基于Android opncv-4.10.0+opencv-contrib-4.10.0 的abi-x86动态库

// // This file is auto-generated. Please don't modify it! // package org.opencv.calib3d; import java.util.ArrayList; import java.util.List; import org.opencv.calib3d.UsacParams; import org.opencv.core.Mat; import org.opencv.core.MatOfDouble; import org.opencv.core.MatOfPoint2f; import org.opencv.core.MatOfPoint3f; import org.opencv.core.Point; import org.opencv.core.Rect; import org.opencv.core.Scalar; import org.opencv.core.Size; import org.opencv.core.TermCriteria; import org.opencv.utils.Converters; // C++: class Calib3d public class Calib3d { // C++: enum <unnamed> public static final int CV_ITERATIVE = 0, CV_EPNP = 1, CV_P3P = 2, CV_DLS = 3, CvLevMarq_DONE = 0, CvLevMarq_STARTED = 1, CvLevMarq_CALC_J = 2, CvLevMarq_CHECK_ERR = 3, LMEDS = 4, RANSAC = 8, RHO = 16, USAC_DEFAULT = 32, USAC_PARALLEL = 33, USAC_FM_8PTS = 34, USAC_FAST = 35, USAC_ACCURATE = 36, USAC_PROSAC = 37, USAC_MAGSAC = 38, CALIB_CB_ADAPTIVE_THRESH = 1, CALIB_CB_NORMALIZE_IMAGE = 2, CALIB_CB_FILTER_QUADS = 4, CALIB_CB_FAST_CHECK = 8, CALIB_CB_EXHAUSTIVE = 16, CALIB_CB_ACCURACY = 32, CALIB_CB_LARGER = 64, CALIB_CB_MARKER = 128, CALIB_CB_PLAIN = 256, CALIB_CB_SYMMETRIC_GRID = 1, CALIB_CB_ASYMMETRIC_GRID = 2, CALIB_CB_CLUSTERING = 4, CALIB_NINTRINSIC = 18, CALIB_USE_INTRINSIC_GUESS = 0x00001, CALIB_FIX_ASPECT_RATIO = 0x00002, CALIB_FIX_PRINCIPAL_POINT = 0x00004, CALIB_ZERO_TANGENT_DIST = 0x00008, CALIB_FIX_FOCAL_LENGTH = 0x00010, CALIB_FIX_K1 = 0x00020, CALIB_FIX_K2 = 0x00040, CALIB_FIX_K3 = 0x00080, CALIB_FIX_K4 = 0x00800, CALIB_FIX_K5 = 0x01000, CALIB_FIX_K6 = 0x02000, CALIB_RATIONAL_MODEL = 0x04000, CALIB_THIN_PRISM_MODEL = 0x08000, CALIB_FIX_S1_S2_S3_S4 = 0x10000, CALIB_TILTED_MODEL = 0x40000, CALIB_FIX_TAUX_TAUY = 0x80000, CALIB_USE_QR = 0x100000, CALIB_FIX_TANGENT_DIST = 0x200000, CALIB_FIX_INTRINSIC = 0x00100, CALIB_SAME_FOCAL_LENGTH = 0x00200, CALIB_ZERO_DISPARITY = 0x00400, CALIB_USE_LU = (1 << 17), CALIB_USE_EXTRINSIC_GUESS = (1 << 22), FM_7POINT = 1, FM_8POINT = 2, FM_LMEDS = 4, FM_RANSAC = 8, fisheye_CALIB_USE_INTRINSIC_GUESS = 1 << 0, fisheye_CALIB_RECOMPUTE_EXTRINSIC = 1 << 1, fisheye_CALIB_CHECK_COND = 1 << 2, fisheye_CALIB_FIX_SKEW = 1 << 3, fisheye_CALIB_FIX_K1 = 1 << 4, fisheye_CALIB_FIX_K2 = 1 << 5, fisheye_CALIB_FIX_K3 = 1 << 6, fisheye_CALIB_FIX_K4 = 1 << 7, fisheye_CALIB_FIX_INTRINSIC = 1 << 8, fisheye_CALIB_FIX_PRINCIPAL_POINT = 1 << 9, fisheye_CALIB_ZERO_DISPARITY = 1 << 10, fisheye_CALIB_FIX_FOCAL_LENGTH = 1 << 11; // C++: enum GridType (cv.CirclesGridFinderParameters.GridType) public static final int CirclesGridFinderParameters_SYMMETRIC_GRID = 0, CirclesGridFinderParameters_ASYMMETRIC_GRID = 1; // C++: enum HandEyeCalibrationMethod (cv.HandEyeCalibrationMethod) public static final int CALIB_HAND_EYE_TSAI = 0, CALIB_HAND_EYE_PARK = 1, CALIB_HAND_EYE_HORAUD = 2, CALIB_HAND_EYE_ANDREFF = 3, CALIB_HAND_EYE_DANIILIDIS = 4; // C++: enum LocalOptimMethod (cv.LocalOptimMethod) public static final int LOCAL_OPTIM_NULL = 0, LOCAL_OPTIM_INNER_LO = 1, LOCAL_OPTIM_INNER_AND_ITER_LO = 2, LOCAL_OPTIM_GC = 3, LOCAL_OPTIM_SIGMA = 4; // C++: enum NeighborSearchMethod (cv.NeighborSearchMethod) public static final int NEIGH_FLANN_KNN = 0, NEIGH_GRID = 1, NEIGH_FLANN_RADIUS = 2; // C++: enum PolishingMethod (cv.PolishingMethod) public static final int NONE_POLISHER = 0, LSQ_POLISHER = 1, MAGSAC = 2, COV_POLISHER = 3; // C++: enum RobotWorldHandEyeCalibrationMethod (cv.RobotWorldHandEyeCalibrationMethod) public static final int CALIB_ROBOT_WORLD_HAND_EYE_SHAH = 0, CALIB_ROBOT_WORLD_HAND_EYE_LI = 1; // C++: enum SamplingMethod (cv.SamplingMethod) public static final int SAMPLING_UNIFORM = 0, SAMPLING_PROGRESSIVE_NAPSAC = 1, SAMPLING_NAPSAC = 2, SAMPLING_PROSAC = 3; // C++: enum ScoreMethod (cv.ScoreMethod) public static final int SCORE_METHOD_RANSAC = 0, SCORE_METHOD_MSAC = 1, SCORE_METHOD_MAGSAC = 2, SCORE_METHOD_LMEDS = 3; // C++: enum SolvePnPMethod (cv.SolvePnPMethod) public static final int SOLVEPNP_ITERATIVE = 0, SOLVEPNP_EPNP = 1, SOLVEPNP_P3P = 2, SOLVEPNP_DLS = 3, SOLVEPNP_UPNP = 4, SOLVEPNP_AP3P = 5, SOLVEPNP_IPPE = 6, SOLVEPNP_IPPE_SQUARE = 7, SOLVEPNP_SQPNP = 8, SOLVEPNP_MAX_COUNT = 8+1; // C++: enum UndistortTypes (cv.UndistortTypes) public static final int PROJ_SPHERICAL_ORTHO = 0, PROJ_SPHERICAL_EQRECT = 1; // // C++: void cv::Rodrigues(Mat src, Mat& dst, Mat& jacobian = Mat()) // /** * Converts a rotation matrix to a rotation vector or vice versa. * * @param src Input rotation vector (3x1 or 1x3) or rotation matrix (3x3). * @param dst Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively. * @param 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 . * * <b>Note:</b> More information about the computation of the derivative of a 3D rotation matrix with respect to its exponential coordinate * can be found in: * <ul> * <li> * A Compact Formula for the Derivative of a 3-D Rotation in Exponential Coordinates, Guillermo Gallego, Anthony J. Yezzi CITE: Gallego2014ACF * </li> * </ul> * * <b>Note:</b> Useful information on SE(3) and Lie Groups can be found in: * <ul> * <li> * A tutorial on SE(3) transformation parameterizations and on-manifold optimization, Jose-Luis Blanco CITE: blanco2010tutorial * </li> * <li> * Lie Groups for 2D and 3D Transformation, Ethan Eade CITE: Eade17 * </li> * <li> * A micro Lie theory for state estimation in robotics, Joan Solà, Jérémie Deray, Dinesh Atchuthan CITE: Sol2018AML * </li> * </ul> */ public static void Rodrigues(Mat src, Mat dst, Mat jacobian) { Rodrigues_0(src.n