Calib3d

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OpenCV 2.4.6

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org.opencv.calib3d
Class Calib3d

java.lang.Object
  org.opencv.calib3d.Calib3d

public class Calib3d
extends java.lang.Object
extends java.lang.Object

Field Summary
`static int`	`CALIB_CB_ADAPTIVE_THRESH`
`static int`	`CALIB_CB_ASYMMETRIC_GRID`
`static int`	`CALIB_CB_CLUSTERING`
`static int`	`CALIB_CB_FAST_CHECK`
`static int`	`CALIB_CB_FILTER_QUADS`
`static int`	`CALIB_CB_NORMALIZE_IMAGE`
`static int`	`CALIB_CB_SYMMETRIC_GRID`
`static int`	`CALIB_FIX_ASPECT_RATIO`
`static int`	`CALIB_FIX_FOCAL_LENGTH`
`static int`	`CALIB_FIX_INTRINSIC`
`static int`	`CALIB_FIX_K1`
`static int`	`CALIB_FIX_K2`
`static int`	`CALIB_FIX_K3`
`static int`	`CALIB_FIX_K4`
`static int`	`CALIB_FIX_K5`
`static int`	`CALIB_FIX_K6`
`static int`	`CALIB_FIX_PRINCIPAL_POINT`
`static int`	`CALIB_RATIONAL_MODEL`
`static int`	`CALIB_SAME_FOCAL_LENGTH`
`static int`	`CALIB_USE_INTRINSIC_GUESS`
`static int`	`CALIB_ZERO_DISPARITY`
`static int`	`CALIB_ZERO_TANGENT_DIST`
`static int`	`CV_EPNP`
`static int`	`CV_ITERATIVE`
`static int`	`CV_P3P`
`static int`	`EPNP`
`static int`	`FM_7POINT`
`static int`	`FM_8POINT`
`static int`	`FM_LMEDS`
`static int`	`FM_RANSAC`
`static int`	`ITERATIVE`
`static int`	`LMEDS`
`static int`	`P3P`
`static int`	`RANSAC`

Constructor Summary
`Calib3d()`

Method Summary
`static double`	`calibrateCamera(java.util.List<Mat> objectPoints, java.util.List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, java.util.List<Mat> rvecs, java.util.List<Mat> tvecs)` Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
`static double`	`calibrateCamera(java.util.List<Mat> objectPoints, java.util.List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, java.util.List<Mat> rvecs, java.util.List<Mat> tvecs, int flags)` Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
`static double`	`calibrateCamera(java.util.List<Mat> objectPoints, java.util.List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, java.util.List<Mat> rvecs, java.util.List<Mat> tvecs, int flags, TermCriteria criteria)` Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
`static void`	`calibrationMatrixValues(Mat cameraMatrix, Size imageSize, double apertureWidth, double apertureHeight, double[] fovx, double[] fovy, double[] focalLength, Point principalPoint, double[] aspectRatio)` Computes useful camera characteristics from the camera matrix.
`static void`	`composeRT(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3)` Combines two rotation-and-shift transformations.
`static void`	`composeRT(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1, Mat dt3dt1, Mat dt3dr2, Mat dt3dt2)` Combines two rotation-and-shift transformations.
`static void`	`computeCorrespondEpilines(Mat points, int whichImage, Mat F, Mat lines)` For points in an image of a stereo pair, computes the corresponding epilines in the other image.
`static void`	`convertPointsFromHomogeneous(Mat src, Mat dst)` Converts points from homogeneous to Euclidean space.
`static void`	`convertPointsToHomogeneous(Mat src, Mat dst)` Converts points from Euclidean to homogeneous space.
`static void`	`correctMatches(Mat F, Mat points1, Mat points2, Mat newPoints1, Mat newPoints2)` Refines coordinates of corresponding points.
`static void`	`decomposeProjectionMatrix(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect)` Decomposes a projection matrix into a rotation matrix and a camera matrix.
`static void`	`decomposeProjectionMatrix(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX, Mat rotMatrixY, Mat rotMatrixZ, Mat eulerAngles)` Decomposes a projection matrix into a rotation matrix and a camera matrix.
`static void`	`drawChessboardCorners(Mat image, Size patternSize, MatOfPoint2f corners, boolean patternWasFound)` Renders the detected chessboard corners.
`static int`	`estimateAffine3D(Mat src, Mat dst, Mat out, Mat inliers)` Computes an optimal affine transformation between two 3D point sets.
`static int`	`estimateAffine3D(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold, double confidence)` Computes an optimal affine transformation between two 3D point sets.
`static void`	`filterSpeckles(Mat img, double newVal, int maxSpeckleSize, double maxDiff)` Filters off small noise blobs (speckles) in the disparity map
`static void`	`filterSpeckles(Mat img, double newVal, int maxSpeckleSize, double maxDiff, Mat buf)` Filters off small noise blobs (speckles) in the disparity map
`static boolean`	`findChessboardCorners(Mat image, Size patternSize, MatOfPoint2f corners)` Finds the positions of internal corners of the chessboard.
`static boolean`	`findChessboardCorners(Mat image, Size patternSize, MatOfPoint2f corners, int flags)` Finds the positions of internal corners of the chessboard.
`static boolean`	`findCirclesGridDefault(Mat image, Size patternSize, Mat centers)`
`static boolean`	`findCirclesGridDefault(Mat image, Size patternSize, Mat centers, int flags)`
`static Mat`	`findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2)` Calculates a fundamental matrix from the corresponding points in two images.
`static Mat`	`findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double param1, double param2)` Calculates a fundamental matrix from the corresponding points in two images.
`static Mat`	`findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double param1, double param2, Mat mask)` Calculates a fundamental matrix from the corresponding points in two images.
`static Mat`	`findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints)` Finds a perspective transformation between two planes.
`static Mat`	`findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold)` Finds a perspective transformation between two planes.
`static Mat`	`findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask)` Finds a perspective transformation between two planes.
`static Mat`	`getOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha)` Returns the new camera matrix based on the free scaling parameter.
`static Mat`	`getOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize, Rect validPixROI, boolean centerPrincipalPoint)` Returns the new camera matrix based on the free scaling parameter.
`static Rect`	`getValidDisparityROI(Rect roi1, Rect roi2, int minDisparity, int numberOfDisparities, int SADWindowSize)`
`static Mat`	`initCameraMatrix2D(java.util.List<MatOfPoint3f> objectPoints, java.util.List<MatOfPoint2f> imagePoints, Size imageSize)` Finds an initial camera matrix from 3D-2D point correspondences.
`static Mat`	`initCameraMatrix2D(java.util.List<MatOfPoint3f> objectPoints, java.util.List<MatOfPoint2f> imagePoints, Size imageSize, double aspectRatio)` Finds an initial camera matrix from 3D-2D point correspondences.
`static void`	`matMulDeriv(Mat A, Mat B, Mat dABdA, Mat dABdB)` Computes partial derivatives of the matrix product for each multiplied matrix.
`static void`	`projectPoints(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints)` Projects 3D points to an image plane.
`static void`	`projectPoints(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian, double aspectRatio)` Projects 3D points to an image plane.
`static float`	`rectify3Collinear(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat cameraMatrix3, Mat distCoeffs3, java.util.List<Mat> imgpt1, java.util.List<Mat> imgpt3, Size imageSize, Mat R12, Mat T12, Mat R13, Mat T13, Mat R1, Mat R2, Mat R3, Mat P1, Mat P2, Mat P3, Mat Q, double alpha, Size newImgSize, Rect roi1, Rect roi2, int flags)`
`static void`	`reprojectImageTo3D(Mat disparity, Mat _3dImage, Mat Q)` Reprojects a disparity image to 3D space.
`static void`	`reprojectImageTo3D(Mat disparity, Mat _3dImage, Mat Q, boolean handleMissingValues)` Reprojects a disparity image to 3D space.
`static void`	`reprojectImageTo3D(Mat disparity, Mat _3dImage, Mat Q, boolean handleMissingValues, int ddepth)` Reprojects a disparity image to 3D space.
`static void`	`Rodrigues(Mat src, Mat dst)` Converts a rotation matrix to a rotation vector or vice versa.
`static void`	`Rodrigues(Mat src, Mat dst, Mat jacobian)` Converts a rotation matrix to a rotation vector or vice versa.
`static double[]`	`RQDecomp3x3(Mat src, Mat mtxR, Mat mtxQ)` Computes an RQ decomposition of 3x3 matrices.
`static double[]`	`RQDecomp3x3(Mat src, Mat mtxR, Mat mtxQ, Mat Qx, Mat Qy, Mat Qz)` Computes an RQ decomposition of 3x3 matrices.
`static boolean`	`solvePnP(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec)` Finds an object pose from 3D-2D point correspondences.
`static boolean`	`solvePnP(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int flags)` Finds an object pose from 3D-2D point correspondences.
`static void`	`solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec)` Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
`static void`	`solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int iterationsCount, float reprojectionError, int minInliersCount, Mat inliers, int flags)` Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
`static double`	`stereoCalibrate(java.util.List<Mat> objectPoints, java.util.List<Mat> imagePoints1, java.util.List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F)` Calibrates the stereo camera.
`static double`	`stereoCalibrate(java.util.List<Mat> objectPoints, java.util.List<Mat> imagePoints1, java.util.List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, TermCriteria criteria, int flags)` Calibrates the stereo camera.
`static void`	`stereoRectify(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q)` Computes rectification transforms for each head of a calibrated stereo camera.
`static void`	`stereoRectify(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, Size newImageSize, Rect validPixROI1, Rect validPixROI2)` Computes rectification transforms for each head of a calibrated stereo camera.
`static boolean`	`stereoRectifyUncalibrated(Mat points1, Mat points2, Mat F, Size imgSize, Mat H1, Mat H2)` Computes a rectification transform for an uncalibrated stereo camera.
`static boolean`	`stereoRectifyUncalibrated(Mat points1, Mat points2, Mat F, Size imgSize, Mat H1, Mat H2, double threshold)` Computes a rectification transform for an uncalibrated stereo camera.
`static void`	`triangulatePoints(Mat projMatr1, Mat projMatr2, Mat projPoints1, Mat projPoints2, Mat points4D)` Reconstructs points by triangulation.
`static void`	`validateDisparity(Mat disparity, Mat cost, int minDisparity, int numberOfDisparities)`
`static void`	`validateDisparity(Mat disparity, Mat cost, int minDisparity, int numberOfDisparities, int disp12MaxDisp)`

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Field Detail

CALIB_CB_ADAPTIVE_THRESH

public static final int CALIB_CB_ADAPTIVE_THRESH

See Also:: Constant Field Values

CALIB_CB_ASYMMETRIC_GRID

public static final int CALIB_CB_ASYMMETRIC_GRID

See Also:: Constant Field Values

CALIB_CB_CLUSTERING

public static final int CALIB_CB_CLUSTERING

See Also:: Constant Field Values

CALIB_CB_FAST_CHECK

public static final int CALIB_CB_FAST_CHECK

See Also:: Constant Field Values

CALIB_CB_FILTER_QUADS

public static final int CALIB_CB_FILTER_QUADS

See Also:: Constant Field Values

CALIB_CB_NORMALIZE_IMAGE

public static final int CALIB_CB_NORMALIZE_IMAGE

See Also:: Constant Field Values

CALIB_CB_SYMMETRIC_GRID

public static final int CALIB_CB_SYMMETRIC_GRID

See Also:: Constant Field Values

CALIB_FIX_ASPECT_RATIO

public static final int CALIB_FIX_ASPECT_RATIO

See Also:: Constant Field Values

CALIB_FIX_FOCAL_LENGTH

public static final int CALIB_FIX_FOCAL_LENGTH

See Also:: Constant Field Values

CALIB_FIX_INTRINSIC

public static final int CALIB_FIX_INTRINSIC

See Also:: Constant Field Values

CALIB_FIX_K1

public static final int CALIB_FIX_K1

See Also:: Constant Field Values

CALIB_FIX_K2

public static final int CALIB_FIX_K2

See Also:: Constant Field Values

CALIB_FIX_K3

public static final int CALIB_FIX_K3

See Also:: Constant Field Values

CALIB_FIX_K4

public static final int CALIB_FIX_K4

See Also:: Constant Field Values

CALIB_FIX_K5

public static final int CALIB_FIX_K5

See Also:: Constant Field Values

CALIB_FIX_K6

public static final int CALIB_FIX_K6

See Also:: Constant Field Values

CALIB_FIX_PRINCIPAL_POINT

public static final int CALIB_FIX_PRINCIPAL_POINT

See Also:: Constant Field Values

CALIB_RATIONAL_MODEL

public static final int CALIB_RATIONAL_MODEL

See Also:: Constant Field Values

CALIB_SAME_FOCAL_LENGTH

public static final int CALIB_SAME_FOCAL_LENGTH

See Also:: Constant Field Values

CALIB_USE_INTRINSIC_GUESS

public static final int CALIB_USE_INTRINSIC_GUESS

See Also:: Constant Field Values

CALIB_ZERO_DISPARITY

public static final int CALIB_ZERO_DISPARITY

See Also:: Constant Field Values

CALIB_ZERO_TANGENT_DIST

public static final int CALIB_ZERO_TANGENT_DIST

See Also:: Constant Field Values

CV_EPNP

public static final int CV_EPNP

See Also:: Constant Field Values

CV_ITERATIVE

public static final int CV_ITERATIVE

See Also:: Constant Field Values

CV_P3P

public static final int CV_P3P

See Also:: Constant Field Values

EPNP

public static final int EPNP

See Also:: Constant Field Values

FM_7POINT

public static final int FM_7POINT

See Also:: Constant Field Values

FM_8POINT

public static final int FM_8POINT

See Also:: Constant Field Values

FM_LMEDS

public static final int FM_LMEDS

See Also:: Constant Field Values

FM_RANSAC

public static final int FM_RANSAC

See Also:: Constant Field Values

ITERATIVE

public static final int ITERATIVE

See Also:: Constant Field Values

LMEDS

public static final int LMEDS

See Also:: Constant Field Values

P3P

public static final int P3P

See Also:: Constant Field Values

RANSAC

public static final int RANSAC

See Also:: Constant Field Values

Constructor Detail

Calib3d

public Calib3d()

Method Detail

calibrateCamera

public static double calibrateCamera(java.util.List<Mat> objectPoints,
                                     java.util.List<Mat> imagePoints,
                                     Size imageSize,
                                     Mat cameraMatrix,
                                     Mat distCoeffs,
                                     java.util.List<Mat> rvecs,
                                     java.util.List<Mat> tvecs)

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000] and [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 a 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 "findChessboardCorners"). Currently, initialization of intrinsic parameters (when CV_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 CV_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.

The function returns the final re-projection error.

Note:

If you use a non-square (=non-NxN) grid and "findChessboardCorners" for calibration, and calibrateCamera returns bad values (zero distortion coefficients, an image center very far from (w/2-0.5,h/2-0.5), and/or large differences between f_x and f_y (ratios of 10:1 or more)), then you have probably used patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in "findChessboardCorners".

Parameters:: objectPoints - In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space. The outer vector contains as many elements as the number of the pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns, or even different patterns in different views. Then, the vectors will be different. The points are 3D, but since they are in a pattern coordinate system, then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that Z-coordinate of each input object point is 0.
In the old interface all the vectors of object points from different views are concatenated together.; imagePoints - In the new interface it is a vector of vectors of the projections of calibration pattern points. imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i.
In the old interface all the vectors of object points from different views are concatenated together.; imageSize - Size of the image used only to initialize the intrinsic camera matrix.; cameraMatrix - Output 3x3 floating-point camera matrix A =
|f_x 0 c_x| |0 f_y c_y| |0 0 1| . If CV_CALIB_USE_INTRINSIC_GUESS and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.; distCoeffs - Output vector of distortion coefficients (k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]]) of 4, 5, or 8 elements.; 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.
See Also:: org.opencv.calib3d.Calib3d.calibrateCamera, initCameraMatrix2D(java.util.List, java.util.List, org.opencv.core.Size, double), stereoCalibrate(java.util.List, java.util.List, java.util.List, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.TermCriteria, int), findChessboardCorners(org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.MatOfPoint2f, int), solvePnP(org.opencv.core.MatOfPoint3f, org.opencv.core.MatOfPoint2f, org.opencv.core.Mat, org.opencv.core.MatOfDouble, org.opencv.core.Mat, org.opencv.core.Mat, boolean, int), Imgproc.undistort(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)

calibrateCamera

public static double calibrateCamera(java.util.List<Mat> objectPoints,
                                     java.util.List<Mat> imagePoints,
                                     Size imageSize,
                                     Mat cameraMatrix,
                                     Mat distCoeffs,
                                     java.util.List<Mat> rvecs,
                                     java.util.List<Mat> tvecs,
                                     int flags)

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

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 CV_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.

The function returns the final re-projection error.

Note:

Parameters:

objectPoints - In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space. The outer vector contains as many elements as the number of the pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns, or even different patterns in different views. Then, the vectors will be different. The points are 3D, but since they are in a pattern coordinate system, then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that Z-coordinate of each input object point is 0.

In the old interface all the vectors of object points from different views are concatenated together.

imagePoints - In the new interface it is a vector of vectors of the projections of calibration pattern points. imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i.

In the old interface all the vectors of object points from different views are concatenated together.

imageSize - Size of the image used only to initialize the intrinsic camera matrix.

cameraMatrix - Output 3x3 floating-point camera matrix A =

distCoeffs - Output vector of distortion coefficients (k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]]) of 4, 5, or 8 elements.

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:

CV_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.
CV_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 CV_CALIB_USE_INTRINSIC_GUESS is set too.
CV_CALIB_FIX_ASPECT_RATIO The functions considers only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix. When CV_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.
CV_CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients (p_1, p_2) are set to zeros and stay zero.
CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
CV_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.

stereoCalibrate(java.util.List, java.util.List, java.util.List, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.TermCriteria, int)

, findChessboardCorners(org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.MatOfPoint2f, int),

solvePnP(org.opencv.core.MatOfPoint3f, org.opencv.core.MatOfPoint2f, org.opencv.core.Mat, org.opencv.core.MatOfDouble, org.opencv.core.Mat, org.opencv.core.Mat, boolean, int)

Imgproc.undistort(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)

calibrateCamera

public static double calibrateCamera(java.util.List<Mat> objectPoints,
                                     java.util.List<Mat> imagePoints,
                                     Size imageSize,
                                     Mat cameraMatrix,
                                     Mat distCoeffs,
                                     java.util.List<Mat> rvecs,
                                     java.util.List<Mat> tvecs,
                                     int flags,
                                     TermCriteria criteria)

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

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 CV_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.

The function returns the final re-projection error.

Note:

Parameters:

In the old interface all the vectors of object points from different views are concatenated together.

imageSize - Size of the image used only to initialize the intrinsic camera matrix.

cameraMatrix - Output 3x3 floating-point camera matrix A =

distCoeffs - Output vector of distortion coefficients (k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]]) of 4, 5, or 8 elements.

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:

CV_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.
CV_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 CV_CALIB_USE_INTRINSIC_GUESS is set too.
CV_CALIB_FIX_ASPECT_RATIO The functions considers only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix. When CV_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.
CV_CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients (p_1, p_2) are set to zeros and stay zero.
CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
CV_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.

criteria - Termination criteria for the iterative optimization algorithm.

stereoCalibrate(java.util.List, java.util.List, java.util.List, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.TermCriteria, int)

, findChessboardCorners(org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.MatOfPoint2f, int),

solvePnP(org.opencv.core.MatOfPoint3f, org.opencv.core.MatOfPoint2f, org.opencv.core.Mat, org.opencv.core.MatOfDouble, org.opencv.core.Mat, org.opencv.core.Mat, boolean, int)

Imgproc.undistort(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)

calibrationMatrixValues

public static void calibrationMatrixValues(Mat cameraMatrix,
                                           Size imageSize,
                                           double apertureWidth,
                                           double apertureHeight,
                                           double[] fovx,
                                           double[] fovy,
                                           double[] focalLength,
                                           Point principalPoint,
                                           double[] aspectRatio)

Computes useful camera characteristics from the camera matrix.

The function computes various useful camera characteristics from the previously estimated camera matrix.

Parameters:: cameraMatrix - Input camera matrix that can be estimated by "calibrateCamera" or "stereoCalibrate".; imageSize - Input image size in pixels.; apertureWidth - Physical width of the sensor.; apertureHeight - Physical height 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 pixels.; aspectRatio - f_y/f_x
See Also:: org.opencv.calib3d.Calib3d.calibrationMatrixValues

composeRT

public static void composeRT(Mat rvec1,
                             Mat tvec1,
                             Mat rvec2,
                             Mat tvec2,
                             Mat rvec3,
                             Mat tvec3)

Combines two rotation-and-shift transformations.

The functions compute:

rvec3 = rodrigues ^(-1)(rodrigues(rvec2) * rodrigues(rvec1)) tvec3 = rodrigues(rvec2) * tvec1 + tvec2,

where rodrigues denotes a rotation vector to a rotation matrix transformation, and 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.

Parameters:: 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.
See Also:: org.opencv.calib3d.Calib3d.composeRT

composeRT

public static void composeRT(Mat rvec1,
                             Mat tvec1,
                             Mat rvec2,
                             Mat tvec2,
                             Mat rvec3,
                             Mat tvec3,
                             Mat dr3dr1,
                             Mat dr3dt1,
                             Mat dr3dr2,
                             Mat dr3dt2,
                             Mat dt3dr1,
                             Mat dt3dt1,
                             Mat dt3dr2,
                             Mat dt3dt2)

Combines two rotation-and-shift transformations.

The functions compute:

rvec3 = rodrigues ^(-1)(rodrigues(rvec2) * rodrigues(rvec1)) tvec3 = rodrigues(rvec2) * tvec1 + tvec2,

where rodrigues denotes a rotation vector to a rotation matrix transformation, and rodrigues^(-1) denotes the inverse transformation. See "Rodrigues" for details.

Parameters:: 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 derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and tvec2, respectively.; dr3dt1 - Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and tvec2, respectively.; dr3dr2 - Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and tvec2, respectively.; dr3dt2 - Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and tvec2, respectively.; dt3dr1 - Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and tvec2, respectively.; dt3dt1 - Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and tvec2, respectively.; dt3dr2 - Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and tvec2, respectively.; dt3dt2 - Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and tvec2, respectively.
See Also:: org.opencv.calib3d.Calib3d.composeRT

computeCorrespondEpilines

public static void computeCorrespondEpilines(Mat points,
                                             int whichImage,
                                             Mat F,
                                             Mat lines)

For points in an image of a stereo pair, computes the corresponding epilines in the other image.

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.

Parameters:: points - Input points. N x 1 or 1 x 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).
See Also:: org.opencv.calib3d.Calib3d.computeCorrespondEpilines

convertPointsFromHomogeneous

public static void convertPointsFromHomogeneous(Mat src,
                                                Mat dst)

Converts points from homogeneous to Euclidean space.

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,...).

Parameters:: src - Input vector of N-dimensional points.; dst - Output vector of N-1-dimensional points.
See Also:: org.opencv.calib3d.Calib3d.convertPointsFromHomogeneous

convertPointsToHomogeneous

public static void convertPointsToHomogeneous(Mat src,
                                              Mat dst)

Converts points from Euclidean to homogeneous space.

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).

Parameters:: src - Input vector of N-dimensional points.; dst - Output vector of N+1-dimensional points.
See Also:: org.opencv.calib3d.Calib3d.convertPointsToHomogeneous

correctMatches

public static void correctMatches(Mat F,
                                  Mat points1,
                                  Mat points2,
                                  Mat newPoints1,
                                  Mat newPoints2)

Refines coordinates of corresponding points.

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.

Parameters:: 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.
See Also:: org.opencv.calib3d.Calib3d.correctMatches

decomposeProjectionMatrix

public static void decomposeProjectionMatrix(Mat projMatrix,
                                             Mat cameraMatrix,
                                             Mat rotMatrix,
                                             Mat transVect)

Decomposes a projection matrix into a rotation matrix and a camera matrix.

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 principle axes that results in the same orientation of an object, eg. see [Slabaugh]. Returned tree rotation matrices and corresponding three Euler angules are only one of the possible solutions.

The function is based on "RQDecomp3x3".

Parameters:: 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.
See Also:: org.opencv.calib3d.Calib3d.decomposeProjectionMatrix

decomposeProjectionMatrix

public static void decomposeProjectionMatrix(Mat projMatrix,
                                             Mat cameraMatrix,
                                             Mat rotMatrix,
                                             Mat transVect,
                                             Mat rotMatrixX,
                                             Mat rotMatrixY,
                                             Mat rotMatrixZ,
                                             Mat eulerAngles)

Decomposes a projection matrix into a rotation matrix and a camera matrix.

The function computes a decomposition of a projection matrix into a calibration and a rotation matrix and the position of a camera.

The function is based on "RQDecomp3x3".

Parameters:: 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 - a rotMatrixX; rotMatrixY - a rotMatrixY; rotMatrixZ - a rotMatrixZ; eulerAngles - Optional three-element vector containing three Euler angles of rotation in degrees.
See Also:: org.opencv.calib3d.Calib3d.decomposeProjectionMatrix

drawChessboardCorners

public static void drawChessboardCorners(Mat image,
                                         Size patternSize,
                                         MatOfPoint2f corners,
                                         boolean patternWasFound)

Renders the detected chessboard corners.

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.

Parameters:: 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.
See Also:: org.opencv.calib3d.Calib3d.drawChessboardCorners

estimateAffine3D

public static int estimateAffine3D(Mat src,
                                   Mat dst,
                                   Mat out,
                                   Mat inliers)

Computes an optimal affine transformation between two 3D point sets.

The function estimates an optimal 3D affine transformation between two 3D point sets using the RANSAC algorithm.

Parameters:: src - First input 3D point set.; dst - Second input 3D point set.; out - Output 3D affine transformation matrix 3 x 4.; inliers - Output vector indicating which points are inliers.
See Also:: org.opencv.calib3d.Calib3d.estimateAffine3D

estimateAffine3D

public static int estimateAffine3D(Mat src,
                                   Mat dst,
                                   Mat out,
                                   Mat inliers,
                                   double ransacThreshold,
                                   double confidence)

Computes an optimal affine transformation between two 3D point sets.

The function estimates an optimal 3D affine transformation between two 3D point sets using the RANSAC algorithm.

Parameters:: src - First input 3D point set.; dst - Second input 3D point set.; out - Output 3D affine transformation matrix 3 x 4.; inliers - Output vector indicating which points are inliers.; 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.
See Also:: org.opencv.calib3d.Calib3d.estimateAffine3D

filterSpeckles

public static void filterSpeckles(Mat img,
                                  double newVal,
                                  int maxSpeckleSize,
                                  double maxDiff)

Filters off small noise blobs (speckles) in the disparity map

Parameters:: img - The input 16-bit signed disparity image; newVal - The disparity value used to paint-off the speckles; maxSpeckleSize - The maximum speckle size to consider it a speckle. Larger blobs are not affected by the algorithm; maxDiff - 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.
See Also:: org.opencv.calib3d.Calib3d.filterSpeckles

filterSpeckles

public static void filterSpeckles(Mat img,
                                  double newVal,
                                  int maxSpeckleSize,
                                  double maxDiff,
                                  Mat buf)

Filters off small noise blobs (speckles) in the disparity map

Parameters:: img - The input 16-bit signed disparity image; newVal - The disparity value used to paint-off the speckles; maxSpeckleSize - The maximum speckle size to consider it a speckle. Larger blobs are not affected by the algorithm; maxDiff - 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.
See Also:: org.opencv.calib3d.Calib3d.filterSpeckles

findChessboardCorners

public static boolean findChessboardCorners(Mat image,
                                            Size patternSize,
                                            MatOfPoint2f corners)

Finds the positions of internal corners of the chessboard.

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:



 // C++ code:

 Size patternsize(8,6); //interior number of corners

 Mat gray =....; //source image

 vector 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.

Parameters:: 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 = cvSize(points_per_row,points_per_colum) = cvSize(columns,rows)).; corners - Output array of detected corners.
See Also:: org.opencv.calib3d.Calib3d.findChessboardCorners

findChessboardCorners

public static boolean findChessboardCorners(Mat image,
                                            Size patternSize,
                                            MatOfPoint2f corners,
                                            int flags)

Finds the positions of internal corners of the chessboard.



 // C++ code:

 Size patternsize(8,6); //interior number of corners

 Mat gray =....; //source image

 vector 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);

Parameters:

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 = cvSize(points_per_row,points_per_colum) =
 cvSize(columns,rows))

corners - Output array of detected corners.

flags - Various operation flags that can be zero or a combination of the following values:

CV_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).
CV_CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with "equalizeHist" before applying fixed or adaptive thresholding.
CV_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.

findCirclesGridDefault

public static boolean findCirclesGridDefault(Mat image,
                                             Size patternSize,
                                             Mat centers)

findCirclesGridDefault

public static boolean findCirclesGridDefault(Mat image,
                                             Size patternSize,
                                             Mat centers,
                                             int flags)

findFundamentalMat

public static Mat findFundamentalMat(MatOfPoint2f points1,
                                     MatOfPoint2f points2)

Calculates a fundamental matrix from the corresponding points in two images.

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 x 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.



 // C++ code:

 // Example. Estimation of fundamental matrix using the RANSAC algorithm

 int point_count = 100;

 vector points1(point_count);

 vector 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);

Parameters:: 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.
See Also:: org.opencv.calib3d.Calib3d.findFundamentalMat





findFundamentalMat
public static Mat findFundamentalMat(MatOfPoint2f points1,
                                     MatOfPoint2f points2,
                                     int method,
                                     double param1,
                                     double param2)

Calculates a fundamental matrix from the corresponding points in two images.

 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 x 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.
 


 // C++ code:

 // Example. Estimation of fundamental matrix using the RANSAC algorithm

 int point_count = 100;

 vector points1(point_count);

 vector 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);


Parameters:
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 >= 8
   
 CV_FM_RANSAC for the RANSAC algorithm. N >= 8
   
 CV_FM_LMEDS for the LMedS algorithm. N >= 8
 
param1 - 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.
param2 - Parameter used for the RANSAC or LMedS methods only. It
 specifies a desirable level of confidence (probability) that the estimated
 matrix is correct.
See Also:
org.opencv.calib3d.Calib3d.findFundamentalMat






findFundamentalMat
public static Mat findFundamentalMat(MatOfPoint2f points1,
                                     MatOfPoint2f points2,
                                     int method,
                                     double param1,
                                     double param2,
                                     Mat mask)

Calculates a fundamental matrix from the corresponding points in two images.

 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 x 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.
 


 // C++ code:

 // Example. Estimation of fundamental matrix using the RANSAC algorithm

 int point_count = 100;

 vector points1(point_count);

 vector 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);


Parameters:
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 >= 8
   
 CV_FM_RANSAC for the RANSAC algorithm. N >= 8
   
 CV_FM_LMEDS for the LMedS algorithm. N >= 8
 
param1 - 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.
param2 - Parameter used for the RANSAC or LMedS methods only. It
 specifies a desirable level of confidence (probability) that the estimated
 matrix is correct.
mask - a mask
See Also:
org.opencv.calib3d.Calib3d.findFundamentalMat






findHomography
public static Mat findHomography(MatOfPoint2f srcPoints,
                                 MatOfPoint2f dstPoints)

Finds a perspective transformation between two planes.

 The functions find and return the perspective transformation H
 between the source and the destination planes:

 s_i [x'_i y'_i 1] ~ H [x_i y_i 1]

 so that the back-projection error

 sum _i(x'_i- (h_11 x_i + h_12 y_i + h_13)/(h_(31) x_i + h_32 y_i +
 h_33))^2+ (y'_i- (h_21 x_i + h_22 y_i + h_23)/(h_(31) x_i + h_32 y_i +
 h_33))^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 two robust methods. Both methods,
 RANSAC and LMeDS, try many different random subsets
 of the corresponding point pairs (of four pairs each), estimate the
 homography matrix using this subset and a simple least-square algorithm, and
 then compute the quality/goodness of the computed homography (which is the
 number of inliers for RANSAC or the 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 method RANSAC can handle practically any ratio of outliers
 but it 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. 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.


Parameters:
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.
See Also:
org.opencv.calib3d.Calib3d.findHomography, 
Imgproc.warpPerspective(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar), 
Core.perspectiveTransform(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat), 
Video.estimateRigidTransform(org.opencv.core.Mat, org.opencv.core.Mat, boolean), 
Imgproc.getAffineTransform(org.opencv.core.MatOfPoint2f, org.opencv.core.MatOfPoint2f), 
Imgproc.getPerspectiveTransform(org.opencv.core.Mat, org.opencv.core.Mat)





findHomography
public static Mat findHomography(MatOfPoint2f srcPoints,
                                 MatOfPoint2f dstPoints,
                                 int method,
                                 double ransacReprojThreshold)

Finds a perspective transformation between two planes.

 The functions find and return the perspective transformation H
 between the source and the destination planes:

 s_i [x'_i y'_i 1] ~ H [x_i y_i 1]

 so that the back-projection error

 sum _i(x'_i- (h_11 x_i + h_12 y_i + h_13)/(h_(31) x_i + h_32 y_i +
 h_33))^2+ (y'_i- (h_21 x_i + h_22 y_i + h_23)/(h_(31) x_i + h_32 y_i +
 h_33))^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 two robust methods. Both methods,
 RANSAC and LMeDS, try many different random subsets
 of the corresponding point pairs (of four pairs each), estimate the
 homography matrix using this subset and a simple least-square algorithm, and
 then compute the quality/goodness of the computed homography (which is the
 number of inliers for RANSAC or the 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 method RANSAC can handle practically any ratio of outliers
 but it 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. 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.


Parameters:
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 computed a homography matrix. The following
 methods are possible:
 
    0 - a regular method using all the points
   
 CV_RANSAC - RANSAC-based robust method
   
 CV_LMEDS - Least-Median robust method
 
ransacReprojThreshold - Maximum allowed reprojection error to treat a
 point pair as an inlier (used in the RANSAC method only). That is, if

 | dstPoints _i - convertPointsHomogeneous(H * srcPoints _i)| >
 ransacReprojThreshold

 then the point i is considered 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.
See Also:
org.opencv.calib3d.Calib3d.findHomography, 
Imgproc.warpPerspective(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar), 
Core.perspectiveTransform(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat), 
Video.estimateRigidTransform(org.opencv.core.Mat, org.opencv.core.Mat, boolean), 
Imgproc.getAffineTransform(org.opencv.core.MatOfPoint2f, org.opencv.core.MatOfPoint2f), 
Imgproc.getPerspectiveTransform(org.opencv.core.Mat, org.opencv.core.Mat)





findHomography
public static Mat findHomography(MatOfPoint2f srcPoints,
                                 MatOfPoint2f dstPoints,
                                 int method,
                                 double ransacReprojThreshold,
                                 Mat mask)

Finds a perspective transformation between two planes.

 The functions find and return the perspective transformation H
 between the source and the destination planes:

 s_i [x'_i y'_i 1] ~ H [x_i y_i 1]

 so that the back-projection error

 sum _i(x'_i- (h_11 x_i + h_12 y_i + h_13)/(h_(31) x_i + h_32 y_i +
 h_33))^2+ (y'_i- (h_21 x_i + h_22 y_i + h_23)/(h_(31) x_i + h_32 y_i +
 h_33))^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 two robust methods. Both methods,
 RANSAC and LMeDS, try many different random subsets
 of the corresponding point pairs (of four pairs each), estimate the
 homography matrix using this subset and a simple least-square algorithm, and
 then compute the quality/goodness of the computed homography (which is the
 number of inliers for RANSAC or the 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 method RANSAC can handle practically any ratio of outliers
 but it 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. 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.


Parameters:
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 computed a homography matrix. The following
 methods are possible:
 
    0 - a regular method using all the points
   
 CV_RANSAC - RANSAC-based robust method
   
 CV_LMEDS - Least-Median robust method
 
ransacReprojThreshold - Maximum allowed reprojection error to treat a
 point pair as an inlier (used in the RANSAC method only). That is, if

 | dstPoints _i - convertPointsHomogeneous(H * srcPoints _i)| >
 ransacReprojThreshold

 then the point i is considered 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 (CV_RANSAC
 or CV_LMEDS). Note that the input mask values are ignored.
See Also:
org.opencv.calib3d.Calib3d.findHomography, 
Imgproc.warpPerspective(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar), 
Core.perspectiveTransform(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat), 
Video.estimateRigidTransform(org.opencv.core.Mat, org.opencv.core.Mat, boolean), 
Imgproc.getAffineTransform(org.opencv.core.MatOfPoint2f, org.opencv.core.MatOfPoint2f), 
Imgproc.getPerspectiveTransform(org.opencv.core.Mat, org.opencv.core.Mat)





getOptimalNewCameraMatrix
public static Mat getOptimalNewCameraMatrix(Mat cameraMatrix,
                                            Mat distCoeffs,
                                            Size imageSize,
                                            double alpha)

Returns the new camera matrix based on the free scaling parameter.

 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 undistortion 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".


Parameters:
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]]) of 4, 5, or 8 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.
See Also:
org.opencv.calib3d.Calib3d.getOptimalNewCameraMatrix





getOptimalNewCameraMatrix
public static Mat getOptimalNewCameraMatrix(Mat cameraMatrix,
                                            Mat distCoeffs,
                                            Size imageSize,
                                            double alpha,
                                            Size newImgSize,
                                            Rect validPixROI,
                                            boolean centerPrincipalPoint)

Returns the new camera matrix based on the free scaling parameter.

 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 undistortion 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".


Parameters:
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]]) of 4, 5, or 8 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 - a newImgSize
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.
See Also:
org.opencv.calib3d.Calib3d.getOptimalNewCameraMatrix





getValidDisparityROI
public static Rect getValidDisparityROI(Rect roi1,
                                        Rect roi2,
                                        int minDisparity,
                                        int numberOfDisparities,
                                        int SADWindowSize)








initCameraMatrix2D
public static Mat initCameraMatrix2D(java.util.List<MatOfPoint3f> objectPoints,
                                     java.util.List<MatOfPoint2f> imagePoints,
                                     Size imageSize)

Finds an initial camera matrix from 3D-2D point correspondences.

 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.


Parameters:
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.
See Also:
org.opencv.calib3d.Calib3d.initCameraMatrix2D





initCameraMatrix2D
public static Mat initCameraMatrix2D(java.util.List<MatOfPoint3f> objectPoints,
                                     java.util.List<MatOfPoint2f> imagePoints,
                                     Size imageSize,
                                     double aspectRatio)

Finds an initial camera matrix from 3D-2D point correspondences.

 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.


Parameters:
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 *
 aspectRatio.
See Also:
org.opencv.calib3d.Calib3d.initCameraMatrix2D





matMulDeriv
public static void matMulDeriv(Mat A,
                               Mat B,
                               Mat dABdA,
                               Mat dABdB)

Computes partial derivatives of the matrix product for each multiplied
 matrix.

 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.


Parameters:
A - First multiplied matrix.
B - Second multiplied matrix.
dABdA - First output derivative matrix d(A*B)/dA of size
 A.rows*B.cols x (A.rows*A.cols).
dABdB - Second output derivative matrix d(A*B)/dB of size
 A.rows*B.cols x (B.rows*B.cols).
See Also:
org.opencv.calib3d.Calib3d.matMulDeriv





projectPoints
public static void projectPoints(MatOfPoint3f objectPoints,
                                 Mat rvec,
                                 Mat tvec,
                                 Mat cameraMatrix,
                                 MatOfDouble distCoeffs,
                                 MatOfPoint2f imagePoints)

Projects 3D points to an image plane.

 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. The Jacobians are used during the
 global optimization in "calibrateCamera", "solvePnP", and "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, you can get various useful partial cases of the
 function. This means that you 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.


Parameters:
objectPoints - Array of object points, 3xN/Nx3 1-channel or 1xN/Nx1
 3-channel (or vector), where N is the number of points
 in the view.
rvec - Rotation vector. See "Rodrigues" for details.
tvec - Translation vector.
cameraMatrix - Camera matrix A =
 |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]]) of 4, 5, or 8 elements. If the vector is
 NULL/empty, the zero distortion coefficients are assumed.
imagePoints - Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1
 2-channel, or vector.
See Also:
org.opencv.calib3d.Calib3d.projectPoints





projectPoints
public static void projectPoints(MatOfPoint3f objectPoints,
                                 Mat rvec,
                                 Mat tvec,
                                 Mat cameraMatrix,
                                 MatOfDouble distCoeffs,
                                 MatOfPoint2f imagePoints,
                                 Mat jacobian,
                                 double aspectRatio)

Projects 3D points to an image plane.

 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. The Jacobians are used during the
 global optimization in "calibrateCamera", "solvePnP", and "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, you can get various useful partial cases of the
 function. This means that you 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.


Parameters:
objectPoints - Array of object points, 3xN/Nx3 1-channel or 1xN/Nx1
 3-channel (or vector), where N is the number of points
 in the view.
rvec - Rotation vector. See "Rodrigues" for details.
tvec - Translation vector.
cameraMatrix - Camera matrix A =
 |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]]) of 4, 5, or 8 elements. If the vector is
 NULL/empty, the zero distortion coefficients are assumed.
imagePoints - Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1
 2-channel, or vector.
jacobian - Optional output 2Nx(10+) jacobian matrix of
 derivatives of image points with respect to components of the rotation
 vector, translation vector, focal lengths, coordinates of the principal point
 and the distortion coefficients. In the old interface different components of
 the jacobian are returned via different output parameters.
aspectRatio - Optional "fixed aspect ratio" parameter. If the parameter
 is not 0, the function assumes that the aspect ratio (*fx/fy*) is fixed and
 correspondingly adjusts the jacobian matrix.
See Also:
org.opencv.calib3d.Calib3d.projectPoints





rectify3Collinear
public static float rectify3Collinear(Mat cameraMatrix1,
                                      Mat distCoeffs1,
                                      Mat cameraMatrix2,
                                      Mat distCoeffs2,
                                      Mat cameraMatrix3,
                                      Mat distCoeffs3,
                                      java.util.List<Mat> imgpt1,
                                      java.util.List<Mat> imgpt3,
                                      Size imageSize,
                                      Mat R12,
                                      Mat T12,
                                      Mat R13,
                                      Mat T13,
                                      Mat R1,
                                      Mat R2,
                                      Mat R3,
                                      Mat P1,
                                      Mat P2,
                                      Mat P3,
                                      Mat Q,
                                      double alpha,
                                      Size newImgSize,
                                      Rect roi1,
                                      Rect roi2,
                                      int flags)








reprojectImageTo3D
public static void reprojectImageTo3D(Mat disparity,
                                      Mat _3dImage,
                                      Mat Q)

Reprojects a disparity image to 3D space.

 The function transforms a single-channel disparity map to a 3-channel image
 representing a 3D surface. That is, for each pixel (x,y) andthe
 corresponding disparity d=disparity(x,y), it computes:

 [X Y Z W]^T = Q *[x y disparity(x,y) 1]^T
 _3dImage(x,y) = (X/W, Y/W, Z/W) 

 The matrix Q can be an arbitrary 4 x 4 matrix (for
 example, the one computed by "stereoRectify"). To reproject a sparse set of
 points {(x,y,d),...} to 3D space, use "perspectiveTransform".


Parameters:
disparity - Input single-channel 8-bit unsigned, 16-bit signed, 32-bit
 signed or 32-bit floating-point disparity image.
_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.
Q - 4 x 4 perspective transformation matrix that can be
 obtained with "stereoRectify".
See Also:
org.opencv.calib3d.Calib3d.reprojectImageTo3D





reprojectImageTo3D
public static void reprojectImageTo3D(Mat disparity,
                                      Mat _3dImage,
                                      Mat Q,
                                      boolean handleMissingValues)

Reprojects a disparity image to 3D space.

 The function transforms a single-channel disparity map to a 3-channel image
 representing a 3D surface. That is, for each pixel (x,y) andthe
 corresponding disparity d=disparity(x,y), it computes:

 [X Y Z W]^T = Q *[x y disparity(x,y) 1]^T
 _3dImage(x,y) = (X/W, Y/W, Z/W) 

 The matrix Q can be an arbitrary 4 x 4 matrix (for
 example, the one computed by "stereoRectify"). To reproject a sparse set of
 points {(x,y,d),...} to 3D space, use "perspectiveTransform".


Parameters:
disparity - Input single-channel 8-bit unsigned, 16-bit signed, 32-bit
 signed or 32-bit floating-point disparity image.
_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.
Q - 4 x 4 perspective transformation matrix that can be
 obtained with "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 :ocv:funcx:"StereoBM.operator()") are
 transformed to 3D points with a very large Z value (currently set to 10000).
See Also:
org.opencv.calib3d.Calib3d.reprojectImageTo3D





reprojectImageTo3D
public static void reprojectImageTo3D(Mat disparity,
                                      Mat _3dImage,
                                      Mat Q,
                                      boolean handleMissingValues,
                                      int ddepth)

Reprojects a disparity image to 3D space.

 The function transforms a single-channel disparity map to a 3-channel image
 representing a 3D surface. That is, for each pixel (x,y) andthe
 corresponding disparity d=disparity(x,y), it computes:

 [X Y Z W]^T = Q *[x y disparity(x,y) 1]^T
 _3dImage(x,y) = (X/W, Y/W, Z/W) 

 The matrix Q can be an arbitrary 4 x 4 matrix (for
 example, the one computed by "stereoRectify"). To reproject a sparse set of
 points {(x,y,d),...} to 3D space, use "perspectiveTransform".


Parameters:
disparity - Input single-channel 8-bit unsigned, 16-bit signed, 32-bit
 signed or 32-bit floating-point disparity image.
_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.
Q - 4 x 4 perspective transformation matrix that can be
 obtained with "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 :ocv:funcx:"StereoBM.operator()") 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.
See Also:
org.opencv.calib3d.Calib3d.reprojectImageTo3D





Rodrigues
public static void Rodrigues(Mat src,
                             Mat dst)

Converts a rotation matrix to a rotation vector or vice versa.

 theta <- norm(r)
 r <- r/ theta
 R = cos(theta) I + (1- cos(theta)) r r^T + sin(theta)
 |0 -r_z r_y|
 |r_z 0 -r_x|
 |-r_y r_x 0|
 

 Inverse transformation can be also done easily, since

 sin(theta)
 |0 -r_z r_y|
 |r_z 0 -r_x|
 |-r_y r_x 0|
 = (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 "calibrateCamera", "stereoCalibrate", or "solvePnP".


Parameters:
src - Input rotation vector (3x1 or 1x3) or rotation matrix (3x3).
dst - Output rotation matrix (3x3) or rotation vector (3x1 or 1x3),
 respectively.
See Also:
org.opencv.calib3d.Calib3d.Rodrigues





Rodrigues
public static void Rodrigues(Mat src,
                             Mat dst,
                             Mat jacobian)

Converts a rotation matrix to a rotation vector or vice versa.

 theta <- norm(r)
 r <- r/ theta
 R = cos(theta) I + (1- cos(theta)) r r^T + sin(theta)
 |0 -r_z r_y|
 |r_z 0 -r_x|
 |-r_y r_x 0|
 

 Inverse transformation can be also done easily, since

 sin(theta)
 |0 -r_z r_y|
 |r_z 0 -r_x|
 |-r_y r_x 0|
 = (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 "calibrateCamera", "stereoCalibrate", or "solvePnP".


Parameters:
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.
See Also:
org.opencv.calib3d.Calib3d.Rodrigues





RQDecomp3x3
public static double[] RQDecomp3x3(Mat src,
                                   Mat mtxR,
                                   Mat mtxQ)

Computes an RQ decomposition of 3x3 matrices.

 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 principle axes that results in the same orientation of an object, eg.
 see [Slabaugh]. Returned tree rotation matrices and corresponding three Euler
 angules are only one of the possible solutions.


Parameters:
src - 3x3 input matrix.
mtxR - Output 3x3 upper-triangular matrix.
mtxQ - Output 3x3 orthogonal matrix.
See Also:
org.opencv.calib3d.Calib3d.RQDecomp3x3





RQDecomp3x3
public static double[] RQDecomp3x3(Mat src,
                                   Mat mtxR,
                                   Mat mtxQ,
                                   Mat Qx,
                                   Mat Qy,
                                   Mat Qz)

Computes an RQ decomposition of 3x3 matrices.

 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 principle axes that results in the same orientation of an object, eg.
 see [Slabaugh]. Returned tree rotation matrices and corresponding three Euler
 angules are only one of the possible solutions.


Parameters:
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.
See Also:
org.opencv.calib3d.Calib3d.RQDecomp3x3





solvePnP
public static boolean solvePnP(MatOfPoint3f objectPoints,
                               MatOfPoint2f imagePoints,
                               Mat cameraMatrix,
                               MatOfDouble distCoeffs,
                               Mat rvec,
                               Mat tvec)

Finds an object pose from 3D-2D point correspondences.

 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.


Parameters:
objectPoints - Array of object points in the object coordinate space,
 3xN/Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points.
 vector can be also passed here.
imagePoints - Array of corresponding image points, 2xN/Nx2 1-channel or
 1xN/Nx1 2-channel, where N is the number of points. vector
 can be also passed here.
cameraMatrix - Input camera matrix A =
 |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]]) of 4, 5, or 8 elements. If the vector is
 NULL/empty, the zero distortion coefficients are assumed.
rvec - Output rotation vector (see "Rodrigues") that, together with
 tvec, brings points from the model coordinate system to the
 camera coordinate system.
tvec - Output translation vector.
See Also:
org.opencv.calib3d.Calib3d.solvePnP





solvePnP
public static boolean solvePnP(MatOfPoint3f objectPoints,
                               MatOfPoint2f imagePoints,
                               Mat cameraMatrix,
                               MatOfDouble distCoeffs,
                               Mat rvec,
                               Mat tvec,
                               boolean useExtrinsicGuess,
                               int flags)

Finds an object pose from 3D-2D point correspondences.

 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.


Parameters:
objectPoints - Array of object points in the object coordinate space,
 3xN/Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points.
 vector can be also passed here.
imagePoints - Array of corresponding image points, 2xN/Nx2 1-channel or
 1xN/Nx1 2-channel, where N is the number of points. vector
 can be also passed here.
cameraMatrix - Input camera matrix A =
 |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]]) of 4, 5, or 8 elements. If the vector is
 NULL/empty, the zero distortion coefficients are assumed.
rvec - Output rotation vector (see "Rodrigues") that, together with
 tvec, brings points from the model coordinate system to the
 camera coordinate system.
tvec - Output translation vector.
useExtrinsicGuess - 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:
 
    CV_ITERATIVE Iterative method is based on 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.
   
 CV_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". In this case the function requires exactly four object and image
 points.
   
 CV_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".
 
See Also:
org.opencv.calib3d.Calib3d.solvePnP





solvePnPRansac
public static void solvePnPRansac(MatOfPoint3f objectPoints,
                                  MatOfPoint2f imagePoints,
                                  Mat cameraMatrix,
                                  MatOfDouble distCoeffs,
                                  Mat rvec,
                                  Mat tvec)

Finds an object pose from 3D-2D point correspondences using the RANSAC
 scheme.

 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
 "projectPoints") objectPoints. The use of RANSAC makes the
 function resistant to outliers. The function is parallelized with the TBB
 library.


Parameters:
objectPoints - Array of object points in the object coordinate space,
 3xN/Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points.
 vector can be also passed here.
imagePoints - Array of corresponding image points, 2xN/Nx2 1-channel or
 1xN/Nx1 2-channel, where N is the number of points. vector
 can be also passed here.
cameraMatrix - Input camera matrix A =
 |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]]) of 4, 5, or 8 elements. If the vector is
 NULL/empty, the zero distortion coefficients are assumed.
rvec - Output rotation vector (see "Rodrigues") that, together with
 tvec, brings points from the model coordinate system to the
 camera coordinate system.
tvec - Output translation vector.
See Also:
org.opencv.calib3d.Calib3d.solvePnPRansac





solvePnPRansac
public static void solvePnPRansac(MatOfPoint3f objectPoints,
                                  MatOfPoint2f imagePoints,
                                  Mat cameraMatrix,
                                  MatOfDouble distCoeffs,
                                  Mat rvec,
                                  Mat tvec,
                                  boolean useExtrinsicGuess,
                                  int iterationsCount,
                                  float reprojectionError,
                                  int minInliersCount,
                                  Mat inliers,
                                  int flags)

Finds an object pose from 3D-2D point correspondences using the RANSAC
 scheme.

 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
 "projectPoints") objectPoints. The use of RANSAC makes the
 function resistant to outliers. The function is parallelized with the TBB
 library.


Parameters:
objectPoints - Array of object points in the object coordinate space,
 3xN/Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points.
 vector can be also passed here.
imagePoints - Array of corresponding image points, 2xN/Nx2 1-channel or
 1xN/Nx1 2-channel, where N is the number of points. vector
 can be also passed here.
cameraMatrix - Input camera matrix A =
 |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]]) of 4, 5, or 8 elements. If the vector is
 NULL/empty, the zero distortion coefficients are assumed.
rvec - Output rotation vector (see "Rodrigues") that, together with
 tvec, brings points from the model coordinate system to the
 camera coordinate system.
tvec - Output translation vector.
useExtrinsicGuess - 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.
minInliersCount - Number of inliers. If the algorithm at some stage
 finds more inliers than minInliersCount, it finishes.
inliers - Output vector that contains indices of inliers in
 objectPoints and imagePoints.
flags - Method for solving a PnP problem (see "solvePnP").
See Also:
org.opencv.calib3d.Calib3d.solvePnPRansac





stereoCalibrate
public static double stereoCalibrate(java.util.List<Mat> objectPoints,
                                     java.util.List<Mat> imagePoints1,
                                     java.util.List<Mat> imagePoints2,
                                     Mat cameraMatrix1,
                                     Mat distCoeffs1,
                                     Mat cameraMatrix2,
                                     Mat distCoeffs2,
                                     Size imageSize,
                                     Mat R,
                                     Mat T,
                                     Mat E,
                                     Mat F)

Calibrates the stereo camera.

 The function estimates transformation between two cameras making a stereo
 pair. If you have a stereo camera where the relative position and orientation
 of two cameras is fixed, and if you computed poses of an object relative to
 the first camera and to the second camera, (R1, T1) and (R2, T2),
 respectively (this can be done with "solvePnP"), then those poses definitely
 relate to each other. This means that, given (R_1,T_1), it
 should be possible to compute (R_2,T_2). You only need to
 know the position and orientation of the second camera relative to the first
 camera. This is what the described function does. It computes
 (R,T) so that:

 R_2=R*R_1<BR>T_2=R*T_1 + T,

 Optionally, it computes the essential matrix E:

 E=
 |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 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
 CV_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
 CV_CALIB_SAME_FOCAL_LENGTH and CV_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.


Parameters:
objectPoints - Vector of vectors of the calibration pattern points.
imagePoints1 - Vector of vectors of the projections of the calibration
 pattern points, observed by the first camera.
imagePoints2 - Vector of vectors of the projections of the calibration
 pattern points, observed by the second camera.
cameraMatrix1 - Input/output first camera matrix: 
 |f_x^j 0 c_x^j|
 |0 f_y^j c_y^j|
 |0 0 1|
 , j = 0, 1. If any of CV_CALIB_USE_INTRINSIC_GUESS,
 CV_CALIB_FIX_ASPECT_RATIO, CV_CALIB_FIX_INTRINSIC,
 or CV_CALIB_FIX_FOCAL_LENGTH are specified, some or all of the
 matrix components must be initialized. See the flags description for details.
distCoeffs1 - Input/output vector of distortion coefficients (k_1,
 k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]]) of 4, 5, or 8 elements. The
 output vector length depends on the flags.
cameraMatrix2 - Input/output second camera matrix. The parameter is
 similar to cameraMatrix1.
distCoeffs2 - Input/output lens distortion coefficients for the second
 camera. The parameter is similar to distCoeffs1.
imageSize - Size of the image used only to initialize intrinsic camera
 matrix.
R - Output rotation matrix between the 1st and the 2nd camera coordinate
 systems.
T - Output translation vector between the coordinate systems of the
 cameras.
E - Output essential matrix.
F - Output fundamental matrix.
See Also:
org.opencv.calib3d.Calib3d.stereoCalibrate





stereoCalibrate
public static double stereoCalibrate(java.util.List<Mat> objectPoints,
                                     java.util.List<Mat> imagePoints1,
                                     java.util.List<Mat> imagePoints2,
                                     Mat cameraMatrix1,
                                     Mat distCoeffs1,
                                     Mat cameraMatrix2,
                                     Mat distCoeffs2,
                                     Size imageSize,
                                     Mat R,
                                     Mat T,
                                     Mat E,
                                     Mat F,
                                     TermCriteria criteria,
                                     int flags)

Calibrates the stereo camera.

 The function estimates transformation between two cameras making a stereo
 pair. If you have a stereo camera where the relative position and orientation
 of two cameras is fixed, and if you computed poses of an object relative to
 the first camera and to the second camera, (R1, T1) and (R2, T2),
 respectively (this can be done with "solvePnP"), then those poses definitely
 relate to each other. This means that, given (R_1,T_1), it
 should be possible to compute (R_2,T_2). You only need to
 know the position and orientation of the second camera relative to the first
 camera. This is what the described function does. It computes
 (R,T) so that:

 R_2=R*R_1<BR>T_2=R*T_1 + T,

 Optionally, it computes the essential matrix E:

 E=
 |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 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
 CV_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
 CV_CALIB_SAME_FOCAL_LENGTH and CV_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.


Parameters:
objectPoints - Vector of vectors of the calibration pattern points.
imagePoints1 - Vector of vectors of the projections of the calibration
 pattern points, observed by the first camera.
imagePoints2 - Vector of vectors of the projections of the calibration
 pattern points, observed by the second camera.
cameraMatrix1 - Input/output first camera matrix: 
 |f_x^j 0 c_x^j|
 |0 f_y^j c_y^j|
 |0 0 1|
 , j = 0, 1. If any of CV_CALIB_USE_INTRINSIC_GUESS,
 CV_CALIB_FIX_ASPECT_RATIO, CV_CALIB_FIX_INTRINSIC,
 or CV_CALIB_FIX_FOCAL_LENGTH are specified, some or all of the
 matrix components must be initialized. See the flags description for details.
distCoeffs1 - Input/output vector of distortion coefficients (k_1,
 k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]]) of 4, 5, or 8 elements. The
 output vector length depends on the flags.
cameraMatrix2 - Input/output second camera matrix. The parameter is
 similar to cameraMatrix1.
distCoeffs2 - Input/output lens distortion coefficients for the second
 camera. The parameter is similar to distCoeffs1.
imageSize - Size of the image used only to initialize intrinsic camera
 matrix.
R - Output rotation matrix between the 1st and the 2nd camera coordinate
 systems.
T - Output translation vector between the coordinate systems of the
 cameras.
E - Output essential matrix.
F - Output fundamental matrix.
criteria - a criteria
flags - Different flags that may be zero or a combination of the
 following values:
 
    CV_CALIB_FIX_INTRINSIC Fix cameraMatrix? and
 distCoeffs? so that only R, T, E, and
 F matrices are estimated.
   
 CV_CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic
 parameters according to the specified flags. Initial values are provided by
 the user.
   
 CV_CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the
 optimization.
   
 CV_CALIB_FIX_FOCAL_LENGTH Fix f^j_x and f^j_y.
   
 CV_CALIB_FIX_ASPECT_RATIO Optimize f^j_y. Fix the ratio
 f^j_x/f^j_y.
   
 CV_CALIB_SAME_FOCAL_LENGTH Enforce f^0_x=f^1_x and
 f^0_y=f^1_y.
   
 CV_CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for
 each camera to zeros and fix there.
   
 CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6 Do not change the corresponding
 radial distortion coefficient during the optimization. If CV_CALIB_USE_INTRINSIC_GUESS
 is set, the coefficient from the supplied distCoeffs matrix is
 used. Otherwise, it is set to 0.
   
 CV_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.
 
See Also:
org.opencv.calib3d.Calib3d.stereoCalibrate





stereoRectify
public static void stereoRectify(Mat cameraMatrix1,
                                 Mat distCoeffs1,
                                 Mat cameraMatrix2,
                                 Mat distCoeffs2,
                                 Size imageSize,
                                 Mat R,
                                 Mat T,
                                 Mat R1,
                                 Mat R2,
                                 Mat P1,
                                 Mat P2,
                                 Mat Q)

Computes rectification transforms for each head of a calibrated stereo
 camera.

 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:
 

 P1 = f 0 cx_1 0
 0 f cy 0
 0 0 1 0 



 P2 = f 0 cx_2 T_x*f
 0 f cy 0
 0 0 1 0,

 where T_x is a horizontal shift between the cameras and
 cx_1=cx_2 if CV_CALIB_ZERO_DISPARITY is set.
 
    Vertical stereo: the first and the second camera views are shifted
 relative to each other mainly in 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:
 

 P1 = f 0 cx 0
 0 f cy_1 0
 0 0 1 0 



 P2 = f 0 cx 0
 0 f cy_2 T_y*f
 0 0 1 0,

 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.


Parameters:
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 between the coordinate systems of the first and the
 second cameras.
T - 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 x 4 disparity-to-depth mapping matrix (see
 "reprojectImageTo3D").
See Also:
org.opencv.calib3d.Calib3d.stereoRectify





stereoRectify
public static void stereoRectify(Mat cameraMatrix1,
                                 Mat distCoeffs1,
                                 Mat cameraMatrix2,
                                 Mat distCoeffs2,
                                 Size imageSize,
                                 Mat R,
                                 Mat T,
                                 Mat R1,
                                 Mat R2,
                                 Mat P1,
                                 Mat P2,
                                 Mat Q,
                                 int flags,
                                 double alpha,
                                 Size newImageSize,
                                 Rect validPixROI1,
                                 Rect validPixROI2)

Computes rectification transforms for each head of a calibrated stereo
 camera.

 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:
 

 P1 = f 0 cx_1 0
 0 f cy 0
 0 0 1 0 



 P2 = f 0 cx_2 T_x*f
 0 f cy 0
 0 0 1 0,

 where T_x is a horizontal shift between the cameras and
 cx_1=cx_2 if CV_CALIB_ZERO_DISPARITY is set.
 
    Vertical stereo: the first and the second camera views are shifted
 relative to each other mainly in 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:
 

 P1 = f 0 cx 0
 0 f cy_1 0
 0 0 1 0 



 P2 = f 0 cx 0
 0 f cy_2 T_y*f
 0 0 1 0,

 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.


Parameters:
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 between the coordinate systems of the first and the
 second cameras.
T - 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 x 4 disparity-to-depth mapping matrix (see
 "reprojectImageTo3D").
flags - Operation flags that may be zero or CV_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). Obviously, 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 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).
See Also:
org.opencv.calib3d.Calib3d.stereoRectify





stereoRectifyUncalibrated
public static boolean stereoRectifyUncalibrated(Mat points1,
                                                Mat points2,
                                                Mat F,
                                                Size imgSize,
                                                Mat H1,
                                                Mat H2)

Computes a rectification transform for an uncalibrated stereo camera.

 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 [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".


Parameters:
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.
See Also:
org.opencv.calib3d.Calib3d.stereoRectifyUncalibrated





stereoRectifyUncalibrated
public static boolean stereoRectifyUncalibrated(Mat points1,
                                                Mat points2,
                                                Mat F,
                                                Size imgSize,
                                                Mat H1,
                                                Mat H2,
                                                double threshold)

Computes a rectification transform for an uncalibrated stereo camera.

 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 [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".


Parameters:
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 |points2[i]^T*F*points1[i]|>threshold)
 are rejected prior to computing the homographies. Otherwise,all the points
 are considered inliers.
See Also:
org.opencv.calib3d.Calib3d.stereoRectifyUncalibrated





triangulatePoints
public static void triangulatePoints(Mat projMatr1,
                                     Mat projMatr2,
                                     Mat projPoints1,
                                     Mat projPoints2,
                                     Mat points4D)

Reconstructs points by triangulation.

 The function reconstructs 3-dimensional points (in homogeneous coordinates)
 by using their observations with a stereo camera. Projections matrices can be
 obtained from "stereoRectify".


Parameters:
projMatr1 - 3x4 projection matrix of the first camera.
projMatr2 - 3x4 projection matrix of the second camera.
projPoints1 - 2xN array of feature points in the first image. In case of
 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
 case of 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.
See Also:
org.opencv.calib3d.Calib3d.triangulatePoints, 
reprojectImageTo3D(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, boolean, int)





validateDisparity
public static void validateDisparity(Mat disparity,
                                     Mat cost,
                                     int minDisparity,
                                     int numberOfDisparities)








validateDisparity
public static void validateDisparity(Mat disparity,
                                     Mat cost,
                                     int minDisparity,
                                     int numberOfDisparities,
                                     int disp12MaxDisp)

















  
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org.opencv.calib3d Class Calib3d

CALIB_CB_ADAPTIVE_THRESH

CALIB_CB_ASYMMETRIC_GRID

CALIB_CB_CLUSTERING

CALIB_CB_FAST_CHECK

CALIB_CB_FILTER_QUADS

CALIB_CB_NORMALIZE_IMAGE

CALIB_CB_SYMMETRIC_GRID

CALIB_FIX_ASPECT_RATIO

CALIB_FIX_FOCAL_LENGTH

CALIB_FIX_INTRINSIC

CALIB_FIX_K1

CALIB_FIX_K2

CALIB_FIX_K3

CALIB_FIX_K4

CALIB_FIX_K5

CALIB_FIX_K6

CALIB_FIX_PRINCIPAL_POINT

CALIB_RATIONAL_MODEL

CALIB_SAME_FOCAL_LENGTH

CALIB_USE_INTRINSIC_GUESS

CALIB_ZERO_DISPARITY

CALIB_ZERO_TANGENT_DIST

CV_EPNP

CV_ITERATIVE

CV_P3P

EPNP

FM_7POINT

FM_8POINT

FM_LMEDS

FM_RANSAC

ITERATIVE

LMEDS

P3P

RANSAC

Calib3d

calibrateCamera

calibrateCamera

calibrateCamera

calibrationMatrixValues

composeRT

composeRT

computeCorrespondEpilines

convertPointsFromHomogeneous

convertPointsToHomogeneous

correctMatches

decomposeProjectionMatrix

decomposeProjectionMatrix

drawChessboardCorners

estimateAffine3D

estimateAffine3D

filterSpeckles

filterSpeckles

findChessboardCorners

findChessboardCorners

findCirclesGridDefault

findCirclesGridDefault

findFundamentalMat

findFundamentalMat

findFundamentalMat

findHomography

findHomography

findHomography

getOptimalNewCameraMatrix

getOptimalNewCameraMatrix

getValidDisparityROI

initCameraMatrix2D

initCameraMatrix2D

matMulDeriv

projectPoints

projectPoints

rectify3Collinear

reprojectImageTo3D

reprojectImageTo3D

reprojectImageTo3D

Rodrigues

Rodrigues

RQDecomp3x3

RQDecomp3x3

solvePnP

org.opencv.calib3d
Class Calib3d