public class opencv_calib3d extends opencv_calib3d
Modifier and Type | Field and Description |
---|---|
static int |
CALIB_CB_ACCURACY
enum cv::
|
static int |
CALIB_CB_ADAPTIVE_THRESH
enum cv::
|
static int |
CALIB_CB_ASYMMETRIC_GRID
enum cv::
|
static int |
CALIB_CB_CLUSTERING
enum cv::
|
static int |
CALIB_CB_EXHAUSTIVE
enum cv::
|
static int |
CALIB_CB_FAST_CHECK
enum cv::
|
static int |
CALIB_CB_FILTER_QUADS
enum cv::
|
static int |
CALIB_CB_LARGER
enum cv::
|
static int |
CALIB_CB_MARKER
enum cv::
|
static int |
CALIB_CB_NORMALIZE_IMAGE
enum cv::
|
static int |
CALIB_CB_SYMMETRIC_GRID
enum cv::
|
static int |
CALIB_FIX_ASPECT_RATIO
enum cv::
|
static int |
CALIB_FIX_FOCAL_LENGTH
enum cv::
|
static int |
CALIB_FIX_INTRINSIC
enum cv::
|
static int |
CALIB_FIX_K1
enum cv::
|
static int |
CALIB_FIX_K2
enum cv::
|
static int |
CALIB_FIX_K3
enum cv::
|
static int |
CALIB_FIX_K4
enum cv::
|
static int |
CALIB_FIX_K5
enum cv::
|
static int |
CALIB_FIX_K6
enum cv::
|
static int |
CALIB_FIX_PRINCIPAL_POINT
enum cv::
|
static int |
CALIB_FIX_S1_S2_S3_S4
enum cv::
|
static int |
CALIB_FIX_TANGENT_DIST
enum cv::
|
static int |
CALIB_FIX_TAUX_TAUY
enum cv::
|
static int |
CALIB_HAND_EYE_ANDREFF
enum cv::HandEyeCalibrationMethod
|
static int |
CALIB_HAND_EYE_DANIILIDIS
enum cv::HandEyeCalibrationMethod
|
static int |
CALIB_HAND_EYE_HORAUD
enum cv::HandEyeCalibrationMethod
|
static int |
CALIB_HAND_EYE_PARK
enum cv::HandEyeCalibrationMethod
|
static int |
CALIB_HAND_EYE_TSAI
enum cv::HandEyeCalibrationMethod
|
static int |
CALIB_NINTRINSIC
enum cv::
|
static int |
CALIB_RATIONAL_MODEL
enum cv::
|
static int |
CALIB_SAME_FOCAL_LENGTH
enum cv::
|
static int |
CALIB_THIN_PRISM_MODEL
enum cv::
|
static int |
CALIB_TILTED_MODEL
enum cv::
|
static int |
CALIB_USE_EXTRINSIC_GUESS
enum cv::
|
static int |
CALIB_USE_INTRINSIC_GUESS
enum cv::
|
static int |
CALIB_USE_LU
enum cv::
|
static int |
CALIB_USE_QR
enum cv::
|
static int |
CALIB_ZERO_DISPARITY
enum cv::
|
static int |
CALIB_ZERO_TANGENT_DIST
enum cv::
|
static int |
CV_CALIB_CB_ADAPTIVE_THRESH |
static int |
CV_CALIB_CB_FAST_CHECK |
static int |
CV_CALIB_CB_FILTER_QUADS |
static int |
CV_CALIB_CB_NORMALIZE_IMAGE |
static int |
CV_CALIB_FIX_ASPECT_RATIO |
static int |
CV_CALIB_FIX_FOCAL_LENGTH |
static int |
CV_CALIB_FIX_INTRINSIC |
static int |
CV_CALIB_FIX_K1 |
static int |
CV_CALIB_FIX_K2 |
static int |
CV_CALIB_FIX_K3 |
static int |
CV_CALIB_FIX_K4 |
static int |
CV_CALIB_FIX_K5 |
static int |
CV_CALIB_FIX_K6 |
static int |
CV_CALIB_FIX_PRINCIPAL_POINT |
static int |
CV_CALIB_FIX_S1_S2_S3_S4 |
static int |
CV_CALIB_FIX_TANGENT_DIST |
static int |
CV_CALIB_FIX_TAUX_TAUY |
static int |
CV_CALIB_NINTRINSIC |
static int |
CV_CALIB_RATIONAL_MODEL |
static int |
CV_CALIB_SAME_FOCAL_LENGTH |
static int |
CV_CALIB_THIN_PRISM_MODEL |
static int |
CV_CALIB_TILTED_MODEL |
static int |
CV_CALIB_USE_INTRINSIC_GUESS |
static int |
CV_CALIB_ZERO_DISPARITY |
static int |
CV_CALIB_ZERO_TANGENT_DIST |
static int |
CV_DLS
enum
|
static int |
CV_EPNP
enum
|
static int |
CV_FM_7POINT |
static int |
CV_FM_8POINT |
static int |
CV_FM_LMEDS |
static int |
CV_FM_LMEDS_ONLY |
static int |
CV_FM_RANSAC |
static int |
CV_FM_RANSAC_ONLY |
static int |
CV_ITERATIVE
enum
|
static int |
CV_LMEDS |
static int |
CV_P3P
enum
|
static int |
CV_RANSAC |
static int |
CV_STEREO_BM_NORMALIZED_RESPONSE |
static int |
CV_STEREO_BM_XSOBEL |
static int |
FISHEYE_CALIB_CHECK_COND
enum cv::fisheye::
|
static int |
FISHEYE_CALIB_FIX_INTRINSIC
enum cv::fisheye::
|
static int |
FISHEYE_CALIB_FIX_K1
enum cv::fisheye::
|
static int |
FISHEYE_CALIB_FIX_K2
enum cv::fisheye::
|
static int |
FISHEYE_CALIB_FIX_K3
enum cv::fisheye::
|
static int |
FISHEYE_CALIB_FIX_K4
enum cv::fisheye::
|
static int |
FISHEYE_CALIB_FIX_PRINCIPAL_POINT
enum cv::fisheye::
|
static int |
FISHEYE_CALIB_FIX_SKEW
enum cv::fisheye::
|
static int |
FISHEYE_CALIB_RECOMPUTE_EXTRINSIC
enum cv::fisheye::
|
static int |
FISHEYE_CALIB_USE_INTRINSIC_GUESS
enum cv::fisheye::
|
static int |
FM_7POINT
enum cv::
|
static int |
FM_8POINT
enum cv::
|
static int |
FM_LMEDS
enum cv::
|
static int |
FM_RANSAC
enum cv::
|
static int |
LMEDS
enum cv::
|
static int |
PROJ_SPHERICAL_EQRECT
enum cv::UndistortTypes
|
static int |
PROJ_SPHERICAL_ORTHO
enum cv::UndistortTypes
|
static int |
RANSAC
enum cv::
|
static int |
RHO
enum cv::
|
static int |
SOLVEPNP_AP3P
enum cv::SolvePnPMethod
|
static int |
SOLVEPNP_DLS
enum cv::SolvePnPMethod
|
static int |
SOLVEPNP_EPNP
enum cv::SolvePnPMethod
|
static int |
SOLVEPNP_IPPE
enum cv::SolvePnPMethod
|
static int |
SOLVEPNP_IPPE_SQUARE
enum cv::SolvePnPMethod
|
static int |
SOLVEPNP_ITERATIVE
enum cv::SolvePnPMethod
|
static int |
SOLVEPNP_MAX_COUNT
enum cv::SolvePnPMethod
|
static int |
SOLVEPNP_P3P
enum cv::SolvePnPMethod
|
static int |
SOLVEPNP_UPNP
enum cv::SolvePnPMethod
|
Constructor and Description |
---|
opencv_calib3d() |
Modifier and Type | Method and Description |
---|---|
static double |
calibrate(GpuMatVector objectPoints,
GpuMatVector imagePoints,
Size image_size,
GpuMat K,
GpuMat D,
GpuMatVector rvecs,
GpuMatVector tvecs) |
static double |
calibrate(GpuMatVector objectPoints,
GpuMatVector imagePoints,
Size image_size,
GpuMat K,
GpuMat D,
GpuMatVector rvecs,
GpuMatVector tvecs,
int flags,
TermCriteria criteria) |
static double |
calibrate(GpuMatVector objectPoints,
GpuMatVector imagePoints,
Size image_size,
Mat K,
Mat D,
GpuMatVector rvecs,
GpuMatVector tvecs) |
static double |
calibrate(GpuMatVector objectPoints,
GpuMatVector imagePoints,
Size image_size,
Mat K,
Mat D,
GpuMatVector rvecs,
GpuMatVector tvecs,
int flags,
TermCriteria criteria) |
static double |
calibrate(GpuMatVector objectPoints,
GpuMatVector imagePoints,
Size image_size,
UMat K,
UMat D,
GpuMatVector rvecs,
GpuMatVector tvecs) |
static double |
calibrate(GpuMatVector objectPoints,
GpuMatVector imagePoints,
Size image_size,
UMat K,
UMat D,
GpuMatVector rvecs,
GpuMatVector tvecs,
int flags,
TermCriteria criteria) |
static double |
calibrate(MatVector objectPoints,
MatVector imagePoints,
Size image_size,
GpuMat K,
GpuMat D,
MatVector rvecs,
MatVector tvecs) |
static double |
calibrate(MatVector objectPoints,
MatVector imagePoints,
Size image_size,
GpuMat K,
GpuMat D,
MatVector rvecs,
MatVector tvecs,
int flags,
TermCriteria criteria) |
static double |
calibrate(MatVector objectPoints,
MatVector imagePoints,
Size image_size,
Mat K,
Mat D,
MatVector rvecs,
MatVector tvecs) |
static double |
calibrate(MatVector objectPoints,
MatVector imagePoints,
Size image_size,
Mat K,
Mat D,
MatVector rvecs,
MatVector tvecs,
int flags,
TermCriteria criteria)
\brief Performs camera calibaration
|
static double |
calibrate(MatVector objectPoints,
MatVector imagePoints,
Size image_size,
UMat K,
UMat D,
MatVector rvecs,
MatVector tvecs) |
static double |
calibrate(MatVector objectPoints,
MatVector imagePoints,
Size image_size,
UMat K,
UMat D,
MatVector rvecs,
MatVector tvecs,
int flags,
TermCriteria criteria) |
static double |
calibrate(UMatVector objectPoints,
UMatVector imagePoints,
Size image_size,
GpuMat K,
GpuMat D,
UMatVector rvecs,
UMatVector tvecs) |
static double |
calibrate(UMatVector objectPoints,
UMatVector imagePoints,
Size image_size,
GpuMat K,
GpuMat D,
UMatVector rvecs,
UMatVector tvecs,
int flags,
TermCriteria criteria) |
static double |
calibrate(UMatVector objectPoints,
UMatVector imagePoints,
Size image_size,
Mat K,
Mat D,
UMatVector rvecs,
UMatVector tvecs) |
static double |
calibrate(UMatVector objectPoints,
UMatVector imagePoints,
Size image_size,
Mat K,
Mat D,
UMatVector rvecs,
UMatVector tvecs,
int flags,
TermCriteria criteria) |
static double |
calibrate(UMatVector objectPoints,
UMatVector imagePoints,
Size image_size,
UMat K,
UMat D,
UMatVector rvecs,
UMatVector tvecs) |
static double |
calibrate(UMatVector objectPoints,
UMatVector imagePoints,
Size image_size,
UMat K,
UMat D,
UMatVector rvecs,
UMatVector tvecs,
int flags,
TermCriteria criteria) |
static double |
calibrateCamera(Point3fVectorVector objectPoints,
Point2fVectorVector imagePoints,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
MatVector rvecs,
MatVector tvecs) |
static double |
calibrateCamera(Point3fVectorVector objectPoints,
Point2fVectorVector imagePoints,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
MatVector rvecs,
MatVector tvecs,
int flags,
TermCriteria criteria) |
static double |
calibrateCameraExtended(Point3fVectorVector objectPoints,
Point2fVectorVector imagePoints,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
MatVector rvecs,
MatVector tvecs,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat perViewErrors) |
static double |
calibrateCameraExtended(Point3fVectorVector objectPoints,
Point2fVectorVector imagePoints,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
MatVector rvecs,
MatVector tvecs,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat perViewErrors,
int flags,
TermCriteria criteria)
\brief Finds the camera intrinsic and extrinsic parameters from several views of a calibration
pattern.
|
static double |
calibrateCameraRO(Point3fVectorVector objectPoints,
Point2fVectorVector imagePoints,
Size imageSize,
int iFixedPoint,
Mat cameraMatrix,
Mat distCoeffs,
MatVector rvecs,
MatVector tvecs,
Mat newObjPoints) |
static double |
calibrateCameraRO(Point3fVectorVector objectPoints,
Point2fVectorVector imagePoints,
Size imageSize,
int iFixedPoint,
Mat cameraMatrix,
Mat distCoeffs,
MatVector rvecs,
MatVector tvecs,
Mat newObjPoints,
int flags,
TermCriteria criteria) |
static double |
calibrateCameraROExtended(Point3fVectorVector objectPoints,
Point2fVectorVector imagePoints,
Size imageSize,
int iFixedPoint,
Mat cameraMatrix,
Mat distCoeffs,
MatVector rvecs,
MatVector tvecs,
Mat newObjPoints,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat stdDeviationsObjPoints,
Mat perViewErrors) |
static double |
calibrateCameraROExtended(Point3fVectorVector objectPoints,
Point2fVectorVector imagePoints,
Size imageSize,
int iFixedPoint,
Mat cameraMatrix,
Mat distCoeffs,
MatVector rvecs,
MatVector tvecs,
Mat newObjPoints,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat stdDeviationsObjPoints,
Mat perViewErrors,
int flags,
TermCriteria criteria)
\brief Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
|
static void |
calibrateHandEye(GpuMatVector R_gripper2base,
GpuMatVector t_gripper2base,
GpuMatVector R_target2cam,
GpuMatVector t_target2cam,
GpuMat R_cam2gripper,
GpuMat t_cam2gripper) |
static void |
calibrateHandEye(GpuMatVector R_gripper2base,
GpuMatVector t_gripper2base,
GpuMatVector R_target2cam,
GpuMatVector t_target2cam,
GpuMat R_cam2gripper,
GpuMat t_cam2gripper,
int method) |
static void |
calibrateHandEye(GpuMatVector R_gripper2base,
GpuMatVector t_gripper2base,
GpuMatVector R_target2cam,
GpuMatVector t_target2cam,
Mat R_cam2gripper,
Mat t_cam2gripper) |
static void |
calibrateHandEye(GpuMatVector R_gripper2base,
GpuMatVector t_gripper2base,
GpuMatVector R_target2cam,
GpuMatVector t_target2cam,
Mat R_cam2gripper,
Mat t_cam2gripper,
int method) |
static void |
calibrateHandEye(GpuMatVector R_gripper2base,
GpuMatVector t_gripper2base,
GpuMatVector R_target2cam,
GpuMatVector t_target2cam,
UMat R_cam2gripper,
UMat t_cam2gripper) |
static void |
calibrateHandEye(GpuMatVector R_gripper2base,
GpuMatVector t_gripper2base,
GpuMatVector R_target2cam,
GpuMatVector t_target2cam,
UMat R_cam2gripper,
UMat t_cam2gripper,
int method) |
static void |
calibrateHandEye(MatVector R_gripper2base,
MatVector t_gripper2base,
MatVector R_target2cam,
MatVector t_target2cam,
GpuMat R_cam2gripper,
GpuMat t_cam2gripper) |
static void |
calibrateHandEye(MatVector R_gripper2base,
MatVector t_gripper2base,
MatVector R_target2cam,
MatVector t_target2cam,
GpuMat R_cam2gripper,
GpuMat t_cam2gripper,
int method) |
static void |
calibrateHandEye(MatVector R_gripper2base,
MatVector t_gripper2base,
MatVector R_target2cam,
MatVector t_target2cam,
Mat R_cam2gripper,
Mat t_cam2gripper) |
static void |
calibrateHandEye(MatVector R_gripper2base,
MatVector t_gripper2base,
MatVector R_target2cam,
MatVector t_target2cam,
Mat R_cam2gripper,
Mat t_cam2gripper,
int method)
\brief Computes Hand-Eye calibration:
_{}^{g}\textrm{T}_c |
static void |
calibrateHandEye(MatVector R_gripper2base,
MatVector t_gripper2base,
MatVector R_target2cam,
MatVector t_target2cam,
UMat R_cam2gripper,
UMat t_cam2gripper) |
static void |
calibrateHandEye(MatVector R_gripper2base,
MatVector t_gripper2base,
MatVector R_target2cam,
MatVector t_target2cam,
UMat R_cam2gripper,
UMat t_cam2gripper,
int method) |
static void |
calibrateHandEye(UMatVector R_gripper2base,
UMatVector t_gripper2base,
UMatVector R_target2cam,
UMatVector t_target2cam,
GpuMat R_cam2gripper,
GpuMat t_cam2gripper) |
static void |
calibrateHandEye(UMatVector R_gripper2base,
UMatVector t_gripper2base,
UMatVector R_target2cam,
UMatVector t_target2cam,
GpuMat R_cam2gripper,
GpuMat t_cam2gripper,
int method) |
static void |
calibrateHandEye(UMatVector R_gripper2base,
UMatVector t_gripper2base,
UMatVector R_target2cam,
UMatVector t_target2cam,
Mat R_cam2gripper,
Mat t_cam2gripper) |
static void |
calibrateHandEye(UMatVector R_gripper2base,
UMatVector t_gripper2base,
UMatVector R_target2cam,
UMatVector t_target2cam,
Mat R_cam2gripper,
Mat t_cam2gripper,
int method) |
static void |
calibrateHandEye(UMatVector R_gripper2base,
UMatVector t_gripper2base,
UMatVector R_target2cam,
UMatVector t_target2cam,
UMat R_cam2gripper,
UMat t_cam2gripper) |
static void |
calibrateHandEye(UMatVector R_gripper2base,
UMatVector t_gripper2base,
UMatVector R_target2cam,
UMatVector t_target2cam,
UMat R_cam2gripper,
UMat t_cam2gripper,
int method) |
static void |
calibrationMatrixValues(GpuMat cameraMatrix,
Size imageSize,
double apertureWidth,
double apertureHeight,
double[] fovx,
double[] fovy,
double[] focalLength,
Point2d principalPoint,
double[] aspectRatio) |
static void |
calibrationMatrixValues(GpuMat cameraMatrix,
Size imageSize,
double apertureWidth,
double apertureHeight,
DoubleBuffer fovx,
DoubleBuffer fovy,
DoubleBuffer focalLength,
Point2d principalPoint,
DoubleBuffer aspectRatio) |
static void |
calibrationMatrixValues(GpuMat cameraMatrix,
Size imageSize,
double apertureWidth,
double apertureHeight,
DoublePointer fovx,
DoublePointer fovy,
DoublePointer focalLength,
Point2d principalPoint,
DoublePointer aspectRatio) |
static void |
calibrationMatrixValues(Mat cameraMatrix,
Size imageSize,
double apertureWidth,
double apertureHeight,
double[] fovx,
double[] fovy,
double[] focalLength,
Point2d principalPoint,
double[] aspectRatio) |
static void |
calibrationMatrixValues(Mat cameraMatrix,
Size imageSize,
double apertureWidth,
double apertureHeight,
DoubleBuffer fovx,
DoubleBuffer fovy,
DoubleBuffer focalLength,
Point2d principalPoint,
DoubleBuffer aspectRatio) |
static void |
calibrationMatrixValues(Mat cameraMatrix,
Size imageSize,
double apertureWidth,
double apertureHeight,
DoublePointer fovx,
DoublePointer fovy,
DoublePointer focalLength,
Point2d principalPoint,
DoublePointer aspectRatio)
\brief Computes useful camera characteristics from the camera matrix.
|
static void |
calibrationMatrixValues(UMat cameraMatrix,
Size imageSize,
double apertureWidth,
double apertureHeight,
double[] fovx,
double[] fovy,
double[] focalLength,
Point2d principalPoint,
double[] aspectRatio) |
static void |
calibrationMatrixValues(UMat cameraMatrix,
Size imageSize,
double apertureWidth,
double apertureHeight,
DoubleBuffer fovx,
DoubleBuffer fovy,
DoubleBuffer focalLength,
Point2d principalPoint,
DoubleBuffer aspectRatio) |
static void |
calibrationMatrixValues(UMat cameraMatrix,
Size imageSize,
double apertureWidth,
double apertureHeight,
DoublePointer fovx,
DoublePointer fovy,
DoublePointer focalLength,
Point2d principalPoint,
DoublePointer aspectRatio) |
static boolean |
checkChessboard(GpuMat img,
Size size) |
static boolean |
checkChessboard(Mat img,
Size size) |
static boolean |
checkChessboard(UMat img,
Size size) |
static void |
composeRT(GpuMat rvec1,
GpuMat tvec1,
GpuMat rvec2,
GpuMat tvec2,
GpuMat rvec3,
GpuMat tvec3) |
static void |
composeRT(GpuMat rvec1,
GpuMat tvec1,
GpuMat rvec2,
GpuMat tvec2,
GpuMat rvec3,
GpuMat tvec3,
GpuMat dr3dr1,
GpuMat dr3dt1,
GpuMat dr3dr2,
GpuMat dr3dt2,
GpuMat dt3dr1,
GpuMat dt3dt1,
GpuMat dt3dr2,
GpuMat dt3dt2) |
static void |
composeRT(Mat rvec1,
Mat tvec1,
Mat rvec2,
Mat tvec2,
Mat rvec3,
Mat tvec3) |
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)
\brief Combines two rotation-and-shift transformations.
|
static void |
composeRT(UMat rvec1,
UMat tvec1,
UMat rvec2,
UMat tvec2,
UMat rvec3,
UMat tvec3) |
static void |
composeRT(UMat rvec1,
UMat tvec1,
UMat rvec2,
UMat tvec2,
UMat rvec3,
UMat tvec3,
UMat dr3dr1,
UMat dr3dt1,
UMat dr3dr2,
UMat dr3dt2,
UMat dt3dr1,
UMat dt3dt1,
UMat dt3dr2,
UMat dt3dt2) |
static void |
computeCorrespondEpilines(GpuMat points,
int whichImage,
GpuMat F,
GpuMat lines) |
static void |
computeCorrespondEpilines(Mat points,
int whichImage,
Mat F,
Mat lines)
\brief For points in an image of a stereo pair, computes the corresponding epilines in the other image.
|
static void |
computeCorrespondEpilines(UMat points,
int whichImage,
UMat F,
UMat lines) |
static void |
convertPointsFromHomogeneous(GpuMat src,
GpuMat dst) |
static void |
convertPointsFromHomogeneous(Mat src,
Mat dst)
\brief Converts points from homogeneous to Euclidean space.
|
static void |
convertPointsFromHomogeneous(UMat src,
UMat dst) |
static void |
convertPointsHomogeneous(GpuMat src,
GpuMat dst) |
static void |
convertPointsHomogeneous(Mat src,
Mat dst)
\brief Converts points to/from homogeneous coordinates.
|
static void |
convertPointsHomogeneous(UMat src,
UMat dst) |
static void |
convertPointsToHomogeneous(GpuMat src,
GpuMat dst) |
static void |
convertPointsToHomogeneous(Mat src,
Mat dst)
\brief Converts points from Euclidean to homogeneous space.
|
static void |
convertPointsToHomogeneous(UMat src,
UMat dst) |
static void |
correctMatches(GpuMat F,
GpuMat points1,
GpuMat points2,
GpuMat newPoints1,
GpuMat newPoints2) |
static void |
correctMatches(Mat F,
Mat points1,
Mat points2,
Mat newPoints1,
Mat newPoints2)
\brief Refines coordinates of corresponding points.
|
static void |
correctMatches(UMat F,
UMat points1,
UMat points2,
UMat newPoints1,
UMat newPoints2) |
static void |
decomposeEssentialMat(GpuMat E,
GpuMat R1,
GpuMat R2,
GpuMat t) |
static void |
decomposeEssentialMat(Mat E,
Mat R1,
Mat R2,
Mat t)
\brief Decompose an essential matrix to possible rotations and translation.
|
static void |
decomposeEssentialMat(UMat E,
UMat R1,
UMat R2,
UMat t) |
static int |
decomposeHomographyMat(GpuMat H,
GpuMat K,
GpuMatVector rotations,
GpuMatVector translations,
GpuMatVector normals) |
static int |
decomposeHomographyMat(GpuMat H,
GpuMat K,
MatVector rotations,
MatVector translations,
MatVector normals) |
static int |
decomposeHomographyMat(GpuMat H,
GpuMat K,
UMatVector rotations,
UMatVector translations,
UMatVector normals) |
static int |
decomposeHomographyMat(Mat H,
Mat K,
GpuMatVector rotations,
GpuMatVector translations,
GpuMatVector normals) |
static int |
decomposeHomographyMat(Mat H,
Mat K,
MatVector rotations,
MatVector translations,
MatVector normals)
\brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s).
|
static int |
decomposeHomographyMat(Mat H,
Mat K,
UMatVector rotations,
UMatVector translations,
UMatVector normals) |
static int |
decomposeHomographyMat(UMat H,
UMat K,
GpuMatVector rotations,
GpuMatVector translations,
GpuMatVector normals) |
static int |
decomposeHomographyMat(UMat H,
UMat K,
MatVector rotations,
MatVector translations,
MatVector normals) |
static int |
decomposeHomographyMat(UMat H,
UMat K,
UMatVector rotations,
UMatVector translations,
UMatVector normals) |
static void |
decomposeProjectionMatrix(GpuMat projMatrix,
GpuMat cameraMatrix,
GpuMat rotMatrix,
GpuMat transVect) |
static void |
decomposeProjectionMatrix(GpuMat projMatrix,
GpuMat cameraMatrix,
GpuMat rotMatrix,
GpuMat transVect,
GpuMat rotMatrixX,
GpuMat rotMatrixY,
GpuMat rotMatrixZ,
GpuMat eulerAngles) |
static void |
decomposeProjectionMatrix(Mat projMatrix,
Mat cameraMatrix,
Mat rotMatrix,
Mat transVect) |
static void |
decomposeProjectionMatrix(Mat projMatrix,
Mat cameraMatrix,
Mat rotMatrix,
Mat transVect,
Mat rotMatrixX,
Mat rotMatrixY,
Mat rotMatrixZ,
Mat eulerAngles)
\brief Decomposes a projection matrix into a rotation matrix and a camera matrix.
|
static void |
decomposeProjectionMatrix(UMat projMatrix,
UMat cameraMatrix,
UMat rotMatrix,
UMat transVect) |
static void |
decomposeProjectionMatrix(UMat projMatrix,
UMat cameraMatrix,
UMat rotMatrix,
UMat transVect,
UMat rotMatrixX,
UMat rotMatrixY,
UMat rotMatrixZ,
UMat eulerAngles) |
static void |
distortPoints(GpuMat undistorted,
GpuMat distorted,
GpuMat K,
GpuMat D) |
static void |
distortPoints(GpuMat undistorted,
GpuMat distorted,
GpuMat K,
GpuMat D,
double alpha) |
static void |
distortPoints(Mat undistorted,
Mat distorted,
Mat K,
Mat D) |
static void |
distortPoints(Mat undistorted,
Mat distorted,
Mat K,
Mat D,
double alpha)
\brief Distorts 2D points using fisheye model.
|
static void |
distortPoints(UMat undistorted,
UMat distorted,
UMat K,
UMat D) |
static void |
distortPoints(UMat undistorted,
UMat distorted,
UMat K,
UMat D,
double alpha) |
static void |
drawChessboardCorners(GpuMat image,
Size patternSize,
GpuMat corners,
boolean patternWasFound) |
static void |
drawChessboardCorners(Mat image,
Size patternSize,
Mat corners,
boolean patternWasFound)
\brief Renders the detected chessboard corners.
|
static void |
drawChessboardCorners(UMat image,
Size patternSize,
UMat corners,
boolean patternWasFound) |
static void |
drawFrameAxes(GpuMat image,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat rvec,
GpuMat tvec,
float length) |
static void |
drawFrameAxes(GpuMat image,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat rvec,
GpuMat tvec,
float length,
int thickness) |
static void |
drawFrameAxes(Mat image,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec,
float length) |
static void |
drawFrameAxes(Mat image,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec,
float length,
int thickness)
\brief Draw axes of the world/object coordinate system from pose estimation.
|
static void |
drawFrameAxes(UMat image,
UMat cameraMatrix,
UMat distCoeffs,
UMat rvec,
UMat tvec,
float length) |
static void |
drawFrameAxes(UMat image,
UMat cameraMatrix,
UMat distCoeffs,
UMat rvec,
UMat tvec,
float length,
int thickness) |
static Mat |
estimateAffine2D(GpuMat from,
GpuMat to) |
static Mat |
estimateAffine2D(GpuMat from,
GpuMat to,
GpuMat inliers,
int method,
double ransacReprojThreshold,
long maxIters,
double confidence,
long refineIters) |
static Mat |
estimateAffine2D(Mat from,
Mat to) |
static Mat |
estimateAffine2D(Mat from,
Mat to,
Mat inliers,
int method,
double ransacReprojThreshold,
long maxIters,
double confidence,
long refineIters)
\brief Computes an optimal affine transformation between two 2D point sets.
|
static Mat |
estimateAffine2D(UMat from,
UMat to) |
static Mat |
estimateAffine2D(UMat from,
UMat to,
UMat inliers,
int method,
double ransacReprojThreshold,
long maxIters,
double confidence,
long refineIters) |
static int |
estimateAffine3D(GpuMat src,
GpuMat dst,
GpuMat out,
GpuMat inliers) |
static int |
estimateAffine3D(GpuMat src,
GpuMat dst,
GpuMat out,
GpuMat inliers,
double ransacThreshold,
double confidence) |
static int |
estimateAffine3D(Mat src,
Mat dst,
Mat out,
Mat inliers) |
static int |
estimateAffine3D(Mat src,
Mat dst,
Mat out,
Mat inliers,
double ransacThreshold,
double confidence)
\brief Computes an optimal affine transformation between two 3D point sets.
|
static int |
estimateAffine3D(UMat src,
UMat dst,
UMat out,
UMat inliers) |
static int |
estimateAffine3D(UMat src,
UMat dst,
UMat out,
UMat inliers,
double ransacThreshold,
double confidence) |
static Mat |
estimateAffinePartial2D(GpuMat from,
GpuMat to) |
static Mat |
estimateAffinePartial2D(GpuMat from,
GpuMat to,
GpuMat inliers,
int method,
double ransacReprojThreshold,
long maxIters,
double confidence,
long refineIters) |
static Mat |
estimateAffinePartial2D(Mat from,
Mat to) |
static Mat |
estimateAffinePartial2D(Mat from,
Mat to,
Mat inliers,
int method,
double ransacReprojThreshold,
long maxIters,
double confidence,
long refineIters)
\brief Computes an optimal limited affine transformation with 4 degrees of freedom between
two 2D point sets.
|
static Mat |
estimateAffinePartial2D(UMat from,
UMat to) |
static Mat |
estimateAffinePartial2D(UMat from,
UMat to,
UMat inliers,
int method,
double ransacReprojThreshold,
long maxIters,
double confidence,
long refineIters) |
static Scalar |
estimateChessboardSharpness(GpuMat image,
Size patternSize,
GpuMat corners) |
static Scalar |
estimateChessboardSharpness(GpuMat image,
Size patternSize,
GpuMat corners,
float rise_distance,
boolean vertical,
GpuMat sharpness) |
static Scalar |
estimateChessboardSharpness(Mat image,
Size patternSize,
Mat corners) |
static Scalar |
estimateChessboardSharpness(Mat image,
Size patternSize,
Mat corners,
float rise_distance,
boolean vertical,
Mat sharpness)
\brief Estimates the sharpness of a detected chessboard.
|
static Scalar |
estimateChessboardSharpness(UMat image,
Size patternSize,
UMat corners) |
static Scalar |
estimateChessboardSharpness(UMat image,
Size patternSize,
UMat corners,
float rise_distance,
boolean vertical,
UMat sharpness) |
static void |
estimateNewCameraMatrixForUndistortRectify(GpuMat K,
GpuMat D,
Size image_size,
GpuMat R,
GpuMat P) |
static void |
estimateNewCameraMatrixForUndistortRectify(GpuMat K,
GpuMat D,
Size image_size,
GpuMat R,
GpuMat P,
double balance,
Size new_size,
double fov_scale) |
static void |
estimateNewCameraMatrixForUndistortRectify(Mat K,
Mat D,
Size image_size,
Mat R,
Mat P) |
static void |
estimateNewCameraMatrixForUndistortRectify(Mat K,
Mat D,
Size image_size,
Mat R,
Mat P,
double balance,
Size new_size,
double fov_scale)
\brief Estimates new camera matrix for undistortion or rectification.
|
static void |
estimateNewCameraMatrixForUndistortRectify(UMat K,
UMat D,
Size image_size,
UMat R,
UMat P) |
static void |
estimateNewCameraMatrixForUndistortRectify(UMat K,
UMat D,
Size image_size,
UMat R,
UMat P,
double balance,
Size new_size,
double fov_scale) |
static void |
filterHomographyDecompByVisibleRefpoints(GpuMatVector rotations,
GpuMatVector normals,
GpuMat beforePoints,
GpuMat afterPoints,
GpuMat possibleSolutions) |
static void |
filterHomographyDecompByVisibleRefpoints(GpuMatVector rotations,
GpuMatVector normals,
GpuMat beforePoints,
GpuMat afterPoints,
GpuMat possibleSolutions,
GpuMat pointsMask) |
static void |
filterHomographyDecompByVisibleRefpoints(GpuMatVector rotations,
GpuMatVector normals,
Mat beforePoints,
Mat afterPoints,
Mat possibleSolutions) |
static void |
filterHomographyDecompByVisibleRefpoints(GpuMatVector rotations,
GpuMatVector normals,
Mat beforePoints,
Mat afterPoints,
Mat possibleSolutions,
Mat pointsMask) |
static void |
filterHomographyDecompByVisibleRefpoints(GpuMatVector rotations,
GpuMatVector normals,
UMat beforePoints,
UMat afterPoints,
UMat possibleSolutions) |
static void |
filterHomographyDecompByVisibleRefpoints(GpuMatVector rotations,
GpuMatVector normals,
UMat beforePoints,
UMat afterPoints,
UMat possibleSolutions,
UMat pointsMask) |
static void |
filterHomographyDecompByVisibleRefpoints(MatVector rotations,
MatVector normals,
GpuMat beforePoints,
GpuMat afterPoints,
GpuMat possibleSolutions) |
static void |
filterHomographyDecompByVisibleRefpoints(MatVector rotations,
MatVector normals,
GpuMat beforePoints,
GpuMat afterPoints,
GpuMat possibleSolutions,
GpuMat pointsMask) |
static void |
filterHomographyDecompByVisibleRefpoints(MatVector rotations,
MatVector normals,
Mat beforePoints,
Mat afterPoints,
Mat possibleSolutions) |
static void |
filterHomographyDecompByVisibleRefpoints(MatVector rotations,
MatVector normals,
Mat beforePoints,
Mat afterPoints,
Mat possibleSolutions,
Mat pointsMask)
\brief Filters homography decompositions based on additional information.
|
static void |
filterHomographyDecompByVisibleRefpoints(MatVector rotations,
MatVector normals,
UMat beforePoints,
UMat afterPoints,
UMat possibleSolutions) |
static void |
filterHomographyDecompByVisibleRefpoints(MatVector rotations,
MatVector normals,
UMat beforePoints,
UMat afterPoints,
UMat possibleSolutions,
UMat pointsMask) |
static void |
filterHomographyDecompByVisibleRefpoints(UMatVector rotations,
UMatVector normals,
GpuMat beforePoints,
GpuMat afterPoints,
GpuMat possibleSolutions) |
static void |
filterHomographyDecompByVisibleRefpoints(UMatVector rotations,
UMatVector normals,
GpuMat beforePoints,
GpuMat afterPoints,
GpuMat possibleSolutions,
GpuMat pointsMask) |
static void |
filterHomographyDecompByVisibleRefpoints(UMatVector rotations,
UMatVector normals,
Mat beforePoints,
Mat afterPoints,
Mat possibleSolutions) |
static void |
filterHomographyDecompByVisibleRefpoints(UMatVector rotations,
UMatVector normals,
Mat beforePoints,
Mat afterPoints,
Mat possibleSolutions,
Mat pointsMask) |
static void |
filterHomographyDecompByVisibleRefpoints(UMatVector rotations,
UMatVector normals,
UMat beforePoints,
UMat afterPoints,
UMat possibleSolutions) |
static void |
filterHomographyDecompByVisibleRefpoints(UMatVector rotations,
UMatVector normals,
UMat beforePoints,
UMat afterPoints,
UMat possibleSolutions,
UMat pointsMask) |
static void |
filterSpeckles(GpuMat img,
double newVal,
int maxSpeckleSize,
double maxDiff) |
static void |
filterSpeckles(GpuMat img,
double newVal,
int maxSpeckleSize,
double maxDiff,
GpuMat buf) |
static void |
filterSpeckles(Mat img,
double newVal,
int maxSpeckleSize,
double maxDiff) |
static void |
filterSpeckles(Mat img,
double newVal,
int maxSpeckleSize,
double maxDiff,
Mat buf)
\brief Filters off small noise blobs (speckles) in the disparity map
|
static void |
filterSpeckles(UMat img,
double newVal,
int maxSpeckleSize,
double maxDiff) |
static void |
filterSpeckles(UMat img,
double newVal,
int maxSpeckleSize,
double maxDiff,
UMat buf) |
static boolean |
find4QuadCornerSubpix(GpuMat img,
GpuMat corners,
Size region_size) |
static boolean |
find4QuadCornerSubpix(Mat img,
Mat corners,
Size region_size)
finds subpixel-accurate positions of the chessboard corners
|
static boolean |
find4QuadCornerSubpix(UMat img,
UMat corners,
Size region_size) |
static boolean |
findChessboardCorners(GpuMat image,
Size patternSize,
GpuMat corners) |
static boolean |
findChessboardCorners(GpuMat image,
Size patternSize,
GpuMat corners,
int flags) |
static boolean |
findChessboardCorners(Mat image,
Size patternSize,
Mat corners) |
static boolean |
findChessboardCorners(Mat image,
Size patternSize,
Mat corners,
int flags)
\brief Finds the positions of internal corners of the chessboard.
|
static boolean |
findChessboardCorners(UMat image,
Size patternSize,
UMat corners) |
static boolean |
findChessboardCorners(UMat image,
Size patternSize,
UMat corners,
int flags) |
static boolean |
findChessboardCornersSB(GpuMat image,
Size patternSize,
GpuMat corners) |
static boolean |
findChessboardCornersSB(GpuMat image,
Size patternSize,
GpuMat corners,
int flags) |
static boolean |
findChessboardCornersSB(Mat image,
Size patternSize,
Mat corners) |
static boolean |
findChessboardCornersSB(Mat image,
Size patternSize,
Mat corners,
int flags)
\overload
|
static boolean |
findChessboardCornersSB(UMat image,
Size patternSize,
UMat corners) |
static boolean |
findChessboardCornersSB(UMat image,
Size patternSize,
UMat corners,
int flags) |
static boolean |
findChessboardCornersSBWithMeta(GpuMat image,
Size patternSize,
GpuMat corners,
int flags,
GpuMat meta) |
static boolean |
findChessboardCornersSBWithMeta(Mat image,
Size patternSize,
Mat corners,
int flags,
Mat meta)
\brief Finds the positions of internal corners of the chessboard using a sector based approach.
|
static boolean |
findChessboardCornersSBWithMeta(UMat image,
Size patternSize,
UMat corners,
int flags,
UMat meta) |
static boolean |
findCirclesGrid(GpuMat image,
Size patternSize,
GpuMat centers) |
static boolean |
findCirclesGrid(GpuMat image,
Size patternSize,
GpuMat centers,
int flags,
Feature2D blobDetector) |
static boolean |
findCirclesGrid(GpuMat image,
Size patternSize,
GpuMat centers,
int flags,
Feature2D blobDetector,
CirclesGridFinderParameters parameters) |
static boolean |
findCirclesGrid(Mat image,
Size patternSize,
Mat centers) |
static boolean |
findCirclesGrid(Mat image,
Size patternSize,
Mat centers,
int flags,
Feature2D blobDetector)
\overload
|
static boolean |
findCirclesGrid(Mat image,
Size patternSize,
Mat centers,
int flags,
Feature2D blobDetector,
CirclesGridFinderParameters parameters)
\brief Finds centers in the grid of circles.
|
static boolean |
findCirclesGrid(UMat image,
Size patternSize,
UMat centers) |
static boolean |
findCirclesGrid(UMat image,
Size patternSize,
UMat centers,
int flags,
Feature2D blobDetector) |
static boolean |
findCirclesGrid(UMat image,
Size patternSize,
UMat centers,
int flags,
Feature2D blobDetector,
CirclesGridFinderParameters parameters) |
static Mat |
findEssentialMat(GpuMat points1,
GpuMat points2) |
static Mat |
findEssentialMat(GpuMat points1,
GpuMat points2,
double focal,
Point2d pp,
int method,
double prob,
double threshold,
GpuMat mask) |
static Mat |
findEssentialMat(GpuMat points1,
GpuMat points2,
GpuMat cameraMatrix) |
static Mat |
findEssentialMat(GpuMat points1,
GpuMat points2,
GpuMat cameraMatrix,
int method,
double prob,
double threshold,
GpuMat mask) |
static Mat |
findEssentialMat(Mat points1,
Mat points2) |
static Mat |
findEssentialMat(Mat points1,
Mat points2,
double focal,
Point2d pp,
int method,
double prob,
double threshold,
Mat mask)
\overload
|
static Mat |
findEssentialMat(Mat points1,
Mat points2,
Mat cameraMatrix) |
static Mat |
findEssentialMat(Mat points1,
Mat points2,
Mat cameraMatrix,
int method,
double prob,
double threshold,
Mat mask)
\brief Calculates an essential matrix from the corresponding points in two images.
|
static Mat |
findEssentialMat(UMat points1,
UMat points2) |
static Mat |
findEssentialMat(UMat points1,
UMat points2,
double focal,
Point2d pp,
int method,
double prob,
double threshold,
UMat mask) |
static Mat |
findEssentialMat(UMat points1,
UMat points2,
UMat cameraMatrix) |
static Mat |
findEssentialMat(UMat points1,
UMat points2,
UMat cameraMatrix,
int method,
double prob,
double threshold,
UMat mask) |
static Mat |
findFundamentalMat(GpuMat points1,
GpuMat points2) |
static Mat |
findFundamentalMat(GpuMat points1,
GpuMat points2,
GpuMat mask) |
static Mat |
findFundamentalMat(GpuMat points1,
GpuMat points2,
GpuMat mask,
int method,
double ransacReprojThreshold,
double confidence) |
static Mat |
findFundamentalMat(GpuMat points1,
GpuMat points2,
int method,
double ransacReprojThreshold,
double confidence,
GpuMat mask) |
static Mat |
findFundamentalMat(GpuMat points1,
GpuMat points2,
int method,
double ransacReprojThreshold,
double confidence,
int maxIters) |
static Mat |
findFundamentalMat(GpuMat points1,
GpuMat points2,
int method,
double ransacReprojThreshold,
double confidence,
int maxIters,
GpuMat mask) |
static Mat |
findFundamentalMat(Mat points1,
Mat points2) |
static Mat |
findFundamentalMat(Mat points1,
Mat points2,
int method,
double ransacReprojThreshold,
double confidence,
int maxIters) |
static Mat |
findFundamentalMat(Mat points1,
Mat points2,
int method,
double ransacReprojThreshold,
double confidence,
int maxIters,
Mat mask)
\brief Calculates a fundamental matrix from the corresponding points in two images.
|
static Mat |
findFundamentalMat(Mat points1,
Mat points2,
int method,
double ransacReprojThreshold,
double confidence,
Mat mask)
\overload
|
static Mat |
findFundamentalMat(Mat points1,
Mat points2,
Mat mask) |
static Mat |
findFundamentalMat(Mat points1,
Mat points2,
Mat mask,
int method,
double ransacReprojThreshold,
double confidence)
\overload
|
static Mat |
findFundamentalMat(UMat points1,
UMat points2) |
static Mat |
findFundamentalMat(UMat points1,
UMat points2,
int method,
double ransacReprojThreshold,
double confidence,
int maxIters) |
static Mat |
findFundamentalMat(UMat points1,
UMat points2,
int method,
double ransacReprojThreshold,
double confidence,
int maxIters,
UMat mask) |
static Mat |
findFundamentalMat(UMat points1,
UMat points2,
int method,
double ransacReprojThreshold,
double confidence,
UMat mask) |
static Mat |
findFundamentalMat(UMat points1,
UMat points2,
UMat mask) |
static Mat |
findFundamentalMat(UMat points1,
UMat points2,
UMat mask,
int method,
double ransacReprojThreshold,
double confidence) |
static Mat |
findHomography(GpuMat srcPoints,
GpuMat dstPoints) |
static Mat |
findHomography(GpuMat srcPoints,
GpuMat dstPoints,
GpuMat mask) |
static Mat |
findHomography(GpuMat srcPoints,
GpuMat dstPoints,
GpuMat mask,
int method,
double ransacReprojThreshold) |
static Mat |
findHomography(GpuMat srcPoints,
GpuMat dstPoints,
int method,
double ransacReprojThreshold,
GpuMat mask,
int maxIters,
double confidence) |
static Mat |
findHomography(Mat srcPoints,
Mat dstPoints) |
static Mat |
findHomography(Mat srcPoints,
Mat dstPoints,
int method,
double ransacReprojThreshold,
Mat mask,
int maxIters,
double confidence)
\brief Finds a perspective transformation between two planes.
|
static Mat |
findHomography(Mat srcPoints,
Mat dstPoints,
Mat mask) |
static Mat |
findHomography(Mat srcPoints,
Mat dstPoints,
Mat mask,
int method,
double ransacReprojThreshold)
\overload
|
static Mat |
findHomography(UMat srcPoints,
UMat dstPoints) |
static Mat |
findHomography(UMat srcPoints,
UMat dstPoints,
int method,
double ransacReprojThreshold,
UMat mask,
int maxIters,
double confidence) |
static Mat |
findHomography(UMat srcPoints,
UMat dstPoints,
UMat mask) |
static Mat |
findHomography(UMat srcPoints,
UMat dstPoints,
UMat mask,
int method,
double ransacReprojThreshold) |
static Mat |
getDefaultNewCameraMatrix(GpuMat cameraMatrix) |
static Mat |
getDefaultNewCameraMatrix(GpuMat cameraMatrix,
Size imgsize,
boolean centerPrincipalPoint) |
static Mat |
getDefaultNewCameraMatrix(Mat cameraMatrix) |
static Mat |
getDefaultNewCameraMatrix(Mat cameraMatrix,
Size imgsize,
boolean centerPrincipalPoint)
\brief Returns the default new camera matrix.
|
static Mat |
getDefaultNewCameraMatrix(UMat cameraMatrix) |
static Mat |
getDefaultNewCameraMatrix(UMat cameraMatrix,
Size imgsize,
boolean centerPrincipalPoint) |
static Mat |
getOptimalNewCameraMatrix(GpuMat cameraMatrix,
GpuMat distCoeffs,
Size imageSize,
double alpha) |
static Mat |
getOptimalNewCameraMatrix(GpuMat cameraMatrix,
GpuMat distCoeffs,
Size imageSize,
double alpha,
Size newImgSize,
Rect validPixROI,
boolean centerPrincipalPoint) |
static Mat |
getOptimalNewCameraMatrix(Mat cameraMatrix,
Mat distCoeffs,
Size imageSize,
double alpha) |
static Mat |
getOptimalNewCameraMatrix(Mat cameraMatrix,
Mat distCoeffs,
Size imageSize,
double alpha,
Size newImgSize,
Rect validPixROI,
boolean centerPrincipalPoint)
\brief Returns the new camera matrix based on the free scaling parameter.
|
static Mat |
getOptimalNewCameraMatrix(UMat cameraMatrix,
UMat distCoeffs,
Size imageSize,
double alpha) |
static Mat |
getOptimalNewCameraMatrix(UMat cameraMatrix,
UMat distCoeffs,
Size imageSize,
double alpha,
Size newImgSize,
Rect validPixROI,
boolean centerPrincipalPoint) |
static Rect |
getValidDisparityROI(Rect roi1,
Rect roi2,
int minDisparity,
int numberOfDisparities,
int blockSize)
computes valid disparity ROI from the valid ROIs of the rectified images (that are returned by cv::stereoRectify())
|
static Mat |
initCameraMatrix2D(Point3fVectorVector objectPoints,
Point2fVectorVector imagePoints,
Size imageSize) |
static Mat |
initCameraMatrix2D(Point3fVectorVector objectPoints,
Point2fVectorVector imagePoints,
Size imageSize,
double aspectRatio)
\brief Finds an initial camera matrix from 3D-2D point correspondences.
|
static void |
initUndistortRectifyMap(GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat R,
GpuMat newCameraMatrix,
Size size,
int m1type,
GpuMat map1,
GpuMat map2) |
static void |
initUndistortRectifyMap(Mat cameraMatrix,
Mat distCoeffs,
Mat R,
Mat newCameraMatrix,
Size size,
int m1type,
Mat map1,
Mat map2)
\brief Computes the undistortion and rectification transformation map.
|
static void |
initUndistortRectifyMap(UMat cameraMatrix,
UMat distCoeffs,
UMat R,
UMat newCameraMatrix,
Size size,
int m1type,
UMat map1,
UMat map2) |
static float |
initWideAngleProjMap(GpuMat cameraMatrix,
GpuMat distCoeffs,
Size imageSize,
int destImageWidth,
int m1type,
GpuMat map1,
GpuMat map2) |
static float |
initWideAngleProjMap(GpuMat cameraMatrix,
GpuMat distCoeffs,
Size imageSize,
int destImageWidth,
int m1type,
GpuMat map1,
GpuMat map2,
int projType) |
static float |
initWideAngleProjMap(GpuMat cameraMatrix,
GpuMat distCoeffs,
Size imageSize,
int destImageWidth,
int m1type,
GpuMat map1,
GpuMat map2,
int projType,
double alpha) |
static float |
initWideAngleProjMap(Mat cameraMatrix,
Mat distCoeffs,
Size imageSize,
int destImageWidth,
int m1type,
Mat map1,
Mat map2) |
static float |
initWideAngleProjMap(Mat cameraMatrix,
Mat distCoeffs,
Size imageSize,
int destImageWidth,
int m1type,
Mat map1,
Mat map2,
int projType) |
static float |
initWideAngleProjMap(Mat cameraMatrix,
Mat distCoeffs,
Size imageSize,
int destImageWidth,
int m1type,
Mat map1,
Mat map2,
int projType,
double alpha)
initializes maps for #remap for wide-angle
|
static float |
initWideAngleProjMap(UMat cameraMatrix,
UMat distCoeffs,
Size imageSize,
int destImageWidth,
int m1type,
UMat map1,
UMat map2) |
static float |
initWideAngleProjMap(UMat cameraMatrix,
UMat distCoeffs,
Size imageSize,
int destImageWidth,
int m1type,
UMat map1,
UMat map2,
int projType) |
static float |
initWideAngleProjMap(UMat cameraMatrix,
UMat distCoeffs,
Size imageSize,
int destImageWidth,
int m1type,
UMat map1,
UMat map2,
int projType,
double alpha) |
static void |
matMulDeriv(GpuMat A,
GpuMat B,
GpuMat dABdA,
GpuMat dABdB) |
static void |
matMulDeriv(Mat A,
Mat B,
Mat dABdA,
Mat dABdB)
\brief Computes partial derivatives of the matrix product for each multiplied matrix.
|
static void |
matMulDeriv(UMat A,
UMat B,
UMat dABdA,
UMat dABdB) |
static void |
projectPoints(GpuMat objectPoints,
GpuMat rvec,
GpuMat tvec,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat imagePoints) |
static void |
projectPoints(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat rvec,
GpuMat tvec,
GpuMat K,
GpuMat D,
double alpha,
GpuMat jacobian) |
static void |
projectPoints(GpuMat objectPoints,
GpuMat rvec,
GpuMat tvec,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat imagePoints,
GpuMat jacobian,
double aspectRatio) |
static void |
projectPoints(GpuMat objectPoints,
GpuMat imagePoints,
Mat affine,
GpuMat K,
GpuMat D) |
static void |
projectPoints(GpuMat objectPoints,
GpuMat imagePoints,
Mat affine,
GpuMat K,
GpuMat D,
double alpha,
GpuMat jacobian) |
static void |
projectPoints(Mat objectPoints,
Mat imagePoints,
Mat affine,
Mat K,
Mat D) |
static void |
projectPoints(Mat objectPoints,
Mat imagePoints,
Mat affine,
Mat K,
Mat D,
double alpha,
Mat jacobian)
\brief Projects points using fisheye model
|
static void |
projectPoints(Mat objectPoints,
Mat rvec,
Mat tvec,
Mat cameraMatrix,
Mat distCoeffs,
Mat imagePoints) |
static void |
projectPoints(Mat objectPoints,
Mat imagePoints,
Mat rvec,
Mat tvec,
Mat K,
Mat D,
double alpha,
Mat jacobian)
\overload
|
static void |
projectPoints(Mat objectPoints,
Mat rvec,
Mat tvec,
Mat cameraMatrix,
Mat distCoeffs,
Mat imagePoints,
Mat jacobian,
double aspectRatio)
\brief Projects 3D points to an image plane.
|
static void |
projectPoints(UMat objectPoints,
UMat imagePoints,
Mat affine,
UMat K,
UMat D) |
static void |
projectPoints(UMat objectPoints,
UMat imagePoints,
Mat affine,
UMat K,
UMat D,
double alpha,
UMat jacobian) |
static void |
projectPoints(UMat objectPoints,
UMat rvec,
UMat tvec,
UMat cameraMatrix,
UMat distCoeffs,
UMat imagePoints) |
static void |
projectPoints(UMat objectPoints,
UMat imagePoints,
UMat rvec,
UMat tvec,
UMat K,
UMat D,
double alpha,
UMat jacobian) |
static void |
projectPoints(UMat objectPoints,
UMat rvec,
UMat tvec,
UMat cameraMatrix,
UMat distCoeffs,
UMat imagePoints,
UMat jacobian,
double aspectRatio) |
static int |
recoverPose(GpuMat E,
GpuMat points1,
GpuMat points2,
GpuMat R,
GpuMat t) |
static int |
recoverPose(GpuMat E,
GpuMat points1,
GpuMat points2,
GpuMat R,
GpuMat t,
double focal,
Point2d pp,
GpuMat mask) |
static int |
recoverPose(GpuMat E,
GpuMat points1,
GpuMat points2,
GpuMat cameraMatrix,
GpuMat R,
GpuMat t) |
static int |
recoverPose(GpuMat E,
GpuMat points1,
GpuMat points2,
GpuMat cameraMatrix,
GpuMat R,
GpuMat t,
double distanceThresh) |
static int |
recoverPose(GpuMat E,
GpuMat points1,
GpuMat points2,
GpuMat cameraMatrix,
GpuMat R,
GpuMat t,
double distanceThresh,
GpuMat mask,
GpuMat triangulatedPoints) |
static int |
recoverPose(GpuMat E,
GpuMat points1,
GpuMat points2,
GpuMat cameraMatrix,
GpuMat R,
GpuMat t,
GpuMat mask) |
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,
Point2d pp,
Mat mask)
\overload
|
static int |
recoverPose(Mat E,
Mat points1,
Mat points2,
Mat cameraMatrix,
Mat R,
Mat t) |
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,
Mat triangulatedPoints)
\overload
|
static int |
recoverPose(Mat E,
Mat points1,
Mat points2,
Mat cameraMatrix,
Mat R,
Mat t,
Mat mask)
\brief Recovers the relative camera rotation and the translation from an estimated essential
matrix and the corresponding points in two images, using cheirality check.
|
static int |
recoverPose(UMat E,
UMat points1,
UMat points2,
UMat R,
UMat t) |
static int |
recoverPose(UMat E,
UMat points1,
UMat points2,
UMat R,
UMat t,
double focal,
Point2d pp,
UMat mask) |
static int |
recoverPose(UMat E,
UMat points1,
UMat points2,
UMat cameraMatrix,
UMat R,
UMat t) |
static int |
recoverPose(UMat E,
UMat points1,
UMat points2,
UMat cameraMatrix,
UMat R,
UMat t,
double distanceThresh) |
static int |
recoverPose(UMat E,
UMat points1,
UMat points2,
UMat cameraMatrix,
UMat R,
UMat t,
double distanceThresh,
UMat mask,
UMat triangulatedPoints) |
static int |
recoverPose(UMat E,
UMat points1,
UMat points2,
UMat cameraMatrix,
UMat R,
UMat t,
UMat mask) |
static float |
rectify3Collinear(GpuMat cameraMatrix1,
GpuMat distCoeffs1,
GpuMat cameraMatrix2,
GpuMat distCoeffs2,
GpuMat cameraMatrix3,
GpuMat distCoeffs3,
GpuMatVector imgpt1,
GpuMatVector imgpt3,
Size imageSize,
GpuMat R12,
GpuMat T12,
GpuMat R13,
GpuMat T13,
GpuMat R1,
GpuMat R2,
GpuMat R3,
GpuMat P1,
GpuMat P2,
GpuMat P3,
GpuMat Q,
double alpha,
Size newImgSize,
Rect roi1,
Rect roi2,
int flags) |
static float |
rectify3Collinear(GpuMat cameraMatrix1,
GpuMat distCoeffs1,
GpuMat cameraMatrix2,
GpuMat distCoeffs2,
GpuMat cameraMatrix3,
GpuMat distCoeffs3,
MatVector imgpt1,
MatVector imgpt3,
Size imageSize,
GpuMat R12,
GpuMat T12,
GpuMat R13,
GpuMat T13,
GpuMat R1,
GpuMat R2,
GpuMat R3,
GpuMat P1,
GpuMat P2,
GpuMat P3,
GpuMat Q,
double alpha,
Size newImgSize,
Rect roi1,
Rect roi2,
int flags) |
static float |
rectify3Collinear(GpuMat cameraMatrix1,
GpuMat distCoeffs1,
GpuMat cameraMatrix2,
GpuMat distCoeffs2,
GpuMat cameraMatrix3,
GpuMat distCoeffs3,
UMatVector imgpt1,
UMatVector imgpt3,
Size imageSize,
GpuMat R12,
GpuMat T12,
GpuMat R13,
GpuMat T13,
GpuMat R1,
GpuMat R2,
GpuMat R3,
GpuMat P1,
GpuMat P2,
GpuMat P3,
GpuMat Q,
double alpha,
Size newImgSize,
Rect roi1,
Rect roi2,
int flags) |
static float |
rectify3Collinear(Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Mat cameraMatrix3,
Mat distCoeffs3,
GpuMatVector imgpt1,
GpuMatVector 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 float |
rectify3Collinear(Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Mat cameraMatrix3,
Mat distCoeffs3,
MatVector imgpt1,
MatVector 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)
computes the rectification transformations for 3-head camera, where all the heads are on the same line.
|
static float |
rectify3Collinear(Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Mat cameraMatrix3,
Mat distCoeffs3,
UMatVector imgpt1,
UMatVector 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 float |
rectify3Collinear(UMat cameraMatrix1,
UMat distCoeffs1,
UMat cameraMatrix2,
UMat distCoeffs2,
UMat cameraMatrix3,
UMat distCoeffs3,
GpuMatVector imgpt1,
GpuMatVector imgpt3,
Size imageSize,
UMat R12,
UMat T12,
UMat R13,
UMat T13,
UMat R1,
UMat R2,
UMat R3,
UMat P1,
UMat P2,
UMat P3,
UMat Q,
double alpha,
Size newImgSize,
Rect roi1,
Rect roi2,
int flags) |
static float |
rectify3Collinear(UMat cameraMatrix1,
UMat distCoeffs1,
UMat cameraMatrix2,
UMat distCoeffs2,
UMat cameraMatrix3,
UMat distCoeffs3,
MatVector imgpt1,
MatVector imgpt3,
Size imageSize,
UMat R12,
UMat T12,
UMat R13,
UMat T13,
UMat R1,
UMat R2,
UMat R3,
UMat P1,
UMat P2,
UMat P3,
UMat Q,
double alpha,
Size newImgSize,
Rect roi1,
Rect roi2,
int flags) |
static float |
rectify3Collinear(UMat cameraMatrix1,
UMat distCoeffs1,
UMat cameraMatrix2,
UMat distCoeffs2,
UMat cameraMatrix3,
UMat distCoeffs3,
UMatVector imgpt1,
UMatVector imgpt3,
Size imageSize,
UMat R12,
UMat T12,
UMat R13,
UMat T13,
UMat R1,
UMat R2,
UMat R3,
UMat P1,
UMat P2,
UMat P3,
UMat Q,
double alpha,
Size newImgSize,
Rect roi1,
Rect roi2,
int flags) |
static void |
reprojectImageTo3D(GpuMat disparity,
GpuMat _3dImage,
GpuMat Q) |
static void |
reprojectImageTo3D(GpuMat disparity,
GpuMat _3dImage,
GpuMat Q,
boolean handleMissingValues,
int ddepth) |
static void |
reprojectImageTo3D(Mat disparity,
Mat _3dImage,
Mat Q) |
static void |
reprojectImageTo3D(Mat disparity,
Mat _3dImage,
Mat Q,
boolean handleMissingValues,
int ddepth)
\brief Reprojects a disparity image to 3D space.
|
static void |
reprojectImageTo3D(UMat disparity,
UMat _3dImage,
UMat Q) |
static void |
reprojectImageTo3D(UMat disparity,
UMat _3dImage,
UMat Q,
boolean handleMissingValues,
int ddepth) |
static void |
Rodrigues(GpuMat src,
GpuMat dst) |
static void |
Rodrigues(GpuMat src,
GpuMat dst,
GpuMat jacobian) |
static void |
Rodrigues(Mat src,
Mat dst) |
static void |
Rodrigues(Mat src,
Mat dst,
Mat jacobian)
\brief Converts a rotation matrix to a rotation vector or vice versa.
|
static void |
Rodrigues(UMat src,
UMat dst) |
static void |
Rodrigues(UMat src,
UMat dst,
UMat jacobian) |
static Point3d |
RQDecomp3x3(GpuMat src,
GpuMat mtxR,
GpuMat mtxQ) |
static Point3d |
RQDecomp3x3(GpuMat src,
GpuMat mtxR,
GpuMat mtxQ,
GpuMat Qx,
GpuMat Qy,
GpuMat Qz) |
static Point3d |
RQDecomp3x3(Mat src,
Mat mtxR,
Mat mtxQ) |
static Point3d |
RQDecomp3x3(Mat src,
Mat mtxR,
Mat mtxQ,
Mat Qx,
Mat Qy,
Mat Qz)
\brief Computes an RQ decomposition of 3x3 matrices.
|
static Point3d |
RQDecomp3x3(UMat src,
UMat mtxR,
UMat mtxQ) |
static Point3d |
RQDecomp3x3(UMat src,
UMat mtxR,
UMat mtxQ,
UMat Qx,
UMat Qy,
UMat Qz) |
static double |
sampsonDistance(GpuMat pt1,
GpuMat pt2,
GpuMat F) |
static double |
sampsonDistance(Mat pt1,
Mat pt2,
Mat F)
\brief Calculates the Sampson Distance between two points.
|
static double |
sampsonDistance(UMat pt1,
UMat pt2,
UMat F) |
static int |
solveP3P(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMatVector rvecs,
GpuMatVector tvecs,
int flags) |
static int |
solveP3P(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
MatVector rvecs,
MatVector tvecs,
int flags) |
static int |
solveP3P(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
UMatVector rvecs,
UMatVector tvecs,
int flags) |
static int |
solveP3P(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
GpuMatVector rvecs,
GpuMatVector tvecs,
int flags) |
static int |
solveP3P(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
MatVector rvecs,
MatVector tvecs,
int flags)
\brief Finds an object pose from 3 3D-2D point correspondences.
|
static int |
solveP3P(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
UMatVector rvecs,
UMatVector tvecs,
int flags) |
static int |
solveP3P(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
GpuMatVector rvecs,
GpuMatVector tvecs,
int flags) |
static int |
solveP3P(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
MatVector rvecs,
MatVector tvecs,
int flags) |
static int |
solveP3P(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
UMatVector rvecs,
UMatVector tvecs,
int flags) |
static boolean |
solvePnP(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat rvec,
GpuMat tvec) |
static boolean |
solvePnP(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat rvec,
GpuMat tvec,
boolean useExtrinsicGuess,
int flags) |
static boolean |
solvePnP(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec) |
static boolean |
solvePnP(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec,
boolean useExtrinsicGuess,
int flags)
\brief Finds an object pose from 3D-2D point correspondences.
|
static boolean |
solvePnP(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
UMat rvec,
UMat tvec) |
static boolean |
solvePnP(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
UMat rvec,
UMat tvec,
boolean useExtrinsicGuess,
int flags) |
static int |
solvePnPGeneric(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMatVector rvecs,
GpuMatVector tvecs) |
static int |
solvePnPGeneric(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMatVector rvecs,
GpuMatVector tvecs,
boolean useExtrinsicGuess,
int flags,
GpuMat rvec,
GpuMat tvec,
GpuMat reprojectionError) |
static int |
solvePnPGeneric(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
MatVector rvecs,
MatVector tvecs) |
static int |
solvePnPGeneric(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
MatVector rvecs,
MatVector tvecs,
boolean useExtrinsicGuess,
int flags,
GpuMat rvec,
GpuMat tvec,
GpuMat reprojectionError) |
static int |
solvePnPGeneric(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
UMatVector rvecs,
UMatVector tvecs) |
static int |
solvePnPGeneric(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
UMatVector rvecs,
UMatVector tvecs,
boolean useExtrinsicGuess,
int flags,
GpuMat rvec,
GpuMat tvec,
GpuMat reprojectionError) |
static int |
solvePnPGeneric(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
GpuMatVector rvecs,
GpuMatVector tvecs) |
static int |
solvePnPGeneric(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
GpuMatVector rvecs,
GpuMatVector tvecs,
boolean useExtrinsicGuess,
int flags,
Mat rvec,
Mat tvec,
Mat reprojectionError) |
static int |
solvePnPGeneric(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
MatVector rvecs,
MatVector tvecs) |
static int |
solvePnPGeneric(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
MatVector rvecs,
MatVector tvecs,
boolean useExtrinsicGuess,
int flags,
Mat rvec,
Mat tvec,
Mat reprojectionError)
\brief Finds an object pose from 3D-2D point correspondences.
|
static int |
solvePnPGeneric(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
UMatVector rvecs,
UMatVector tvecs) |
static int |
solvePnPGeneric(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
UMatVector rvecs,
UMatVector tvecs,
boolean useExtrinsicGuess,
int flags,
Mat rvec,
Mat tvec,
Mat reprojectionError) |
static int |
solvePnPGeneric(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
GpuMatVector rvecs,
GpuMatVector tvecs) |
static int |
solvePnPGeneric(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
GpuMatVector rvecs,
GpuMatVector tvecs,
boolean useExtrinsicGuess,
int flags,
UMat rvec,
UMat tvec,
UMat reprojectionError) |
static int |
solvePnPGeneric(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
MatVector rvecs,
MatVector tvecs) |
static int |
solvePnPGeneric(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
MatVector rvecs,
MatVector tvecs,
boolean useExtrinsicGuess,
int flags,
UMat rvec,
UMat tvec,
UMat reprojectionError) |
static int |
solvePnPGeneric(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
UMatVector rvecs,
UMatVector tvecs) |
static int |
solvePnPGeneric(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
UMatVector rvecs,
UMatVector tvecs,
boolean useExtrinsicGuess,
int flags,
UMat rvec,
UMat tvec,
UMat reprojectionError) |
static boolean |
solvePnPRansac(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat rvec,
GpuMat tvec) |
static boolean |
solvePnPRansac(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat rvec,
GpuMat tvec,
boolean useExtrinsicGuess,
int iterationsCount,
float reprojectionError,
double confidence,
GpuMat inliers,
int flags) |
static boolean |
solvePnPRansac(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec) |
static boolean |
solvePnPRansac(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec,
boolean useExtrinsicGuess,
int iterationsCount,
float reprojectionError,
double confidence,
Mat inliers,
int flags)
\brief Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
|
static boolean |
solvePnPRansac(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
UMat rvec,
UMat tvec) |
static boolean |
solvePnPRansac(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
UMat rvec,
UMat tvec,
boolean useExtrinsicGuess,
int iterationsCount,
float reprojectionError,
double confidence,
UMat inliers,
int flags) |
static void |
solvePnPRefineLM(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat rvec,
GpuMat tvec) |
static void |
solvePnPRefineLM(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat rvec,
GpuMat tvec,
TermCriteria criteria) |
static void |
solvePnPRefineLM(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec) |
static void |
solvePnPRefineLM(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec,
TermCriteria criteria)
\brief 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(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
UMat rvec,
UMat tvec) |
static void |
solvePnPRefineLM(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
UMat rvec,
UMat tvec,
TermCriteria criteria) |
static void |
solvePnPRefineVVS(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat rvec,
GpuMat tvec) |
static void |
solvePnPRefineVVS(GpuMat objectPoints,
GpuMat imagePoints,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat rvec,
GpuMat tvec,
TermCriteria criteria,
double VVSlambda) |
static void |
solvePnPRefineVVS(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec) |
static void |
solvePnPRefineVVS(Mat objectPoints,
Mat imagePoints,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec,
TermCriteria criteria,
double VVSlambda)
\brief 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(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
UMat rvec,
UMat tvec) |
static void |
solvePnPRefineVVS(UMat objectPoints,
UMat imagePoints,
UMat cameraMatrix,
UMat distCoeffs,
UMat rvec,
UMat tvec,
TermCriteria criteria,
double VVSlambda) |
static double |
stereoCalibrate(Point3fVectorVector objectPoints,
Point2fVectorVector imagePoints1,
Point2fVectorVector imagePoints2,
Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat E,
Mat F) |
static double |
stereoCalibrate(Point3fVectorVector objectPoints,
Point2fVectorVector imagePoints1,
Point2fVectorVector 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 |
stereoCalibrateExtended(Point3fVectorVector objectPoints,
Point2fVectorVector imagePoints1,
Point2fVectorVector imagePoints2,
Mat cameraMatrix1,
Mat distCoeffs1,
Mat cameraMatrix2,
Mat distCoeffs2,
Size imageSize,
Mat R,
Mat T,
Mat E,
Mat F,
Mat perViewErrors) |
static double |
stereoCalibrateExtended(Point3fVectorVector objectPoints,
Point2fVectorVector imagePoints1,
Point2fVectorVector 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)
\brief Calibrates a stereo camera set up.
|
static void |
stereoRectify(GpuMat cameraMatrix1,
GpuMat distCoeffs1,
GpuMat cameraMatrix2,
GpuMat distCoeffs2,
Size imageSize,
GpuMat R,
GpuMat T,
GpuMat R1,
GpuMat R2,
GpuMat P1,
GpuMat P2,
GpuMat Q) |
static void |
stereoRectify(GpuMat K1,
GpuMat D1,
GpuMat K2,
GpuMat D2,
Size imageSize,
GpuMat R,
GpuMat tvec,
GpuMat R1,
GpuMat R2,
GpuMat P1,
GpuMat P2,
GpuMat Q,
int flags) |
static void |
stereoRectify(GpuMat cameraMatrix1,
GpuMat distCoeffs1,
GpuMat cameraMatrix2,
GpuMat distCoeffs2,
Size imageSize,
GpuMat R,
GpuMat T,
GpuMat R1,
GpuMat R2,
GpuMat P1,
GpuMat P2,
GpuMat Q,
int flags,
double alpha,
Size newImageSize,
Rect validPixROI1,
Rect validPixROI2) |
static void |
stereoRectify(GpuMat K1,
GpuMat D1,
GpuMat K2,
GpuMat D2,
Size imageSize,
GpuMat R,
GpuMat tvec,
GpuMat R1,
GpuMat R2,
GpuMat P1,
GpuMat P2,
GpuMat Q,
int flags,
Size newImageSize,
double balance,
double fov_scale) |
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) |
static void |
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) |
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)
\brief Computes rectification transforms for each head of a calibrated stereo camera.
|
static void |
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)
\brief Stereo rectification for fisheye camera model
|
static void |
stereoRectify(UMat cameraMatrix1,
UMat distCoeffs1,
UMat cameraMatrix2,
UMat distCoeffs2,
Size imageSize,
UMat R,
UMat T,
UMat R1,
UMat R2,
UMat P1,
UMat P2,
UMat Q) |
static void |
stereoRectify(UMat K1,
UMat D1,
UMat K2,
UMat D2,
Size imageSize,
UMat R,
UMat tvec,
UMat R1,
UMat R2,
UMat P1,
UMat P2,
UMat Q,
int flags) |
static void |
stereoRectify(UMat cameraMatrix1,
UMat distCoeffs1,
UMat cameraMatrix2,
UMat distCoeffs2,
Size imageSize,
UMat R,
UMat T,
UMat R1,
UMat R2,
UMat P1,
UMat P2,
UMat Q,
int flags,
double alpha,
Size newImageSize,
Rect validPixROI1,
Rect validPixROI2) |
static void |
stereoRectify(UMat K1,
UMat D1,
UMat K2,
UMat D2,
Size imageSize,
UMat R,
UMat tvec,
UMat R1,
UMat R2,
UMat P1,
UMat P2,
UMat Q,
int flags,
Size newImageSize,
double balance,
double fov_scale) |
static boolean |
stereoRectifyUncalibrated(GpuMat points1,
GpuMat points2,
GpuMat F,
Size imgSize,
GpuMat H1,
GpuMat H2) |
static boolean |
stereoRectifyUncalibrated(GpuMat points1,
GpuMat points2,
GpuMat F,
Size imgSize,
GpuMat H1,
GpuMat H2,
double threshold) |
static boolean |
stereoRectifyUncalibrated(Mat points1,
Mat points2,
Mat F,
Size imgSize,
Mat H1,
Mat H2) |
static boolean |
stereoRectifyUncalibrated(Mat points1,
Mat points2,
Mat F,
Size imgSize,
Mat H1,
Mat H2,
double threshold)
\brief Computes a rectification transform for an uncalibrated stereo camera.
|
static boolean |
stereoRectifyUncalibrated(UMat points1,
UMat points2,
UMat F,
Size imgSize,
UMat H1,
UMat H2) |
static boolean |
stereoRectifyUncalibrated(UMat points1,
UMat points2,
UMat F,
Size imgSize,
UMat H1,
UMat H2,
double threshold) |
static void |
triangulatePoints(GpuMat projMatr1,
GpuMat projMatr2,
GpuMat projPoints1,
GpuMat projPoints2,
GpuMat points4D) |
static void |
triangulatePoints(Mat projMatr1,
Mat projMatr2,
Mat projPoints1,
Mat projPoints2,
Mat points4D)
\brief This function reconstructs 3-dimensional points (in homogeneous coordinates) by using
their observations with a stereo camera.
|
static void |
triangulatePoints(UMat projMatr1,
UMat projMatr2,
UMat projPoints1,
UMat projPoints2,
UMat points4D) |
static void |
undistort(GpuMat src,
GpuMat dst,
GpuMat cameraMatrix,
GpuMat distCoeffs) |
static void |
undistort(GpuMat src,
GpuMat dst,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat newCameraMatrix) |
static void |
undistort(Mat src,
Mat dst,
Mat cameraMatrix,
Mat distCoeffs) |
static void |
undistort(Mat src,
Mat dst,
Mat cameraMatrix,
Mat distCoeffs,
Mat newCameraMatrix)
\brief Transforms an image to compensate for lens distortion.
|
static void |
undistort(UMat src,
UMat dst,
UMat cameraMatrix,
UMat distCoeffs) |
static void |
undistort(UMat src,
UMat dst,
UMat cameraMatrix,
UMat distCoeffs,
UMat newCameraMatrix) |
static void |
undistortImage(GpuMat distorted,
GpuMat undistorted,
GpuMat K,
GpuMat D) |
static void |
undistortImage(GpuMat distorted,
GpuMat undistorted,
GpuMat K,
GpuMat D,
GpuMat Knew,
Size new_size) |
static void |
undistortImage(Mat distorted,
Mat undistorted,
Mat K,
Mat D) |
static void |
undistortImage(Mat distorted,
Mat undistorted,
Mat K,
Mat D,
Mat Knew,
Size new_size)
\brief Transforms an image to compensate for fisheye lens distortion.
|
static void |
undistortImage(UMat distorted,
UMat undistorted,
UMat K,
UMat D) |
static void |
undistortImage(UMat distorted,
UMat undistorted,
UMat K,
UMat D,
UMat Knew,
Size new_size) |
static void |
undistortPoints(GpuMat src,
GpuMat dst,
GpuMat cameraMatrix,
GpuMat distCoeffs) |
static void |
undistortPoints(GpuMat src,
GpuMat dst,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat R,
GpuMat P) |
static void |
undistortPoints(Mat src,
Mat dst,
Mat cameraMatrix,
Mat distCoeffs) |
static void |
undistortPoints(Mat src,
Mat dst,
Mat cameraMatrix,
Mat distCoeffs,
Mat R,
Mat P)
\brief Computes the ideal point coordinates from the observed point coordinates.
|
static void |
undistortPoints(UMat src,
UMat dst,
UMat cameraMatrix,
UMat distCoeffs) |
static void |
undistortPoints(UMat src,
UMat dst,
UMat cameraMatrix,
UMat distCoeffs,
UMat R,
UMat P) |
static void |
undistortPointsIter(GpuMat src,
GpuMat dst,
GpuMat cameraMatrix,
GpuMat distCoeffs,
GpuMat R,
GpuMat P,
TermCriteria criteria) |
static void |
undistortPointsIter(Mat src,
Mat dst,
Mat cameraMatrix,
Mat distCoeffs,
Mat R,
Mat P,
TermCriteria criteria)
\overload
\note Default version of #undistortPoints does 5 iterations to compute undistorted points.
|
static void |
undistortPointsIter(UMat src,
UMat dst,
UMat cameraMatrix,
UMat distCoeffs,
UMat R,
UMat P,
TermCriteria criteria) |
static void |
validateDisparity(GpuMat disparity,
GpuMat cost,
int minDisparity,
int numberOfDisparities) |
static void |
validateDisparity(GpuMat disparity,
GpuMat cost,
int minDisparity,
int numberOfDisparities,
int disp12MaxDisp) |
static void |
validateDisparity(Mat disparity,
Mat cost,
int minDisparity,
int numberOfDisparities) |
static void |
validateDisparity(Mat disparity,
Mat cost,
int minDisparity,
int numberOfDisparities,
int disp12MaxDisp)
validates disparity using the left-right check.
|
static void |
validateDisparity(UMat disparity,
UMat cost,
int minDisparity,
int numberOfDisparities) |
static void |
validateDisparity(UMat disparity,
UMat cost,
int minDisparity,
int numberOfDisparities,
int disp12MaxDisp) |
map
public static final int CV_FM_7POINT
public static final int CV_FM_8POINT
public static final int CV_LMEDS
public static final int CV_RANSAC
public static final int CV_FM_LMEDS_ONLY
public static final int CV_FM_RANSAC_ONLY
public static final int CV_FM_LMEDS
public static final int CV_FM_RANSAC
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 CV_CALIB_CB_ADAPTIVE_THRESH
public static final int CV_CALIB_CB_NORMALIZE_IMAGE
public static final int CV_CALIB_CB_FILTER_QUADS
public static final int CV_CALIB_CB_FAST_CHECK
public static final int CV_CALIB_USE_INTRINSIC_GUESS
public static final int CV_CALIB_FIX_ASPECT_RATIO
public static final int CV_CALIB_FIX_PRINCIPAL_POINT
public static final int CV_CALIB_ZERO_TANGENT_DIST
public static final int CV_CALIB_FIX_FOCAL_LENGTH
public static final int CV_CALIB_FIX_K1
public static final int CV_CALIB_FIX_K2
public static final int CV_CALIB_FIX_K3
public static final int CV_CALIB_FIX_K4
public static final int CV_CALIB_FIX_K5
public static final int CV_CALIB_FIX_K6
public static final int CV_CALIB_RATIONAL_MODEL
public static final int CV_CALIB_THIN_PRISM_MODEL
public static final int CV_CALIB_FIX_S1_S2_S3_S4
public static final int CV_CALIB_TILTED_MODEL
public static final int CV_CALIB_FIX_TAUX_TAUY
public static final int CV_CALIB_FIX_TANGENT_DIST
public static final int CV_CALIB_NINTRINSIC
public static final int CV_CALIB_FIX_INTRINSIC
public static final int CV_CALIB_SAME_FOCAL_LENGTH
public static final int CV_CALIB_ZERO_DISPARITY
public static final int CV_STEREO_BM_NORMALIZED_RESPONSE
public static final int CV_STEREO_BM_XSOBEL
public static final int LMEDS
public static final int RANSAC
public static final int RHO
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_MAX_COUNT
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_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 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 PROJ_SPHERICAL_ORTHO
public static final int PROJ_SPHERICAL_EQRECT
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
@Namespace(value="cv") public static void Rodrigues(@ByVal Mat src, @ByVal Mat dst, @ByVal(nullValue="cv::OutputArray(cv::noArray())") 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: - A Compact Formula for the Derivative of a 3-D Rotation in Exponential Coordinates, Guillermo Gallego, Anthony J. Yezzi \cite Gallego2014ACF
\note Useful information on SE(3) and Lie Groups can be found in: - A tutorial on SE(3) transformation parameterizations and on-manifold optimization, Jose-Luis Blanco \cite blanco2010tutorial - Lie Groups for 2D and 3D Transformation, Ethan Eade \cite Eade17 - A micro Lie theory for state estimation in robotics, Joan Sol??, J??r??mie Deray, Dinesh Atchuthan \cite Sol2018AML
@Namespace(value="cv") public static void Rodrigues(@ByVal UMat src, @ByVal UMat dst, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat jacobian)
@Namespace(value="cv") public static void Rodrigues(@ByVal GpuMat src, @ByVal GpuMat dst, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat jacobian)
@Namespace(value="cv") @ByVal public static Mat findHomography(@ByVal Mat srcPoints, @ByVal Mat dstPoints, int method, double ransacReprojThreshold, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat mask, int maxIters, double confidence)
srcPoints
- Coordinates of the points in the original plane, a matrix of the type CV_32FC2
or vector\dstPoints
- Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or
a vector\method
- Method used to compute a homography matrix. The following methods are possible:
- **0** - a regular method using all the points, i.e., the least squares method
- **RANSAC** - RANSAC-based robust method
- **LMEDS** - Least-Median robust method
- **RHO** - PROSAC-based robust methodransacReprojThreshold
- Maximum allowed reprojection error to treat a point pair as an inlier
(used in the RANSAC and RHO methods only). That is, if
\[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\]
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. Thus, it is normalized so that h_{33}=1
. Note that whenever an H
matrix
cannot be estimated, an empty one will be returned.
getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective,
perspectiveTransform
@Namespace(value="cv") @ByVal public static Mat findHomography(@ByVal Mat srcPoints, @ByVal Mat dstPoints)
@Namespace(value="cv") @ByVal public static Mat findHomography(@ByVal UMat srcPoints, @ByVal UMat dstPoints, int method, double ransacReprojThreshold, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat mask, int maxIters, double confidence)
@Namespace(value="cv") @ByVal public static Mat findHomography(@ByVal UMat srcPoints, @ByVal UMat dstPoints)
@Namespace(value="cv") @ByVal public static Mat findHomography(@ByVal GpuMat srcPoints, @ByVal GpuMat dstPoints, int method, double ransacReprojThreshold, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat mask, int maxIters, double confidence)
@Namespace(value="cv") @ByVal public static Mat findHomography(@ByVal GpuMat srcPoints, @ByVal GpuMat dstPoints)
@Namespace(value="cv") @ByVal public static Mat findHomography(@ByVal Mat srcPoints, @ByVal Mat dstPoints, @ByVal Mat mask, int method, double ransacReprojThreshold)
@Namespace(value="cv") @ByVal public static Mat findHomography(@ByVal Mat srcPoints, @ByVal Mat dstPoints, @ByVal Mat mask)
@Namespace(value="cv") @ByVal public static Mat findHomography(@ByVal UMat srcPoints, @ByVal UMat dstPoints, @ByVal UMat mask, int method, double ransacReprojThreshold)
@Namespace(value="cv") @ByVal public static Mat findHomography(@ByVal UMat srcPoints, @ByVal UMat dstPoints, @ByVal UMat mask)
@Namespace(value="cv") @ByVal public static Mat findHomography(@ByVal GpuMat srcPoints, @ByVal GpuMat dstPoints, @ByVal GpuMat mask, int method, double ransacReprojThreshold)
@Namespace(value="cv") @ByVal public static Mat findHomography(@ByVal GpuMat srcPoints, @ByVal GpuMat dstPoints, @ByVal GpuMat mask)
@Namespace(value="cv") @ByVal @Cast(value="cv::Vec3d*") public static Point3d RQDecomp3x3(@ByVal Mat src, @ByVal Mat mtxR, @ByVal Mat mtxQ, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat Qx, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat Qy, @ByVal(nullValue="cv::OutputArray(cv::noArray())") 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 tree rotation matrices and corresponding three Euler angles are only one of the possible solutions.
@Namespace(value="cv") @ByVal @Cast(value="cv::Vec3d*") public static Point3d RQDecomp3x3(@ByVal Mat src, @ByVal Mat mtxR, @ByVal Mat mtxQ)
@Namespace(value="cv") @ByVal @Cast(value="cv::Vec3d*") public static Point3d RQDecomp3x3(@ByVal UMat src, @ByVal UMat mtxR, @ByVal UMat mtxQ, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat Qx, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat Qy, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat Qz)
@Namespace(value="cv") @ByVal @Cast(value="cv::Vec3d*") public static Point3d RQDecomp3x3(@ByVal UMat src, @ByVal UMat mtxR, @ByVal UMat mtxQ)
@Namespace(value="cv") @ByVal @Cast(value="cv::Vec3d*") public static Point3d RQDecomp3x3(@ByVal GpuMat src, @ByVal GpuMat mtxR, @ByVal GpuMat mtxQ, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat Qx, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat Qy, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat Qz)
@Namespace(value="cv") @ByVal @Cast(value="cv::Vec3d*") public static Point3d RQDecomp3x3(@ByVal GpuMat src, @ByVal GpuMat mtxR, @ByVal GpuMat mtxQ)
@Namespace(value="cv") public static void decomposeProjectionMatrix(@ByVal Mat projMatrix, @ByVal Mat cameraMatrix, @ByVal Mat rotMatrix, @ByVal Mat transVect, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat rotMatrixX, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat rotMatrixY, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat rotMatrixZ, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat eulerAngles)
projMatrix
- 3x4 input projection matrix P.cameraMatrix
- Output 3x3 camera matrix K.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 tree rotation matrices and corresponding three Euler angles are only one of the possible solutions.
The function is based on RQDecomp3x3 .
@Namespace(value="cv") public static void decomposeProjectionMatrix(@ByVal Mat projMatrix, @ByVal Mat cameraMatrix, @ByVal Mat rotMatrix, @ByVal Mat transVect)
@Namespace(value="cv") public static void decomposeProjectionMatrix(@ByVal UMat projMatrix, @ByVal UMat cameraMatrix, @ByVal UMat rotMatrix, @ByVal UMat transVect, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat rotMatrixX, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat rotMatrixY, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat rotMatrixZ, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat eulerAngles)
@Namespace(value="cv") public static void decomposeProjectionMatrix(@ByVal UMat projMatrix, @ByVal UMat cameraMatrix, @ByVal UMat rotMatrix, @ByVal UMat transVect)
@Namespace(value="cv") public static void decomposeProjectionMatrix(@ByVal GpuMat projMatrix, @ByVal GpuMat cameraMatrix, @ByVal GpuMat rotMatrix, @ByVal GpuMat transVect, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat rotMatrixX, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat rotMatrixY, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat rotMatrixZ, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat eulerAngles)
@Namespace(value="cv") public static void decomposeProjectionMatrix(@ByVal GpuMat projMatrix, @ByVal GpuMat cameraMatrix, @ByVal GpuMat rotMatrix, @ByVal GpuMat transVect)
@Namespace(value="cv") public static void matMulDeriv(@ByVal Mat A, @ByVal Mat B, @ByVal Mat dABdA, @ByVal 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.
@Namespace(value="cv") public static void matMulDeriv(@ByVal UMat A, @ByVal UMat B, @ByVal UMat dABdA, @ByVal UMat dABdB)
@Namespace(value="cv") public static void matMulDeriv(@ByVal GpuMat A, @ByVal GpuMat B, @ByVal GpuMat dABdA, @ByVal GpuMat dABdB)
@Namespace(value="cv") public static void composeRT(@ByVal Mat rvec1, @ByVal Mat tvec1, @ByVal Mat rvec2, @ByVal Mat tvec2, @ByVal Mat rvec3, @ByVal Mat tvec3, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat dr3dr1, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat dr3dt1, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat dr3dr2, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat dr3dt2, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat dt3dr1, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat dt3dt1, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat dt3dr2, @ByVal(nullValue="cv::OutputArray(cv::noArray())") 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.
@Namespace(value="cv") public static void composeRT(@ByVal Mat rvec1, @ByVal Mat tvec1, @ByVal Mat rvec2, @ByVal Mat tvec2, @ByVal Mat rvec3, @ByVal Mat tvec3)
@Namespace(value="cv") public static void composeRT(@ByVal UMat rvec1, @ByVal UMat tvec1, @ByVal UMat rvec2, @ByVal UMat tvec2, @ByVal UMat rvec3, @ByVal UMat tvec3, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat dr3dr1, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat dr3dt1, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat dr3dr2, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat dr3dt2, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat dt3dr1, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat dt3dt1, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat dt3dr2, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat dt3dt2)
@Namespace(value="cv") public static void composeRT(@ByVal UMat rvec1, @ByVal UMat tvec1, @ByVal UMat rvec2, @ByVal UMat tvec2, @ByVal UMat rvec3, @ByVal UMat tvec3)
@Namespace(value="cv") public static void composeRT(@ByVal GpuMat rvec1, @ByVal GpuMat tvec1, @ByVal GpuMat rvec2, @ByVal GpuMat tvec2, @ByVal GpuMat rvec3, @ByVal GpuMat tvec3, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat dr3dr1, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat dr3dt1, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat dr3dr2, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat dr3dt2, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat dt3dr1, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat dt3dt1, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat dt3dr2, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat dt3dt2)
@Namespace(value="cv") public static void composeRT(@ByVal GpuMat rvec1, @ByVal GpuMat tvec1, @ByVal GpuMat rvec2, @ByVal GpuMat tvec2, @ByVal GpuMat rvec3, @ByVal GpuMat tvec3)
@Namespace(value="cv") public static void projectPoints(@ByVal Mat objectPoints, @ByVal Mat rvec, @ByVal Mat tvec, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Mat imagePoints, @ByVal(nullValue="cv::OutputArray(cv::noArray())") 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\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 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 empty, the zero distortion coefficients are assumed.imagePoints
- Output array of image points, 1xN/Nx1 2-channel, or
vector\jacobian
- Optional output 2Nx(10+\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.
@Namespace(value="cv") public static void projectPoints(@ByVal Mat objectPoints, @ByVal Mat rvec, @ByVal Mat tvec, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Mat imagePoints)
@Namespace(value="cv") public static void projectPoints(@ByVal UMat objectPoints, @ByVal UMat rvec, @ByVal UMat tvec, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMat imagePoints, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat jacobian, double aspectRatio)
@Namespace(value="cv") public static void projectPoints(@ByVal UMat objectPoints, @ByVal UMat rvec, @ByVal UMat tvec, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMat imagePoints)
@Namespace(value="cv") public static void projectPoints(@ByVal GpuMat objectPoints, @ByVal GpuMat rvec, @ByVal GpuMat tvec, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMat imagePoints, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat jacobian, double aspectRatio)
@Namespace(value="cv") public static void projectPoints(@ByVal GpuMat objectPoints, @ByVal GpuMat rvec, @ByVal GpuMat tvec, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMat imagePoints)
@Namespace(value="cv") @Cast(value="bool") public static boolean solvePnP(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Mat rvec, @ByVal Mat tvec, @Cast(value="bool") 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\imagePoints
- Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
where N is the number of points. vector\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.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:
- **SOLVEPNP_ITERATIVE** Iterative method is based on a Levenberg-Marquardt optimization. In
this case the 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
projectPoints ) objectPoints .
- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
"Complete Solution Classification for the Perspective-Three-Point Problem" (\cite gao2003complete).
In this case the function requires exactly four object and image points.
- **SOLVEPNP_AP3P** Method is based on the paper of T. Ke, S. Roumeliotis
"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (\cite Ke17).
In this case the function requires exactly four object and image points.
- **SOLVEPNP_EPNP** Method has been introduced by F. Moreno-Noguer, V. Lepetit and P. Fua in the
paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" (\cite lepetit2009epnp).
- **SOLVEPNP_DLS** Method is based on the paper of J. Hesch and S. Roumeliotis.
"A Direct Least-Squares (DLS) Method for PnP" (\cite hesch2011direct).
- **SOLVEPNP_UPNP** Method is based on the paper of A. Penate-Sanchez, J. Andrade-Cetto,
F. Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length
Estimation" (\cite penate2013exhaustive). In this case the function also estimates the parameters f_x
and f_y
assuming that both have the same value. Then the cameraMatrix is updated with the estimated
focal length.
- **SOLVEPNP_IPPE** Method is based on the paper of T. Collins and A. Bartoli.
"Infinitesimal Plane-Based Pose Estimation" (\cite Collins14). This method requires coplanar object points.
- **SOLVEPNP_IPPE_SQUARE** Method is based on the paper of Toby Collins and Adrien Bartoli.
"Infinitesimal Plane-Based Pose Estimation" (\cite Collins14). This method is suitable for marker pose estimation.
It requires 4 coplanar object points defined in the following order:
- point 0: [-squareLength / 2, squareLength / 2, 0]
- point 1: [ squareLength / 2, squareLength / 2, 0]
- point 2: [ squareLength / 2, -squareLength / 2, 0]
- point 3: [-squareLength / 2, -squareLength / 2, 0]
The function estimates the object pose given a set of object points, their corresponding image projections, as well as the camera matrix and the distortion coefficients, see the figure below (more precisely, the X-axis of the camera frame is pointing to the right, the Y-axis downward and the Z-axis forward).
![](pnp.jpg)
Points expressed in the world frame \bf{X}_w
are projected into the image plane \left[ u, v \right]
using the perspective projection model \Pi
and the camera intrinsic parameters matrix \bf{A}
:
\[
\begin{align*}
\begin{bmatrix}
u \\
v \\
1
\end{bmatrix} &=
\bf{A} \hspace{0.1em} \Pi \hspace{0.2em} ^{c}\bf{T}_w
\begin{bmatrix}
X_{w} \\
Y_{w} \\
Z_{w} \\
1
\end{bmatrix} \\
\begin{bmatrix}
u \\
v \\
1
\end{bmatrix} &=
\begin{bmatrix}
f_x & 0 & c_x \\
0 & f_y & c_y \\
0 & 0 & 1
\end{bmatrix}
\begin{bmatrix}
1 & 0 & 0 & 0 \\
0 & 1 & 0 & 0 \\
0 & 0 & 1 & 0
\end{bmatrix}
\begin{bmatrix}
r_{11} & r_{12} & r_{13} & t_x \\
r_{21} & r_{22} & r_{23} & t_y \\
r_{31} & r_{32} & r_{33} & t_z \\
0 & 0 & 0 & 1
\end{bmatrix}
\begin{bmatrix}
X_{w} \\
Y_{w} \\
Z_{w} \\
1
\end{bmatrix}
\end{align*}
\]
The estimated pose is thus the rotation (rvec
) and the translation (tvec
) vectors that allow transforming
a 3D point expressed in the world frame into the camera frame:
\[
\begin{align*}
\begin{bmatrix}
X_c \\
Y_c \\
Z_c \\
1
\end{bmatrix} &=
\hspace{0.2em} ^{c}\bf{T}_w
\begin{bmatrix}
X_{w} \\
Y_{w} \\
Z_{w} \\
1
\end{bmatrix} \\
\begin{bmatrix}
X_c \\
Y_c \\
Z_c \\
1
\end{bmatrix} &=
\begin{bmatrix}
r_{11} & r_{12} & r_{13} & t_x \\
r_{21} & r_{22} & r_{23} & t_y \\
r_{31} & r_{32} & r_{33} & t_z \\
0 & 0 & 0 & 1
\end{bmatrix}
\begin{bmatrix}
X_{w} \\
Y_{w} \\
Z_{w} \\
1
\end{bmatrix}
\end{align*}
\]
\note
- An example of how to use solvePnP for planar augmented reality can be found at
opencv_source_code/samples/python/plane_ar.py
- If you are using Python:
- Numpy array slices won't work as input because solvePnP requires contiguous
arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of
modules/calib3d/src/solvepnp.cpp version 2.4.9)
- The P3P algorithm requires image points to be in an array of shape (N,1,2) due
to its calling of cv::undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
which requires 2-channel information.
- Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of
it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints =
np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
- The methods **SOLVEPNP_DLS** and **SOLVEPNP_UPNP** cannot be used as the current implementations are
unstable and sometimes give completely wrong results. If you pass one of these two
flags, **SOLVEPNP_EPNP** method will be used instead.
- The minimum number of points is 4 in the general case. In the case of **SOLVEPNP_P3P** and **SOLVEPNP_AP3P**
methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions
of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
- With **SOLVEPNP_ITERATIVE** method and useExtrinsicGuess=true
, the minimum number of points is 3 (3 points
are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the
global solution to converge.
- With **SOLVEPNP_IPPE** input points must be >= 4 and object points must be coplanar.
- With **SOLVEPNP_IPPE_SQUARE** this is a special case suitable for marker pose estimation.
Number of input points must be 4. Object points must be defined in the following order:
- point 0: [-squareLength / 2, squareLength / 2, 0]
- point 1: [ squareLength / 2, squareLength / 2, 0]
- point 2: [ squareLength / 2, -squareLength / 2, 0]
- point 3: [-squareLength / 2, -squareLength / 2, 0]
@Namespace(value="cv") @Cast(value="bool") public static boolean solvePnP(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Mat rvec, @ByVal Mat tvec)
@Namespace(value="cv") @Cast(value="bool") public static boolean solvePnP(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMat rvec, @ByVal UMat tvec, @Cast(value="bool") boolean useExtrinsicGuess, int flags)
@Namespace(value="cv") @Cast(value="bool") public static boolean solvePnP(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMat rvec, @ByVal UMat tvec)
@Namespace(value="cv") @Cast(value="bool") public static boolean solvePnP(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMat rvec, @ByVal GpuMat tvec, @Cast(value="bool") boolean useExtrinsicGuess, int flags)
@Namespace(value="cv") @Cast(value="bool") public static boolean solvePnP(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMat rvec, @ByVal GpuMat tvec)
@Namespace(value="cv") @Cast(value="bool") public static boolean solvePnPRansac(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Mat rvec, @ByVal Mat tvec, @Cast(value="bool") boolean useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, @ByVal(nullValue="cv::OutputArray(cv::noArray())") 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\imagePoints
- Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
where N is the number of points. vector\cameraMatrix
- Input camera matrix A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{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.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 matrix and the distortion coefficients. This function finds such a pose that minimizes reprojection error, that is, the sum of squared distances between the observed projections imagePoints and the projected (using \ref projectPoints ) objectPoints. The use of RANSAC makes the function resistant to outliers.
\note - An example of how to use solvePNPRansac for object detection can be found at opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/ - The default method used to estimate the camera pose for the Minimal Sample Sets step is #SOLVEPNP_EPNP. Exceptions are: - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used. - if the number of input points is equal to 4, #SOLVEPNP_P3P is used. - The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case, the method #SOLVEPNP_EPNP will be used instead.
@Namespace(value="cv") @Cast(value="bool") public static boolean solvePnPRansac(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Mat rvec, @ByVal Mat tvec)
@Namespace(value="cv") @Cast(value="bool") public static boolean solvePnPRansac(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMat rvec, @ByVal UMat tvec, @Cast(value="bool") boolean useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat inliers, int flags)
@Namespace(value="cv") @Cast(value="bool") public static boolean solvePnPRansac(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMat rvec, @ByVal UMat tvec)
@Namespace(value="cv") @Cast(value="bool") public static boolean solvePnPRansac(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMat rvec, @ByVal GpuMat tvec, @Cast(value="bool") boolean useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat inliers, int flags)
@Namespace(value="cv") @Cast(value="bool") public static boolean solvePnPRansac(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMat rvec, @ByVal GpuMat tvec)
@Namespace(value="cv") public static int solveP3P(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs, int flags)
objectPoints
- Array of object points in the object coordinate space, 3x3 1-channel or
1x3/3x1 3-channel. vector\imagePoints
- Array of corresponding image points, 3x2 1-channel or 1x3/3x1 2-channel.
vector\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.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:
- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
"Complete Solution Classification for the Perspective-Three-Point Problem" (\cite gao2003complete).
- **SOLVEPNP_AP3P** Method is based on the paper of T. Ke and S. Roumeliotis.
"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (\cite Ke17).
The function estimates the object pose given 3 object points, their corresponding image projections, as well as the camera matrix and the distortion coefficients.
\note The solutions are sorted by reprojection errors (lowest to highest).
@Namespace(value="cv") public static int solveP3P(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs, int flags)
@Namespace(value="cv") public static int solveP3P(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs, int flags)
@Namespace(value="cv") public static int solveP3P(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs, int flags)
@Namespace(value="cv") public static int solveP3P(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs, int flags)
@Namespace(value="cv") public static int solveP3P(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs, int flags)
@Namespace(value="cv") public static int solveP3P(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs, int flags)
@Namespace(value="cv") public static int solveP3P(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs, int flags)
@Namespace(value="cv") public static int solveP3P(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs, int flags)
@Namespace(value="cv") public static void solvePnPRefineLM(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Mat rvec, @ByVal Mat tvec, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 20, FLT_EPSILON)") 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\imagePoints
- Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
where N is the number of points. vector\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.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 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.
@Namespace(value="cv") public static void solvePnPRefineLM(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Mat rvec, @ByVal Mat tvec)
@Namespace(value="cv") public static void solvePnPRefineLM(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMat rvec, @ByVal UMat tvec, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 20, FLT_EPSILON)") TermCriteria criteria)
@Namespace(value="cv") public static void solvePnPRefineLM(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMat rvec, @ByVal UMat tvec)
@Namespace(value="cv") public static void solvePnPRefineLM(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMat rvec, @ByVal GpuMat tvec, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 20, FLT_EPSILON)") TermCriteria criteria)
@Namespace(value="cv") public static void solvePnPRefineLM(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMat rvec, @ByVal GpuMat tvec)
@Namespace(value="cv") public static void solvePnPRefineVVS(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Mat rvec, @ByVal Mat tvec, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 20, FLT_EPSILON)") 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\imagePoints
- Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
where N is the number of points. vector\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.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 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.
@Namespace(value="cv") public static void solvePnPRefineVVS(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Mat rvec, @ByVal Mat tvec)
@Namespace(value="cv") public static void solvePnPRefineVVS(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMat rvec, @ByVal UMat tvec, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 20, FLT_EPSILON)") TermCriteria criteria, double VVSlambda)
@Namespace(value="cv") public static void solvePnPRefineVVS(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMat rvec, @ByVal UMat tvec)
@Namespace(value="cv") public static void solvePnPRefineVVS(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMat rvec, @ByVal GpuMat tvec, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 20, FLT_EPSILON)") TermCriteria criteria, double VVSlambda)
@Namespace(value="cv") public static void solvePnPRefineVVS(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMat rvec, @ByVal GpuMat tvec)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs, @Cast(value="bool") boolean useExtrinsicGuess, @Cast(value="cv::SolvePnPMethod") int flags, @ByVal(nullValue="cv::InputArray(cv::noArray())") Mat rvec, @ByVal(nullValue="cv::InputArray(cv::noArray())") Mat tvec, @ByVal(nullValue="cv::OutputArray(cv::noArray())") 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\imagePoints
- Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
where N is the number of points. vector\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.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:
- **SOLVEPNP_ITERATIVE** Iterative method is based on a Levenberg-Marquardt optimization. In
this case the 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
projectPoints ) objectPoints .
- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
"Complete Solution Classification for the Perspective-Three-Point Problem" (\cite gao2003complete).
In this case the function requires exactly four object and image points.
- **SOLVEPNP_AP3P** Method is based on the paper of T. Ke, S. Roumeliotis
"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (\cite Ke17).
In this case the function requires exactly four object and image points.
- **SOLVEPNP_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the
paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" (\cite lepetit2009epnp).
- **SOLVEPNP_DLS** Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis.
"A Direct Least-Squares (DLS) Method for PnP" (\cite hesch2011direct).
- **SOLVEPNP_UPNP** Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto,
F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length
Estimation" (\cite penate2013exhaustive). In this case the function also estimates the parameters f_x
and f_y
assuming that both have the same value. Then the cameraMatrix is updated with the estimated
focal length.
- **SOLVEPNP_IPPE** Method is based on the paper of T. Collins and A. Bartoli.
"Infinitesimal Plane-Based Pose Estimation" (\cite Collins14). This method requires coplanar object points.
- **SOLVEPNP_IPPE_SQUARE** Method is based on the paper of Toby Collins and Adrien Bartoli.
"Infinitesimal Plane-Based Pose Estimation" (\cite Collins14). This method is suitable for marker pose estimation.
It requires 4 coplanar object points defined in the following order:
- point 0: [-squareLength / 2, squareLength / 2, 0]
- point 1: [ squareLength / 2, squareLength / 2, 0]
- point 2: [ squareLength / 2, -squareLength / 2, 0]
- point 3: [-squareLength / 2, -squareLength / 2, 0]rvec
- Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE
and useExtrinsicGuess is set to true.tvec
- Translation vector used to initialize an iterative PnP refinement algorithm, when flag is 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.
The function estimates the object pose given a set of object points, their corresponding image projections, as well as the camera matrix and the distortion coefficients, see the figure below (more precisely, the X-axis of the camera frame is pointing to the right, the Y-axis downward and the Z-axis forward).
![](pnp.jpg)
Points expressed in the world frame \bf{X}_w
are projected into the image plane \left[ u, v \right]
using the perspective projection model \Pi
and the camera intrinsic parameters matrix \bf{A}
:
\[
\begin{align*}
\begin{bmatrix}
u \\
v \\
1
\end{bmatrix} &=
\bf{A} \hspace{0.1em} \Pi \hspace{0.2em} ^{c}\bf{T}_w
\begin{bmatrix}
X_{w} \\
Y_{w} \\
Z_{w} \\
1
\end{bmatrix} \\
\begin{bmatrix}
u \\
v \\
1
\end{bmatrix} &=
\begin{bmatrix}
f_x & 0 & c_x \\
0 & f_y & c_y \\
0 & 0 & 1
\end{bmatrix}
\begin{bmatrix}
1 & 0 & 0 & 0 \\
0 & 1 & 0 & 0 \\
0 & 0 & 1 & 0
\end{bmatrix}
\begin{bmatrix}
r_{11} & r_{12} & r_{13} & t_x \\
r_{21} & r_{22} & r_{23} & t_y \\
r_{31} & r_{32} & r_{33} & t_z \\
0 & 0 & 0 & 1
\end{bmatrix}
\begin{bmatrix}
X_{w} \\
Y_{w} \\
Z_{w} \\
1
\end{bmatrix}
\end{align*}
\]
The estimated pose is thus the rotation (rvec
) and the translation (tvec
) vectors that allow transforming
a 3D point expressed in the world frame into the camera frame:
\[
\begin{align*}
\begin{bmatrix}
X_c \\
Y_c \\
Z_c \\
1
\end{bmatrix} &=
\hspace{0.2em} ^{c}\bf{T}_w
\begin{bmatrix}
X_{w} \\
Y_{w} \\
Z_{w} \\
1
\end{bmatrix} \\
\begin{bmatrix}
X_c \\
Y_c \\
Z_c \\
1
\end{bmatrix} &=
\begin{bmatrix}
r_{11} & r_{12} & r_{13} & t_x \\
r_{21} & r_{22} & r_{23} & t_y \\
r_{31} & r_{32} & r_{33} & t_z \\
0 & 0 & 0 & 1
\end{bmatrix}
\begin{bmatrix}
X_{w} \\
Y_{w} \\
Z_{w} \\
1
\end{bmatrix}
\end{align*}
\]
\note
- An example of how to use solvePnP for planar augmented reality can be found at
opencv_source_code/samples/python/plane_ar.py
- If you are using Python:
- Numpy array slices won't work as input because solvePnP requires contiguous
arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of
modules/calib3d/src/solvepnp.cpp version 2.4.9)
- The P3P algorithm requires image points to be in an array of shape (N,1,2) due
to its calling of cv::undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
which requires 2-channel information.
- Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of
it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints =
np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
- The methods **SOLVEPNP_DLS** and **SOLVEPNP_UPNP** cannot be used as the current implementations are
unstable and sometimes give completely wrong results. If you pass one of these two
flags, **SOLVEPNP_EPNP** method will be used instead.
- The minimum number of points is 4 in the general case. In the case of **SOLVEPNP_P3P** and **SOLVEPNP_AP3P**
methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions
of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
- With **SOLVEPNP_ITERATIVE** method and useExtrinsicGuess=true
, the minimum number of points is 3 (3 points
are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the
global solution to converge.
- With **SOLVEPNP_IPPE** input points must be >= 4 and object points must be coplanar.
- With **SOLVEPNP_IPPE_SQUARE** this is a special case suitable for marker pose estimation.
Number of input points must be 4. Object points must be defined in the following order:
- point 0: [-squareLength / 2, squareLength / 2, 0]
- point 1: [ squareLength / 2, squareLength / 2, 0]
- point 2: [ squareLength / 2, -squareLength / 2, 0]
- point 3: [-squareLength / 2, -squareLength / 2, 0]
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs, @Cast(value="bool") boolean useExtrinsicGuess, @Cast(value="cv::SolvePnPMethod") int flags, @ByVal(nullValue="cv::InputArray(cv::noArray())") Mat rvec, @ByVal(nullValue="cv::InputArray(cv::noArray())") Mat tvec, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat reprojectionError)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs, @Cast(value="bool") boolean useExtrinsicGuess, @Cast(value="cv::SolvePnPMethod") int flags, @ByVal(nullValue="cv::InputArray(cv::noArray())") Mat rvec, @ByVal(nullValue="cv::InputArray(cv::noArray())") Mat tvec, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat reprojectionError)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs, @Cast(value="bool") boolean useExtrinsicGuess, @Cast(value="cv::SolvePnPMethod") int flags, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat rvec, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat tvec, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat reprojectionError)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs, @Cast(value="bool") boolean useExtrinsicGuess, @Cast(value="cv::SolvePnPMethod") int flags, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat rvec, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat tvec, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat reprojectionError)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs, @Cast(value="bool") boolean useExtrinsicGuess, @Cast(value="cv::SolvePnPMethod") int flags, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat rvec, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat tvec, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat reprojectionError)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs, @Cast(value="bool") boolean useExtrinsicGuess, @Cast(value="cv::SolvePnPMethod") int flags, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat rvec, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat tvec, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat reprojectionError)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs, @Cast(value="bool") boolean useExtrinsicGuess, @Cast(value="cv::SolvePnPMethod") int flags, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat rvec, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat tvec, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat reprojectionError)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs, @Cast(value="bool") boolean useExtrinsicGuess, @Cast(value="cv::SolvePnPMethod") int flags, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat rvec, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat tvec, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat reprojectionError)
@Namespace(value="cv") public static int solvePnPGeneric(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs)
@Namespace(value="cv") @ByVal public static Mat initCameraMatrix2D(@ByVal Point3fVectorVector objectPoints, @ByVal Point2fVectorVector imagePoints, @ByVal 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 * \texttt{aspectRatio}
.
The function estimates and returns an initial camera 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.
@Namespace(value="cv") @ByVal public static Mat initCameraMatrix2D(@ByVal Point3fVectorVector objectPoints, @ByVal Point2fVectorVector imagePoints, @ByVal Size imageSize)
@Namespace(value="cv") @Cast(value="bool") public static boolean findChessboardCorners(@ByVal Mat image, @ByVal Size patternSize, @ByVal Mat 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:
- **CALIB_CB_ADAPTIVE_THRESH** Use adaptive thresholding to convert the image to black
and white, rather than a fixed threshold level (computed from the average image brightness).
- **CALIB_CB_NORMALIZE_IMAGE** Normalize the image gamma with equalizeHist before
applying fixed or adaptive thresholding.
- **CALIB_CB_FILTER_QUADS** Use additional criteria (like contour area, perimeter,
square-like shape) to filter out false quads extracted at the contour retrieval stage.
- **CALIB_CB_FAST_CHECK** Run a fast check on the image that looks for chessboard corners,
and shortcut the call if none is found. This can drastically speed up the call in the
degenerate condition when no chessboard is observed.
The function attempts to determine whether the input image is a view of the chessboard pattern and locate the internal chessboard corners. The function returns a non-zero value if all of the corners are found and they are placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black squares touch each other. The detected coordinates are approximate, and to determine their positions more accurately, the function calls cornerSubPix. You also may use the function cornerSubPix with different parameters if returned coordinates are not accurate enough.
Sample usage of detecting and drawing chessboard 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.@Namespace(value="cv") @Cast(value="bool") public static boolean findChessboardCorners(@ByVal Mat image, @ByVal Size patternSize, @ByVal Mat corners)
@Namespace(value="cv") @Cast(value="bool") public static boolean findChessboardCorners(@ByVal UMat image, @ByVal Size patternSize, @ByVal UMat corners, int flags)
@Namespace(value="cv") @Cast(value="bool") public static boolean findChessboardCorners(@ByVal UMat image, @ByVal Size patternSize, @ByVal UMat corners)
@Namespace(value="cv") @Cast(value="bool") public static boolean findChessboardCorners(@ByVal GpuMat image, @ByVal Size patternSize, @ByVal GpuMat corners, int flags)
@Namespace(value="cv") @Cast(value="bool") public static boolean findChessboardCorners(@ByVal GpuMat image, @ByVal Size patternSize, @ByVal GpuMat corners)
@Namespace(value="cv") @Cast(value="bool") public static boolean checkChessboard(@ByVal Mat img, @ByVal Size size)
@Namespace(value="cv") @Cast(value="bool") public static boolean checkChessboard(@ByVal UMat img, @ByVal Size size)
@Namespace(value="cv") @Cast(value="bool") public static boolean checkChessboard(@ByVal GpuMat img, @ByVal Size size)
@Namespace(value="cv") @Cast(value="bool") @Name(value="findChessboardCornersSB") public static boolean findChessboardCornersSBWithMeta(@ByVal Mat image, @ByVal Size patternSize, @ByVal Mat corners, int flags, @ByVal 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:
- **CALIB_CB_NORMALIZE_IMAGE** Normalize the image gamma with equalizeHist before detection.
- **CALIB_CB_EXHAUSTIVE** Run an exhaustive search to improve detection rate.
- **CALIB_CB_ACCURACY** Up sample input image to improve sub-pixel accuracy due to aliasing effects.
- **CALIB_CB_LARGER** The detected pattern is allowed to be larger than patternSize (see description).
- **CALIB_CB_MARKER** The detected pattern must have a marker (see description).
This should be used if an accurate camera calibration is required.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:
- 0 = no meta data attached
- 1 = left-top corner of a black cell
- 2 = left-top corner of a white cell
- 3 = left-top corner of a black cell with a white marker dot
- 4 = left-top corner of a white cell with a black marker dot (pattern origin in case of markers otherwise first corner)
The function is analog to findchessboardCorners but uses a localized radon transformation approximated by box filters being more robust to all sort of noise, faster on larger images and is able to directly return the sub-pixel position of the internal chessboard corners. The Method is based on the paper \cite duda2018 "Accurate Detection and Localization of Checkerboard Corners for Calibration" demonstrating that the returned sub-pixel positions are more accurate than the one returned by cornerSubPix allowing a precise camera calibration for demanding applications.
In the case, the flags **CALIB_CB_LARGER** or **CALIB_CB_MARKER** are given, the result can be recovered from the optional meta array. Both flags are helpful to use calibration patterns exceeding the field of view of the camera. These oversized patterns allow more accurate calibrations as corners can be utilized, which are as close as possible to the image borders. For a consistent coordinate system across all images, the optional marker (see image below) can be used to move the origin of the board to the location where the black circle is located.
\note The function requires a white boarder with roughly the same width as one of the checkerboard fields around the whole board to improve the detection in various environments. In addition, because of the localized radon transformation it is beneficial to use round corners for the field corners which are located on the outside of the board. The following figure illustrates a sample checkerboard optimized for the detection. However, any other checkerboard can be used as well. ![Checkerboard](pics/checkerboard_radon.png)
@Namespace(value="cv") @Cast(value="bool") @Name(value="findChessboardCornersSB") public static boolean findChessboardCornersSBWithMeta(@ByVal UMat image, @ByVal Size patternSize, @ByVal UMat corners, int flags, @ByVal UMat meta)
@Namespace(value="cv") @Cast(value="bool") @Name(value="findChessboardCornersSB") public static boolean findChessboardCornersSBWithMeta(@ByVal GpuMat image, @ByVal Size patternSize, @ByVal GpuMat corners, int flags, @ByVal GpuMat meta)
@Namespace(value="cv") @Cast(value="bool") public static boolean findChessboardCornersSB(@ByVal Mat image, @ByVal Size patternSize, @ByVal Mat corners, int flags)
@Namespace(value="cv") @Cast(value="bool") public static boolean findChessboardCornersSB(@ByVal Mat image, @ByVal Size patternSize, @ByVal Mat corners)
@Namespace(value="cv") @Cast(value="bool") public static boolean findChessboardCornersSB(@ByVal UMat image, @ByVal Size patternSize, @ByVal UMat corners, int flags)
@Namespace(value="cv") @Cast(value="bool") public static boolean findChessboardCornersSB(@ByVal UMat image, @ByVal Size patternSize, @ByVal UMat corners)
@Namespace(value="cv") @Cast(value="bool") public static boolean findChessboardCornersSB(@ByVal GpuMat image, @ByVal Size patternSize, @ByVal GpuMat corners, int flags)
@Namespace(value="cv") @Cast(value="bool") public static boolean findChessboardCornersSB(@ByVal GpuMat image, @ByVal Size patternSize, @ByVal GpuMat corners)
@Namespace(value="cv") @ByVal public static Scalar estimateChessboardSharpness(@ByVal Mat image, @ByVal Size patternSize, @ByVal Mat corners, float rise_distance, @Cast(value="bool") boolean vertical, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat sharpness)
Image sharpness, as well as brightness, are a critical parameter for accuracte camera calibration. For accessing these parameters for filtering out problematic calibraiton images, this method calculates edge profiles by traveling from black to white chessboard cell centers. Based on this, the number of pixels is calculated required to transit from black to white. This width of the transition area is a good indication of how sharp the chessboard is imaged and should be below ~3.0 pixels.
image
- Gray image used to find chessboard cornerspatternSize
- Size of a found chessboard patterncorners
- Corners found by findChessboardCorners(SB)rise_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)
@Namespace(value="cv") @ByVal public static Scalar estimateChessboardSharpness(@ByVal Mat image, @ByVal Size patternSize, @ByVal Mat corners)
@Namespace(value="cv") @ByVal public static Scalar estimateChessboardSharpness(@ByVal UMat image, @ByVal Size patternSize, @ByVal UMat corners, float rise_distance, @Cast(value="bool") boolean vertical, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat sharpness)
@Namespace(value="cv") @ByVal public static Scalar estimateChessboardSharpness(@ByVal UMat image, @ByVal Size patternSize, @ByVal UMat corners)
@Namespace(value="cv") @ByVal public static Scalar estimateChessboardSharpness(@ByVal GpuMat image, @ByVal Size patternSize, @ByVal GpuMat corners, float rise_distance, @Cast(value="bool") boolean vertical, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat sharpness)
@Namespace(value="cv") @ByVal public static Scalar estimateChessboardSharpness(@ByVal GpuMat image, @ByVal Size patternSize, @ByVal GpuMat corners)
@Namespace(value="cv") @Cast(value="bool") public static boolean find4QuadCornerSubpix(@ByVal Mat img, @ByVal Mat corners, @ByVal Size region_size)
@Namespace(value="cv") @Cast(value="bool") public static boolean find4QuadCornerSubpix(@ByVal UMat img, @ByVal UMat corners, @ByVal Size region_size)
@Namespace(value="cv") @Cast(value="bool") public static boolean find4QuadCornerSubpix(@ByVal GpuMat img, @ByVal GpuMat corners, @ByVal Size region_size)
@Namespace(value="cv") public static void drawChessboardCorners(@ByVal Mat image, @ByVal Size patternSize, @ByVal Mat corners, @Cast(value="bool") 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.
@Namespace(value="cv") public static void drawChessboardCorners(@ByVal UMat image, @ByVal Size patternSize, @ByVal UMat corners, @Cast(value="bool") boolean patternWasFound)
@Namespace(value="cv") public static void drawChessboardCorners(@ByVal GpuMat image, @ByVal Size patternSize, @ByVal GpuMat corners, @Cast(value="bool") boolean patternWasFound)
@Namespace(value="cv") public static void drawFrameAxes(@ByVal Mat image, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Mat rvec, @ByVal 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.
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 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.
@Namespace(value="cv") public static void drawFrameAxes(@ByVal Mat image, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Mat rvec, @ByVal Mat tvec, float length)
@Namespace(value="cv") public static void drawFrameAxes(@ByVal UMat image, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMat rvec, @ByVal UMat tvec, float length, int thickness)
@Namespace(value="cv") public static void drawFrameAxes(@ByVal UMat image, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMat rvec, @ByVal UMat tvec, float length)
@Namespace(value="cv") public static void drawFrameAxes(@ByVal GpuMat image, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMat rvec, @ByVal GpuMat tvec, float length, int thickness)
@Namespace(value="cv") public static void drawFrameAxes(@ByVal GpuMat image, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMat rvec, @ByVal GpuMat tvec, float length)
@Namespace(value="cv") @Cast(value="bool") public static boolean findCirclesGrid(@ByVal Mat image, @ByVal Size patternSize, @ByVal Mat centers, int flags, @Cast(value="cv::FeatureDetector*") @opencv_core.Ptr Feature2D blobDetector, @Const @ByRef CirclesGridFinderParameters parameters)
image
- grid view of input circles; it must be an 8-bit grayscale or color image.patternSize
- number of circles per row and column
( patternSize = Size(points_per_row, points_per_colum) ).centers
- output array of detected centers.flags
- various operation flags that can be one of the following values:
- **CALIB_CB_SYMMETRIC_GRID** uses symmetric pattern of circles.
- **CALIB_CB_ASYMMETRIC_GRID** uses asymmetric pattern of circles.
- **CALIB_CB_CLUSTERING** uses a special algorithm for grid detection. It is more robust to
perspective distortions but much more sensitive to background clutter.blobDetector
- feature detector that finds blobs like dark circles on light background.parameters
- struct for finding circles in a grid pattern.
The function attempts to determine whether the input image contains a grid of circles. If it is, the function locates centers of the circles. The function returns a non-zero value if all of the centers have been found and they have been placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0.
Sample usage of detecting and drawing the centers of circles: :
Size patternsize(7,7); //number of centers
Mat gray = ....; //source image
vector<Point2f> centers; //this will be filled by the detected centers
bool patternfound = findCirclesGrid(gray, patternsize, centers);
drawChessboardCorners(img, patternsize, Mat(centers), 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.@Namespace(value="cv") @Cast(value="bool") public static boolean findCirclesGrid(@ByVal UMat image, @ByVal Size patternSize, @ByVal UMat centers, int flags, @Cast(value="cv::FeatureDetector*") @opencv_core.Ptr Feature2D blobDetector, @Const @ByRef CirclesGridFinderParameters parameters)
@Namespace(value="cv") @Cast(value="bool") public static boolean findCirclesGrid(@ByVal GpuMat image, @ByVal Size patternSize, @ByVal GpuMat centers, int flags, @Cast(value="cv::FeatureDetector*") @opencv_core.Ptr Feature2D blobDetector, @Const @ByRef CirclesGridFinderParameters parameters)
@Namespace(value="cv") @Cast(value="bool") public static boolean findCirclesGrid(@ByVal Mat image, @ByVal Size patternSize, @ByVal Mat centers, int flags, @Cast(value="cv::FeatureDetector*") @opencv_core.Ptr Feature2D blobDetector)
@Namespace(value="cv") @Cast(value="bool") public static boolean findCirclesGrid(@ByVal Mat image, @ByVal Size patternSize, @ByVal Mat centers)
@Namespace(value="cv") @Cast(value="bool") public static boolean findCirclesGrid(@ByVal UMat image, @ByVal Size patternSize, @ByVal UMat centers, int flags, @Cast(value="cv::FeatureDetector*") @opencv_core.Ptr Feature2D blobDetector)
@Namespace(value="cv") @Cast(value="bool") public static boolean findCirclesGrid(@ByVal UMat image, @ByVal Size patternSize, @ByVal UMat centers)
@Namespace(value="cv") @Cast(value="bool") public static boolean findCirclesGrid(@ByVal GpuMat image, @ByVal Size patternSize, @ByVal GpuMat centers, int flags, @Cast(value="cv::FeatureDetector*") @opencv_core.Ptr Feature2D blobDetector)
@Namespace(value="cv") @Cast(value="bool") public static boolean findCirclesGrid(@ByVal GpuMat image, @ByVal Size patternSize, @ByVal GpuMat centers)
@Namespace(value="cv") @Name(value="calibrateCamera") public static double calibrateCameraExtended(@ByVal Point3fVectorVector objectPoints, @ByVal Point2fVectorVector imagePoints, @ByVal Size imageSize, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs, @ByVal Mat stdDeviationsIntrinsics, @ByVal Mat stdDeviationsExtrinsics, @ByVal Mat perViewErrors, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 30, DBL_EPSILON)") 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::vectorimagePoints
- In the new interface it is a vector of vectors of the projections of calibration
pattern points (e.g. std::vectorimageSize
- Size of the image used only to initialize the intrinsic camera matrix.cameraMatrix
- Input/output 3x3 floating-point camera matrix
A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}
. If CV\_CALIB\_USE\_INTRINSIC\_GUESS
and/or CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be
initialized before calling the function.distCoeffs
- Input/output 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.rvecs
- Output vector of rotation vectors (\ref Rodrigues ) estimated for each pattern view
(e.g. std::vectortvecs
- 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:
- **CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of
fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
center ( imageSize is used), and focal distances are computed in a least-squares fashion.
Note, that if intrinsic parameters are known, there is no need to use this function just to
estimate extrinsic parameters. Use solvePnP instead.
- **CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global
optimization. It stays at the center or at a different location specified when
CALIB_USE_INTRINSIC_GUESS is set too.
- **CALIB_FIX_ASPECT_RATIO** The functions consider only fy as a free parameter. The
ratio fx/fy stays the same as in the input cameraMatrix . When
CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are
ignored, only their ratio is computed and used further.
- **CALIB_ZERO_TANGENT_DIST** Tangential distortion coefficients (p_1, p_2)
are set
to zeros and stay zero.
- **CALIB_FIX_K1,...,CALIB_FIX_K6** The corresponding radial distortion
coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is
set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
- **CALIB_RATIONAL_MODEL** Coefficients k4, k5, and k6 are enabled. To provide the
backward compatibility, this extra flag should be explicitly specified to make the
calibration function use the rational model and return 8 coefficients. If the flag is not
set, the function computes and returns only 5 distortion coefficients.
- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the
backward compatibility, this extra flag should be explicitly specified to make the
calibration function use the thin prism model and return 12 coefficients. If the flag is not
set, the function computes and returns only 5 distortion coefficients.
- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during
the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
supplied distCoeffs matrix is used. Otherwise, it is set to 0.
- **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the
backward compatibility, this extra flag should be explicitly specified to make the
calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
set, the function computes and returns only 5 distortion coefficients.
- **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during
the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
supplied distCoeffs matrix is used. Otherwise, it is set to 0.criteria
- Termination criteria for the iterative optimization algorithm.
The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on \cite Zhang2000 and \cite BouguetMCT . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see \ref findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.
The algorithm performs the following steps:
- Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
- Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using solvePnP .
- Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See projectPoints for details.
\note
If you use a non-square (i.e. non-N-by-N) grid and \ref findChessboardCorners for calibration,
and \ref calibrateCamera returns bad values (zero distortion coefficients, c_x
and
c_y
very far from the image center, and/or large differences between f_x
and
f_y
(ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols)
instead of using patternSize=cvSize(cols,rows) in \ref findChessboardCorners.
calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate,
undistort
@Namespace(value="cv") @Name(value="calibrateCamera") public static double calibrateCameraExtended(@ByVal Point3fVectorVector objectPoints, @ByVal Point2fVectorVector imagePoints, @ByVal Size imageSize, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs, @ByVal Mat stdDeviationsIntrinsics, @ByVal Mat stdDeviationsExtrinsics, @ByVal Mat perViewErrors)
@Namespace(value="cv") public static double calibrateCamera(@ByVal Point3fVectorVector objectPoints, @ByVal Point2fVectorVector imagePoints, @ByVal Size imageSize, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 30, DBL_EPSILON)") TermCriteria criteria)
@Namespace(value="cv") public static double calibrateCamera(@ByVal Point3fVectorVector objectPoints, @ByVal Point2fVectorVector imagePoints, @ByVal Size imageSize, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs)
@Namespace(value="cv") @Name(value="calibrateCameraRO") public static double calibrateCameraROExtended(@ByVal Point3fVectorVector objectPoints, @ByVal Point2fVectorVector imagePoints, @ByVal Size imageSize, int iFixedPoint, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs, @ByVal Mat newObjPoints, @ByVal Mat stdDeviationsIntrinsics, @ByVal Mat stdDeviationsExtrinsics, @ByVal Mat stdDeviationsObjPoints, @ByVal Mat perViewErrors, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 30, DBL_EPSILON)") TermCriteria criteria)
This function is an extension of calibrateCamera() with the method of releasing object which was proposed in \cite strobl2011iccv. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter **iFixedPoint** for method selection. In the internal implementation, calibrateCamera() is a wrapper for this function.
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.
The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on \cite Zhang2000, \cite BouguetMCT and \cite strobl2011iccv. See calibrateCamera() for other detailed explanations.
calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort
@Namespace(value="cv") @Name(value="calibrateCameraRO") public static double calibrateCameraROExtended(@ByVal Point3fVectorVector objectPoints, @ByVal Point2fVectorVector imagePoints, @ByVal Size imageSize, int iFixedPoint, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs, @ByVal Mat newObjPoints, @ByVal Mat stdDeviationsIntrinsics, @ByVal Mat stdDeviationsExtrinsics, @ByVal Mat stdDeviationsObjPoints, @ByVal Mat perViewErrors)
@Namespace(value="cv") public static double calibrateCameraRO(@ByVal Point3fVectorVector objectPoints, @ByVal Point2fVectorVector imagePoints, @ByVal Size imageSize, int iFixedPoint, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs, @ByVal Mat newObjPoints, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 30, DBL_EPSILON)") TermCriteria criteria)
@Namespace(value="cv") public static double calibrateCameraRO(@ByVal Point3fVectorVector objectPoints, @ByVal Point2fVectorVector imagePoints, @ByVal Size imageSize, int iFixedPoint, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal MatVector rvecs, @ByVal MatVector tvecs, @ByVal Mat newObjPoints)
@Namespace(value="cv") public static void calibrationMatrixValues(@ByVal Mat cameraMatrix, @ByVal Size imageSize, double apertureWidth, double apertureHeight, @ByRef DoublePointer fovx, @ByRef DoublePointer fovy, @ByRef DoublePointer focalLength, @ByRef Point2d principalPoint, @ByRef DoublePointer aspectRatio)
cameraMatrix
- Input camera 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).
@Namespace(value="cv") public static void calibrationMatrixValues(@ByVal Mat cameraMatrix, @ByVal Size imageSize, double apertureWidth, double apertureHeight, @ByRef DoubleBuffer fovx, @ByRef DoubleBuffer fovy, @ByRef DoubleBuffer focalLength, @ByRef Point2d principalPoint, @ByRef DoubleBuffer aspectRatio)
@Namespace(value="cv") public static void calibrationMatrixValues(@ByVal Mat cameraMatrix, @ByVal Size imageSize, double apertureWidth, double apertureHeight, @ByRef double[] fovx, @ByRef double[] fovy, @ByRef double[] focalLength, @ByRef Point2d principalPoint, @ByRef double[] aspectRatio)
@Namespace(value="cv") public static void calibrationMatrixValues(@ByVal UMat cameraMatrix, @ByVal Size imageSize, double apertureWidth, double apertureHeight, @ByRef DoublePointer fovx, @ByRef DoublePointer fovy, @ByRef DoublePointer focalLength, @ByRef Point2d principalPoint, @ByRef DoublePointer aspectRatio)
@Namespace(value="cv") public static void calibrationMatrixValues(@ByVal UMat cameraMatrix, @ByVal Size imageSize, double apertureWidth, double apertureHeight, @ByRef DoubleBuffer fovx, @ByRef DoubleBuffer fovy, @ByRef DoubleBuffer focalLength, @ByRef Point2d principalPoint, @ByRef DoubleBuffer aspectRatio)
@Namespace(value="cv") public static void calibrationMatrixValues(@ByVal UMat cameraMatrix, @ByVal Size imageSize, double apertureWidth, double apertureHeight, @ByRef double[] fovx, @ByRef double[] fovy, @ByRef double[] focalLength, @ByRef Point2d principalPoint, @ByRef double[] aspectRatio)
@Namespace(value="cv") public static void calibrationMatrixValues(@ByVal GpuMat cameraMatrix, @ByVal Size imageSize, double apertureWidth, double apertureHeight, @ByRef DoublePointer fovx, @ByRef DoublePointer fovy, @ByRef DoublePointer focalLength, @ByRef Point2d principalPoint, @ByRef DoublePointer aspectRatio)
@Namespace(value="cv") public static void calibrationMatrixValues(@ByVal GpuMat cameraMatrix, @ByVal Size imageSize, double apertureWidth, double apertureHeight, @ByRef DoubleBuffer fovx, @ByRef DoubleBuffer fovy, @ByRef DoubleBuffer focalLength, @ByRef Point2d principalPoint, @ByRef DoubleBuffer aspectRatio)
@Namespace(value="cv") public static void calibrationMatrixValues(@ByVal GpuMat cameraMatrix, @ByVal Size imageSize, double apertureWidth, double apertureHeight, @ByRef double[] fovx, @ByRef double[] fovy, @ByRef double[] focalLength, @ByRef Point2d principalPoint, @ByRef double[] aspectRatio)
@Namespace(value="cv") @Name(value="stereoCalibrate") public static double stereoCalibrateExtended(@ByVal Point3fVectorVector objectPoints, @ByVal Point2fVectorVector imagePoints1, @ByVal Point2fVectorVector imagePoints2, @ByVal Mat cameraMatrix1, @ByVal Mat distCoeffs1, @ByVal Mat cameraMatrix2, @ByVal Mat distCoeffs2, @ByVal Size imageSize, @ByVal Mat R, @ByVal Mat T, @ByVal Mat E, @ByVal Mat F, @ByVal Mat perViewErrors, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 30, 1e-6)") 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 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 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 intrinsic camera 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.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:
- **CALIB_FIX_INTRINSIC** Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F
matrices are estimated.
- **CALIB_USE_INTRINSIC_GUESS** Optimize some or all of the intrinsic parameters
according to the specified flags. Initial values are provided by the user.
- **CALIB_USE_EXTRINSIC_GUESS** R and T contain valid initial values that are optimized further.
Otherwise R and T are initialized to the median value of the pattern views (each dimension separately).
- **CALIB_FIX_PRINCIPAL_POINT** Fix the principal points during the optimization.
- **CALIB_FIX_FOCAL_LENGTH** Fix f^{(j)}_x
and f^{(j)}_y
.
- **CALIB_FIX_ASPECT_RATIO** Optimize f^{(j)}_y
. Fix the ratio f^{(j)}_x/f^{(j)}_y
.
- **CALIB_SAME_FOCAL_LENGTH** Enforce f^{(0)}_x=f^{(1)}_x
and f^{(0)}_y=f^{(1)}_y
.
- **CALIB_ZERO_TANGENT_DIST** Set tangential distortion coefficients for each camera to
zeros and fix there.
- **CALIB_FIX_K1,...,CALIB_FIX_K6** Do not change the corresponding radial
distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set,
the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
- **CALIB_RATIONAL_MODEL** Enable coefficients k4, k5, and k6. To provide the backward
compatibility, this extra flag should be explicitly specified to make the calibration
function use the rational model and return 8 coefficients. If the flag is not set, the
function computes and returns only 5 distortion coefficients.
- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the
backward compatibility, this extra flag should be explicitly specified to make the
calibration function use the thin prism model and return 12 coefficients. If the flag is not
set, the function computes and returns only 5 distortion coefficients.
- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during
the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
supplied distCoeffs matrix is used. Otherwise, it is set to 0.
- **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the
backward compatibility, this extra flag should be explicitly specified to make the
calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
set, the function computes and returns only 5 distortion coefficients.
- **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during
the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
supplied distCoeffs matrix is used. Otherwise, it is set to 0.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:
\[F = cameraMatrix2^{-T} E cameraMatrix1^{-1}\]
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 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 CALIB_SAME_FOCAL_LENGTH and 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.
@Namespace(value="cv") @Name(value="stereoCalibrate") public static double stereoCalibrateExtended(@ByVal Point3fVectorVector objectPoints, @ByVal Point2fVectorVector imagePoints1, @ByVal Point2fVectorVector imagePoints2, @ByVal Mat cameraMatrix1, @ByVal Mat distCoeffs1, @ByVal Mat cameraMatrix2, @ByVal Mat distCoeffs2, @ByVal Size imageSize, @ByVal Mat R, @ByVal Mat T, @ByVal Mat E, @ByVal Mat F, @ByVal Mat perViewErrors)
@Namespace(value="cv") public static double stereoCalibrate(@ByVal Point3fVectorVector objectPoints, @ByVal Point2fVectorVector imagePoints1, @ByVal Point2fVectorVector imagePoints2, @ByVal Mat cameraMatrix1, @ByVal Mat distCoeffs1, @ByVal Mat cameraMatrix2, @ByVal Mat distCoeffs2, @ByVal Size imageSize, @ByVal Mat R, @ByVal Mat T, @ByVal Mat E, @ByVal Mat F, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 30, 1e-6)") TermCriteria criteria)
@Namespace(value="cv") public static double stereoCalibrate(@ByVal Point3fVectorVector objectPoints, @ByVal Point2fVectorVector imagePoints1, @ByVal Point2fVectorVector imagePoints2, @ByVal Mat cameraMatrix1, @ByVal Mat distCoeffs1, @ByVal Mat cameraMatrix2, @ByVal Mat distCoeffs2, @ByVal Size imageSize, @ByVal Mat R, @ByVal Mat T, @ByVal Mat E, @ByVal Mat F)
@Namespace(value="cv") public static void stereoRectify(@ByVal Mat cameraMatrix1, @ByVal Mat distCoeffs1, @ByVal Mat cameraMatrix2, @ByVal Mat distCoeffs2, @ByVal Size imageSize, @ByVal Mat R, @ByVal Mat T, @ByVal Mat R1, @ByVal Mat R2, @ByVal Mat P1, @ByVal Mat P2, @ByVal Mat Q, int flags, double alpha, @ByVal(nullValue="cv::Size()") Size newImageSize, Rect validPixROI1, Rect validPixROI2)
cameraMatrix1
- First camera matrix.distCoeffs1
- First camera distortion parameters.cameraMatrix2
- Second camera 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 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:
- **Horizontal stereo**: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:
\[\texttt{P1} = \begin{bmatrix}
f & 0 & cx_1 & 0 \\
0 & f & cy & 0 \\
0 & 0 & 1 & 0
\end{bmatrix}\]
\[\texttt{P2} = \begin{bmatrix}
f & 0 & cx_2 & T_x*f \\
0 & f & cy & 0 \\
0 & 0 & 1 & 0
\end{bmatrix} ,\]
where T_x
is a horizontal shift between the cameras and cx_1=cx_2
if
CALIB_ZERO_DISPARITY is set.
- **Vertical stereo**: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:
\[\texttt{P1} = \begin{bmatrix}
f & 0 & cx & 0 \\
0 & f & cy_1 & 0 \\
0 & 0 & 1 & 0
\end{bmatrix}\]
\[\texttt{P2} = \begin{bmatrix}
f & 0 & cx & 0 \\
0 & f & cy_2 & T_y*f \\
0 & 0 & 1 & 0
\end{bmatrix},\]
where T_y
is a vertical shift between the cameras and cy_1=cy_2
if
CALIB_ZERO_DISPARITY is set.
As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to initialize the rectification map for each camera.
See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.
![image](pics/stereo_undistort.jpg)
@Namespace(value="cv") public static void stereoRectify(@ByVal Mat cameraMatrix1, @ByVal Mat distCoeffs1, @ByVal Mat cameraMatrix2, @ByVal Mat distCoeffs2, @ByVal Size imageSize, @ByVal Mat R, @ByVal Mat T, @ByVal Mat R1, @ByVal Mat R2, @ByVal Mat P1, @ByVal Mat P2, @ByVal Mat Q)
@Namespace(value="cv") public static void stereoRectify(@ByVal UMat cameraMatrix1, @ByVal UMat distCoeffs1, @ByVal UMat cameraMatrix2, @ByVal UMat distCoeffs2, @ByVal Size imageSize, @ByVal UMat R, @ByVal UMat T, @ByVal UMat R1, @ByVal UMat R2, @ByVal UMat P1, @ByVal UMat P2, @ByVal UMat Q, int flags, double alpha, @ByVal(nullValue="cv::Size()") Size newImageSize, Rect validPixROI1, Rect validPixROI2)
@Namespace(value="cv") public static void stereoRectify(@ByVal UMat cameraMatrix1, @ByVal UMat distCoeffs1, @ByVal UMat cameraMatrix2, @ByVal UMat distCoeffs2, @ByVal Size imageSize, @ByVal UMat R, @ByVal UMat T, @ByVal UMat R1, @ByVal UMat R2, @ByVal UMat P1, @ByVal UMat P2, @ByVal UMat Q)
@Namespace(value="cv") public static void stereoRectify(@ByVal GpuMat cameraMatrix1, @ByVal GpuMat distCoeffs1, @ByVal GpuMat cameraMatrix2, @ByVal GpuMat distCoeffs2, @ByVal Size imageSize, @ByVal GpuMat R, @ByVal GpuMat T, @ByVal GpuMat R1, @ByVal GpuMat R2, @ByVal GpuMat P1, @ByVal GpuMat P2, @ByVal GpuMat Q, int flags, double alpha, @ByVal(nullValue="cv::Size()") Size newImageSize, Rect validPixROI1, Rect validPixROI2)
@Namespace(value="cv") public static void stereoRectify(@ByVal GpuMat cameraMatrix1, @ByVal GpuMat distCoeffs1, @ByVal GpuMat cameraMatrix2, @ByVal GpuMat distCoeffs2, @ByVal Size imageSize, @ByVal GpuMat R, @ByVal GpuMat T, @ByVal GpuMat R1, @ByVal GpuMat R2, @ByVal GpuMat P1, @ByVal GpuMat P2, @ByVal GpuMat Q)
@Namespace(value="cv") @Cast(value="bool") public static boolean stereoRectifyUncalibrated(@ByVal Mat points1, @ByVal Mat points2, @ByVal Mat F, @ByVal Size imgSize, @ByVal Mat H1, @ByVal 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*\texttt{F}*\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 .
@Namespace(value="cv") @Cast(value="bool") public static boolean stereoRectifyUncalibrated(@ByVal Mat points1, @ByVal Mat points2, @ByVal Mat F, @ByVal Size imgSize, @ByVal Mat H1, @ByVal Mat H2)
@Namespace(value="cv") @Cast(value="bool") public static boolean stereoRectifyUncalibrated(@ByVal UMat points1, @ByVal UMat points2, @ByVal UMat F, @ByVal Size imgSize, @ByVal UMat H1, @ByVal UMat H2, double threshold)
@Namespace(value="cv") @Cast(value="bool") public static boolean stereoRectifyUncalibrated(@ByVal UMat points1, @ByVal UMat points2, @ByVal UMat F, @ByVal Size imgSize, @ByVal UMat H1, @ByVal UMat H2)
@Namespace(value="cv") @Cast(value="bool") public static boolean stereoRectifyUncalibrated(@ByVal GpuMat points1, @ByVal GpuMat points2, @ByVal GpuMat F, @ByVal Size imgSize, @ByVal GpuMat H1, @ByVal GpuMat H2, double threshold)
@Namespace(value="cv") @Cast(value="bool") public static boolean stereoRectifyUncalibrated(@ByVal GpuMat points1, @ByVal GpuMat points2, @ByVal GpuMat F, @ByVal Size imgSize, @ByVal GpuMat H1, @ByVal GpuMat H2)
@Namespace(value="cv") public static float rectify3Collinear(@ByVal Mat cameraMatrix1, @ByVal Mat distCoeffs1, @ByVal Mat cameraMatrix2, @ByVal Mat distCoeffs2, @ByVal Mat cameraMatrix3, @ByVal Mat distCoeffs3, @ByVal MatVector imgpt1, @ByVal MatVector imgpt3, @ByVal Size imageSize, @ByVal Mat R12, @ByVal Mat T12, @ByVal Mat R13, @ByVal Mat T13, @ByVal Mat R1, @ByVal Mat R2, @ByVal Mat R3, @ByVal Mat P1, @ByVal Mat P2, @ByVal Mat P3, @ByVal Mat Q, double alpha, @ByVal Size newImgSize, Rect roi1, Rect roi2, int flags)
@Namespace(value="cv") public static float rectify3Collinear(@ByVal Mat cameraMatrix1, @ByVal Mat distCoeffs1, @ByVal Mat cameraMatrix2, @ByVal Mat distCoeffs2, @ByVal Mat cameraMatrix3, @ByVal Mat distCoeffs3, @ByVal UMatVector imgpt1, @ByVal UMatVector imgpt3, @ByVal Size imageSize, @ByVal Mat R12, @ByVal Mat T12, @ByVal Mat R13, @ByVal Mat T13, @ByVal Mat R1, @ByVal Mat R2, @ByVal Mat R3, @ByVal Mat P1, @ByVal Mat P2, @ByVal Mat P3, @ByVal Mat Q, double alpha, @ByVal Size newImgSize, Rect roi1, Rect roi2, int flags)
@Namespace(value="cv") public static float rectify3Collinear(@ByVal Mat cameraMatrix1, @ByVal Mat distCoeffs1, @ByVal Mat cameraMatrix2, @ByVal Mat distCoeffs2, @ByVal Mat cameraMatrix3, @ByVal Mat distCoeffs3, @ByVal GpuMatVector imgpt1, @ByVal GpuMatVector imgpt3, @ByVal Size imageSize, @ByVal Mat R12, @ByVal Mat T12, @ByVal Mat R13, @ByVal Mat T13, @ByVal Mat R1, @ByVal Mat R2, @ByVal Mat R3, @ByVal Mat P1, @ByVal Mat P2, @ByVal Mat P3, @ByVal Mat Q, double alpha, @ByVal Size newImgSize, Rect roi1, Rect roi2, int flags)
@Namespace(value="cv") public static float rectify3Collinear(@ByVal UMat cameraMatrix1, @ByVal UMat distCoeffs1, @ByVal UMat cameraMatrix2, @ByVal UMat distCoeffs2, @ByVal UMat cameraMatrix3, @ByVal UMat distCoeffs3, @ByVal MatVector imgpt1, @ByVal MatVector imgpt3, @ByVal Size imageSize, @ByVal UMat R12, @ByVal UMat T12, @ByVal UMat R13, @ByVal UMat T13, @ByVal UMat R1, @ByVal UMat R2, @ByVal UMat R3, @ByVal UMat P1, @ByVal UMat P2, @ByVal UMat P3, @ByVal UMat Q, double alpha, @ByVal Size newImgSize, Rect roi1, Rect roi2, int flags)
@Namespace(value="cv") public static float rectify3Collinear(@ByVal UMat cameraMatrix1, @ByVal UMat distCoeffs1, @ByVal UMat cameraMatrix2, @ByVal UMat distCoeffs2, @ByVal UMat cameraMatrix3, @ByVal UMat distCoeffs3, @ByVal UMatVector imgpt1, @ByVal UMatVector imgpt3, @ByVal Size imageSize, @ByVal UMat R12, @ByVal UMat T12, @ByVal UMat R13, @ByVal UMat T13, @ByVal UMat R1, @ByVal UMat R2, @ByVal UMat R3, @ByVal UMat P1, @ByVal UMat P2, @ByVal UMat P3, @ByVal UMat Q, double alpha, @ByVal Size newImgSize, Rect roi1, Rect roi2, int flags)
@Namespace(value="cv") public static float rectify3Collinear(@ByVal UMat cameraMatrix1, @ByVal UMat distCoeffs1, @ByVal UMat cameraMatrix2, @ByVal UMat distCoeffs2, @ByVal UMat cameraMatrix3, @ByVal UMat distCoeffs3, @ByVal GpuMatVector imgpt1, @ByVal GpuMatVector imgpt3, @ByVal Size imageSize, @ByVal UMat R12, @ByVal UMat T12, @ByVal UMat R13, @ByVal UMat T13, @ByVal UMat R1, @ByVal UMat R2, @ByVal UMat R3, @ByVal UMat P1, @ByVal UMat P2, @ByVal UMat P3, @ByVal UMat Q, double alpha, @ByVal Size newImgSize, Rect roi1, Rect roi2, int flags)
@Namespace(value="cv") public static float rectify3Collinear(@ByVal GpuMat cameraMatrix1, @ByVal GpuMat distCoeffs1, @ByVal GpuMat cameraMatrix2, @ByVal GpuMat distCoeffs2, @ByVal GpuMat cameraMatrix3, @ByVal GpuMat distCoeffs3, @ByVal MatVector imgpt1, @ByVal MatVector imgpt3, @ByVal Size imageSize, @ByVal GpuMat R12, @ByVal GpuMat T12, @ByVal GpuMat R13, @ByVal GpuMat T13, @ByVal GpuMat R1, @ByVal GpuMat R2, @ByVal GpuMat R3, @ByVal GpuMat P1, @ByVal GpuMat P2, @ByVal GpuMat P3, @ByVal GpuMat Q, double alpha, @ByVal Size newImgSize, Rect roi1, Rect roi2, int flags)
@Namespace(value="cv") public static float rectify3Collinear(@ByVal GpuMat cameraMatrix1, @ByVal GpuMat distCoeffs1, @ByVal GpuMat cameraMatrix2, @ByVal GpuMat distCoeffs2, @ByVal GpuMat cameraMatrix3, @ByVal GpuMat distCoeffs3, @ByVal UMatVector imgpt1, @ByVal UMatVector imgpt3, @ByVal Size imageSize, @ByVal GpuMat R12, @ByVal GpuMat T12, @ByVal GpuMat R13, @ByVal GpuMat T13, @ByVal GpuMat R1, @ByVal GpuMat R2, @ByVal GpuMat R3, @ByVal GpuMat P1, @ByVal GpuMat P2, @ByVal GpuMat P3, @ByVal GpuMat Q, double alpha, @ByVal Size newImgSize, Rect roi1, Rect roi2, int flags)
@Namespace(value="cv") public static float rectify3Collinear(@ByVal GpuMat cameraMatrix1, @ByVal GpuMat distCoeffs1, @ByVal GpuMat cameraMatrix2, @ByVal GpuMat distCoeffs2, @ByVal GpuMat cameraMatrix3, @ByVal GpuMat distCoeffs3, @ByVal GpuMatVector imgpt1, @ByVal GpuMatVector imgpt3, @ByVal Size imageSize, @ByVal GpuMat R12, @ByVal GpuMat T12, @ByVal GpuMat R13, @ByVal GpuMat T13, @ByVal GpuMat R1, @ByVal GpuMat R2, @ByVal GpuMat R3, @ByVal GpuMat P1, @ByVal GpuMat P2, @ByVal GpuMat P3, @ByVal GpuMat Q, double alpha, @ByVal Size newImgSize, Rect roi1, Rect roi2, int flags)
@Namespace(value="cv") @ByVal public static Mat getOptimalNewCameraMatrix(@ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Size imageSize, double alpha, @ByVal(nullValue="cv::Size()") Size newImgSize, Rect validPixROI, @Cast(value="bool") boolean centerPrincipalPoint)
cameraMatrix
- Input camera matrix.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.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 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.The function computes and returns the optimal new camera matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. When alpha\>0 , the undistorted result is likely to have some black pixels corresponding to "virtual" pixels outside of the captured distorted image. The original camera matrix, distortion coefficients, the computed new camera matrix, and newImageSize should be passed to initUndistortRectifyMap to produce the maps for remap .
@Namespace(value="cv") @ByVal public static Mat getOptimalNewCameraMatrix(@ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Size imageSize, double alpha)
@Namespace(value="cv") @ByVal public static Mat getOptimalNewCameraMatrix(@ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal Size imageSize, double alpha, @ByVal(nullValue="cv::Size()") Size newImgSize, Rect validPixROI, @Cast(value="bool") boolean centerPrincipalPoint)
@Namespace(value="cv") @ByVal public static Mat getOptimalNewCameraMatrix(@ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal Size imageSize, double alpha)
@Namespace(value="cv") @ByVal public static Mat getOptimalNewCameraMatrix(@ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal Size imageSize, double alpha, @ByVal(nullValue="cv::Size()") Size newImgSize, Rect validPixROI, @Cast(value="bool") boolean centerPrincipalPoint)
@Namespace(value="cv") @ByVal public static Mat getOptimalNewCameraMatrix(@ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal Size imageSize, double alpha)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal MatVector R_gripper2base, @ByVal MatVector t_gripper2base, @ByVal MatVector R_target2cam, @ByVal MatVector t_target2cam, @ByVal Mat R_cam2gripper, @ByVal Mat t_cam2gripper, @Cast(value="cv::HandEyeCalibrationMethod") int method)
_{}^{g}\textrm{T}_c
R_gripper
- [in] 2base 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 matrices for all the transformations
from gripper frame to robot base frame.t_gripper
- [in] 2base 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 translation vectors for all the transformations
from gripper frame to robot base frame.R_target
- [in] 2cam 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 matrices for all the transformations
from calibration target frame to camera frame.t_target
- [in] 2cam 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 translation vectors for all the transformations
from calibration target frame to camera frame.R_cam
- [out] 2gripper Estimated 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_cam
- [out] 2gripper Estimated 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
- [in] 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: - R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/EyeCalibration \cite Tsai89 - F. Park, B. Martin Robot Sensor Calibration: Solving AX = XB on the Euclidean Group \cite Park94 - R. Horaud, F. Dornaika Hand-Eye Calibration \cite Horaud95
Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented method: - N. Andreff, R. Horaud, B. Espiau On-line Hand-Eye Calibration \cite Andreff99 - K. Daniilidis Hand-Eye Calibration Using Dual Quaternions \cite Daniilidis98
The following picture describes the Hand-Eye calibration problem where the transformation between a camera ("eye") mounted on a robot gripper ("hand") has to be estimated.
![](pics/hand-eye_figure.png)
The calibration procedure is the following: - a static calibration pattern is used to estimate the transformation between the target frame and the camera frame - the robot gripper is moved in order to acquire several poses - for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for instance the robot kinematics
\[
\begin{bmatrix}
X_b\\
Y_b\\
Z_b\\
1
\end{bmatrix}
=
\begin{bmatrix}
_{}^{b}\textrm{R}_g & _{}^{b}\textrm{t}_g \\
0_{1 \times 3} & 1
\end{bmatrix}
\begin{bmatrix}
X_g\\
Y_g\\
Z_g\\
1
\end{bmatrix}
\]
- for each pose, the homogeneous transformation between the calibration target frame and the camera frame is recorded using
for instance a pose estimation method (PnP) from 2D-3D point correspondences
\[
\begin{bmatrix}
X_c\\
Y_c\\
Z_c\\
1
\end{bmatrix}
=
\begin{bmatrix}
_{}^{c}\textrm{R}_t & _{}^{c}\textrm{t}_t \\
0_{1 \times 3} & 1
\end{bmatrix}
\begin{bmatrix}
X_t\\
Y_t\\
Z_t\\
1
\end{bmatrix}
\]
The Hand-Eye calibration procedure returns the following homogeneous transformation
\[
\begin{bmatrix}
X_g\\
Y_g\\
Z_g\\
1
\end{bmatrix}
=
\begin{bmatrix}
_{}^{g}\textrm{R}_c & _{}^{g}\textrm{t}_c \\
0_{1 \times 3} & 1
\end{bmatrix}
\begin{bmatrix}
X_c\\
Y_c\\
Z_c\\
1
\end{bmatrix}
\]
This problem is also known as solving the \mathbf{A}\mathbf{X}=\mathbf{X}\mathbf{B}
equation:
\[
\begin{align*}
^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &=
\hspace{0.1em} ^{b}{\textrm{T}_g}^{(2)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\
(^{b}{\textrm{T}_g}^{(2)})^{-1} \hspace{0.2em} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c &=
\hspace{0.1em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\
\textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\
\end{align*}
\]
\note Additional information can be found on this [website](http://campar.in.tum.de/Chair/HandEyeCalibration). \note A minimum of 2 motions with non parallel rotation axes are necessary to determine the hand-eye transformation. So at least 3 different poses are required, but it is strongly recommended to use many more poses.
@Namespace(value="cv") public static void calibrateHandEye(@ByVal MatVector R_gripper2base, @ByVal MatVector t_gripper2base, @ByVal MatVector R_target2cam, @ByVal MatVector t_target2cam, @ByVal Mat R_cam2gripper, @ByVal Mat t_cam2gripper)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal UMatVector R_gripper2base, @ByVal UMatVector t_gripper2base, @ByVal UMatVector R_target2cam, @ByVal UMatVector t_target2cam, @ByVal Mat R_cam2gripper, @ByVal Mat t_cam2gripper, @Cast(value="cv::HandEyeCalibrationMethod") int method)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal UMatVector R_gripper2base, @ByVal UMatVector t_gripper2base, @ByVal UMatVector R_target2cam, @ByVal UMatVector t_target2cam, @ByVal Mat R_cam2gripper, @ByVal Mat t_cam2gripper)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal GpuMatVector R_gripper2base, @ByVal GpuMatVector t_gripper2base, @ByVal GpuMatVector R_target2cam, @ByVal GpuMatVector t_target2cam, @ByVal Mat R_cam2gripper, @ByVal Mat t_cam2gripper, @Cast(value="cv::HandEyeCalibrationMethod") int method)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal GpuMatVector R_gripper2base, @ByVal GpuMatVector t_gripper2base, @ByVal GpuMatVector R_target2cam, @ByVal GpuMatVector t_target2cam, @ByVal Mat R_cam2gripper, @ByVal Mat t_cam2gripper)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal MatVector R_gripper2base, @ByVal MatVector t_gripper2base, @ByVal MatVector R_target2cam, @ByVal MatVector t_target2cam, @ByVal UMat R_cam2gripper, @ByVal UMat t_cam2gripper, @Cast(value="cv::HandEyeCalibrationMethod") int method)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal MatVector R_gripper2base, @ByVal MatVector t_gripper2base, @ByVal MatVector R_target2cam, @ByVal MatVector t_target2cam, @ByVal UMat R_cam2gripper, @ByVal UMat t_cam2gripper)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal UMatVector R_gripper2base, @ByVal UMatVector t_gripper2base, @ByVal UMatVector R_target2cam, @ByVal UMatVector t_target2cam, @ByVal UMat R_cam2gripper, @ByVal UMat t_cam2gripper, @Cast(value="cv::HandEyeCalibrationMethod") int method)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal UMatVector R_gripper2base, @ByVal UMatVector t_gripper2base, @ByVal UMatVector R_target2cam, @ByVal UMatVector t_target2cam, @ByVal UMat R_cam2gripper, @ByVal UMat t_cam2gripper)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal GpuMatVector R_gripper2base, @ByVal GpuMatVector t_gripper2base, @ByVal GpuMatVector R_target2cam, @ByVal GpuMatVector t_target2cam, @ByVal UMat R_cam2gripper, @ByVal UMat t_cam2gripper, @Cast(value="cv::HandEyeCalibrationMethod") int method)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal GpuMatVector R_gripper2base, @ByVal GpuMatVector t_gripper2base, @ByVal GpuMatVector R_target2cam, @ByVal GpuMatVector t_target2cam, @ByVal UMat R_cam2gripper, @ByVal UMat t_cam2gripper)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal MatVector R_gripper2base, @ByVal MatVector t_gripper2base, @ByVal MatVector R_target2cam, @ByVal MatVector t_target2cam, @ByVal GpuMat R_cam2gripper, @ByVal GpuMat t_cam2gripper, @Cast(value="cv::HandEyeCalibrationMethod") int method)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal MatVector R_gripper2base, @ByVal MatVector t_gripper2base, @ByVal MatVector R_target2cam, @ByVal MatVector t_target2cam, @ByVal GpuMat R_cam2gripper, @ByVal GpuMat t_cam2gripper)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal UMatVector R_gripper2base, @ByVal UMatVector t_gripper2base, @ByVal UMatVector R_target2cam, @ByVal UMatVector t_target2cam, @ByVal GpuMat R_cam2gripper, @ByVal GpuMat t_cam2gripper, @Cast(value="cv::HandEyeCalibrationMethod") int method)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal UMatVector R_gripper2base, @ByVal UMatVector t_gripper2base, @ByVal UMatVector R_target2cam, @ByVal UMatVector t_target2cam, @ByVal GpuMat R_cam2gripper, @ByVal GpuMat t_cam2gripper)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal GpuMatVector R_gripper2base, @ByVal GpuMatVector t_gripper2base, @ByVal GpuMatVector R_target2cam, @ByVal GpuMatVector t_target2cam, @ByVal GpuMat R_cam2gripper, @ByVal GpuMat t_cam2gripper, @Cast(value="cv::HandEyeCalibrationMethod") int method)
@Namespace(value="cv") public static void calibrateHandEye(@ByVal GpuMatVector R_gripper2base, @ByVal GpuMatVector t_gripper2base, @ByVal GpuMatVector R_target2cam, @ByVal GpuMatVector t_target2cam, @ByVal GpuMat R_cam2gripper, @ByVal GpuMat t_cam2gripper)
@Namespace(value="cv") public static void convertPointsToHomogeneous(@ByVal Mat src, @ByVal 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).
@Namespace(value="cv") public static void convertPointsToHomogeneous(@ByVal UMat src, @ByVal UMat dst)
@Namespace(value="cv") public static void convertPointsToHomogeneous(@ByVal GpuMat src, @ByVal GpuMat dst)
@Namespace(value="cv") public static void convertPointsFromHomogeneous(@ByVal Mat src, @ByVal 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,...).
@Namespace(value="cv") public static void convertPointsFromHomogeneous(@ByVal UMat src, @ByVal UMat dst)
@Namespace(value="cv") public static void convertPointsFromHomogeneous(@ByVal GpuMat src, @ByVal GpuMat dst)
@Namespace(value="cv") public static void convertPointsHomogeneous(@ByVal Mat src, @ByVal Mat dst)
src
- Input array or vector of 2D, 3D, or 4D points.dst
- Output vector of 2D, 3D, or 4D points.
The function converts 2D or 3D points from/to homogeneous coordinates by calling either convertPointsToHomogeneous or convertPointsFromHomogeneous.
\note The function is obsolete. Use one of the previous two functions instead.
@Namespace(value="cv") public static void convertPointsHomogeneous(@ByVal UMat src, @ByVal UMat dst)
@Namespace(value="cv") public static void convertPointsHomogeneous(@ByVal GpuMat src, @ByVal GpuMat dst)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal Mat points1, @ByVal Mat points2, int method, double ransacReprojThreshold, double confidence, int maxIters, @ByVal(nullValue="cv::OutputArray(cv::noArray())") 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.
- **CV_FM_7POINT** for a 7-point algorithm. N = 7
- **CV_FM_8POINT** for an 8-point algorithm. N \ge 8
- **CV_FM_RANSAC** for the RANSAC algorithm. N \ge 8
- **CV_FM_LMEDS** for the LMedS algorithm. N \ge 8
ransacReprojThreshold
- Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar
line in pixels, beyond which the point is considered an outlier and is not used for computing the
final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
point localization, image resolution, and the image noise.confidence
- Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level
of confidence (probability) that the estimated matrix is correct.mask
- 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);
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal Mat points1, @ByVal Mat points2, int method, double ransacReprojThreshold, double confidence, int maxIters)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal UMat points1, @ByVal UMat points2, int method, double ransacReprojThreshold, double confidence, int maxIters, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat mask)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal UMat points1, @ByVal UMat points2, int method, double ransacReprojThreshold, double confidence, int maxIters)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal GpuMat points1, @ByVal GpuMat points2, int method, double ransacReprojThreshold, double confidence, int maxIters, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat mask)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal GpuMat points1, @ByVal GpuMat points2, int method, double ransacReprojThreshold, double confidence, int maxIters)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal Mat points1, @ByVal Mat points2, int method, double ransacReprojThreshold, double confidence, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat mask)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal Mat points1, @ByVal Mat points2)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal UMat points1, @ByVal UMat points2, int method, double ransacReprojThreshold, double confidence, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat mask)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal UMat points1, @ByVal UMat points2)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal GpuMat points1, @ByVal GpuMat points2, int method, double ransacReprojThreshold, double confidence, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat mask)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal GpuMat points1, @ByVal GpuMat points2)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal Mat points1, @ByVal Mat points2, @ByVal Mat mask, int method, double ransacReprojThreshold, double confidence)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal Mat points1, @ByVal Mat points2, @ByVal Mat mask)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal UMat points1, @ByVal UMat points2, @ByVal UMat mask, int method, double ransacReprojThreshold, double confidence)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal UMat points1, @ByVal UMat points2, @ByVal UMat mask)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal GpuMat points1, @ByVal GpuMat points2, @ByVal GpuMat mask, int method, double ransacReprojThreshold, double confidence)
@Namespace(value="cv") @ByVal public static Mat findFundamentalMat(@ByVal GpuMat points1, @ByVal GpuMat points2, @ByVal GpuMat mask)
@Namespace(value="cv") @ByVal public static Mat findEssentialMat(@ByVal Mat points1, @ByVal Mat points2, @ByVal Mat cameraMatrix, int method, double prob, double threshold, @ByVal(nullValue="cv::OutputArray(cv::noArray())") 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 matrix K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}
.
Note that this function assumes that points1 and points2 are feature points from cameras with the
same camera matrix.method
- Method for computing an essential matrix.
- **RANSAC** for the RANSAC algorithm.
- **LMEDS** for the LMedS algorithm.prob
- Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
confidence (probability) that the estimated matrix is correct.threshold
- Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
line in pixels, beyond which the point is considered an outlier and is not used for computing the
final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
point localization, image resolution, and the image noise.mask
- Output array of N elements, every element of which is set to 0 for outliers and to 1
for the other points. The array is computed only in the RANSAC and LMedS methods.
This function estimates essential matrix based on the five-point algorithm solver in \cite Nister03 . \cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation:
\[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\]
where E
is an essential matrix, p_1
and p_2
are corresponding points in the first and the
second images, respectively. The result of this function may be passed further to
decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
@Namespace(value="cv") @ByVal public static Mat findEssentialMat(@ByVal Mat points1, @ByVal Mat points2, @ByVal Mat cameraMatrix)
@Namespace(value="cv") @ByVal public static Mat findEssentialMat(@ByVal UMat points1, @ByVal UMat points2, @ByVal UMat cameraMatrix, int method, double prob, double threshold, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat mask)
@Namespace(value="cv") @ByVal public static Mat findEssentialMat(@ByVal UMat points1, @ByVal UMat points2, @ByVal UMat cameraMatrix)
@Namespace(value="cv") @ByVal public static Mat findEssentialMat(@ByVal GpuMat points1, @ByVal GpuMat points2, @ByVal GpuMat cameraMatrix, int method, double prob, double threshold, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat mask)
@Namespace(value="cv") @ByVal public static Mat findEssentialMat(@ByVal GpuMat points1, @ByVal GpuMat points2, @ByVal GpuMat cameraMatrix)
@Namespace(value="cv") @ByVal public static Mat findEssentialMat(@ByVal Mat points1, @ByVal Mat points2, double focal, @ByVal(nullValue="cv::Point2d(0, 0)") Point2d pp, int method, double prob, double threshold, @ByVal(nullValue="cv::OutputArray(cv::noArray())") 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.
- **RANSAC** for the RANSAC algorithm.
- **LMEDS** for the LMedS algorithm.threshold
- Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
line in pixels, beyond which the point is considered an outlier and is not used for computing the
final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
point localization, image resolution, and the image noise.prob
- Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
confidence (probability) that the estimated matrix is correct.mask
- Output array of N elements, every element of which is set to 0 for outliers and to 1
for the other points. The array is computed only in the RANSAC and LMedS methods.
This function differs from the one above that it computes camera matrix from focal length and principal point:
\[K =
\begin{bmatrix}
f & 0 & x_{pp} \\
0 & f & y_{pp} \\
0 & 0 & 1
\end{bmatrix}\]
@Namespace(value="cv") @ByVal public static Mat findEssentialMat(@ByVal Mat points1, @ByVal Mat points2)
@Namespace(value="cv") @ByVal public static Mat findEssentialMat(@ByVal UMat points1, @ByVal UMat points2, double focal, @ByVal(nullValue="cv::Point2d(0, 0)") Point2d pp, int method, double prob, double threshold, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat mask)
@Namespace(value="cv") @ByVal public static Mat findEssentialMat(@ByVal UMat points1, @ByVal UMat points2)
@Namespace(value="cv") @ByVal public static Mat findEssentialMat(@ByVal GpuMat points1, @ByVal GpuMat points2, double focal, @ByVal(nullValue="cv::Point2d(0, 0)") Point2d pp, int method, double prob, double threshold, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat mask)
@Namespace(value="cv") @ByVal public static Mat findEssentialMat(@ByVal GpuMat points1, @ByVal GpuMat points2)
@Namespace(value="cv") public static void decomposeEssentialMat(@ByVal Mat E, @ByVal Mat R1, @ByVal Mat R2, @ByVal 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.
@Namespace(value="cv") public static void decomposeEssentialMat(@ByVal UMat E, @ByVal UMat R1, @ByVal UMat R2, @ByVal UMat t)
@Namespace(value="cv") public static void decomposeEssentialMat(@ByVal GpuMat E, @ByVal GpuMat R1, @ByVal GpuMat R2, @ByVal GpuMat t)
@Namespace(value="cv") public static int recoverPose(@ByVal Mat E, @ByVal Mat points1, @ByVal Mat points2, @ByVal Mat cameraMatrix, @ByVal Mat R, @ByVal Mat t, @ByVal(nullValue="cv::InputOutputArray(cv::noArray())") 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 matrix A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}
.
Note that this function assumes that points1 and points2 are feature points from cameras with the
same camera 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 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] = ...;
}
// 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);
@Namespace(value="cv") public static int recoverPose(@ByVal Mat E, @ByVal Mat points1, @ByVal Mat points2, @ByVal Mat cameraMatrix, @ByVal Mat R, @ByVal Mat t)
@Namespace(value="cv") public static int recoverPose(@ByVal UMat E, @ByVal UMat points1, @ByVal UMat points2, @ByVal UMat cameraMatrix, @ByVal UMat R, @ByVal UMat t, @ByVal(nullValue="cv::InputOutputArray(cv::noArray())") UMat mask)
@Namespace(value="cv") public static int recoverPose(@ByVal UMat E, @ByVal UMat points1, @ByVal UMat points2, @ByVal UMat cameraMatrix, @ByVal UMat R, @ByVal UMat t)
@Namespace(value="cv") public static int recoverPose(@ByVal GpuMat E, @ByVal GpuMat points1, @ByVal GpuMat points2, @ByVal GpuMat cameraMatrix, @ByVal GpuMat R, @ByVal GpuMat t, @ByVal(nullValue="cv::InputOutputArray(cv::noArray())") GpuMat mask)
@Namespace(value="cv") public static int recoverPose(@ByVal GpuMat E, @ByVal GpuMat points1, @ByVal GpuMat points2, @ByVal GpuMat cameraMatrix, @ByVal GpuMat R, @ByVal GpuMat t)
@Namespace(value="cv") public static int recoverPose(@ByVal Mat E, @ByVal Mat points1, @ByVal Mat points2, @ByVal Mat R, @ByVal Mat t, double focal, @ByVal(nullValue="cv::Point2d(0, 0)") Point2d pp, @ByVal(nullValue="cv::InputOutputArray(cv::noArray())") 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 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 differs from the one above that it computes camera matrix from focal length and principal point:
\[A =
\begin{bmatrix}
f & 0 & x_{pp} \\
0 & f & y_{pp} \\
0 & 0 & 1
\end{bmatrix}\]
@Namespace(value="cv") public static int recoverPose(@ByVal Mat E, @ByVal Mat points1, @ByVal Mat points2, @ByVal Mat R, @ByVal Mat t)
@Namespace(value="cv") public static int recoverPose(@ByVal UMat E, @ByVal UMat points1, @ByVal UMat points2, @ByVal UMat R, @ByVal UMat t, double focal, @ByVal(nullValue="cv::Point2d(0, 0)") Point2d pp, @ByVal(nullValue="cv::InputOutputArray(cv::noArray())") UMat mask)
@Namespace(value="cv") public static int recoverPose(@ByVal UMat E, @ByVal UMat points1, @ByVal UMat points2, @ByVal UMat R, @ByVal UMat t)
@Namespace(value="cv") public static int recoverPose(@ByVal GpuMat E, @ByVal GpuMat points1, @ByVal GpuMat points2, @ByVal GpuMat R, @ByVal GpuMat t, double focal, @ByVal(nullValue="cv::Point2d(0, 0)") Point2d pp, @ByVal(nullValue="cv::InputOutputArray(cv::noArray())") GpuMat mask)
@Namespace(value="cv") public static int recoverPose(@ByVal GpuMat E, @ByVal GpuMat points1, @ByVal GpuMat points2, @ByVal GpuMat R, @ByVal GpuMat t)
@Namespace(value="cv") public static int recoverPose(@ByVal Mat E, @ByVal Mat points1, @ByVal Mat points2, @ByVal Mat cameraMatrix, @ByVal Mat R, @ByVal Mat t, double distanceThresh, @ByVal(nullValue="cv::InputOutputArray(cv::noArray())") Mat mask, @ByVal(nullValue="cv::OutputArray(cv::noArray())") 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 matrix A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}
.
Note that this function assumes that points1 and points2 are feature points from cameras with the
same camera 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 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.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 cheirality check.
@Namespace(value="cv") public static int recoverPose(@ByVal Mat E, @ByVal Mat points1, @ByVal Mat points2, @ByVal Mat cameraMatrix, @ByVal Mat R, @ByVal Mat t, double distanceThresh)
@Namespace(value="cv") public static int recoverPose(@ByVal UMat E, @ByVal UMat points1, @ByVal UMat points2, @ByVal UMat cameraMatrix, @ByVal UMat R, @ByVal UMat t, double distanceThresh, @ByVal(nullValue="cv::InputOutputArray(cv::noArray())") UMat mask, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat triangulatedPoints)
@Namespace(value="cv") public static int recoverPose(@ByVal UMat E, @ByVal UMat points1, @ByVal UMat points2, @ByVal UMat cameraMatrix, @ByVal UMat R, @ByVal UMat t, double distanceThresh)
@Namespace(value="cv") public static int recoverPose(@ByVal GpuMat E, @ByVal GpuMat points1, @ByVal GpuMat points2, @ByVal GpuMat cameraMatrix, @ByVal GpuMat R, @ByVal GpuMat t, double distanceThresh, @ByVal(nullValue="cv::InputOutputArray(cv::noArray())") GpuMat mask, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat triangulatedPoints)
@Namespace(value="cv") public static int recoverPose(@ByVal GpuMat E, @ByVal GpuMat points1, @ByVal GpuMat points2, @ByVal GpuMat cameraMatrix, @ByVal GpuMat R, @ByVal GpuMat t, double distanceThresh)
@Namespace(value="cv") public static void computeCorrespondEpilines(@ByVal Mat points, int whichImage, @ByVal Mat F, @ByVal Mat lines)
points
- Input points. N \times 1
or 1 \times N
matrix of type CV_32FC2 or
vector\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
.
@Namespace(value="cv") public static void computeCorrespondEpilines(@ByVal UMat points, int whichImage, @ByVal UMat F, @ByVal UMat lines)
@Namespace(value="cv") public static void computeCorrespondEpilines(@ByVal GpuMat points, int whichImage, @ByVal GpuMat F, @ByVal GpuMat lines)
@Namespace(value="cv") public static void triangulatePoints(@ByVal Mat projMatr1, @ByVal Mat projMatr2, @ByVal Mat projPoints1, @ByVal Mat projPoints2, @ByVal 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.
reprojectImageTo3D
@Namespace(value="cv") public static void triangulatePoints(@ByVal UMat projMatr1, @ByVal UMat projMatr2, @ByVal UMat projPoints1, @ByVal UMat projPoints2, @ByVal UMat points4D)
@Namespace(value="cv") public static void triangulatePoints(@ByVal GpuMat projMatr1, @ByVal GpuMat projMatr2, @ByVal GpuMat projPoints1, @ByVal GpuMat projPoints2, @ByVal GpuMat points4D)
@Namespace(value="cv") public static void correctMatches(@ByVal Mat F, @ByVal Mat points1, @ByVal Mat points2, @ByVal Mat newPoints1, @ByVal 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 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 * F * newPoints1 = 0
.
@Namespace(value="cv") public static void correctMatches(@ByVal UMat F, @ByVal UMat points1, @ByVal UMat points2, @ByVal UMat newPoints1, @ByVal UMat newPoints2)
@Namespace(value="cv") public static void correctMatches(@ByVal GpuMat F, @ByVal GpuMat points1, @ByVal GpuMat points2, @ByVal GpuMat newPoints1, @ByVal GpuMat newPoints2)
@Namespace(value="cv") public static void filterSpeckles(@ByVal Mat img, double newVal, int maxSpeckleSize, double maxDiff, @ByVal(nullValue="cv::InputOutputArray(cv::noArray())") 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.@Namespace(value="cv") public static void filterSpeckles(@ByVal Mat img, double newVal, int maxSpeckleSize, double maxDiff)
@Namespace(value="cv") public static void filterSpeckles(@ByVal UMat img, double newVal, int maxSpeckleSize, double maxDiff, @ByVal(nullValue="cv::InputOutputArray(cv::noArray())") UMat buf)
@Namespace(value="cv") public static void filterSpeckles(@ByVal UMat img, double newVal, int maxSpeckleSize, double maxDiff)
@Namespace(value="cv") public static void filterSpeckles(@ByVal GpuMat img, double newVal, int maxSpeckleSize, double maxDiff, @ByVal(nullValue="cv::InputOutputArray(cv::noArray())") GpuMat buf)
@Namespace(value="cv") public static void filterSpeckles(@ByVal GpuMat img, double newVal, int maxSpeckleSize, double maxDiff)
@Namespace(value="cv") @ByVal public static Rect getValidDisparityROI(@ByVal Rect roi1, @ByVal Rect roi2, int minDisparity, int numberOfDisparities, int blockSize)
@Namespace(value="cv") public static void validateDisparity(@ByVal Mat disparity, @ByVal Mat cost, int minDisparity, int numberOfDisparities, int disp12MaxDisp)
@Namespace(value="cv") public static void validateDisparity(@ByVal Mat disparity, @ByVal Mat cost, int minDisparity, int numberOfDisparities)
@Namespace(value="cv") public static void validateDisparity(@ByVal UMat disparity, @ByVal UMat cost, int minDisparity, int numberOfDisparities, int disp12MaxDisp)
@Namespace(value="cv") public static void validateDisparity(@ByVal UMat disparity, @ByVal UMat cost, int minDisparity, int numberOfDisparities)
@Namespace(value="cv") public static void validateDisparity(@ByVal GpuMat disparity, @ByVal GpuMat cost, int minDisparity, int numberOfDisparities, int disp12MaxDisp)
@Namespace(value="cv") public static void validateDisparity(@ByVal GpuMat disparity, @ByVal GpuMat cost, int minDisparity, int numberOfDisparities)
@Namespace(value="cv") public static void reprojectImageTo3D(@ByVal Mat disparity, @ByVal Mat _3dImage, @ByVal Mat Q, @Cast(value="bool") 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:
\[\begin{bmatrix}
X \\
Y \\
Z \\
W
\end{bmatrix} = Q \begin{bmatrix}
x \\
y \\
\texttt{disparity} (x,y) \\
z
\end{bmatrix}.\]
reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform.
@Namespace(value="cv") public static void reprojectImageTo3D(@ByVal Mat disparity, @ByVal Mat _3dImage, @ByVal Mat Q)
@Namespace(value="cv") public static void reprojectImageTo3D(@ByVal UMat disparity, @ByVal UMat _3dImage, @ByVal UMat Q, @Cast(value="bool") boolean handleMissingValues, int ddepth)
@Namespace(value="cv") public static void reprojectImageTo3D(@ByVal UMat disparity, @ByVal UMat _3dImage, @ByVal UMat Q)
@Namespace(value="cv") public static void reprojectImageTo3D(@ByVal GpuMat disparity, @ByVal GpuMat _3dImage, @ByVal GpuMat Q, @Cast(value="bool") boolean handleMissingValues, int ddepth)
@Namespace(value="cv") public static void reprojectImageTo3D(@ByVal GpuMat disparity, @ByVal GpuMat _3dImage, @ByVal GpuMat Q)
@Namespace(value="cv") public static double sampsonDistance(@ByVal Mat pt1, @ByVal Mat pt2, @ByVal Mat F)
The function cv::sampsonDistance calculates and returns the first order approximation of the geometric error as:
\[
sd( \texttt{pt1} , \texttt{pt2} )=
\frac{(\texttt{pt2}^t \cdot \texttt{F} \cdot \texttt{pt1})^2}
{((\texttt{F} \cdot \texttt{pt1})(0))^2 +
((\texttt{F} \cdot \texttt{pt1})(1))^2 +
((\texttt{F}^t \cdot \texttt{pt2})(0))^2 +
((\texttt{F}^t \cdot \texttt{pt2})(1))^2}
\]
The fundamental matrix may be calculated using the cv::findFundamentalMat function. See \cite HartleyZ00 11.4.3 for details.pt1
- first homogeneous 2d pointpt2
- second homogeneous 2d pointF
- fundamental matrix@Namespace(value="cv") public static double sampsonDistance(@ByVal UMat pt1, @ByVal UMat pt2, @ByVal UMat F)
@Namespace(value="cv") public static double sampsonDistance(@ByVal GpuMat pt1, @ByVal GpuMat pt2, @ByVal GpuMat F)
@Namespace(value="cv") public static int estimateAffine3D(@ByVal Mat src, @ByVal Mat dst, @ByVal Mat out, @ByVal Mat inliers, double ransacThreshold, double confidence)
It computes
\[
\begin{bmatrix}
x\\
y\\
z\\
\end{bmatrix}
=
\begin{bmatrix}
a_{11} & a_{12} & a_{13}\\
a_{21} & a_{22} & a_{23}\\
a_{31} & a_{32} & a_{33}\\
\end{bmatrix}
\begin{bmatrix}
X\\
Y\\
Z\\
\end{bmatrix}
+
\begin{bmatrix}
b_1\\
b_2\\
b_3\\
\end{bmatrix}
\]
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.
@Namespace(value="cv") public static int estimateAffine3D(@ByVal Mat src, @ByVal Mat dst, @ByVal Mat out, @ByVal Mat inliers)
@Namespace(value="cv") public static int estimateAffine3D(@ByVal UMat src, @ByVal UMat dst, @ByVal UMat out, @ByVal UMat inliers, double ransacThreshold, double confidence)
@Namespace(value="cv") public static int estimateAffine3D(@ByVal UMat src, @ByVal UMat dst, @ByVal UMat out, @ByVal UMat inliers)
@Namespace(value="cv") public static int estimateAffine3D(@ByVal GpuMat src, @ByVal GpuMat dst, @ByVal GpuMat out, @ByVal GpuMat inliers, double ransacThreshold, double confidence)
@Namespace(value="cv") public static int estimateAffine3D(@ByVal GpuMat src, @ByVal GpuMat dst, @ByVal GpuMat out, @ByVal GpuMat inliers)
@Namespace(value="cv") @ByVal public static Mat estimateAffine2D(@ByVal Mat from, @ByVal Mat to, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat inliers, int method, double ransacReprojThreshold, @Cast(value="size_t") long maxIters, double confidence, @Cast(value="size_t") long refineIters)
It computes
\[
\begin{bmatrix}
x\\
y\\
\end{bmatrix}
=
\begin{bmatrix}
a_{11} & a_{12}\\
a_{21} & a_{22}\\
\end{bmatrix}
\begin{bmatrix}
X\\
Y\\
\end{bmatrix}
+
\begin{bmatrix}
b_1\\
b_2\\
\end{bmatrix}
\]
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:
- cv::RANSAC - RANSAC-based robust method
- cv::LMEDS - Least-Median robust method
RANSAC is the default method.ransacReprojThreshold
- Maximum reprojection error in the RANSAC algorithm to consider
a point as an inlier. Applies only to RANSAC.maxIters
- The maximum number of robust method iterations.confidence
- Confidence level, between 0 and 1, for the estimated transformation. Anything
between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.refineIters
- Maximum number of iterations of refining algorithm (Levenberg-Marquardt).
Passing 0 will disable refining, so the output matrix will be output of robust method.
2 \times 3
or empty matrix if transformation
could not be estimated. The returned matrix has the following form:
\[
\begin{bmatrix}
a_{11} & a_{12} & b_1\\
a_{21} & a_{22} & b_2\\
\end{bmatrix}
\]
The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
\note The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
estimateAffinePartial2D, getAffineTransform
@Namespace(value="cv") @ByVal public static Mat estimateAffine2D(@ByVal Mat from, @ByVal Mat to)
@Namespace(value="cv") @ByVal public static Mat estimateAffine2D(@ByVal UMat from, @ByVal UMat to, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat inliers, int method, double ransacReprojThreshold, @Cast(value="size_t") long maxIters, double confidence, @Cast(value="size_t") long refineIters)
@Namespace(value="cv") @ByVal public static Mat estimateAffine2D(@ByVal UMat from, @ByVal UMat to)
@Namespace(value="cv") @ByVal public static Mat estimateAffine2D(@ByVal GpuMat from, @ByVal GpuMat to, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat inliers, int method, double ransacReprojThreshold, @Cast(value="size_t") long maxIters, double confidence, @Cast(value="size_t") long refineIters)
@Namespace(value="cv") @ByVal public static Mat estimateAffine2D(@ByVal GpuMat from, @ByVal GpuMat to)
@Namespace(value="cv") @ByVal public static Mat estimateAffinePartial2D(@ByVal Mat from, @ByVal Mat to, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat inliers, int method, double ransacReprojThreshold, @Cast(value="size_t") long maxIters, double confidence, @Cast(value="size_t") 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:
- cv::RANSAC - RANSAC-based robust method
- cv::LMEDS - Least-Median robust method
RANSAC is the default method.ransacReprojThreshold
- Maximum reprojection error in the RANSAC algorithm to consider
a point as an inlier. Applies only to RANSAC.maxIters
- The maximum number of robust method iterations.confidence
- Confidence level, between 0 and 1, for the estimated transformation. Anything
between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.refineIters
- Maximum number of iterations of refining algorithm (Levenberg-Marquardt).
Passing 0 will disable refining, so the output matrix will be output of robust method.
2 \times 3
or
empty matrix if transformation could not be estimated.
The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.
The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.
Estimated transformation matrix is:
\[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\
\sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y
\end{bmatrix} \]
Where \theta
is the rotation angle, s
the scaling factor and t_x, t_y
are
translations in x, y
axes respectively.
\note The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.
estimateAffine2D, getAffineTransform
@Namespace(value="cv") @ByVal public static Mat estimateAffinePartial2D(@ByVal Mat from, @ByVal Mat to)
@Namespace(value="cv") @ByVal public static Mat estimateAffinePartial2D(@ByVal UMat from, @ByVal UMat to, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat inliers, int method, double ransacReprojThreshold, @Cast(value="size_t") long maxIters, double confidence, @Cast(value="size_t") long refineIters)
@Namespace(value="cv") @ByVal public static Mat estimateAffinePartial2D(@ByVal UMat from, @ByVal UMat to)
@Namespace(value="cv") @ByVal public static Mat estimateAffinePartial2D(@ByVal GpuMat from, @ByVal GpuMat to, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat inliers, int method, double ransacReprojThreshold, @Cast(value="size_t") long maxIters, double confidence, @Cast(value="size_t") long refineIters)
@Namespace(value="cv") @ByVal public static Mat estimateAffinePartial2D(@ByVal GpuMat from, @ByVal GpuMat to)
@Namespace(value="cv") public static int decomposeHomographyMat(@ByVal Mat H, @ByVal Mat K, @ByVal MatVector rotations, @ByVal MatVector translations, @ByVal MatVector normals)
H
- The input homography matrix between two images.K
- The input intrinsic camera calibration 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 Malis.
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.
@Namespace(value="cv") public static int decomposeHomographyMat(@ByVal Mat H, @ByVal Mat K, @ByVal UMatVector rotations, @ByVal UMatVector translations, @ByVal UMatVector normals)
@Namespace(value="cv") public static int decomposeHomographyMat(@ByVal Mat H, @ByVal Mat K, @ByVal GpuMatVector rotations, @ByVal GpuMatVector translations, @ByVal GpuMatVector normals)
@Namespace(value="cv") public static int decomposeHomographyMat(@ByVal UMat H, @ByVal UMat K, @ByVal MatVector rotations, @ByVal MatVector translations, @ByVal MatVector normals)
@Namespace(value="cv") public static int decomposeHomographyMat(@ByVal UMat H, @ByVal UMat K, @ByVal UMatVector rotations, @ByVal UMatVector translations, @ByVal UMatVector normals)
@Namespace(value="cv") public static int decomposeHomographyMat(@ByVal UMat H, @ByVal UMat K, @ByVal GpuMatVector rotations, @ByVal GpuMatVector translations, @ByVal GpuMatVector normals)
@Namespace(value="cv") public static int decomposeHomographyMat(@ByVal GpuMat H, @ByVal GpuMat K, @ByVal MatVector rotations, @ByVal MatVector translations, @ByVal MatVector normals)
@Namespace(value="cv") public static int decomposeHomographyMat(@ByVal GpuMat H, @ByVal GpuMat K, @ByVal UMatVector rotations, @ByVal UMatVector translations, @ByVal UMatVector normals)
@Namespace(value="cv") public static int decomposeHomographyMat(@ByVal GpuMat H, @ByVal GpuMat K, @ByVal GpuMatVector rotations, @ByVal GpuMatVector translations, @ByVal GpuMatVector normals)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal MatVector rotations, @ByVal MatVector normals, @ByVal Mat beforePoints, @ByVal Mat afterPoints, @ByVal Mat possibleSolutions, @ByVal(nullValue="cv::InputArray(cv::noArray())") 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 Malis . 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.
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal MatVector rotations, @ByVal MatVector normals, @ByVal Mat beforePoints, @ByVal Mat afterPoints, @ByVal Mat possibleSolutions)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal UMatVector rotations, @ByVal UMatVector normals, @ByVal Mat beforePoints, @ByVal Mat afterPoints, @ByVal Mat possibleSolutions, @ByVal(nullValue="cv::InputArray(cv::noArray())") Mat pointsMask)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal UMatVector rotations, @ByVal UMatVector normals, @ByVal Mat beforePoints, @ByVal Mat afterPoints, @ByVal Mat possibleSolutions)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal GpuMatVector rotations, @ByVal GpuMatVector normals, @ByVal Mat beforePoints, @ByVal Mat afterPoints, @ByVal Mat possibleSolutions, @ByVal(nullValue="cv::InputArray(cv::noArray())") Mat pointsMask)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal GpuMatVector rotations, @ByVal GpuMatVector normals, @ByVal Mat beforePoints, @ByVal Mat afterPoints, @ByVal Mat possibleSolutions)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal MatVector rotations, @ByVal MatVector normals, @ByVal UMat beforePoints, @ByVal UMat afterPoints, @ByVal UMat possibleSolutions, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat pointsMask)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal MatVector rotations, @ByVal MatVector normals, @ByVal UMat beforePoints, @ByVal UMat afterPoints, @ByVal UMat possibleSolutions)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal UMatVector rotations, @ByVal UMatVector normals, @ByVal UMat beforePoints, @ByVal UMat afterPoints, @ByVal UMat possibleSolutions, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat pointsMask)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal UMatVector rotations, @ByVal UMatVector normals, @ByVal UMat beforePoints, @ByVal UMat afterPoints, @ByVal UMat possibleSolutions)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal GpuMatVector rotations, @ByVal GpuMatVector normals, @ByVal UMat beforePoints, @ByVal UMat afterPoints, @ByVal UMat possibleSolutions, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat pointsMask)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal GpuMatVector rotations, @ByVal GpuMatVector normals, @ByVal UMat beforePoints, @ByVal UMat afterPoints, @ByVal UMat possibleSolutions)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal MatVector rotations, @ByVal MatVector normals, @ByVal GpuMat beforePoints, @ByVal GpuMat afterPoints, @ByVal GpuMat possibleSolutions, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat pointsMask)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal MatVector rotations, @ByVal MatVector normals, @ByVal GpuMat beforePoints, @ByVal GpuMat afterPoints, @ByVal GpuMat possibleSolutions)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal UMatVector rotations, @ByVal UMatVector normals, @ByVal GpuMat beforePoints, @ByVal GpuMat afterPoints, @ByVal GpuMat possibleSolutions, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat pointsMask)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal UMatVector rotations, @ByVal UMatVector normals, @ByVal GpuMat beforePoints, @ByVal GpuMat afterPoints, @ByVal GpuMat possibleSolutions)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal GpuMatVector rotations, @ByVal GpuMatVector normals, @ByVal GpuMat beforePoints, @ByVal GpuMat afterPoints, @ByVal GpuMat possibleSolutions, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat pointsMask)
@Namespace(value="cv") public static void filterHomographyDecompByVisibleRefpoints(@ByVal GpuMatVector rotations, @ByVal GpuMatVector normals, @ByVal GpuMat beforePoints, @ByVal GpuMat afterPoints, @ByVal GpuMat possibleSolutions)
@Namespace(value="cv") public static void undistort(@ByVal Mat src, @ByVal Mat dst, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal(nullValue="cv::InputArray(cv::noArray())") Mat newCameraMatrix)
The function transforms an image to compensate radial and tangential lens distortion.
The function is simply a combination of #initUndistortRectifyMap (with unity R ) and #remap (with bilinear interpolation). See the former function for details of the transformation being performed.
Those pixels in the destination image, for which there is no correspondent pixels in the source image, are filled with zeros (black color).
A particular subset of the source image that will be visible in the corrected image can be regulated by newCameraMatrix. You can use #getOptimalNewCameraMatrix to compute the appropriate newCameraMatrix depending on your requirements.
The camera matrix and the distortion parameters can be determined using #calibrateCamera. If
the resolution of images is different from the resolution used at the calibration stage, f_x,
f_y, c_x
and c_y
need to be scaled accordingly, while the distortion coefficients remain
the same.
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.@Namespace(value="cv") public static void undistort(@ByVal Mat src, @ByVal Mat dst, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs)
@Namespace(value="cv") public static void undistort(@ByVal UMat src, @ByVal UMat dst, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat newCameraMatrix)
@Namespace(value="cv") public static void undistort(@ByVal UMat src, @ByVal UMat dst, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs)
@Namespace(value="cv") public static void undistort(@ByVal GpuMat src, @ByVal GpuMat dst, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat newCameraMatrix)
@Namespace(value="cv") public static void undistort(@ByVal GpuMat src, @ByVal GpuMat dst, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs)
@Namespace(value="cv") public static void initUndistortRectifyMap(@ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Mat R, @ByVal Mat newCameraMatrix, @ByVal Size size, int m1type, @ByVal Mat map1, @ByVal Mat map2)
The function computes the joint undistortion and rectification transformation and represents the result in the form of maps for remap. The undistorted image looks like original, as if it is captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by #getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify .
Also, this new camera is oriented differently in the coordinate space, according to R. That, for example, helps to align two heads of a stereo camera so that the epipolar lines on both images become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).
The function actually builds the maps for the inverse mapping algorithm that is used by remap. That
is, for each pixel (u, v)
in the destination (corrected and rectified) image, the function
computes the corresponding coordinates in the source image (that is, in the original image from
camera). The following process is applied:
\[
\begin{array}{l}
x \leftarrow (u - {c'}_x)/{f'}_x \\
y \leftarrow (v - {c'}_y)/{f'}_y \\
{[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\
x' \leftarrow X/W \\
y' \leftarrow Y/W \\
r^2 \leftarrow x'^2 + y'^2 \\
x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}
+ 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\
y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}
+ p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\
s\vecthree{x'''}{y'''}{1} =
\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)}
{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)}
{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\
map_x(u,v) \leftarrow x''' f_x + c_x \\
map_y(u,v) \leftarrow y''' f_y + c_y
\end{array}
\]
where (k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])
are the distortion coefficients.
In case of a stereo camera, this function is called twice: once for each camera head, after stereoRectify, which in its turn is called after #stereoCalibrate. But if the stereo camera was not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using #stereoRectifyUncalibrated. For each camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as
\[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\]
where cameraMatrix can be chosen arbitrarily.
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 cvInitUndistortMap 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.@Namespace(value="cv") public static void initUndistortRectifyMap(@ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMat R, @ByVal UMat newCameraMatrix, @ByVal Size size, int m1type, @ByVal UMat map1, @ByVal UMat map2)
@Namespace(value="cv") public static void initUndistortRectifyMap(@ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMat R, @ByVal GpuMat newCameraMatrix, @ByVal Size size, int m1type, @ByVal GpuMat map1, @ByVal GpuMat map2)
@Namespace(value="cv") public static float initWideAngleProjMap(@ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Size imageSize, int destImageWidth, int m1type, @ByVal Mat map1, @ByVal Mat map2, @Cast(value="cv::UndistortTypes") int projType, double alpha)
@Namespace(value="cv") public static float initWideAngleProjMap(@ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Size imageSize, int destImageWidth, int m1type, @ByVal Mat map1, @ByVal Mat map2)
@Namespace(value="cv") public static float initWideAngleProjMap(@ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal Size imageSize, int destImageWidth, int m1type, @ByVal UMat map1, @ByVal UMat map2, @Cast(value="cv::UndistortTypes") int projType, double alpha)
@Namespace(value="cv") public static float initWideAngleProjMap(@ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal Size imageSize, int destImageWidth, int m1type, @ByVal UMat map1, @ByVal UMat map2)
@Namespace(value="cv") public static float initWideAngleProjMap(@ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal Size imageSize, int destImageWidth, int m1type, @ByVal GpuMat map1, @ByVal GpuMat map2, @Cast(value="cv::UndistortTypes") int projType, double alpha)
@Namespace(value="cv") public static float initWideAngleProjMap(@ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal Size imageSize, int destImageWidth, int m1type, @ByVal GpuMat map1, @ByVal GpuMat map2)
@Namespace(value="cv") public static float initWideAngleProjMap(@ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Size imageSize, int destImageWidth, int m1type, @ByVal Mat map1, @ByVal Mat map2, int projType)
@Namespace(value="cv") public static float initWideAngleProjMap(@ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal Size imageSize, int destImageWidth, int m1type, @ByVal UMat map1, @ByVal UMat map2, int projType)
@Namespace(value="cv") public static float initWideAngleProjMap(@ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal Size imageSize, int destImageWidth, int m1type, @ByVal GpuMat map1, @ByVal GpuMat map2, int projType)
@Namespace(value="cv") @ByVal public static Mat getDefaultNewCameraMatrix(@ByVal Mat cameraMatrix, @ByVal(nullValue="cv::Size()") Size imgsize, @Cast(value="bool") boolean centerPrincipalPoint)
The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).
In the latter case, the new camera matrix will be:
\[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\]
where f_x
and f_y
are (0,0)
and (1,1)
elements of cameraMatrix, respectively.
By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.
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.@Namespace(value="cv") @ByVal public static Mat getDefaultNewCameraMatrix(@ByVal Mat cameraMatrix)
@Namespace(value="cv") @ByVal public static Mat getDefaultNewCameraMatrix(@ByVal UMat cameraMatrix, @ByVal(nullValue="cv::Size()") Size imgsize, @Cast(value="bool") boolean centerPrincipalPoint)
@Namespace(value="cv") @ByVal public static Mat getDefaultNewCameraMatrix(@ByVal UMat cameraMatrix)
@Namespace(value="cv") @ByVal public static Mat getDefaultNewCameraMatrix(@ByVal GpuMat cameraMatrix, @ByVal(nullValue="cv::Size()") Size imgsize, @Cast(value="bool") boolean centerPrincipalPoint)
@Namespace(value="cv") @ByVal public static Mat getDefaultNewCameraMatrix(@ByVal GpuMat cameraMatrix)
@Namespace(value="cv") public static void undistortPoints(@ByVal Mat src, @ByVal Mat dst, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal(nullValue="cv::InputArray(cv::noArray())") Mat R, @ByVal(nullValue="cv::InputArray(cv::noArray())") Mat P)
The function is similar to #undistort and #initUndistortRectifyMap but it operates on a sparse set of points instead of a raster image. Also the function performs a reverse transformation to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a planar object, it does, up to a translation vector, if the proper R is specified.
For each observed point coordinate (u, v)
the function computes:
\[
\begin{array}{l}
x^{"} \leftarrow (u - c_x)/f_x \\
y^{"} \leftarrow (v - c_y)/f_y \\
(x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\
{[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\
x \leftarrow X/W \\
y \leftarrow Y/W \\
\text{only performed if P is specified:} \\
u' \leftarrow x {f'}_x + {c'}_x \\
v' \leftarrow y {f'}_y + {c'}_y
\end{array}
\]
where *undistort* is an approximate iterative algorithm that estimates the normalized original point coordinates out of the normalized distorted point coordinates ("normalized" means that the coordinates do not depend on the camera matrix).
The function can be used for both a stereo camera head or a monocular camera (when R is empty).
src
- Observed point coordinates, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or
vector\dst
- Output ideal point coordinates (1xN/Nx1 2-channel or vector\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.@Namespace(value="cv") public static void undistortPoints(@ByVal Mat src, @ByVal Mat dst, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs)
@Namespace(value="cv") public static void undistortPoints(@ByVal UMat src, @ByVal UMat dst, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat R, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat P)
@Namespace(value="cv") public static void undistortPoints(@ByVal UMat src, @ByVal UMat dst, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs)
@Namespace(value="cv") public static void undistortPoints(@ByVal GpuMat src, @ByVal GpuMat dst, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat R, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat P)
@Namespace(value="cv") public static void undistortPoints(@ByVal GpuMat src, @ByVal GpuMat dst, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs)
@Namespace(value="cv") @Name(value="undistortPoints") public static void undistortPointsIter(@ByVal Mat src, @ByVal Mat dst, @ByVal Mat cameraMatrix, @ByVal Mat distCoeffs, @ByVal Mat R, @ByVal Mat P, @ByVal TermCriteria criteria)
@Namespace(value="cv") @Name(value="undistortPoints") public static void undistortPointsIter(@ByVal UMat src, @ByVal UMat dst, @ByVal UMat cameraMatrix, @ByVal UMat distCoeffs, @ByVal UMat R, @ByVal UMat P, @ByVal TermCriteria criteria)
@Namespace(value="cv") @Name(value="undistortPoints") public static void undistortPointsIter(@ByVal GpuMat src, @ByVal GpuMat dst, @ByVal GpuMat cameraMatrix, @ByVal GpuMat distCoeffs, @ByVal GpuMat R, @ByVal GpuMat P, @ByVal TermCriteria criteria)
@Namespace(value="cv::fisheye") public static void projectPoints(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @Const @ByRef Mat affine, @ByVal Mat K, @ByVal Mat D, double alpha, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat jacobian)
objectPoints
- Array of object points, 1xN/Nx1 3-channel (or vector\imagePoints
- Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or
vector\affine
- K
- Camera matrix K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}
.D
- Input vector of distortion coefficients (k_1, k_2, k_3, k_4)
.alpha
- The skew coefficient.jacobian
- Optional output 2Nx15 jacobian matrix of derivatives of image points with respect
to components of the focal lengths, coordinates of the principal point, distortion coefficients,
rotation vector, translation vector, and the skew. In the old interface different components of
the jacobian are returned via different output parameters.
The function computes 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.
@Namespace(value="cv::fisheye") public static void projectPoints(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @Const @ByRef Mat affine, @ByVal Mat K, @ByVal Mat D)
@Namespace(value="cv::fisheye") public static void projectPoints(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @Const @ByRef Mat affine, @ByVal UMat K, @ByVal UMat D, double alpha, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat jacobian)
@Namespace(value="cv::fisheye") public static void projectPoints(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @Const @ByRef Mat affine, @ByVal UMat K, @ByVal UMat D)
@Namespace(value="cv::fisheye") public static void projectPoints(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @Const @ByRef Mat affine, @ByVal GpuMat K, @ByVal GpuMat D, double alpha, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat jacobian)
@Namespace(value="cv::fisheye") public static void projectPoints(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @Const @ByRef Mat affine, @ByVal GpuMat K, @ByVal GpuMat D)
@Namespace(value="cv::fisheye") public static void projectPoints(@ByVal Mat objectPoints, @ByVal Mat imagePoints, @ByVal Mat rvec, @ByVal Mat tvec, @ByVal Mat K, @ByVal Mat D, double alpha, @ByVal(nullValue="cv::OutputArray(cv::noArray())") Mat jacobian)
@Namespace(value="cv::fisheye") public static void projectPoints(@ByVal UMat objectPoints, @ByVal UMat imagePoints, @ByVal UMat rvec, @ByVal UMat tvec, @ByVal UMat K, @ByVal UMat D, double alpha, @ByVal(nullValue="cv::OutputArray(cv::noArray())") UMat jacobian)
@Namespace(value="cv::fisheye") public static void projectPoints(@ByVal GpuMat objectPoints, @ByVal GpuMat imagePoints, @ByVal GpuMat rvec, @ByVal GpuMat tvec, @ByVal GpuMat K, @ByVal GpuMat D, double alpha, @ByVal(nullValue="cv::OutputArray(cv::noArray())") GpuMat jacobian)
@Namespace(value="cv::fisheye") public static void distortPoints(@ByVal Mat undistorted, @ByVal Mat distorted, @ByVal Mat K, @ByVal Mat D, double alpha)
undistorted
- Array of object points, 1xN/Nx1 2-channel (or vector\K
- Camera matrix K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}
.D
- Input vector of distortion coefficients (k_1, k_2, k_3, k_4)
.alpha
- The skew coefficient.distorted
- Output array of image points, 1xN/Nx1 2-channel, or vector\
Note that the function assumes the camera matrix of the undistorted points to be identity.
This means if you want to transform back points undistorted with undistortPoints() you have to
multiply them with P^{-1}
.
@Namespace(value="cv::fisheye") public static void distortPoints(@ByVal Mat undistorted, @ByVal Mat distorted, @ByVal Mat K, @ByVal Mat D)
@Namespace(value="cv::fisheye") public static void distortPoints(@ByVal UMat undistorted, @ByVal UMat distorted, @ByVal UMat K, @ByVal UMat D, double alpha)
@Namespace(value="cv::fisheye") public static void distortPoints(@ByVal UMat undistorted, @ByVal UMat distorted, @ByVal UMat K, @ByVal UMat D)
@Namespace(value="cv::fisheye") public static void distortPoints(@ByVal GpuMat undistorted, @ByVal GpuMat distorted, @ByVal GpuMat K, @ByVal GpuMat D, double alpha)
@Namespace(value="cv::fisheye") public static void distortPoints(@ByVal GpuMat undistorted, @ByVal GpuMat distorted, @ByVal GpuMat K, @ByVal GpuMat D)
@Namespace(value="cv::fisheye") public static void undistortImage(@ByVal Mat distorted, @ByVal Mat undistorted, @ByVal Mat K, @ByVal Mat D, @ByVal(nullValue="cv::InputArray(cv::noArray())") Mat Knew, @Const @ByRef(nullValue="cv::Size()") Size new_size)
distorted
- image with fisheye lens distortion.undistorted
- Output image with compensated fisheye lens distortion.K
- Camera matrix K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}
.D
- Input vector of distortion coefficients (k_1, k_2, k_3, k_4)
.Knew
- Camera matrix of the distorted image. By default, it is the identity matrix but you
may additionally scale and shift the result by using a different matrix.new_size
- the new size
The function transforms an image to compensate radial and tangential lens distortion.
The function is simply a combination of fisheye::initUndistortRectifyMap (with unity R ) and remap (with bilinear interpolation). See the former function for details of the transformation being performed.
See below the results of undistortImage. - a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, k_4, k_5, k_6) of distortion were optimized under calibration) - b\) result of fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, k_3, k_4) of fisheye distortion were optimized under calibration) - c\) original image was captured with fisheye lens
Pictures a) and b) almost the same. But if we consider points of image located far from the center of image, we can notice that on image a) these points are distorted.
![image](pics/fisheye_undistorted.jpg)
@Namespace(value="cv::fisheye") public static void undistortImage(@ByVal Mat distorted, @ByVal Mat undistorted, @ByVal Mat K, @ByVal Mat D)
@Namespace(value="cv::fisheye") public static void undistortImage(@ByVal UMat distorted, @ByVal UMat undistorted, @ByVal UMat K, @ByVal UMat D, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat Knew, @Const @ByRef(nullValue="cv::Size()") Size new_size)
@Namespace(value="cv::fisheye") public static void undistortImage(@ByVal UMat distorted, @ByVal UMat undistorted, @ByVal UMat K, @ByVal UMat D)
@Namespace(value="cv::fisheye") public static void undistortImage(@ByVal GpuMat distorted, @ByVal GpuMat undistorted, @ByVal GpuMat K, @ByVal GpuMat D, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat Knew, @Const @ByRef(nullValue="cv::Size()") Size new_size)
@Namespace(value="cv::fisheye") public static void undistortImage(@ByVal GpuMat distorted, @ByVal GpuMat undistorted, @ByVal GpuMat K, @ByVal GpuMat D)
@Namespace(value="cv::fisheye") public static void estimateNewCameraMatrixForUndistortRectify(@ByVal Mat K, @ByVal Mat D, @Const @ByRef Size image_size, @ByVal Mat R, @ByVal Mat P, double balance, @Const @ByRef(nullValue="cv::Size()") Size new_size, double fov_scale)
K
- Camera matrix K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}
.image_size
- Size of the imageD
- Input vector of distortion coefficients (k_1, k_2, k_3, k_4)
.R
- Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
1-channel or 1x1 3-channelP
- New camera 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.@Namespace(value="cv::fisheye") public static void estimateNewCameraMatrixForUndistortRectify(@ByVal Mat K, @ByVal Mat D, @Const @ByRef Size image_size, @ByVal Mat R, @ByVal Mat P)
@Namespace(value="cv::fisheye") public static void estimateNewCameraMatrixForUndistortRectify(@ByVal UMat K, @ByVal UMat D, @Const @ByRef Size image_size, @ByVal UMat R, @ByVal UMat P, double balance, @Const @ByRef(nullValue="cv::Size()") Size new_size, double fov_scale)
@Namespace(value="cv::fisheye") public static void estimateNewCameraMatrixForUndistortRectify(@ByVal UMat K, @ByVal UMat D, @Const @ByRef Size image_size, @ByVal UMat R, @ByVal UMat P)
@Namespace(value="cv::fisheye") public static void estimateNewCameraMatrixForUndistortRectify(@ByVal GpuMat K, @ByVal GpuMat D, @Const @ByRef Size image_size, @ByVal GpuMat R, @ByVal GpuMat P, double balance, @Const @ByRef(nullValue="cv::Size()") Size new_size, double fov_scale)
@Namespace(value="cv::fisheye") public static void estimateNewCameraMatrixForUndistortRectify(@ByVal GpuMat K, @ByVal GpuMat D, @Const @ByRef Size image_size, @ByVal GpuMat R, @ByVal GpuMat P)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal MatVector objectPoints, @ByVal MatVector imagePoints, @Const @ByRef Size image_size, @ByVal Mat K, @ByVal Mat D, @ByVal MatVector rvecs, @ByVal MatVector tvecs, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 100, DBL_EPSILON)") 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 intrinsic camera matrix.K
- Output 3x3 floating-point camera matrix
A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}
. If
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 (k_1, k_2, k_3, k_4)
.rvecs
- Output vector of rotation vectors (see 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:
- **fisheye::CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of
fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
center ( imageSize is used), and focal distances are computed in a least-squares fashion.
- **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration
of intrinsic optimization.
- **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number.
- **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero.
- **fisheye::CALIB_FIX_K1..fisheye::CALIB_FIX_K4** Selected distortion coefficients
are set to zeros and stay zero.
- **fisheye::CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global
optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too.criteria
- Termination criteria for the iterative optimization algorithm.@Namespace(value="cv::fisheye") public static double calibrate(@ByVal MatVector objectPoints, @ByVal MatVector imagePoints, @Const @ByRef Size image_size, @ByVal Mat K, @ByVal Mat D, @ByVal MatVector rvecs, @ByVal MatVector tvecs)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal UMatVector objectPoints, @ByVal UMatVector imagePoints, @Const @ByRef Size image_size, @ByVal Mat K, @ByVal Mat D, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 100, DBL_EPSILON)") TermCriteria criteria)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal UMatVector objectPoints, @ByVal UMatVector imagePoints, @Const @ByRef Size image_size, @ByVal Mat K, @ByVal Mat D, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal GpuMatVector objectPoints, @ByVal GpuMatVector imagePoints, @Const @ByRef Size image_size, @ByVal Mat K, @ByVal Mat D, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 100, DBL_EPSILON)") TermCriteria criteria)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal GpuMatVector objectPoints, @ByVal GpuMatVector imagePoints, @Const @ByRef Size image_size, @ByVal Mat K, @ByVal Mat D, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal MatVector objectPoints, @ByVal MatVector imagePoints, @Const @ByRef Size image_size, @ByVal UMat K, @ByVal UMat D, @ByVal MatVector rvecs, @ByVal MatVector tvecs, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 100, DBL_EPSILON)") TermCriteria criteria)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal MatVector objectPoints, @ByVal MatVector imagePoints, @Const @ByRef Size image_size, @ByVal UMat K, @ByVal UMat D, @ByVal MatVector rvecs, @ByVal MatVector tvecs)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal UMatVector objectPoints, @ByVal UMatVector imagePoints, @Const @ByRef Size image_size, @ByVal UMat K, @ByVal UMat D, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 100, DBL_EPSILON)") TermCriteria criteria)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal UMatVector objectPoints, @ByVal UMatVector imagePoints, @Const @ByRef Size image_size, @ByVal UMat K, @ByVal UMat D, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal GpuMatVector objectPoints, @ByVal GpuMatVector imagePoints, @Const @ByRef Size image_size, @ByVal UMat K, @ByVal UMat D, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 100, DBL_EPSILON)") TermCriteria criteria)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal GpuMatVector objectPoints, @ByVal GpuMatVector imagePoints, @Const @ByRef Size image_size, @ByVal UMat K, @ByVal UMat D, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal MatVector objectPoints, @ByVal MatVector imagePoints, @Const @ByRef Size image_size, @ByVal GpuMat K, @ByVal GpuMat D, @ByVal MatVector rvecs, @ByVal MatVector tvecs, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 100, DBL_EPSILON)") TermCriteria criteria)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal MatVector objectPoints, @ByVal MatVector imagePoints, @Const @ByRef Size image_size, @ByVal GpuMat K, @ByVal GpuMat D, @ByVal MatVector rvecs, @ByVal MatVector tvecs)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal UMatVector objectPoints, @ByVal UMatVector imagePoints, @Const @ByRef Size image_size, @ByVal GpuMat K, @ByVal GpuMat D, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 100, DBL_EPSILON)") TermCriteria criteria)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal UMatVector objectPoints, @ByVal UMatVector imagePoints, @Const @ByRef Size image_size, @ByVal GpuMat K, @ByVal GpuMat D, @ByVal UMatVector rvecs, @ByVal UMatVector tvecs)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal GpuMatVector objectPoints, @ByVal GpuMatVector imagePoints, @Const @ByRef Size image_size, @ByVal GpuMat K, @ByVal GpuMat D, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs, int flags, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 100, DBL_EPSILON)") TermCriteria criteria)
@Namespace(value="cv::fisheye") public static double calibrate(@ByVal GpuMatVector objectPoints, @ByVal GpuMatVector imagePoints, @Const @ByRef Size image_size, @ByVal GpuMat K, @ByVal GpuMat D, @ByVal GpuMatVector rvecs, @ByVal GpuMatVector tvecs)
@Namespace(value="cv::fisheye") public static void stereoRectify(@ByVal Mat K1, @ByVal Mat D1, @ByVal Mat K2, @ByVal Mat D2, @Const @ByRef Size imageSize, @ByVal Mat R, @ByVal Mat tvec, @ByVal Mat R1, @ByVal Mat R2, @ByVal Mat P1, @ByVal Mat P2, @ByVal Mat Q, int flags, @Const @ByRef(nullValue="cv::Size()") Size newImageSize, double balance, double fov_scale)
K1
- First camera matrix.D1
- First camera distortion parameters.K2
- Second camera 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 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.@Namespace(value="cv::fisheye") public static void stereoRectify(@ByVal Mat K1, @ByVal Mat D1, @ByVal Mat K2, @ByVal Mat D2, @Const @ByRef Size imageSize, @ByVal Mat R, @ByVal Mat tvec, @ByVal Mat R1, @ByVal Mat R2, @ByVal Mat P1, @ByVal Mat P2, @ByVal Mat Q, int flags)
@Namespace(value="cv::fisheye") public static void stereoRectify(@ByVal UMat K1, @ByVal UMat D1, @ByVal UMat K2, @ByVal UMat D2, @Const @ByRef Size imageSize, @ByVal UMat R, @ByVal UMat tvec, @ByVal UMat R1, @ByVal UMat R2, @ByVal UMat P1, @ByVal UMat P2, @ByVal UMat Q, int flags, @Const @ByRef(nullValue="cv::Size()") Size newImageSize, double balance, double fov_scale)
@Namespace(value="cv::fisheye") public static void stereoRectify(@ByVal UMat K1, @ByVal UMat D1, @ByVal UMat K2, @ByVal UMat D2, @Const @ByRef Size imageSize, @ByVal UMat R, @ByVal UMat tvec, @ByVal UMat R1, @ByVal UMat R2, @ByVal UMat P1, @ByVal UMat P2, @ByVal UMat Q, int flags)
@Namespace(value="cv::fisheye") public static void stereoRectify(@ByVal GpuMat K1, @ByVal GpuMat D1, @ByVal GpuMat K2, @ByVal GpuMat D2, @Const @ByRef Size imageSize, @ByVal GpuMat R, @ByVal GpuMat tvec, @ByVal GpuMat R1, @ByVal GpuMat R2, @ByVal GpuMat P1, @ByVal GpuMat P2, @ByVal GpuMat Q, int flags, @Const @ByRef(nullValue="cv::Size()") Size newImageSize, double balance, double fov_scale)
@Namespace(value="cv::fisheye") public static void stereoRectify(@ByVal GpuMat K1, @ByVal GpuMat D1, @ByVal GpuMat K2, @ByVal GpuMat D2, @Const @ByRef Size imageSize, @ByVal GpuMat R, @ByVal GpuMat tvec, @ByVal GpuMat R1, @ByVal GpuMat R2, @ByVal GpuMat P1, @ByVal GpuMat P2, @ByVal GpuMat Q, int flags)
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