mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2025-08-06 06:26:29 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Fixed hundreds of documentation problems

Browse Source
This commit is contained in:
Andrey Kamaev 2012-05-28 07:36:14 +00:00
parent 7e698726e4
commit eb2f1f81ed
65 changed files with 1708 additions and 1680 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

83
modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst
View File

@ -115,7 +115,7 @@ Finds the camera intrinsic and extrinsic parameters from several views of a cali
.. ocv:pyfunction:: cv2.calibrateCamera(objectPoints, imagePoints, imageSize[, cameraMatrix[, distCoeffs[, rvecs[, tvecs[, flags[, criteria]]]]]]) -> retval, cameraMatrix, distCoeffs, rvecs, tvecs
.. ocv:cfunction:: double cvCalibrateCamera2(const CvMat* objectPoints, const CvMat* imagePoints, const CvMat* pointCounts, CvSize imageSize, CvMat* cameraMatrix, CvMat* distCoeffs, CvMat* rvecs=NULL, CvMat* tvecs=NULL, int flags=0, CvTermCriteria term_crit = cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,DBL_EPSILON) )
.. ocv:cfunction:: double cvCalibrateCamera2( const CvMat* object_points, const CvMat* image_points, const CvMat* point_counts, CvSize image_size, CvMat* camera_matrix, CvMat* distortion_coeffs, CvMat* rotation_vectors=NULL, CvMat* translation_vectors=NULL, int flags=0, CvTermCriteria term_crit=cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,DBL_EPSILON) )
.. ocv:pyoldfunction:: cv.CalibrateCamera2(objectPoints, imagePoints, pointCounts, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs, flags=0)-> None
@ -268,7 +268,7 @@ For points in an image of a stereo pair, computes the corresponding epilines in
.. ocv:function:: void computeCorrespondEpilines( InputArray points, int whichImage, InputArray F, OutputArray lines )
.. ocv:cfunction:: void cvComputeCorrespondEpilines( const CvMat* points, int whichImage, const CvMat* F, CvMat* lines)
.. ocv:cfunction:: void cvComputeCorrespondEpilines( const CvMat* points, int which_image, const CvMat* fundamental_matrix, CvMat* correspondent_lines )
.. ocv:pyoldfunction:: cv.ComputeCorrespondEpilines(points, whichImage, F, lines) -> None
@ -344,8 +344,6 @@ Converts points to/from homogeneous coordinates.
.. ocv:function:: void convertPointsHomogeneous( InputArray src, OutputArray dst )
.. ocv:pyfunction:: cv2.convertPointsHomogeneous(src[, dst]) -> dst
.. ocv:cfunction:: void cvConvertPointsHomogeneous( const CvMat* src, CvMat* dst )
.. ocv:pyoldfunction:: cv.ConvertPointsHomogeneous(src, dst) -> None
@ -391,7 +389,7 @@ Decomposes a projection matrix into a rotation matrix and a camera matrix.
.. ocv:pyfunction:: cv2.decomposeProjectionMatrix(projMatrix[, cameraMatrix[, rotMatrix[, transVect[, rotMatrixX[, rotMatrixY[, rotMatrixZ[, eulerAngles]]]]]]]) -> cameraMatrix, rotMatrix, transVect, rotMatrixX, rotMatrixY, rotMatrixZ, eulerAngles
.. ocv:cfunction:: void cvDecomposeProjectionMatrix( const CvMat *projMatrix, CvMat *cameraMatrix, CvMat *rotMatrix, CvMat *transVect, CvMat *rotMatrX=NULL, CvMat *rotMatrY=NULL, CvMat *rotMatrZ=NULL, CvPoint3D64f *eulerAngles=NULL)
.. ocv:cfunction:: void cvDecomposeProjectionMatrix( const CvMat * projMatr, CvMat * calibMatr, CvMat * rotMatr, CvMat * posVect, CvMat * rotMatrX=NULL, CvMat * rotMatrY=NULL, CvMat * rotMatrZ=NULL, CvPoint3D64f * eulerAngles=NULL )
.. ocv:pyoldfunction:: cv.DecomposeProjectionMatrix(projMatrix, cameraMatrix, rotMatrix, transVect, rotMatrX=None, rotMatrY=None, rotMatrZ=None) -> eulerAngles
@ -428,7 +426,7 @@ Renders the detected chessboard corners.
.. ocv:pyfunction:: cv2.drawChessboardCorners(image, patternSize, corners, patternWasFound) -> None
.. ocv:cfunction:: void cvDrawChessboardCorners( CvArr* image, CvSize patternSize, CvPoint2D32f* corners, int count, int patternWasFound )
.. ocv:cfunction:: void cvDrawChessboardCorners( CvArr* image, CvSize pattern_size, CvPoint2D32f* corners, int count, int pattern_was_found )
.. ocv:pyoldfunction:: cv.DrawChessboardCorners(image, patternSize, corners, patternWasFound)-> None
:param image: Destination image. It must be an 8-bit color image.
@ -447,11 +445,11 @@ findChessboardCorners
-------------------------
Finds the positions of internal corners of the chessboard.
.. ocv:function:: bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners, int flags=CV_CALIB_CB_ADAPTIVE_THRESH+CV_CALIB_CB_NORMALIZE_IMAGE )
.. ocv:function:: bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners, int flags=CALIB_CB_ADAPTIVE_THRESH+CALIB_CB_NORMALIZE_IMAGE )
.. ocv:pyfunction:: cv2.findChessboardCorners(image, patternSize[, corners[, flags]]) -> retval, corners
.. ocv:cfunction:: int cvFindChessboardCorners( const void* image, CvSize patternSize, CvPoint2D32f* corners, int* cornerCount=NULL, int flags=CV_CALIB_CB_ADAPTIVE_THRESH )
.. ocv:cfunction:: int cvFindChessboardCorners( const void* image, CvSize pattern_size, CvPoint2D32f* corners, int* corner_count=NULL, int flags=CV_CALIB_CB_ADAPTIVE_THRESH+CV_CALIB_CB_NORMALIZE_IMAGE )
.. ocv:pyoldfunction:: cv.FindChessboardCorners(image, patternSize, flags=CV_CALIB_CB_ADAPTIVE_THRESH) -> corners
:param image: Source chessboard view. It must be an 8-bit grayscale or color image.
@ -508,7 +506,7 @@ Finds the centers in the grid of circles.
.. ocv:function:: bool findCirclesGrid( InputArray image, Size patternSize, OutputArray centers, int flags=CALIB_CB_SYMMETRIC_GRID, const Ptr<FeatureDetector> &blobDetector = new SimpleBlobDetector() )
.. ocv:pyfunction:: cv2.findCirclesGridDefault(image, patternSize[, centers[, flags]]) -> centers
.. ocv:pyfunction:: cv2.findCirclesGridDefault(image, patternSize[, centers[, flags]]) -> retval, centers
:param image: Grid view of source circles. It must be an 8-bit grayscale or color image.
@ -551,11 +549,11 @@ solvePnP
------------
Finds an object pose from 3D-2D point correspondences.
.. ocv:function:: void solvePnP( InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess=false, int flags = CV_ITERATIVE )
.. ocv:function:: bool solvePnP( InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess=false, int flags=ITERATIVE )
.. ocv:pyfunction:: cv2.solvePnP( objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, flags]]]] ) -> rvec, tvec
.. ocv:pyfunction:: cv2.solvePnP(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, flags]]]]) -> retval, rvec, tvec
.. ocv:cfunction:: void cvFindExtrinsicCameraParams2( const CvMat* objectPoints, const CvMat* imagePoints, const CvMat* cameraMatrix, const CvMat* distCoeffs, CvMat* rvec, CvMat* tvec, int useExtrinsicGuess=0 )
.. ocv:cfunction:: void cvFindExtrinsicCameraParams2( const CvMat* object_points, const CvMat* image_points, const CvMat* camera_matrix, const CvMat* distortion_coeffs, CvMat* rotation_vector, CvMat* translation_vector, int use_extrinsic_guess=0 )
.. ocv:pyoldfunction:: cv.FindExtrinsicCameraParams2(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec, useExtrinsicGuess=0 ) -> None
@ -587,7 +585,7 @@ solvePnPRansac
------------------
Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
.. ocv:function:: void solvePnPRansac( InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess=false, int iterationsCount = 100, float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = CV_ITERATIVE )
.. ocv:function:: void solvePnPRansac( InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess=false, int iterationsCount = 100, float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = ITERATIVE )
.. ocv:pyfunction:: cv2.solvePnPRansac(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, iterationsCount[, reprojectionError[, minInliersCount[, inliers[, flags]]]]]]]]) -> rvec, tvec, inliers
@ -628,8 +626,8 @@ Calculates a fundamental matrix from the corresponding points in two images.
.. ocv:pyfunction:: cv2.findFundamentalMat(points1, points2[, method[, param1[, param2[, mask]]]]) -> retval, mask
.. ocv:cfunction:: int cvFindFundamentalMat( const CvMat* points1, const CvMat* points2, CvMat* fundamentalMatrix, int method=CV_FM_RANSAC, double param1=1., double param2=0.99, CvMat* status=NULL)
.. ocv:pyoldfunction:: cv.FindFundamentalMat(points1, points2, fundamentalMatrix, method=CV_FM_RANSAC, param1=1., param2=0.99, status=None) -> None
.. ocv:cfunction:: int cvFindFundamentalMat( const CvMat* points1, const CvMat* points2, CvMat* fundamental_matrix, int method=CV_FM_RANSAC, double param1=3., double param2=0.99, CvMat* status=NULL )
.. ocv:pyoldfunction:: cv.FindFundamentalMat(points1, points2, fundamentalMatrix, method=CV_FM_RANSAC, param1=1., param2=0.99, status=None) -> retval
:param points1: Array of ``N`` points from the first image. The point coordinates should be floating-point (single or double precision).
@ -693,9 +691,9 @@ Finds a perspective transformation between two planes.
.. ocv:pyfunction:: cv2.findHomography(srcPoints, dstPoints[, method[, ransacReprojThreshold[, mask]]]) -> retval, mask
.. ocv:cfunction:: void cvFindHomography( const CvMat* srcPoints, const CvMat* dstPoints, CvMat* H, int method=0, double ransacReprojThreshold=3, CvMat* status=NULL)
.. ocv:cfunction:: int cvFindHomography( const CvMat* src_points, const CvMat* dst_points, CvMat* homography, int method=0, double ransacReprojThreshold=3, CvMat* mask=0 )
.. ocv:pyoldfunction:: cv.FindHomography(srcPoints, dstPoints, H, method, ransacReprojThreshold=3.0, status=None)-> None
.. ocv:pyoldfunction:: cv.FindHomography(srcPoints, dstPoints, H, method=0, ransacReprojThreshold=3.0, status=None) -> None
:param srcPoints: Coordinates of the points in the original plane, a matrix of the type ``CV_32FC2`` or ``vector<Point2f>`` .
@ -773,9 +771,9 @@ estimateAffine3D
--------------------
Computes an optimal affine transformation between two 3D point sets.
.. ocv:function:: int estimateAffine3D(InputArray srcpt, InputArray dstpt, OutputArray out, OutputArray inliers, double ransacThreshold = 3.0, double confidence = 0.99)
.. ocv:function:: int estimateAffine3D(InputArray src, InputArray dst, OutputArray out, OutputArray inliers, double ransacThreshold = 3, double confidence = 0.99)
.. ocv:pyfunction:: cv2.estimateAffine3D(srcpt, dstpt[, out[, inliers[, ransacThreshold[, confidence]]]]) -> retval, out, inliers
.. ocv:pyfunction:: cv2.estimateAffine3D(src, dst[, out[, inliers[, ransacThreshold[, confidence]]]]) -> retval, out, inliers
:param srcpt: First input 3D point set.
@ -815,13 +813,13 @@ getOptimalNewCameraMatrix
-----------------------------
Returns the new camera matrix based on the free scaling parameter.
.. ocv:function:: Mat getOptimalNewCameraMatrix( InputArray cameraMatrix, InputArray distCoeffs, Size imageSize, double alpha, Size newImageSize=Size(), Rect* validPixROI=0, bool centerPrincipalPoint=false)
.. ocv:function:: Mat getOptimalNewCameraMatrix( InputArray cameraMatrix, InputArray distCoeffs, Size imageSize, double alpha, Size newImgSize=Size(), Rect* validPixROI=0, bool centerPrincipalPoint=false )
.. ocv:pyfunction:: cv2.getOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, alpha[, newImgSize[, centerPrincipalPoint]]) -> retval, validPixROI
.. ocv:cfunction:: void cvGetOptimalNewCameraMatrix( const CvMat* cameraMatrix, const CvMat* distCoeffs, CvSize imageSize, double alpha, CvMat* newCameraMatrix, CvSize newImageSize=cvSize(0, 0), CvRect* validPixROI=0, int centerPrincipalPoint=0)
.. ocv:cfunction:: void cvGetOptimalNewCameraMatrix( const CvMat* camera_matrix, const CvMat* dist_coeffs, CvSize image_size, double alpha, CvMat* new_camera_matrix, CvSize new_imag_size=cvSize(0,0), CvRect* valid_pixel_ROI=0, int center_principal_point=0 )
.. ocv:pyoldfunction:: cv.GetOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, alpha, newCameraMatrix, newImageSize=(0, 0), validPixROI=0) -> None
.. ocv:pyoldfunction:: cv.GetOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, alpha, newCameraMatrix, newImageSize=(0, 0), validPixROI=0, centerPrincipalPoint=0) -> None
:param cameraMatrix: Input camera matrix.
@ -854,9 +852,9 @@ Finds an initial camera matrix from 3D-2D point correspondences.
.. ocv:pyfunction:: cv2.initCameraMatrix2D(objectPoints, imagePoints, imageSize[, aspectRatio]) -> retval
.. ocv:cfunction:: void cvInitIntrinsicParams2D( const CvMat* objectPoints, const CvMat* imagePoints, const CvMat* pointCounts, CvSize imageSize, CvMat* cameraMatrix, double aspectRatio=1.)
.. ocv:cfunction:: void cvInitIntrinsicParams2D( const CvMat* object_points, const CvMat* image_points, const CvMat* npoints, CvSize image_size, CvMat* camera_matrix, double aspect_ratio=1. )
.. ocv:pyoldfunction:: cv.InitIntrinsicParams2D(objectPoints, imagePoints, pointCounts, imageSize, cameraMatrix, aspectRatio=1.) -> None
.. ocv:pyoldfunction:: cv.InitIntrinsicParams2D(objectPoints, imagePoints, npoints, imageSize, cameraMatrix, aspectRatio=1.) -> None
:param 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 :ocv:func:`calibrateCamera` for details.
@ -903,7 +901,8 @@ Projects 3D points to an image plane.
.. ocv:pyfunction:: cv2.projectPoints(objectPoints, rvec, tvec, cameraMatrix, distCoeffs[, imagePoints[, jacobian[, aspectRatio]]]) -> imagePoints, jacobian
.. ocv:cfunction:: void cvProjectPoints2( const CvMat* objectPoints, const CvMat* rvec, const CvMat* tvec, const CvMat* cameraMatrix, const CvMat* distCoeffs, CvMat* imagePoints, CvMat* dpdrot=NULL, CvMat* dpdt=NULL, CvMat* dpdf=NULL, CvMat* dpdc=NULL, CvMat* dpddist=NULL )
.. ocv:cfunction:: void cvProjectPoints2( const CvMat* object_points, const CvMat* rotation_vector, const CvMat* translation_vector, const CvMat* camera_matrix, const CvMat* distortion_coeffs, CvMat* image_points, CvMat* dpdrot=NULL, CvMat* dpdt=NULL, CvMat* dpdf=NULL, CvMat* dpdc=NULL, CvMat* dpddist=NULL, double aspect_ratio=0 )
.. ocv:pyoldfunction:: cv.ProjectPoints2(objectPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints, dpdrot=None, dpdt=None, dpdf=None, dpdc=None, dpddist=None)-> None
:param objectPoints: Array of object points, 3xN/Nx3 1-channel or 1xN/Nx1 3-channel (or ``vector<Point3f>`` ), where N is the number of points in the view.
@ -943,11 +942,11 @@ reprojectImageTo3D
----------------------
Reprojects a disparity image to 3D space.
.. ocv:function:: void reprojectImageTo3D( InputArray disparity, OutputArray _3dImage, InputArray Q, bool handleMissingValues=false, int depth=-1 )
.. ocv:function:: void reprojectImageTo3D( InputArray disparity, OutputArray _3dImage, InputArray Q, bool handleMissingValues=false, int ddepth=-1 )
.. ocv:pyfunction:: cv2.reprojectImageTo3D(disparity, Q[, _3dImage[, handleMissingValues[, ddepth]]]) -> _3dImage
.. ocv:cfunction:: void cvReprojectImageTo3D( const CvArr* disparity, CvArr* _3dImage, const CvMat* Q, int handleMissingValues=0)
.. ocv:cfunction:: void cvReprojectImageTo3D( const CvArr* disparityImage, CvArr* _3dImage, const CvMat* Q, int handleMissingValues=0 )
.. ocv:pyoldfunction:: cv.ReprojectImageTo3D(disparity, _3dImage, Q, handleMissingValues=0) -> None
@ -978,11 +977,11 @@ RQDecomp3x3
---------------
Computes an RQ decomposition of 3x3 matrices.
.. ocv:function:: Vec3d RQDecomp3x3( InputArray M, OutputArray R, OutputArray Q, OutputArray Qx=noArray(), OutputArray Qy=noArray(), OutputArray Qz=noArray() )
.. ocv:function:: Vec3d RQDecomp3x3( InputArray src, OutputArray mtxR, OutputArray mtxQ, OutputArray Qx=noArray(), OutputArray Qy=noArray(), OutputArray Qz=noArray() )
.. ocv:pyfunction:: cv2.RQDecomp3x3(src[, mtxR[, mtxQ[, Qx[, Qy[, Qz]]]]]) -> retval, mtxR, mtxQ, Qx, Qy, Qz
.. ocv:cfunction:: void cvRQDecomp3x3( const CvMat *M, CvMat *R, CvMat *Q, CvMat *Qx=NULL, CvMat *Qy=NULL, CvMat *Qz=NULL, CvPoint3D64f *eulerAngles=NULL)
.. ocv:cfunction:: void cvRQDecomp3x3( const CvMat * matrixM, CvMat * matrixR, CvMat * matrixQ, CvMat * matrixQx=NULL, CvMat * matrixQy=NULL, CvMat * matrixQz=NULL, CvPoint3D64f * eulerAngles=NULL )
.. ocv:pyoldfunction:: cv.RQDecomp3x3(M, R, Q, Qx=None, Qy=None, Qz=None) -> eulerAngles
:param M: 3x3 input matrix.
@ -1083,11 +1082,11 @@ The constructors.
.. ocv:function:: StereoBM::StereoBM()
.. ocv:function:: StereoBM::StereoBM(int preset, int ndisparities=0, int SADWindowSize=21)
.. ocv:pyfunction:: cv2.StereoBM.StereoBM(preset[, ndisparities[, SADWindowSize]]) -> <StereoBM object>
.. ocv:pyfunction:: cv2.StereoBM([preset[, ndisparities[, SADWindowSize]]]) -> <StereoBM object>
.. ocv:cfunction:: CvStereoBMState* cvCreateStereoBMState( int preset=CV_STEREO_BM_BASIC, int ndisparities=0 )
.. ocv:cfunction:: CvStereoBMState* cvCreateStereoBMState( int preset=CV_STEREO_BM_BASIC, int numberOfDisparities=0 )
.. ocv:pyoldfunction:: cv.CreateStereoBMState(preset=CV_STEREO_BM_BASIC, ndisparities=0)-> StereoBMState
.. ocv:pyoldfunction:: cv.CreateStereoBMState(preset=CV_STEREO_BM_BASIC, numberOfDisparities=0)-> CvStereoBMState
:param preset: specifies the whole set of algorithm parameters, one of:
@ -1109,7 +1108,7 @@ StereoBM::operator()
-----------------------
Computes disparity using the BM algorithm for a rectified stereo pair.
.. ocv:function:: void StereoBM::operator()(InputArray left, InputArray right, OutputArray disp, int disptype=CV_16S )
.. ocv:function:: void StereoBM::operator()( InputArray left, InputArray right, OutputArray disparity, int disptype=CV_16S )
.. ocv:pyfunction:: cv2.StereoBM.compute(left, right[, disparity[, disptype]]) -> disparity
@ -1182,7 +1181,7 @@ StereoSGBM::StereoSGBM
.. ocv:function:: StereoSGBM::StereoSGBM( int minDisparity, int numDisparities, int SADWindowSize, int P1=0, int P2=0, int disp12MaxDiff=0, int preFilterCap=0, int uniquenessRatio=0, int speckleWindowSize=0, int speckleRange=0, bool fullDP=false)
.. ocv:pyfunction:: cv2.StereoSGBM.StereoSGBM(minDisparity, numDisparities, SADWindowSize[, P1[, P2[, disp12MaxDiff[, preFilterCap[, uniquenessRatio[, speckleWindowSize[, speckleRange[, fullDP]]]]]]]]) -> <StereoSGBM object>
.. ocv:pyfunction:: cv2.StereoSGBM([minDisparity, numDisparities, SADWindowSize[, P1[, P2[, disp12MaxDiff[, preFilterCap[, uniquenessRatio[, speckleWindowSize[, speckleRange[, fullDP]]]]]]]]]) -> <StereoSGBM object>
Initializes ``StereoSGBM`` and sets parameters to custom values.??
@ -1349,12 +1348,13 @@ stereoCalibrate
-------------------
Calibrates the stereo camera.
.. ocv:function:: double stereoCalibrate( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, Size imageSize, OutputArray R, OutputArray T, OutputArray E, OutputArray F, TermCriteria term_crit = TermCriteria(TermCriteria::COUNT+ TermCriteria::EPS, 30, 1e-6), int flags=CALIB_FIX_INTRINSIC )
.. ocv:function:: double stereoCalibrate( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, Size imageSize, OutputArray R, OutputArray T, OutputArray E, OutputArray F, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6), int flags=CALIB_FIX_INTRINSIC )
.. ocv:pyfunction:: cv2.stereoCalibrate(objectPoints, imagePoints1, imagePoints2, imageSize[, cameraMatrix1[, distCoeffs1[, cameraMatrix2[, distCoeffs2[, R[, T[, E[, F[, criteria[, flags]]]]]]) -> retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F
.. ocv:pyfunction:: cv2.stereoCalibrate(objectPoints, imagePoints1, imagePoints2, imageSize[, cameraMatrix1[, distCoeffs1[, cameraMatrix2[, distCoeffs2[, R[, T[, E[, F[, criteria[, flags]]]]]]]]]]) -> retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F
.. ocv:cfunction:: double cvStereoCalibrate( const CvMat* objectPoints, const CvMat* imagePoints1, const CvMat* imagePoints2, const CvMat* pointCounts, CvMat* cameraMatrix1, CvMat* distCoeffs1, CvMat* cameraMatrix2, CvMat* distCoeffs2, CvSize imageSize, CvMat* R, CvMat* T, CvMat* E=0, CvMat* F=0, CvTermCriteria termCrit=cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 30, 1e-6), int flags=CV_CALIB_FIX_INTRINSIC )
.. ocv:pyoldfunction:: cv.StereoCalibrate( objectPoints, imagePoints1, imagePoints2, pointCounts, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T, E=None, F=None, termCrit=(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 30, 1e-6), flags=CV_CALIB_FIX_INTRINSIC)-> None
.. ocv:cfunction:: double cvStereoCalibrate( const CvMat* object_points, const CvMat* image_points1, const CvMat* image_points2, const CvMat* npoints, CvMat* camera_matrix1, CvMat* dist_coeffs1, CvMat* camera_matrix2, CvMat* dist_coeffs2, CvSize image_size, CvMat* R, CvMat* T, CvMat* E=0, CvMat* F=0, CvTermCriteria term_crit=cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,1e-6), int flags=CV_CALIB_FIX_INTRINSIC )
.. ocv:pyoldfunction:: cv.StereoCalibrate(objectPoints, imagePoints1, imagePoints2, pointCounts, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T, E=None, F=None, term_crit=(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 30, 1e-6), flags=CV_CALIB_FIX_INTRINSIC)-> None
:param objectPoints: Vector of vectors of the calibration pattern points.
@ -1439,9 +1439,10 @@ stereoRectify
-----------------
Computes rectification transforms for each head of a calibrated stereo camera.
.. ocv:function:: void stereoRectify( InputArray cameraMatrix1, InputArray distCoeffs1, InputArray cameraMatrix2, InputArray distCoeffs2, Size imageSize, InputArray R, InputArray T, OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags=CALIB_ZERO_DISPARITY, double alpha, Size newImageSize=Size(), Rect* roi1=0, Rect* roi2=0 )
.. ocv:function:: void stereoRectify( InputArray cameraMatrix1, InputArray distCoeffs1, InputArray cameraMatrix2, InputArray distCoeffs2, Size imageSize, InputArray R, InputArray T, OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags=CALIB_ZERO_DISPARITY, double alpha=-1, Size newImageSize=Size(), Rect* validPixROI1=0, Rect* validPixROI2=0 )
.. ocv:cfunction:: void cvStereoRectify( const CvMat* camera_matrix1, const CvMat* camera_matrix2, const CvMat* dist_coeffs1, const CvMat* dist_coeffs2, CvSize image_size, const CvMat* R, const CvMat* T, CvMat* R1, CvMat* R2, CvMat* P1, CvMat* P2, CvMat* Q=0, int flags=CV_CALIB_ZERO_DISPARITY, double alpha=-1, CvSize new_image_size=cvSize(0,0), CvRect* valid_pix_ROI1=0, CvRect* valid_pix_ROI2=0 )
.. ocv:cfunction:: void cvStereoRectify( const CvMat* cameraMatrix1, const CvMat* cameraMatrix2, const CvMat* distCoeffs1, const CvMat* distCoeffs2, CvSize imageSize, const CvMat* R, const CvMat* T, CvMat* R1, CvMat* R2, CvMat* P1, CvMat* P2, CvMat* Q=0, int flags=CV_CALIB_ZERO_DISPARITY, double alpha=-1, CvSize newImageSize=cvSize(0, 0), CvRect* roi1=0, CvRect* roi2=0)
.. ocv:pyoldfunction:: cv.StereoRectify(cameraMatrix1, cameraMatrix2, distCoeffs1, distCoeffs2, imageSize, R, T, R1, R2, P1, P2, Q=None, flags=CV_CALIB_ZERO_DISPARITY, alpha=-1, newImageSize=(0, 0)) -> (roi1, roi2)
:param cameraMatrix1: First camera matrix.
@ -1529,7 +1530,7 @@ Computes a rectification transform for an uncalibrated stereo camera.
.. ocv:pyfunction:: cv2.stereoRectifyUncalibrated(points1, points2, F, imgSize[, H1[, H2[, threshold]]]) -> retval, H1, H2
.. ocv:cfunction:: void cvStereoRectifyUncalibrated( const CvMat* points1, const CvMat* points2, const CvMat* F, CvSize imageSize, CvMat* H1, CvMat* H2, double threshold=5 )
.. ocv:cfunction:: int cvStereoRectifyUncalibrated( const CvMat* points1, const CvMat* points2, const CvMat* F, CvSize img_size, CvMat* H1, CvMat* H2, double threshold=5 )
.. ocv:pyoldfunction:: cv.StereoRectifyUncalibrated(points1, points2, F, imageSize, H1, H2, threshold=5)-> None

21
modules/calib3d/include/opencv2/calib3d/calib3d.hpp
View File

@ -215,8 +215,7 @@ CVAPI(int) cvCheckChessboard(IplImage* src, CvSize size);
CVAPI(int) cvFindChessboardCorners( const void* image, CvSize pattern_size,
CvPoint2D32f* corners,
int* corner_count CV_DEFAULT(NULL),
int flags CV_DEFAULT(CV_CALIB_CB_ADAPTIVE_THRESH+
CV_CALIB_CB_NORMALIZE_IMAGE) );
int flags CV_DEFAULT(CV_CALIB_CB_ADAPTIVE_THRESH+CV_CALIB_CB_NORMALIZE_IMAGE) );
/* Draws individual chessboard corners or the whole chessboard detected */
CVAPI(void) cvDrawChessboardCorners( CvArr* image, CvSize pattern_size,
@ -474,7 +473,7 @@ enum
CV_EXPORTS_W bool solvePnP( InputArray objectPoints, InputArray imagePoints,
InputArray cameraMatrix, InputArray distCoeffs,
OutputArray rvec, OutputArray tvec,
bool useExtrinsicGuess=false, int flags=0);
bool useExtrinsicGuess=false, int flags=ITERATIVE);
//! computes the camera pose from a few 3D points and the corresponding projections. The outliers are possible.
CV_EXPORTS_W void solvePnPRansac( InputArray objectPoints,
@ -488,7 +487,7 @@ CV_EXPORTS_W void solvePnPRansac( InputArray objectPoints,
float reprojectionError = 8.0,
int minInliersCount = 100,
OutputArray inliers = noArray(),
int flags = 0);
int flags = ITERATIVE);
//! initializes camera matrix from a few 3D points and the corresponding projections.
CV_EXPORTS_W Mat initCameraMatrix2D( InputArrayOfArrays objectPoints,
@ -501,8 +500,7 @@ enum { CALIB_CB_ADAPTIVE_THRESH = 1, CALIB_CB_NORMALIZE_IMAGE = 2,
//! finds checkerboard pattern of the specified size in the image
CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize,
OutputArray corners,
int flags=CALIB_CB_ADAPTIVE_THRESH+
CALIB_CB_NORMALIZE_IMAGE );
int flags=CALIB_CB_ADAPTIVE_THRESH+CALIB_CB_NORMALIZE_IMAGE );
//! finds subpixel-accurate positions of the chessboard corners
CV_EXPORTS bool find4QuadCornerSubpix(InputArray img, InputOutputArray corners, Size region_size);
@ -574,8 +572,7 @@ CV_EXPORTS_W double stereoCalibrate( InputArrayOfArrays objectPoints,
CV_OUT InputOutputArray distCoeffs2,
Size imageSize, OutputArray R,
OutputArray T, OutputArray E, OutputArray F,
TermCriteria criteria = TermCriteria(TermCriteria::COUNT+
TermCriteria::EPS, 30, 1e-6),
TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6),
int flags=CALIB_FIX_INTRINSIC );
@ -642,7 +639,7 @@ CV_EXPORTS Mat findFundamentalMat( InputArray points1, InputArray points2,
double param1=3., double param2=0.99);
//! finds coordinates of epipolar lines corresponding the specified points
CV_EXPORTS void computeCorrespondEpilines( InputArray points1,
CV_EXPORTS void computeCorrespondEpilines( InputArray points,
int whichImage, InputArray F,
OutputArray lines );
@ -743,9 +740,9 @@ CV_EXPORTS_W void reprojectImageTo3D( InputArray disparity,
bool handleMissingValues=false,
int ddepth=-1 );
CV_EXPORTS_W int estimateAffine3D(InputArray _from, InputArray _to,
OutputArray _out, OutputArray _inliers,
double param1=3, double param2=0.99);
CV_EXPORTS_W int estimateAffine3D(InputArray src, InputArray dst,
OutputArray out, OutputArray inliers,
double ransacThreshold=3, double confidence=0.99);
}

2
modules/core/doc/clustering.rst
View File

@ -13,7 +13,7 @@ Finds centers of clusters and groups input samples around the clusters.
.. ocv:cfunction:: int cvKMeans2(const CvArr* samples, int clusterCount, CvArr* labels, CvTermCriteria criteria, int attempts=1, CvRNG* rng=0, int flags=0, CvArr* centers=0, double* compactness=0)
.. ocv:pyoldfunction:: cv.KMeans2(samples, clusterCount, labels, criteria)-> None
.. ocv:pyoldfunction:: cv.KMeans2(samples, nclusters, labels, termcrit, attempts=1, flags=0, centers=None) -> float
:param samples: Floating-point matrix of input samples, one row per sample.

8
modules/core/doc/drawing_functions.rst
View File

@ -64,7 +64,7 @@ Clips the line against the image rectangle.
.. ocv:pyfunction:: cv2.clipLine(imgRect, pt1, pt2) -> retval, pt1, pt2
.. ocv:cfunction:: int cvClipLine( CvSize imgSize, CvPoint* pt1, CvPoint* pt2 )
.. ocv:pyoldfunction:: cv.ClipLine(imgSize, pt1, pt2) -> (clippedPt1, clippedPt2)
.. ocv:pyoldfunction:: cv.ClipLine(imgSize, pt1, pt2) -> (point1, point2)
:param imgSize: Image size. The image rectangle is ``Rect(0, 0, imgSize.width, imgSize.height)`` .
@ -89,7 +89,7 @@ Draws a simple or thick elliptic arc or fills an ellipse sector.
.. ocv:pyfunction:: cv2.ellipse(img, box, color[, thickness[, lineType]]) -> None
.. ocv:cfunction:: void cvEllipse( CvArr* img, CvPoint center, CvSize axes, double angle, double startAngle, double endAngle, CvScalar color, int thickness=1, int lineType=8, int shift=0 )
.. ocv:pyoldfunction:: cv.Ellipse(img, center, axes, angle, startAngle, endAngle, color, thickness=1, lineType=8, shift=0)-> None
.. ocv:pyoldfunction:: cv.Ellipse(img, center, axes, angle, start_angle, end_angle, color, thickness=1, lineType=8, shift=0)-> None
.. ocv:cfunction:: void cvEllipseBox( CvArr* img, CvBox2D box, CvScalar color, int thickness=1, int lineType=8, int shift=0 )
.. ocv:pyoldfunction:: cv.EllipseBox(img, box, color, thickness=1, lineType=8, shift=0)-> None
@ -441,7 +441,7 @@ Draws several polygonal curves.
.. ocv:cfunction:: void cvPolyLine( CvArr* img, CvPoint** pts, int* npts, int contours, int isClosed, CvScalar color, int thickness=1, int lineType=8, int shift=0 )
.. ocv:pyoldfunction:: cv.PolyLine(img, polys, isClosed, color, thickness=1, lineType=8, shift=0)-> None
.. ocv:pyoldfunction:: cv.PolyLine(img, polys, is_closed, color, thickness=1, lineType=8, shift=0) -> None
:param img: Image.
@ -471,7 +471,7 @@ Draws a text string.
.. ocv:function:: void putText( Mat& img, const string& text, Point org, int fontFace, double fontScale, Scalar color, int thickness=1, int lineType=8, bool bottomLeftOrigin=false )
.. ocv:pyfunction:: cv2.putText(img, text, org, fontFace, fontScale, color[, thickness[, linetype[, bottomLeftOrigin]]]) -> None
.. ocv:pyfunction:: cv2.putText(img, text, org, fontFace, fontScale, color[, thickness[, lineType[, bottomLeftOrigin]]]) -> None
.. ocv:cfunction:: void cvPutText( CvArr* img, const char* text, CvPoint org, const CvFont* font, CvScalar color )
.. ocv:pyoldfunction:: cv.PutText(img, text, org, font, color)-> None

28
modules/core/doc/old_basic_structures.rst
View File

@ -524,7 +524,7 @@ CloneImage
Makes a full copy of an image, including the header, data, and ROI.
.. ocv:cfunction:: IplImage* cvCloneImage(const IplImage* image)
.. ocv:pyoldfunction:: cv.CloneImage(image)-> copy
.. ocv:pyoldfunction:: cv.CloneImage(image) -> image
:param image: The original image
@ -533,7 +533,7 @@ CloneMat
Creates a full matrix copy.
.. ocv:cfunction:: CvMat* cvCloneMat(const CvMat* mat)
.. ocv:pyoldfunction:: cv.CloneMat(mat)-> copy
.. ocv:pyoldfunction:: cv.CloneMat(mat) -> mat
:param mat: Matrix to be copied
@ -544,7 +544,7 @@ CloneMatND
Creates full copy of a multi-dimensional array and returns a pointer to the copy.
.. ocv:cfunction:: CvMatND* cvCloneMatND(const CvMatND* mat)
.. ocv:pyoldfunction:: cv.CloneMatND(mat)-> copy
.. ocv:pyoldfunction:: cv.CloneMatND(mat) -> matND
:param mat: Input array
@ -701,7 +701,7 @@ CreateMatND
Creates the header and allocates the data for a multi-dimensional dense array.
.. ocv:cfunction:: CvMatND* cvCreateMatND( int dims, const int* sizes, int type)
.. ocv:pyoldfunction:: cv.CreateMatND(dims, type) -> None
.. ocv:pyoldfunction:: cv.CreateMatND(dims, type) -> matND
:param dims: Number of array dimensions. This must not exceed CV_MAX_DIM (32 by default, but can be changed at build time).
@ -719,7 +719,7 @@ CreateMatNDHeader
Creates a new matrix header but does not allocate the matrix data.
.. ocv:cfunction:: CvMatND* cvCreateMatNDHeader( int dims, const int* sizes, int type)
.. ocv:pyoldfunction:: cv.CreateMatNDHeader(dims, type) -> None
.. ocv:pyoldfunction:: cv.CreateMatNDHeader(dims, type) -> matND
:param dims: Number of array dimensions
@ -776,7 +776,7 @@ DotProduct
Calculates the dot product of two arrays in Euclidean metrics.
.. ocv:cfunction:: double cvDotProduct(const CvArr* src1, const CvArr* src2)
.. ocv:pyoldfunction:: cv.DotProduct(src1, src2)-> double
.. ocv:pyoldfunction:: cv.DotProduct(src1, src2) -> float
:param src1: The first source array
@ -862,7 +862,7 @@ GetDims
Return number of array dimensions
.. ocv:cfunction:: int cvGetDims(const CvArr* arr, int* sizes=NULL)
.. ocv:pyoldfunction:: cv.GetDims(arr)-> list
.. ocv:pyoldfunction:: cv.GetDims(arr) -> (dim1, dim2, ...)
:param arr: Input array
@ -921,7 +921,7 @@ GetImageCOI
Returns the index of the channel of interest.
.. ocv:cfunction:: int cvGetImageCOI(const IplImage* image)
.. ocv:pyoldfunction:: cv.GetImageCOI(image)-> channel
.. ocv:pyoldfunction:: cv.GetImageCOI(image) -> int
:param image: A pointer to the image header
@ -944,7 +944,7 @@ GetMat
Returns matrix header for arbitrary array.
.. ocv:cfunction:: CvMat* cvGetMat(const CvArr* arr, CvMat* header, int* coi=NULL, int allowND=0)
.. ocv:pyoldfunction:: cv.GetMat(arr, allowND=0) -> cvmat
.. ocv:pyoldfunction:: cv.GetMat(arr, allowND=0) -> mat
:param arr: Input array
@ -1404,7 +1404,7 @@ Reshape
Changes shape of matrix/image without copying data.
.. ocv:cfunction:: CvMat* cvReshape(const CvArr* arr, CvMat* header, int newCn, int newRows=0)
.. ocv:pyoldfunction:: cv.Reshape(arr, newCn, newRows=0) -> cvmat
.. ocv:pyoldfunction:: cv.Reshape(arr, newCn, newRows=0) -> mat
:param arr: Input array
@ -1441,7 +1441,7 @@ ReshapeMatND
Changes the shape of a multi-dimensional array without copying the data.
.. ocv:cfunction:: CvArr* cvReshapeMatND(const CvArr* arr, int sizeofHeader, CvArr* header, int newCn, int newDims, int* newSizes)
.. ocv:pyoldfunction:: cv.ReshapeMatND(arr, newCn, newDims) -> cvmat
.. ocv:pyoldfunction:: cv.ReshapeMatND(arr, newCn, newDims) -> mat
:param arr: Input array
@ -1628,7 +1628,7 @@ mGet
Returns the particular element of single-channel floating-point matrix.
.. ocv:cfunction:: double cvmGet(const CvMat* mat, int row, int col)
.. ocv:pyoldfunction:: cv.mGet(mat, row, col)-> double
.. ocv:pyoldfunction:: cv.mGet(mat, row, col) -> float
:param mat: Input matrix
@ -1725,7 +1725,7 @@ RandReal
Returns a floating-point random number and updates RNG.
.. ocv:cfunction:: double cvRandReal(CvRNG* rng)
.. ocv:pyoldfunction:: cv.RandReal(rng)-> double
.. ocv:pyoldfunction:: cv.RandReal(rng) -> float
:param rng: RNG state initialized by :ocv:cfunc:`RNG`
@ -1736,7 +1736,7 @@ fromarray
---------
Create a CvMat from an object that supports the array interface.
.. ocv:pyoldfunction:: cv.fromarray(object, allowND=False) -> CvMat
.. ocv:pyoldfunction:: cv.fromarray(array, allowND=False) -> mat
:param object: Any object that supports the array interface

59
modules/core/doc/operations_on_arrays.rst
View File

@ -460,7 +460,7 @@ Checks every element of an input array for invalid values.
.. ocv:function:: bool checkRange(InputArray src, bool quiet=true, Point* pos=0, double minVal=-DBL_MAX, double maxVal=DBL_MAX)
.. ocv:pyfunction:: cv2.checkRange(a[, quiet[, minVal[, maxVal]]]) -> retval, pt
.. ocv:pyfunction:: cv2.checkRange(a[, quiet[, minVal[, maxVal]]]) -> retval, pos
:param src: Array to check.
@ -493,7 +493,7 @@ Performs the per-element comparison of two arrays or an array and scalar value.
.. ocv:cfunction:: void cvCmpS(const CvArr* src1, double src2, CvArr* dst, int cmpOp)
.. ocv:pyoldfunction:: cv.CmpS(src1, src2, dst, cmpOp)-> None
.. ocv:pyoldfunction:: cv.CmpS(src, value, dst, cmpOp)-> None
:param src1: First source array or a scalar (in the case of ``cvCmp``, ``cv.Cmp``, ``cvCmpS``, ``cv.CmpS`` it is always an array). When it is array, it must have a single channel.
@ -976,7 +976,7 @@ Performs per-element division of two arrays or a scalar by an array.
.. ocv:pyfunction:: cv2.divide(scale, src2[, dst[, dtype]]) -> dst
.. ocv:cfunction:: void cvDiv(const CvArr* src1, const CvArr* src2, CvArr* dst, double scale=1)
.. ocv:pyoldfunction:: cv.Div(src1, src2, dst, scale)-> None
.. ocv:pyoldfunction:: cv.Div(src1, src2, dst, scale=1) -> None
:param src1: First source array.
@ -1022,7 +1022,7 @@ Returns the determinant of a square floating-point matrix.
.. ocv:pyfunction:: cv2.determinant(mtx) -> retval
.. ocv:cfunction:: double cvDet(const CvArr* mtx)
.. ocv:pyoldfunction:: cv.Det(mtx)-> double
.. ocv:pyoldfunction:: cv.Det(mat) -> float
:param mtx: Input matrix that must have ``CV_32FC1`` or ``CV_64FC1`` type and square size.
@ -1043,18 +1043,17 @@ For symmetric positively-determined matrices, it is also possible to use :ocv:fu
eigen
-----
Computes eigenvalues and eigenvectors of a symmetric matrix.
.. ocv:function:: bool eigen(InputArray src, OutputArray eigenvalues, int lowindex=-1, int highindex=-1)
.. ocv:function:: bool eigen(InputArray src, OutputArray eigenvalues, OutputArray eigenvectors, int lowindex=-1,int highindex=-1)
.. ocv:pyfunction:: cv2.eigen(src, computeEigenvectors[, eigenvalues[, eigenvectors]]) -> retval, eigenvalues, eigenvectors
.. ocv:cfunction:: void cvEigenVV( CvArr* src, CvArr* eigenvectors, CvArr* eigenvalues, double eps=0, int lowindex=-1, int highindex=-1)
.. ocv:pyoldfunction:: cv.EigenVV(src, eigenvectors, eigenvalues, eps, lowindex=-1, highindex=-1)-> None
Computes eigenvalues and eigenvectors of a symmetric matrix.
.. ocv:pyfunction:: cv2.eigen(src, computeEigenvectors[, eigenvalues[, eigenvectors[, lowindex[, highindex]]]]) -> retval, eigenvalues, eigenvectors
.. ocv:pyoldfunction:: cv.EigenVV(mat, evects, evals, eps, lowindex=-1, highindex=-1)-> None
:param src: Input matrix that must have ``CV_32FC1`` or ``CV_64FC1`` type, square size and be symmetrical (``src`` :sup:`T` == ``src``).
@ -1417,7 +1416,7 @@ Finds the inverse or pseudo-inverse of a matrix.
.. ocv:cfunction:: double cvInvert(const CvArr* src, CvArr* dst, int flags=CV_LU)
.. ocv:pyoldfunction:: cv.Invert(src, dst, flags=CV_LU)-> double
.. ocv:pyoldfunction:: cv.Invert(src, dst, method=CV_LU) -> float
:param src: Source floating-point ``M x N`` matrix.
@ -1558,7 +1557,7 @@ Calculates the Mahalanobis distance between two vectors.
.. ocv:cfunction:: double cvMahalanobis( const CvArr* vec1, const CvArr* vec2, CvArr* icovar)
.. ocv:pyoldfunction:: cv.Mahalanobis(vec1, vec2, icovar)-> None
.. ocv:pyoldfunction:: cv.Mahalonobis(vec1, vec2, mat) -> None
:param vec1: First 1D source vector.
@ -1644,7 +1643,7 @@ Calculates an average (mean) of array elements.
.. ocv:pyfunction:: cv2.mean(src[, mask]) -> retval
.. ocv:cfunction:: CvScalar cvAvg(const CvArr* src, const CvArr* mask=NULL)
.. ocv:pyoldfunction:: cv.Avg(src, mask=None)-> CvScalar
.. ocv:pyoldfunction:: cv.Avg(arr, mask=None) -> scalar
:param src: Source array that should have from 1 to 4 channels so that the result can be stored in :ocv:class:`Scalar_` .
@ -1676,7 +1675,7 @@ Calculates a mean and standard deviation of array elements.
.. ocv:pyfunction:: cv2.meanStdDev(src[, mean[, stddev[, mask]]]) -> mean, stddev
.. ocv:cfunction:: void cvAvgSdv(const CvArr* src, CvScalar* mean, CvScalar* stdDev, const CvArr* mask=NULL)
.. ocv:pyoldfunction:: cv.AvgSdv(src, mask=None)-> (mean, stdDev)
.. ocv:pyoldfunction:: cv.AvgSdv(arr, mask=None) -> (mean, stdDev)
:param src: Source array that should have from 1 to 4 channels so that the results can be stored in :ocv:class:`Scalar_` 's.
@ -1970,7 +1969,7 @@ Calculates the per-element scaled product of two arrays.
.. ocv:pyfunction:: cv2.multiply(src1, src2[, dst[, scale[, dtype]]]) -> dst
.. ocv:cfunction:: void cvMul(const CvArr* src1, const CvArr* src2, CvArr* dst, double scale=1)
.. ocv:pyoldfunction:: cv.Mul(src1, src2, dst, scale)-> None
.. ocv:pyoldfunction:: cv.Mul(src1, src2, dst, scale=1) -> None
:param src1: First source array.
@ -2019,7 +2018,7 @@ Calculates the product of a matrix and its transposition.
.. ocv:pyfunction:: cv2.mulTransposed(src, aTa[, dst[, delta[, scale[, dtype]]]]) -> dst
.. ocv:cfunction:: void cvMulTransposed(const CvArr* src, CvArr* dst, int order, const CvArr* delta=NULL, double scale=1.0)
.. ocv:pyoldfunction:: cv.MulTransposed(src, dst, order, delta=None, scale)-> None
.. ocv:pyoldfunction:: cv.MulTransposed(src, dst, order, delta=None, scale=1.0) -> None
:param src: Source single-channel matrix. Note that unlike :ocv:func:`gemm`, the function can multiply not only floating-point matrices.
@ -2070,7 +2069,7 @@ Calculates an absolute array norm, an absolute difference norm, or a relative di
.. ocv:pyfunction:: cv2.norm(src1, src2[, normType[, mask]]) -> retval
.. ocv:cfunction:: double cvNorm(const CvArr* arr1, const CvArr* arr2=NULL, int normType=CV_L2, const CvArr* mask=NULL)
.. ocv:pyoldfunction:: cv.Norm(arr1, arr2, normType=CV_L2, mask=None)-> double
.. ocv:pyoldfunction:: cv.Norm(arr1, arr2, normType=CV_L2, mask=None) -> float
:param src1: First source array.
@ -2279,7 +2278,7 @@ Projects vector(s) to the principal component subspace.
.. ocv:function:: void PCA::project(InputArray vec, OutputArray result) const
.. ocv:pyfunction:: cv2.PCAProject(vec, mean, eigenvectors[, result]) -> result
.. ocv:pyfunction:: cv2.PCAProject(data, mean, eigenvectors[, result]) -> result
:param vec: Input vector(s). They must have the same dimensionality and the same layout as the input data used at PCA phase. That is, if ``CV_PCA_DATA_AS_ROW`` are specified, then ``vec.cols==data.cols`` (vector dimensionality) and ``vec.rows`` is the number of vectors to project. The same is true for the ``CV_PCA_DATA_AS_COL`` case.
@ -2297,7 +2296,7 @@ Reconstructs vectors from their PC projections.
.. ocv:function:: void PCA::backProject(InputArray vec, OutputArray result) const
.. ocv:pyfunction:: cv2.PCABackProject(vec, mean, eigenvectors[, result]) -> result
.. ocv:pyfunction:: cv2.PCABackProject(data, mean, eigenvectors[, result]) -> result
:param vec: Coordinates of the vectors in the principal component subspace. The layout and size are the same as of ``PCA::project`` output vectors.
@ -2673,7 +2672,7 @@ Shuffles the array elements randomly.
.. ocv:function:: void randShuffle(InputOutputArray mtx, double iterFactor=1., RNG* rng=0)
.. ocv:pyfunction:: cv2.randShuffle(src[, dst[, iterFactor]]) -> dst
.. ocv:pyfunction:: cv2.randShuffle(dst[, iterFactor]) -> None
:param mtx: Input/output numerical 1D array.
@ -3026,7 +3025,7 @@ Divides a multi-channel array into several single-channel arrays.
.. ocv:function:: void split(const Mat& mtx, vector<Mat>& mv)
.. ocv:pyfunction:: cv2.split(m, mv) -> None
.. ocv:pyfunction:: cv2.split(m[, mv]) -> mv
.. ocv:cfunction:: void cvSplit(const CvArr* src, CvArr* dst0, CvArr* dst1, CvArr* dst2, CvArr* dst3)
.. ocv:pyoldfunction:: cv.Split(src, dst0, dst1, dst2, dst3)-> None
@ -3089,8 +3088,8 @@ Calculates the per-element difference between two arrays or array and a scalar.
.. ocv:cfunction:: void cvSubS(const CvArr* src1, CvScalar src2, CvArr* dst, const CvArr* mask=NULL)
.. ocv:pyoldfunction:: cv.Sub(src1, src2, dst, mask=None) -> None
.. ocv:pyoldfunction:: cv.SubRS(src1, src2, dst, mask=None)-> None
.. ocv:pyoldfunction:: cv.SubS(src1, src2, dst, mask=None)-> None
.. ocv:pyoldfunction:: cv.SubRS(src, value, dst, mask=None) -> None
.. ocv:pyoldfunction:: cv.SubS(src, value, dst, mask=None) -> None
:param src1: First source array or a scalar.
@ -3224,7 +3223,7 @@ Performs SVD of a matrix
.. ocv:cfunction:: void cvSVD( CvArr* src, CvArr* w, CvArr* u=NULL, CvArr* v=NULL, int flags=0)
.. ocv:pyoldfunction:: cv.SVD(src, w, u=None, v=None, flags=0)-> None
.. ocv:pyoldfunction:: cv.SVD(A, W, U=None, V=None, flags=0) -> None
:param src: Decomposed matrix
@ -3274,7 +3273,7 @@ Performs a singular value back substitution.
.. ocv:cfunction:: void cvSVBkSb( const CvArr* w, const CvArr* u, const CvArr* v, const CvArr* rhs, CvArr* dst, int flags)
.. ocv:pyoldfunction:: cv.SVBkSb(w, u, v, rhs, dst, flags)-> None
.. ocv:pyoldfunction:: cv.SVBkSb(W, U, V, B, X, flags) -> None
:param w: Singular values
@ -3306,10 +3305,10 @@ Calculates the sum of array elements.
.. ocv:function:: Scalar sum(InputArray arr)
.. ocv:pyfunction:: cv2.sumElems(arr) -> retval
.. ocv:pyfunction:: cv2.sumElems(src) -> retval
.. ocv:cfunction:: CvScalar cvSum(const CvArr* arr)
.. ocv:pyoldfunction:: cv.Sum(arr)-> CvScalar
.. ocv:pyoldfunction:: cv.Sum(arr) -> scalar
:param arr: Source array that must have from 1 to 4 channels.
@ -3350,10 +3349,10 @@ Returns the trace of a matrix.
.. ocv:function:: Scalar trace(InputArray mat)
.. ocv:pyfunction:: cv2.trace(mat) -> retval
.. ocv:pyfunction:: cv2.trace(mtx) -> retval
.. ocv:cfunction:: CvScalar cvTrace(const CvArr* mat)
.. ocv:pyoldfunction:: cv.Trace(mat)-> CvScalar
.. ocv:pyoldfunction:: cv.Trace(mat) -> scalar
:param mat: Source matrix.
@ -3371,10 +3370,10 @@ Performs the matrix transformation of every array element.
.. ocv:function:: void transform(InputArray src, OutputArray dst, InputArray mtx )
.. ocv:pyfunction:: cv2.transform(src, mtx [, dst]) -> dst
.. ocv:pyfunction:: cv2.transform(src, m[, dst]) -> dst
.. ocv:cfunction:: void cvTransform(const CvArr* src, CvArr* dst, const CvMat* mtx, const CvMat* shiftvec=NULL)
.. ocv:pyoldfunction:: cv.Transform(src, dst, mtx, shiftvec=None)-> None
.. ocv:pyoldfunction:: cv.Transform(src, dst, transmat, shiftvec=None)-> None
:param src: Source array that must have as many channels (1 to 4) as ``mtx.cols`` or ``mtx.cols-1``.

6
modules/core/doc/utility_and_system_functions_and_macros.rst
View File

@ -95,7 +95,7 @@ Computes the cube root of an argument.
.. ocv:cfunction:: float cvCbrt(float val)
.. ocv:pyoldfunction:: cv.Cbrt(val)-> float
.. ocv:pyoldfunction:: cv.Cbrt(value)-> float
:param val: A function argument.
@ -209,7 +209,7 @@ The macro ``CV_Error_`` can be used to construct an error message on-fly to incl
Exception
---------
.. ocv:class:: Exception
.. ocv:class:: Exception : public std::exception
Exception class passed to an error. ::
@ -286,7 +286,7 @@ Returns true if the specified feature is supported by the host hardware.
.. ocv:function:: bool checkHardwareSupport(int feature)
.. ocv:cfunction:: int cvCheckHardwareSupport(int feature)
.. ocv:pyfunction:: checkHardwareSupport(feature) -> Bool
.. ocv:pyfunction:: cv2.checkHardwareSupport(feature) -> retval
:param feature: The feature of interest, one of:

6
modules/features2d/doc/common_interfaces_of_descriptor_extractors.rst
View File

@ -13,7 +13,7 @@ descriptor extractors inherit the
DescriptorExtractor
-------------------
.. ocv:class:: DescriptorExtractor
.. ocv:class:: DescriptorExtractor : public Algorithm
Abstract base class for computing descriptors for image keypoints. ::
@ -107,7 +107,7 @@ for example: ``"OpponentSIFT"`` .
OpponentColorDescriptorExtractor
--------------------------------
.. ocv:class:: OpponentColorDescriptorExtractor
.. ocv:class:: OpponentColorDescriptorExtractor : public DescriptorExtractor
Class adapting a descriptor extractor to compute descriptors in the Opponent Color Space
(refer to Van de Sande et al., CGIV 2008 *Color Descriptors for Object Category Recognition*).
@ -132,7 +132,7 @@ them into a single color descriptor. ::
BriefDescriptorExtractor
------------------------
.. ocv:class:: BriefDescriptorExtractor
.. ocv:class:: BriefDescriptorExtractor : public DescriptorExtractor
Class for computing BRIEF descriptors described in a paper of Calonder M., Lepetit V.,
Strecha C., Fua P. *BRIEF: Binary Robust Independent Elementary Features* ,

12
modules/features2d/doc/common_interfaces_of_descriptor_matchers.rst
View File

@ -11,7 +11,7 @@ descriptor matchers inherit the
DMatch
------
.. ocv:class:: DMatch
.. ocv:struct:: DMatch
Class for matching keypoint descriptors: query descriptor index,
train descriptor index, train image index, and distance between descriptors. ::
@ -40,7 +40,7 @@ train descriptor index, train image index, and distance between descriptors. ::
DescriptorMatcher
-----------------
.. ocv:class:: DescriptorMatcher
.. ocv:class:: DescriptorMatcher : public Algorithm
Abstract base class for matching keypoint descriptors. It has two groups
of match methods: for matching descriptors of an image with another image or
@ -227,7 +227,7 @@ DescriptorMatcher::clone
----------------------------
Clones the matcher.
.. ocv:function:: Ptr<DescriptorMatcher> DescriptorMatcher::clone( bool emptyTrainData ) const
.. ocv:function:: Ptr<DescriptorMatcher> DescriptorMatcher::clone( bool emptyTrainData=false )
:param emptyTrainData: If ``emptyTrainData`` is false, the method creates a deep copy of the object, that is, copies both parameters and train data. If ``emptyTrainData`` is true, the method creates an object copy with the current parameters but with empty train data.
@ -258,7 +258,7 @@ Creates a descriptor matcher of a given type with the default parameters (using
BFMatcher
-----------------
.. ocv:class::BFMatcher
.. ocv:class::BFMatcher : public DescriptorMatcher
Brute-force descriptor matcher. For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one. This descriptor matcher supports masking permissible matches of descriptor sets. ::
@ -267,7 +267,7 @@ BFMatcher::BFMatcher
--------------------
Brute-force matcher constructor.
.. ocv:function:: BFMatcher::BFMatcher( int distanceType, bool crossCheck=false )
.. ocv:function:: BFMatcher::BFMatcher( int normType, bool crossCheck=false )
:param distanceType: One of ``NORM_L1``, ``NORM_L2``, ``NORM_HAMMING``, ``NORM_HAMMING2``. ``L1`` and ``L2`` norms are preferable choices for SIFT and SURF descriptors, ``NORM_HAMMING`` should be used with ORB and BRIEF, ``NORM_HAMMING2`` should be used with ORB when ``WTA_K==3`` or ``4`` (see ORB::ORB constructor description).
@ -276,7 +276,7 @@ Brute-force matcher constructor.
FlannBasedMatcher
-----------------
.. ocv:class:: FlannBasedMatcher
.. ocv:class:: FlannBasedMatcher : public DescriptorMatcher
Flann-based descriptor matcher. This matcher trains :ocv:class:`flann::Index_` on a train descriptor collection and calls its nearest search methods to find the best matches. So, this matcher may be faster when matching a large train collection than the brute force matcher. ``FlannBasedMatcher`` does not support masking permissible matches of descriptor sets because ``flann::Index`` does not support this. ::

24
modules/features2d/doc/common_interfaces_of_feature_detectors.rst
View File

@ -48,7 +48,7 @@ The keypoint constructors
.. ocv:function:: KeyPoint::KeyPoint(float x, float y, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1)
.. ocv:pyfunction:: cv2.KeyPoint(x, y, _size[, _angle[, _response[, _octave[, _class_id]]]]) -> <KeyPoint object>
.. ocv:pyfunction:: cv2.KeyPoint([x, y, _size[, _angle[, _response[, _octave[, _class_id]]]]]) -> <KeyPoint object>
:param x: x-coordinate of the keypoint
@ -69,7 +69,7 @@ The keypoint constructors
FeatureDetector
---------------
.. ocv:class:: FeatureDetector
.. ocv:class:: FeatureDetector : public Algorithm
Abstract base class for 2D image feature detectors. ::
@ -156,7 +156,7 @@ for example: ``"GridFAST"``, ``"PyramidSTAR"`` .
FastFeatureDetector
-------------------
.. ocv:class:: FastFeatureDetector
.. ocv:class:: FastFeatureDetector : public FeatureDetector
Wrapping class for feature detection using the
:ocv:func:`FAST` method. ::
@ -252,7 +252,7 @@ Wrapping class for feature detection using the
DenseFeatureDetector
--------------------
.. ocv:class:: DenseFeatureDetector
.. ocv:class:: DenseFeatureDetector : public FeatureDetector
Class for generation of image features which are distributed densely and regularly over the image. ::
@ -279,7 +279,7 @@ The detector generates several levels (in the amount of ``featureScaleLevels``)
SimpleBlobDetector
-------------------
.. ocv:class:: SimpleBlobDetector
.. ocv:class:: SimpleBlobDetector : public FeatureDetector
Class for extracting blobs from an image. ::
@ -344,7 +344,7 @@ Default values of parameters are tuned to extract dark circular blobs.
GridAdaptedFeatureDetector
--------------------------
.. ocv:class:: GridAdaptedFeatureDetector
.. ocv:class:: GridAdaptedFeatureDetector : public FeatureDetector
Class adapting a detector to partition the source image into a grid and detect points in each cell. ::
@ -369,7 +369,7 @@ Class adapting a detector to partition the source image into a grid and detect p
PyramidAdaptedFeatureDetector
-----------------------------
.. ocv:class:: PyramidAdaptedFeatureDetector
.. ocv:class:: PyramidAdaptedFeatureDetector : public FeatureDetector
Class adapting a detector to detect points over multiple levels of a Gaussian pyramid. Consider using this class for detectors that are not inherently scaled. ::
@ -387,7 +387,7 @@ Class adapting a detector to detect points over multiple levels of a Gaussian py
DynamicAdaptedFeatureDetector
-----------------------------
.. ocv:class:: DynamicAdaptedFeatureDetector
.. ocv:class:: DynamicAdaptedFeatureDetector : public FeatureDetector
Adaptively adjusting detector that iteratively detects features until the desired number is found. ::
@ -431,7 +431,7 @@ DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector
----------------------------------------------------------------
The constructor
.. ocv:function:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjuster, int min_features, int max_features, int max_iters )
.. ocv:function:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjaster, int min_features=400, int max_features=500, int max_iters=5 )
:param adjuster: :ocv:class:`AdjusterAdapter` that detects features and adjusts parameters.
@ -443,7 +443,7 @@ The constructor
AdjusterAdapter
---------------
.. ocv:class:: AdjusterAdapter
.. ocv:class:: AdjusterAdapter : public FeatureDetector
Class providing an interface for adjusting parameters of a feature detector. This interface is used by :ocv:class:`DynamicAdaptedFeatureDetector` . It is a wrapper for :ocv:class:`FeatureDetector` that enables adjusting parameters after feature detection. ::
@ -522,7 +522,7 @@ Creates an adjuster adapter by name
FastAdjuster
------------
.. ocv:class:: FastAdjuster
.. ocv:class:: FastAdjuster : public AdjusterAdapter
:ocv:class:`AdjusterAdapter` for :ocv:class:`FastFeatureDetector`. This class decreases or increases the threshold value by 1. ::
@ -535,7 +535,7 @@ FastAdjuster
StarAdjuster
------------
.. ocv:class:: StarAdjuster
.. ocv:class:: StarAdjuster : public AdjusterAdapter
:ocv:class:`AdjusterAdapter` for :ocv:class:`StarFeatureDetector`. This class adjusts the ``responseThreshhold`` of ``StarFeatureDetector``. ::

6
modules/features2d/doc/common_interfaces_of_generic_descriptor_matchers.rst
View File

@ -130,7 +130,7 @@ GenericDescriptorMatcher::isMaskSupported
---------------------------------------------
Returns ``true`` if a generic descriptor matcher supports masking permissible matches.
.. ocv:function:: void GenericDescriptorMatcher::isMaskSupported()
.. ocv:function:: bool GenericDescriptorMatcher::isMaskSupported()
@ -231,7 +231,7 @@ GenericDescriptorMatcher::clone
-----------------------------------
Clones the matcher.
.. ocv:function:: Ptr<GenericDescriptorMatcher> GenericDescriptorMatcher::clone( bool emptyTrainData ) const
.. ocv:function:: Ptr<GenericDescriptorMatcher> GenericDescriptorMatcher::clone( bool emptyTrainData=false ) const
:param emptyTrainData: If ``emptyTrainData`` is false, the method creates a deep copy of the object, that is, copies
both parameters and train data. If ``emptyTrainData`` is true, the method creates an object copy with the current parameters
@ -240,7 +240,7 @@ Clones the matcher.
VectorDescriptorMatcher
-----------------------
.. ocv:class:: VectorDescriptorMatcher
.. ocv:class:: VectorDescriptorMatcher : public GenericDescriptorMatcher
Class used for matching descriptors that can be described as vectors in a finite-dimensional space. ::

2
modules/features2d/doc/drawing_function_of_keypoints_and_matches.rst
View File

@ -65,7 +65,7 @@ drawKeypoints
-----------------
Draws keypoints.
.. ocv:function:: void drawKeypoints( const Mat& image, const vector<KeyPoint>& keypoints, Mat& outImg, const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT )
.. ocv:function:: void drawKeypoints( const Mat& image, const vector<KeyPoint>& keypoints, Mat& outImage, const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT )
:param image: Source image.

6
modules/features2d/doc/feature_detection_and_description.rst
View File

@ -24,7 +24,7 @@ Detects corners using the FAST algorithm by [Rosten06]_.
MSER
----
.. ocv:class:: MSER
.. ocv:class:: MSER : public FeatureDetector
Maximally stable extremal region extractor. ::
@ -50,7 +50,7 @@ http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions). Also see http:/
ORB
---
.. ocv:class:: ORB
.. ocv:class:: ORB : public Feature2D
Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor, described in [RRKB11]_. The algorithm uses FAST in pyramids to detect stable keypoints, selects the strongest features using FAST or Harris response, finds their orientation using first-order moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or k-tuples) are rotated according to the measured orientation).
@ -60,8 +60,6 @@ ORB::ORB
--------
The ORB constructor
.. ocv:function:: ORB::ORB()
.. ocv:function:: ORB::ORB(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31, int firstLevel = 0, int WTA_K=2, int scoreType=HARRIS_SCORE, int patchSize=31)
:param nfeatures: The maximum number of features to retain.

4
modules/features2d/doc/object_categorization.rst
View File

@ -54,7 +54,7 @@ BOWTrainer::descripotorsCount
---------------------------------
Returns the count of all descriptors stored in the training set.
.. ocv:function:: const vector<Mat>& BOWTrainer::descripotorsCount() const
.. ocv:function:: int BOWTrainer::descripotorsCount() const
@ -72,7 +72,7 @@ The vocabulary consists of cluster centers. So, this method returns the vocabula
BOWKMeansTrainer
----------------
.. ocv:class:: BOWKMeansTrainer
.. ocv:class:: BOWKMeansTrainer : public BOWTrainer
:ocv:func:`kmeans` -based class to train visual vocabulary using the *bag of visual words* approach.
::

6
modules/features2d/include/opencv2/features2d/features2d.hpp
View File

@ -276,7 +276,7 @@ public:
enum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 };
explicit ORB(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31,
int firstLevel = 0, int WTA_K=2, int scoreType=0, int patchSize=31 );
int firstLevel = 0, int WTA_K=2, int scoreType=HARRIS_SCORE, int patchSize=31 );
// returns the descriptor size in bytes
int descriptorSize() const;
@ -1158,9 +1158,9 @@ public:
const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
// Reads matcher object from a file node
virtual void read( const FileNode& );
virtual void read( const FileNode& fn );
// Writes matcher object to a file storage
virtual void write( FileStorage& ) const;
virtual void write( FileStorage& fs ) const;
// Return true if matching object is empty (e.g. feature detector or descriptor matcher are empty)
virtual bool empty() const;

2
modules/gpu/doc/data_structures.rst
View File

@ -338,7 +338,7 @@ Blocks the current CPU thread until all operations in the stream are complete.
gpu::StreamAccessor
-------------------
.. ocv:class:: gpu::StreamAccessor
.. ocv:struct:: gpu::StreamAccessor
Class that enables getting ``cudaStream_t`` from :ocv:class:`gpu::Stream` and is declared in ``stream_accessor.hpp`` because it is the only public header that depends on the CUDA Runtime API. Including it brings a dependency to your code. ::

4
modules/gpu/doc/image_processing.rst
View File

@ -238,7 +238,7 @@ The source matrix should be continuous, otherwise reallocation and data copying
gpu::ConvolveBuf
----------------
.. ocv:class:: gpu::ConvolveBuf
.. ocv:struct:: gpu::ConvolveBuf
Class providing a memory buffer for :ocv:func:`gpu::convolve` function, plus it allows to adjust some specific parameters. ::
@ -290,7 +290,7 @@ Computes a convolution (or cross-correlation) of two images.
gpu::MatchTemplateBuf
---------------------
.. ocv:class:: gpu::MatchTemplateBuf
.. ocv:struct:: gpu::MatchTemplateBuf
Class providing memory buffers for :ocv:func:`gpu::matchTemplate` function, plus it allows to adjust some specific parameters. ::

2
modules/gpu/doc/object_detection.rst
View File

@ -7,7 +7,7 @@ Object Detection
gpu::HOGDescriptor
------------------
.. ocv:class:: gpu::HOGDescriptor
.. ocv:struct:: gpu::HOGDescriptor
The class implements Histogram of Oriented Gradients ([Dalal2005]_) object detector. ::

4
modules/gpu/doc/video.rst
View File

@ -377,7 +377,7 @@ The method write the specified image to video file. The image must have the same
gpu::VideoWriter_GPU::EncoderParams
-----------------------------------
.. ocv:class:: gpu::VideoWriter_GPU::EncoderParams
.. ocv:struct:: gpu::VideoWriter_GPU::EncoderParams
Different parameters for CUDA video encoder. ::
@ -561,7 +561,7 @@ Chroma formats supported by ocv:class:`gpu::VideoReader_GPU` . ::
gpu::VideoReader_GPU::FormatInfo
--------------------------------
.. ocv:class:: gpu::VideoReader_GPU::FormatInfo
.. ocv:struct:: gpu::VideoReader_GPU::FormatInfo
Struct providing information about video file format. ::

24
modules/highgui/doc/qt_new_functions.rst
View File

@ -61,11 +61,11 @@ setWindowProperty
---------------------
Changes parameters of a window dynamically.
.. ocv:function:: void setWindowProperty(const string& name, int prop_id, double prop_value)
.. ocv:function:: void setWindowProperty( const string& winname, int prop_id, double prop_value )
.. ocv:pyfunction:: cv2.setWindowProperty(winname, prop_id, prop_value) -> None
.. ocv:cfunction:: void cvSetWindowProperty(const char* name, int propId, double propValue)
.. ocv:cfunction:: void cvSetWindowProperty( const char* name, int prop_id, double prop_value )
:param name: Name of the window.
@ -97,11 +97,11 @@ getWindowProperty
---------------------
Provides parameters of a window.
.. ocv:function:: void getWindowProperty(const string& name, int prop_id)
.. ocv:function:: double getWindowProperty( const string& winname, int prop_id )
.. ocv:pyfunction:: cv2.getWindowProperty(winname, prop_id) -> retval
.. ocv:cfunction:: void cvGetWindowProperty(const char* name, int propId)
.. ocv:cfunction:: double cvGetWindowProperty( const char* name, int prop_id )
:param name: Name of the window.
@ -169,9 +169,9 @@ addText
-----------
Creates the font to draw a text on an image.
.. ocv:function:: void addText(const Mat& img, const string& text, Point location, CvFont *font)
.. ocv:function:: void addText( const Mat& img, const string& text, Point org, CvFont font )
.. ocv:cfunction:: void cvAddText(const CvArr* img, const char* text, CvPoint location, CvFont *font)
.. ocv:cfunction:: void cvAddText( const CvArr* img, const char* text, CvPoint org, CvFont * arg2 )
:param img: 8-bit 3-channel image where the text should be drawn.
@ -195,7 +195,7 @@ displayOverlay
------------------
Displays a text on a window image as an overlay for a specified duration.
.. ocv:function:: void displayOverlay(const string& name, const string& text, int delayms = 0)
.. ocv:function:: void displayOverlay( const string& winname, const string& text, int delayms=0 )
.. ocv:cfunction:: void cvDisplayOverlay(const char* name, const char* text, int delayms = 0)
@ -212,7 +212,7 @@ displayStatusBar
--------------------
Displays a text on the window statusbar during the specified period of time.
.. ocv:function:: void displayStatusBar(const string& name, const string& text, int delayms = 0)
.. ocv:function:: void displayStatusBar( const string& winname, const string& text, int delayms=0 )
.. ocv:cfunction:: void cvDisplayStatusBar(const char* name, const char* text, int delayms = 0)
@ -282,7 +282,7 @@ saveWindowParameters
------------------------
Saves parameters of the specified window.
.. ocv:function:: void saveWindowParameters(const string& name)
.. ocv:function:: void saveWindowParameters( const string& windowName )
.. ocv:cfunction:: void cvSaveWindowParameters(const char* name)
@ -295,7 +295,7 @@ loadWindowParameters
------------------------
Loads parameters of the specified window.
.. ocv:function:: void loadWindowParameters(const string& name)
.. ocv:function:: void loadWindowParameters( const string& windowName )
.. ocv:cfunction:: void cvLoadWindowParameters(const char* name)
@ -308,9 +308,9 @@ createButton
----------------
Attaches a button to the control panel.
.. ocv:function:: createButton( const string& button_name=NULL, ButtonCallback on_change=NULL, void* userdata=NULL, int button_type=CV_PUSH_BUTTON, int initial_button_state=0 )
.. ocv:function:: int createButton( const string& bar_name, ButtonCallback on_change, void* userdata=NULL, int type=CV_PUSH_BUTTON, bool initial_button_state=0 )
.. ocv:cfunction:: cvCreateButton( const char* buttonName=NULL, CvButtonCallback onChange=NULL, void* userdata=NULL, int buttonType=CV_PUSH_BUTTON, int initialButtonState=0 )
.. ocv:cfunction:: int cvCreateButton( const char* button_name=NULL, CvButtonCallback on_change=NULL, void* userdata=NULL, int button_type=CV_PUSH_BUTTON, int initial_button_state=0 )
:param button_name: Name of the button.

54
modules/highgui/doc/reading_and_writing_images_and_video.rst
View File

@ -31,9 +31,9 @@ Encodes an image into a memory buffer.
.. ocv:function:: bool imencode( const string& ext, InputArray img, vector<uchar>& buf, const vector<int>& params=vector<int>())
.. ocv:cfunction:: CvMat* cvEncodeImage(const char* ext, const CvArr* image, const int* params=NULL )
.. ocv:cfunction:: CvMat* cvEncodeImage( const char* ext, const CvArr* image, const int* params=0 )
.. ocv:pyfunction:: cv2.imencode(ext, img, buf[, params]) -> retval
.. ocv:pyfunction:: cv2.imencode(ext, img[, params]) -> retval, buf
:param ext: File extension that defines the output format.
@ -57,13 +57,13 @@ Loads an image from a file.
.. ocv:pyfunction:: cv2.imread(filename[, flags]) -> retval
.. ocv:cfunction:: IplImage* cvLoadImage( const char* filename, int flags=CV_LOAD_IMAGE_COLOR )
.. ocv:cfunction:: IplImage* cvLoadImage( const char* filename, int iscolor=CV_LOAD_IMAGE_COLOR )
.. ocv:cfunction:: CvMat* cvLoadImageM( const char* filename, int flags=CV_LOAD_IMAGE_COLOR )
.. ocv:cfunction:: CvMat* cvLoadImageM( const char* filename, int iscolor=CV_LOAD_IMAGE_COLOR )
.. ocv:pyoldfunction:: cv.LoadImage(filename, flags=CV_LOAD_IMAGE_COLOR)->None
.. ocv:pyoldfunction:: cv.LoadImage(filename, iscolor=CV_LOAD_IMAGE_COLOR) -> None
.. ocv:pyoldfunction:: cv.LoadImageM(filename, flags=CV_LOAD_IMAGE_COLOR)->None
.. ocv:pyoldfunction:: cv.LoadImageM(filename, iscolor=CV_LOAD_IMAGE_COLOR) -> None
:param filename: Name of file to be loaded.
@ -103,11 +103,11 @@ imwrite
-----------
Saves an image to a specified file.
.. ocv:function:: bool imwrite( const string& filename, InputArray image, const vector<int>& params=vector<int>())
.. ocv:function:: bool imwrite( const string& filename, InputArray img, const vector<int>& params=vector<int>() )
.. ocv:pyfunction:: cv2.imwrite(filename, image[, params]) -> retval
.. ocv:pyfunction:: cv2.imwrite(filename, img[, params]) -> retval
.. ocv:cfunction:: int cvSaveImage( const char* filename, const CvArr* image )
.. ocv:cfunction:: int cvSaveImage( const char* filename, const CvArr* image, const int* params=0 )
.. ocv:pyoldfunction:: cv.SaveImage(filename, image)-> None
@ -225,7 +225,7 @@ VideoCapture constructors.
.. ocv:pyfunction:: cv2.VideoCapture(device) -> <VideoCapture object>
.. ocv:cfunction:: CvCapture* cvCaptureFromCAM( int device )
.. ocv:pyoldfunction:: cv.CaptureFromCAM(device) -> CvCapture
.. ocv:pyoldfunction:: cv.CaptureFromCAM(index) -> CvCapture
.. ocv:cfunction:: CvCapture* cvCaptureFromFile( const char* filename )
.. ocv:pyoldfunction:: cv.CaptureFromFile(filename) -> CvCapture
@ -243,8 +243,8 @@ Open video file or a capturing device for video capturing
.. ocv:function:: bool VideoCapture::open(const string& filename)
.. ocv:function:: bool VideoCapture::open(int device)
.. ocv:pyfunction:: cv2.VideoCapture.open(filename) -> successFlag
.. ocv:pyfunction:: cv2.VideoCapture.open(device) -> successFlag
.. ocv:pyfunction:: cv2.VideoCapture.open(filename) -> retval
.. ocv:pyfunction:: cv2.VideoCapture.open(device) -> retval
:param filename: name of the opened video file
@ -259,7 +259,7 @@ Returns true if video capturing has been initialized already.
.. ocv:function:: bool VideoCapture::isOpened()
.. ocv:pyfunction:: cv2.VideoCapture.isOpened() -> flag
.. ocv:pyfunction:: cv2.VideoCapture.isOpened() -> retval
If the previous call to ``VideoCapture`` constructor or ``VideoCapture::open`` succeeded, the method returns true.
@ -269,7 +269,7 @@ Closes video file or capturing device.
.. ocv:function:: void VideoCapture::release()
.. ocv:pyfunction:: cv2.VideoCapture.release()
.. ocv:pyfunction:: cv2.VideoCapture.release() -> None
.. ocv:cfunction:: void cvReleaseCapture(CvCapture** capture)
@ -284,7 +284,7 @@ Grabs the next frame from video file or capturing device.
.. ocv:function:: bool VideoCapture::grab()
.. ocv:pyfunction:: cv2.VideoCapture.grab() -> successFlag
.. ocv:pyfunction:: cv2.VideoCapture.grab() -> retval
.. ocv:cfunction:: int cvGrabFrame(CvCapture* capture)
@ -303,11 +303,11 @@ Decodes and returns the grabbed video frame.
.. ocv:function:: bool VideoCapture::retrieve(Mat& image, int channel=0)
.. ocv:pyfunction:: cv2.VideoCapture.retrieve([image[, channel]]) -> successFlag, image
.. ocv:pyfunction:: cv2.VideoCapture.retrieve([image[, channel]]) -> retval, image
.. ocv:cfunction:: IplImage* cvRetrieveFrame(CvCapture* capture)
.. ocv:cfunction:: IplImage* cvRetrieveFrame( CvCapture* capture, int streamIdx=0 )
.. ocv:pyoldfunction:: cv.RetrieveFrame(capture) -> iplimage
.. ocv:pyoldfunction:: cv.RetrieveFrame(capture) -> image
The methods/functions decode and return the just grabbed frame. If no frames has been grabbed (camera has been disconnected, or there are no more frames in video file), the methods return false and the functions return NULL pointer.
@ -322,11 +322,11 @@ Grabs, decodes and returns the next video frame.
.. ocv:function:: bool VideoCapture::read(Mat& image)
.. ocv:pyfunction:: cv2.VideoCapture.read([image]) -> successFlag, image
.. ocv:pyfunction:: cv2.VideoCapture.read([image]) -> retval, image
.. ocv:cfunction:: IplImage* cvQueryFrame(CvCapture* capture)
.. ocv:pyoldfunction:: cv.QueryFrame(capture) -> iplimage
.. ocv:pyoldfunction:: cv.QueryFrame(capture) -> image
The methods/functions combine :ocv:func:`VideoCapture::grab` and :ocv:func:`VideoCapture::retrieve` in one call. This is the most convenient method for reading video files or capturing data from decode and return the just grabbed frame. If no frames has been grabbed (camera has been disconnected, or there are no more frames in video file), the methods return false and the functions return NULL pointer.
@ -341,9 +341,9 @@ Returns the specified ``VideoCapture`` property
.. ocv:pyfunction:: cv2.VideoCapture.get(propId) -> retval
.. ocv:cfunction:: double cvGetCaptureProperty( CvCapture* capture, int propId )
.. ocv:cfunction:: double cvGetCaptureProperty( CvCapture* capture, int property_id )
.. ocv:pyoldfunction:: cv.GetCaptureProperty(capture, propId)->double
.. ocv:pyoldfunction:: cv.GetCaptureProperty(capture, property_id) -> float
:param propId: Property identifier. It can be one of the following:
@ -393,13 +393,13 @@ VideoCapture::set
---------------------
Sets a property in the ``VideoCapture``.
.. ocv:function:: bool VideoCapture::set(int propertyId, double value)
.. ocv:function:: bool VideoCapture::set( int propId, double value )
.. ocv:pyfunction:: cv2.VideoCapture.set(propId, value) -> retval
.. ocv:cfunction:: int cvSetCaptureProperty( CvCapture* capture, int propId, double value )
.. ocv:cfunction:: int cvSetCaptureProperty( CvCapture* capture, int property_id, double value )
.. ocv:pyoldfunction:: cv.SetCaptureProperty(capture, propId, value)->None
.. ocv:pyoldfunction:: cv.SetCaptureProperty(capture, property_id, value) -> retval
:param propId: Property identifier. It can be one of the following:
@ -463,8 +463,8 @@ VideoWriter constructors
.. ocv:pyfunction:: cv2.VideoWriter([filename, fourcc, fps, frameSize[, isColor]]) -> <VideoWriter object>
.. ocv:cfunction:: CvVideoWriter* cvCreateVideoWriter( const char* filename, int fourcc, double fps, CvSize frameSize, int isColor=1 )
.. ocv:pyoldfunction:: cv.CreateVideoWriter(filename, fourcc, fps, frameSize, isColor) -> CvVideoWriter
.. ocv:cfunction:: CvVideoWriter* cvCreateVideoWriter( const char* filename, int fourcc, double fps, CvSize frame_size, int is_color=1 )
.. ocv:pyoldfunction:: cv.CreateVideoWriter(filename, fourcc, fps, frame_size, is_color=true) -> CvVideoWriter
.. ocv:pyfunction:: cv2.VideoWriter.isOpened() -> retval
.. ocv:pyfunction:: cv2.VideoWriter.open(filename, fourcc, fps, frameSize[, isColor]) -> retval

26
modules/highgui/doc/user_interface.rst
View File

@ -9,7 +9,7 @@ Creates a trackbar and attaches it to the specified window.
.. ocv:function:: int createTrackbar( const string& trackbarname, const string& winname, int* value, int count, TrackbarCallback onChange=0, void* userdata=0)
.. ocv:cfunction:: int cvCreateTrackbar( const char* trackbarName, const char* windowName, int* value, int count, CvTrackbarCallback onChange )
.. ocv:cfunction:: int cvCreateTrackbar( const char* trackbar_name, const char* window_name, int* value, int count, CvTrackbarCallback on_change=NULL )
.. ocv:pyoldfunction:: cv.CreateTrackbar(trackbarName, windowName, value, count, onChange) -> None
:param trackbarname: Name of the created trackbar.
@ -40,8 +40,8 @@ Returns the trackbar position.
.. ocv:pyfunction:: cv2.getTrackbarPos(trackbarname, winname) -> retval
.. ocv:cfunction:: int cvGetTrackbarPos( const char* trackbarName, const char* windowName )
.. ocv:pyoldfunction:: cv.GetTrackbarPos(trackbarName, windowName)-> None
.. ocv:cfunction:: int cvGetTrackbarPos( const char* trackbar_name, const char* window_name )
.. ocv:pyoldfunction:: cv.GetTrackbarPos(trackbarName, windowName) -> retval
:param trackbarname: Name of the trackbar.
@ -57,12 +57,12 @@ imshow
----------
Displays an image in the specified window.
.. ocv:function:: void imshow( const string& winname, InputArray image )
.. ocv:function:: void imshow( const string& winname, InputArray mat )
.. ocv:pyfunction:: cv2.imshow(winname, image) -> None
.. ocv:pyfunction:: cv2.imshow(winname, mat) -> None
.. ocv:cfunction:: void cvShowImage( const char* winname, const CvArr* image )
.. ocv:pyoldfunction:: cv.ShowImage(winname, image)-> None
.. ocv:cfunction:: void cvShowImage( const char* name, const CvArr* image )
.. ocv:pyoldfunction:: cv.ShowImage(name, image) -> None
:param winname: Name of the window.
@ -81,11 +81,11 @@ namedWindow
---------------
Creates a window.
.. ocv:function:: void namedWindow( const string& winname, int flags )
.. ocv:function:: void namedWindow( const string& winname, int flags=WINDOW_AUTOSIZE )
.. ocv:pyfunction:: cv2.namedWindow(winname[, flags]) -> None
.. ocv:cfunction:: int cvNamedWindow( const char* name, int flags )
.. ocv:cfunction:: int cvNamedWindow( const char* name, int flags=CV_WINDOW_AUTOSIZE )
.. ocv:pyoldfunction:: cv.NamedWindow(name, flags=CV_WINDOW_AUTOSIZE)-> None
:param name: Name of the window in the window caption that may be used as a window identifier.
@ -179,8 +179,8 @@ SetMouseCallback
----------------
Sets mouse handler for the specified window
.. ocv:cfunction:: void cvSetMouseCallback( const char* name, CvMouseCallback onMouse, void* param=NULL )
.. ocv:pyoldfunction:: cv.SetMouseCallback(name, onMouse, param) -> None
.. ocv:cfunction:: void cvSetMouseCallback( const char* window_name, CvMouseCallback on_mouse, void* param=NULL )
.. ocv:pyoldfunction:: cv.SetMouseCallback(windowName, onMouse, param=None) -> None
:param name: Window name
@ -197,7 +197,7 @@ Sets the trackbar position.
.. ocv:pyfunction:: cv2.setTrackbarPos(trackbarname, winname, pos) -> None
.. ocv:cfunction:: void cvSetTrackbarPos( const char* trackbarName, const char* windowName, int pos )
.. ocv:cfunction:: void cvSetTrackbarPos( const char* trackbar_name, const char* window_name, int pos )
.. ocv:pyoldfunction:: cv.SetTrackbarPos(trackbarName, windowName, pos)-> None
:param trackbarname: Name of the trackbar.
@ -218,7 +218,7 @@ Waits for a pressed key.
.. ocv:function:: int waitKey(int delay=0)
.. ocv:pyfunction:: cv2.waitKey([, delay]) -> retval
.. ocv:pyfunction:: cv2.waitKey([delay]) -> retval
.. ocv:cfunction:: int cvWaitKey( int delay=0 )
.. ocv:pyoldfunction:: cv.WaitKey(delay=0)-> int

42
modules/imgproc/doc/feature_detection.rst
View File

@ -13,8 +13,9 @@ Finds edges in an image using the [Canny86]_ algorithm.
.. ocv:pyfunction:: cv2.Canny(image, threshold1, threshold2[, edges[, apertureSize[, L2gradient]]]) -> edges
.. ocv:cfunction:: void cvCanny( const CvArr* image, CvArr* edges, double threshold1, double threshold2, int apertureSize=3 )
.. ocv:pyoldfunction:: cv.Canny(image, edges, threshold1, threshold2, apertureSize=3)-> None
.. ocv:cfunction:: void cvCanny( const CvArr* image, CvArr* edges, double threshold1, double threshold2, int aperture_size=3 )
.. ocv:pyoldfunction:: cv.Canny(image, edges, threshold1, threshold2, aperture_size=3) -> None
:param image: Single-channel 8-bit input image.
@ -37,12 +38,13 @@ cornerEigenValsAndVecs
----------------------
Calculates eigenvalues and eigenvectors of image blocks for corner detection.
.. ocv:function:: void cornerEigenValsAndVecs( InputArray src, OutputArray dst, int blockSize, int apertureSize, int borderType=BORDER_DEFAULT )
.. ocv:function:: void cornerEigenValsAndVecs( InputArray src, OutputArray dst, int blockSize, int ksize, int borderType=BORDER_DEFAULT )
.. ocv:pyfunction:: cv2.cornerEigenValsAndVecs(src, blockSize, ksize[, dst[, borderType]]) -> dst
.. ocv:cfunction:: void cvCornerEigenValsAndVecs( const CvArr* image, CvArr* eigenvv, int blockSize, int apertureSize=3 )
.. ocv:pyoldfunction:: cv.CornerEigenValsAndVecs(image, eigenvv, blockSize, apertureSize=3)-> None
.. ocv:cfunction:: void cvCornerEigenValsAndVecs( const CvArr* image, CvArr* eigenvv, int block_size, int aperture_size=3 )
.. ocv:pyoldfunction:: cv.CornerEigenValsAndVecs(image, eigenvv, blockSize, aperture_size=3) -> None
:param src: Input single-channel 8-bit or floating-point image.
@ -89,12 +91,13 @@ cornerHarris
------------
Harris edge detector.
.. ocv:function:: void cornerHarris( InputArray src, OutputArray dst, int blockSize, int apertureSize, double k, int borderType=BORDER_DEFAULT )
.. ocv:function:: void cornerHarris( InputArray src, OutputArray dst, int blockSize, int ksize, double k, int borderType=BORDER_DEFAULT )
.. ocv:pyfunction:: cv2.cornerHarris(src, blockSize, ksize, k[, dst[, borderType]]) -> dst
.. ocv:cfunction:: void cvCornerHarris( const CvArr* image, CvArr* harrisDst, int blockSize, int apertureSize=3, double k=0.04 )
.. ocv:pyoldfunction:: cv.CornerHarris(image, harrisDst, blockSize, apertureSize=3, k=0.04)-> None
.. ocv:cfunction:: void cvCornerHarris( const CvArr* image, CvArr* harris_responce, int block_size, int aperture_size=3, double k=0.04 )
.. ocv:pyoldfunction:: cv.CornerHarris(image, harris_dst, blockSize, aperture_size=3, k=0.04) -> None
:param src: Input single-channel 8-bit or floating-point image.
@ -128,13 +131,13 @@ cornerMinEigenVal
-----------------
Calculates the minimal eigenvalue of gradient matrices for corner detection.
.. ocv:function:: void cornerMinEigenVal( InputArray src, OutputArray dst, int blockSize, int apertureSize=3, int borderType=BORDER_DEFAULT )
.. ocv:function:: void cornerMinEigenVal( InputArray src, OutputArray dst, int blockSize, int ksize=3, int borderType=BORDER_DEFAULT )
.. ocv:pyfunction:: cv2.cornerMinEigenVal(src, blockSize[, dst[, ksize[, borderType]]]) -> dst
.. ocv:cfunction:: void cvCornerMinEigenVal( const CvArr* image, CvArr* eigenval, int blockSize, int apertureSize=3 )
.. ocv:cfunction:: void cvCornerMinEigenVal( const CvArr* image, CvArr* eigenval, int block_size, int aperture_size=3 )
.. ocv:pyoldfunction:: cv.CornerMinEigenVal(image, eigenval, blockSize, apertureSize=3)-> None
.. ocv:pyoldfunction:: cv.CornerMinEigenVal(image, eigenval, blockSize, aperture_size=3) -> None
:param src: Input single-channel 8-bit or floating-point image.
@ -161,9 +164,9 @@ Refines the corner locations.
.. ocv:pyfunction:: cv2.cornerSubPix(image, corners, winSize, zeroZone, criteria) -> None
.. ocv:cfunction:: void cvFindCornerSubPix( const CvArr* image, CvPoint2D32f* corners, int count, CvSize winSize, CvSize zeroZone, CvTermCriteria criteria )
.. ocv:cfunction:: void cvFindCornerSubPix( const CvArr* image, CvPoint2D32f* corners, int count, CvSize win, CvSize zero_zone, CvTermCriteria criteria )
.. ocv:pyoldfunction:: cv.FindCornerSubPix(image, corners, winSize, zeroZone, criteria)-> corners
.. ocv:pyoldfunction:: cv.FindCornerSubPix(image, corners, win, zero_zone, criteria) -> corners
:param image: Input image.
@ -223,9 +226,9 @@ Determines strong corners on an image.
.. ocv:pyfunction:: cv2.goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]]) -> corners
.. ocv:cfunction:: void cvGoodFeaturesToTrack( const CvArr* image, CvArr* eigImage, CvArr* tempImage, CvPoint2D32f* corners, int* cornerCount, double qualityLevel, double minDistance, const CvArr* mask=NULL, int blockSize=3, int useHarris=0, double k=0.04 )
.. ocv:cfunction:: void cvGoodFeaturesToTrack( const CvArr* image, CvArr* eig_image, CvArr* temp_image, CvPoint2D32f* corners, int* corner_count, double quality_level, double min_distance, const CvArr* mask=NULL, int block_size=3, int use_harris=0, double k=0.04 )
.. ocv:pyoldfunction:: cv.GoodFeaturesToTrack(image, eigImage, tempImage, cornerCount, qualityLevel, minDistance, mask=None, blockSize=3, useHarris=0, k=0.04)-> corners
.. ocv:pyoldfunction:: cv.GoodFeaturesToTrack(image, eigImage, tempImage, cornerCount, qualityLevel, minDistance, mask=None, blockSize=3, useHarris=0, k=0.04) -> cornerCount
:param image: Input 8-bit or floating-point 32-bit, single-channel image.
@ -287,7 +290,7 @@ Finds circles in a grayscale image using the Hough transform.
.. ocv:function:: void HoughCircles( InputArray image, OutputArray circles, int method, double dp, double minDist, double param1=100, double param2=100, int minRadius=0, int maxRadius=0 )
.. ocv:cfunction:: CvSeq* cvHoughCircles( CvArr* image, CvMemStorage* circleStorage, int method, double dp, double minDist, double param1=100, double param2=100, int minRadius=0, int maxRadius=0 )
.. ocv:cfunction:: CvSeq* cvHoughCircles( CvArr* image, void* circle_storage, int method, double dp, double min_dist, double param1=100, double param2=100, int min_radius=0, int max_radius=0 )
.. ocv:pyfunction:: cv2.HoughCircles(image, method, dp, minDist[, circles[, param1[, param2[, minRadius[, maxRadius]]]]]) -> circles
@ -362,7 +365,7 @@ Finds lines in a binary image using the standard Hough transform.
.. ocv:pyfunction:: cv2.HoughLines(image, rho, theta, threshold[, lines[, srn[, stn]]]) -> lines
.. ocv:cfunction:: CvSeq* cvHoughLines2( CvArr* image, void* storage, int method, double rho, double theta, int threshold, double param1=0, double param2=0 )
.. ocv:cfunction:: CvSeq* cvHoughLines2( CvArr* image, void* line_storage, int method, double rho, double theta, int threshold, double param1=0, double param2=0 )
.. ocv:pyoldfunction:: cv.HoughLines2(image, storage, method, rho, theta, threshold, param1=0, param2=0)-> lines
@ -501,11 +504,12 @@ preCornerDetect
---------------
Calculates a feature map for corner detection.
.. ocv:function:: void preCornerDetect( InputArray src, OutputArray dst, int apertureSize, int borderType=BORDER_DEFAULT )
.. ocv:function:: void preCornerDetect( InputArray src, OutputArray dst, int ksize, int borderType=BORDER_DEFAULT )
.. ocv:pyfunction:: cv2.preCornerDetect(src, ksize[, dst[, borderType]]) -> dst
.. ocv:cfunction:: void cvPreCornerDetect( const CvArr* image, CvArr* corners, int apertureSize=3 )
.. ocv:cfunction:: void cvPreCornerDetect( const CvArr* image, CvArr* corners, int aperture_size=3 )
.. ocv:pyoldfunction:: cv.PreCornerDetect(image, corners, apertureSize=3)-> None
:param src: Source single-channel 8-bit of floating-point image.

55
modules/imgproc/doc/filtering.rst
View File

@ -523,7 +523,7 @@ buildPyramid
----------------
Constructs the Gaussian pyramid for an image.
.. ocv:function:: void buildPyramid( InputArray src, OutputArrayOfArrays dst, int maxlevel )
.. ocv:function:: void buildPyramid( InputArray src, OutputArrayOfArrays dst, int maxlevel, int borderType=BORDER_DEFAULT )
:param src: Source image. Check :ocv:func:`pyrDown` for the list of supported types.
@ -672,7 +672,7 @@ createGaussianFilter
------------------------
Returns an engine for smoothing images with the Gaussian filter.
.. ocv:function:: Ptr<FilterEngine> createGaussianFilter( int type, Size ksize, double sigmaX, double sigmaY=0, int borderType=BORDER_DEFAULT)
.. ocv:function:: Ptr<FilterEngine> createGaussianFilter( int type, Size ksize, double sigma1, double sigma2=0, int borderType=BORDER_DEFAULT )
:param type: Source and destination image type.
@ -701,7 +701,7 @@ createLinearFilter
----------------------
Creates a non-separable linear filter engine.
.. ocv:function:: Ptr<FilterEngine> createLinearFilter(int srcType, int dstType, InputArray kernel, Point _anchor=Point(-1,-1), double delta=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, const Scalar& borderValue=Scalar())
.. ocv:function:: Ptr<FilterEngine> createLinearFilter( int srcType, int dstType, InputArray kernel, Point _anchor=Point(-1,-1), double delta=0, int _rowBorderType=BORDER_DEFAULT, int _columnBorderType=-1, const Scalar& _borderValue=Scalar() )
.. ocv:function:: Ptr<BaseFilter> getLinearFilter(int srcType, int dstType, InputArray kernel, Point anchor=Point(-1,-1), double delta=0, int bits=0)
@ -737,13 +737,13 @@ createMorphologyFilter
--------------------------
Creates an engine for non-separable morphological operations.
.. ocv:function:: Ptr<FilterEngine> createMorphologyFilter(int op, int type, InputArray element, Point anchor=Point(-1,-1), int rowBorderType=BORDER_CONSTANT, int columnBorderType=-1, const Scalar& borderValue=morphologyDefaultBorderValue())
.. ocv:function:: Ptr<FilterEngine> createMorphologyFilter( int op, int type, InputArray kernel, Point anchor=Point(-1,-1), int _rowBorderType=BORDER_CONSTANT, int _columnBorderType=-1, const Scalar& _borderValue=morphologyDefaultBorderValue() )
.. ocv:function:: Ptr<BaseFilter> getMorphologyFilter(int op, int type, InputArray element, Point anchor=Point(-1,-1))
.. ocv:function:: Ptr<BaseFilter> getMorphologyFilter( int op, int type, InputArray kernel, Point anchor=Point(-1,-1) )
.. ocv:function:: Ptr<BaseRowFilter> getMorphologyRowFilter(int op, int type, int esize, int anchor=-1)
.. ocv:function:: Ptr<BaseRowFilter> getMorphologyRowFilter( int op, int type, int ksize, int anchor=-1 )
.. ocv:function:: Ptr<BaseColumnFilter> getMorphologyColumnFilter(int op, int type, int esize, int anchor=-1)
.. ocv:function:: Ptr<BaseColumnFilter> getMorphologyColumnFilter( int op, int type, int ksize, int anchor=-1 )
.. ocv:function:: Scalar morphologyDefaultBorderValue()
@ -783,11 +783,11 @@ createSeparableLinearFilter
-------------------------------
Creates an engine for a separable linear filter.
.. ocv:function:: Ptr<FilterEngine> createSeparableLinearFilter(int srcType, int dstType, InputArray rowKernel, InputArray columnKernel, Point anchor=Point(-1,-1), double delta=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, const Scalar& borderValue=Scalar())
.. ocv:function:: Ptr<FilterEngine> createSeparableLinearFilter( int srcType, int dstType, InputArray rowKernel, InputArray columnKernel, Point _anchor=Point(-1,-1), double delta=0, int _rowBorderType=BORDER_DEFAULT, int _columnBorderType=-1, const Scalar& _borderValue=Scalar() )
.. ocv:function:: Ptr<BaseColumnFilter> getLinearColumnFilter(int bufType, int dstType, InputArray columnKernel, int anchor, int symmetryType, double delta=0, int bits=0)
.. ocv:function:: Ptr<BaseColumnFilter> getLinearColumnFilter( int bufType, int dstType, InputArray kernel, int anchor, int symmetryType, double delta=0, int bits=0 )
.. ocv:function:: Ptr<BaseRowFilter> getLinearRowFilter(int srcType, int bufType, InputArray rowKernel, int anchor, int symmetryType)
.. ocv:function:: Ptr<BaseRowFilter> getLinearRowFilter( int srcType, int bufType, InputArray kernel, int anchor, int symmetryType )
:param srcType: Source array type.
@ -831,7 +831,7 @@ dilate
----------
Dilates an image by using a specific structuring element.
.. ocv:function:: void dilate( InputArray src, OutputArray dst, InputArray element, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
.. ocv:function:: void dilate( InputArray src, OutputArray dst, InputArray kernel, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
.. ocv:pyfunction:: cv2.dilate(src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) -> dst
@ -871,7 +871,7 @@ erode
---------
Erodes an image by using a specific structuring element.
.. ocv:function:: void erode( InputArray src, OutputArray dst, InputArray element, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
.. ocv:function:: void erode( InputArray src, OutputArray dst, InputArray kernel, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
.. ocv:pyfunction:: cv2.erode(src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) -> dst
@ -917,6 +917,7 @@ Convolves an image with the kernel.
.. ocv:pyfunction:: cv2.filter2D(src, ddepth, kernel[, dst[, anchor[, delta[, borderType]]]]) -> dst
.. ocv:cfunction:: void cvFilter2D( const CvArr* src, CvArr* dst, const CvMat* kernel, CvPoint anchor=cvPoint(-1,-1) )
.. ocv:pyoldfunction:: cv.Filter2D(src, dst, kernel, anchor=(-1, -1))-> None
:param src: Source image.
@ -1095,7 +1096,7 @@ Returns a structuring element of the specified size and shape for morphological
.. ocv:pyfunction:: cv2.getStructuringElement(shape, ksize[, anchor]) -> retval
.. ocv:cfunction:: IplConvKernel* cvCreateStructuringElementEx( int cols, int rows, int anchorX, int anchorY, int shape, int* values=NULL )
.. ocv:cfunction:: IplConvKernel* cvCreateStructuringElementEx( int cols, int rows, int anchor_x, int anchor_y, int shape, int* values=NULL )
.. ocv:pyoldfunction:: cv.CreateStructuringElementEx(cols, rows, anchorX, anchorY, shape, values=None)-> kernel
@ -1170,7 +1171,7 @@ morphologyEx
----------------
Performs advanced morphological transformations.
.. ocv:function:: void morphologyEx( InputArray src, OutputArray dst, int op, InputArray element, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
.. ocv:function:: void morphologyEx( InputArray src, OutputArray dst, int op, InputArray kernel, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
.. ocv:pyfunction:: cv2.morphologyEx(src, op, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) -> dst
@ -1242,7 +1243,6 @@ Any of the operations can be done in-place. In case of multi-channel images, eac
:ocv:func:`createMorphologyFilter`
Laplacian
-------------
Calculates the Laplacian of an image.
@ -1251,9 +1251,9 @@ Calculates the Laplacian of an image.
.. ocv:pyfunction:: cv2.Laplacian(src, ddepth[, dst[, ksize[, scale[, delta[, borderType]]]]]) -> dst
.. ocv:cfunction:: void cvLaplace( const CvArr* src, CvArr* dst, int ksize=3)
.. ocv:cfunction:: void cvLaplace( const CvArr* src, CvArr* dst, int aperture_size=3 )
.. ocv:pyoldfunction:: cv.Laplace(src, dst, ksize=3)-> None
.. ocv:pyoldfunction:: cv.Laplace(src, dst, apertureSize=3) -> None
:param src: Source image.
@ -1293,9 +1293,9 @@ pyrDown
-----------
Smoothes an image and downsamples it.
.. ocv:function:: void pyrDown( InputArray src, OutputArray dst, const Size& dstsize=Size())
.. ocv:function:: void pyrDown( InputArray src, OutputArray dst, const Size& dstsize=Size(), int borderType=BORDER_DEFAULT )
.. ocv:pyfunction:: cv2.pyrDown(src[, dst[, dstsize]]) -> dst
.. ocv:pyfunction:: cv2.pyrDown(src[, dst[, dstsize[, borderType]]]) -> dst
.. ocv:cfunction:: void cvPyrDown( const CvArr* src, CvArr* dst, int filter=CV_GAUSSIAN_5x5 )
@ -1326,9 +1326,9 @@ pyrUp
---------
Upsamples an image and then smoothes it.
.. ocv:function:: void pyrUp( InputArray src, OutputArray dst, const Size& dstsize=Size())
.. ocv:function:: void pyrUp( InputArray src, OutputArray dst, const Size& dstsize=Size(), int borderType=BORDER_DEFAULT )
.. ocv:pyfunction:: cv2.pyrUp(src[, dst[, dstsize]]) -> dst
.. ocv:pyfunction:: cv2.pyrUp(src[, dst[, dstsize[, borderType]]]) -> dst
.. ocv:cfunction:: cvPyrUp( const CvArr* src, CvArr* dst, int filter=CV_GAUSSIAN_5x5 )
@ -1359,7 +1359,7 @@ Performs initial step of meanshift segmentation of an image.
.. ocv:cfunction:: void cvPyrMeanShiftFiltering( const CvArr* src, CvArr* dst, double sp, double sr, int max_level=1, CvTermCriteria termcrit= cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,5,1))
.. ocv:pyoldfunction:: cv.PyrMeanShiftFiltering(src, dst, sp, sr, maxLevel=1, termcrit=(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 5, 1))-> None
.. ocv:pyoldfunction:: cv.PyrMeanShiftFiltering(src, dst, sp, sr, max_level=1, termcrit=(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 5, 1)) -> None
:param src: The source 8-bit, 3-channel image.
@ -1402,7 +1402,7 @@ sepFilter2D
---------------
Applies a separable linear filter to an image.
.. ocv:function:: void sepFilter2D( InputArray src, OutputArray dst, int ddepth, InputArray rowKernel, InputArray columnKernel, Point anchor=Point(-1,-1), double delta=0, int borderType=BORDER_DEFAULT )
.. ocv:function:: void sepFilter2D( InputArray src, OutputArray dst, int ddepth, InputArray kernelX, InputArray kernelY, Point anchor=Point(-1,-1), double delta=0, int borderType=BORDER_DEFAULT )
.. ocv:pyfunction:: cv2.sepFilter2D(src, ddepth, kernelX, kernelY[, dst[, anchor[, delta[, borderType]]]]) -> dst
@ -1444,7 +1444,7 @@ Smooth
------
Smooths the image in one of several ways.
.. ocv:cfunction:: void cvSmooth( const CvArr* src, CvArr* dst, int smoothtype=CV_GAUSSIAN, int param1=3, int param2=0, double param3=0, double param4=0)
.. ocv:cfunction:: void cvSmooth( const CvArr* src, CvArr* dst, int smoothtype=CV_GAUSSIAN, int size1=3, int size2=0, double sigma1=0, double sigma2=0 )
.. ocv:pyoldfunction:: cv.Smooth(src, dst, smoothtype=CV_GAUSSIAN, param1=3, param2=0, param3=0, param4=0)-> None
@ -1496,11 +1496,12 @@ Sobel
---------
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
.. ocv:function:: void Sobel( InputArray src, OutputArray dst, int ddepth, int xorder, int yorder, int ksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
.. ocv:function:: void Sobel( InputArray src, OutputArray dst, int ddepth, int dx, int dy, int ksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
.. ocv:pyfunction:: cv2.Sobel(src, ddepth, dx, dy[, dst[, ksize[, scale[, delta[, borderType]]]]]) -> dst
.. ocv:cfunction:: void cvSobel( const CvArr* src, CvArr* dst, int xorder, int yorder, int apertureSize=3 )
.. ocv:cfunction:: void cvSobel( const CvArr* src, CvArr* dst, int xorder, int yorder, int aperture_size=3 )
.. ocv:pyoldfunction:: cv.Sobel(src, dst, xorder, yorder, apertureSize=3)-> None
:param src: Source image.
@ -1582,7 +1583,7 @@ Scharr
----------
Calculates the first x- or y- image derivative using Scharr operator.
.. ocv:function:: void Scharr( InputArray src, OutputArray dst, int ddepth, int xorder, int yorder, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
.. ocv:function:: void Scharr( InputArray src, OutputArray dst, int ddepth, int dx, int dy, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
.. ocv:pyfunction:: cv2.Scharr(src, ddepth, dx, dy[, dst[, scale[, delta[, borderType]]]]) -> dst

33
modules/imgproc/doc/geometric_transformations.rst
View File

@ -77,11 +77,13 @@ getAffineTransform
----------------------
Calculates an affine transform from three pairs of the corresponding points.
.. ocv:function:: Mat getAffineTransform( const Point2f* src, const Point2f* dst )
.. ocv:function:: Mat getAffineTransform( InputArray src, InputArray dst )
.. ocv:function:: Mat getAffineTransform( const Point2f src[], const Point2f dst[] )
.. ocv:pyfunction:: cv2.getAffineTransform(src, dst) -> retval
.. ocv:cfunction:: CvMat* cvGetAffineTransform( const CvPoint2D32f* src, const CvPoint2D32f* dst, CvMat* mapMatrix )
.. ocv:cfunction:: CvMat* cvGetAffineTransform( const CvPoint2D32f * src, const CvPoint2D32f * dst, CvMat * map_matrix )
.. ocv:pyoldfunction:: cv.GetAffineTransform(src, dst, mapMatrix)-> None
@ -114,11 +116,13 @@ getPerspectiveTransform
---------------------------
Calculates a perspective transform from four pairs of the corresponding points.
.. ocv:function:: Mat getPerspectiveTransform( const Point2f* src, const Point2f* dst )
.. ocv:function:: Mat getPerspectiveTransform( InputArray src, InputArray dst )
.. ocv:function:: Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] )
.. ocv:pyfunction:: cv2.getPerspectiveTransform(src, dst) -> retval
.. ocv:cfunction:: CvMat* cvGetPerspectiveTransform( const CvPoint2D32f* src, const CvPoint2D32f* dst, CvMat* mapMatrix )
.. ocv:cfunction:: CvMat* cvGetPerspectiveTransform( const CvPoint2D32f* src, const CvPoint2D32f* dst, CvMat* map_matrix )
.. ocv:pyoldfunction:: cv.GetPerspectiveTransform(src, dst, mapMatrix)-> None
@ -151,7 +155,7 @@ getRectSubPix
-----------------
Retrieves a pixel rectangle from an image with sub-pixel accuracy.
.. ocv:function:: void getRectSubPix( InputArray image, Size patchSize, Point2f center, OutputArray dst, int patchType=-1 )
.. ocv:function:: void getRectSubPix( InputArray image, Size patchSize, Point2f center, OutputArray patch, int patchType=-1 )
.. ocv:pyfunction:: cv2.getRectSubPix(image, patchSize, center[, patch[, patchType]]) -> patch
@ -196,7 +200,7 @@ Calculates an affine matrix of 2D rotation.
.. ocv:pyfunction:: cv2.getRotationMatrix2D(center, angle, scale) -> retval
.. ocv:cfunction:: CvMat* cv2DRotationMatrix( CvPoint2D32f center, double angle, double scale, CvMat* mapMatrix )
.. ocv:cfunction:: CvMat* cv2DRotationMatrix( CvPoint2D32f center, double angle, double scale, CvMat* map_matrix )
.. ocv:pyoldfunction:: cv.GetRotationMatrix2D(center, angle, scale, mapMatrix)-> None
@ -428,10 +432,12 @@ Applies an affine transformation to an image.
.. ocv:pyfunction:: cv2.warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]) -> dst
.. ocv:cfunction:: void cvWarpAffine( const CvArr* src, CvArr* dst, const CvMat* mapMatrix, int flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, CvScalar fillval=cvScalarAll(0) )
.. ocv:cfunction:: void cvWarpAffine( const CvArr* src, CvArr* dst, const CvMat* map_matrix, int flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, CvScalar fillval=cvScalarAll(0) )
.. ocv:pyoldfunction:: cv.WarpAffine(src, dst, mapMatrix, flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, fillval=(0, 0, 0, 0))-> None
.. ocv:cfunction:: void cvGetQuadrangleSubPix( const CvArr* src, CvArr* dst, const CvMat* mapMatrix )
.. ocv:cfunction:: void cvGetQuadrangleSubPix( const CvArr* src, CvArr* dst, const CvMat* map_matrix )
.. ocv:pyoldfunction:: cv.GetQuadrangleSubPix(src, dst, mapMatrix)-> None
:param src: Source image.
@ -477,7 +483,8 @@ Applies a perspective transformation to an image.
.. ocv:pyfunction:: cv2.warpPerspective(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]) -> dst
.. ocv:cfunction:: void cvWarpPerspective( const CvArr* src, CvArr* dst, const CvMat* mapMatrix, int flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, CvScalar fillval=cvScalarAll(0) )
.. ocv:cfunction:: void cvWarpPerspective( const CvArr* src, CvArr* dst, const CvMat* map_matrix, int flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, CvScalar fillval=cvScalarAll(0) )
.. ocv:pyoldfunction:: cv.WarpPerspective(src, dst, mapMatrix, flags=CV_INNER_LINEAR+CV_WARP_FILL_OUTLIERS, fillval=(0, 0, 0, 0))-> None
:param src: Source image.
@ -524,8 +531,8 @@ Computes the undistortion and rectification transformation map.
.. ocv:pyfunction:: cv2.initUndistortRectifyMap(cameraMatrix, distCoeffs, R, newCameraMatrix, size, m1type[, map1[, map2]]) -> map1, map2
.. ocv:cfunction:: void cvInitUndistortRectifyMap( const CvMat* cameraMatrix, const CvMat* distCoeffs, const CvMat* R, const CvMat* newCameraMatrix, CvArr* map1, CvArr* map2 )
.. ocv:cfunction:: void cvInitUndistortMap( const CvMat* cameraMatrix, const CvMat* distCoeffs, CvArr* map1, CvArr* map2 )
.. ocv:cfunction:: void cvInitUndistortRectifyMap( const CvMat* camera_matrix, const CvMat* dist_coeffs, const CvMat * R, const CvMat* new_camera_matrix, CvArr* mapx, CvArr* mapy )
.. ocv:cfunction:: void cvInitUndistortMap( const CvMat* camera_matrix, const CvMat* distortion_coeffs, CvArr* mapx, CvArr* mapy )
.. ocv:pyoldfunction:: cv.InitUndistortRectifyMap(cameraMatrix, distCoeffs, R, newCameraMatrix, map1, map2)-> None
.. ocv:pyoldfunction:: cv.InitUndistortMap(cameraMatrix, distCoeffs, map1, map2)-> None
@ -621,7 +628,7 @@ Transforms an image to compensate for lens distortion.
.. ocv:pyfunction:: cv2.undistort(src, cameraMatrix, distCoeffs[, dst[, newCameraMatrix]]) -> dst
.. ocv:cfunction:: void cvUndistort2( const CvArr* src, CvArr* dst, const CvMat* cameraMatrix, const CvMat* distCoeffs, const CvMat* newCameraMatrix=NULL )
.. ocv:cfunction:: void cvUndistort2( const CvArr* src, CvArr* dst, const CvMat* camera_matrix, const CvMat* distortion_coeffs, const CvMat* new_camera_matrix=0 )
.. ocv:pyoldfunction:: cv.Undistort2(src, dst, cameraMatrix, distCoeffs)-> None
@ -660,7 +667,7 @@ Computes the ideal point coordinates from the observed point coordinates.
.. ocv:function:: void undistortPoints( InputArray src, OutputArray dst, InputArray cameraMatrix, InputArray distCoeffs, InputArray R=noArray(), InputArray P=noArray())
.. ocv:cfunction:: void cvUndistortPoints( const CvMat* src, CvMat* dst, const CvMat* cameraMatrix, const CvMat* distCoeffs, const CvMat* R=NULL, const CvMat* P=NULL)
.. ocv:cfunction:: void cvUndistortPoints( const CvMat* src, CvMat* dst, const CvMat* camera_matrix, const CvMat* dist_coeffs, const CvMat* R=0, const CvMat* P=0 )
.. ocv:pyoldfunction:: cv.UndistortPoints(src, dst, cameraMatrix, distCoeffs, R=None, P=None)-> None
:param src: Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2).

30
modules/imgproc/doc/histograms.rst
View File

@ -9,9 +9,9 @@ calcHist
------------
Calculates a histogram of a set of arrays.
.. ocv:function:: void calcHist( const Mat* arrays, int narrays, const int* channels, InputArray mask, OutputArray hist, int dims, const int* histSize, const float** ranges, bool uniform=true, bool accumulate=false )
.. ocv:function:: void calcHist( const Mat* images, int nimages, const int* channels, InputArray mask, OutputArray hist, int dims, const int* histSize, const float** ranges, bool uniform=true, bool accumulate=false )
.. ocv:function:: void calcHist( const Mat* arrays, int narrays, const int* channels, InputArray mask, SparseMat& hist, int dims, const int* histSize, const float** ranges, bool uniform=true, bool accumulate=false )
.. ocv:function:: void calcHist( const Mat* images, int nimages, const int* channels, InputArray mask, SparseMat& hist, int dims, const int* histSize, const float** ranges, bool uniform=true, bool accumulate=false )
.. ocv:pyfunction:: cv2.calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]]) -> hist
@ -106,14 +106,14 @@ calcBackProject
-------------------
Calculates the back projection of a histogram.
.. ocv:function:: void calcBackProject( const Mat* arrays, int narrays, const int* channels, InputArray hist, OutputArray backProject, const float** ranges, double scale=1, bool uniform=true )
.. ocv:function:: void calcBackProject( const Mat* images, int nimages, const int* channels, InputArray hist, OutputArray backProject, const float** ranges, double scale=1, bool uniform=true )
.. ocv:function:: void calcBackProject( const Mat* arrays, int narrays, const int* channels, const SparseMat& hist, OutputArray backProject, const float** ranges, double scale=1, bool uniform=true )
.. ocv:function:: void calcBackProject( const Mat* images, int nimages, const int* channels, const SparseMat& hist, OutputArray backProject, const float** ranges, double scale=1, bool uniform=true )
.. ocv:pyfunction:: cv2.calcBackProject(images, channels, hist, ranges[, dst[, scale]]) -> dst
.. ocv:pyfunction:: cv2.calcBackProject(images, channels, hist, ranges, scale[, dst]) -> dst
.. ocv:cfunction:: void cvCalcBackProject( IplImage** image, CvArr* backProject, const CvHistogram* hist )
.. ocv:pyoldfunction:: cv.CalcBackProject(image, backProject, hist)-> None
.. ocv:pyoldfunction:: cv.CalcBackProject(image, back_project, hist) -> None
:param arrays: Source arrays. They all should have the same depth, ``CV_8U`` or ``CV_32F`` , and the same size. Each of them can have an arbitrary number of channels.
@ -225,8 +225,9 @@ Computes the "minimal work" distance between two weighted point configurations.
.. ocv:function:: float EMD( InputArray signature1, InputArray signature2, int distType, InputArray cost=noArray(), float* lowerBound=0, OutputArray flow=noArray() )
.. ocv:cfunction:: float cvCalcEMD2( const CvArr* signature1, const CvArr* signature2, int distType, CvDistanceFunction distFunc=NULL, const CvArr* cost=NULL, CvArr* flow=NULL, float* lowerBound=NULL, void* userdata=NULL )
.. ocv:pyoldfunction:: cv.CalcEMD2(signature1, signature2, distType, distFunc=None, cost=None, flow=None, lowerBound=None, userdata=None) -> float
.. ocv:cfunction:: float cvCalcEMD2( const CvArr* signature1, const CvArr* signature2, int distance_type, CvDistanceFunction distance_func=NULL, const CvArr* cost_matrix=NULL, CvArr* flow=NULL, float* lower_bound=NULL, void* userdata=NULL )
.. ocv:pyoldfunction:: cv.CalcEMD2(signature1, signature2, distance_type, distance_func=None, cost_matrix=None, flow=None, lower_bound=None, userdata=None) -> float
:param signature1: First signature, a :math:`\texttt{size1}\times \texttt{dims}+1` floating-point matrix. Each row stores the point weight followed by the point coordinates. The matrix is allowed to have a single column (weights only) if the user-defined cost matrix is used.
@ -301,7 +302,7 @@ Locates a template within an image by using a histogram comparison.
.. ocv:cfunction:: void cvCalcBackProjectPatch( IplImage** images, CvArr* dst, CvSize patch_size, CvHistogram* hist, int method, double factor )
.. ocv:pyoldfunction:: cv.CalcBackProjectPatch(images, dst, patchSize, hist, method, factor)-> None
.. ocv:pyoldfunction:: cv.CalcBackProjectPatch(images, dst, patch_size, hist, method, factor)-> None
:param images: Source images (though, you may pass CvMat** as well).
@ -324,9 +325,9 @@ CalcProbDensity
---------------
Divides one histogram by another.
.. ocv:cfunction:: void cvCalcProbDensity( const CvHistogram* hist1, const CvHistogram* hist2, CvHistogram* dsthist, double scale=255 )
.. ocv:cfunction:: void cvCalcProbDensity( const CvHistogram* hist1, const CvHistogram* hist2, CvHistogram* dst_hist, double scale=255 )
.. ocv:pyoldfunction:: cv.CalcProbDensity(hist1, hist2, dsthist, scale=255)-> None
.. ocv:pyoldfunction:: cv.CalcProbDensity(hist1, hist2, dst_hist, scale=255) -> None
:param hist1: First histogram (the divisor).
@ -375,7 +376,7 @@ Creates a histogram.
.. ocv:cfunction:: CvHistogram* cvCreateHist( int dims, int* sizes, int type, float** ranges=NULL, int uniform=1 )
.. ocv:pyoldfunction:: cv.CreateHist(dims, type, ranges, uniform=1) -> hist
.. ocv:pyoldfunction:: cv.CreateHist(dims, type, ranges=None, uniform=1) -> hist
:param dims: Number of histogram dimensions.
@ -449,7 +450,7 @@ Finds the minimum and maximum histogram bins.
.. ocv:cfunction:: void cvGetMinMaxHistValue( const CvHistogram* hist, float* min_value, float* max_value, int* min_idx=NULL, int* max_idx=NULL )
.. ocv:pyoldfunction:: cv.GetMinMaxHistValue(hist)-> (minValue, maxValue, minIdx, maxIdx)
.. ocv:pyoldfunction:: cv.GetMinMaxHistValue(hist)-> (min_value, max_value, min_idx, max_idx)
:param hist: Histogram.
@ -510,7 +511,7 @@ Queries the value of the histogram bin.
.. ocv:pyoldfunction:: cv.QueryHistValue_1D(hist, idx0) -> float
.. ocv:pyoldfunction:: cv.QueryHistValue_2D(hist, idx0, idx1) -> float
.. ocv:pyoldfunction:: cv.QueryHistValue_3D(hist, idx0, idx1, idx2) -> float
.. ocv:pyoldfunction:: cv.QueryHistValueND(hist, idx) -> float
.. ocv:pyoldfunction:: cv.QueryHistValue_nD(hist, idx) -> float
:param hist: Histogram.
@ -569,7 +570,6 @@ CalcPGH
Calculates a pair-wise geometrical histogram for a contour.
.. ocv:cfunction:: void cvCalcPGH( const CvSeq* contour, CvHistogram* hist )
.. ocv:pyoldfunction:: cv.CalcPGH(contour, hist)-> None
:param contour: Input contour. Currently, only integer point coordinates are allowed.

35
modules/imgproc/doc/miscellaneous_transformations.rst
View File

@ -12,8 +12,9 @@ Applies an adaptive threshold to an array.
.. ocv:pyfunction:: cv2.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst]) -> dst
.. ocv:cfunction:: void cvAdaptiveThreshold( const CvArr* src, CvArr* dst, double maxValue, int adaptiveMethod=CV_ADAPTIVE_THRESH_MEAN_C, int thresholdType=CV_THRESH_BINARY, int blockSize=3, double param1=5 )
.. ocv:pyoldfunction:: cv.AdaptiveThreshold(src, dst, maxValue, adaptiveMethod=CV_ADAPTIVE_THRESH_MEAN_C, thresholdType=CV_THRESH_BINARY, blockSize=3, param1=5)-> None
.. ocv:cfunction:: void cvAdaptiveThreshold( const CvArr* src, CvArr* dst, double max_value, int adaptive_method=CV_ADAPTIVE_THRESH_MEAN_C, int threshold_type=CV_THRESH_BINARY, int block_size=3, double param1=5 )
.. ocv:pyoldfunction:: cv.AdaptiveThreshold(src, dst, maxValue, adaptive_method=CV_ADAPTIVE_THRESH_MEAN_C, thresholdType=CV_THRESH_BINARY, blockSize=3, param1=5)-> None
:param src: Source 8-bit single-channel image.
@ -414,11 +415,11 @@ Calculates the distance to the closest zero pixel for each pixel of the source i
.. ocv:function:: void distanceTransform( InputArray src, OutputArray dst, OutputArray labels, int distanceType, int maskSize, int labelType=DIST_LABEL_CCOMP )
.. ocv:pyfunction:: cv2.distanceTransform(src, distanceType, maskSize[, dst[, labels[, labelType=cv2.DIST_LABEL_CCOMP]]]) -> dst, labels
.. ocv:pyfunction:: cv2.distanceTransform(src, distanceType, maskSize[, dst]) -> dst
.. ocv:cfunction:: void cvDistTransform( const CvArr* src, CvArr* dst, int distanceType=CV_DIST_L2, int maskSize=3, const float* mask=NULL, CvArr* labels=NULL, int labelType=CV_DIST_LABEL_CCOMP )
.. ocv:cfunction:: void cvDistTransform( const CvArr* src, CvArr* dst, int distance_type=CV_DIST_L2, int mask_size=3, const float* mask=NULL, CvArr* labels=NULL, int labelType=CV_DIST_LABEL_CCOMP )
.. ocv:pyoldfunction:: cv.DistTransform(src, dst, distanceType=CV_DIST_L2, maskSize=3, mask=None, labels=None)-> None
.. ocv:pyoldfunction:: cv.DistTransform(src, dst, distance_type=CV_DIST_L2, mask_size=3, mask=None, labels=None) -> None
:param src: 8-bit, single-channel (binary) source image.
@ -483,14 +484,14 @@ floodFill
-------------
Fills a connected component with the given color.
.. ocv:function:: int floodFill( InputOutputArray image, Point seed, Scalar newVal, Rect* rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4 )
.. ocv:function:: int floodFill( InputOutputArray image, Point seedPoint, Scalar newVal, Rect* rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4 )
.. ocv:function:: int floodFill( InputOutputArray image, InputOutputArray mask, Point seed, Scalar newVal, Rect* rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4 )
.. ocv:function:: int floodFill( InputOutputArray image, InputOutputArray mask, Point seedPoint, Scalar newVal, Rect* rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4 )
.. ocv:pyfunction:: cv2.floodFill(image, mask, seedPoint, newVal[, loDiff[, upDiff[, flags]]]) -> retval, rect
.. ocv:cfunction:: void cvFloodFill( CvArr* image, CvPoint seedPoint, CvScalar newVal, CvScalar loDiff=cvScalarAll(0), CvScalar upDiff=cvScalarAll(0), CvConnectedComp* comp=NULL, int flags=4, CvArr* mask=NULL )
.. ocv:pyoldfunction:: cv.FloodFill(image, seedPoint, newVal, loDiff=(0, 0, 0, 0), upDiff=(0, 0, 0, 0), flags=4, mask=None)-> comp
.. ocv:cfunction:: void cvFloodFill( CvArr* image, CvPoint seed_point, CvScalar new_val, CvScalar lo_diff=cvScalarAll(0), CvScalar up_diff=cvScalarAll(0), CvConnectedComp* comp=NULL, int flags=4, CvArr* mask=NULL )
.. ocv:pyoldfunction:: cv.FloodFill(image, seed_point, new_val, lo_diff=(0, 0, 0, 0), up_diff=(0, 0, 0, 0), flags=4, mask=None)-> comp
:param image: Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the ``FLOODFILL_MASK_ONLY`` flag is set in the second variant of the function. See the details below.
@ -588,11 +589,11 @@ integral
------------
Calculates the integral of an image.
.. ocv:function:: void integral( InputArray image, OutputArray sum, int sdepth=-1 )
.. ocv:function:: void integral( InputArray src, OutputArray sum, int sdepth=-1 )
.. ocv:function:: void integral( InputArray image, OutputArray sum, OutputArray sqsum, int sdepth=-1 )
.. ocv:function:: void integral( InputArray src, OutputArray sum, OutputArray sqsum, int sdepth=-1 )
.. ocv:function:: void integral( InputArray image, OutputArray sum, OutputArray sqsum, OutputArray tilted, int sdepth=-1 )
.. ocv:function:: void integral( InputArray src, OutputArray sum, OutputArray sqsum, OutputArray tilted, int sdepth=-1 )
.. ocv:pyfunction:: cv2.integral(src[, sum[, sdepth]]) -> sum
@ -600,7 +601,8 @@ Calculates the integral of an image.
.. ocv:pyfunction:: cv2.integral3(src[, sum[, sqsum[, tilted[, sdepth]]]]) -> sum, sqsum, tilted
.. ocv:cfunction:: void cvIntegral( const CvArr* image, CvArr* sum, CvArr* sqsum=NULL, CvArr* tiltedSum=NULL )
.. ocv:cfunction:: void cvIntegral( const CvArr* image, CvArr* sum, CvArr* sqsum=NULL, CvArr* tilted_sum=NULL )
.. ocv:pyoldfunction:: cv.Integral(image, sum, sqsum=None, tiltedSum=None)-> None
:param image: Source image as :math:`W \times H` , 8-bit or floating-point (32f or 64f).
@ -647,11 +649,12 @@ threshold
-------------
Applies a fixed-level threshold to each array element.
.. ocv:function:: double threshold( InputArray src, OutputArray dst, double thresh, double maxVal, int thresholdType )
.. ocv:function:: double threshold( InputArray src, OutputArray dst, double thresh, double maxval, int type )
.. ocv:pyfunction:: cv2.threshold(src, thresh, maxval, type[, dst]) -> retval, dst
.. ocv:cfunction:: double cvThreshold( const CvArr* src, CvArr* dst, double threshold, double maxValue, int thresholdType )
.. ocv:cfunction:: double cvThreshold( const CvArr* src, CvArr* dst, double threshold, double max_value, int threshold_type )
.. ocv:pyoldfunction:: cv.Threshold(src, dst, threshold, maxValue, thresholdType)-> None
:param src: Source array (single-channel, 8-bit of 32-bit floating point).
@ -748,7 +751,7 @@ grabCut
-------
Runs the GrabCut algorithm.
.. ocv:function:: void grabCut(InputArray image, InputOutputArray mask, Rect rect, InputOutputArray bgdModel, InputOutputArray fgdModel, int iterCount, int mode )
.. ocv:function:: void grabCut( InputArray img, InputOutputArray mask, Rect rect, InputOutputArray bgdModel, InputOutputArray fgdModel, int iterCount, int mode=GC_EVAL )
.. ocv:pyfunction:: cv2.grabCut(img, mask, rect, bgdModel, fgdModel, iterCount[, mode]) -> None

19
modules/imgproc/doc/motion_analysis_and_object_tracking.rst
View File

@ -11,8 +11,9 @@ Adds an image to the accumulator.
.. ocv:pyfunction:: cv2.accumulate(src, dst[, mask]) -> None
.. ocv:cfunction:: void cvAcc( const CvArr* src, CvArr* dst, const CvArr* mask=NULL )
.. ocv:pyoldfunction:: cv.Acc(src, dst, mask=None)-> None
.. ocv:cfunction:: void cvAcc( const CvArr* image, CvArr* sum, const CvArr* mask=NULL )
.. ocv:pyoldfunction:: cv.Acc(image, sum, mask=None) -> None
:param src: Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
@ -46,8 +47,9 @@ Adds the square of a source image to the accumulator.
.. ocv:pyfunction:: cv2.accumulateSquare(src, dst[, mask]) -> None
.. ocv:cfunction:: void cvSquareAcc( const CvArr* src, CvArr* dst, const CvArr* mask=NULL )
.. ocv:pyoldfunction:: cv.SquareAcc(src, dst, mask=None)-> None
.. ocv:cfunction:: void cvSquareAcc( const CvArr* image, CvArr* sqsum, const CvArr* mask=NULL )
.. ocv:pyoldfunction:: cv.SquareAcc(image, sqsum, mask=None) -> None
:param src: Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
@ -79,8 +81,9 @@ Adds the per-element product of two input images to the accumulator.
.. ocv:pyfunction:: cv2.accumulateProduct(src1, src2, dst[, mask]) -> None
.. ocv:cfunction:: void cvMultiplyAcc( const CvArr* src1, const CvArr* src2, CvArr* dst, const CvArr* mask=NULL )
.. ocv:pyoldfunction:: cv.MultiplyAcc(src1, src2, dst, mask=None)-> None
.. ocv:cfunction:: void cvMultiplyAcc( const CvArr* image1, const CvArr* image2, CvArr* acc, const CvArr* mask=NULL )
.. ocv:pyoldfunction:: cv.MultiplyAcc(image1, image2, acc, mask=None)-> None
:param src1: First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
@ -114,8 +117,8 @@ Updates a running average.
.. ocv:pyfunction:: cv2.accumulateWeighted(src, dst, alpha[, mask]) -> None
.. ocv:cfunction:: void cvRunningAvg( const CvArr* src, CvArr* dst, double alpha, const CvArr* mask=NULL )
.. ocv:pyoldfunction:: cv.RunningAvg(src, dst, alpha, mask=None)-> None
.. ocv:cfunction:: void cvRunningAvg( const CvArr* image, CvArr* acc, double alpha, const CvArr* mask=NULL )
.. ocv:pyoldfunction:: cv.RunningAvg(image, acc, alpha, mask=None)-> None
:param src: Input image as 1- or 3-channel, 8-bit or 32-bit floating point.

2
modules/imgproc/doc/object_detection.rst
View File

@ -7,7 +7,7 @@ matchTemplate
-----------------
Compares a template against overlapped image regions.
.. ocv:function:: void matchTemplate( InputArray image, InputArray temp, OutputArray result, int method )
.. ocv:function:: void matchTemplate( InputArray image, InputArray templ, OutputArray result, int method )
.. ocv:pyfunction:: cv2.matchTemplate(image, templ, method[, result]) -> result

61
modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst
View File

@ -11,8 +11,9 @@ Calculates all of the moments up to the third order of a polygon or rasterized s
.. ocv:pyfunction:: cv2.moments(array[, binaryImage]) -> retval
.. ocv:cfunction:: void cvMoments( const CvArr* array, CvMoments* moments, int binary=0 )
.. ocv:pyoldfunction:: cv.Moments(array, binary=0) -> moments
.. ocv:cfunction:: void cvMoments( const CvArr* arr, CvMoments* moments, int binary=0 )
.. ocv:pyoldfunction:: cv.Moments(arr, binary=0) -> moments
:param array: Raster image (single-channel, 8-bit or floating-point 2D array) or an array ( :math:`1 \times N` or :math:`N \times 1` ) of 2D points (``Point`` or ``Point2f`` ).
@ -89,11 +90,13 @@ HuMoments
-------------
Calculates seven Hu invariants.
.. ocv:function:: void HuMoments( const Moments& moments, double* hu )
.. ocv:function:: void HuMoments( const Moments& m, OutputArray hu )
.. ocv:pyfunction:: cv2.HuMoments(m) -> hu
.. ocv:function:: void HuMoments( const Moments& moments, double hu[7] )
.. ocv:cfunction:: void cvGetHuMoments( const CvMoments* moments, CvHuMoments* hu )
.. ocv:pyfunction:: cv2.HuMoments(m[, hu]) -> hu
.. ocv:cfunction:: void cvGetHuMoments( CvMoments* moments, CvHuMoments* hu_moments )
.. ocv:pyoldfunction:: cv.GetHuMoments(moments) -> hu
@ -126,9 +129,9 @@ Finds contours in a binary image.
.. ocv:pyfunction:: cv2.findContours(image, mode, method[, contours[, hierarchy[, offset]]]) -> contours, hierarchy
.. ocv:cfunction:: int cvFindContours( CvArr* image, CvMemStorage* storage, CvSeq** firstContour, int headerSize=sizeof(CvContour), int mode=CV_RETR_LIST, int method=CV_CHAIN_APPROX_SIMPLE, CvPoint offset=cvPoint(0, 0) )
.. ocv:cfunction:: int cvFindContours( CvArr* image, CvMemStorage* storage, CvSeq** first_contour, int header_size=sizeof(CvContour), int mode=CV_RETR_LIST, int method=CV_CHAIN_APPROX_SIMPLE, CvPoint offset=cvPoint(0,0) )
.. ocv:pyoldfunction:: cv.FindContours(image, storage, mode=CV_RETR_LIST, method=CV_CHAIN_APPROX_SIMPLE, offset=(0, 0)) -> cvseq
.. ocv:pyoldfunction:: cv.FindContours(image, storage, mode=CV_RETR_LIST, method=CV_CHAIN_APPROX_SIMPLE, offset=(0, 0)) -> contours
:param image: Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as ``binary`` . You can use :ocv:func:`compare` , :ocv:func:`inRange` , :ocv:func:`threshold` , :ocv:func:`adaptiveThreshold` , :ocv:func:`Canny` , and others to create a binary image out of a grayscale or color one. The function modifies the ``image`` while extracting the contours.
@ -172,7 +175,7 @@ Draws contours outlines or filled contours.
.. ocv:pyfunction:: cv2.drawContours(image, contours, contourIdx, color[, thickness[, lineType[, hierarchy[, maxLevel[, offset]]]]]) -> None
.. ocv:cfunction:: void cvDrawContours( CvArr *img, CvSeq* contour, CvScalar externalColor, CvScalar holeColor, int maxLevel, int thickness=1, int lineType=8 )
.. ocv:pyoldfunction:: cv.DrawContours(img, contour, externalColor, holeColor, maxLevel, thickness=1, lineType=8, offset=(0, 0))-> None
.. ocv:pyoldfunction:: cv.DrawContours(img, contour, external_color, hole_color, max_level, thickness=1, lineType=8, offset=(0, 0))-> None
:param image: Destination image.
@ -253,7 +256,7 @@ Approximates a polygonal curve(s) with the specified precision.
.. ocv:pyfunction:: cv2.approxPolyDP(curve, epsilon, closed[, approxCurve]) -> approxCurve
.. ocv:cfunction:: CvSeq* cvApproxPoly( const void* curve, int headerSize, CvMemStorage* storage, int method, double epsilon, int recursive=0 )
.. ocv:cfunction:: CvSeq* cvApproxPoly( const void* src_seq, int header_size, CvMemStorage* storage, int method, double parameter, int parameter2=0 )
:param curve: Input vector of a 2D point stored in:
@ -287,9 +290,9 @@ ApproxChains
-------------
Approximates Freeman chain(s) with a polygonal curve.
.. ocv:cfunction:: CvSeq* cvApproxChains( CvSeq* chain, CvMemStorage* storage, int method=CV_CHAIN_APPROX_SIMPLE, double parameter=0, int minimalPerimeter=0, int recursive=0 )
.. ocv:cfunction:: CvSeq* cvApproxChains( CvSeq* src_seq, CvMemStorage* storage, int method=CV_CHAIN_APPROX_SIMPLE, double parameter=0, int minimal_perimeter=0, int recursive=0 )
.. ocv:pyoldfunction:: cv.ApproxChains(chain, storage, method=CV_CHAIN_APPROX_SIMPLE, parameter=0, minimalPerimeter=0, recursive=0)-> contours
.. ocv:pyoldfunction:: cv.ApproxChains(src_seq, storage, method=CV_CHAIN_APPROX_SIMPLE, parameter=0, minimal_perimeter=0, recursive=0)-> contours
:param chain: Pointer to the approximated Freeman chain that can refer to other chains.
@ -314,8 +317,9 @@ Calculates a contour perimeter or a curve length.
.. ocv:pyfunction:: cv2.arcLength(curve, closed) -> retval
.. ocv:cfunction:: double cvArcLength( const void* curve, CvSlice slice=CV_WHOLE_SEQ, int isClosed=-1 )
.. ocv:pyoldfunction:: cv.ArcLength(curve, slice=CV_WHOLE_SEQ, isClosed=-1)-> double
.. ocv:cfunction:: double cvArcLength( const void* curve, CvSlice slice=CV_WHOLE_SEQ, int is_closed=-1 )
.. ocv:pyoldfunction:: cv.ArcLength(curve, slice=CV_WHOLE_SEQ, isClosed=-1) -> float
:param curve: Input vector of 2D points, stored in ``std::vector`` or ``Mat``.
@ -351,9 +355,9 @@ Calculates a contour area.
.. ocv:pyfunction:: cv2.contourArea(contour[, oriented]) -> retval
.. ocv:cfunction:: double cvContourArea( const CvArr* contour, CvSlice slice=CV_WHOLE_SEQ )
.. ocv:cfunction:: double cvContourArea( const CvArr* contour, CvSlice slice=CV_WHOLE_SEQ, int oriented=0 )
.. ocv:pyoldfunction:: cv.ContourArea(contour, slice=CV_WHOLE_SEQ)-> double
.. ocv:pyoldfunction:: cv.ContourArea(contour, slice=CV_WHOLE_SEQ) -> float
:param contour: Input vector of 2D points (contour vertices), stored in ``std::vector`` or ``Mat``.
@ -390,11 +394,11 @@ Finds the convex hull of a point set.
.. ocv:function:: void convexHull( InputArray points, OutputArray hull, bool clockwise=false, bool returnPoints=true )
.. ocv:pyfunction:: cv2.convexHull(points[, hull[, returnPoints[, clockwise]]]) -> hull
.. ocv:pyfunction:: cv2.convexHull(points[, hull[, clockwise[, returnPoints]]]) -> hull
.. ocv:cfunction:: CvSeq* cvConvexHull2( const CvArr* input, void* storage=NULL, int orientation=CV_CLOCKWISE, int returnPoints=0 )
.. ocv:cfunction:: CvSeq* cvConvexHull2( const CvArr* input, void* hull_storage=NULL, int orientation=CV_CLOCKWISE, int return_points=0 )
.. ocv:pyoldfunction:: cv.ConvexHull2(points, storage, orientation=CV_CLOCKWISE, returnPoints=0)-> convexHull
.. ocv:pyoldfunction:: cv.ConvexHull2(points, storage, orientation=CV_CLOCKWISE, return_points=0) -> convexHull
:param points: Input 2D point set, stored in ``std::vector`` or ``Mat``.
@ -420,7 +424,7 @@ Finds the convexity defects of a contour.
.. ocv:function:: void convexityDefects( InputArray contour, InputArray convexhull, OutputArray convexityDefects )
.. ocv:pyfunction:: cv2.ConvexityDefects(contour, convexhull)-> convexityDefects
.. ocv:pyfunction:: cv2.convexityDefects(contour, convexhull[, convexityDefects]) -> convexityDefects
.. ocv:cfunction:: CvSeq* cvConvexityDefects( const CvArr* contour, const CvArr* convexhull, CvMemStorage* storage=NULL )
@ -475,10 +479,11 @@ Fits a line to a 2D or 3D point set.
.. ocv:function:: void fitLine( InputArray points, OutputArray line, int distType, double param, double reps, double aeps )
.. ocv:pyfunction:: cv2.fitLine(points, distType, param, reps, aeps) -> line
.. ocv:pyfunction:: cv2.fitLine(points, distType, param, reps, aeps[, line]) -> line
.. ocv:cfunction:: void cvFitLine( const CvArr* points, int distType, double param, double reps, double aeps, float* line )
.. ocv:pyoldfunction:: cv.FitLine(points, distType, param, reps, aeps) -> line
.. ocv:cfunction:: void cvFitLine( const CvArr* points, int dist_type, double param, double reps, double aeps, float* line )
.. ocv:pyoldfunction:: cv.FitLine(points, dist_type, param, reps, aeps) -> line
:param points: Input vector of 2D or 3D points, stored in ``std::vector<>`` or ``Mat``.
@ -575,7 +580,7 @@ Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
.. ocv:cfunction:: CvBox2D cvMinAreaRect2( const CvArr* points, CvMemStorage* storage=NULL )
.. ocv:pyoldfunction:: cv.MinAreaRect2(points, storage=None)-> CvBox2D
.. ocv:pyoldfunction:: cv.MinAreaRect2(points, storage=None) -> Box2D
:param points: Input vector of 2D points, stored in:
@ -595,7 +600,7 @@ Finds a circle of the minimum area enclosing a 2D point set.
.. ocv:function:: void minEnclosingCircle( InputArray points, Point2f& center, float& radius )
.. ocv:pyfunction:: cv2.minEnclosingCircle(points, center, radius) -> None
.. ocv:pyfunction:: cv2.minEnclosingCircle(points) -> center, radius
.. ocv:cfunction:: int cvMinEnclosingCircle( const CvArr* points, CvPoint2D32f* center, float* radius )
@ -621,12 +626,12 @@ matchShapes
---------------
Compares two shapes.
.. ocv:function:: double matchShapes( InputArray object1, InputArray object2, int method, double parameter=0 )
.. ocv:function:: double matchShapes( InputArray contour1, InputArray contour2, int method, double parameter )
.. ocv:pyfunction:: cv2.matchShapes(contour1, contour2, method, parameter) -> retval
.. ocv:cfunction:: double cvMatchShapes( const void* object1, const void* object2, int method, double parameter=0 )
.. ocv:pyoldfunction:: cv.MatchShapes(object1, object2, method, parameter=0)-> None
.. ocv:pyoldfunction:: cv.MatchShapes(object1, object2, method, parameter=0) -> float
:param object1: First contour or grayscale image.
@ -680,8 +685,8 @@ Performs a point-in-contour test.
.. ocv:pyfunction:: cv2.pointPolygonTest(contour, pt, measureDist) -> retval
.. ocv:cfunction:: double cvPointPolygonTest( const CvArr* contour, CvPoint2D32f pt, int measureDist )
.. ocv:pyoldfunction:: cv.PointPolygonTest(contour, pt, measureDist)-> double
.. ocv:cfunction:: double cvPointPolygonTest( const CvArr* contour, CvPoint2D32f pt, int measure_dist )
.. ocv:pyoldfunction:: cv.PointPolygonTest(contour, pt, measure_dist) -> float
:param contour: Input contour.

2
modules/imgproc/include/opencv2/imgproc/imgproc.hpp
View File

@ -392,7 +392,7 @@ CV_EXPORTS_W void medianBlur( InputArray src, OutputArray dst, int ksize );
//! smooths the image using Gaussian filter.
CV_EXPORTS_W void GaussianBlur( InputArray src,
OutputArray dst, Size ksize,
double sigma1, double sigma2=0,
double sigmaX, double sigmaY=0,
int borderType=BORDER_DEFAULT );
//! smooths the image using bilateral filter
CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d,

4
modules/legacy/doc/common_interfaces_of_generic_descriptor_matchers.rst
View File

@ -5,7 +5,7 @@ Common Interfaces of Generic Descriptor Matchers
OneWayDescriptorMatcher
-----------------------
.. ocv:class:: OneWayDescriptorMatcher
.. ocv:class:: OneWayDescriptorMatcher : public GenericDescriptorMatcher
Wrapping class for computing, matching, and classifying descriptors using the
:ocv:class:`OneWayDescriptorBase` class. ::
@ -64,7 +64,7 @@ Wrapping class for computing, matching, and classifying descriptors using the
FernDescriptorMatcher
---------------------
.. ocv:class:: FernDescriptorMatcher
.. ocv:class:: FernDescriptorMatcher : public GenericDescriptorMatcher
Wrapping class for computing, matching, and classifying descriptors using the
:ocv:class:`FernClassifier` class. ::

2
modules/legacy/doc/feature_detection_and_description.rst
View File

@ -142,7 +142,7 @@ RandomizedTree::applyQuantization
RTreeNode
---------
.. ocv:class:: RTreeNode
.. ocv:struct:: RTreeNode
Class containing a base structure for ``RandomizedTree``. ::

8
modules/legacy/doc/motion_analysis.rst
View File

@ -8,9 +8,9 @@ CalcOpticalFlowBM
-----------------
Calculates the optical flow for two images by using the block matching method.
.. ocv:cfunction:: void cvCalcOpticalFlowBM( const CvArr* prev, const CvArr* curr, CvSize blockSize, CvSize shiftSize, CvSize maxRange, int usePrevious, CvArr* velx, CvArr* vely )
.. ocv:cfunction:: void cvCalcOpticalFlowBM( const CvArr* prev, const CvArr* curr, CvSize block_size, CvSize shift_size, CvSize max_range, int use_previous, CvArr* velx, CvArr* vely )
.. ocv:pyoldfunction:: cv.CalcOpticalFlowBM(prev, curr, blockSize, shiftSize, maxRange, usePrevious, velx, vely)-> None
.. ocv:pyoldfunction:: cv.CalcOpticalFlowBM(prev, curr, blockSize, shiftSize, max_range, usePrevious, velx, vely)-> None
:param prev: First image, 8-bit, single-channel
@ -43,7 +43,7 @@ CalcOpticalFlowHS
-----------------
Calculates the optical flow for two images using Horn-Schunck algorithm.
.. ocv:cfunction:: void cvCalcOpticalFlowHS(const CvArr* prev, const CvArr* curr, int usePrevious, CvArr* velx, CvArr* vely, double lambda, CvTermCriteria criteria)
.. ocv:cfunction:: void cvCalcOpticalFlowHS(const CvArr* prev, const CvArr* curr, int use_previous, CvArr* velx, CvArr* vely, double lambda, CvTermCriteria criteria)
.. ocv:pyoldfunction:: cv.CalcOpticalFlowHS(prev, curr, usePrevious, velx, vely, lambda, criteria)-> None
@ -69,7 +69,7 @@ CalcOpticalFlowLK
Calculates the optical flow for two images using Lucas-Kanade algorithm.
.. ocv:cfunction:: void cvCalcOpticalFlowLK( const CvArr* prev, const CvArr* curr, CvSize winSize, CvArr* velx, CvArr* vely )
.. ocv:cfunction:: void cvCalcOpticalFlowLK( const CvArr* prev, const CvArr* curr, CvSize win_size, CvArr* velx, CvArr* vely )
.. ocv:pyoldfunction:: cv.CalcOpticalFlowLK(prev, curr, winSize, velx, vely)-> None

4
modules/legacy/include/opencv2/legacy/legacy.hpp
View File

@ -1768,8 +1768,8 @@ protected:
struct CV_EXPORTS_W_MAP CvEMParams
{
CvEMParams();
CvEMParams( int nclusters, int cov_mat_type=1/*CvEM::COV_MAT_DIAGONAL*/,
int start_step=0/*CvEM::START_AUTO_STEP*/,
CvEMParams( int nclusters, int cov_mat_type=cv::EM::COV_MAT_DIAGONAL,
int start_step=cv::EM::START_AUTO_STEP,
CvTermCriteria term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON),
const CvMat* probs=0, const CvMat* weights=0, const CvMat* means=0, const CvMat** covs=0 );

8
modules/ml/doc/boosting.rst
View File

@ -65,7 +65,7 @@ training examples are recomputed at each training iteration. Examples deleted at
CvBoostParams
-------------
.. ocv:class:: CvBoostParams
.. ocv:struct:: CvBoostParams : public CvDTreeParams
Boosting training parameters.
@ -122,7 +122,7 @@ Default parameters are:
CvBoostTree
-----------
.. ocv:class:: CvBoostTree
.. ocv:class:: CvBoostTree : public CvDTree
The weak tree classifier, a component of the boosted tree classifier :ocv:class:`CvBoost`, is a derivative of :ocv:class:`CvDTree`. Normally, there is no need to use the weak classifiers directly. However, they can be accessed as elements of the sequence :ocv:member:`CvBoost::weak`, retrieved by :ocv:func:`CvBoost::get_weak_predictors`.
@ -130,7 +130,7 @@ The weak tree classifier, a component of the boosted tree classifier :ocv:class:
CvBoost
-------
.. ocv:class:: CvBoost
.. ocv:class:: CvBoost : public CvStatModel
Boosted tree classifier derived from :ocv:class:`CvStatModel`.
@ -144,7 +144,7 @@ Default and training constructors.
.. ocv:function:: CvBoost::CvBoost( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvBoostParams params=CvBoostParams() )
.. ocv:pyfunction:: cv2.Boost(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]) -> <Boost object>
.. ocv:pyfunction:: cv2.Boost([trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]]) -> <Boost object>
The constructors follow conventions of :ocv:func:`CvStatModel::CvStatModel`. See :ocv:func:`CvStatModel::train` for parameters descriptions.

16
modules/ml/doc/decision_trees.rst
View File

@ -57,7 +57,7 @@ Importance of each variable is computed over all the splits on this variable in
CvDTreeSplit
------------
.. ocv:class:: CvDTreeSplit
.. ocv:struct:: CvDTreeSplit
The structure represents a possible decision tree node split. It has public members:
@ -100,7 +100,7 @@ The structure represents a possible decision tree node split. It has public memb
CvDTreeNode
-----------
.. ocv:class:: CvDTreeNode
.. ocv:struct:: CvDTreeNode
The structure represents a node in a decision tree. It has public members:
@ -145,7 +145,7 @@ Other numerous fields of ``CvDTreeNode`` are used internally at the training sta
CvDTreeParams
-------------
.. ocv:class:: CvDTreeParams
.. ocv:struct:: CvDTreeParams
The structure contains all the decision tree training parameters. You can initialize it by default constructor and then override any parameters directly before training, or the structure may be fully initialized using the advanced variant of the constructor.
@ -187,7 +187,7 @@ The default constructor initializes all the parameters with the default values t
CvDTreeTrainData
----------------
.. ocv:class:: CvDTreeTrainData
.. ocv:struct:: CvDTreeTrainData
Decision tree training data and shared data for tree ensembles. The structure is mostly used internally for storing both standalone trees and tree ensembles efficiently. Basically, it contains the following types of information:
@ -212,7 +212,7 @@ There are two ways of using this structure. In simple cases (for example, a stan
CvDTree
-------
.. ocv:class:: CvDTree
.. ocv:class:: CvDTree : public CvStatModel
The class implements a decision tree as described in the beginning of this section.
@ -221,7 +221,7 @@ CvDTree::train
--------------
Trains a decision tree.
.. ocv:function:: bool CvDTree::train( const Mat& train_data, int tflag, const Mat& responses, const Mat& var_idx=Mat(), const Mat& sample_idx=Mat(), const Mat& var_type=Mat(), const Mat& missing_mask=Mat(), CvDTreeParams params=CvDTreeParams() )
.. ocv:function:: bool CvDTree::train( const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvDTreeParams params=CvDTreeParams() )
.. ocv:function:: bool CvDTree::train( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvDTreeParams params=CvDTreeParams() )
@ -290,7 +290,7 @@ Returns the variable importance array.
.. ocv:function:: const CvMat* CvDTree::get_var_importance()
.. ocv:pyfunction:: cv2.DTree.getVarImportance() -> importanceVector
.. ocv:pyfunction:: cv2.DTree.getVarImportance() -> retval
CvDTree::get_root
-----------------
@ -311,7 +311,7 @@ CvDTree::get_data
-----------------
Returns used train data of the decision tree.
.. ocv:function:: const CvDTreeTrainData* CvDTree::get_data() const
.. ocv:function:: CvDTreeTrainData* CvDTree::get_data() const
Example: building a tree for classifying mushrooms. See the ``mushroom.cpp`` sample that demonstrates how to build and use the
decision tree.

2
modules/ml/doc/ertrees.rst
View File

@ -10,6 +10,6 @@ Extremely randomized trees have been introduced by Pierre Geurts, Damien Ernst a
CvERTrees
----------
.. ocv:class:: CvERTrees
.. ocv:class:: CvERTrees : public CvRTrees
The class implements the Extremely randomized trees algorithm. ``CvERTrees`` is inherited from :ocv:class:`CvRTrees` and has the same interface, so see description of :ocv:class:`CvRTrees` class to get details. To set the training parameters of Extremely randomized trees the same class :ocv:class:`CvRTParams` is used.

13
modules/ml/doc/expectation_maximization.rst
View File

@ -91,7 +91,7 @@ already a good enough approximation).
EM
--
.. ocv:class:: EM
.. ocv:class:: EM : public Algorithm
The class implements the EM algorithm as described in the beginning of this section. It is inherited from :ocv:class:`Algorithm`.
@ -102,6 +102,7 @@ The constructor of the class
.. ocv:function:: EM::EM(int nclusters=EM::DEFAULT_NCLUSTERS, int covMatType=EM::COV_MAT_DIAGONAL, const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, EM::DEFAULT_MAX_ITERS, FLT_EPSILON) )
.. ocv:pyfunction:: cv2.EM([nclusters[, covMatType[, termCrit]]]) -> <EM object>
:param nclusters: The number of mixture components in the Gaussian mixture model. Default value of the parameter is ``EM::DEFAULT_NCLUSTERS=5``. Some of EM implementation could determine the optimal number of mixtures within a specified value range, but that is not the case in ML yet.
@ -125,6 +126,12 @@ Estimates the Gaussian mixture parameters from a samples set.
.. ocv:function:: bool EM::trainM(InputArray samples, InputArray probs0, OutputArray logLikelihoods=noArray(), OutputArray labels=noArray(), OutputArray probs=noArray())
.. ocv:pyfunction:: cv2.EM.train(samples[, logLikelihoods[, labels[, probs]]]) -> retval, logLikelihoods, labels, probs
.. ocv:pyfunction:: cv2.EM.trainE(samples, means0[, covs0[, weights0[, logLikelihoods[, labels[, probs]]]]]) -> retval, logLikelihoods, labels, probs
.. ocv:pyfunction:: cv2.EM.trainM(samples, probs0[, logLikelihoods[, labels[, probs]]]) -> retval, logLikelihoods, labels, probs
:param samples: Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have ``CV_64F`` type it will be converted to the inner matrix of such type for the further computing.
:param means0: Initial means :math:`a_k` of mixture components. It is a one-channel matrix of :math:`nclusters \times dims` size. If the matrix does not have ``CV_64F`` type it will be converted to the inner matrix of such type for the further computing.
@ -168,6 +175,8 @@ Returns a likelihood logarithm value and an index of the most probable mixture c
.. ocv:function:: Vec2d predict(InputArray sample, OutputArray probs=noArray()) const
.. ocv:pyfunction:: cv2.EM.predict(sample[, probs]) -> retval, probs
:param sample: A sample for classification. It should be a one-channel matrix of :math:`1 \times dims` or :math:`dims \times 1` size.
:param probs: Optional output matrix that contains posterior probabilities of each component given the sample. It has :math:`1 \times nclusters` size and ``CV_64FC1`` type.
@ -180,6 +189,8 @@ Returns ``true`` if the Gaussian mixture model was trained.
.. ocv:function:: bool EM::isTrained() const
.. ocv:pyfunction:: cv2.EM.isTrained() -> retval
EM::read, EM::write
-------------------
See :ocv:func:`Algorithm::read` and :ocv:func:`Algorithm::write`.

4
modules/ml/doc/gradient_boosted_trees.rst
View File

@ -104,7 +104,7 @@ For classification problems, the result is :math:`\arg\max_{i=1..K}(f_i(x))`.
CvGBTreesParams
---------------
.. ocv:class:: CvGBTreesParams
.. ocv:struct:: CvGBTreesParams : public CvDTreeParams
GBT training parameters.
@ -149,7 +149,7 @@ By default the following constructor is used:
CvGBTrees
---------
.. ocv:class:: CvGBTrees
.. ocv:class:: CvGBTrees : public CvStatModel
The class implements the Gradient boosted tree model as described in the beginning of this section.

2
modules/ml/doc/k_nearest_neighbors.rst
View File

@ -7,7 +7,7 @@ The algorithm caches all training samples and predicts the response for a new sa
CvKNearest
----------
.. ocv:class:: CvKNearest
.. ocv:class:: CvKNearest : public CvStatModel
The class implements K-Nearest Neighbors model as described in the beginning of this section.

2
modules/ml/doc/mldata.rst
View File

@ -251,7 +251,7 @@ The method returns a map that converts string class labels to the numerical clas
CvTrainTestSplit
----------------
.. ocv:class:: CvTrainTestSplit
.. ocv:struct:: CvTrainTestSplit
Structure setting the split of a data set read by :ocv:class:`CvMLData`.
::

10
modules/ml/doc/neural_networks.rst
View File

@ -97,7 +97,7 @@ The second (default) one is a batch RPROP algorithm.
CvANN_MLP_TrainParams
---------------------
.. ocv:class:: CvANN_MLP_TrainParams
.. ocv:struct:: CvANN_MLP_TrainParams
Parameters of the MLP training algorithm. You can initialize the structure by a constructor or the individual parameters can be adjusted after the structure is created.
@ -169,7 +169,7 @@ By default the RPROP algorithm is used:
CvANN_MLP
---------
.. ocv:class:: CvANN_MLP
.. ocv:class:: CvANN_MLP : public CvStatModel
MLP model.
@ -184,7 +184,7 @@ The constructors.
.. ocv:function:: CvANN_MLP::CvANN_MLP( const CvMat* layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 )
.. ocv:pyfunction:: cv2.ANN_MLP(layerSizes[, activateFunc[, fparam1[, fparam2]]]) -> <ANN_MLP object>
.. ocv:pyfunction:: cv2.ANN_MLP([layerSizes[, activateFunc[, fparam1[, fparam2]]]]) -> <ANN_MLP object>
The advanced constructor allows to create MLP with the specified topology. See :ocv:func:`CvANN_MLP::create` for details.
@ -216,7 +216,7 @@ Trains/updates MLP.
.. ocv:function:: int CvANN_MLP::train( const CvMat* inputs, const CvMat* outputs, const CvMat* sampleWeights, const CvMat* sampleIdx=0, CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(), int flags=0 )
.. ocv:pyfunction:: cv2.ANN_MLP.train(inputs, outputs, sampleWeights[, sampleIdx[, params[, flags]]]) -> niterations
.. ocv:pyfunction:: cv2.ANN_MLP.train(inputs, outputs, sampleWeights[, sampleIdx[, params[, flags]]]) -> retval
:param inputs: Floating-point matrix of input vectors, one vector per row.
@ -249,7 +249,7 @@ Predicts responses for input samples.
.. ocv:function:: float CvANN_MLP::predict( const CvMat* inputs, CvMat* outputs ) const
.. ocv:pyfunction:: cv2.ANN_MLP.predict(inputs, outputs) -> retval
.. ocv:pyfunction:: cv2.ANN_MLP.predict(inputs[, outputs]) -> retval, outputs
:param inputs: Input samples.

4
modules/ml/doc/normal_bayes_classifier.rst
View File

@ -11,7 +11,7 @@ This simple classification model assumes that feature vectors from each class ar
CvNormalBayesClassifier
-----------------------
.. ocv:class:: CvNormalBayesClassifier
.. ocv:class:: CvNormalBayesClassifier : public CvStatModel
Bayes classifier for normally distributed data.
@ -25,7 +25,7 @@ Default and training constructors.
.. ocv:function:: CvNormalBayesClassifier::CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0 )
.. ocv:pyfunction:: cv2.NormalBayesClassifier(trainData, responses[, varIdx[, sampleIdx]]) -> <NormalBayesClassifier object>
.. ocv:pyfunction:: cv2.NormalBayesClassifier([trainData, responses[, varIdx[, sampleIdx]]]) -> <NormalBayesClassifier object>
The constructors follow conventions of :ocv:func:`CvStatModel::CvStatModel`. See :ocv:func:`CvStatModel::train` for parameters descriptions.

8
modules/ml/doc/random_trees.rst
View File

@ -42,7 +42,7 @@ For the random trees usage example, please, see letter_recog.cpp sample in OpenC
CvRTParams
----------
.. ocv:class:: CvRTParams
.. ocv:struct:: CvRTParams : public CvDTreeParams
Training parameters of random trees.
@ -94,7 +94,7 @@ The default constructor sets all parameters to default values which are differen
CvRTrees
--------
.. ocv:class:: CvRTrees
.. ocv:class:: CvRTrees : public CvStatModel
The class implements the random forest predictor as described in the beginning of this section.
@ -118,7 +118,7 @@ CvRTrees::predict
-----------------
Predicts the output for an input sample.
.. ocv:function:: double CvRTrees::predict( const Mat& sample, const Mat& missing=Mat() ) const
.. ocv:function:: float CvRTrees::predict( const Mat& sample, const Mat& missing=Mat() ) const
.. ocv:function:: float CvRTrees::predict( const CvMat* sample, const CvMat* missing = 0 ) const
@ -156,7 +156,7 @@ Returns the variable importance array.
.. ocv:function:: const CvMat* CvRTrees::get_var_importance()
.. ocv:pyfunction:: cv2.RTrees.getVarImportance() -> importanceVector
.. ocv:pyfunction:: cv2.RTrees.getVarImportance() -> retval
The method returns the variable importance vector, computed at the training stage when ``CvRTParams::calc_var_importance`` is set to true. If this flag was set to false, the ``NULL`` pointer is returned. This differs from the decision trees where variable importance can be computed anytime after the training.

2
modules/ml/doc/statistical_models.rst
View File

@ -52,7 +52,7 @@ CvStatModel::CvStatModel(...)
-----------------------------
The training constructor.
.. ocv:function:: CvStatModel::CvStatModel( const Mat& train_data ... )
.. ocv:function:: CvStatModel::CvStatModel()
Most ML classes provide a single-step constructor and train constructors. This constructor is equivalent to the default constructor, followed by the :ocv:func:`CvStatModel::train` method with the parameters that are passed to the constructor.

12
modules/ml/doc/support_vector_machines.rst
View File

@ -16,7 +16,7 @@ SVM implementation in OpenCV is based on [LibSVM]_.
CvParamGrid
-----------
.. ocv:class:: CvParamGrid
.. ocv:struct:: CvParamGrid
The structure represents the logarithmic grid range of statmodel parameters. It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate being computed by cross-validation.
@ -77,7 +77,7 @@ Returns ``true`` if the grid is valid and ``false`` otherwise. The grid is valid
CvSVMParams
-----------
.. ocv:class:: CvSVMParams
.. ocv:struct:: CvSVMParams
SVM training parameters.
@ -146,7 +146,7 @@ The default constructor initialize the structure with following values:
CvSVM
-----
.. ocv:class:: CvSVM
.. ocv:class:: CvSVM : public CvStatModel
Support Vector Machines.
@ -160,7 +160,7 @@ Default and training constructors.
.. ocv:function:: CvSVM::CvSVM( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, CvSVMParams params=CvSVMParams() )
.. ocv:pyfunction:: cv2.SVM(trainData, responses[, varIdx[, sampleIdx[, params]]]) -> <SVM object>
.. ocv:pyfunction:: cv2.SVM([trainData, responses[, varIdx[, sampleIdx[, params]]]]) -> <SVM object>
The constructors follow conventions of :ocv:func:`CvStatModel::CvStatModel`. See :ocv:func:`CvStatModel::train` for parameters descriptions.
@ -285,7 +285,7 @@ Retrieves a number of support vectors and the particular vector.
.. ocv:function:: const float* CvSVM::get_support_vector(int i) const
.. ocv:pyfunction:: cv2.SVM.get_support_vector_count() -> nsupportVectors
.. ocv:pyfunction:: cv2.SVM.get_support_vector_count() -> retval
:param i: Index of the particular support vector.
@ -297,4 +297,4 @@ Returns the number of used features (variables count).
.. ocv:function:: int CvSVM::get_var_count() const
.. ocv:pyfunction:: cv2.SVM.get_var_count() -> nvars
.. ocv:pyfunction:: cv2.SVM.get_var_count() -> retval

19
modules/ml/include/opencv2/ml/ml.hpp
View File

@ -170,10 +170,10 @@ struct CV_EXPORTS_W_MAP CvParamGrid
min_val = max_val = step = 0;
}
CvParamGrid( double _min_val, double _max_val, double log_step )
CvParamGrid( double min_val, double max_val, double log_step )
{
min_val = _min_val;
max_val = _max_val;
this->min_val = min_val;
this->max_val = max_val;
step = log_step;
}
//CvParamGrid( int param_id );
@ -291,10 +291,10 @@ protected:
struct CV_EXPORTS_W_MAP CvSVMParams
{
CvSVMParams();
CvSVMParams( int _svm_type, int _kernel_type,
double _degree, double _gamma, double _coef0,
double Cvalue, double _nu, double _p,
CvMat* _class_weights, CvTermCriteria _term_crit );
CvSVMParams( int svm_type, int kernel_type,
double degree, double gamma, double coef0,
double Cvalue, double nu, double p,
CvMat* class_weights, CvTermCriteria term_crit );
CV_PROP_RW int svm_type;
CV_PROP_RW int kernel_type;
@ -569,8 +569,7 @@ public:
enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
CV_WRAP EM(int nclusters=EM::DEFAULT_NCLUSTERS, int covMatType=EM::COV_MAT_DIAGONAL,
const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+
TermCriteria::EPS,
const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,
EM::DEFAULT_MAX_ITERS, FLT_EPSILON));
virtual ~EM();
@ -1026,7 +1025,7 @@ public:
virtual float get_proximity( const CvMat* sample1, const CvMat* sample2,
const CvMat* missing1 = 0, const CvMat* missing2 = 0 ) const;
virtual float calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
virtual float calc_error( CvMLData* data, int type , std::vector<float>* resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
virtual float get_train_error();

16
modules/nonfree/doc/feature_detection.rst
View File

@ -3,7 +3,7 @@ Feature Detection and Description
SIFT
----
.. ocv:class:: SIFT
.. ocv:class:: SIFT : public Feature2D
Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [Lowe04]_.
@ -31,7 +31,7 @@ SIFT::operator ()
-----------------
Extract features and computes their descriptors using SIFT algorithm
.. ocv:function:: void SIFT::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
.. ocv:function:: void SIFT::operator()(InputArray img, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
:param image: Input 8-bit grayscale image
@ -46,7 +46,7 @@ Extract features and computes their descriptors using SIFT algorithm
SURF
----
.. ocv:class:: SURF
.. ocv:class:: SURF : public Feature2D
Class for extracting Speeded Up Robust Features from an image [Bay06]_. The class is derived from ``CvSURFParams`` structure, which specifies the algorithm parameters:
@ -82,9 +82,9 @@ The SURF extractor constructors.
.. ocv:function:: SURF::SURF()
.. ocv:function:: SURF::SURF(double hessianThreshold, int nOctaves=4, int nOctaveLayers=2, bool extended=false, bool upright=false)
.. ocv:function:: SURF::SURF( double hessianThreshold, int nOctaves=4, int nOctaveLayers=2, bool extended=true, bool upright=false )
.. ocv:pyfunction:: cv2.SURF(_hessianThreshold[, _nOctaves[, _nOctaveLayers[, _extended[, _upright]]]]) -> <SURF object>
.. ocv:pyfunction:: cv2.SURF([hessianThreshold[, nOctaves[, nOctaveLayers[, extended[, upright]]]]]) -> <SURF object>
:param hessianThreshold: Threshold for hessian keypoint detector used in SURF.
@ -101,11 +101,11 @@ SURF::operator()
----------------
Detects keypoints and computes SURF descriptors for them.
.. ocv:function:: void SURF::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints) const
.. ocv:function:: void SURF::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
.. ocv:function:: void SURF::operator()(InputArray img, InputArray mask, vector<KeyPoint>& keypoints) const
.. ocv:function:: void SURF::operator()(InputArray img, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
.. ocv:pyfunction:: cv2.SURF.detect(img, mask) -> keypoints
.. ocv:pyfunction:: cv2.SURF.detect(img, mask[, useProvidedKeypoints]) -> keypoints, descriptors
.. ocv:pyfunction:: cv2.SURF.detect(img, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
.. ocv:cfunction:: void cvExtractSURF( const CvArr* image, const CvArr* mask, CvSeq** keypoints, CvSeq** descriptors, CvMemStorage* storage, CvSURFParams params )

22
modules/nonfree/include/opencv2/nonfree/features2d.hpp
View File

@ -58,9 +58,9 @@ namespace cv
class CV_EXPORTS_W SIFT : public Feature2D
{
public:
explicit SIFT( int _nfeatures=0, int _nOctaveLayers=3,
double _contrastThreshold=0.04, double _edgeThreshold=10,
double _sigma=1.6);
explicit SIFT( int nfeatures=0, int nOctaveLayers=3,
double contrastThreshold=0.04, double edgeThreshold=10,
double sigma=1.6);
//! returns the descriptor size in floats (128)
int descriptorSize() const;
@ -108,23 +108,23 @@ class CV_EXPORTS_W SURF : public Feature2D
{
public:
//! the default constructor
SURF();
CV_WRAP SURF();
//! the full constructor taking all the necessary parameters
explicit SURF(double _hessianThreshold,
int _nOctaves=4, int _nOctaveLayers=2,
bool _extended=true, bool _upright=false);
explicit CV_WRAP SURF(double hessianThreshold,
int nOctaves=4, int nOctaveLayers=2,
bool extended=true, bool upright=false);
//! returns the descriptor size in float's (64 or 128)
int descriptorSize() const;
CV_WRAP int descriptorSize() const;
//! returns the descriptor type
int descriptorType() const;
CV_WRAP int descriptorType() const;
//! finds the keypoints using fast hessian detector used in SURF
void operator()(InputArray img, InputArray mask,
CV_WRAP_AS(detect) void operator()(InputArray img, InputArray mask,
CV_OUT vector<KeyPoint>& keypoints) const;
//! finds the keypoints and computes their descriptors. Optionally it can compute descriptors for the user-provided keypoints
void operator()(InputArray img, InputArray mask,
CV_WRAP_AS(detect) void operator()(InputArray img, InputArray mask,
CV_OUT vector<KeyPoint>& keypoints,
OutputArray descriptors,
bool useProvidedKeypoints=false) const;

12
modules/objdetect/doc/cascade_classification.rst
View File

@ -131,7 +131,7 @@ FeatureEvaluator::create
----------------------------
Constructs the feature evaluator.
.. ocv:function:: static Ptr<FeatureEvaluator> FeatureEvaluator::create(int type)
.. ocv:function:: Ptr<FeatureEvaluator> FeatureEvaluator::create(int type)
:param type: Type of features evaluated by cascade (``HAAR`` or ``LBP`` for now).
@ -148,7 +148,7 @@ Loads a classifier from a file.
.. ocv:function:: CascadeClassifier::CascadeClassifier(const string& filename)
.. ocv:pyfunction:: cv2.CascadeClassifier(filename) -> <CascadeClassifier object>
.. ocv:pyfunction:: cv2.CascadeClassifier([filename]) -> <CascadeClassifier object>
:param filename: Name of the file from which the classifier is loaded.
@ -193,9 +193,9 @@ Detects objects of different sizes in the input image. The detected objects are
.. ocv:pyfunction:: cv2.CascadeClassifier.detectMultiScale(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]) -> objects
.. ocv:pyfunction:: cv2.CascadeClassifier.detectMultiScale(image, rejectLevels, levelWeights[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize[, outputRejectLevels]]]]]]) -> objects
.. ocv:cfunction:: CvSeq* cvHaarDetectObjects( const CvArr* image, CvHaarClassifierCascade* cascade, CvMemStorage* storage, double scaleFactor=1.1, int minNeighbors=3, int flags=0, CvSize minSize=cvSize(0, 0), CvSize maxSize=cvSize(0, 0) )
.. ocv:cfunction:: CvSeq* cvHaarDetectObjects( const CvArr* image, CvHaarClassifierCascade* cascade, CvMemStorage* storage, double scale_factor=1.1, int min_neighbors=3, int flags=0, CvSize min_size=cvSize(0,0), CvSize max_size=cvSize(0,0) )
.. ocv:pyoldfunction:: cv.HaarDetectObjects(image, cascade, storage, scaleFactor=1.1, minNeighbors=3, flags=0, minSize=(0, 0))-> detectedObjects
.. ocv:pyoldfunction:: cv.HaarDetectObjects(image, cascade, storage, scale_factor=1.1, min_neighbors=3, flags=0, min_size=(0, 0)) -> detectedObjects
:param cascade: Haar classifier cascade (OpenCV 1.x API only). It can be loaded from XML or YAML file using :ocv:cfunc:`Load`. When the cascade is not needed anymore, release it using ``cvReleaseHaarClassifierCascade(&cascade)``.
@ -222,7 +222,7 @@ Sets an image for detection.
.. ocv:function:: bool CascadeClassifier::setImage( Ptr<FeatureEvaluator>& feval, const Mat& image )
.. ocv:cfunction:: void cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade, const CvArr* sum, const CvArr* sqsum, const CvArr* tiltedSum, double scale )
.. ocv:cfunction:: void cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade, const CvArr* sum, const CvArr* sqsum, const CvArr* tilted_sum, double scale )
:param cascade: Haar classifier cascade (OpenCV 1.x API only). See :ocv:func:`CascadeClassifier::detectMultiScale` for more information.
@ -241,7 +241,7 @@ Runs the detector at the specified point.
.. ocv:function:: int CascadeClassifier::runAt( Ptr<FeatureEvaluator>& feval, Point pt )
.. ocv:cfunction:: int cvRunHaarClassifierCascade( CvHaarClassifierCascade* cascade, CvPoint pt, int startStage=0 )
.. ocv:cfunction:: int cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade, CvPoint pt, int start_stage=0 )
:param cascade: Haar classifier cascade (OpenCV 1.x API only). See :ocv:func:`CascadeClassifier::detectMultiScale` for more information.

8
modules/objdetect/doc/latent_svm.rst
View File

@ -161,7 +161,7 @@ cvLatentSvmDetectObjects
Find rectangular regions in the given image that are likely to contain objects
and corresponding confidence levels.
.. ocv:function:: CvSeq* cvLatentSvmDetectObjects(IplImage* image, CvLatentSvmDetector* detector, CvMemStorage* storage, float overlap_threshold, int numThreads)
.. ocv:function:: CvSeq* cvLatentSvmDetectObjects( IplImage* image, CvLatentSvmDetector* detector, CvMemStorage* storage, float overlap_threshold=0.5f, int numThreads=-1 )
:param image: image
:param detector: LatentSVM detector in internal representation
@ -181,7 +181,7 @@ using them.
LatentSvmDetector::ObjectDetection
----------------------------------
.. ocv:class:: LatentSvmDetector::ObjectDetection
.. ocv:struct:: LatentSvmDetector::ObjectDetection
Structure contains the detection information.
@ -228,7 +228,7 @@ LatentSvmDetector::load
-----------------------
Load the trained models from given ``.xml`` files and return ``true`` if at least one model was loaded.
.. ocv:function:: bool LatentSvmDetector::load(const vector<string>& filenames, const vector<string>& classNames)
.. ocv:function:: bool LatentSvmDetector::load( const vector<string>& filenames, const vector<string>& classNames=vector<string>() )
:param filenames: A set of filenames storing the trained detectors (models). Each file contains one model. See examples of such files here /opencv_extra/testdata/cv/latentsvmdetector/models_VOC2007/.
@ -239,7 +239,7 @@ LatentSvmDetector::detect
Find rectangular regions in the given image that are likely to contain objects of loaded classes (models)
and corresponding confidence levels.
.. ocv:function:: void LatentSvmDetector::detect( const Mat& image, vector<ObjectDetection>& objectDetections, float overlapThreshold=0.5, int numThreads=-1 )
.. ocv:function:: void LatentSvmDetector::detect( const Mat& image, vector<ObjectDetection>& objectDetections, float overlapThreshold=0.5f, int numThreads=-1 )
:param image: An image.
:param objectDetections: The detections: rectangulars, scores and class IDs.

2
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
View File

@ -346,7 +346,7 @@ public:
virtual Ptr<FeatureEvaluator> clone() const;
virtual int getFeatureType() const;
virtual bool setImage(const Mat&, Size origWinSize);
virtual bool setImage(const Mat& img, Size origWinSize);
virtual bool setWindow(Point p);
virtual double calcOrd(int featureIdx) const;

4
modules/photo/doc/inpainting.rst
View File

@ -9,9 +9,9 @@ Restores the selected region in an image using the region neighborhood.
.. ocv:function:: void inpaint( InputArray src, InputArray inpaintMask, OutputArray dst, double inpaintRadius, int flags )
.. ocv:pyfunction:: cv2.inpaint(src, inpaintMask, inpaintRange, flags[, dst]) -> dst
.. ocv:pyfunction:: cv2.inpaint(src, inpaintMask, inpaintRadius, flags[, dst]) -> dst
.. ocv:cfunction:: void cvInpaint( const CvArr* src, const CvArr* mask, CvArr* dst, double inpaintRadius, int flags)
.. ocv:cfunction:: void cvInpaint( const CvArr* src, const CvArr* inpaint_mask, CvArr* dst, double inpaintRange, int flags )
.. ocv:pyoldfunction:: cv.Inpaint(src, mask, dst, inpaintRadius, flags) -> None
:param src: Input 8-bit 1-channel or 3-channel image.

22
modules/python/src2/api
View File

@ -1669,7 +1669,7 @@ ApproxPoly /doconly
int method
double parameter 0.0
int parameter2 0
CalcEMD2 /doconly
CalcEMD2 float /doconly
CvArr signature1
CvArr signature2
int distance_type
@ -1688,7 +1688,7 @@ CalcOpticalFlowPyrLK currFeatures,status,track_error /doconly
int level
CvTermCriteria criteria
int flags
CvPoint2D32f* guesses
CvPoint2D32f* guesses NULL
CvPoint2D32f currFeatures /O
char status /O
float track_error /O
@ -1726,7 +1726,7 @@ CreateMatND CvMatND /doconly
ints dims
int type
CreateMemStorage CvMemStorage /doconly
int blockSize
int blockSize 0
CreateTrackbar /doconly
char* trackbarName
char* windowName
@ -1738,7 +1738,7 @@ FindChessboardCorners corners /doconly
CvSize patternSize
CvPoint2D32fs corners /O
int flags CV_CALIB_CB_ADAPTIVE_THRESH
FindContours /doconly
FindContours CvSeq /doconly
CvArr image
CvMemStorage storage
int mode CV_RETR_LIST
@ -1751,14 +1751,14 @@ FitLine line /doconly
double reps
double aeps
PyObject* line /O
GetDims /doconly
GetDims dim1,dim2,... /doconly
CvArr arr
GetHuMoments hu /doconly
CvMoments moments
PyObject* hu /O
GetImage /doconly
GetImage iplimage /doconly
CvMat arr
GetMat /doconly
GetMat CvMat /doconly
IplImage arr
int allowND 0
GetMinMaxHistValue min_value,max_value,min_idx,max_idx /doconly
@ -1780,14 +1780,14 @@ LoadImageM /doconly
LoadImage /doconly
char* filename
int iscolor CV_LOAD_IMAGE_COLOR
ReshapeMatND /doconly
ReshapeMatND CvMat /doconly
CvMat arr
int newCn
ints newDims
Reshape /doconly
Reshape CvMat /doconly
CvArr arr
int newCn
int newRows
int newRows 0
SetData /doconly
CvArr arr
PyObject* data
@ -1801,5 +1801,5 @@ Subdiv2DLocate loc,where /doconly
CvPoint2D32f pt
int loc /O
edgeorpoint where /O
WaitKey /doconly
WaitKey int /doconly
int delay 0

2
modules/python/src2/cv2.cv.hpp
View File

@ -3844,7 +3844,7 @@ static double cppKMeans(const CvArr* _samples, int cluster_count, CvArr* _labels
static PyMethodDef old_methods[] = {
#if PYTHON_USE_NUMPY
{"fromarray", (PyCFunction)pycvfromarray, METH_KEYWORDS, "fromarray(array) -> cvmatnd"},
{"fromarray", (PyCFunction)pycvfromarray, METH_KEYWORDS, "fromarray(array [, allowND]) -> CvMat"},
#endif
{"FindDataMatrix", pyfinddatamatrix, METH_VARARGS},

4
modules/stitching/doc/blenders.rst
View File

@ -64,7 +64,7 @@ Blends and returns the final pano.
detail::FeatherBlender
----------------------
.. ocv:class:: detail::FeatherBlender
.. ocv:class:: detail::FeatherBlender : public Blender
Simple blender which mixes images at its borders. ::
@ -93,7 +93,7 @@ Simple blender which mixes images at its borders. ::
detail::MultiBandBlender
------------------------
.. ocv:class:: detail::MultiBandBlender
.. ocv:class:: detail::MultiBandBlender : public Blender
Blender which uses multi-band blending algorithm (see [BA83]_). ::

2
modules/stitching/doc/camera.rst
View File

@ -5,7 +5,7 @@ Camera
detail::CameraParams
--------------------
.. ocv:class:: detail::CameraParams
.. ocv:struct:: detail::CameraParams
Describes camera parameters.

6
modules/stitching/doc/exposure_compensation.rst
View File

@ -54,7 +54,7 @@ Compensate exposure in the specified image.
detail::NoExposureCompensator
-----------------------------
.. ocv:class:: detail::NoExposureCompensator
.. ocv:class:: detail::NoExposureCompensator : public ExposureCompensator
Stub exposure compensator which does nothing. ::
@ -70,7 +70,7 @@ Stub exposure compensator which does nothing. ::
detail::GainCompensator
-----------------------
.. ocv:class:: detail::GainCompensator
.. ocv:class:: detail::GainCompensator : public ExposureCompensator
Exposure compensator which tries to remove exposure related artifacts by adjusting image intensities, see [BL07]_ and [WJ10]_ for details. ::
@ -90,7 +90,7 @@ Exposure compensator which tries to remove exposure related artifacts by adjusti
detail::BlocksGainCompensator
-----------------------------
.. ocv:class:: detail::BlocksGainCompensator
.. ocv:class:: detail::BlocksGainCompensator : public ExposureCompensator
Exposure compensator which tries to remove exposure related artifacts by adjusting image block intensities, see [UES01]_ for details. ::

6
modules/stitching/doc/high_level.rst
View File

@ -164,7 +164,7 @@ Image warper factories base class. ::
PlaneWarper
-----------
.. ocv:class:: PlaneWarper
.. ocv:class:: PlaneWarper : public WarperCreator
Plane warper factory class. ::
@ -178,7 +178,7 @@ Plane warper factory class. ::
CylindricalWarper
-----------------
.. ocv:class:: CylindricalWarper
.. ocv:class:: CylindricalWarper : public WarperCreator
Cylindrical warper factory class. ::
@ -192,7 +192,7 @@ Cylindrical warper factory class. ::
SphericalWarper
---------------
.. ocv:class:: SphericalWarper
.. ocv:class:: SphericalWarper : public WarperCreator
Spherical warper factory class. ::

8
modules/stitching/doc/matching.rst
View File

@ -74,7 +74,7 @@ This method must implement features finding logic in order to make the wrappers
detail::SurfFeaturesFinder
--------------------------
.. ocv:class:: detail::SurfFeaturesFinder
.. ocv:class:: detail::SurfFeaturesFinder : public FeaturesFinder
SURF features finder. ::
@ -92,7 +92,7 @@ SURF features finder. ::
detail::OrbFeaturesFinder
-------------------------
.. ocv:class:: detail::OrbFeaturesFinder
.. ocv:class:: detail::OrbFeaturesFinder : public FeaturesFinder
ORB features finder. ::
@ -212,7 +212,7 @@ This method must implement matching logic in order to make the wrappers `detail:
detail::BestOf2NearestMatcher
-----------------------------
.. ocv:class:: detail::BestOf2NearestMatcher
.. ocv:class:: detail::BestOf2NearestMatcher : public FeaturesMatcher
Features matcher which finds two best matches for each feature and leaves the best one only if the ratio between descriptor distances is greater than the threshold ``match_conf``. ::
@ -239,7 +239,7 @@ detail::BestOf2NearestMatcher::BestOf2NearestMatcher
Constructs a "best of 2 nearest" matcher.
.. ocv:function:: detail::BestOf2NearestMatcher::BestOf2NearestMatcher(bool try_use_gpu = false, float match_conf = 0.65f, int num_matches_thresh1 = 6, int num_matches_thresh2 = 6)
.. ocv:function:: detail::BestOf2NearestMatcher::BestOf2NearestMatcher(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6, int num_matches_thresh2 = 6)
:param try_use_gpu: Should try to use GPU or not

8
modules/stitching/doc/motion_estimation.rst
View File

@ -55,7 +55,7 @@ This method must implement camera parameters estimation logic in order to make t
detail::HomographyBasedEstimator
--------------------------------
.. ocv:class:: detail::HomographyBasedEstimator
.. ocv:class:: detail::HomographyBasedEstimator : public Estimator
Homography based rotation estimator. ::
@ -71,7 +71,7 @@ Homography based rotation estimator. ::
detail::BundleAdjusterBase
--------------------------
.. ocv:class:: detail::BundleAdjusterBase
.. ocv:class:: detail::BundleAdjusterBase : public Estimator
Base class for all camera parameters refinement methods. ::
@ -187,7 +187,7 @@ Gets the refined camera parameters.
detail::BundleAdjusterReproj
----------------------------
.. ocv:class:: detail::BundleAdjusterReproj
.. ocv:class:: detail::BundleAdjusterReproj : public BundleAdjusterBase
Implementation of the camera parameters refinement algorithm which minimizes sum of the reprojection error squares. ::
@ -204,7 +204,7 @@ Implementation of the camera parameters refinement algorithm which minimizes sum
detail::BundleAdjusterRay
-------------------------
.. ocv:class:: detail::BundleAdjusterRay
.. ocv:class:: detail::BundleAdjusterRay : public BundleAdjusterBase
Implementation of the camera parameters refinement algorithm which minimizes sum of the distances between the rays passing through the camera center and a feature. ::

8
modules/stitching/doc/seam_estimation.rst
View File

@ -33,7 +33,7 @@ Estimates seams.
detail::NoSeamFinder
--------------------
.. ocv:class:: detail::NoSeamFinder
.. ocv:class:: detail::NoSeamFinder : public SeamFinder
Stub seam estimator which does nothing. ::
@ -47,7 +47,7 @@ Stub seam estimator which does nothing. ::
detail::PairwiseSeamFinder
--------------------------
.. ocv:class:: detail::PairwiseSeamFinder
.. ocv:class:: detail::PairwiseSeamFinder : public SeamFinder
Base class for all pairwise seam estimators. ::
@ -84,7 +84,7 @@ Resolves masks intersection of two specified images in the given ROI.
detail::VoronoiSeamFinder
-------------------------
.. ocv:class:: detail::VoronoiSeamFinder
.. ocv:class:: detail::VoronoiSeamFinder : public PairwiseSeamFinder
Voronoi diagram-based seam estimator. ::
@ -113,7 +113,7 @@ Base class for all minimum graph-cut-based seam estimators. ::
detail::GraphCutSeamFinder
--------------------------
.. ocv:class:: detail::GraphCutSeamFinder
.. ocv:class:: detail::GraphCutSeamFinder : public GraphCutSeamFinderBase, public SeamFinder
Minimum graph cut-based seam estimator. See details in [V03]_. ::

6
modules/stitching/doc/warpers.rst
View File

@ -172,7 +172,7 @@ Base class for rotation-based warper using a `detail::ProjectorBase`_ derived cl
detail::PlaneWarper
-------------------
.. ocv:class:: detail::PlaneWarper
.. ocv:class:: detail::PlaneWarper : public RotationWarperBase<PlaneProjector>
Warper that maps an image onto the z = 1 plane. ::
@ -209,7 +209,7 @@ Construct an instance of the plane warper class.
detail::SphericalWarper
-----------------------
.. ocv:class:: detail::SphericalWarper
.. ocv:class:: detail::SphericalWarper : public RotationWarperBase<SphericalProjector>
Warper that maps an image onto the unit sphere located at the origin. ::
@ -235,7 +235,7 @@ Construct an instance of the spherical warper class.
detail::CylindricalWarper
-------------------------
.. ocv:class:: detail::CylindricalWarper
.. ocv:class:: detail::CylindricalWarper : public RotationWarperBase<CylindricalProjector>
Warper that maps an image onto the x*x + z*z = 1 cylinder. ::

42
modules/video/doc/motion_analysis_and_object_tracking.rst
View File

@ -8,12 +8,12 @@ calcOpticalFlowPyrLK
------------------------
Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.
.. ocv:function:: void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg, InputArray prevPts, InputOutputArray nextPts, OutputArray status, OutputArray err, Size winSize=Size(15,15), int maxLevel=3, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags=0, double minEigThreshold=1e-4)
.. ocv:function:: void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg, InputArray prevPts, InputOutputArray nextPts, OutputArray status, OutputArray err, Size winSize=Size(21,21), int maxLevel=3, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags=0, double minEigThreshold=1e-4 )
.. ocv:pyfunction:: cv2.calcOpticalFlowPyrLK(prevImg, nextImg, prevPts[, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, flags[, minEigThreshold]]]]]]]]) -> nextPts, status, err
.. ocv:cfunction:: void cvCalcOpticalFlowPyrLK( const CvArr* prev, const CvArr* curr, CvArr* prevPyr, CvArr* currPyr, const CvPoint2D32f* prevFeatures, CvPoint2D32f* currFeatures, int count, CvSize winSize, int level, char* status, float* trackError, CvTermCriteria criteria, int flags )
.. ocv:pyoldfunction:: cv.CalcOpticalFlowPyrLK( prev, curr, prevPyr, currPyr, prevFeatures, winSize, level, criteria, flags, guesses=None) -> (currFeatures, status, trackError)
.. ocv:cfunction:: void cvCalcOpticalFlowPyrLK( const CvArr* prev, const CvArr* curr, CvArr* prev_pyr, CvArr* curr_pyr, const CvPoint2D32f* prev_features, CvPoint2D32f* curr_features, int count, CvSize win_size, int level, char* status, float* track_error, CvTermCriteria criteria, int flags )
.. ocv:pyoldfunction:: cv.CalcOpticalFlowPyrLK(prev, curr, prevPyr, currPyr, prevFeatures, winSize, level, criteria, flags, guesses=None) -> (currFeatures, status, track_error)
:param prevImg: First 8-bit input image or pyramid constructed by :ocv:func:`buildOpticalFlowPyramid`.
@ -73,11 +73,11 @@ calcOpticalFlowFarneback
----------------------------
Computes a dense optical flow using the Gunnar Farneback's algorithm.
.. ocv:function:: void calcOpticalFlowFarneback( InputArray prevImg, InputArray nextImg, InputOutputArray flow, double pyrScale, int levels, int winsize, int iterations, int polyN, double polySigma, int flags )
.. ocv:function:: void calcOpticalFlowFarneback( InputArray prev, InputArray next, InputOutputArray flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags )
.. ocv:cfunction:: void cvCalcOpticalFlowFarneback( const CvArr* prevImg, const CvArr* nextImg, CvArr* flow, double pyrScale, int levels, int winsize, int iterations, int polyN, double polySigma, int flags )
.. ocv:cfunction:: void cvCalcOpticalFlowFarneback( const CvArr* prev, const CvArr* next, CvArr* flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags )
.. ocv:pyfunction:: cv2.calcOpticalFlowFarneback(prevImg, nextImg, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags[, flow]) -> flow
.. ocv:pyfunction:: cv2.calcOpticalFlowFarneback(prev, next, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags[, flow]) -> flow
:param prevImg: First 8-bit single-channel input image.
@ -197,7 +197,7 @@ Calculates a gradient orientation of a motion history image.
.. ocv:pyfunction:: cv2.calcMotionGradient(mhi, delta1, delta2[, mask[, orientation[, apertureSize]]]) -> mask, orientation
.. ocv:cfunction:: void cvCalcMotionGradient( const CvArr* mhi, CvArr* mask, CvArr* orientation, double delta1, double delta2, int apertureSize=3 )
.. ocv:cfunction:: void cvCalcMotionGradient( const CvArr* mhi, CvArr* mask, CvArr* orientation, double delta1, double delta2, int aperture_size=3 )
.. ocv:pyoldfunction:: cv.CalcMotionGradient(mhi, mask, orientation, delta1, delta2, apertureSize=3)-> None
:param mhi: Motion history single-channel floating-point image.
@ -267,8 +267,8 @@ Splits a motion history image into a few parts corresponding to separate indepen
.. ocv:pyfunction:: cv2.segmentMotion(mhi, timestamp, segThresh[, segmask]) -> segmask, boundingRects
.. ocv:cfunction:: CvSeq* cvSegmentMotion( const CvArr* mhi, CvArr* segMask, CvMemStorage* storage, double timestamp, double segThresh )
.. ocv:pyoldfunction:: cv.SegmentMotion(mhi, segMask, storage, timestamp, segThresh)-> None
.. ocv:cfunction:: CvSeq* cvSegmentMotion( const CvArr* mhi, CvArr* seg_mask, CvMemStorage* storage, double timestamp, double seg_thresh )
.. ocv:pyoldfunction:: cv.SegmentMotion(mhi, seg_mask, storage, timestamp, seg_thresh) -> boundingRects
:param mhi: Motion history image.
@ -294,9 +294,9 @@ Finds an object center, size, and orientation.
.. ocv:pyfunction:: cv2.CamShift(probImage, window, criteria) -> retval, window
.. ocv:cfunction:: int cvCamShift( const CvArr* probImage, CvRect window, CvTermCriteria criteria, CvConnectedComp* comp, CvBox2D* box=NULL )
.. ocv:cfunction:: int cvCamShift( const CvArr* prob_image, CvRect window, CvTermCriteria criteria, CvConnectedComp* comp, CvBox2D* box=NULL )
.. ocv:pyoldfunction:: cv.CamShift(probImage, window, criteria)-> (int, comp, box)
.. ocv:pyoldfunction:: cv.CamShift(prob_image, window, criteria) -> (int, comp, box)
:param probImage: Back projection of the object histogram. See :ocv:func:`calcBackProject` .
@ -323,8 +323,8 @@ Finds an object on a back projection image.
.. ocv:pyfunction:: cv2.meanShift(probImage, window, criteria) -> retval, window
.. ocv:cfunction:: int cvMeanShift( const CvArr* probImage, CvRect window, CvTermCriteria criteria, CvConnectedComp* comp )
.. ocv:pyoldfunction:: cv.MeanShift(probImage, window, criteria)-> comp
.. ocv:cfunction:: int cvMeanShift( const CvArr* prob_image, CvRect window, CvTermCriteria criteria, CvConnectedComp* comp )
.. ocv:pyoldfunction:: cv.MeanShift(prob_image, window, criteria) -> comp
:param probImage: Back projection of the object histogram. See :ocv:func:`calcBackProject` for details.
@ -364,10 +364,10 @@ The constructors.
.. ocv:function:: KalmanFilter::KalmanFilter(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F)
.. ocv:pyfunction:: cv2.KalmanFilter(dynamParams, measureParams[, controlParams[, type]]) -> <KalmanFilter object>
.. ocv:pyfunction:: cv2.KalmanFilter([dynamParams, measureParams[, controlParams[, type]]]) -> <KalmanFilter object>
.. ocv:cfunction:: CvKalman* cvCreateKalman( int dynamParams, int measureParams, int controlParams=0 )
.. ocv:pyoldfunction:: cv.CreateKalman(dynamParams, measureParams, controlParams=0) -> CvKalman
.. ocv:cfunction:: CvKalman* cvCreateKalman( int dynam_params, int measure_params, int control_params=0 )
.. ocv:pyoldfunction:: cv.CreateKalman(dynam_params, measure_params, control_params=0) -> CvKalman
The full constructor.
@ -402,10 +402,10 @@ Computes a predicted state.
.. ocv:function:: const Mat& KalmanFilter::predict(const Mat& control=Mat())
.. ocv:pyfunction:: cv2.KalmanFilter.predict([, control]) -> retval
.. ocv:pyfunction:: cv2.KalmanFilter.predict([control]) -> retval
.. ocv:cfunction:: const CvMat* cvKalmanPredict( CvKalman* kalman, const CvMat* control=NULL)
.. ocv:pyoldfunction:: cv.KalmanPredict(kalman, control=None) -> cvmat
.. ocv:pyoldfunction:: cv.KalmanPredict(kalman, control=None) -> mat
:param control: The optional input control
@ -420,7 +420,7 @@ Updates the predicted state from the measurement.
.. ocv:cfunction:: const CvMat* cvKalmanCorrect( CvKalman* kalman, const CvMat* measurement )
.. ocv:pyoldfunction:: cv.KalmanCorrect(kalman, measurement) -> cvmat
.. ocv:pyoldfunction:: cv.KalmanCorrect(kalman, measurement) -> mat
:param measurement: The measured system parameters
@ -487,7 +487,7 @@ The constructors.
.. ocv:function:: BackgroundSubtractorMOG::BackgroundSubtractorMOG(int history, int nmixtures, double backgroundRatio, double noiseSigma=0)
.. ocv:pyfunction:: cv2.BackgroundSubtractorMOG(history, nmixtures, backgroundRatio[, noiseSigma]) -> <BackgroundSubtractorMOG object>
.. ocv:pyfunction:: cv2.BackgroundSubtractorMOG([history, nmixtures, backgroundRatio[, noiseSigma]]) -> <BackgroundSubtractorMOG object>
:param history: Length of the history.
@ -569,7 +569,7 @@ The constructors.
.. ocv:function:: BackgroundSubtractorMOG2::BackgroundSubtractorMOG2()
.. ocv:function:: BackgroundSubtractorMOG2::BackgroundSubtractorMOG2(int history, float varThreshold, bool bShadowDetection=1)
.. ocv:function:: BackgroundSubtractorMOG2::BackgroundSubtractorMOG2( int history, float varThreshold, bool bShadowDetection=true )
:param history: Length of the history.