//
// 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.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,
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 UndistortTypes
public static final int
PROJ_SPHERICAL_ORTHO = 0,
PROJ_SPHERICAL_EQRECT = 1;
// 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 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 GridType
public static final int
CirclesGridFinderParameters_SYMMETRIC_GRID = 0,
CirclesGridFinderParameters_ASYMMETRIC_GRID = 1;
//
// 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
* \(
* \begin{bmatrix}
* x\\
* y\\
* \end{bmatrix}
* =
* \begin{bmatrix}
* a_{11} & a_{12}\\
* a_{21} & a_{22}\\
* \end{bmatrix}
* \begin{bmatrix}
* X\\
* Y\\
* \end{bmatrix}
* +
* \begin{bmatrix}
* b_1\\
* b_2\\
* \end{bmatrix}
* \)
*
* @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:
* \(
* \begin{bmatrix}
* a_{11} & a_{12} & b_1\\
* a_{21} & a_{22} & b_2\\
* \end{bmatrix}
* \)
*
* 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.
*
* <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.
*
* SEE: estimateAffinePartial2D, getAffineTransform
*/
public static Mat estimateAffine2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters) {
return new Mat(estimateAffine2D_0(from.nativeObj, to.nativeObj, inliers.nativeObj, method, ransacReprojThreshold, maxIters, confidence, refineIters));
}
/**
* Computes an optimal affine transformation between two 2D point sets.
*
* It computes
* \(
* \begin{bmatrix}
* x\\
* y\\
* \end{bmatrix}
* =
* \begin{bmatrix}
* a_{11} & a_{12}\\
* a_{21} & a_{22}\\
* \end{bmatrix}
* \begin{bmatrix}
* X\\
* Y\\
* \end{bmatrix}
* +
* \begin{bmatrix}
* b_1\\
* b_2\\
* \end{bmatrix}
* \)
*
* @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 robus
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opencv+contrib(4.3.0) 完整版Android sdk
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