public class Calib3d extends Object
| Constructor and Description |
|---|
Calib3d() |
| Modifier and Type | Method and Description |
|---|---|
static double |
calibrateCamera(List<Mat> objectPoints,
List<Mat> imagePoints,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs) |
static double |
calibrateCamera(List<Mat> objectPoints,
List<Mat> imagePoints,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
int flags) |
static double |
calibrateCamera(List<Mat> objectPoints,
List<Mat> imagePoints,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
int flags,
TermCriteria criteria) |
static double |
calibrateCameraExtended(List<Mat> objectPoints,
List<Mat> imagePoints,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat perViewErrors)
Finds the camera intrinsic and extrinsic parameters from several views of a calibration
pattern.
|
static double |
calibrateCameraExtended(List<Mat> objectPoints,
List<Mat> imagePoints,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat perViewErrors,
int flags)
Finds the camera intrinsic and extrinsic parameters from several views of a calibration
pattern.
|
static double |
calibrateCameraExtended(List<Mat> objectPoints,
List<Mat> imagePoints,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat perViewErrors,
int flags,
TermCriteria criteria)
Finds the camera intrinsic and extrinsic parameters from several views of a calibration
pattern.
|
static double |
calibrateCameraRO(List<Mat> objectPoints,
List<Mat> imagePoints,
Size imageSize,
int iFixedPoint,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat newObjPoints) |
static double |
calibrateCameraRO(List<Mat> objectPoints,
List<Mat> imagePoints,
Size imageSize,
int iFixedPoint,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat newObjPoints,
int flags) |
static double |
calibrateCameraRO(List<Mat> objectPoints,
List<Mat> imagePoints,
Size imageSize,
int iFixedPoint,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat newObjPoints,
int flags,
TermCriteria criteria) |
static double |
calibrateCameraROExtended(List<Mat> objectPoints,
List<Mat> imagePoints,
Size imageSize,
int iFixedPoint,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat newObjPoints,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat stdDeviationsObjPoints,
Mat perViewErrors)
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
|
static double |
calibrateCameraROExtended(List<Mat> objectPoints,
List<Mat> imagePoints,
Size imageSize,
int iFixedPoint,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat newObjPoints,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat stdDeviationsObjPoints,
Mat perViewErrors,
int flags)
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
|
static double |
calibrateCameraROExtended(List<Mat> objectPoints,
List<Mat> imagePoints,
Size imageSize,
int iFixedPoint,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat newObjPoints,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat stdDeviationsObjPoints,
Mat perViewErrors,
int flags,
TermCriteria criteria)
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
|
static void |
calibrateHandEye(List<Mat> R_gripper2base,
List<Mat> t_gripper2base,
List<Mat> R_target2cam,
List<Mat> t_target2cam,
Mat R_cam2gripper,
Mat t_cam2gripper)
Computes Hand-Eye calibration: \(_{}^{g}\textrm{T}_c\)
|
static void |
calibrateHandEye(List<Mat> R_gripper2base,
List<Mat> t_gripper2base,
List<Mat> R_target2cam,
List<Mat> t_target2cam,
Mat R_cam2gripper,
Mat t_cam2gripper,
int method)
Computes Hand-Eye calibration: \(_{}^{g}\textrm{T}_c\)
|
static void |
calibrateRobotWorldHandEye(List<Mat> R_world2cam,
List<Mat> t_world2cam,
List<Mat> R_base2gripper,
List<Mat> t_base2gripper,
Mat R_base2world,
Mat t_base2world,
Mat R_gripper2cam,
Mat t_gripper2cam)
Computes Robot-World/Hand-Eye calibration: \(_{}^{w}\textrm{T}_b\) and \(_{}^{c}\textrm{T}_g\)
|
static void |
calibrateRobotWorldHandEye(List<Mat> R_world2cam,
List<Mat> t_world2cam,
List<Mat> R_base2gripper,
List<Mat> t_base2gripper,
Mat R_base2world,
Mat t_base2world,
Mat R_gripper2cam,
Mat t_gripper2cam,
int method)
Computes Robot-World/Hand-Eye calibration: \(_{}^{w}\textrm{T}_b\) and \(_{}^{c}\textrm{T}_g\)
|
static void |
calibrationMatrixValues(Mat cameraMatrix,
Size imageSize,
double apertureWidth,
double apertureHeight,
double[] fovx,
double[] fovy,
double[] focalLength,
Point principalPoint,
double[] aspectRatio)
Computes useful camera characteristics from the camera intrinsic matrix.
|
static boolean |
checkChessboard(Mat img,
Size size) |
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 |
convertPointsToHomogeneous(Mat src,
Mat dst)
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,
List<Mat> rotations,
List<Mat> translations,
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 |
drawChessboardCorners(Mat image,
Size patternSize,
MatOfPoint2f corners,
boolean patternWasFound)
Renders the detected chessboard corners.
|
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 Scalar |
estimateChessboardSharpness(Mat image,
Size patternSize,
Mat corners)
Estimates the sharpness of a detected chessboard.
|
static Scalar |
estimateChessboardSharpness(Mat image,
Size patternSize,
Mat corners,
float rise_distance)
Estimates the sharpness of a detected chessboard.
|
static Scalar |
estimateChessboardSharpness(Mat image,
Size patternSize,
Mat corners,
float rise_distance,
boolean vertical)
Estimates the sharpness of a detected chessboard.
|
static Scalar |
estimateChessboardSharpness(Mat image,
Size patternSize,
Mat corners,
float rise_distance,
boolean vertical,
Mat sharpness)
Estimates the sharpness of a detected chessboard.
|
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(List<Mat> rotations,
List<Mat> normals,
Mat beforePoints,
Mat afterPoints,
Mat possibleSolutions)
Filters homography decompositions based on additional information.
|
static void |
filterHomographyDecompByVisibleRefpoints(List<Mat> rotations,
List<Mat> normals,
Mat beforePoints,
Mat afterPoints,
Mat possibleSolutions,
Mat pointsMask)
Filters homography decompositions based on additional information.
|
static void |
filterSpeckles(Mat img,
double newVal,
int maxSpeckleSize,
double maxDiff)
Filters off small noise blobs (speckles) in the disparity map
|
static void |
filterSpeckles(Mat img,
double newVal,
int maxSpeckleSize,
double maxDiff,
Mat buf)
Filters off small noise blobs (speckles) in the disparity map
|
static boolean |
find4QuadCornerSubpix(Mat img,
Mat corners,
Size region_size) |
static boolean |
findChessboardCorners(Mat image,
Size patternSize,
MatOfPoint2f corners)
Finds the positions of internal corners of the chessboard.
|
static boolean |
findChessboardCorners(Mat image,
Size patternSize,
MatOfPoint2f corners,
int flags)
Finds the positions of internal corners of the chessboard.
|
static boolean |
findChessboardCornersSB(Mat image,
Size patternSize,
Mat corners) |
static boolean |
findChessboardCornersSB(Mat image,
Size patternSize,
Mat corners,
int flags) |
static boolean |
findChessboardCornersSBWithMeta(Mat image,
Size patternSize,
Mat corners,
int flags,
Mat meta)
Finds the positions of internal corners of the chessboard using a sector based approach.
|
static boolean |
findCirclesGrid(Mat image,
Size patternSize,
Mat centers) |
static boolean |
findCirclesGrid(Mat image,
Size patternSize,
Mat centers,
int flags) |
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 double |
fisheye_calibrate(List<Mat> objectPoints,
List<Mat> imagePoints,
Size image_size,
Mat K,
Mat D,
List<Mat> rvecs,
List<Mat> tvecs)
Performs camera calibration
|
static double |
fisheye_calibrate(List<Mat> objectPoints,
List<Mat> imagePoints,
Size image_size,
Mat K,
Mat D,
List<Mat> rvecs,
List<Mat> tvecs,
int flags)
Performs camera calibration
|
static double |
fisheye_calibrate(List<Mat> objectPoints,
List<Mat> imagePoints,
Size image_size,
Mat K,
Mat D,
List<Mat> rvecs,
List<Mat> tvecs,
int flags,
TermCriteria criteria)
Performs camera calibration
|
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 obtained 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 obtained 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 #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 double |
fisheye_stereoCalibrate(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat K1,
Mat D1,
Mat K2,
Mat D2,
Size imageSize,
Mat R,
Mat T) |
static double |
fisheye_stereoCalibrate(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat K1,
Mat D1,
Mat K2,
Mat D2,
Size imageSize,
Mat R,
Mat T,
int flags) |
static double |
fisheye_stereoCalibrate(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat K1,
Mat D1,
Mat K2,
Mat D2,
Size imageSize,
Mat R,
Mat T,
int flags,
TermCriteria criteria) |
static double |
fisheye_stereoCalibrate(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat K1,
Mat D1,
Mat K2,
Mat D2,
Size imageSize,
Mat R,
Mat T,
List<Mat> rvecs,
List<Mat> tvecs)
Performs stereo calibration
|
static double |
fisheye_stereoCalibrate(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat K1,
Mat D1,
Mat K2,
Mat D2,
Size imageSize,
Mat R,
Mat T,
List<Mat> rvecs,
List<Mat> tvecs,
int flags)
Performs stereo calibration
|
static double |
fisheye_stereoCalibrate(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat K1,
Mat D1,
Mat K2,
Mat D2,
Size imageSize,
Mat R,
Mat T,
List<Mat> rvecs,
List<Mat> tvecs,
int flags,
TermCriteria criteria)
Performs stereo calibration
|
static void |
fisheye_stereoRectify(Mat K1,
Mat D1,
Mat K2,
Mat D2,
Size imageSize,
Mat R,
Mat tvec,
Mat R1,
Mat R2,
Mat P1,
Mat P2,
Mat Q,
int flags)
Stereo rectification for fisheye camera model
|
static void |
fisheye_stereoRectify(Mat K1,
Mat D1,
Mat K2,
Mat D2,
Size imageSize,
Mat R,
Mat tvec,
Mat R1,
Mat R2,
Mat P1,
Mat P2,
Mat Q,
int flags,
Size newImageSize)
Stereo rectification for fisheye camera model
|
static void |
fisheye_stereoRectify(Mat K1,
Mat D1,
Mat K2,
Mat D2,
Size imageSize,
Mat R,
Mat tvec,
Mat R1,
Mat R2,
Mat P1,
Mat P2,
Mat Q,
int flags,
Size newImageSize,
double balance)
Stereo rectification for fisheye camera model
|
static void |
fisheye_stereoRectify(Mat K1,
Mat D1,
Mat K2,
Mat D2,
Size imageSize,
Mat R,
Mat tvec,
Mat R1,
Mat R2,
Mat P1,
Mat P2,
Mat Q,
int flags,
Size newImageSize,
double balance,
double fov_scale)
Stereo rectification for fisheye camera model
|
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 Rect |
getValidDisparityROI(Rect roi1,
Rect roi2,
int minDisparity,
int numberOfDisparities,
int blockSize) |
static Mat |
initCameraMatrix2D(List<MatOfPoint3f> objectPoints,
List<MatOfPoint2f> imagePoints,
Size imageSize)
Finds an initial camera intrinsic matrix from 3D-2D point correspondences.
|
static Mat |
initCameraMatrix2D(List<MatOfPoint3f> objectPoints,
List<MatOfPoint2f> imagePoints,
Size imageSize,
double aspectRatio)
Finds an initial camera intrinsic matrix from 3D-2D point correspondences.
|
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 |
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 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 cheirality 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 cheirality 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 cheirality 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 cheirality 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 cheirality check.
|
static float |
rectify3Collinear(Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Mat cameraMatrix3,
Mat distCoeffs3,
List<Mat> imgpt1,
List<Mat> imgpt3,
Size imageSize,
Mat R12,
Mat T12,
Mat R13,
Mat T13,
Mat R1,
Mat R2,
Mat R3,
Mat P1,
Mat P2,
Mat P3,
Mat Q,
double alpha,
Size newImgSize,
Rect roi1,
Rect roi2,
int flags) |
static void |
reprojectImageTo3D(Mat disparity,
Mat _3dImage,
Mat Q)
Reprojects a disparity image to 3D space.
|
static void |
reprojectImageTo3D(Mat disparity,
Mat _3dImage,
Mat Q,
boolean handleMissingValues)
Reprojects a disparity image to 3D space.
|
static void |
reprojectImageTo3D(Mat disparity,
Mat _3dImage,
Mat Q,
boolean handleMissingValues,
int ddepth)
Reprojects a disparity image to 3D space.
|
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 int |
solveP3P(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
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,
List<Mat> rvecs,
List<Mat> tvecs)
Finds an object pose from 3D-2D point correspondences.
|
static int |
solvePnPGeneric(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
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,
List<Mat> rvecs,
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,
List<Mat> rvecs,
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,
List<Mat> rvecs,
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,
List<Mat> rvecs,
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 double |
stereoCalibrate(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat E,
Mat F) |
static double |
stereoCalibrate(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat E,
Mat F,
int flags) |
static double |
stereoCalibrate(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat E,
Mat F,
int flags,
TermCriteria criteria) |
static double |
stereoCalibrate(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat E,
Mat F,
Mat perViewErrors) |
static double |
stereoCalibrate(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat E,
Mat F,
Mat perViewErrors,
int flags) |
static double |
stereoCalibrate(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat E,
Mat F,
Mat perViewErrors,
int flags,
TermCriteria criteria) |
static double |
stereoCalibrateExtended(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat E,
Mat F,
List<Mat> rvecs,
List<Mat> tvecs,
Mat perViewErrors)
Calibrates a stereo camera set up.
|
static double |
stereoCalibrateExtended(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat E,
Mat F,
List<Mat> rvecs,
List<Mat> tvecs,
Mat perViewErrors,
int flags)
Calibrates a stereo camera set up.
|
static double |
stereoCalibrateExtended(List<Mat> objectPoints,
List<Mat> imagePoints1,
List<Mat> imagePoints2,
Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat E,
Mat F,
List<Mat> rvecs,
List<Mat> tvecs,
Mat perViewErrors,
int flags,
TermCriteria criteria)
Calibrates a stereo camera set up.
|
static void |
stereoRectify(Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat R1,
Mat R2,
Mat P1,
Mat P2,
Mat Q)
Computes rectification transforms for each head of a calibrated stereo camera.
|
static void |
stereoRectify(Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat R1,
Mat R2,
Mat P1,
Mat P2,
Mat Q,
int flags)
Computes rectification transforms for each head of a calibrated stereo camera.
|
static void |
stereoRectify(Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat R1,
Mat R2,
Mat P1,
Mat P2,
Mat Q,
int flags,
double alpha)
Computes rectification transforms for each head of a calibrated stereo camera.
|
static void |
stereoRectify(Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat R1,
Mat R2,
Mat P1,
Mat P2,
Mat Q,
int flags,
double alpha,
Size newImageSize)
Computes rectification transforms for each head of a calibrated stereo camera.
|
static void |
stereoRectify(Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat R1,
Mat R2,
Mat P1,
Mat P2,
Mat Q,
int flags,
double alpha,
Size newImageSize,
Rect validPixROI1)
Computes rectification transforms for each head of a calibrated stereo camera.
|
static void |
stereoRectify(Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat R1,
Mat R2,
Mat P1,
Mat P2,
Mat Q,
int flags,
double alpha,
Size newImageSize,
Rect validPixROI1,
Rect validPixROI2)
Computes rectification transforms for each head of a calibrated stereo camera.
|
static boolean |
stereoRectifyUncalibrated(Mat points1,
Mat points2,
Mat F,
Size imgSize,
Mat H1,
Mat H2)
Computes a rectification transform for an uncalibrated stereo camera.
|
static boolean |
stereoRectifyUncalibrated(Mat points1,
Mat points2,
Mat F,
Size imgSize,
Mat H1,
Mat H2,
double threshold)
Computes a rectification transform for an uncalibrated stereo camera.
|
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 |
undistortPointsIter(Mat src,
Mat dst,
Mat cameraMatrix,
Mat distCoeffs,
Mat R,
Mat P,
TermCriteria criteria)
Note: Default version of #undistortPoints does 5 iterations to compute undistorted points.
|
static void |
validateDisparity(Mat disparity,
Mat cost,
int minDisparity,
int numberOfDisparities) |
static void |
validateDisparity(Mat disparity,
Mat cost,
int minDisparity,
int numberOfDisparities,
int disp12MaxDisp) |
public static final int CV_ITERATIVE
public static final int CV_EPNP
public static final int CV_P3P
public static final int CV_DLS
public static final int CvLevMarq_DONE
public static final int CvLevMarq_STARTED
public static final int CvLevMarq_CALC_J
public static final int CvLevMarq_CHECK_ERR
public static final int LMEDS
public static final int RANSAC
public static final int RHO
public static final int USAC_DEFAULT
public static final int USAC_PARALLEL
public static final int USAC_FM_8PTS
public static final int USAC_FAST
public static final int USAC_ACCURATE
public static final int USAC_PROSAC
public static final int USAC_MAGSAC
public static final int CALIB_CB_ADAPTIVE_THRESH
public static final int CALIB_CB_NORMALIZE_IMAGE
public static final int CALIB_CB_FILTER_QUADS
public static final int CALIB_CB_FAST_CHECK
public static final int CALIB_CB_EXHAUSTIVE
public static final int CALIB_CB_ACCURACY
public static final int CALIB_CB_LARGER
public static final int CALIB_CB_MARKER
public static final int CALIB_CB_PLAIN
public static final int CALIB_CB_SYMMETRIC_GRID
public static final int CALIB_CB_ASYMMETRIC_GRID
public static final int CALIB_CB_CLUSTERING
public static final int CALIB_NINTRINSIC
public static final int CALIB_USE_INTRINSIC_GUESS
public static final int CALIB_FIX_ASPECT_RATIO
public static final int CALIB_FIX_PRINCIPAL_POINT
public static final int CALIB_ZERO_TANGENT_DIST
public static final int CALIB_FIX_FOCAL_LENGTH
public static final int CALIB_FIX_K1
public static final int CALIB_FIX_K2
public static final int CALIB_FIX_K3
public static final int CALIB_FIX_K4
public static final int CALIB_FIX_K5
public static final int CALIB_FIX_K6
public static final int CALIB_RATIONAL_MODEL
public static final int CALIB_THIN_PRISM_MODEL
public static final int CALIB_FIX_S1_S2_S3_S4
public static final int CALIB_TILTED_MODEL
public static final int CALIB_FIX_TAUX_TAUY
public static final int CALIB_USE_QR
public static final int CALIB_FIX_TANGENT_DIST
public static final int CALIB_FIX_INTRINSIC
public static final int CALIB_SAME_FOCAL_LENGTH
public static final int CALIB_ZERO_DISPARITY
public static final int CALIB_USE_LU
public static final int CALIB_USE_EXTRINSIC_GUESS
public static final int FM_7POINT
public static final int FM_8POINT
public static final int FM_LMEDS
public static final int FM_RANSAC
public static final int fisheye_CALIB_USE_INTRINSIC_GUESS
public static final int fisheye_CALIB_RECOMPUTE_EXTRINSIC
public static final int fisheye_CALIB_CHECK_COND
public static final int fisheye_CALIB_FIX_SKEW
public static final int fisheye_CALIB_FIX_K1
public static final int fisheye_CALIB_FIX_K2
public static final int fisheye_CALIB_FIX_K3
public static final int fisheye_CALIB_FIX_K4
public static final int fisheye_CALIB_FIX_INTRINSIC
public static final int fisheye_CALIB_FIX_PRINCIPAL_POINT
public static final int fisheye_CALIB_ZERO_DISPARITY
public static final int fisheye_CALIB_FIX_FOCAL_LENGTH
public static final int CirclesGridFinderParameters_SYMMETRIC_GRID
public static final int CirclesGridFinderParameters_ASYMMETRIC_GRID
public static final int CALIB_HAND_EYE_TSAI
public static final int CALIB_HAND_EYE_PARK
public static final int CALIB_HAND_EYE_HORAUD
public static final int CALIB_HAND_EYE_ANDREFF
public static final int CALIB_HAND_EYE_DANIILIDIS
public static final int LOCAL_OPTIM_NULL
public static final int LOCAL_OPTIM_INNER_LO
public static final int LOCAL_OPTIM_INNER_AND_ITER_LO
public static final int LOCAL_OPTIM_GC
public static final int LOCAL_OPTIM_SIGMA
public static final int NEIGH_FLANN_KNN
public static final int NEIGH_GRID
public static final int NEIGH_FLANN_RADIUS
public static final int NONE_POLISHER
public static final int LSQ_POLISHER
public static final int MAGSAC
public static final int COV_POLISHER
public static final int CALIB_ROBOT_WORLD_HAND_EYE_SHAH
public static final int CALIB_ROBOT_WORLD_HAND_EYE_LI
public static final int SAMPLING_UNIFORM
public static final int SAMPLING_PROGRESSIVE_NAPSAC
public static final int SAMPLING_NAPSAC
public static final int SAMPLING_PROSAC
public static final int SCORE_METHOD_RANSAC
public static final int SCORE_METHOD_MSAC
public static final int SCORE_METHOD_MAGSAC
public static final int SCORE_METHOD_LMEDS
public static final int SOLVEPNP_ITERATIVE
public static final int SOLVEPNP_EPNP
public static final int SOLVEPNP_P3P
public static final int SOLVEPNP_DLS
public static final int SOLVEPNP_UPNP
public static final int SOLVEPNP_AP3P
public static final int SOLVEPNP_IPPE
public static final int SOLVEPNP_IPPE_SQUARE
public static final int SOLVEPNP_SQPNP
public static final int SOLVEPNP_MAX_COUNT
public static final int PROJ_SPHERICAL_ORTHO
public static final int PROJ_SPHERICAL_EQRECT
public static void Rodrigues(Mat src, Mat dst, Mat jacobian)
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:
public static void Rodrigues(Mat src, Mat dst)
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:
public static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters, double confidence)
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:
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,
perspectiveTransformpublic static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters)
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:
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,
perspectiveTransformpublic static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask)
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:
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,
perspectiveTransformpublic static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold)
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:
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,
perspectiveTransformpublic static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method)
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:
public static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints)
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> .
public static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, Mat mask, UsacParams params)
public static double[] RQDecomp3x3(Mat src, Mat mtxR, Mat mtxQ, Mat Qx, Mat Qy, Mat Qz)
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.public static double[] RQDecomp3x3(Mat src, Mat mtxR, Mat mtxQ, Mat Qx, Mat Qy)
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.public static double[] RQDecomp3x3(Mat src, Mat mtxR, Mat mtxQ, Mat Qx)
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.public static double[] RQDecomp3x3(Mat src, Mat mtxR, Mat mtxQ)
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.public static void decomposeProjectionMatrix(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX, Mat rotMatrixY, Mat rotMatrixZ, Mat eulerAngles)
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 .public static void decomposeProjectionMatrix(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX, Mat rotMatrixY, Mat rotMatrixZ)
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 .public static void decomposeProjectionMatrix(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX, Mat rotMatrixY)
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 .public static void decomposeProjectionMatrix(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX)
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 .public static void decomposeProjectionMatrix(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect)
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 .public static void matMulDeriv(Mat A, Mat B, Mat dABdA, Mat dABdB)
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.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)
rvec1 - First rotation vector.tvec1 - First translation vector.rvec2 - Second rotation vector.tvec2 - Second translation vector.rvec3 - Output rotation vector of the superposition.tvec3 - Output translation vector of the superposition.dr3dr1 - Optional output derivative of rvec3 with regard to rvec1dr3dt1 - Optional output derivative of rvec3 with regard to tvec1dr3dr2 - Optional output derivative of rvec3 with regard to rvec2dr3dt2 - Optional output derivative of rvec3 with regard to tvec2dt3dr1 - Optional output derivative of tvec3 with regard to rvec1dt3dt1 - Optional output derivative of tvec3 with regard to tvec1dt3dr2 - Optional output derivative of tvec3 with regard to rvec2dt3dt2 - Optional output derivative of tvec3 with regard to tvec2
The functions compute:
\(\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\)
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and
\(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See #Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input
vectors (see #matMulDeriv ). The functions are used inside #stereoCalibrate but can also be used in
your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a
function that contains a matrix multiplication.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)
rvec1 - First rotation vector.tvec1 - First translation vector.rvec2 - Second rotation vector.tvec2 - Second translation vector.rvec3 - Output rotation vector of the superposition.tvec3 - Output translation vector of the superposition.dr3dr1 - Optional output derivative of rvec3 with regard to rvec1dr3dt1 - Optional output derivative of rvec3 with regard to tvec1dr3dr2 - Optional output derivative of rvec3 with regard to rvec2dr3dt2 - Optional output derivative of rvec3 with regard to tvec2dt3dr1 - Optional output derivative of tvec3 with regard to rvec1dt3dt1 - Optional output derivative of tvec3 with regard to tvec1dt3dr2 - Optional output derivative of tvec3 with regard to rvec2
The functions compute:
\(\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\)
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and
\(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See #Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input
vectors (see #matMulDeriv ). The functions are used inside #stereoCalibrate but can also be used in
your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a
function that contains a matrix multiplication.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)
rvec1 - First rotation vector.tvec1 - First translation vector.rvec2 - Second rotation vector.tvec2 - Second translation vector.rvec3 - Output rotation vector of the superposition.tvec3 - Output translation vector of the superposition.dr3dr1 - Optional output derivative of rvec3 with regard to rvec1dr3dt1 - Optional output derivative of rvec3 with regard to tvec1dr3dr2 - Optional output derivative of rvec3 with regard to rvec2dr3dt2 - Optional output derivative of rvec3 with regard to tvec2dt3dr1 - Optional output derivative of tvec3 with regard to rvec1dt3dt1 - Optional output derivative of tvec3 with regard to tvec1
The functions compute:
\(\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\)
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and
\(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See #Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input
vectors (see #matMulDeriv ). The functions are used inside #stereoCalibrate but can also be used in
your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a
function that contains a matrix multiplication.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)
rvec1 - First rotation vector.tvec1 - First translation vector.rvec2 - Second rotation vector.tvec2 - Second translation vector.rvec3 - Output rotation vector of the superposition.tvec3 - Output translation vector of the superposition.dr3dr1 - Optional output derivative of rvec3 with regard to rvec1dr3dt1 - Optional output derivative of rvec3 with regard to tvec1dr3dr2 - Optional output derivative of rvec3 with regard to rvec2dr3dt2 - Optional output derivative of rvec3 with regard to tvec2dt3dr1 - Optional output derivative of tvec3 with regard to rvec1
The functions compute:
\(\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\)
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and
\(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See #Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input
vectors (see #matMulDeriv ). The functions are used inside #stereoCalibrate but can also be used in
your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a
function that contains a matrix multiplication.public static void composeRT(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2)
rvec1 - First rotation vector.tvec1 - First translation vector.rvec2 - Second rotation vector.tvec2 - Second translation vector.rvec3 - Output rotation vector of the superposition.tvec3 - Output translation vector of the superposition.dr3dr1 - Optional output derivative of rvec3 with regard to rvec1dr3dt1 - Optional output derivative of rvec3 with regard to tvec1dr3dr2 - Optional output derivative of rvec3 with regard to rvec2dr3dt2 - Optional output derivative of rvec3 with regard to tvec2
The functions compute:
\(\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\)
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and
\(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See #Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input
vectors (see #matMulDeriv ). The functions are used inside #stereoCalibrate but can also be used in
your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a
function that contains a matrix multiplication.public static void composeRT(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2)
rvec1 - First rotation vector.tvec1 - First translation vector.rvec2 - Second rotation vector.tvec2 - Second translation vector.rvec3 - Output rotation vector of the superposition.tvec3 - Output translation vector of the superposition.dr3dr1 - Optional output derivative of rvec3 with regard to rvec1dr3dt1 - Optional output derivative of rvec3 with regard to tvec1dr3dr2 - Optional output derivative of rvec3 with regard to rvec2
The functions compute:
\(\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\)
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and
\(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See #Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input
vectors (see #matMulDeriv ). The functions are used inside #stereoCalibrate but can also be used in
your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a
function that contains a matrix multiplication.public static void composeRT(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1)
rvec1 - First rotation vector.tvec1 - First translation vector.rvec2 - Second rotation vector.tvec2 - Second translation vector.rvec3 - Output rotation vector of the superposition.tvec3 - Output translation vector of the superposition.dr3dr1 - Optional output derivative of rvec3 with regard to rvec1dr3dt1 - Optional output derivative of rvec3 with regard to tvec1
The functions compute:
\(\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\)
where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and
\(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See #Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input
vectors (see #matMulDeriv ). The functions are used inside #stereoCalibrate but can also be used in
your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a
function that contains a matrix multiplication.public static void composeRT(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1)
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.public static void composeRT(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3)
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.public static void projectPoints(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian, double aspectRatio)
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.public static void projectPoints(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian)
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.public static void projectPoints(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints)
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.public static boolean solvePnP(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int flags)
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:
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.
public static boolean solvePnP(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess)
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:
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.
public static boolean solvePnP(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec)
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:
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.
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)
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:
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)
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:
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence)
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:
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int iterationsCount, float reprojectionError)
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:
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int iterationsCount)
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:
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess)
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:
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec)
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:
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers, UsacParams params)
public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, Mat inliers)
public static int solveP3P(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, int flags)
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:
public static void solvePnPRefineLM(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria)
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.public static void solvePnPRefineLM(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec)
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.public static void solvePnPRefineVVS(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria, double VVSlambda)
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.public static void solvePnPRefineVVS(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, TermCriteria criteria)
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.public static void solvePnPRefineVVS(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec)
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.public static int solvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess, int flags, Mat rvec, Mat tvec, Mat reprojectionError)
objectPoints - Array of object points in the object coordinate space, Nx3 1-channel or
1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.imagePoints - Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
where N is the number of points. vector<Point2d> can be also passed here.cameraMatrix - Input camera intrinsic matrix \(\cameramatrix{A}\) .distCoeffs - Input vector of distortion coefficients
\(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are
assumed.rvecs - Vector of output rotation vectors (see REF: Rodrigues ) that, together with tvecs, brings points from
the model coordinate system to the camera coordinate system.tvecs - Vector of output translation vectors.useExtrinsicGuess - Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
the provided rvec and tvec values as initial approximations of the rotation and translation
vectors, respectively, and further optimizes them.flags - Method for solving a PnP problem: see REF: calib3d_solvePnP_flagsrvec - Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is REF: SOLVEPNP_ITERATIVE
and useExtrinsicGuess is set to true.tvec - Translation vector used to initialize an iterative PnP refinement algorithm, when flag is REF: SOLVEPNP_ITERATIVE
and useExtrinsicGuess is set to true.reprojectionError - Optional vector of reprojection error, that is the RMS error
(\( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points
and the 3D object points projected with the estimated pose.
More information is described in REF: calib3d_solvePnP
Note:
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.
public static int solvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess, int flags, Mat rvec, Mat tvec)
objectPoints - Array of object points in the object coordinate space, Nx3 1-channel or
1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.imagePoints - Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
where N is the number of points. vector<Point2d> can be also passed here.cameraMatrix - Input camera intrinsic matrix \(\cameramatrix{A}\) .distCoeffs - Input vector of distortion coefficients
\(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are
assumed.rvecs - Vector of output rotation vectors (see REF: Rodrigues ) that, together with tvecs, brings points from
the model coordinate system to the camera coordinate system.tvecs - Vector of output translation vectors.useExtrinsicGuess - Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
the provided rvec and tvec values as initial approximations of the rotation and translation
vectors, respectively, and further optimizes them.flags - Method for solving a PnP problem: see REF: calib3d_solvePnP_flagsrvec - Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is REF: SOLVEPNP_ITERATIVE
and useExtrinsicGuess is set to true.tvec - Translation vector used to initialize an iterative PnP refinement algorithm, when flag is REF: SOLVEPNP_ITERATIVE
and useExtrinsicGuess is set to true.
(\( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points
and the 3D object points projected with the estimated pose.
More information is described in REF: calib3d_solvePnP
Note:
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.
public static int solvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess, int flags, Mat rvec)
objectPoints - Array of object points in the object coordinate space, Nx3 1-channel or
1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.imagePoints - Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
where N is the number of points. vector<Point2d> can be also passed here.cameraMatrix - Input camera intrinsic matrix \(\cameramatrix{A}\) .distCoeffs - Input vector of distortion coefficients
\(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are
assumed.rvecs - Vector of output rotation vectors (see REF: Rodrigues ) that, together with tvecs, brings points from
the model coordinate system to the camera coordinate system.tvecs - Vector of output translation vectors.useExtrinsicGuess - Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
the provided rvec and tvec values as initial approximations of the rotation and translation
vectors, respectively, and further optimizes them.flags - Method for solving a PnP problem: see REF: calib3d_solvePnP_flagsrvec - Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is REF: SOLVEPNP_ITERATIVE
and useExtrinsicGuess is set to true.
and useExtrinsicGuess is set to true.
(\( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points
and the 3D object points projected with the estimated pose.
More information is described in REF: calib3d_solvePnP
Note:
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.
public static int solvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess, int flags)
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:
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.
public static int solvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, boolean useExtrinsicGuess)
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:
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.
public static int solvePnPGeneric(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs)
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:
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.
public static Mat initCameraMatrix2D(List<MatOfPoint3f> objectPoints, List<MatOfPoint2f> imagePoints, Size imageSize, double aspectRatio)
objectPoints - Vector of vectors of the calibration pattern points in the calibration pattern
coordinate space. In the old interface all the per-view vectors are concatenated. See
#calibrateCamera for details.imagePoints - Vector of vectors of the projections of the calibration pattern points. In the
old interface all the per-view vectors are concatenated.imageSize - Image size in pixels used to initialize the principal point.aspectRatio - If it is zero or negative, both \(f_x\) and \(f_y\) are estimated independently.
Otherwise, \(f_x = f_y \cdot \texttt{aspectRatio}\) .
The function estimates and returns an initial camera intrinsic matrix for the camera calibration process.
Currently, the function only supports planar calibration patterns, which are patterns where each
object point has z-coordinate =0.public static Mat initCameraMatrix2D(List<MatOfPoint3f> objectPoints, List<MatOfPoint2f> imagePoints, Size imageSize)
objectPoints - Vector of vectors of the calibration pattern points in the calibration pattern
coordinate space. In the old interface all the per-view vectors are concatenated. See
#calibrateCamera for details.imagePoints - Vector of vectors of the projections of the calibration pattern points. In the
old interface all the per-view vectors are concatenated.imageSize - Image size in pixels used to initialize the principal point.
Otherwise, \(f_x = f_y \cdot \texttt{aspectRatio}\) .
The function estimates and returns an initial camera intrinsic matrix for the camera calibration process.
Currently, the function only supports planar calibration patterns, which are patterns where each
object point has z-coordinate =0.public static boolean findChessboardCorners(Mat image, Size patternSize, MatOfPoint2f corners, int flags)
image - Source chessboard view. It must be an 8-bit grayscale or color image.patternSize - Number of inner corners per a chessboard row and column
( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).corners - Output array of detected corners.flags - Various operation flags that can be zero or a combination of the following values:
Size patternsize(8,6); //interior number of corners
Mat gray = ....; //source image
vector<Point2f> corners; //this will be filled by the detected corners
//CALIB_CB_FAST_CHECK saves a lot of time on images
//that do not contain any chessboard corners
bool patternfound = findChessboardCorners(gray, patternsize, corners,
CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
+ CALIB_CB_FAST_CHECK);
if(patternfound)
cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
drawChessboardCorners(img, patternsize, Mat(corners), patternfound);
Note: The function requires white space (like a square-thick border, the wider the better) around
the board to make the detection more robust in various environments. Otherwise, if there is no
border and the background is dark, the outer black squares cannot be segmented properly and so the
square grouping and ordering algorithm fails.
Use gen_pattern.py (REF: tutorial_camera_calibration_pattern) to create checkerboard.public static boolean findChessboardCorners(Mat image, Size patternSize, MatOfPoint2f corners)
image - Source chessboard view. It must be an 8-bit grayscale or color image.patternSize - Number of inner corners per a chessboard row and column
( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).corners - Output array of detected corners.
Size patternsize(8,6); //interior number of corners
Mat gray = ....; //source image
vector<Point2f> corners; //this will be filled by the detected corners
//CALIB_CB_FAST_CHECK saves a lot of time on images
//that do not contain any chessboard corners
bool patternfound = findChessboardCorners(gray, patternsize, corners,
CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
+ CALIB_CB_FAST_CHECK);
if(patternfound)
cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
drawChessboardCorners(img, patternsize, Mat(corners), patternfound);
Note: The function requires white space (like a square-thick border, the wider the better) around
the board to make the detection more robust in various environments. Otherwise, if there is no
border and the background is dark, the outer black squares cannot be segmented properly and so the
square grouping and ordering algorithm fails.
Use gen_pattern.py (REF: tutorial_camera_calibration_pattern) to create checkerboard.public static boolean findChessboardCornersSBWithMeta(Mat image, Size patternSize, Mat corners, int flags, Mat meta)
image - Source chessboard view. It must be an 8-bit grayscale or color image.patternSize - Number of inner corners per a chessboard row and column
( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).corners - Output array of detected corners.flags - Various operation flags that can be zero or a combination of the following values:
meta - Optional output arrray of detected corners (CV_8UC1 and size = cv::Size(columns,rows)).
Each entry stands for one corner of the pattern and can have one of the following values:
public static boolean findChessboardCornersSB(Mat image, Size patternSize, Mat corners, int flags)
public static boolean findChessboardCornersSB(Mat image, Size patternSize, Mat corners)
public static Scalar estimateChessboardSharpness(Mat image, Size patternSize, Mat corners, float rise_distance, boolean vertical, Mat sharpness)
image - Gray image used to find chessboard cornerspatternSize - Size of a found chessboard patterncorners - Corners found by #findChessboardCornersSBrise_distance - Rise distance 0.8 means 10% ... 90% of the final signal strengthvertical - By default edge responses for horizontal lines are calculatedsharpness - Optional output array with a sharpness value for calculated edge responses (see description)
The optional sharpness array is of type CV_32FC1 and has for each calculated
profile one row with the following five entries:
0 = x coordinate of the underlying edge in the image
1 = y coordinate of the underlying edge in the image
2 = width of the transition area (sharpness)
3 = signal strength in the black cell (min brightness)
4 = signal strength in the white cell (max brightness)public static Scalar estimateChessboardSharpness(Mat image, Size patternSize, Mat corners, float rise_distance, boolean vertical)
image - Gray image used to find chessboard cornerspatternSize - Size of a found chessboard patterncorners - Corners found by #findChessboardCornersSBrise_distance - Rise distance 0.8 means 10% ... 90% of the final signal strengthvertical - By default edge responses for horizontal lines are calculated
The optional sharpness array is of type CV_32FC1 and has for each calculated
profile one row with the following five entries:
0 = x coordinate of the underlying edge in the image
1 = y coordinate of the underlying edge in the image
2 = width of the transition area (sharpness)
3 = signal strength in the black cell (min brightness)
4 = signal strength in the white cell (max brightness)public static Scalar estimateChessboardSharpness(Mat image, Size patternSize, Mat corners, float rise_distance)
image - Gray image used to find chessboard cornerspatternSize - Size of a found chessboard patterncorners - Corners found by #findChessboardCornersSBrise_distance - Rise distance 0.8 means 10% ... 90% of the final signal strength
The optional sharpness array is of type CV_32FC1 and has for each calculated
profile one row with the following five entries:
0 = x coordinate of the underlying edge in the image
1 = y coordinate of the underlying edge in the image
2 = width of the transition area (sharpness)
3 = signal strength in the black cell (min brightness)
4 = signal strength in the white cell (max brightness)public static Scalar estimateChessboardSharpness(Mat image, Size patternSize, Mat corners)
image - Gray image used to find chessboard cornerspatternSize - Size of a found chessboard patterncorners - Corners found by #findChessboardCornersSB
The optional sharpness array is of type CV_32FC1 and has for each calculated
profile one row with the following five entries:
0 = x coordinate of the underlying edge in the image
1 = y coordinate of the underlying edge in the image
2 = width of the transition area (sharpness)
3 = signal strength in the black cell (min brightness)
4 = signal strength in the white cell (max brightness)public static boolean find4QuadCornerSubpix(Mat img, Mat corners, Size region_size)
public static void drawChessboardCorners(Mat image, Size patternSize, MatOfPoint2f corners, boolean patternWasFound)
image - Destination image. It must be an 8-bit color image.patternSize - Number of inner corners per a chessboard row and column
(patternSize = cv::Size(points_per_row,points_per_column)).corners - Array of detected corners, the output of #findChessboardCorners.patternWasFound - Parameter indicating whether the complete board was found or not. The
return value of #findChessboardCorners should be passed here.
The function draws individual chessboard corners detected either as red circles if the board was not
found, or as colored corners connected with lines if the board was found.public static void drawFrameAxes(Mat image, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, float length, int thickness)
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.public static void drawFrameAxes(Mat image, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, float length)
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.public static boolean findCirclesGrid(Mat image, Size patternSize, Mat centers, int flags)
public static double calibrateCameraExtended(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags, TermCriteria criteria)
objectPoints - In the new interface it is a vector of vectors of calibration pattern points in
the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer
vector contains as many elements as the number of pattern views. If the same calibration pattern
is shown in each view and it is fully visible, all the vectors will be the same. Although, it is
possible to use partially occluded patterns or even different patterns in different views. Then,
the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's
XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig.
In the old interface all the vectors of object points from different views are concatenated
together.imagePoints - In the new interface it is a vector of vectors of the projections of calibration
pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and
objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal,
respectively. In the old interface all the vectors of object points from different views are
concatenated together.imageSize - Size of the image used only to initialize the camera intrinsic matrix.cameraMatrix - Input/output 3x3 floating-point camera intrinsic matrix
\(\cameramatrix{A}\) . If REF: CALIB_USE_INTRINSIC_GUESS
and/or REF: CALIB_FIX_ASPECT_RATIO, REF: CALIB_FIX_PRINCIPAL_POINT or REF: CALIB_FIX_FOCAL_LENGTH
are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.distCoeffs - Input/output vector of distortion coefficients
\(\distcoeffs\).rvecs - Output vector of rotation vectors (REF: Rodrigues ) estimated for each pattern view
(e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding
i-th translation vector (see the next output parameter description) brings the calibration pattern
from the object coordinate space (in which object points are specified) to the camera coordinate
space. In more technical terms, the tuple of the i-th rotation and translation vector performs
a change of basis from object coordinate space to camera coordinate space. Due to its duality, this
tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate
space.tvecs - Output vector of translation vectors estimated for each pattern view, see parameter
describtion above.stdDeviationsIntrinsics - Output vector of standard deviations estimated for intrinsic
parameters. Order of deviations values:
\((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3,
s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.stdDeviationsExtrinsics - Output vector of standard deviations estimated for extrinsic
parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is
the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors.perViewErrors - Output vector of the RMS re-projection error estimated for each pattern view.flags - Different flags that may be zero or a combination of the following values:
criteria - Termination criteria for the iterative optimization algorithm.public static double calibrateCameraExtended(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags)
objectPoints - In the new interface it is a vector of vectors of calibration pattern points in
the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer
vector contains as many elements as the number of pattern views. If the same calibration pattern
is shown in each view and it is fully visible, all the vectors will be the same. Although, it is
possible to use partially occluded patterns or even different patterns in different views. Then,
the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's
XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig.
In the old interface all the vectors of object points from different views are concatenated
together.imagePoints - In the new interface it is a vector of vectors of the projections of calibration
pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and
objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal,
respectively. In the old interface all the vectors of object points from different views are
concatenated together.imageSize - Size of the image used only to initialize the camera intrinsic matrix.cameraMatrix - Input/output 3x3 floating-point camera intrinsic matrix
\(\cameramatrix{A}\) . If REF: CALIB_USE_INTRINSIC_GUESS
and/or REF: CALIB_FIX_ASPECT_RATIO, REF: CALIB_FIX_PRINCIPAL_POINT or REF: CALIB_FIX_FOCAL_LENGTH
are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.distCoeffs - Input/output vector of distortion coefficients
\(\distcoeffs\).rvecs - Output vector of rotation vectors (REF: Rodrigues ) estimated for each pattern view
(e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding
i-th translation vector (see the next output parameter description) brings the calibration pattern
from the object coordinate space (in which object points are specified) to the camera coordinate
space. In more technical terms, the tuple of the i-th rotation and translation vector performs
a change of basis from object coordinate space to camera coordinate space. Due to its duality, this
tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate
space.tvecs - Output vector of translation vectors estimated for each pattern view, see parameter
describtion above.stdDeviationsIntrinsics - Output vector of standard deviations estimated for intrinsic
parameters. Order of deviations values:
\((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3,
s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.stdDeviationsExtrinsics - Output vector of standard deviations estimated for extrinsic
parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is
the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors.perViewErrors - Output vector of the RMS re-projection error estimated for each pattern view.flags - Different flags that may be zero or a combination of the following values:
public static double calibrateCameraExtended(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors)
objectPoints - In the new interface it is a vector of vectors of calibration pattern points in
the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer
vector contains as many elements as the number of pattern views. If the same calibration pattern
is shown in each view and it is fully visible, all the vectors will be the same. Although, it is
possible to use partially occluded patterns or even different patterns in different views. Then,
the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's
XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig.
In the old interface all the vectors of object points from different views are concatenated
together.imagePoints - In the new interface it is a vector of vectors of the projections of calibration
pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and
objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal,
respectively. In the old interface all the vectors of object points from different views are
concatenated together.imageSize - Size of the image used only to initialize the camera intrinsic matrix.cameraMatrix - Input/output 3x3 floating-point camera intrinsic matrix
\(\cameramatrix{A}\) . If REF: CALIB_USE_INTRINSIC_GUESS
and/or REF: CALIB_FIX_ASPECT_RATIO, REF: CALIB_FIX_PRINCIPAL_POINT or REF: CALIB_FIX_FOCAL_LENGTH
are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.distCoeffs - Input/output vector of distortion coefficients
\(\distcoeffs\).rvecs - Output vector of rotation vectors (REF: Rodrigues ) estimated for each pattern view
(e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding
i-th translation vector (see the next output parameter description) brings the calibration pattern
from the object coordinate space (in which object points are specified) to the camera coordinate
space. In more technical terms, the tuple of the i-th rotation and translation vector performs
a change of basis from object coordinate space to camera coordinate space. Due to its duality, this
tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate
space.tvecs - Output vector of translation vectors estimated for each pattern view, see parameter
describtion above.stdDeviationsIntrinsics - Output vector of standard deviations estimated for intrinsic
parameters. Order of deviations values:
\((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3,
s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.stdDeviationsExtrinsics - Output vector of standard deviations estimated for extrinsic
parameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is
the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors.perViewErrors - Output vector of the RMS re-projection error estimated for each pattern view.
public static double calibrateCamera(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, int flags, TermCriteria criteria)
public static double calibrateCamera(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, int flags)
public static double calibrateCamera(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs)
public static double calibrateCameraROExtended(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat newObjPoints, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat stdDeviationsObjPoints, Mat perViewErrors, int flags, TermCriteria criteria)
objectPoints - Vector of vectors of calibration pattern points in the calibration pattern
coordinate space. See #calibrateCamera for details. If the method of releasing object to be used,
the identical calibration board must be used in each view and it must be fully visible, and all
objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration
target has to be rigid, or at least static if the camera (rather than the calibration target) is
shifted for grabbing images.imagePoints - Vector of vectors of the projections of calibration pattern points. See
#calibrateCamera for details.imageSize - Size of the image used only to initialize the intrinsic camera matrix.iFixedPoint - The index of the 3D object point in objectPoints[0] to be fixed. It also acts as
a switch for calibration method selection. If object-releasing method to be used, pass in the
parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will
make standard calibration method selected. Usually the top-right corner point of the calibration
board grid is recommended to be fixed when object-releasing method being utilized. According to
\cite strobl2011iccv, two other points are also fixed. In this implementation, objectPoints[0].front
and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and
newObjPoints are only possible if coordinates of these three fixed points are accurate enough.cameraMatrix - Output 3x3 floating-point camera matrix. See #calibrateCamera for details.distCoeffs - Output vector of distortion coefficients. See #calibrateCamera for details.rvecs - Output vector of rotation vectors estimated for each pattern view. See #calibrateCamera
for details.tvecs - Output vector of translation vectors estimated for each pattern view.newObjPoints - The updated output vector of calibration pattern points. The coordinates might
be scaled based on three fixed points. The returned coordinates are accurate only if the above
mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter
is ignored with standard calibration method.stdDeviationsIntrinsics - Output vector of standard deviations estimated for intrinsic parameters.
See #calibrateCamera for details.stdDeviationsExtrinsics - Output vector of standard deviations estimated for extrinsic parameters.
See #calibrateCamera for details.stdDeviationsObjPoints - Output vector of standard deviations estimated for refined coordinates
of calibration pattern points. It has the same size and order as objectPoints[0] vector. This
parameter is ignored with standard calibration method.perViewErrors - Output vector of the RMS re-projection error estimated for each pattern view.flags - Different flags that may be zero or a combination of some predefined values. See
#calibrateCamera for details. If the method of releasing object is used, the calibration time may
be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially
less precise and less stable in some rare cases.criteria - Termination criteria for the iterative optimization algorithm.public static double calibrateCameraROExtended(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat newObjPoints, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat stdDeviationsObjPoints, Mat perViewErrors, int flags)
objectPoints - Vector of vectors of calibration pattern points in the calibration pattern
coordinate space. See #calibrateCamera for details. If the method of releasing object to be used,
the identical calibration board must be used in each view and it must be fully visible, and all
objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration
target has to be rigid, or at least static if the camera (rather than the calibration target) is
shifted for grabbing images.imagePoints - Vector of vectors of the projections of calibration pattern points. See
#calibrateCamera for details.imageSize - Size of the image used only to initialize the intrinsic camera matrix.iFixedPoint - The index of the 3D object point in objectPoints[0] to be fixed. It also acts as
a switch for calibration method selection. If object-releasing method to be used, pass in the
parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will
make standard calibration method selected. Usually the top-right corner point of the calibration
board grid is recommended to be fixed when object-releasing method being utilized. According to
\cite strobl2011iccv, two other points are also fixed. In this implementation, objectPoints[0].front
and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and
newObjPoints are only possible if coordinates of these three fixed points are accurate enough.cameraMatrix - Output 3x3 floating-point camera matrix. See #calibrateCamera for details.distCoeffs - Output vector of distortion coefficients. See #calibrateCamera for details.rvecs - Output vector of rotation vectors estimated for each pattern view. See #calibrateCamera
for details.tvecs - Output vector of translation vectors estimated for each pattern view.newObjPoints - The updated output vector of calibration pattern points. The coordinates might
be scaled based on three fixed points. The returned coordinates are accurate only if the above
mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter
is ignored with standard calibration method.stdDeviationsIntrinsics - Output vector of standard deviations estimated for intrinsic parameters.
See #calibrateCamera for details.stdDeviationsExtrinsics - Output vector of standard deviations estimated for extrinsic parameters.
See #calibrateCamera for details.stdDeviationsObjPoints - Output vector of standard deviations estimated for refined coordinates
of calibration pattern points. It has the same size and order as objectPoints[0] vector. This
parameter is ignored with standard calibration method.perViewErrors - Output vector of the RMS re-projection error estimated for each pattern view.flags - Different flags that may be zero or a combination of some predefined values. See
#calibrateCamera for details. If the method of releasing object is used, the calibration time may
be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially
less precise and less stable in some rare cases.public static double calibrateCameraROExtended(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat newObjPoints, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat stdDeviationsObjPoints, Mat perViewErrors)
objectPoints - Vector of vectors of calibration pattern points in the calibration pattern
coordinate space. See #calibrateCamera for details. If the method of releasing object to be used,
the identical calibration board must be used in each view and it must be fully visible, and all
objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration
target has to be rigid, or at least static if the camera (rather than the calibration target) is
shifted for grabbing images.imagePoints - Vector of vectors of the projections of calibration pattern points. See
#calibrateCamera for details.imageSize - Size of the image used only to initialize the intrinsic camera matrix.iFixedPoint - The index of the 3D object point in objectPoints[0] to be fixed. It also acts as
a switch for calibration method selection. If object-releasing method to be used, pass in the
parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will
make standard calibration method selected. Usually the top-right corner point of the calibration
board grid is recommended to be fixed when object-releasing method being utilized. According to
\cite strobl2011iccv, two other points are also fixed. In this implementation, objectPoints[0].front
and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and
newObjPoints are only possible if coordinates of these three fixed points are accurate enough.cameraMatrix - Output 3x3 floating-point camera matrix. See #calibrateCamera for details.distCoeffs - Output vector of distortion coefficients. See #calibrateCamera for details.rvecs - Output vector of rotation vectors estimated for each pattern view. See #calibrateCamera
for details.tvecs - Output vector of translation vectors estimated for each pattern view.newObjPoints - The updated output vector of calibration pattern points. The coordinates might
be scaled based on three fixed points. The returned coordinates are accurate only if the above
mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter
is ignored with standard calibration method.stdDeviationsIntrinsics - Output vector of standard deviations estimated for intrinsic parameters.
See #calibrateCamera for details.stdDeviationsExtrinsics - Output vector of standard deviations estimated for extrinsic parameters.
See #calibrateCamera for details.stdDeviationsObjPoints - Output vector of standard deviations estimated for refined coordinates
of calibration pattern points. It has the same size and order as objectPoints[0] vector. This
parameter is ignored with standard calibration method.perViewErrors - Output vector of the RMS re-projection error estimated for each pattern view.
#calibrateCamera for details. If the method of releasing object is used, the calibration time may
be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially
less precise and less stable in some rare cases.public static double calibrateCameraRO(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat newObjPoints, int flags, TermCriteria criteria)
public static double calibrateCameraRO(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat newObjPoints, int flags)
public static double calibrateCameraRO(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, int iFixedPoint, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat newObjPoints)
public static void calibrationMatrixValues(Mat cameraMatrix, Size imageSize, double apertureWidth, double apertureHeight, double[] fovx, double[] fovy, double[] focalLength, Point principalPoint, double[] aspectRatio)
cameraMatrix - Input camera intrinsic matrix that can be estimated by #calibrateCamera or
#stereoCalibrate .imageSize - Input image size in pixels.apertureWidth - Physical width in mm of the sensor.apertureHeight - Physical height in mm of the sensor.fovx - Output field of view in degrees along the horizontal sensor axis.fovy - Output field of view in degrees along the vertical sensor axis.focalLength - Focal length of the lens in mm.principalPoint - Principal point in mm.aspectRatio - \(f_y/f_x\)
The function computes various useful camera characteristics from the previously estimated camera
matrix.
Note:
Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for
the chessboard pitch (it can thus be any value).public static double stereoCalibrateExtended(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, List<Mat> rvecs, List<Mat> tvecs, Mat perViewErrors, int flags, TermCriteria criteria)
objectPoints - Vector of vectors of the calibration pattern points. The same structure as
in REF: calibrateCamera. For each pattern view, both cameras need to see the same object
points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be
equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to
be equal for each i.imagePoints1 - Vector of vectors of the projections of the calibration pattern points,
observed by the first camera. The same structure as in REF: calibrateCamera.imagePoints2 - Vector of vectors of the projections of the calibration pattern points,
observed by the second camera. The same structure as in REF: calibrateCamera.cameraMatrix1 - Input/output camera intrinsic 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 second camera intrinsic matrix for the second camera. See description for
cameraMatrix1.distCoeffs2 - Input/output lens distortion coefficients for the second camera. See
description for distCoeffs1.imageSize - Size of the image used only to initialize the camera intrinsic matrices.R - Output rotation matrix. Together with the translation vector T, this matrix brings
points given in the first camera's coordinate system to points in the second camera's
coordinate system. In more technical terms, the tuple of R and T performs a change of basis
from the first camera's coordinate system to the second camera's coordinate system. Due to its
duality, this tuple is equivalent to the position of the first camera with respect to the
second camera coordinate system.T - Output translation vector, see description above.E - Output essential matrix.F - Output fundamental matrix.rvecs - Output vector of rotation vectors ( REF: Rodrigues ) estimated for each pattern view in the
coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
description) brings the calibration pattern from the object coordinate space (in which object points are
specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
to camera coordinate space of the first camera of the stereo pair.tvecs - Output vector of translation vectors estimated for each pattern view, see parameter description
of previous output parameter ( rvecs ).perViewErrors - Output vector of the RMS re-projection error estimated for each pattern view.flags - Different flags that may be zero or a combination of the following values:
criteria - Termination criteria for the iterative optimization algorithm.
The function estimates the transformation between two cameras making a stereo pair. If one computes
the poses of an object relative to the first camera and to the second camera,
( \(R_1\),\(T_1\) ) and (\(R_2\),\(T_2\)), respectively, for a stereo camera where the
relative position and orientation between the two cameras are fixed, then those poses definitely
relate to each other. This means, if the relative position and orientation (\(R\),\(T\)) of the
two cameras is known, it is possible to compute (\(R_2\),\(T_2\)) when (\(R_1\),\(T_1\)) is
given. This is what the described function does. It computes (\(R\),\(T\)) such that:
\(R_2=R R_1\)
\(T_2=R T_1 + T.\)
Therefore, one can compute the coordinate representation of a 3D point for the second camera's
coordinate system when given the point's coordinate representation in the first camera's coordinate
system:
\(\begin{bmatrix}
X_2 \\
Y_2 \\
Z_2 \\
1
\end{bmatrix} = \begin{bmatrix}
R & T \\
0 & 1
\end{bmatrix} \begin{bmatrix}
X_1 \\
Y_1 \\
Z_1 \\
1
\end{bmatrix}.\)
Optionally, it computes the essential matrix E:
\(E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} R\)
where \(T_i\) are components of the translation vector \(T\) : \(T=[T_0, T_1, T_2]^T\) .
And the function can also compute the fundamental matrix F:
\(F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\)
Besides the stereo-related information, the function can also perform a full calibration of each of
the two cameras. However, due to the high dimensionality of the parameter space and noise in the
input data, the function can diverge from the correct solution. If the intrinsic parameters can be
estimated with high accuracy for each of the cameras individually (for example, using
#calibrateCamera ), you are recommended to do so and then pass REF: CALIB_FIX_INTRINSIC flag to the
function along with the computed intrinsic parameters. Otherwise, if all the parameters are
estimated at once, it makes sense to restrict some parameters, for example, pass
REF: CALIB_SAME_FOCAL_LENGTH and REF: CALIB_ZERO_TANGENT_DIST flags, which is usually a
reasonable assumption.
Similarly to #calibrateCamera, the function minimizes the total re-projection error for all the
points in all the available views from both cameras. The function returns the final value of the
re-projection error.public static double stereoCalibrateExtended(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, List<Mat> rvecs, List<Mat> tvecs, Mat perViewErrors, int flags)
objectPoints - Vector of vectors of the calibration pattern points. The same structure as
in REF: calibrateCamera. For each pattern view, both cameras need to see the same object
points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be
equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to
be equal for each i.imagePoints1 - Vector of vectors of the projections of the calibration pattern points,
observed by the first camera. The same structure as in REF: calibrateCamera.imagePoints2 - Vector of vectors of the projections of the calibration pattern points,
observed by the second camera. The same structure as in REF: calibrateCamera.cameraMatrix1 - Input/output camera intrinsic 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 second camera intrinsic matrix for the second camera. See description for
cameraMatrix1.distCoeffs2 - Input/output lens distortion coefficients for the second camera. See
description for distCoeffs1.imageSize - Size of the image used only to initialize the camera intrinsic matrices.R - Output rotation matrix. Together with the translation vector T, this matrix brings
points given in the first camera's coordinate system to points in the second camera's
coordinate system. In more technical terms, the tuple of R and T performs a change of basis
from the first camera's coordinate system to the second camera's coordinate system. Due to its
duality, this tuple is equivalent to the position of the first camera with respect to the
second camera coordinate system.T - Output translation vector, see description above.E - Output essential matrix.F - Output fundamental matrix.rvecs - Output vector of rotation vectors ( REF: Rodrigues ) estimated for each pattern view in the
coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
description) brings the calibration pattern from the object coordinate space (in which object points are
specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
to camera coordinate space of the first camera of the stereo pair.tvecs - Output vector of translation vectors estimated for each pattern view, see parameter description
of previous output parameter ( rvecs ).perViewErrors - Output vector of the RMS re-projection error estimated for each pattern view.flags - Different flags that may be zero or a combination of the following values:
public static double stereoCalibrateExtended(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, List<Mat> rvecs, List<Mat> tvecs, Mat perViewErrors)
objectPoints - Vector of vectors of the calibration pattern points. The same structure as
in REF: calibrateCamera. For each pattern view, both cameras need to see the same object
points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be
equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to
be equal for each i.imagePoints1 - Vector of vectors of the projections of the calibration pattern points,
observed by the first camera. The same structure as in REF: calibrateCamera.imagePoints2 - Vector of vectors of the projections of the calibration pattern points,
observed by the second camera. The same structure as in REF: calibrateCamera.cameraMatrix1 - Input/output camera intrinsic 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 second camera intrinsic matrix for the second camera. See description for
cameraMatrix1.distCoeffs2 - Input/output lens distortion coefficients for the second camera. See
description for distCoeffs1.imageSize - Size of the image used only to initialize the camera intrinsic matrices.R - Output rotation matrix. Together with the translation vector T, this matrix brings
points given in the first camera's coordinate system to points in the second camera's
coordinate system. In more technical terms, the tuple of R and T performs a change of basis
from the first camera's coordinate system to the second camera's coordinate system. Due to its
duality, this tuple is equivalent to the position of the first camera with respect to the
second camera coordinate system.T - Output translation vector, see description above.E - Output essential matrix.F - Output fundamental matrix.rvecs - Output vector of rotation vectors ( REF: Rodrigues ) estimated for each pattern view in the
coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
description) brings the calibration pattern from the object coordinate space (in which object points are
specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
to camera coordinate space of the first camera of the stereo pair.tvecs - Output vector of translation vectors estimated for each pattern view, see parameter description
of previous output parameter ( rvecs ).perViewErrors - Output vector of the RMS re-projection error estimated for each pattern view.
public static double stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, int flags, TermCriteria criteria)
public static double stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, int flags)
public static double stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F)
public static double stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors, int flags, TermCriteria criteria)
public static double stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors, int flags)
public static double stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors)
public static void stereoRectify(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, Size newImageSize, Rect validPixROI1, Rect validPixROI2)
cameraMatrix1 - First camera intrinsic matrix.distCoeffs1 - First camera distortion parameters.cameraMatrix2 - Second camera intrinsic matrix.distCoeffs2 - Second camera distortion parameters.imageSize - Size of the image used for stereo calibration.R - Rotation matrix from the coordinate system of the first camera to the second camera,
see REF: stereoCalibrate.T - Translation vector from the coordinate system of the first camera to the second camera,
see REF: stereoCalibrate.R1 - Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix
brings points given in the unrectified first camera's coordinate system to points in the rectified
first camera's coordinate system. In more technical terms, it performs a change of basis from the
unrectified first camera's coordinate system to the rectified first camera's coordinate system.R2 - Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix
brings points given in the unrectified second camera's coordinate system to points in the rectified
second camera's coordinate system. In more technical terms, it performs a change of basis from the
unrectified second camera's coordinate system to the rectified second camera's coordinate system.P1 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
camera, i.e. it projects points given in the rectified first camera coordinate system into the
rectified first camera's image.P2 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
camera, i.e. it projects points given in the rectified first camera coordinate system into the
rectified second camera's image.Q - Output \(4 \times 4\) disparity-to-depth mapping matrix (see REF: reprojectImageTo3D).flags - Operation flags that may be zero or REF: CALIB_ZERO_DISPARITY . If the flag is set,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views. And if the flag is not set, the function may still shift the images in the
horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
useful image area.alpha - Free scaling parameter. If it is -1 or absent, the function performs the default
scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified
images are zoomed and shifted so that only valid pixels are visible (no black areas after
rectification). alpha=1 means that the rectified image is decimated and shifted so that all the
pixels from the original images from the cameras are retained in the rectified images (no source
image pixels are lost). Any intermediate value yields an intermediate result between
those two extreme cases.newImageSize - New image resolution after rectification. The same size should be passed to
#initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
is passed (default), it is set to the original imageSize . Setting it to a larger value can help you
preserve details in the original image, especially when there is a big radial distortion.validPixROI1 - Optional output rectangles inside the rectified images where all the pixels
are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
(see the picture below).validPixROI2 - Optional output rectangles inside the rectified images where all the pixels
are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
(see the picture below).
The function computes the rotation matrices for each camera that (virtually) make both camera image
planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies
the dense stereo correspondence problem. The function takes the matrices computed by #stereoCalibrate
as input. As output, it provides two rotation matrices and also two projection matrices in the new
coordinates. The function distinguishes the following two cases:
public static void stereoRectify(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, Size newImageSize, Rect validPixROI1)
cameraMatrix1 - First camera intrinsic matrix.distCoeffs1 - First camera distortion parameters.cameraMatrix2 - Second camera intrinsic matrix.distCoeffs2 - Second camera distortion parameters.imageSize - Size of the image used for stereo calibration.R - Rotation matrix from the coordinate system of the first camera to the second camera,
see REF: stereoCalibrate.T - Translation vector from the coordinate system of the first camera to the second camera,
see REF: stereoCalibrate.R1 - Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix
brings points given in the unrectified first camera's coordinate system to points in the rectified
first camera's coordinate system. In more technical terms, it performs a change of basis from the
unrectified first camera's coordinate system to the rectified first camera's coordinate system.R2 - Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix
brings points given in the unrectified second camera's coordinate system to points in the rectified
second camera's coordinate system. In more technical terms, it performs a change of basis from the
unrectified second camera's coordinate system to the rectified second camera's coordinate system.P1 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
camera, i.e. it projects points given in the rectified first camera coordinate system into the
rectified first camera's image.P2 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
camera, i.e. it projects points given in the rectified first camera coordinate system into the
rectified second camera's image.Q - Output \(4 \times 4\) disparity-to-depth mapping matrix (see REF: reprojectImageTo3D).flags - Operation flags that may be zero or REF: CALIB_ZERO_DISPARITY . If the flag is set,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views. And if the flag is not set, the function may still shift the images in the
horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
useful image area.alpha - Free scaling parameter. If it is -1 or absent, the function performs the default
scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified
images are zoomed and shifted so that only valid pixels are visible (no black areas after
rectification). alpha=1 means that the rectified image is decimated and shifted so that all the
pixels from the original images from the cameras are retained in the rectified images (no source
image pixels are lost). Any intermediate value yields an intermediate result between
those two extreme cases.newImageSize - New image resolution after rectification. The same size should be passed to
#initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
is passed (default), it is set to the original imageSize . Setting it to a larger value can help you
preserve details in the original image, especially when there is a big radial distortion.validPixROI1 - Optional output rectangles inside the rectified images where all the pixels
are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
(see the picture below).
are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
(see the picture below).
The function computes the rotation matrices for each camera that (virtually) make both camera image
planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies
the dense stereo correspondence problem. The function takes the matrices computed by #stereoCalibrate
as input. As output, it provides two rotation matrices and also two projection matrices in the new
coordinates. The function distinguishes the following two cases:
public static void stereoRectify(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, Size newImageSize)
cameraMatrix1 - First camera intrinsic matrix.distCoeffs1 - First camera distortion parameters.cameraMatrix2 - Second camera intrinsic matrix.distCoeffs2 - Second camera distortion parameters.imageSize - Size of the image used for stereo calibration.R - Rotation matrix from the coordinate system of the first camera to the second camera,
see REF: stereoCalibrate.T - Translation vector from the coordinate system of the first camera to the second camera,
see REF: stereoCalibrate.R1 - Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix
brings points given in the unrectified first camera's coordinate system to points in the rectified
first camera's coordinate system. In more technical terms, it performs a change of basis from the
unrectified first camera's coordinate system to the rectified first camera's coordinate system.R2 - Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix
brings points given in the unrectified second camera's coordinate system to points in the rectified
second camera's coordinate system. In more technical terms, it performs a change of basis from the
unrectified second camera's coordinate system to the rectified second camera's coordinate system.P1 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
camera, i.e. it projects points given in the rectified first camera coordinate system into the
rectified first camera's image.P2 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
camera, i.e. it projects points given in the rectified first camera coordinate system into the
rectified second camera's image.Q - Output \(4 \times 4\) disparity-to-depth mapping matrix (see REF: reprojectImageTo3D).flags - Operation flags that may be zero or REF: CALIB_ZERO_DISPARITY . If the flag is set,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views. And if the flag is not set, the function may still shift the images in the
horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
useful image area.alpha - Free scaling parameter. If it is -1 or absent, the function performs the default
scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified
images are zoomed and shifted so that only valid pixels are visible (no black areas after
rectification). alpha=1 means that the rectified image is decimated and shifted so that all the
pixels from the original images from the cameras are retained in the rectified images (no source
image pixels are lost). Any intermediate value yields an intermediate result between
those two extreme cases.newImageSize - New image resolution after rectification. The same size should be passed to
#initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
is passed (default), it is set to the original imageSize . Setting it to a larger value can help you
preserve details in the original image, especially when there is a big radial distortion.
are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
(see the picture below).
are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
(see the picture below).
The function computes the rotation matrices for each camera that (virtually) make both camera image
planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies
the dense stereo correspondence problem. The function takes the matrices computed by #stereoCalibrate
as input. As output, it provides two rotation matrices and also two projection matrices in the new
coordinates. The function distinguishes the following two cases:
public static void stereoRectify(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha)
cameraMatrix1 - First camera intrinsic matrix.distCoeffs1 - First camera distortion parameters.cameraMatrix2 - Second camera intrinsic matrix.distCoeffs2 - Second camera distortion parameters.imageSize - Size of the image used for stereo calibration.R - Rotation matrix from the coordinate system of the first camera to the second camera,
see REF: stereoCalibrate.T - Translation vector from the coordinate system of the first camera to the second camera,
see REF: stereoCalibrate.R1 - Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix
brings points given in the unrectified first camera's coordinate system to points in the rectified
first camera's coordinate system. In more technical terms, it performs a change of basis from the
unrectified first camera's coordinate system to the rectified first camera's coordinate system.R2 - Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix
brings points given in the unrectified second camera's coordinate system to points in the rectified
second camera's coordinate system. In more technical terms, it performs a change of basis from the
unrectified second camera's coordinate system to the rectified second camera's coordinate system.P1 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
camera, i.e. it projects points given in the rectified first camera coordinate system into the
rectified first camera's image.P2 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
camera, i.e. it projects points given in the rectified first camera coordinate system into the
rectified second camera's image.Q - Output \(4 \times 4\) disparity-to-depth mapping matrix (see REF: reprojectImageTo3D).flags - Operation flags that may be zero or REF: CALIB_ZERO_DISPARITY . If the flag is set,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views. And if the flag is not set, the function may still shift the images in the
horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
useful image area.alpha - Free scaling parameter. If it is -1 or absent, the function performs the default
scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified
images are zoomed and shifted so that only valid pixels are visible (no black areas after
rectification). alpha=1 means that the rectified image is decimated and shifted so that all the
pixels from the original images from the cameras are retained in the rectified images (no source
image pixels are lost). Any intermediate value yields an intermediate result between
those two extreme cases.
#initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
is passed (default), it is set to the original imageSize . Setting it to a larger value can help you
preserve details in the original image, especially when there is a big radial distortion.
are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
(see the picture below).
are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
(see the picture below).
The function computes the rotation matrices for each camera that (virtually) make both camera image
planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies
the dense stereo correspondence problem. The function takes the matrices computed by #stereoCalibrate
as input. As output, it provides two rotation matrices and also two projection matrices in the new
coordinates. The function distinguishes the following two cases:
public static void stereoRectify(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags)
cameraMatrix1 - First camera intrinsic matrix.distCoeffs1 - First camera distortion parameters.cameraMatrix2 - Second camera intrinsic matrix.distCoeffs2 - Second camera distortion parameters.imageSize - Size of the image used for stereo calibration.R - Rotation matrix from the coordinate system of the first camera to the second camera,
see REF: stereoCalibrate.T - Translation vector from the coordinate system of the first camera to the second camera,
see REF: stereoCalibrate.R1 - Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix
brings points given in the unrectified first camera's coordinate system to points in the rectified
first camera's coordinate system. In more technical terms, it performs a change of basis from the
unrectified first camera's coordinate system to the rectified first camera's coordinate system.R2 - Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix
brings points given in the unrectified second camera's coordinate system to points in the rectified
second camera's coordinate system. In more technical terms, it performs a change of basis from the
unrectified second camera's coordinate system to the rectified second camera's coordinate system.P1 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
camera, i.e. it projects points given in the rectified first camera coordinate system into the
rectified first camera's image.P2 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
camera, i.e. it projects points given in the rectified first camera coordinate system into the
rectified second camera's image.Q - Output \(4 \times 4\) disparity-to-depth mapping matrix (see REF: reprojectImageTo3D).flags - Operation flags that may be zero or REF: CALIB_ZERO_DISPARITY . If the flag is set,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views. And if the flag is not set, the function may still shift the images in the
horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
useful image area.
scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified
images are zoomed and shifted so that only valid pixels are visible (no black areas after
rectification). alpha=1 means that the rectified image is decimated and shifted so that all the
pixels from the original images from the cameras are retained in the rectified images (no source
image pixels are lost). Any intermediate value yields an intermediate result between
those two extreme cases.
#initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
is passed (default), it is set to the original imageSize . Setting it to a larger value can help you
preserve details in the original image, especially when there is a big radial distortion.
are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
(see the picture below).
are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
(see the picture below).
The function computes the rotation matrices for each camera that (virtually) make both camera image
planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies
the dense stereo correspondence problem. The function takes the matrices computed by #stereoCalibrate
as input. As output, it provides two rotation matrices and also two projection matrices in the new
coordinates. The function distinguishes the following two cases:
public static void stereoRectify(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q)
cameraMatrix1 - First camera intrinsic matrix.distCoeffs1 - First camera distortion parameters.cameraMatrix2 - Second camera intrinsic matrix.distCoeffs2 - Second camera distortion parameters.imageSize - Size of the image used for stereo calibration.R - Rotation matrix from the coordinate system of the first camera to the second camera,
see REF: stereoCalibrate.T - Translation vector from the coordinate system of the first camera to the second camera,
see REF: stereoCalibrate.R1 - Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix
brings points given in the unrectified first camera's coordinate system to points in the rectified
first camera's coordinate system. In more technical terms, it performs a change of basis from the
unrectified first camera's coordinate system to the rectified first camera's coordinate system.R2 - Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix
brings points given in the unrectified second camera's coordinate system to points in the rectified
second camera's coordinate system. In more technical terms, it performs a change of basis from the
unrectified second camera's coordinate system to the rectified second camera's coordinate system.P1 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
camera, i.e. it projects points given in the rectified first camera coordinate system into the
rectified first camera's image.P2 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
camera, i.e. it projects points given in the rectified first camera coordinate system into the
rectified second camera's image.Q - Output \(4 \times 4\) disparity-to-depth mapping matrix (see REF: reprojectImageTo3D).
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views. And if the flag is not set, the function may still shift the images in the
horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
useful image area.
scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified
images are zoomed and shifted so that only valid pixels are visible (no black areas after
rectification). alpha=1 means that the rectified image is decimated and shifted so that all the
pixels from the original images from the cameras are retained in the rectified images (no source
image pixels are lost). Any intermediate value yields an intermediate result between
those two extreme cases.
#initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
is passed (default), it is set to the original imageSize . Setting it to a larger value can help you
preserve details in the original image, especially when there is a big radial distortion.
are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
(see the picture below).
are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
(see the picture below).
The function computes the rotation matrices for each camera that (virtually) make both camera image
planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies
the dense stereo correspondence problem. The function takes the matrices computed by #stereoCalibrate
as input. As output, it provides two rotation matrices and also two projection matrices in the new
coordinates. The function distinguishes the following two cases:
public static boolean stereoRectifyUncalibrated(Mat points1, Mat points2, Mat F, Size imgSize, Mat H1, Mat H2, double threshold)
points1 - Array of feature points in the first image.points2 - The corresponding points in the second image. The same formats as in
#findFundamentalMat are supported.F - Input fundamental matrix. It can be computed from the same set of point pairs using
#findFundamentalMat .imgSize - Size of the image.H1 - Output rectification homography matrix for the first image.H2 - Output rectification homography matrix for the second image.threshold - Optional threshold used to filter out the outliers. If the parameter is greater
than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points
for which \(|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}|>\texttt{threshold}\) )
are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.
The function computes the rectification transformations without knowing intrinsic parameters of the
cameras and their relative position in the space, which explains the suffix "uncalibrated". Another
related difference from #stereoRectify is that the function outputs not the rectification
transformations in the object (3D) space, but the planar perspective transformations encoded by the
homography matrices H1 and H2 . The function implements the algorithm CITE: Hartley99 .
Note:
While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily
depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion,
it would be better to correct it before computing the fundamental matrix and calling this
function. For example, distortion coefficients can be estimated for each head of stereo camera
separately by using #calibrateCamera . Then, the images can be corrected using #undistort , or
just the point coordinates can be corrected with #undistortPoints .public static boolean stereoRectifyUncalibrated(Mat points1, Mat points2, Mat F, Size imgSize, Mat H1, Mat H2)
points1 - Array of feature points in the first image.points2 - The corresponding points in the second image. The same formats as in
#findFundamentalMat are supported.F - Input fundamental matrix. It can be computed from the same set of point pairs using
#findFundamentalMat .imgSize - Size of the image.H1 - Output rectification homography matrix for the first image.H2 - Output rectification homography matrix for the second image.
than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points
for which \(|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}|>\texttt{threshold}\) )
are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.
The function computes the rectification transformations without knowing intrinsic parameters of the
cameras and their relative position in the space, which explains the suffix "uncalibrated". Another
related difference from #stereoRectify is that the function outputs not the rectification
transformations in the object (3D) space, but the planar perspective transformations encoded by the
homography matrices H1 and H2 . The function implements the algorithm CITE: Hartley99 .
Note:
While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily
depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion,
it would be better to correct it before computing the fundamental matrix and calling this
function. For example, distortion coefficients can be estimated for each head of stereo camera
separately by using #calibrateCamera . Then, the images can be corrected using #undistort , or
just the point coordinates can be corrected with #undistortPoints .public static float rectify3Collinear(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat cameraMatrix3, Mat distCoeffs3, List<Mat> imgpt1, List<Mat> imgpt3, Size imageSize, Mat R12, Mat T12, Mat R13, Mat T13, Mat R1, Mat R2, Mat R3, Mat P1, Mat P2, Mat P3, Mat Q, double alpha, Size newImgSize, Rect roi1, Rect roi2, int flags)
public static Mat getOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize, Rect validPixROI, boolean centerPrincipalPoint)
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.public static Mat getOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize, Rect validPixROI)
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.public static Mat getOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize)
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.public static Mat getOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha)
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.public static void calibrateHandEye(List<Mat> R_gripper2base, List<Mat> t_gripper2base, List<Mat> R_target2cam, List<Mat> t_target2cam, Mat R_cam2gripper, Mat t_cam2gripper, int method)
R_gripper2base - Rotation part extracted from the homogeneous matrix that transforms a point
expressed in the gripper frame to the robot base frame (\(_{}^{b}\textrm{T}_g\)).
This is a vector (vector<Mat>) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors,
for all the transformations from gripper frame to robot base frame.t_gripper2base - Translation part extracted from the homogeneous matrix that transforms a point
expressed in the gripper frame to the robot base frame (\(_{}^{b}\textrm{T}_g\)).
This is a vector (vector<Mat>) that contains the (3x1) translation vectors for all the transformations
from gripper frame to robot base frame.R_target2cam - Rotation part extracted from the homogeneous matrix that transforms a point
expressed in the target frame to the camera frame (\(_{}^{c}\textrm{T}_t\)).
This is a vector (vector<Mat>) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors,
for all the transformations from calibration target frame to camera frame.t_target2cam - Rotation part extracted from the homogeneous matrix that transforms a point
expressed in the target frame to the camera frame (\(_{}^{c}\textrm{T}_t\)).
This is a vector (vector<Mat>) that contains the (3x1) translation vectors for all the transformations
from calibration target frame to camera frame.R_cam2gripper - Estimated (3x3) rotation part extracted from the homogeneous matrix that transforms a point
expressed in the camera frame to the gripper frame (\(_{}^{g}\textrm{T}_c\)).t_cam2gripper - Estimated (3x1) translation part extracted from the homogeneous matrix that transforms a point
expressed in the camera frame to the gripper frame (\(_{}^{g}\textrm{T}_c\)).method - One of the implemented Hand-Eye calibration method, see cv::HandEyeCalibrationMethod
The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the
rotation then the translation (separable solutions) and the following methods are implemented:
public static void calibrateHandEye(List<Mat> R_gripper2base, List<Mat> t_gripper2base, List<Mat> R_target2cam, List<Mat> t_target2cam, Mat R_cam2gripper, Mat t_cam2gripper)
R_gripper2base - Rotation part extracted from the homogeneous matrix that transforms a point
expressed in the gripper frame to the robot base frame (\(_{}^{b}\textrm{T}_g\)).
This is a vector (vector<Mat>) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors,
for all the transformations from gripper frame to robot base frame.t_gripper2base - Translation part extracted from the homogeneous matrix that transforms a point
expressed in the gripper frame to the robot base frame (\(_{}^{b}\textrm{T}_g\)).
This is a vector (vector<Mat>) that contains the (3x1) translation vectors for all the transformations
from gripper frame to robot base frame.R_target2cam - Rotation part extracted from the homogeneous matrix that transforms a point
expressed in the target frame to the camera frame (\(_{}^{c}\textrm{T}_t\)).
This is a vector (vector<Mat>) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors,
for all the transformations from calibration target frame to camera frame.t_target2cam - Rotation part extracted from the homogeneous matrix that transforms a point
expressed in the target frame to the camera frame (\(_{}^{c}\textrm{T}_t\)).
This is a vector (vector<Mat>) that contains the (3x1) translation vectors for all the transformations
from calibration target frame to camera frame.R_cam2gripper - Estimated (3x3) rotation part extracted from the homogeneous matrix that transforms a point
expressed in the camera frame to the gripper frame (\(_{}^{g}\textrm{T}_c\)).t_cam2gripper - Estimated (3x1) translation part extracted from the homogeneous matrix that transforms a point
expressed in the camera frame to the gripper frame (\(_{}^{g}\textrm{T}_c\)).
The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the
rotation then the translation (separable solutions) and the following methods are implemented:
public static void calibrateRobotWorldHandEye(List<Mat> R_world2cam, List<Mat> t_world2cam, List<Mat> R_base2gripper, List<Mat> t_base2gripper, Mat R_base2world, Mat t_base2world, Mat R_gripper2cam, Mat t_gripper2cam, int method)
R_world2cam - Rotation part extracted from the homogeneous matrix that transforms a point
expressed in the world frame to the camera frame (\(_{}^{c}\textrm{T}_w\)).
This is a vector (vector<Mat>) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors,
for all the transformations from world frame to the camera frame.t_world2cam - Translation part extracted from the homogeneous matrix that transforms a point
expressed in the world frame to the camera frame (\(_{}^{c}\textrm{T}_w\)).
This is a vector (vector<Mat>) that contains the (3x1) translation vectors for all the transformations
from world frame to the camera frame.R_base2gripper - Rotation part extracted from the homogeneous matrix that transforms a point
expressed in the robot base frame to the gripper frame (\(_{}^{g}\textrm{T}_b\)).
This is a vector (vector<Mat>) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors,
for all the transformations from robot base frame to the gripper frame.t_base2gripper - Rotation part extracted from the homogeneous matrix that transforms a point
expressed in the robot base frame to the gripper frame (\(_{}^{g}\textrm{T}_b\)).
This is a vector (vector<Mat>) that contains the (3x1) translation vectors for all the transformations
from robot base frame to the gripper frame.R_base2world - Estimated (3x3) rotation part extracted from the homogeneous matrix that transforms a point
expressed in the robot base frame to the world frame (\(_{}^{w}\textrm{T}_b\)).t_base2world - Estimated (3x1) translation part extracted from the homogeneous matrix that transforms a point
expressed in the robot base frame to the world frame (\(_{}^{w}\textrm{T}_b\)).R_gripper2cam - Estimated (3x3) rotation part extracted from the homogeneous matrix that transforms a point
expressed in the gripper frame to the camera frame (\(_{}^{c}\textrm{T}_g\)).t_gripper2cam - Estimated (3x1) translation part extracted from the homogeneous matrix that transforms a point
expressed in the gripper frame to the camera frame (\(_{}^{c}\textrm{T}_g\)).method - One of the implemented Robot-World/Hand-Eye calibration method, see cv::RobotWorldHandEyeCalibrationMethod
The function performs the Robot-World/Hand-Eye calibration using various methods. One approach consists in estimating the
rotation then the translation (separable solutions):
public static void calibrateRobotWorldHandEye(List<Mat> R_world2cam, List<Mat> t_world2cam, List<Mat> R_base2gripper, List<Mat> t_base2gripper, Mat R_base2world, Mat t_base2world, Mat R_gripper2cam, Mat t_gripper2cam)
R_world2cam - Rotation part extracted from the homogeneous matrix that transforms a point
expressed in the world frame to the camera frame (\(_{}^{c}\textrm{T}_w\)).
This is a vector (vector<Mat>) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors,
for all the transformations from world frame to the camera frame.t_world2cam - Translation part extracted from the homogeneous matrix that transforms a point
expressed in the world frame to the camera frame (\(_{}^{c}\textrm{T}_w\)).
This is a vector (vector<Mat>) that contains the (3x1) translation vectors for all the transformations
from world frame to the camera frame.R_base2gripper - Rotation part extracted from the homogeneous matrix that transforms a point
expressed in the robot base frame to the gripper frame (\(_{}^{g}\textrm{T}_b\)).
This is a vector (vector<Mat>) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors,
for all the transformations from robot base frame to the gripper frame.t_base2gripper - Rotation part extracted from the homogeneous matrix that transforms a point
expressed in the robot base frame to the gripper frame (\(_{}^{g}\textrm{T}_b\)).
This is a vector (vector<Mat>) that contains the (3x1) translation vectors for all the transformations
from robot base frame to the gripper frame.R_base2world - Estimated (3x3) rotation part extracted from the homogeneous matrix that transforms a point
expressed in the robot base frame to the world frame (\(_{}^{w}\textrm{T}_b\)).t_base2world - Estimated (3x1) translation part extracted from the homogeneous matrix that transforms a point
expressed in the robot base frame to the world frame (\(_{}^{w}\textrm{T}_b\)).R_gripper2cam - Estimated (3x3) rotation part extracted from the homogeneous matrix that transforms a point
expressed in the gripper frame to the camera frame (\(_{}^{c}\textrm{T}_g\)).t_gripper2cam - Estimated (3x1) translation part extracted from the homogeneous matrix that transforms a point
expressed in the gripper frame to the camera frame (\(_{}^{c}\textrm{T}_g\)).
The function performs the Robot-World/Hand-Eye calibration using various methods. One approach consists in estimating the
rotation then the translation (separable solutions):
public static void convertPointsToHomogeneous(Mat src, Mat dst)
src - Input vector of N-dimensional points.dst - Output vector of N+1-dimensional points.
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).public static void convertPointsFromHomogeneous(Mat src, Mat dst)
src - Input vector of N-dimensional points.dst - Output vector of N-1-dimensional points.
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,...).public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, int maxIters, Mat mask)
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.
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 maskmaxIters - 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);
public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, int maxIters)
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.
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);
public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, Mat mask)
public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence)
public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold)
public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method)
public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2)
public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, Mat mask, UsacParams params)
public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold, int maxIters, Mat mask)
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.
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.public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold, int maxIters)
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.
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.public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold)
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.
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.public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix, int method, double prob)
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.
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.public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix, int method)
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.
public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix)
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.
public static Mat findEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold, int maxIters, Mat mask)
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.
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:
\(A =
\begin{bmatrix}
f & 0 & x_{pp} \\
0 & f & y_{pp} \\
0 & 0 & 1
\end{bmatrix}\)public static Mat findEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold, int maxIters)
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.
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:
\(A =
\begin{bmatrix}
f & 0 & x_{pp} \\
0 & f & y_{pp} \\
0 & 0 & 1
\end{bmatrix}\)public static Mat findEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold)
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.
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:
\(A =
\begin{bmatrix}
f & 0 & x_{pp} \\
0 & f & y_{pp} \\
0 & 0 & 1
\end{bmatrix}\)public static Mat findEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method, double prob)
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.
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:
\(A =
\begin{bmatrix}
f & 0 & x_{pp} \\
0 & f & y_{pp} \\
0 & 0 & 1
\end{bmatrix}\)public static Mat findEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method)
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.
public static Mat findEssentialMat(Mat points1, Mat points2, double focal, Point pp)
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.
public static Mat findEssentialMat(Mat points1, Mat points2, double focal)
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.
public static Mat findEssentialMat(Mat points1, Mat points2)
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.
public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method, double prob, double threshold, Mat mask)
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.
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.public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method, double prob, double threshold)
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.
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.public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method, double prob)
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.
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.public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, int method)
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.
public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2)
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.
public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix1, Mat cameraMatrix2, Mat dist_coeff1, Mat dist_coeff2, Mat mask, UsacParams params)
public static void decomposeEssentialMat(Mat E, Mat R1, Mat R2, Mat t)
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.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)
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.
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 then given essential matrix E. Only these inliers will be used to
recover pose. In the output mask only inliers which pass the cheirality check.
This function decomposes an essential matrix using REF: decomposeEssentialMat and then verifies
possible pose hypotheses by doing cheirality check. The cheirality 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);
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)
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.
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 then given essential matrix E. Only these inliers will be used to
recover pose. In the output mask only inliers which pass the cheirality check.
This function decomposes an essential matrix using REF: decomposeEssentialMat and then verifies
possible pose hypotheses by doing cheirality check. The cheirality 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);
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)
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.
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 then given essential matrix E. Only these inliers will be used to
recover pose. In the output mask only inliers which pass the cheirality check.
This function decomposes an essential matrix using REF: decomposeEssentialMat and then verifies
possible pose hypotheses by doing cheirality check. The cheirality 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);
public static int recoverPose(Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat E, Mat R, Mat t, int method)
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.
// 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);
public static int recoverPose(Mat points1, Mat points2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat E, Mat R, Mat t)
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.
// 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);
public static int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, Mat mask)
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);
public static int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t)
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);
public static int recoverPose(Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, Point pp, Mat mask)
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:
\(A =
\begin{bmatrix}
f & 0 & x_{pp} \\
0 & f & y_{pp} \\
0 & 0 & 1
\end{bmatrix}\)public static int recoverPose(Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, Point pp)
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:
\(A =
\begin{bmatrix}
f & 0 & x_{pp} \\
0 & f & y_{pp} \\
0 & 0 & 1
\end{bmatrix}\)public static int recoverPose(Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal)
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:
\(A =
\begin{bmatrix}
f & 0 & x_{pp} \\
0 & f & y_{pp} \\
0 & 0 & 1
\end{bmatrix}\)public static int recoverPose(Mat E, Mat points1, Mat points2, Mat R, Mat t)
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:
\(A =
\begin{bmatrix}
f & 0 & x_{pp} \\
0 & f & y_{pp} \\
0 & 0 & 1
\end{bmatrix}\)public static int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh, Mat mask, Mat triangulatedPoints)
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.public static int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh, Mat mask)
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.public static int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh)
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.public static void computeCorrespondEpilines(Mat points, int whichImage, Mat F, Mat lines)
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 \(a_i^2+b_i^2=1\) .public static void triangulatePoints(Mat projMatr1, Mat projMatr2, Mat projPoints1, Mat projPoints2, Mat points4D)
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:
reprojectImageTo3Dpublic static void correctMatches(Mat F, Mat points1, Mat points2, Mat newPoints1, Mat newPoints2)
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
\(newPoints2^T \cdot F \cdot newPoints1 = 0\) .public static void filterSpeckles(Mat img, double newVal, int maxSpeckleSize, double maxDiff, Mat buf)
img - The input 16-bit signed disparity imagenewVal - The disparity value used to paint-off the specklesmaxSpeckleSize - The maximum speckle size to consider it a speckle. Larger blobs are not
affected by the algorithmmaxDiff - Maximum difference between neighbor disparity pixels to put them into the same
blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point
disparity map, where disparity values are multiplied by 16, this scale factor should be taken into
account when specifying this parameter value.buf - The optional temporary buffer to avoid memory allocation within the function.public static void filterSpeckles(Mat img, double newVal, int maxSpeckleSize, double maxDiff)
img - The input 16-bit signed disparity imagenewVal - The disparity value used to paint-off the specklesmaxSpeckleSize - The maximum speckle size to consider it a speckle. Larger blobs are not
affected by the algorithmmaxDiff - Maximum difference between neighbor disparity pixels to put them into the same
blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point
disparity map, where disparity values are multiplied by 16, this scale factor should be taken into
account when specifying this parameter value.public static Rect getValidDisparityROI(Rect roi1, Rect roi2, int minDisparity, int numberOfDisparities, int blockSize)
public static void validateDisparity(Mat disparity, Mat cost, int minDisparity, int numberOfDisparities, int disp12MaxDisp)
public static void validateDisparity(Mat disparity, Mat cost, int minDisparity, int numberOfDisparities)
public static void reprojectImageTo3D(Mat disparity, Mat _3dImage, Mat Q, boolean handleMissingValues, int ddepth)
disparity - Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit
floating-point disparity image. The values of 8-bit / 16-bit signed formats are assumed to have no
fractional bits. If the disparity is 16-bit signed format, as computed by REF: StereoBM or
REF: StereoSGBM and maybe other algorithms, it should be divided by 16 (and scaled to float) before
being used here._3dImage - Output 3-channel floating-point image of the same size as disparity. Each element of
_3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity map. If one
uses Q obtained by REF: stereoRectify, then the returned points are represented in the first
camera's rectified coordinate system.Q - \(4 \times 4\) perspective transformation matrix that can be obtained with
REF: stereoRectify.handleMissingValues - Indicates, whether the function should handle missing values (i.e.
points where the disparity was not computed). If handleMissingValues=true, then pixels with the
minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed
to 3D points with a very large Z value (currently set to 10000).ddepth - The optional output array depth. If it is -1, the output image will have CV_32F
depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.
The function transforms a single-channel disparity map to a 3-channel image representing a 3D
surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it
computes:
\(\begin{bmatrix}
X \\
Y \\
Z \\
W
\end{bmatrix} = Q \begin{bmatrix}
x \\
y \\
\texttt{disparity} (x,y) \\
1
\end{bmatrix}.\)
SEE:
To reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform.public static void reprojectImageTo3D(Mat disparity, Mat _3dImage, Mat Q, boolean handleMissingValues)
disparity - Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit
floating-point disparity image. The values of 8-bit / 16-bit signed formats are assumed to have no
fractional bits. If the disparity is 16-bit signed format, as computed by REF: StereoBM or
REF: StereoSGBM and maybe other algorithms, it should be divided by 16 (and scaled to float) before
being used here._3dImage - Output 3-channel floating-point image of the same size as disparity. Each element of
_3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity map. If one
uses Q obtained by REF: stereoRectify, then the returned points are represented in the first
camera's rectified coordinate system.Q - \(4 \times 4\) perspective transformation matrix that can be obtained with
REF: stereoRectify.handleMissingValues - Indicates, whether the function should handle missing values (i.e.
points where the disparity was not computed). If handleMissingValues=true, then pixels with the
minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed
to 3D points with a very large Z value (currently set to 10000).
depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.
The function transforms a single-channel disparity map to a 3-channel image representing a 3D
surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it
computes:
\(\begin{bmatrix}
X \\
Y \\
Z \\
W
\end{bmatrix} = Q \begin{bmatrix}
x \\
y \\
\texttt{disparity} (x,y) \\
1
\end{bmatrix}.\)
SEE:
To reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform.public static void reprojectImageTo3D(Mat disparity, Mat _3dImage, Mat Q)
disparity - Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit
floating-point disparity image. The values of 8-bit / 16-bit signed formats are assumed to have no
fractional bits. If the disparity is 16-bit signed format, as computed by REF: StereoBM or
REF: StereoSGBM and maybe other algorithms, it should be divided by 16 (and scaled to float) before
being used here._3dImage - Output 3-channel floating-point image of the same size as disparity. Each element of
_3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity map. If one
uses Q obtained by REF: stereoRectify, then the returned points are represented in the first
camera's rectified coordinate system.Q - \(4 \times 4\) perspective transformation matrix that can be obtained with
REF: stereoRectify.
points where the disparity was not computed). If handleMissingValues=true, then pixels with the
minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed
to 3D points with a very large Z value (currently set to 10000).
depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.
The function transforms a single-channel disparity map to a 3-channel image representing a 3D
surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it
computes:
\(\begin{bmatrix}
X \\
Y \\
Z \\
W
\end{bmatrix} = Q \begin{bmatrix}
x \\
y \\
\texttt{disparity} (x,y) \\
1
\end{bmatrix}.\)
SEE:
To reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform.public static double sampsonDistance(Mat pt1, Mat pt2, Mat F)
pt1 - first homogeneous 2d pointpt2 - second homogeneous 2d pointF - fundamental matrixpublic static int estimateAffine3D(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold, double confidence)
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.public static int estimateAffine3D(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold)
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.public static int estimateAffine3D(Mat src, Mat dst, Mat out, Mat inliers)
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.public static Mat estimateAffine3D(Mat src, Mat dst, double[] scale, boolean force_rotation)
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.public static Mat estimateAffine3D(Mat src, Mat dst, double[] scale)
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.public static Mat estimateAffine3D(Mat src, Mat dst)
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.public static int estimateTranslation3D(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold, double confidence)
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.public static int estimateTranslation3D(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold)
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.public static int estimateTranslation3D(Mat src, Mat dst, Mat out, Mat inliers)
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.public static Mat estimateAffine2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters)
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:
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.public static Mat estimateAffine2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence)
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:
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.public static Mat estimateAffine2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters)
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:
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.public static Mat estimateAffine2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold)
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:
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.public static Mat estimateAffine2D(Mat from, Mat to, Mat inliers, int method)
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:
public static Mat estimateAffine2D(Mat from, Mat to, Mat inliers)
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).
public static Mat estimateAffine2D(Mat from, Mat to)
from - First input 2D point set containing \((X,Y)\).to - Second input 2D point set containing \((x,y)\).
public static Mat estimateAffine2D(Mat pts1, Mat pts2, Mat inliers, UsacParams params)
public static Mat estimateAffinePartial2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters)
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:
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.public static Mat estimateAffinePartial2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence)
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:
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.public static Mat estimateAffinePartial2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters)
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:
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.public static Mat estimateAffinePartial2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold)
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:
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.public static Mat estimateAffinePartial2D(Mat from, Mat to, Mat inliers, int method)
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:
public static Mat estimateAffinePartial2D(Mat from, Mat to, Mat inliers)
from - First input 2D point set.to - Second input 2D point set.inliers - Output vector indicating which points are inliers.
public static Mat estimateAffinePartial2D(Mat from, Mat to)
from - First input 2D point set.to - Second input 2D point set.
public static int decomposeHomographyMat(Mat H, Mat K, List<Mat> rotations, List<Mat> translations, List<Mat> normals)
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.public static void filterHomographyDecompByVisibleRefpoints(List<Mat> rotations, List<Mat> normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions, Mat pointsMask)
rotations - Vector of rotation matrices.normals - Vector of plane normal matrices.beforePoints - Vector of (rectified) visible reference points before the homography is appliedafterPoints - Vector of (rectified) visible reference points after the homography is appliedpossibleSolutions - Vector of int indices representing the viable solution set after filteringpointsMask - optional Mat/Vector of 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.public static void filterHomographyDecompByVisibleRefpoints(List<Mat> rotations, List<Mat> normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions)
rotations - Vector of rotation matrices.normals - Vector of plane normal matrices.beforePoints - Vector of (rectified) visible reference points before the homography is appliedafterPoints - Vector of (rectified) visible reference points after the homography is appliedpossibleSolutions - Vector of int indices representing the viable solution set after filtering
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.public static void undistort(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat newCameraMatrix)
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.public static void undistort(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs)
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.public static void initUndistortRectifyMap(Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, Size size, int m1type, Mat map1, Mat map2)
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 #convertMapsmap1 - The first output map.map2 - The second output map.public static void initInverseRectificationMap(Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, Size size, int m1type, Mat map1, Mat map2)
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 #convertMapsmap1 - The first output map for #remap.map2 - The second output map for #remap.public static Mat getDefaultNewCameraMatrix(Mat cameraMatrix, Size imgsize, boolean centerPrincipalPoint)
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.public static Mat getDefaultNewCameraMatrix(Mat cameraMatrix, Size imgsize)
cameraMatrix - Input camera matrix.imgsize - Camera view image size in pixels.
parameter indicates whether this location should be at the image center or not.public static Mat getDefaultNewCameraMatrix(Mat cameraMatrix)
cameraMatrix - Input camera matrix.
parameter indicates whether this location should be at the image center or not.public static void undistortPoints(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P)
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.public static void undistortPoints(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R)
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.public static void undistortPoints(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs)
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.public static void undistortPointsIter(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, TermCriteria criteria)
src - automatically generateddst - automatically generatedcameraMatrix - automatically generateddistCoeffs - automatically generatedR - automatically generatedP - automatically generatedcriteria - automatically generatedpublic static void undistortImagePoints(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, TermCriteria arg1)
src - Observed points position, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or
CV_64FC2) (or vector<Point2f> ).dst - Output undistorted points position (1xN/Nx1 2-channel or vector<Point2f> ).cameraMatrix - Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .distCoeffs - Distortion coefficientsarg1 - automatically generatedpublic static void undistortImagePoints(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs)
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 coefficientspublic static void fisheye_projectPoints(Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D, double alpha, Mat jacobian)
public static void fisheye_projectPoints(Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D, double alpha)
public static void fisheye_projectPoints(Mat objectPoints, Mat imagePoints, Mat rvec, Mat tvec, Mat K, Mat D)
public static void fisheye_distortPoints(Mat undistorted, Mat distorted, Mat K, Mat D, double alpha)
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.public static void fisheye_distortPoints(Mat undistorted, Mat distorted, Mat K, Mat D)
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.public static void fisheye_distortPoints(Mat undistorted, Mat distorted, Mat Kundistorted, Mat K, Mat D, double alpha)
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: estimateNewCameraMatrixForUndistortRectifypublic static void fisheye_distortPoints(Mat undistorted, Mat distorted, Mat Kundistorted, Mat K, Mat D)
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: estimateNewCameraMatrixForUndistortRectifypublic static void fisheye_undistortPoints(Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P, TermCriteria criteria)
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-channelP - New camera intrinsic matrix (3x3) or new projection matrix (3x4)criteria - Termination criteriaundistorted - Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .public static void fisheye_undistortPoints(Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P)
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-channelP - New camera intrinsic matrix (3x3) or new projection matrix (3x4)undistorted - Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .public static void fisheye_undistortPoints(Mat distorted, Mat undistorted, Mat K, Mat D, Mat R)
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-channelundistorted - Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .public static void fisheye_undistortPoints(Mat distorted, Mat undistorted, Mat K, Mat D)
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-channelundistorted - Output array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .public static void fisheye_initUndistortRectifyMap(Mat K, Mat D, Mat R, Mat P, Size size, int m1type, Mat map1, Mat map2)
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-channelP - 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.public static void fisheye_undistortImage(Mat distorted, Mat undistorted, Mat K, Mat D, Mat Knew, Size new_size)
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.
public static void fisheye_undistortImage(Mat distorted, Mat undistorted, Mat K, Mat D, Mat Knew)
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.
public static void fisheye_undistortImage(Mat distorted, Mat undistorted, Mat K, Mat D)
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.
public static void fisheye_estimateNewCameraMatrixForUndistortRectify(Mat K, Mat D, Size image_size, Mat R, Mat P, double balance, Size new_size, double fov_scale)
K - Camera intrinsic matrix \(\cameramatrix{K}\).image_size - Size of the imageD - 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-channelP - New camera intrinsic matrix (3x3) or new projection matrix (3x4)balance - Sets the new focal length in range between the min focal length and the max focal
length. Balance is in range of [0, 1].new_size - the new sizefov_scale - Divisor for new focal length.public static void fisheye_estimateNewCameraMatrixForUndistortRectify(Mat K, Mat D, Size image_size, Mat R, Mat P, double balance, Size new_size)
K - Camera intrinsic matrix \(\cameramatrix{K}\).image_size - Size of the imageD - 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-channelP - New camera intrinsic matrix (3x3) or new projection matrix (3x4)balance - Sets the new focal length in range between the min focal length and the max focal
length. Balance is in range of [0, 1].new_size - the new sizepublic static void fisheye_estimateNewCameraMatrixForUndistortRectify(Mat K, Mat D, Size image_size, Mat R, Mat P, double balance)
K - Camera intrinsic matrix \(\cameramatrix{K}\).image_size - Size of the imageD - 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-channelP - New camera intrinsic matrix (3x3) or new projection matrix (3x4)balance - Sets the new focal length in range between the min focal length and the max focal
length. Balance is in range of [0, 1].public static void fisheye_estimateNewCameraMatrixForUndistortRectify(Mat K, Mat D, Size image_size, Mat R, Mat P)
K - Camera intrinsic matrix \(\cameramatrix{K}\).image_size - Size of the imageD - 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-channelP - New camera intrinsic matrix (3x3) or new projection matrix (3x4)
length. Balance is in range of [0, 1].public static double fisheye_calibrate(List<Mat> objectPoints, List<Mat> imagePoints, Size image_size, Mat K, Mat D, List<Mat> rvecs, List<Mat> tvecs, int flags, TermCriteria criteria)
objectPoints - vector of vectors of calibration pattern points in the calibration pattern
coordinate space.imagePoints - vector of vectors of the projections of calibration pattern points.
imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to
objectPoints[i].size() for each i.image_size - Size of the image used only to initialize the camera intrinsic matrix.K - Output 3x3 floating-point camera intrinsic matrix
\(\cameramatrix{A}\) . If
REF: fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be
initialized before calling the function.D - Output vector of distortion coefficients \(\distcoeffsfisheye\).rvecs - Output vector of rotation vectors (see REF: Rodrigues ) estimated for each pattern view.
That is, each k-th rotation vector together with the corresponding k-th translation vector (see
the next output parameter description) brings the calibration pattern from the model coordinate
space (in which object points are specified) to the world coordinate space, that is, a real
position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).tvecs - Output vector of translation vectors estimated for each pattern view.flags - Different flags that may be zero or a combination of the following values:
criteria - Termination criteria for the iterative optimization algorithm.public static double fisheye_calibrate(List<Mat> objectPoints, List<Mat> imagePoints, Size image_size, Mat K, Mat D, List<Mat> rvecs, List<Mat> tvecs, int flags)
objectPoints - vector of vectors of calibration pattern points in the calibration pattern
coordinate space.imagePoints - vector of vectors of the projections of calibration pattern points.
imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to
objectPoints[i].size() for each i.image_size - Size of the image used only to initialize the camera intrinsic matrix.K - Output 3x3 floating-point camera intrinsic matrix
\(\cameramatrix{A}\) . If
REF: fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be
initialized before calling the function.D - Output vector of distortion coefficients \(\distcoeffsfisheye\).rvecs - Output vector of rotation vectors (see REF: Rodrigues ) estimated for each pattern view.
That is, each k-th rotation vector together with the corresponding k-th translation vector (see
the next output parameter description) brings the calibration pattern from the model coordinate
space (in which object points are specified) to the world coordinate space, that is, a real
position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).tvecs - Output vector of translation vectors estimated for each pattern view.flags - Different flags that may be zero or a combination of the following values:
public static double fisheye_calibrate(List<Mat> objectPoints, List<Mat> imagePoints, Size image_size, Mat K, Mat D, List<Mat> rvecs, List<Mat> tvecs)
objectPoints - vector of vectors of calibration pattern points in the calibration pattern
coordinate space.imagePoints - vector of vectors of the projections of calibration pattern points.
imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to
objectPoints[i].size() for each i.image_size - Size of the image used only to initialize the camera intrinsic matrix.K - Output 3x3 floating-point camera intrinsic matrix
\(\cameramatrix{A}\) . If
REF: fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be
initialized before calling the function.D - Output vector of distortion coefficients \(\distcoeffsfisheye\).rvecs - Output vector of rotation vectors (see REF: Rodrigues ) estimated for each pattern view.
That is, each k-th rotation vector together with the corresponding k-th translation vector (see
the next output parameter description) brings the calibration pattern from the model coordinate
space (in which object points are specified) to the world coordinate space, that is, a real
position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).tvecs - Output vector of translation vectors estimated for each pattern view.
public static void fisheye_stereoRectify(Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, Size newImageSize, double balance, double fov_scale)
K1 - First camera intrinsic matrix.D1 - First camera distortion parameters.K2 - Second camera intrinsic matrix.D2 - Second camera distortion parameters.imageSize - Size of the image used for stereo calibration.R - Rotation matrix between the coordinate systems of the first and the second
cameras.tvec - Translation vector between coordinate systems of the cameras.R1 - Output 3x3 rectification transform (rotation matrix) for the first camera.R2 - Output 3x3 rectification transform (rotation matrix) for the second camera.P1 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
camera.P2 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
camera.Q - Output \(4 \times 4\) disparity-to-depth mapping matrix (see #reprojectImageTo3D ).flags - Operation flags that may be zero or REF: fisheye::CALIB_ZERO_DISPARITY . If the flag is set,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views. And if the flag is not set, the function may still shift the images in the
horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
useful image area.newImageSize - New image resolution after rectification. The same size should be passed to
#initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
is passed (default), it is set to the original imageSize . Setting it to larger value can help you
preserve details in the original image, especially when there is a big radial distortion.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].fov_scale - Divisor for new focal length.public static void fisheye_stereoRectify(Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, Size newImageSize, double balance)
K1 - First camera intrinsic matrix.D1 - First camera distortion parameters.K2 - Second camera intrinsic matrix.D2 - Second camera distortion parameters.imageSize - Size of the image used for stereo calibration.R - Rotation matrix between the coordinate systems of the first and the second
cameras.tvec - Translation vector between coordinate systems of the cameras.R1 - Output 3x3 rectification transform (rotation matrix) for the first camera.R2 - Output 3x3 rectification transform (rotation matrix) for the second camera.P1 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
camera.P2 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
camera.Q - Output \(4 \times 4\) disparity-to-depth mapping matrix (see #reprojectImageTo3D ).flags - Operation flags that may be zero or REF: fisheye::CALIB_ZERO_DISPARITY . If the flag is set,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views. And if the flag is not set, the function may still shift the images in the
horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
useful image area.newImageSize - New image resolution after rectification. The same size should be passed to
#initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
is passed (default), it is set to the original imageSize . Setting it to larger value can help you
preserve details in the original image, especially when there is a big radial distortion.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].public static void fisheye_stereoRectify(Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, Size newImageSize)
K1 - First camera intrinsic matrix.D1 - First camera distortion parameters.K2 - Second camera intrinsic matrix.D2 - Second camera distortion parameters.imageSize - Size of the image used for stereo calibration.R - Rotation matrix between the coordinate systems of the first and the second
cameras.tvec - Translation vector between coordinate systems of the cameras.R1 - Output 3x3 rectification transform (rotation matrix) for the first camera.R2 - Output 3x3 rectification transform (rotation matrix) for the second camera.P1 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
camera.P2 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
camera.Q - Output \(4 \times 4\) disparity-to-depth mapping matrix (see #reprojectImageTo3D ).flags - Operation flags that may be zero or REF: fisheye::CALIB_ZERO_DISPARITY . If the flag is set,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views. And if the flag is not set, the function may still shift the images in the
horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
useful image area.newImageSize - New image resolution after rectification. The same size should be passed to
#initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
is passed (default), it is set to the original imageSize . Setting it to larger value can help you
preserve details in the original image, especially when there is a big radial distortion.
length. Balance is in range of [0, 1].public static void fisheye_stereoRectify(Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags)
K1 - First camera intrinsic matrix.D1 - First camera distortion parameters.K2 - Second camera intrinsic matrix.D2 - Second camera distortion parameters.imageSize - Size of the image used for stereo calibration.R - Rotation matrix between the coordinate systems of the first and the second
cameras.tvec - Translation vector between coordinate systems of the cameras.R1 - Output 3x3 rectification transform (rotation matrix) for the first camera.R2 - Output 3x3 rectification transform (rotation matrix) for the second camera.P1 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
camera.P2 - Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
camera.Q - Output \(4 \times 4\) disparity-to-depth mapping matrix (see #reprojectImageTo3D ).flags - Operation flags that may be zero or REF: fisheye::CALIB_ZERO_DISPARITY . If the flag is set,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views. And if the flag is not set, the function may still shift the images in the
horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
useful image area.
#initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
is passed (default), it is set to the original imageSize . Setting it to larger value can help you
preserve details in the original image, especially when there is a big radial distortion.
length. Balance is in range of [0, 1].public static double fisheye_stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T, List<Mat> rvecs, List<Mat> tvecs, int flags, TermCriteria criteria)
objectPoints - Vector of vectors of the calibration pattern points.imagePoints1 - Vector of vectors of the projections of the calibration pattern points,
observed by the first camera.imagePoints2 - Vector of vectors of the projections of the calibration pattern points,
observed by the second camera.K1 - Input/output first camera intrinsic matrix:
\(\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\) , \(j = 0,\, 1\) . If
any of REF: fisheye::CALIB_USE_INTRINSIC_GUESS , REF: fisheye::CALIB_FIX_INTRINSIC are specified,
some or all of the matrix components must be initialized.D1 - Input/output vector of distortion coefficients \(\distcoeffsfisheye\) of 4 elements.K2 - Input/output second camera intrinsic matrix. The parameter is similar to K1 .D2 - Input/output lens distortion coefficients for the second camera. The parameter is
similar to D1 .imageSize - Size of the image used only to initialize camera intrinsic matrix.R - Output rotation matrix between the 1st and the 2nd camera coordinate systems.T - Output translation vector between the coordinate systems of the cameras.rvecs - Output vector of rotation vectors ( REF: Rodrigues ) estimated for each pattern view in the
coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
description) brings the calibration pattern from the object coordinate space (in which object points are
specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
to camera coordinate space of the first camera of the stereo pair.tvecs - Output vector of translation vectors estimated for each pattern view, see parameter description
of previous output parameter ( rvecs ).flags - Different flags that may be zero or a combination of the following values:
criteria - Termination criteria for the iterative optimization algorithm.public static double fisheye_stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T, List<Mat> rvecs, List<Mat> tvecs, int flags)
objectPoints - Vector of vectors of the calibration pattern points.imagePoints1 - Vector of vectors of the projections of the calibration pattern points,
observed by the first camera.imagePoints2 - Vector of vectors of the projections of the calibration pattern points,
observed by the second camera.K1 - Input/output first camera intrinsic matrix:
\(\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\) , \(j = 0,\, 1\) . If
any of REF: fisheye::CALIB_USE_INTRINSIC_GUESS , REF: fisheye::CALIB_FIX_INTRINSIC are specified,
some or all of the matrix components must be initialized.D1 - Input/output vector of distortion coefficients \(\distcoeffsfisheye\) of 4 elements.K2 - Input/output second camera intrinsic matrix. The parameter is similar to K1 .D2 - Input/output lens distortion coefficients for the second camera. The parameter is
similar to D1 .imageSize - Size of the image used only to initialize camera intrinsic matrix.R - Output rotation matrix between the 1st and the 2nd camera coordinate systems.T - Output translation vector between the coordinate systems of the cameras.rvecs - Output vector of rotation vectors ( REF: Rodrigues ) estimated for each pattern view in the
coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
description) brings the calibration pattern from the object coordinate space (in which object points are
specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
to camera coordinate space of the first camera of the stereo pair.tvecs - Output vector of translation vectors estimated for each pattern view, see parameter description
of previous output parameter ( rvecs ).flags - Different flags that may be zero or a combination of the following values:
public static double fisheye_stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T, List<Mat> rvecs, List<Mat> tvecs)
objectPoints - Vector of vectors of the calibration pattern points.imagePoints1 - Vector of vectors of the projections of the calibration pattern points,
observed by the first camera.imagePoints2 - Vector of vectors of the projections of the calibration pattern points,
observed by the second camera.K1 - Input/output first camera intrinsic matrix:
\(\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\) , \(j = 0,\, 1\) . If
any of REF: fisheye::CALIB_USE_INTRINSIC_GUESS , REF: fisheye::CALIB_FIX_INTRINSIC are specified,
some or all of the matrix components must be initialized.D1 - Input/output vector of distortion coefficients \(\distcoeffsfisheye\) of 4 elements.K2 - Input/output second camera intrinsic matrix. The parameter is similar to K1 .D2 - Input/output lens distortion coefficients for the second camera. The parameter is
similar to D1 .imageSize - Size of the image used only to initialize camera intrinsic matrix.R - Output rotation matrix between the 1st and the 2nd camera coordinate systems.T - Output translation vector between the coordinate systems of the cameras.rvecs - Output vector of rotation vectors ( REF: Rodrigues ) estimated for each pattern view in the
coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
description) brings the calibration pattern from the object coordinate space (in which object points are
specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
to camera coordinate space of the first camera of the stereo pair.tvecs - Output vector of translation vectors estimated for each pattern view, see parameter description
of previous output parameter ( rvecs ).
public static double fisheye_stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T, int flags, TermCriteria criteria)
public static double fisheye_stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T, int flags)
public static double fisheye_stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T)
public static boolean fisheye_solvePnP(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int flags, TermCriteria criteria)
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:
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. More information about Perspective-n-Points is described in REF: calib3d_solvePnP
for more information.public static boolean fisheye_solvePnP(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int flags)
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:
public static boolean fisheye_solvePnP(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess)
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:
public static boolean fisheye_solvePnP(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec)
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:
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