基于ESP32-CAM开发板实现的智能视力障碍辅助手杖项目源码(含APP).zip

// // 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 RobotWorldHandEyeCalibrationMethod public static final int CALIB_ROBOT_WORLD_HAND_EYE_SHAH = 0, CALIB_ROBOT_WORLD_HAND_EYE_LI = 1; // C++: enum 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 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_MAX_COUNT = 7+1; // C++: enum ScoreMethod public static final int SCORE_METHOD_RANSAC = 0, SCORE_METHOD_MSAC = 1, SCORE_METHOD_MAGSAC = 2, SCORE_METHOD_LMEDS = 3; // 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_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; // C++: enum 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 GridType public static final int CirclesGridFinderParameters_SYMMETRIC_GRID = 0, CirclesGridFinderParameters_ASYMMETRIC_GRID = 1; // C++: enum UndistortTypes public static final int PROJ_SPHERICAL_ORTHO = 0, PROJ_SPHERICAL_EQRECT = 1; // C++: enum SamplingMethod public static final int SAMPLING_UNIFORM = 0, SAMPLING_PROGRESSIVE_NAPSAC = 1, SAMPLING_NAPSAC = 2, SAMPLING_PROSAC = 3; // C++: enum NeighborSearchMethod public static final int NEIGH_FLANN_KNN = 0, NEIGH_GRID = 1, NEIGH_FLANN_RADIUS = 2; // // C++: Mat cv::estimateAffine2D(Mat from, Mat to, Mat& inliers = Mat(), int method = RANSAC, double ransacReprojThreshold = 3, size_t maxIters = 2000, double confidence = 0.99, size_t refineIters = 10) // /** * Computes an optimal affine transformation between two 2D point sets. * * It computes * \( * \begin{bmatrix} * x\\ * y\\ * \end{bmatrix} * = * \begin{bmatrix} * a_{11} &amp; a_{12}\\ * a_{21} &amp; a_{22}\\ * \end{bmatrix} * \begin{bmatrix} * X\\ * Y\\ * \end{bmatrix} * + * \begin{bmatrix} * b_1\\ * b_2\\ * \end{bmatrix} * \) * * @param from First input 2D point set containing \((X,Y)\). * @param to Second input 2D point set containing \((x,y)\). * @param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). * @param method Robust method used to compute transformation. The following methods are possible: * <ul> * <li> * cv::RANSAC - RANSAC-based robust method * </li> * <li> * cv::LMEDS - Least-Median robust method * RANSAC is the default method. * @param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider * a point as an inlier. Applies only to RANSAC. * @param maxIters The maximum number of robust method iterations. * @param 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. * @param 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. * </li> * </ul> * * @return Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformation * could not be estimated. The returned matrix has the following form: * \( * \begin{bmatrix} * a_{11} &amp; a_{12} &amp; b_1\\ * a_{21} &amp; a_{22} &amp; b_2\\ * \end{bmatrix} * \) * * 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. * * <b>Note:</b> * 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.