffs,
                                               Mat imagePoints,
                                               Mat dpdr,
                                               Mat dpdt,
                                               Mat dpdf,
                                               Mat dpdc,
                                               Mat dpdk,
                                               Mat dpdo)

    - #### projectPointsSepJ

          public static void projectPointsSepJ​(Mat objectPoints,
                                               Mat rvec,
                                               Mat tvec,
                                               Mat cameraMatrix,
                                               Mat distCoeffs,
                                               Mat imagePoints,
                                               Mat dpdr,
                                               Mat dpdt,
                                               Mat dpdf,
                                               Mat dpdc,
                                               Mat dpdk)

    - #### projectPointsSepJ

          public static void projectPointsSepJ​(Mat objectPoints,
                                               Mat rvec,
                                               Mat tvec,
                                               Mat cameraMatrix,
                                               Mat distCoeffs,
                                               Mat imagePoints,
                                               Mat dpdr,
                                               Mat dpdt,
                                               Mat dpdf,
                                               Mat dpdc)

    - #### projectPointsSepJ

          public static void projectPointsSepJ​(Mat objectPoints,
                                               Mat rvec,
                                               Mat tvec,
                                               Mat cameraMatrix,
                                               Mat distCoeffs,
                                               Mat imagePoints,
                                               Mat dpdr,
                                               Mat dpdt,
                                               Mat dpdf)

    - #### projectPointsSepJ

          public static void projectPointsSepJ​(Mat objectPoints,
                                               Mat rvec,
                                               Mat tvec,
                                               Mat cameraMatrix,
                                               Mat distCoeffs,
                                               Mat imagePoints,
                                               Mat dpdr,
                                               Mat dpdt)

    - #### 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 from 3D-2D point correspondences. 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 methods REF: SOLVEPNP_DLS and REF: SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, REF: SOLVEPNP_EPNP method will be used instead.
        - 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

    - #### solvePnP

          public static boolean solvePnP​(MatOfPoint3f objectPoints,
                                         MatOfPoint2f imagePoints,
                                         Mat cameraMatrix,
                                         MatOfDouble distCoeffs,
                                         Mat rvec,
                                         Mat tvec,
                                         boolean useExtrinsicGuess)

      Finds an object pose from 3D-2D point correspondences. 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 methods REF: SOLVEPNP_DLS and REF: SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, REF: SOLVEPNP_EPNP method will be used instead.
        - 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

    - #### solvePnP

          public static boolean solvePnP​(MatOfPoint3f objectPoints,
                                         MatOfPoint2f imagePoints,
                                         Mat cameraMatrix,
                                         MatOfDouble distCoeffs,
                                         Mat rvec,
                                         Mat tvec)

      Finds an object pose from 3D-2D point correspondences. 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 methods REF: SOLVEPNP_DLS and REF: SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, REF: SOLVEPNP_EPNP method will be used instead.
        - 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

    - #### 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 from 3D-2D point correspondences using the RANSAC scheme. 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 opencv_source_code/samples/cpp/tutorial_code/3d/real_time_pose_estimation/
        - The default method used to estimate the camera pose for the Minimal Sample Sets step is #SOLVEPNP_EPNP. Exceptions are:
          - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used.
          - if the number of input points is equal to 4, #SOLVEPNP_P3P is used.
        - The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case, the method #SOLVEPNP_EPNP will be used instead.
      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 from 3D-2D point correspondences using the RANSAC scheme. 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 opencv_source_code/samples/cpp/tutorial_code/3d/real_time_pose_estimation/
        - The default method used to estimate the camera pose for the Minimal Sample Sets step is #SOLVEPNP_EPNP. Exceptions are:
          - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used.
          - if the number of input points is equal to 4, #SOLVEPNP_P3P is used.
        - The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case, the method #SOLVEPNP_EPNP will be used instead.
      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 from 3D-2D point correspondences using the RANSAC scheme. 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 opencv_source_code/samples/cpp/tutorial_code/3d/real_time_pose_estimation/
        - The default method used to estimate the camera pose for the Minimal Sample Sets step is #SOLVEPNP_EPNP. Exceptions are:
          - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used.
          - if the number of input points is equal to 4, #SOLVEPNP_P3P is used.
        - The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case, the method #SOLVEPNP_EPNP will be used instead.
      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 from 3D-2D point correspondences using the RANSAC scheme. 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 opencv_source_code/samples/cpp/tutorial_code/3d/real_time_pose_estimation/
        - The default method used to estimate the camera pose for the Minimal Sample Sets step is #SOLVEPNP_EPNP. Exceptions are:
          - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used.
          - if the number of input points is equal to 4, #SOLVEPNP_P3P is used.
        - The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case, the method #SOLVEPNP_EPNP will be used instead.
      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 from 3D-2D point correspondences using the RANSAC scheme. 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 opencv_source_code/samples/cpp/tutorial_code/3d/real_time_pose_estimation/
        - The default method used to estimate the camera pose for the Minimal Sample Sets step is #SOLVEPNP_EPNP. Exceptions are:
          - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used.
          - if the number of input points is equal to 4, #SOLVEPNP_P3P is used.
        - The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case, the method #SOLVEPNP_EPNP will be used instead.
      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 from 3D-2D point correspondences using the RANSAC scheme. 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 opencv_source_code/samples/cpp/tutorial_code/3d/real_time_pose_estimation/
        - The default method used to estimate the camera pose for the Minimal Sample Sets step is #SOLVEPNP_EPNP. Exceptions are:
          - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used.
          - if the number of input points is equal to 4, #SOLVEPNP_P3P is used.
        - The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case, the method #SOLVEPNP_EPNP will be used instead.
      Returns:
        automatically generated

    - #### solvePnPRansac

          public static boolean solvePnPRansac​(MatOfPoint3f objectPoints,
                                               MatOfPoint2f imagePoints,
                                               Mat cameraMatrix,
                                               MatOfDouble distCoeffs,
                                               Mat rvec,
                                               Mat tvec)

      Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. 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 opencv_source_code/samples/cpp/tutorial_code/3d/real_time_pose_estimation/
        - The default method used to estimate the camera pose for the Minimal Sample Sets step is #SOLVEPNP_EPNP. Exceptions are:
          - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used.
          - if the number of input points is equal to 4, #SOLVEPNP_P3P is used.
        - The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case, the method #SOLVEPNP_EPNP will be used instead.
      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,
                                     java.util.List<Mat> rvecs,
                                     java.util.List<Mat> tvecs,
                                     int flags)

      Finds an object pose from 3 3D-2D point correspondences. 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 X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang "Complete Solution Classification for the Perspective-Three-Point Problem" (CITE: gao2003complete).
        - 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).
        The function estimates the object pose given 3 object points, their corresponding image projections, as well as the camera intrinsic matrix and the distortion coefficients. Note: The solutions are sorted by reprojection errors (lowest to highest).
      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,
                                            java.util.List<Mat> rvecs,
                                            java.util.List<Mat> tvecs,
                                            boolean useExtrinsicGuess,
                                            int flags,
                                            Mat rvec,
                                            Mat tvec,
                                            Mat reprojectionError)

      Finds an object pose from 3D-2D point correspondences. 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
        `rvec` - 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 methods REF: SOLVEPNP_DLS and REF: SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, REF: SOLVEPNP_EPNP method will be used instead.
        - 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]
      Returns:
        automatically generated

    - #### solvePnPGeneric

          public static int solvePnPGeneric​(Mat objectPoints,
                                            Mat imagePoints,
                                            Mat cameraMatrix,
                                            Mat distCoeffs,
                                            java.util.List<Mat> rvecs,
                                            java.util.List<Mat> tvecs,
                                            boolean useExtrinsicGuess,
                                            int flags,
                                            Mat rvec,
                                            Mat tvec)

      Finds an object pose from 3D-2D point correspondences. 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
        `rvec` - 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 methods REF: SOLVEPNP_DLS and REF: SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, REF: SOLVEPNP_EPNP method will be used instead.
        - 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]
      Returns:
        automatically generated

    - #### solvePnPGeneric

          public static int solvePnPGeneric​(Mat objectPoints,
                                            Mat imagePoints,
                                            Mat cameraMatrix,
                                            Mat distCoeffs,
                                            java.util.List<Mat> rvecs,
                                            java.util.List<Mat> tvecs,
                                            boolean useExtrinsicGuess,
                                            int flags,
                                            Mat rvec)

      Finds an object pose from 3D-2D point correspondences. 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
        `rvec` - 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 methods REF: SOLVEPNP_DLS and REF: SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, REF: SOLVEPNP_EPNP method will be used instead.
        - 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]
      Returns:
        automatically generated

    - #### solvePnPGeneric

          public static int solvePnPGeneric​(Mat objectPoints,
                                            Mat imagePoints,
                                            Mat cameraMatrix,
                                            Mat distCoeffs,
                                            java.util.List<Mat> rvecs,
                                            java.util.List<Mat> tvecs,
                                            boolean useExtrinsicGuess,
                                            int flags)

      Finds an object pose from 3D-2D point correspondences. 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 methods REF: SOLVEPNP_DLS and REF: SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, REF: SOLVEPNP_EPNP method will be used instead.
        - 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]
      Returns:
        automatically generated

    - #### solvePnPGeneric

          public static int solvePnPGeneric​(Mat objectPoints,
                                            Mat imagePoints,
                                            Mat cameraMatrix,
                                            Mat distCoeffs,
                                            java.util.List<Mat> rvecs,
                                            java.util.List<Mat> tvecs,
                                            boolean useExtrinsicGuess)

      Finds an object pose from 3D-2D point correspondences. 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 methods REF: SOLVEPNP_DLS and REF: SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, REF: SOLVEPNP_EPNP method will be used instead.
        - 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]
      Returns:
        automatically generated

    - #### solvePnPGeneric

          public static int solvePnPGeneric​(Mat objectPoints,
                                            Mat imagePoints,
                                            Mat cameraMatrix,
                                            Mat distCoeffs,
                                            java.util.List<Mat> rvecs,
                                            java.util.List<Mat> tvecs)

      Finds an object pose from 3D-2D point correspondences. 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 methods REF: SOLVEPNP_DLS and REF: SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, REF: SOLVEPNP_EPNP method will be used instead.
        - 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]
      Returns:
        automatically generated

    - #### drawFrameAxes

          public static void drawFrameAxes​(Mat image,
                                           Mat cameraMatrix,
                                           Mat distCoeffs,
                                           Mat rvec,
                                           Mat tvec,
                                           float length,
                                           int thickness)

      Draw axes of the world/object coordinate system from pose estimation. SEE: solvePnP
      Parameters:
        `image` - Input/output image. It must have 1 or 3 channels. The number of channels is not altered.
        `cameraMatrix` - Input 3x3 floating-point matrix of camera intrinsic parameters. \(\cameramatrix{A}\)
        `distCoeffs` - Input vector of distortion coefficients \(\distcoeffs\). If the vector is empty, the zero distortion coefficients are assumed.
        `rvec` - Rotation vector (see REF: Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
        `tvec` - Translation vector.
        `length` - Length of the painted axes in the same unit than tvec (usually in meters).
        `thickness` - Line thickness of the painted axes. This function draws the axes of the world/object coordinate system w.r.t. to the camera frame. OX is drawn in red, OY in green and OZ in blue.

    - #### drawFrameAxes

          public static void drawFrameAxes​(Mat image,
                                           Mat cameraMatrix,
                                           Mat distCoeffs,
                                           Mat rvec,
                                           Mat tvec,
                                           float length)

      Draw axes of the world/object coordinate system from pose estimation. SEE: solvePnP
      Parameters:
        `image` - Input/output image. It must have 1 or 3 channels. The number of channels is not altered.
        `cameraMatrix` - Input 3x3 floating-point matrix of camera intrinsic parameters. \(\cameramatrix{A}\)
        `distCoeffs` - Input vector of distortion coefficients \(\distcoeffs\). If the vector is empty, the zero distortion coefficients are assumed.
        `rvec` - Rotation vector (see REF: Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
        `tvec` - Translation vector.
        `length` - Length of the painted axes in the same unit than tvec (usually in meters). This function draws the axes of the world/object coordinate system w.r.t. to the camera frame. OX is drawn in red, OY in green and OZ in blue.

    - #### convertPointsToHomogeneous

          public static void convertPointsToHomogeneous​(Mat src,
                                                        Mat dst,
                                                        int dtype)

      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

          public static void convertPointsToHomogeneous​(Mat src,
                                                        Mat dst)

      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

          public static void convertPointsFromHomogeneous​(Mat src,
                                                          Mat dst,
                                                          int dtype)

      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

          public static void convertPointsFromHomogeneous​(Mat src,
                                                          Mat dst)

      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.
        `mask` - optional output mask
        `maxIters` - The maximum number of robust method iterations. The epipolar geometry is described by the following equation: \([p_2; 1]^T F [p_1; 1] = 0\) where \(F\) is a fundamental matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The function calculates the fundamental matrix using one of four methods listed above and returns the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point algorithm, the function may return up to 3 solutions ( \(9 \times 3\) matrix that stores all 3 matrices sequentially). The calculated fundamental matrix may be passed further to #computeCorrespondEpilines that finds the epipolar lines corresponding to the specified points. It can also be passed to #stereoRectifyUncalibrated to compute the rectification transformation. : `// Example. Estimation of fundamental matrix using the RANSAC algorithm int point_count = 100; vector<Point2f> points1(point_count); vector<Point2f> points2(point_count); // initialize the points here ... for( int i = 0; i < point_count; i++ ) { points1[i] = ...; points2[i] = ...; } Mat fundamental_matrix = findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);`
      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

          public static Mat findFundamentalMat​(MatOfPoint2f points1,
                                               MatOfPoint2f points2,
                                               int method)

    - #### findFundamentalMat

          public static Mat findFundamentalMat​(MatOfPoint2f points1,
                                               MatOfPoint2f points2)

    - #### 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 with `P = cv::NoArray()` for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.
        `method` - Method for computing an essential matrix.
        - 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.
        `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,
                                             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 with `P = cv::NoArray()` for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.
        `method` - Method for computing an essential matrix.
        - 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 with `P = cv::NoArray()` for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.
        `method` - Method for computing an essential matrix.
        - 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 with `P = cv::NoArray()` for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.
        `method` - Method for computing an essential matrix.
        - 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 cameraMatrix,
                                             int method)

      Calculates an essential matrix from the corresponding points in two images.
      Parameters:
        `points1` - Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
        `points2` - Array of the second image points of the same size and format as points1.
        `cameraMatrix` - Camera intrinsic matrix \(\cameramatrix{A}\) . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix. If this assumption does not hold for your use case, use another function overload or #undistortPoints with `P = cv::NoArray()` for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.
        `method` - Method for computing an essential matrix.
        - REF: RANSAC for the RANSAC algorithm.
        - REF: LMEDS for the LMedS algorithm.
        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 cameraMatrix)

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

          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.
        line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
        `prob` - Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. 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

          public static Mat findEssentialMat​(Mat points1,
                                             Mat points2,
                                             double focal,
                                             Point pp,
                                             int method)

      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.
        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 (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

          public static Mat findEssentialMat​(Mat points1,
                                             Mat points2,
                                             double focal,
                                             Point pp)

      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.
        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 (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

          public static Mat findEssentialMat​(Mat points1,
                                             Mat points2,
                                             double focal)

      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.
        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 (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

          public static Mat findEssentialMat​(Mat points1,
                                             Mat points2)

      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.
        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 (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

          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 \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `cameraMatrix2` - Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `distCoeffs1` - Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `distCoeffs2` - Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `method` - Method for computing an essential matrix.
        - 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 \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `cameraMatrix2` - Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `distCoeffs1` - Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `distCoeffs2` - Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `method` - Method for computing an essential matrix.
        - 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 \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `cameraMatrix2` - Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `distCoeffs1` - Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `distCoeffs2` - Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `method` - Method for computing an essential matrix.
        - 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 \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `cameraMatrix2` - Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `distCoeffs1` - Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `distCoeffs2` - Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `method` - Method for computing an essential matrix.
        - REF: RANSAC for the RANSAC algorithm.
        - REF: LMEDS for the LMedS algorithm.
        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)

      Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
      Parameters:
        `points1` - Array of N (N >= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision).
        `points2` - Array of the second image points of the same size and format as points1.
        `cameraMatrix1` - Camera matrix for the first camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `cameraMatrix2` - Camera matrix for the second camera \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `distCoeffs1` - Input vector of distortion coefficients for the first camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `distCoeffs2` - Input vector of distortion coefficients for the second camera \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        - REF: RANSAC for the RANSAC algorithm.
        - REF: LMEDS for the LMedS algorithm.
        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 cameraMatrix2,
                                             Mat dist_coeff1,
                                             Mat dist_coeff2,
                                             Mat mask,
                                             UsacParams params)

    - #### decomposeEssentialMat

          public static void decomposeEssentialMat​(Mat E,
                                                   Mat R1,
                                                   Mat R2,
                                                   Mat t)

      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.
        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)

      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.
        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 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

          public static int recoverPose​(Mat E,
                                        Mat points1,
                                        Mat points2,
                                        Mat cameraMatrix,
                                        Mat R,
                                        Mat t)

      Recovers the relative camera rotation and the translation from an estimated essential matrix and the corresponding points in two images, using chirality check. Returns the number of inliers that pass the check.
      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

          public static int recoverPose​(Mat E,
                                        Mat points1,
                                        Mat points2,
                                        Mat R,
                                        Mat t,
                                        double focal)

      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

          public static int recoverPose​(Mat E,
                                        Mat points1,
                                        Mat points2,
                                        Mat R,
                                        Mat t)

      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

          public static void computeCorrespondEpilines​(Mat points,
                                                       int whichImage,
                                                       Mat F,
                                                       Mat lines)

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

          public static void correctMatches​(Mat F,
                                            Mat points1,
                                            Mat points2,
                                            Mat newPoints1,
                                            Mat newPoints2)

      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

          public static double sampsonDistance​(Mat pt1,
                                               Mat pt2,
                                               Mat F)

      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 point
        `pt2` - second homogeneous 2d point
        `F` - fundamental matrix
      Returns:
        The computed Sampson distance.

    - #### estimateAffine3D

          public static int estimateAffine3D​(Mat src,
                                             Mat dst,
                                             Mat out,
                                             Mat inliers,
                                             double ransacThreshold,
                                             double confidence)

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

    - #### estimateAffine3D

          public static int 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. The function estimates an optimal 3D affine transformation between two 3D point sets using the RANSAC algorithm.
      Returns:
        automatically generated

    - #### estimateAffine3D

          public static int estimateAffine3D​(Mat src,
                                             Mat dst,
                                             Mat out,
                                             Mat inliers)

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

    - #### estimateAffine3D

          public static Mat estimateAffine3D​(Mat src,
                                             Mat dst,
                                             double[] scale,
                                             boolean force_rotation)

      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

          public static Mat estimateAffine3D​(Mat src,
                                             Mat dst,
                                             double[] scale)

      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

          public static Mat estimateAffine3D​(Mat src,
                                             Mat dst)

      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 int 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. The function estimates an optimal 3D translation between two 3D point sets using the RANSAC algorithm.
      Returns:
        automatically generated

    - #### estimateTranslation3D

          public static int 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. The function estimates an optimal 3D translation between two 3D point sets using the RANSAC algorithm.
      Returns:
        automatically generated

    - #### estimateTranslation3D

          public static int estimateTranslation3D​(Mat src,
                                                  Mat dst,
                                                  Mat out,
                                                  Mat inliers)

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

    - #### 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

          public static Mat estimateAffine2D​(Mat from,
                                             Mat to,
                                             Mat inliers,
                                             int method)

      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.
        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

          public static Mat estimateAffine2D​(Mat from,
                                             Mat to,
                                             Mat inliers)

      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.
        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

          public static Mat estimateAffine2D​(Mat from,
                                             Mat to)

      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.
        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

          public static Mat estimateAffine2D​(Mat pts1,
                                             Mat pts2,
                                             Mat inliers,
                                             UsacParams params)

    - #### 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

          public static Mat estimateAffinePartial2D​(Mat from,
                                                    Mat to,
                                                    Mat inliers,
                                                    int method)

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

          public static Mat estimateAffinePartial2D​(Mat from,
                                                    Mat to,
                                                    Mat inliers)

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

          public static Mat estimateAffinePartial2D​(Mat from,
                                                    Mat to)

      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.
        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

    - #### decomposeHomographyMat

          public static int decomposeHomographyMat​(Mat H,
                                                   Mat K,
                                                   java.util.List<Mat> rotations,
                                                   java.util.List<Mat> translations,
                                                   java.util.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​(java.util.List<Mat> rotations,
                                                                      java.util.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 applied
        `afterPoints` - Vector of (rectified) visible reference points after the homography is applied
        `possibleSolutions` - Vector of int indices representing the viable solution set after filtering
        `pointsMask` - optional Mat/Vector of 8u type representing the mask for the inliers as given by the #findHomography function This function is intended to filter the output of the #decomposeHomographyMat based on additional information as described in 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​(java.util.List<Mat> rotations,
                                                                      java.util.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 applied
        `afterPoints` - Vector of (rectified) visible reference points after the homography is applied
        `possibleSolutions` - Vector of int indices representing the viable solution set after filtering 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.

    - #### undistort

          public static void undistort​(Mat src,
                                       Mat dst,
                                       Mat cameraMatrix,
                                       Mat distCoeffs,
                                       Mat newCameraMatrix)

      Transforms an image to compensate for lens distortion. The function transforms an image to compensate radial and tangential lens distortion. The function is simply a combination of #initUndistortRectifyMap (with unity R ) and #remap (with bilinear interpolation). See the former function for details of the transformation being performed. Those pixels in the destination image, for which there is no correspondent pixels in the source image, are filled with zeros (black color). A particular subset of the source image that will be visible in the corrected image can be regulated by newCameraMatrix. You can use #getOptimalNewCameraMatrix to compute the appropriate newCameraMatrix depending on your requirements. The camera matrix and the distortion parameters can be determined using #calibrateCamera. If the resolution of images is different from the resolution used at the calibration stage, \(f_x, f_y, c_x\) and \(c_y\) need to be scaled accordingly, while the distortion coefficients remain the same.
      Parameters:
        `src` - Input (distorted) image.
        `dst` - Output (corrected) image that has the same size and type as src .
        `cameraMatrix` - Input camera matrix \(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `distCoeffs` - Input vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `newCameraMatrix` - Camera matrix of the distorted image. By default, it is the same as cameraMatrix but you may additionally scale and shift the result by using a different matrix.

    - #### undistort

          public static void undistort​(Mat src,
                                       Mat dst,
                                       Mat cameraMatrix,
                                       Mat distCoeffs)

      Transforms an image to compensate for lens distortion. The function transforms an image to compensate radial and tangential lens distortion. The function is simply a combination of #initUndistortRectifyMap (with unity R ) and #remap (with bilinear interpolation). See the former function for details of the transformation being performed. Those pixels in the destination image, for which there is no correspondent pixels in the source image, are filled with zeros (black color). A particular subset of the source image that will be visible in the corrected image can be regulated by newCameraMatrix. You can use #getOptimalNewCameraMatrix to compute the appropriate newCameraMatrix depending on your requirements. The camera matrix and the distortion parameters can be determined using #calibrateCamera. If the resolution of images is different from the resolution used at the calibration stage, \(f_x, f_y, c_x\) and \(c_y\) need to be scaled accordingly, while the distortion coefficients remain the same.
      Parameters:
        `src` - Input (distorted) image.
        `dst` - Output (corrected) image that has the same size and type as src .
        `cameraMatrix` - Input camera matrix \(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `distCoeffs` - Input vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. cameraMatrix but you may additionally scale and shift the result by using a different matrix.

    - #### initUndistortRectifyMap

          public static void initUndistortRectifyMap​(Mat cameraMatrix,
                                                     Mat distCoeffs,
                                                     Mat R,
                                                     Mat newCameraMatrix,
                                                     Size size,
                                                     int m1type,
                                                     Mat map1,
                                                     Mat map2)

      Computes the undistortion and rectification transformation map. The function computes the joint undistortion and rectification transformation and represents the result in the form of maps for #remap. The undistorted image looks like original, as if it is captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by #getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify . Also, this new camera is oriented differently in the coordinate space, according to R. That, for example, helps to align two heads of a stereo camera so that the epipolar lines on both images become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera). The function actually builds the maps for the inverse mapping algorithm that is used by #remap. That is, for each pixel \((u, v)\) in the destination (corrected and rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original image from camera). The following process is applied: \( \begin{array}{l} x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\ {[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ x' \leftarrow X/W \\ y' \leftarrow Y/W \\ r^2 \leftarrow x'^2 + y'^2 \\ x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\ y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ s\vecthree{x'''}{y'''}{1} = \vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} {0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} {0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ map_x(u,v) \leftarrow x''' f_x + c_x \\ map_y(u,v) \leftarrow y''' f_y + c_y \end{array} \) where \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) are the distortion coefficients. In case of a stereo camera, this function is called twice: once for each camera head, after #stereoRectify, which in its turn is called after #stereoCalibrate. But if the stereo camera was not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using #stereoRectifyUncalibrated. For each camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as \(\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\) where cameraMatrix can be chosen arbitrarily.
      Parameters:
        `cameraMatrix` - Input camera matrix \(A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `distCoeffs` - Input vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `R` - Optional rectification transformation in the object space (3x3 matrix). R1 or R2 , computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation is assumed. In #initUndistortRectifyMap R assumed to be an identity matrix.
        `newCameraMatrix` - New camera matrix \(A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\).
        `size` - Undistorted image size.
        `m1type` - Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
        `map1` - The first output map.
        `map2` - The second output map.

    - #### initInverseRectificationMap

          public static void initInverseRectificationMap​(Mat cameraMatrix,
                                                         Mat distCoeffs,
                                                         Mat R,
                                                         Mat newCameraMatrix,
                                                         Size size,
                                                         int m1type,
                                                         Mat map1,
                                                         Mat map2)

      Computes the projection and inverse-rectification transformation map. In essense, this is the inverse of #initUndistortRectifyMap to accomodate stereo-rectification of projectors ('inverse-cameras') in projector-camera pairs. The function computes the joint projection and inverse rectification transformation and represents the result in the form of maps for #remap. The projected image looks like a distorted version of the original which, once projected by a projector, should visually match the original. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by #getOptimalNewCameraMatrix for a better control over scaling. In case of a projector-camera pair, newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify . The projector is oriented differently in the coordinate space, according to R. In case of projector-camera pairs, this helps align the projector (in the same manner as #initUndistortRectifyMap for the camera) to create a stereo-rectified pair. This allows epipolar lines on both images to become horizontal and have the same y-coordinate (in case of a horizontally aligned projector-camera pair). The function builds the maps for the inverse mapping algorithm that is used by #remap. That is, for each pixel \((u, v)\) in the destination (projected and inverse-rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original digital image). The following process is applied: \( \begin{array}{l} \text{newCameraMatrix}\\ x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\ \\\text{Undistortion} \\\scriptsize{\textit{though equation shown is for radial undistortion, function implements cv::undistortPoints()}}\\ r^2 \leftarrow x^2 + y^2 \\ \theta \leftarrow \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}\\ x' \leftarrow \frac{x}{\theta} \\ y' \leftarrow \frac{y}{\theta} \\ \\\text{Rectification}\\ {[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ x'' \leftarrow X/W \\ y'' \leftarrow Y/W \\ \\\text{cameraMatrix}\\ map_x(u,v) \leftarrow x'' f_x + c_x \\ map_y(u,v) \leftarrow y'' f_y + c_y \end{array} \) where \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) are the distortion coefficients vector distCoeffs. In case of a stereo-rectified projector-camera pair, this function is called for the projector while #initUndistortRectifyMap is called for the camera head. This is done after #stereoRectify, which in turn is called after #stereoCalibrate. If the projector-camera pair is not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using #stereoRectifyUncalibrated. For the projector and camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as \(\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\) where cameraMatrix can be chosen arbitrarily.
      Parameters:
        `cameraMatrix` - Input camera matrix \(A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `distCoeffs` - Input vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `R` - Optional rectification transformation in the object space (3x3 matrix). R1 or R2, computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation is assumed.
        `newCameraMatrix` - New camera matrix \(A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\).
        `size` - Distorted image size.
        `m1type` - Type of the first output map. Can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
        `map1` - The first output map for #remap.
        `map2` - The second output map for #remap.

    - #### 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

          public static Mat getDefaultNewCameraMatrix​(Mat cameraMatrix,
                                                      Size imgsize)

      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

          public static Mat getDefaultNewCameraMatrix​(Mat cameraMatrix)

      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).
      Parameters:
        `src` - Observed point coordinates, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or vector<Point2f> ).
        `dst` - Output ideal point coordinates (1xN/Nx1 2-channel or vector<Point2f> ) after undistortion and reverse perspective transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
        `cameraMatrix` - Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `distCoeffs` - Input vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `R` - Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation is used.
        `P` - New camera matrix (3x3) or new projection matrix (3x4) \(\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\). P1 or P2 computed by #stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.
        `criteria` - termination criteria for the iterative point undistortion algorithm

    - #### 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).
      Parameters:
        `src` - Observed point coordinates, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or vector<Point2f> ).
        `dst` - Output ideal point coordinates (1xN/Nx1 2-channel or vector<Point2f> ) after undistortion and reverse perspective transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
        `cameraMatrix` - Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `distCoeffs` - Input vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `R` - Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation is used.
        `P` - New camera matrix (3x3) or new projection matrix (3x4) \(\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\). P1 or P2 computed by #stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.

    - #### undistortPoints

          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).
      Parameters:
        `src` - Observed point coordinates, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or vector<Point2f> ).
        `dst` - Output ideal point coordinates (1xN/Nx1 2-channel or vector<Point2f> ) after undistortion and reverse perspective transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
        `cameraMatrix` - Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `distCoeffs` - Input vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
        `R` - Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation is used. #stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.

    - #### 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).
      Parameters:
        `src` - Observed point coordinates, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or vector<Point2f> ).
        `dst` - Output ideal point coordinates (1xN/Nx1 2-channel or vector<Point2f> ) after undistortion and reverse perspective transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
        `cameraMatrix` - Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
        `distCoeffs` - Input vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. #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.

    - #### 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 coefficients
        `arg1` - automatically generated

    - #### undistortImagePoints

          public static void undistortImagePoints​(Mat src,
                                                  Mat dst,
                                                  Mat cameraMatrix,
                                                  Mat distCoeffs)

      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

          public static void fisheye_projectPoints​(Mat objectPoints,
                                                   Mat imagePoints,
                                                   Mat rvec,
                                                   Mat tvec,
                                                   Mat K,
                                                   Mat D,
                                                   double alpha,
                                                   Mat jacobian)

    - #### fisheye_projectPoints

          public static void fisheye_projectPoints​(Mat objectPoints,
                                                   Mat imagePoints,
                                                   Mat rvec,
                                                   Mat tvec,
                                                   Mat K,
                                                   Mat D,
                                                   double alpha)

    - #### fisheye_projectPoints

          public static void fisheye_projectPoints​(Mat objectPoints,
                                                   Mat imagePoints,
                                                   Mat rvec,
                                                   Mat tvec,
                                                   Mat K,
                                                   Mat D)

    - #### 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.
        `K` - Camera intrinsic matrix \(cameramatrix{K}\).
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\).
        `alpha` - The skew coefficient.
        `distorted` - Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> . Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity. This means if you want to distort image points you have to multiply them with \(K^{-1}\) or use another function overload.

    - #### fisheye_distortPoints

          public static void fisheye_distortPoints​(Mat undistorted,
                                                   Mat distorted,
                                                   Mat K,
                                                   Mat D)

      Distorts 2D points using fisheye model.
      Parameters:
        `undistorted` - Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
        `K` - Camera intrinsic matrix \(cameramatrix{K}\).
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\).
        `distorted` - Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> . Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity. This means if you want to distort image points you have to multiply them with \(K^{-1}\) or use another function overload.

    - #### fisheye_distortPoints

          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.
        `Kundistorted` - Camera intrinsic matrix used as new camera matrix for undistortion.
        `K` - Camera intrinsic matrix \(cameramatrix{K}\).
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\).
        `alpha` - The skew coefficient.
        `distorted` - Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> . SEE: estimateNewCameraMatrixForUndistortRectify

    - #### 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.
        `Kundistorted` - Camera intrinsic matrix used as new camera matrix for undistortion.
        `K` - Camera intrinsic matrix \(cameramatrix{K}\).
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\).
        `distorted` - Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> . SEE: estimateNewCameraMatrixForUndistortRectify

    - #### 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 model
      Parameters:
        `distorted` - Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
        `K` - Camera intrinsic matrix \(cameramatrix{K}\).
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\).
        `R` - Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
        `P` - New camera intrinsic matrix (3x3) or new projection matrix (3x4)
        `criteria` - Termination criteria
        `undistorted` - Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .

    - #### fisheye_undistortPoints

          public static void fisheye_undistortPoints​(Mat distorted,
                                                     Mat undistorted,
                                                     Mat K,
                                                     Mat D,
                                                     Mat R,
                                                     Mat P)

      Undistorts 2D points using fisheye model
      Parameters:
        `distorted` - Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
        `K` - Camera intrinsic matrix \(cameramatrix{K}\).
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\).
        `R` - Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
        `P` - New camera intrinsic matrix (3x3) or new projection matrix (3x4)
        `undistorted` - Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .

    - #### fisheye_undistortPoints

          public static void fisheye_undistortPoints​(Mat distorted,
                                                     Mat undistorted,
                                                     Mat K,
                                                     Mat D,
                                                     Mat R)

      Undistorts 2D points using fisheye model
      Parameters:
        `distorted` - Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
        `K` - Camera intrinsic matrix \(cameramatrix{K}\).
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\).
        `R` - Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
        `undistorted` - Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .

    - #### fisheye_undistortPoints

          public static void fisheye_undistortPoints​(Mat distorted,
                                                     Mat undistorted,
                                                     Mat K,
                                                     Mat D)

      Undistorts 2D points using fisheye model
      Parameters:
        `distorted` - Array of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
        `K` - Camera intrinsic matrix \(cameramatrix{K}\).
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\). 1-channel or 1x1 3-channel
        `undistorted` - Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .

    - #### 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 \(cameramatrix{K}\).
        `image_size` - Size of the image
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\).
        `R` - Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
        `P` - New camera intrinsic matrix (3x3) or new projection matrix (3x4)
        `balance` - Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
        `new_size` - the new size
        `fov_scale` - Divisor for new focal length.

    - #### fisheye_estimateNewCameraMatrixForUndistortRectify

          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 \(cameramatrix{K}\).
        `image_size` - Size of the image
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\).
        `R` - Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
        `P` - New camera intrinsic matrix (3x3) or new projection matrix (3x4)
        `balance` - Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
        `new_size` - the new size

    - #### 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 \(cameramatrix{K}\).
        `image_size` - Size of the image
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\).
        `R` - Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
        `P` - New camera intrinsic matrix (3x3) or new projection matrix (3x4)
        `balance` - Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].

    - #### 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 \(cameramatrix{K}\).
        `image_size` - Size of the image
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\).
        `R` - Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
        `P` - New camera intrinsic matrix (3x3) or new projection matrix (3x4) length. Balance is in range of [0, 1].

    - #### fisheye_initUndistortRectifyMap

          public static void fisheye_initUndistortRectifyMap​(Mat K,
                                                             Mat D,
                                                             Mat R,
                                                             Mat P,
                                                             Size size,
                                                             int m1type,
                                                             Mat map1,
                                                             Mat map2)

      Computes undistortion and rectification maps for image transform by cv::remap(). If D is empty zero distortion is used, if R or P is empty identity matrixes are used.
      Parameters:
        `K` - Camera intrinsic matrix \(cameramatrix{K}\).
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\).
        `R` - Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
        `P` - New camera intrinsic matrix (3x3) or new projection matrix (3x4)
        `size` - Undistorted image size.
        `m1type` - Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps() for details.
        `map1` - The first output map.
        `map2` - The second output map.

    - #### fisheye_undistortImage

          public static void fisheye_undistortImage​(Mat distorted,
                                                    Mat undistorted,
                                                    Mat K,
                                                    Mat D,
                                                    Mat Knew,
                                                    Size new_size)

      Transforms an image to compensate for fisheye lens distortion.
      Parameters:
        `distorted` - image with fisheye lens distortion.
        `undistorted` - Output image with compensated fisheye lens distortion.
        `K` - Camera intrinsic matrix \(cameramatrix{K}\).
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\).
        `Knew` - Camera intrinsic matrix of the distorted image. By default, it is the identity matrix but you may additionally scale and shift the result by using a different matrix.
        `new_size` - the new size The function transforms an image to compensate radial and tangential lens distortion. The function is simply a combination of fisheye::initUndistortRectifyMap (with unity R ) and remap (with bilinear interpolation). See the former function for details of the transformation being performed. See below the results of undistortImage.
        - a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, k_4, k_5, k_6) of distortion were optimized under calibration)
          - b\) result of fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, k_3, k_4) of fisheye distortion were optimized under calibration)
          - c\) original image was captured with fisheye lens
          Pictures a) and b) almost the same. But if we consider points of image located far from the center of image, we can notice that on image a) these points are distorted.
        ![image](pics/fisheye_undistorted.jpg)

    - #### fisheye_undistortImage

          public static void fisheye_undistortImage​(Mat distorted,
                                                    Mat undistorted,
                                                    Mat K,
                                                    Mat D,
                                                    Mat Knew)

      Transforms an image to compensate for fisheye lens distortion.
      Parameters:
        `distorted` - image with fisheye lens distortion.
        `undistorted` - Output image with compensated fisheye lens distortion.
        `K` - Camera intrinsic matrix \(cameramatrix{K}\).
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\).
        `Knew` - Camera intrinsic matrix of the distorted image. By default, it is the identity matrix but you may additionally scale and shift the result by using a different matrix. The function transforms an image to compensate radial and tangential lens distortion. The function is simply a combination of fisheye::initUndistortRectifyMap (with unity R ) and remap (with bilinear interpolation). See the former function for details of the transformation being performed. See below the results of undistortImage.
        - a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, k_4, k_5, k_6) of distortion were optimized under calibration)
          - b\) result of fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, k_3, k_4) of fisheye distortion were optimized under calibration)
          - c\) original image was captured with fisheye lens
          Pictures a) and b) almost the same. But if we consider points of image located far from the center of image, we can notice that on image a) these points are distorted.
        ![image](pics/fisheye_undistorted.jpg)

    - #### fisheye_undistortImage

          public static void fisheye_undistortImage​(Mat distorted,
                                                    Mat undistorted,
                                                    Mat K,
                                                    Mat D)

      Transforms an image to compensate for fisheye lens distortion.
      Parameters:
        `distorted` - image with fisheye lens distortion.
        `undistorted` - Output image with compensated fisheye lens distortion.
        `K` - Camera intrinsic matrix \(cameramatrix{K}\).
        `D` - Input vector of distortion coefficients \(\distcoeffsfisheye\). may additionally scale and shift the result by using a different matrix. The function transforms an image to compensate radial and tangential lens distortion. The function is simply a combination of fisheye::initUndistortRectifyMap (with unity R ) and remap (with bilinear interpolation). See the former function for details of the transformation being performed. See below the results of undistortImage.
        - a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, k_4, k_5, k_6) of distortion were optimized under calibration)
          - b\) result of fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, k_3, k_4) of fisheye distortion were optimized under calibration)
          - c\) original image was captured with fisheye lens
          Pictures a) and b) almost the same. But if we consider points of image located far from the center of image, we can notice that on image a) these points are distorted.
        ![image](pics/fisheye_undistorted.jpg)

    - #### 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 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.
        `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 interally 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 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.
        `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.
        The function interally 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)

      Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
      Parameters:
        `objectPoints` - Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
        `imagePoints` - Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
        `cameraMatrix` - Input camera intrinsic matrix \(\cameramatrix{A}\) .
        `distCoeffs` - Input vector of distortion coefficients (4x1/1x4).
        `rvec` - Output rotation vector (see 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.
        The function interally 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)

      Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
      Parameters:
        `objectPoints` - Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
        `imagePoints` - Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
        `cameraMatrix` - Input camera intrinsic matrix \(\cameramatrix{A}\) .
        `distCoeffs` - Input vector of distortion coefficients (4x1/1x4).
        `rvec` - Output rotation vector (see 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.
        The function interally 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

    - #### loadPointCloud

          public static void loadPointCloud​(java.lang.String filename,
                                            Mat vertices,
                                            Mat normals,
                                            Mat rgb)

      Loads a point cloud from a file. The function loads point cloud from the specified file and returns it. If the cloud cannot be read, throws an error. Vertex coordinates, normals and colors are returned as they are saved in the file even if these arrays have different sizes and their elements do not correspond to each other (which is typical for OBJ files for example) Currently, the following file formats are supported: - [Wavefront obj file *.obj](https://en.wikipedia.org/wiki/Wavefront_.obj_file) - [Polygon File Format *.ply](https://en.wikipedia.org/wiki/PLY_(file_format))
      Parameters:
        `filename` - Name of the file
        `vertices` - vertex coordinates, each value contains 3 floats
        `normals` - per-vertex normals, each value contains 3 floats
        `rgb` - per-vertex colors, each value contains 3 floats

    - #### loadPointCloud

          public static void loadPointCloud​(java.lang.String filename,
                                            Mat vertices,
                                            Mat normals)

      Loads a point cloud from a file. The function loads point cloud from the specified file and returns it. If the cloud cannot be read, throws an error. Vertex coordinates, normals and colors are returned as they are saved in the file even if these arrays have different sizes and their elements do not correspond to each other (which is typical for OBJ files for example) Currently, the following file formats are supported: - [Wavefront obj file *.obj](https://en.wikipedia.org/wiki/Wavefront_.obj_file) - [Polygon File Format *.ply](https://en.wikipedia.org/wiki/PLY_(file_format))
      Parameters:
        `filename` - Name of the file
        `vertices` - vertex coordinates, each value contains 3 floats
        `normals` - per-vertex normals, each value contains 3 floats

    - #### loadPointCloud

          public static void loadPointCloud​(java.lang.String filename,
                                            Mat vertices)

      Loads a point cloud from a file. The function loads point cloud from the specified file and returns it. If the cloud cannot be read, throws an error. Vertex coordinates, normals and colors are returned as they are saved in the file even if these arrays have different sizes and their elements do not correspond to each other (which is typical for OBJ files for example) Currently, the following file formats are supported: - [Wavefront obj file *.obj](https://en.wikipedia.org/wiki/Wavefront_.obj_file) - [Polygon File Format *.ply](https://en.wikipedia.org/wiki/PLY_(file_format))
      Parameters:
        `filename` - Name of the file
        `vertices` - vertex coordinates, each value contains 3 floats

    - #### savePointCloud

          public static void savePointCloud​(java.lang.String filename,
                                            Mat vertices,
                                            Mat normals,
                                            Mat rgb)

      Saves a point cloud to a specified file. The function saves point cloud to the specified file. File format is chosen based on the filename extension.
      Parameters:
        `filename` - Name of the file
        `vertices` - vertex coordinates, each value contains 3 floats
        `normals` - per-vertex normals, each value contains 3 floats
        `rgb` - per-vertex colors, each value contains 3 floats

    - #### savePointCloud

          public static void savePointCloud​(java.lang.String filename,
                                            Mat vertices,
                                            Mat normals)

      Saves a point cloud to a specified file. The function saves point cloud to the specified file. File format is chosen based on the filename extension.
      Parameters:
        `filename` - Name of the file
        `vertices` - vertex coordinates, each value contains 3 floats
        `normals` - per-vertex normals, each value contains 3 floats

    - #### savePointCloud

          public static void savePointCloud​(java.lang.String filename,
                                            Mat vertices)

      Saves a point cloud to a specified file. The function saves point cloud to the specified file. File format is chosen based on the filename extension.
      Parameters:
        `filename` - Name of the file
        `vertices` - vertex coordinates, each value contains 3 floats

    - #### loadMesh

          public static void loadMesh​(java.lang.String filename,
                                      Mat vertices,
                                      java.util.List<Mat> indices,
                                      Mat normals,
                                      Mat colors,
                                      Mat texCoords)

      Loads a mesh from a file. The function loads mesh from the specified file and returns it. If the mesh cannot be read, throws an error Vertex attributes (i.e. space and texture coodinates, normals and colors) are returned in same-sized arrays with corresponding elements having the same indices. This means that if a face uses a vertex with a normal or a texture coordinate with different indices (which is typical for OBJ files for example), this vertex will be duplicated for each face it uses. Currently, the following file formats are supported: - [Wavefront obj file *.obj](https://en.wikipedia.org/wiki/Wavefront_.obj_file) (ONLY TRIANGULATED FACES) - [Polygon File Format *.ply](https://en.wikipedia.org/wiki/PLY_(file_format))
      Parameters:
        `filename` - Name of the file
        `vertices` - vertex coordinates, each value contains 3 floats
        `indices` - per-face list of vertices, each value is a vector of ints
        `normals` - per-vertex normals, each value contains 3 floats
        `colors` - per-vertex colors, each value contains 3 floats
        `texCoords` - per-vertex texture coordinates, each value contains 2 or 3 floats

    - #### loadMesh

          public static void loadMesh​(java.lang.String filename,
                                      Mat vertices,
                                      java.util.List<Mat> indices,
                                      Mat normals,
                                      Mat colors)

      Loads a mesh from a file. The function loads mesh from the specified file and returns it. If the mesh cannot be read, throws an error Vertex attributes (i.e. space and texture coodinates, normals and colors) are returned in same-sized arrays with corresponding elements having the same indices. This means that if a face uses a vertex with a normal or a texture coordinate with different indices (which is typical for OBJ files for example), this vertex will be duplicated for each face it uses. Currently, the following file formats are supported: - [Wavefront obj file *.obj](https://en.wikipedia.org/wiki/Wavefront_.obj_file) (ONLY TRIANGULATED FACES) - [Polygon File Format *.ply](https://en.wikipedia.org/wiki/PLY_(file_format))
      Parameters:
        `filename` - Name of the file
        `vertices` - vertex coordinates, each value contains 3 floats
        `indices` - per-face list of vertices, each value is a vector of ints
        `normals` - per-vertex normals, each value contains 3 floats
        `colors` - per-vertex colors, each value contains 3 floats

    - #### loadMesh

          public static void loadMesh​(java.lang.String filename,
                                      Mat vertices,
                                      java.util.List<Mat> indices,
                                      Mat normals)

      Loads a mesh from a file. The function loads mesh from the specified file and returns it. If the mesh cannot be read, throws an error Vertex attributes (i.e. space and texture coodinates, normals and colors) are returned in same-sized arrays with corresponding elements having the same indices. This means that if a face uses a vertex with a normal or a texture coordinate with different indices (which is typical for OBJ files for example), this vertex will be duplicated for each face it uses. Currently, the following file formats are supported: - [Wavefront obj file *.obj](https://en.wikipedia.org/wiki/Wavefront_.obj_file) (ONLY TRIANGULATED FACES) - [Polygon File Format *.ply](https://en.wikipedia.org/wiki/PLY_(file_format))
      Parameters:
        `filename` - Name of the file
        `vertices` - vertex coordinates, each value contains 3 floats
        `indices` - per-face list of vertices, each value is a vector of ints
        `normals` - per-vertex normals, each value contains 3 floats

    - #### loadMesh

          public static void loadMesh​(java.lang.String filename,
                                      Mat vertices,
                                      java.util.List<Mat> indices)

      Loads a mesh from a file. The function loads mesh from the specified file and returns it. If the mesh cannot be read, throws an error Vertex attributes (i.e. space and texture coodinates, normals and colors) are returned in same-sized arrays with corresponding elements having the same indices. This means that if a face uses a vertex with a normal or a texture coordinate with different indices (which is typical for OBJ files for example), this vertex will be duplicated for each face it uses. Currently, the following file formats are supported: - [Wavefront obj file *.obj](https://en.wikipedia.org/wiki/Wavefront_.obj_file) (ONLY TRIANGULATED FACES) - [Polygon File Format *.ply](https://en.wikipedia.org/wiki/PLY_(file_format))
      Parameters:
        `filename` - Name of the file
        `vertices` - vertex coordinates, each value contains 3 floats
        `indices` - per-face list of vertices, each value is a vector of ints

    - #### saveMesh

          public static void saveMesh​(java.lang.String filename,
                                      Mat vertices,
                                      java.util.List<Mat> indices,
                                      Mat normals,
                                      Mat colors,
                                      Mat texCoords)

      Saves a mesh to a specified file. The function saves mesh to the specified file. File format is chosen based on the filename extension.
      Parameters:
        `filename` - Name of the file.
        `vertices` - vertex coordinates, each value contains 3 floats
        `indices` - per-face list of vertices, each value is a vector of ints
        `normals` - per-vertex normals, each value contains 3 floats
        `colors` - per-vertex colors, each value contains 3 floats
        `texCoords` - per-vertex texture coordinates, each value contains 2 or 3 floats

    - #### saveMesh

          public static void saveMesh​(java.lang.String filename,
                                      Mat vertices,
                                      java.util.List<Mat> indices,
                                      Mat normals,
                                      Mat colors)

      Saves a mesh to a specified file. The function saves mesh to the specified file. File format is chosen based on the filename extension.
      Parameters:
        `filename` - Name of the file.
        `vertices` - vertex coordinates, each value contains 3 floats
        `indices` - per-face list of vertices, each value is a vector of ints
        `normals` - per-vertex normals, each value contains 3 floats
        `colors` - per-vertex colors, each value contains 3 floats

    - #### saveMesh

          public static void saveMesh​(java.lang.String filename,
                                      Mat vertices,
                                      java.util.List<Mat> indices,
                                      Mat normals)

      Saves a mesh to a specified file. The function saves mesh to the specified file. File format is chosen based on the filename extension.
      Parameters:
        `filename` - Name of the file.
        `vertices` - vertex coordinates, each value contains 3 floats
        `indices` - per-face list of vertices, each value is a vector of ints
        `normals` - per-vertex normals, each value contains 3 floats

    - #### saveMesh

          public static void saveMesh​(java.lang.String filename,
                                      Mat vertices,
                                      java.util.List<Mat> indices)

      Saves a mesh to a specified file. The function saves mesh to the specified file. File format is chosen based on the filename extension.
      Parameters:
        `filename` - Name of the file.
        `vertices` - vertex coordinates, each value contains 3 floats
        `indices` - per-face list of vertices, each value is a vector of ints

    - #### triangleRasterize

          public static void triangleRasterize​(Mat vertices,
                                               Mat indices,
                                               Mat colors,
                                               Mat colorBuf,
                                               Mat depthBuf,
                                               Mat world2cam,
                                               double fovY,
                                               double zNear,
                                               double zFar,
                                               TriangleRasterizeSettings settings)

      Renders a set of triangles on a depth and color image Triangles can be drawn white (1.0, 1.0, 1.0), flat-shaded or with a color interpolation between vertices. In flat-shaded mode the 1st vertex color of each triangle is used to fill the whole triangle. The world2cam is an inverted camera pose matrix in fact. It transforms vertices from world to camera coordinate system. The camera coordinate system emulates the OpenGL's coordinate system having coordinate origin in a screen center, X axis pointing right, Y axis pointing up and Z axis pointing towards the viewer except that image is vertically flipped after the render. This means that all visible objects are placed in z-negative area, or exactly in -zNear > z > -zFar since zNear and zFar are positive. For example, at fovY = PI/2 the point (0, 1, -1) will be projected to (width/2, 0) screen point, (1, 0, -1) to (width/2 + height/2, height/2). Increasing fovY makes projection smaller and vice versa. The function does not create or clear output images before the rendering. This means that it can be used for drawing over an existing image or for rendering a model into a 3D scene using pre-filled Z-buffer. Empty scene results in a depth buffer filled by the maximum value since every pixel is infinitely far from the camera. Therefore, before rendering anything from scratch the depthBuf should be filled by zFar values (or by ones in INVDEPTH mode). There are special versions of this function named triangleRasterizeDepth and triangleRasterizeColor for cases if a user needs a color image or a depth image alone; they may run slightly faster.
      Parameters:
        `vertices` - vertices coordinates array. Should contain values of CV_32FC3 type or a compatible one (e.g. cv::Vec3f, etc.)
        `indices` - triangle vertices index array, 3 per triangle. Each index indicates a vertex in a vertices array. Should contain CV_32SC3 values or compatible
        `colors` - per-vertex colors of CV_32FC3 type or compatible. Can be empty or the same size as vertices array. If the values are out of [0; 1] range, the result correctness is not guaranteed
        `colorBuf` - an array representing the final rendered image. Should containt CV_32FC3 values and be the same size as depthBuf. Not cleared before rendering, i.e. the content is reused as there is some pre-rendered scene.
        `depthBuf` - an array of floats containing resulting Z buffer. Should contain float values and be the same size as colorBuf. Not cleared before rendering, i.e. the content is reused as there is some pre-rendered scene. Empty scene corresponds to all values set to zFar (or to 1.0 in INVDEPTH mode)
        `world2cam` - a 4x3 or 4x4 float or double matrix containing inverted (sic!) camera pose
        `fovY` - field of view in vertical direction, given in radians
        `zNear` - minimum Z value to render, everything closer is clipped
        `zFar` - maximum Z value to render, everything farther is clipped
        `settings` - see TriangleRasterizeSettings. By default the smooth shading is on, with CW culling and with disabled GL compatibility

    - #### triangleRasterize

          public static void triangleRasterize​(Mat vertices,
                                               Mat indices,
                                               Mat colors,
                                               Mat colorBuf,
                                               Mat depthBuf,
                                               Mat world2cam,
                                               double fovY,
                                               double zNear,
                                               double zFar)

      Renders a set of triangles on a depth and color image Triangles can be drawn white (1.0, 1.0, 1.0), flat-shaded or with a color interpolation between vertices. In flat-shaded mode the 1st vertex color of each triangle is used to fill the whole triangle. The world2cam is an inverted camera pose matrix in fact. It transforms vertices from world to camera coordinate system. The camera coordinate system emulates the OpenGL's coordinate system having coordinate origin in a screen center, X axis pointing right, Y axis pointing up and Z axis pointing towards the viewer except that image is vertically flipped after the render. This means that all visible objects are placed in z-negative area, or exactly in -zNear > z > -zFar since zNear and zFar are positive. For example, at fovY = PI/2 the point (0, 1, -1) will be projected to (width/2, 0) screen point, (1, 0, -1) to (width/2 + height/2, height/2). Increasing fovY makes projection smaller and vice versa. The function does not create or clear output images before the rendering. This means that it can be used for drawing over an existing image or for rendering a model into a 3D scene using pre-filled Z-buffer. Empty scene results in a depth buffer filled by the maximum value since every pixel is infinitely far from the camera. Therefore, before rendering anything from scratch the depthBuf should be filled by zFar values (or by ones in INVDEPTH mode). There are special versions of this function named triangleRasterizeDepth and triangleRasterizeColor for cases if a user needs a color image or a depth image alone; they may run slightly faster.
      Parameters:
        `vertices` - vertices coordinates array. Should contain values of CV_32FC3 type or a compatible one (e.g. cv::Vec3f, etc.)
        `indices` - triangle vertices index array, 3 per triangle. Each index indicates a vertex in a vertices array. Should contain CV_32SC3 values or compatible
        `colors` - per-vertex colors of CV_32FC3 type or compatible. Can be empty or the same size as vertices array. If the values are out of [0; 1] range, the result correctness is not guaranteed
        `colorBuf` - an array representing the final rendered image. Should containt CV_32FC3 values and be the same size as depthBuf. Not cleared before rendering, i.e. the content is reused as there is some pre-rendered scene.
        `depthBuf` - an array of floats containing resulting Z buffer. Should contain float values and be the same size as colorBuf. Not cleared before rendering, i.e. the content is reused as there is some pre-rendered scene. Empty scene corresponds to all values set to zFar (or to 1.0 in INVDEPTH mode)
        `world2cam` - a 4x3 or 4x4 float or double matrix containing inverted (sic!) camera pose
        `fovY` - field of view in vertical direction, given in radians
        `zNear` - minimum Z value to render, everything closer is clipped
        `zFar` - maximum Z value to render, everything farther is clipped with CW culling and with disabled GL compatibility

    - #### triangleRasterizeDepth

          public static void triangleRasterizeDepth​(Mat vertices,
                                                    Mat indices,
                                                    Mat depthBuf,
                                                    Mat world2cam,
                                                    double fovY,
                                                    double zNear,
                                                    double zFar,
                                                    TriangleRasterizeSettings settings)

      Overloaded version of triangleRasterize() with depth-only rendering
      Parameters:
        `vertices` - vertices coordinates array. Should contain values of CV_32FC3 type or a compatible one (e.g. cv::Vec3f, etc.)
        `indices` - triangle vertices index array, 3 per triangle. Each index indicates a vertex in a vertices array. Should contain CV_32SC3 values or compatible
        `depthBuf` - an array of floats containing resulting Z buffer. Should contain float values and be the same size as colorBuf. Not cleared before rendering, i.e. the content is reused as there is some pre-rendered scene. Empty scene corresponds to all values set to zFar (or to 1.0 in INVDEPTH mode)
        `world2cam` - a 4x3 or 4x4 float or double matrix containing inverted (sic!) camera pose
        `fovY` - field of view in vertical direction, given in radians
        `zNear` - minimum Z value to render, everything closer is clipped
        `zFar` - maximum Z value to render, everything farther is clipped
        `settings` - see TriangleRasterizeSettings. By default the smooth shading is on, with CW culling and with disabled GL compatibility

    - #### triangleRasterizeDepth

          public static void triangleRasterizeDepth​(Mat vertices,
                                                    Mat indices,
                                                    Mat depthBuf,
                                                    Mat world2cam,
                                                    double fovY,
                                                    double zNear,
                                                    double zFar)

      Overloaded version of triangleRasterize() with depth-only rendering
      Parameters:
        `vertices` - vertices coordinates array. Should contain values of CV_32FC3 type or a compatible one (e.g. cv::Vec3f, etc.)
        `indices` - triangle vertices index array, 3 per triangle. Each index indicates a vertex in a vertices array. Should contain CV_32SC3 values or compatible
        `depthBuf` - an array of floats containing resulting Z buffer. Should contain float values and be the same size as colorBuf. Not cleared before rendering, i.e. the content is reused as there is some pre-rendered scene. Empty scene corresponds to all values set to zFar (or to 1.0 in INVDEPTH mode)
        `world2cam` - a 4x3 or 4x4 float or double matrix containing inverted (sic!) camera pose
        `fovY` - field of view in vertical direction, given in radians
        `zNear` - minimum Z value to render, everything closer is clipped
        `zFar` - maximum Z value to render, everything farther is clipped with CW culling and with disabled GL compatibility

    - #### triangleRasterizeColor

          public static void triangleRasterizeColor​(Mat vertices,
                                                    Mat indices,
                                                    Mat colors,
                                                    Mat colorBuf,
                                                    Mat world2cam,
                                                    double fovY,
                                                    double zNear,
                                                    double zFar,
                                                    TriangleRasterizeSettings settings)

      Overloaded version of triangleRasterize() with color-only rendering
      Parameters:
        `vertices` - vertices coordinates array. Should contain values of CV_32FC3 type or a compatible one (e.g. cv::Vec3f, etc.)
        `indices` - triangle vertices index array, 3 per triangle. Each index indicates a vertex in a vertices array. Should contain CV_32SC3 values or compatible
        `colors` - per-vertex colors of CV_32FC3 type or compatible. Can be empty or the same size as vertices array. If the values are out of [0; 1] range, the result correctness is not guaranteed
        `colorBuf` - an array representing the final rendered image. Should containt CV_32FC3 values and be the same size as depthBuf. Not cleared before rendering, i.e. the content is reused as there is some pre-rendered scene.
        `world2cam` - a 4x3 or 4x4 float or double matrix containing inverted (sic!) camera pose
        `fovY` - field of view in vertical direction, given in radians
        `zNear` - minimum Z value to render, everything closer is clipped
        `zFar` - maximum Z value to render, everything farther is clipped
        `settings` - see TriangleRasterizeSettings. By default the smooth shading is on, with CW culling and with disabled GL compatibility

    - #### triangleRasterizeColor

          public static void triangleRasterizeColor​(Mat vertices,
                                                    Mat indices,
                                                    Mat colors,
                                                    Mat colorBuf,
                                                    Mat world2cam,
                                                    double fovY,
                                                    double zNear,
                                                    double zFar)

      Overloaded version of triangleRasterize() with color-only rendering
      Parameters:
        `vertices` - vertices coordinates array. Should contain values of CV_32FC3 type or a compatible one (e.g. cv::Vec3f, etc.)
        `indices` - triangle vertices index array, 3 per triangle. Each index indicates a vertex in a vertices array. Should contain CV_32SC3 values or compatible
        `colors` - per-vertex colors of CV_32FC3 type or compatible. Can be empty or the same size as vertices array. If the values are out of [0; 1] range, the result correctness is not guaranteed
        `colorBuf` - an array representing the final rendered image. Should containt CV_32FC3 values and be the same size as depthBuf. Not cleared before rendering, i.e. the content is reused as there is some pre-rendered scene.
        `world2cam` - a 4x3 or 4x4 float or double matrix containing inverted (sic!) camera pose
        `fovY` - field of view in vertical direction, given in radians
        `zNear` - minimum Z value to render, everything closer is clipped
        `zFar` - maximum Z value to render, everything farther is clipped with CW culling and with disabled GL compatibility

    - #### registerDepth

          public static void registerDepth​(Mat unregisteredCameraMatrix,
                                           Mat registeredCameraMatrix,
                                           Mat registeredDistCoeffs,
                                           Mat Rt,
                                           Mat unregisteredDepth,
                                           Size outputImagePlaneSize,
                                           Mat registeredDepth,
                                           boolean depthDilation)

      Registers depth data to an external camera Registration is performed by creating a depth cloud, transforming the cloud by the rigid body transformation between the cameras, and then projecting the transformed points into the RGB camera. uv_rgb = K_rgb * [R | t] * z * inv(K_ir) * uv_ir Currently does not check for negative depth values.
      Parameters:
        `unregisteredCameraMatrix` - the camera matrix of the depth camera
        `registeredCameraMatrix` - the camera matrix of the external camera
        `registeredDistCoeffs` - the distortion coefficients of the external camera
        `Rt` - the rigid body transform between the cameras. Transforms points from depth camera frame to external camera frame.
        `unregisteredDepth` - the input depth data
        `outputImagePlaneSize` - the image plane dimensions of the external camera (width, height)
        `registeredDepth` - the result of transforming the depth into the external camera
        `depthDilation` - whether or not the depth is dilated to avoid holes and occlusion errors (optional)

    - #### registerDepth

          public static void registerDepth​(Mat unregisteredCameraMatrix,
                                           Mat registeredCameraMatrix,
                                           Mat registeredDistCoeffs,
                                           Mat Rt,
                                           Mat unregisteredDepth,
                                           Size outputImagePlaneSize,
                                           Mat registeredDepth)

      Registers depth data to an external camera Registration is performed by creating a depth cloud, transforming the cloud by the rigid body transformation between the cameras, and then projecting the transformed points into the RGB camera. uv_rgb = K_rgb * [R | t] * z * inv(K_ir) * uv_ir Currently does not check for negative depth values.
      Parameters:
        `unregisteredCameraMatrix` - the camera matrix of the depth camera
        `registeredCameraMatrix` - the camera matrix of the external camera
        `registeredDistCoeffs` - the distortion coefficients of the external camera
        `Rt` - the rigid body transform between the cameras. Transforms points from depth camera frame to external camera frame.
        `unregisteredDepth` - the input depth data
        `outputImagePlaneSize` - the image plane dimensions of the external camera (width, height)
        `registeredDepth` - the result of transforming the depth into the external camera

    - #### depthTo3dSparse

          public static void depthTo3dSparse​(Mat depth,
                                             Mat in_K,
                                             Mat in_points,
                                             Mat points3d)

      Parameters:
        `depth` - the depth image
        `in_K` -
        `in_points` - the list of xy coordinates
        `points3d` - the resulting 3d points (point is represented by 4 chanels value [x, y, z, 0])

    - #### depthTo3d

          public static void depthTo3d​(Mat depth,
                                       Mat K,
                                       Mat points3d,
                                       Mat mask)

      Converts a depth image to 3d points. If the mask is empty then the resulting array has the same dimensions as `depth`, otherwise it is 1d vector containing mask-enabled values only. The coordinate system is x pointing left, y down and z away from the camera
      Parameters:
        `depth` - the depth image (if given as short int CV_U, it is assumed to be the depth in millimeters (as done with the Microsoft Kinect), otherwise, if given as CV_32F or CV_64F, it is assumed in meters)
        `K` - The calibration matrix
        `points3d` - the resulting 3d points (point is represented by 4 channels value [x, y, z, 0]). They are of the same depth as `depth` if it is CV_32F or CV_64F, and the depth of `K` if `depth` is of depth CV_16U or CV_16S
        `mask` - the mask of the points to consider (can be empty)

    - #### depthTo3d

          public static void depthTo3d​(Mat depth,
                                       Mat K,
                                       Mat points3d)

      Converts a depth image to 3d points. If the mask is empty then the resulting array has the same dimensions as `depth`, otherwise it is 1d vector containing mask-enabled values only. The coordinate system is x pointing left, y down and z away from the camera
      Parameters:
        `depth` - the depth image (if given as short int CV_U, it is assumed to be the depth in millimeters (as done with the Microsoft Kinect), otherwise, if given as CV_32F or CV_64F, it is assumed in meters)
        `K` - The calibration matrix
        `points3d` - the resulting 3d points (point is represented by 4 channels value [x, y, z, 0]). They are of the same depth as `depth` if it is CV_32F or CV_64F, and the depth of `K` if `depth` is of depth CV_16U or CV_16S

    - #### rescaleDepth

          public static void rescaleDepth​(Mat in,
                                          int type,
                                          Mat out,
                                          double depth_factor)

      If the input image is of type CV_16UC1 (like the Kinect one), the image is converted to floats, divided by depth_factor to get a depth in meters, and the values 0 are converted to std::numeric_limits<float>::quiet_NaN() Otherwise, the image is simply converted to floats
      Parameters:
        `in` - the depth image (if given as short int CV_U, it is assumed to be the depth in millimeters (as done with the Microsoft Kinect), it is assumed in meters)
        `type` - the desired output depth (CV_32F or CV_64F)
        `out` - The rescaled float depth image
        `depth_factor` - (optional) factor by which depth is converted to distance (by default = 1000.0 for Kinect sensor)

    - #### rescaleDepth

          public static void rescaleDepth​(Mat in,
                                          int type,
                                          Mat out)

      If the input image is of type CV_16UC1 (like the Kinect one), the image is converted to floats, divided by depth_factor to get a depth in meters, and the values 0 are converted to std::numeric_limits<float>::quiet_NaN() Otherwise, the image is simply converted to floats
      Parameters:
        `in` - the depth image (if given as short int CV_U, it is assumed to be the depth in millimeters (as done with the Microsoft Kinect), it is assumed in meters)
        `type` - the desired output depth (CV_32F or CV_64F)
        `out` - The rescaled float depth image

    - #### warpFrame

          public static void warpFrame​(Mat depth,
                                       Mat image,
                                       Mat mask,
                                       Mat Rt,
                                       Mat cameraMatrix,
                                       Mat warpedDepth,
                                       Mat warpedImage,
                                       Mat warpedMask)

      Warps depth or RGB-D image by reprojecting it in 3d, applying Rt transformation and then projecting it back onto the image plane. This function can be used to visualize the results of the Odometry algorithm.
      Parameters:
        `depth` - Depth data, should be 1-channel CV_16U, CV_16S, CV_32F or CV_64F
        `image` - RGB image (optional), should be 1-, 3- or 4-channel CV_8U
        `mask` - Mask of used pixels (optional), should be CV_8UC1
        `Rt` - Rotation+translation matrix (3x4 or 4x4) to be applied to depth points
        `cameraMatrix` - Camera intrinsics matrix (3x3)
        `warpedDepth` - The warped depth data (optional)
        `warpedImage` - The warped RGB image (optional)
        `warpedMask` - The mask of valid pixels in warped image (optional)

    - #### warpFrame

          public static void warpFrame​(Mat depth,
                                       Mat image,
                                       Mat mask,
                                       Mat Rt,
                                       Mat cameraMatrix,
                                       Mat warpedDepth,
                                       Mat warpedImage)

      Warps depth or RGB-D image by reprojecting it in 3d, applying Rt transformation and then projecting it back onto the image plane. This function can be used to visualize the results of the Odometry algorithm.
      Parameters:
        `depth` - Depth data, should be 1-channel CV_16U, CV_16S, CV_32F or CV_64F
        `image` - RGB image (optional), should be 1-, 3- or 4-channel CV_8U
        `mask` - Mask of used pixels (optional), should be CV_8UC1
        `Rt` - Rotation+translation matrix (3x4 or 4x4) to be applied to depth points
        `cameraMatrix` - Camera intrinsics matrix (3x3)
        `warpedDepth` - The warped depth data (optional)
        `warpedImage` - The warped RGB image (optional)

    - #### warpFrame

          public static void warpFrame​(Mat depth,
                                       Mat image,
                                       Mat mask,
                                       Mat Rt,
                                       Mat cameraMatrix,
                                       Mat warpedDepth)

      Warps depth or RGB-D image by reprojecting it in 3d, applying Rt transformation and then projecting it back onto the image plane. This function can be used to visualize the results of the Odometry algorithm.
      Parameters:
        `depth` - Depth data, should be 1-channel CV_16U, CV_16S, CV_32F or CV_64F
        `image` - RGB image (optional), should be 1-, 3- or 4-channel CV_8U
        `mask` - Mask of used pixels (optional), should be CV_8UC1
        `Rt` - Rotation+translation matrix (3x4 or 4x4) to be applied to depth points
        `cameraMatrix` - Camera intrinsics matrix (3x3)
        `warpedDepth` - The warped depth data (optional)

    - #### warpFrame

          public static void warpFrame​(Mat depth,
                                       Mat image,
                                       Mat mask,
                                       Mat Rt,
                                       Mat cameraMatrix)

      Warps depth or RGB-D image by reprojecting it in 3d, applying Rt transformation and then projecting it back onto the image plane. This function can be used to visualize the results of the Odometry algorithm.
      Parameters:
        `depth` - Depth data, should be 1-channel CV_16U, CV_16S, CV_32F or CV_64F
        `image` - RGB image (optional), should be 1-, 3- or 4-channel CV_8U
        `mask` - Mask of used pixels (optional), should be CV_8UC1
        `Rt` - Rotation+translation matrix (3x4 or 4x4) to be applied to depth points
        `cameraMatrix` - Camera intrinsics matrix (3x3)

    - #### findPlanes

          public static void findPlanes​(Mat points3d,
                                        Mat normals,
                                        Mat mask,
                                        Mat plane_coefficients,
                                        int block_size,
                                        int min_size,
                                        double threshold,
                                        double sensor_error_a,
                                        double sensor_error_b,
                                        double sensor_error_c,
                                        int method)

      Find the planes in a depth image
      Parameters:
        `points3d` - the 3d points organized like the depth image: rows x cols with 3 channels
        `normals` - the normals for every point in the depth image; optional, can be empty
        `mask` - An image where each pixel is labeled with the plane it belongs to and 255 if it does not belong to any plane
        `plane_coefficients` - the coefficients of the corresponding planes (a,b,c,d) such that ax+by+cz+d=0, norm(a,b,c)=1 and c < 0 (so that the normal points towards the camera)
        `block_size` - The size of the blocks to look at for a stable MSE
        `min_size` - The minimum size of a cluster to be considered a plane
        `threshold` - The maximum distance of a point from a plane to belong to it (in meters)
        `sensor_error_a` - coefficient of the sensor error. 0 by default, use 0.0075 for a Kinect
        `sensor_error_b` - coefficient of the sensor error. 0 by default
        `sensor_error_c` - coefficient of the sensor error. 0 by default
        `method` - The method to use to compute the planes.

    - #### findPlanes

          public static void findPlanes​(Mat points3d,
                                        Mat normals,
                                        Mat mask,
                                        Mat plane_coefficients,
                                        int block_size,
                                        int min_size,
                                        double threshold,
                                        double sensor_error_a,
                                        double sensor_error_b,
                                        double sensor_error_c)

      Find the planes in a depth image
      Parameters:
        `points3d` - the 3d points organized like the depth image: rows x cols with 3 channels
        `normals` - the normals for every point in the depth image; optional, can be empty
        `mask` - An image where each pixel is labeled with the plane it belongs to and 255 if it does not belong to any plane
        `plane_coefficients` - the coefficients of the corresponding planes (a,b,c,d) such that ax+by+cz+d=0, norm(a,b,c)=1 and c < 0 (so that the normal points towards the camera)
        `block_size` - The size of the blocks to look at for a stable MSE
        `min_size` - The minimum size of a cluster to be considered a plane
        `threshold` - The maximum distance of a point from a plane to belong to it (in meters)
        `sensor_error_a` - coefficient of the sensor error. 0 by default, use 0.0075 for a Kinect
        `sensor_error_b` - coefficient of the sensor error. 0 by default
        `sensor_error_c` - coefficient of the sensor error. 0 by default

    - #### findPlanes

          public static void findPlanes​(Mat points3d,
                                        Mat normals,
                                        Mat mask,
                                        Mat plane_coefficients,
                                        int block_size,
                                        int min_size,
                                        double threshold,
                                        double sensor_error_a,
                                        double sensor_error_b)

      Find the planes in a depth image
      Parameters:
        `points3d` - the 3d points organized like the depth image: rows x cols with 3 channels
        `normals` - the normals for every point in the depth image; optional, can be empty
        `mask` - An image where each pixel is labeled with the plane it belongs to and 255 if it does not belong to any plane
        `plane_coefficients` - the coefficients of the corresponding planes (a,b,c,d) such that ax+by+cz+d=0, norm(a,b,c)=1 and c < 0 (so that the normal points towards the camera)
        `block_size` - The size of the blocks to look at for a stable MSE
        `min_size` - The minimum size of a cluster to be considered a plane
        `threshold` - The maximum distance of a point from a plane to belong to it (in meters)
        `sensor_error_a` - coefficient of the sensor error. 0 by default, use 0.0075 for a Kinect
        `sensor_error_b` - coefficient of the sensor error. 0 by default

    - #### findPlanes

          public static void findPlanes​(Mat points3d,
                                        Mat normals,
                                        Mat mask,
                                        Mat plane_coefficients,
                                        int block_size,
                                        int min_size,
                                        double threshold,
                                        double sensor_error_a)

      Find the planes in a depth image
      Parameters:
        `points3d` - the 3d points organized like the depth image: rows x cols with 3 channels
        `normals` - the normals for every point in the depth image; optional, can be empty
        `mask` - An image where each pixel is labeled with the plane it belongs to and 255 if it does not belong to any plane
        `plane_coefficients` - the coefficients of the corresponding planes (a,b,c,d) such that ax+by+cz+d=0, norm(a,b,c)=1 and c < 0 (so that the normal points towards the camera)
        `block_size` - The size of the blocks to look at for a stable MSE
        `min_size` - The minimum size of a cluster to be considered a plane
        `threshold` - The maximum distance of a point from a plane to belong to it (in meters)
        `sensor_error_a` - coefficient of the sensor error. 0 by default, use 0.0075 for a Kinect

    - #### findPlanes

          public static void findPlanes​(Mat points3d,
                                        Mat normals,
                                        Mat mask,
                                        Mat plane_coefficients,
                                        int block_size,
                                        int min_size,
                                        double threshold)

      Find the planes in a depth image
      Parameters:
        `points3d` - the 3d points organized like the depth image: rows x cols with 3 channels
        `normals` - the normals for every point in the depth image; optional, can be empty
        `mask` - An image where each pixel is labeled with the plane it belongs to and 255 if it does not belong to any plane
        `plane_coefficients` - the coefficients of the corresponding planes (a,b,c,d) such that ax+by+cz+d=0, norm(a,b,c)=1 and c < 0 (so that the normal points towards the camera)
        `block_size` - The size of the blocks to look at for a stable MSE
        `min_size` - The minimum size of a cluster to be considered a plane
        `threshold` - The maximum distance of a point from a plane to belong to it (in meters)

    - #### findPlanes

          public static void findPlanes​(Mat points3d,
                                        Mat normals,
                                        Mat mask,
                                        Mat plane_coefficients,
                                        int block_size,
                                        int min_size)

      Find the planes in a depth image
      Parameters:
        `points3d` - the 3d points organized like the depth image: rows x cols with 3 channels
        `normals` - the normals for every point in the depth image; optional, can be empty
        `mask` - An image where each pixel is labeled with the plane it belongs to and 255 if it does not belong to any plane
        `plane_coefficients` - the coefficients of the corresponding planes (a,b,c,d) such that ax+by+cz+d=0, norm(a,b,c)=1 and c < 0 (so that the normal points towards the camera)
        `block_size` - The size of the blocks to look at for a stable MSE
        `min_size` - The minimum size of a cluster to be considered a plane

    - #### findPlanes

          public static void findPlanes​(Mat points3d,
                                        Mat normals,
                                        Mat mask,
                                        Mat plane_coefficients,
                                        int block_size)

      Find the planes in a depth image
      Parameters:
        `points3d` - the 3d points organized like the depth image: rows x cols with 3 channels
        `normals` - the normals for every point in the depth image; optional, can be empty
        `mask` - An image where each pixel is labeled with the plane it belongs to and 255 if it does not belong to any plane
        `plane_coefficients` - the coefficients of the corresponding planes (a,b,c,d) such that ax+by+cz+d=0, norm(a,b,c)=1 and c < 0 (so that the normal points towards the camera)
        `block_size` - The size of the blocks to look at for a stable MSE

    - #### findPlanes

          public static void findPlanes​(Mat points3d,
                                        Mat normals,
                                        Mat mask,
                                        Mat plane_coefficients)

      Find the planes in a depth image
      Parameters:
        `points3d` - the 3d points organized like the depth image: rows x cols with 3 channels
        `normals` - the normals for every point in the depth image; optional, can be empty
        `mask` - An image where each pixel is labeled with the plane it belongs to and 255 if it does not belong to any plane
        `plane_coefficients` - the coefficients of the corresponding planes (a,b,c,d) such that ax+by+cz+d=0, norm(a,b,c)=1 and c < 0 (so that the normal points towards the camera)