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started work on API & doc synchronization (in particular, Mat& => Input/OutputArray in the descriptions)

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This commit is contained in:
Vadim Pisarevsky 2011-06-07 22:51:31 +00:00
parent 927b5c88ea
commit c7a42e9682
52 changed files with 1782 additions and 2048 deletions
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21
doc/check_docs.py
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@ -16,13 +16,13 @@ opencv_hdr_list = [
]
opencv_module_list = [
"core",
"imgproc",
"calib3d",
"features2d",
"video",
"objdetect",
"highgui",
#"core",
#"imgproc",
#"calib3d",
#"features2d",
#"video",
#"objdetect",
#"highgui",
"ml"
]
@ -33,7 +33,7 @@ class RSTParser(object):
fdecl = ""
balance = 0
lineno = 0
for l in df.readlines():
lineno += 1
ll = l.strip()
@ -48,6 +48,7 @@ class RSTParser(object):
if balance > 0:
continue
rst_decl = self.parser.parse_func_decl_no_wrap(fdecl)
hdr_decls = self.fmap.get(rst_decl[0], [])
if not hdr_decls:
print "Documented function %s (%s) in %s:%d is not in the headers" % (fdecl, rst_decl[0], docname, lineno)
@ -68,6 +69,7 @@ class RSTParser(object):
self.fmap[rst_decl[0]] = hdr_decls[:decl_idx] + hdr_decls[decl_idx+1:]
continue
print "Documented function %s in %s:%d does not have a match" % (fdecl, docname, lineno)
df.close()
def check_module_docs(self, name):
self.parser = hp.CppHeaderParser()
@ -94,11 +96,14 @@ class RSTParser(object):
self.process_rst(d)
print "\n\n########## The list of undocumented functions: ###########\n\n"
misscount = 0
fkeys = sorted(self.fmap.keys())
for f in fkeys:
decls = self.fmap[f]
for d in decls:
misscount += 1
print "%s %s(%s)" % (d[1], d[0], ", ".join([a[0] + " " + a[1] for a in d[3]]))
print "\n\n\nundocumented functions in %s: %d" % (name, misscount)
p = RSTParser()
for m in opencv_module_list:

12
doc/opencv1/c/calib3d_camera_calibration_and_3d_reconstruction.rst
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@ -308,7 +308,7 @@ The algorithm does the following:
The function returns the final re-projection error.
Note: if you're using a non-square (=non-NxN) grid and
:func:`findChessboardCorners`
:cpp:func:`findChessboardCorners`
for calibration, and
``calibrateCamera``
returns
@ -333,7 +333,7 @@ See also:
,
:ref:`FindExtrinsicCameraParams2`
,
:func:`initCameraMatrix2D`
:cpp:func:`initCameraMatrix2D`
,
:ref:`StereoCalibrate`
,
@ -2303,7 +2303,7 @@ StereoRectify
:param P1, P2: The output :math:`3 \times 4` projection matrices in the new (rectified) coordinate systems.
:param Q: The output :math:`4 \times 4` disparity-to-depth mapping matrix, see :func:`reprojectImageTo3D` .
:param Q: The output :math:`4 \times 4` disparity-to-depth mapping matrix, see :cpp:func:`reprojectImageTo3D` .
:param flags: The operation flags; may be 0 or ``CV_CALIB_ZERO_DISPARITY`` . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in horizontal or vertical direction (depending on the orientation of epipolar lines) in order to maximize the useful image area.
@ -2320,7 +2320,7 @@ StereoRectify
The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, that makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. On input the function takes the matrices computed by
:func:`stereoCalibrate`
:cpp:func:`stereoCalibrate`
and on output it gives 2 rotation matrices and also 2 projection matrices in the new coordinates. The 2 cases are distinguished by the function are:
@ -2562,10 +2562,10 @@ UndistortPoints
:param distCoeffs: The input vector of distortion coefficients :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])` of 4, 5 or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
:param R: The rectification transformation in object space (3x3 matrix). ``R1`` or ``R2`` , computed by :func:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is used
:param R: The rectification transformation in object space (3x3 matrix). ``R1`` or ``R2`` , computed by :cpp:func:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is used
:param P: The new camera matrix (3x3) or the new projection matrix (3x4). ``P1`` or ``P2`` , computed by :func:`StereoRectify` can be passed here. If the matrix is empty, the identity new camera matrix is used
:param P: The new camera matrix (3x3) or the new projection matrix (3x4). ``P1`` or ``P2`` , computed by :cpp:func:`StereoRectify` can be passed here. If the matrix is empty, the identity new camera matrix is used

12
doc/opencv1/c/core_drawing_functions.rst
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@ -9,20 +9,20 @@ The boundaries of the shapes can be rendered with antialiasing (implemented only
All the functions include the parameter color that uses a rgb value (that may be constructed
with
``CV_RGB``
macro or the :func:`cvScalar` function
macro or the :cpp:func:`cvScalar` function
) for color
images and brightness for grayscale images. For color images the order channel
is normally
*Blue, Green, Red*
, this is what
:func:`imshow`
:cpp:func:`imshow`
,
:func:`imread`
:cpp:func:`imread`
and
:func:`imwrite`
:cpp:func:`imwrite`
expect
, so if you form a color using
:func:`cvScalar`
:cpp:func:`cvScalar`
, it should look like:
@ -32,7 +32,7 @@ expect
If you are using your own image rendering and I/O functions, you can use any channel ordering, the drawing functions process each channel independently and do not depend on the channel order or even on the color space used. The whole image can be converted from BGR to RGB or to a different color space using
:func:`cvtColor`
:cpp:func:`cvtColor`
.
If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, many drawing functions can handle pixel coordinates specified with sub-pixel accuracy, that is, the coordinates can be passed as fixed-point numbers, encoded as integers. The number of fractional bits is specified by the

2
doc/opencv1/c/imgproc_image_filtering.rst
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@ -5,7 +5,7 @@ Image Filtering
Functions and classes described in this section are used to perform various linear or non-linear filtering operations on 2D images (represented as
:func:`Mat`
:cpp:func:`Mat`
's), that is, for each pixel location
:math:`(x,y)`
in the source image some its (normally rectangular) neighborhood is considered and used to compute the response. In case of a linear filter it is a weighted sum of pixel values, in case of morphological operations it is the minimum or maximum etc. The computed response is stored to the destination image at the same location

12
doc/opencv1/py/calib3d_camera_calibration_and_3d_reconstruction.rst
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@ -278,7 +278,7 @@ The algorithm does the following:
Note: if you're using a non-square (=non-NxN) grid and
:func:`findChessboardCorners`
:cpp:func:`findChessboardCorners`
for calibration, and
``calibrateCamera``
returns
@ -303,7 +303,7 @@ See also:
,
:ref:`FindExtrinsicCameraParams2`
,
:func:`initCameraMatrix2D`
:cpp:func:`initCameraMatrix2D`
,
:ref:`StereoCalibrate`
,
@ -2312,7 +2312,7 @@ StereoRectify
:param P1, P2: The output :math:`3 \times 4` projection matrices in the new (rectified) coordinate systems.
:param Q: The output :math:`4 \times 4` disparity-to-depth mapping matrix, see :func:`reprojectImageTo3D` .
:param Q: The output :math:`4 \times 4` disparity-to-depth mapping matrix, see :cpp:func:`reprojectImageTo3D` .
:type Q: :class:`CvMat`
@ -2337,7 +2337,7 @@ StereoRectify
The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, that makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. On input the function takes the matrices computed by
:func:`stereoCalibrate`
:cpp:func:`stereoCalibrate`
and on output it gives 2 rotation matrices and also 2 projection matrices in the new coordinates. The 2 cases are distinguished by the function are:
@ -2595,12 +2595,12 @@ UndistortPoints
:type distCoeffs: :class:`CvMat`
:param R: The rectification transformation in object space (3x3 matrix). ``R1`` or ``R2`` , computed by :func:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is used
:param R: The rectification transformation in object space (3x3 matrix). ``R1`` or ``R2`` , computed by :cpp:func:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is used
:type R: :class:`CvMat`
:param P: The new camera matrix (3x3) or the new projection matrix (3x4). ``P1`` or ``P2`` , computed by :func:`StereoRectify` can be passed here. If the matrix is empty, the identity new camera matrix is used
:param P: The new camera matrix (3x3) or the new projection matrix (3x4). ``P1`` or ``P2`` , computed by :cpp:func:`StereoRectify` can be passed here. If the matrix is empty, the identity new camera matrix is used
:type P: :class:`CvMat`

8
doc/opencv1/py/core_drawing_functions.rst
View File

@ -14,14 +14,14 @@ images and brightness for grayscale images. For color images the order channel
is normally
*Blue, Green, Red*
, this is what
:func:`imshow`
:cpp:func:`imshow`
,
:func:`imread`
:cpp:func:`imread`
and
:func:`imwrite`
:cpp:func:`imwrite`
expect
If you are using your own image rendering and I/O functions, you can use any channel ordering, the drawing functions process each channel independently and do not depend on the channel order or even on the color space used. The whole image can be converted from BGR to RGB or to a different color space using
:func:`cvtColor`
:cpp:func:`cvtColor`
.
If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, many drawing functions can handle pixel coordinates specified with sub-pixel accuracy, that is, the coordinates can be passed as fixed-point numbers, encoded as integers. The number of fractional bits is specified by the

2
doc/opencv1/py/imgproc_image_filtering.rst
View File

@ -5,7 +5,7 @@ Image Filtering
Functions and classes described in this section are used to perform various linear or non-linear filtering operations on 2D images (represented as
:func:`Mat`
:cpp:func:`Mat`
's), that is, for each pixel location
:math:`(x,y)`
in the source image some its (normally rectangular) neighborhood is considered and used to compute the response. In case of a linear filter it is a weighted sum of pixel values, in case of morphological operations it is the minimum or maximum etc. The computed response is stored to the destination image at the same location

251
modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst
View File

@ -108,7 +108,7 @@ The functions below use the above model to do the following:
calibrateCamera
---------------
.. c:function:: double calibrateCamera( const vector<vector<Point3f> >& objectPoints, const vector<vector<Point2f> >& imagePoints, Size imageSize, Mat& cameraMatrix, Mat& distCoeffs, vector<Mat>& rvecs, vector<Mat>& tvecs, int flags=0 )
.. cpp:function:: double calibrateCamera( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArray rvecs, OutputArray tvecs, int flags=0 )
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
@ -190,7 +190,7 @@ See Also:
calibrationMatrixValues
-----------------------
.. c:function:: void calibrationMatrixValues( const Mat& cameraMatrix, Size imageSize, double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, Point2d& principalPoint, double& aspectRatio )
.. cpp:function:: void calibrationMatrixValues( InputArray cameraMatrix, Size imageSize, double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, Point2d& principalPoint, double& aspectRatio )
Computes useful camera characteristics from the camera matrix.
@ -220,9 +220,8 @@ The function computes various useful camera characteristics from the previously
composeRT
-------------
.. c:function:: void composeRT( const Mat& rvec1, const Mat& tvec1, const Mat& rvec2, const Mat& tvec2, Mat& rvec3, Mat& tvec3 )
.. c:function:: void composeRT( const Mat& rvec1, const Mat& tvec1, const Mat& rvec2, const Mat& tvec2, Mat& rvec3, Mat& tvec3, Mat& dr3dr1, Mat& dr3dt1, Mat& dr3dr2, Mat& dr3dt2, Mat& dt3dr1, Mat& dt3dt1, Mat& dt3dr2, Mat& dt3dt2 )
.. cpp:function:: void composeRT( InputArray rvec1, InputArray tvec1, InputArray rvec2, InputArray tvec2, OutputArray rvec3, OutputArray tvec3, OutputArray dr3dr1=None(), OutputArray dr3dt1=None(), OutputArray dr3dr2=None(), OutputArray dr3dt2=None(), OutputArray dt3dr1=None(), OutputArray dt3dt1=None(), OutputArray dt3dr2=None(), OutputArray dt3dt2=None() )
Combines two rotation-and-shift transformations.
@ -258,7 +257,7 @@ The functions are used inside :ref:`stereoCalibrate` but can also be used in you
computeCorrespondEpilines
-----------------------------
.. c:function:: void computeCorrespondEpilines( const Mat& points, int whichImage, const Mat& F, vector<Vec3f>& lines )
.. cpp:function:: void computeCorrespondEpilines( InputArray points, int whichImage, InputArray F, OutputArray lines )
For points in an image of a stereo pair, computes the corresponding epilines in the other image.
@ -301,9 +300,7 @@ Line coefficients are defined up to a scale. They are normalized so that
convertPointsHomogeneous
------------------------
.. c:function:: void convertPointsHomogeneous( const Mat& src, vector<Point3f>& dst )
.. c:function:: void convertPointsHomogeneous( const Mat& src, vector<Point2f>& dst )
.. cpp:function:: void convertPointsHomogeneous( InputArray src, OutputArray dst )
Converts points to/from homogeneous coordinates.
@ -326,9 +323,9 @@ If the output array dimensionality is larger, an extra 1 is appended to each poi
decomposeProjectionMatrix
-----------------------------
.. c:function:: void decomposeProjectionMatrix( const Mat& projMatrix, Mat& cameraMatrix, Mat& rotMatrix, Mat& transVect )
.. cpp:function:: void decomposeProjectionMatrix( InputArray projMatrix, OutputArray cameraMatrix, OutputArray rotMatrix, OutputArray transVect )
.. c:function:: void decomposeProjectionMatrix( const Mat& projMatrix, Mat& cameraMatrix, Mat& rotMatrix, Mat& transVect, Mat& rotMatrixX, Mat& rotMatrixY, Mat& rotMatrixZ, Vec3d& eulerAngles )
.. cpp:function:: void decomposeProjectionMatrix( InputArray projMatrix, OutputArray cameraMatrix, OutputArray rotMatrix, OutputArray transVect, OutputArray rotMatrixX, OutputArray rotMatrixY, OutputArray rotMatrixZ, Vec3d& eulerAngles )
Decomposes a projection matrix into a rotation matrix and a camera matrix.
@ -361,7 +358,7 @@ The function is based on
drawChessboardCorners
-------------------------
.. c:function:: void drawChessboardCorners( Mat& image, Size patternSize, const Mat& corners, bool patternWasFound )
.. cpp:function:: void drawChessboardCorners( InputOutputArray image, Size patternSize, InputArray corners, bool patternWasFound )
Renders the detected chessboard corners.
@ -381,7 +378,7 @@ The function draws individual chessboard corners detected either as red circles
findChessboardCorners
-------------------------
.. c:function:: bool findChessboardCorners( const Mat& image, Size patternSize, vector<Point2f>& corners, int flags=CV_CALIB_CB_ADAPTIVE_THRESH+CV_CALIB_CB_NORMALIZE_IMAGE )
.. cpp:function:: bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners, int flags=CV_CALIB_CB_ADAPTIVE_THRESH+CV_CALIB_CB_NORMALIZE_IMAGE )
Finds the positions of internal corners of the chessboard.
@ -441,7 +438,7 @@ The function requires white space (like a square-thick border, the wider the bet
findCirclesGrid
-------------------
.. c:function:: bool findCirclesGrid( const Mat& image, Size patternSize, vector<Point2f>& centers, int flags=CALIB_CB_SYMMETRIC_GRID )
.. cpp:function:: bool findCirclesGrid( InputArray image, Size patternSize, vector<Point2f>& centers, int flags=CALIB_CB_SYMMETRIC_GRID )
Finds the centers in the grid of circles.
@ -483,7 +480,7 @@ The function requires white space (like a square-thick border, the wider the bet
solvePnP
------------
.. c:function:: void solvePnP( const Mat& objectPoints, const Mat& imagePoints, const Mat& cameraMatrix, const Mat& distCoeffs, Mat& rvec, Mat& tvec, bool useExtrinsicGuess=false )
.. cpp:function:: void solvePnP( InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess=false )
Finds an object pose from 3D-2D point correspondences.
@ -511,7 +508,7 @@ The function estimates the object pose given a set of object points, their corre
solvePnPRansac
------------------
.. c:function:: void solvePnPRansac( const Mat& objectPoints, const Mat& imagePoints, const Mat& cameraMatrix, const Mat& distCoeffs, Mat& rvec, Mat& tvec, bool useExtrinsicGuess=false, int iterationsCount = 100, float reprojectionError = 8.0, int minInliersCount = 100, vector<int>* inliers = NULL )
.. cpp: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 = None() )
Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
@ -546,9 +543,7 @@ The function estimates an object pose given a set of object points, their corres
findFundamentalMat
----------------------
.. c:function:: Mat findFundamentalMat( const Mat& points1, const Mat& points2, vector<uchar>& status, int method=FM_RANSAC, double param1=3., double param2=0.99 )
.. c:function:: Mat findFundamentalMat( const Mat& points1, const Mat& points2, int method=FM_RANSAC, double param1=3., double param2=0.99 )
.. cpp:function:: Mat findFundamentalMat( InputArray points1, InputArray points2, int method=FM_RANSAC, double param1=3., double param2=0.99, OutputArray mask=None() )
Calculates a fundamental matrix from the corresponding points in two images.
@ -610,11 +605,7 @@ corresponding to the specified points. It can also be passed to
findHomography
------------------
.. c:function:: Mat findHomography( const Mat& srcPoints, const Mat& dstPoints, Mat& status, int method=0, double ransacReprojThreshold=3 )
.. c:function:: Mat findHomography( const Mat& srcPoints, const Mat& dstPoints, vector<uchar>& status, int method=0, double ransacReprojThreshold=3 )
.. c:function:: Mat findHomography( const Mat& srcPoints, const Mat& dstPoints, int method=0, double ransacReprojThreshold=3 )
.. cpp:function:: Mat findHomography( InputArray srcPoints, InputArray dstPoints, int method=0, double ransacReprojThreshold=3, OutputArray mask=None() )
Finds a perspective transformation between two planes.
@ -688,41 +679,6 @@ See Also:
:ref:`WarpPerspective`,
:ref:`PerspectiveTransform`
.. index:: getDefaultNewCameraMatrix
.. index:: getDefaultNewCameraMatrix
.. _getDefaultNewCameraMatrix:
getDefaultNewCameraMatrix
-----------------------------
.. c:function:: Mat getDefaultNewCameraMatrix( const Mat& cameraMatrix, Size imgSize=Size(), bool centerPrincipalPoint=false )
Returns the default new camera matrix.
:param cameraMatrix: Input camera matrix.
:param imageSize: Camera view image size in pixels.
:param centerPrincipalPoint: Location of the principal point in the new camera matrix. The parameter indicates whether this location should be at the image center or not.
The function returns the camera matrix that is either an exact copy of the input ``cameraMatrix`` (when ``centerPrinicipalPoint=false`` ), or the modified one (when ``centerPrincipalPoint`` =true).
In the latter case, the new camera matrix will be:
.. math::
\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,
where
:math:`f_x` and
:math:`f_y` are
:math:`(0,0)` and
:math:`(1,1)` elements of ``cameraMatrix`` , respectively.
By default, the undistortion functions in OpenCV (see
:ref:`initUndistortRectifyMap`,
:ref:`undistort`) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.
.. index:: getOptimalNewCameraMatrix
@ -730,7 +686,7 @@ By default, the undistortion functions in OpenCV (see
getOptimalNewCameraMatrix
-----------------------------
.. c:function:: Mat getOptimalNewCameraMatrix( const Mat& cameraMatrix, const Mat& distCoeffs, Size imageSize, double alpha, Size newImageSize=Size(), Rect* validPixROI=0)
.. cpp:function:: Mat getOptimalNewCameraMatrix( InputArray cameraMatrix, InputArray distCoeffs, Size imageSize, double alpha, Size newImageSize=Size(), Rect* validPixROI=0)
Returns the new camera matrix based on the free scaling parameter.
@ -759,7 +715,7 @@ the optimal new camera matrix based on the free scaling parameter. By varying t
initCameraMatrix2D
----------------------
.. c:function:: Mat initCameraMatrix2D( const vector<vector<Point3f> >& objectPoints, const vector<vector<Point2f> >& imagePoints, Size imageSize, double aspectRatio=1.)
.. cpp:function:: Mat initCameraMatrix2D( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, double aspectRatio=1.)
Finds an initial camera matrix from 3D-2D point correspondences.
@ -774,62 +730,6 @@ initCameraMatrix2D
The function estimates and returns an initial camera matrix for the camera calibration process.
Currently, the function only supports planar calibration patterns, which are patterns where each object point has z-coordinate =0.
.. index:: initUndistortRectifyMap
.. _initUndistortRectifyMap:
initUndistortRectifyMap
---------------------------
.. c:function:: void initUndistortRectifyMap( const Mat& cameraMatrix, const Mat& distCoeffs, const Mat& R, const Mat& newCameraMatrix, Size size, int m1type, Mat& map1, Mat& map2 )
Computes the undistortion and rectification transformation map.
:param cameraMatrix: Input camera matrix :math:`A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}` .
:param distCoeffs: Input vector of distortion coefficients :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])` of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
:param R: Optional rectification transformation in the object space (3x3 matrix). ``R1`` or ``R2`` , computed by :ref:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is assumed.
:param newCameraMatrix: New camera matrix :math:`A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}` .
:param size: Undistorted image size.
:param m1type: Type of the first output map that can be ``CV_32FC1`` or ``CV_16SC2`` . See :ref:`convertMaps` for details.
:param map1: The first output map.
:param map2: The second output map.
The function computes the joint undistortion and rectification transformation and represents the result in the form of maps for
:ref:`Remap` . The undistorted image looks like original, as if it is captured with a camera using the camera matrix ``=newCameraMatrix`` and zero distortion. In case of a monocular camera, ``newCameraMatrix`` is usually equal to ``cameraMatrix`` , or it can be computed by
:ref:`GetOptimalNewCameraMatrix` for a better control over scaling. In case of a stereo camera, ``newCameraMatrix`` is normally set to ``P1`` or ``P2`` computed by
:ref:`StereoRectify` .
Also, this new camera is oriented differently in the coordinate space, according to ``R`` . That, for example, helps to align two heads of a stereo camera so that the epipolar lines on both images become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).
The function actually builds the maps for the inverse mapping algorithm that is used by
:ref:`Remap` . That is, for each pixel
:math:`(u, v)` in the destination (corrected and rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original image from camera). The following process is applied:
.. math::
\begin{array}{l} x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\{[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ x' \leftarrow X/W \\ y' \leftarrow Y/W \\ x" \leftarrow x' (1 + k_1 r^2 + k_2 r^4 + k_3 r^6) + 2p_1 x' y' + p_2(r^2 + 2 x'^2) \\ y" \leftarrow y' (1 + k_1 r^2 + k_2 r^4 + k_3 r^6) + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' \\ map_x(u,v) \leftarrow x" f_x + c_x \\ map_y(u,v) \leftarrow y" f_y + c_y \end{array}
where
:math:`(k_1, k_2, p_1, p_2[, k_3])` are the distortion coefficients.
In case of a stereo camera, this function is called twice: once for each camera head, after
:ref:`StereoRectify` , which in its turn is called after
:ref:`StereoCalibrate` . But if the stereo camera was not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using
:ref:`StereoRectifyUncalibrated` . For each camera, the function computes homography ``H`` as the rectification transformation in a pixel domain, not a rotation matrix ``R`` in 3D space. ``R`` can be computed from ``H`` as
.. math::
\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}
where ``cameraMatrix`` can be chosen arbitrarily.
.. index:: matMulDeriv
.. _matMulDeriv:
@ -837,7 +737,7 @@ where ``cameraMatrix`` can be chosen arbitrarily.
matMulDeriv
---------------
.. c:function:: void matMulDeriv( const Mat& A, const Mat& B, Mat& dABdA, Mat& dABdB )
.. cpp:function:: void matMulDeriv( InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB )
Computes partial derivatives of the matrix product for each multiplied matrix.
@ -860,9 +760,7 @@ The function computes partial derivatives of the elements of the matrix product
projectPoints
-----------------
.. c:function:: void projectPoints( const Mat& objectPoints, const Mat& rvec, const Mat& tvec, const Mat& cameraMatrix, const Mat& distCoeffs, vector<Point2f>& imagePoints )
.. c:function:: void projectPoints( const Mat& objectPoints, const Mat& rvec, const Mat& tvec, const Mat& cameraMatrix, const Mat& distCoeffs, vector<Point2f>& imagePoints, Mat& dpdrot, Mat& dpdt, Mat& dpdf, Mat& dpdc, Mat& dpddist, double aspectRatio=0 )
.. cpp:function:: void projectPoints( InputArray objectPoints, InputArray rvec, InputArray tvec, InputArray cameraMatrix, InputArray distCoeffs, OutputArray imagePoints, OutputArray dpdrot=None(), OutputArray dpdt=None(), OutputArray dpdf=None(), OutputArray dpdc=None(), OutputArray dpddist=None(), double aspectRatio=0 )
Projects 3D points to an image plane.
@ -912,7 +810,7 @@ By setting ``rvec=tvec=(0,0,0)`` or by setting ``cameraMatrix`` to a 3x3 identi
reprojectImageTo3D
----------------------
.. c:function:: void reprojectImageTo3D( const Mat& disparity, Mat& _3dImage, const Mat& Q, bool handleMissingValues=false )
.. cpp:function:: void reprojectImageTo3D( InputArray disparity, OutputArray _3dImage, InputArray Q, bool handleMissingValues=false )
Reprojects a disparity image to 3D space.
@ -941,9 +839,9 @@ The matrix ``Q`` can be an arbitrary
RQDecomp3x3
---------------
.. c:function:: void RQDecomp3x3( const Mat& M, Mat& R, Mat& Q )
.. cpp:function:: void RQDecomp3x3( InputArray M, OutputArray R, OutputArray Q )
.. c:function:: Vec3d RQDecomp3x3( const Mat& M, Mat& R, Mat& Q, Mat& Qx, Mat& Qy, Mat& Qz )
.. cpp:function:: Vec3d RQDecomp3x3( InputArray M, OutputArray R, OutputArray Q, OutputArray Qx, OutputArray Qy, OutputArray Qz )
Computes an RQ decomposition of 3x3 matrices.
@ -972,9 +870,7 @@ that could be used in OpenGL.
Rodrigues
-------------
.. c:function:: void Rodrigues(const Mat& src, Mat& dst)
.. c:function:: void Rodrigues(const Mat& src, Mat& dst, Mat& jacobian)
.. cpp:function:: void Rodrigues(InputArray src, OutputArray dst, OutputArray jacobian=None())
Converts a rotation matrix to a rotation vector or vice versa.
@ -1029,7 +925,7 @@ Class for computing stereo correspondence using the block matching algorithm ::
void init(int preset, int ndisparities=0, int SADWindowSize=21);
// computes the disparity for the two rectified 8-bit single-channel images.
// the disparity will be 16-bit signed (fixed-point) or 32-bit floating-point image of the same size as left.
void operator()( const Mat& left, const Mat& right, Mat& disparity, int disptype=CV_16S );
void operator()( InputArray left, InputArray right, OutputArray disparity, int disptype=CV_16S );
Ptr<CvStereoBMState> state;
};
@ -1045,7 +941,7 @@ The class is a C++ wrapper for the associated functions. In particular, ``Stereo
StereoBM::operator ()
-----------------------
.. c:function:: void StereoBM::operator()(const Mat& left, const Mat& right, Mat& disp, int disptype=CV_16S )
.. cpp:function:: void StereoBM::operator()(InputArray left, InputArray right, OutputArray disp, int disptype=CV_16S )
Computes disparity using the BM algorithm for a rectified stereo pair.
@ -1081,7 +977,7 @@ Class for computing stereo correspondence using the semi-global block matching a
bool fullDP=false);
virtual ~StereoSGBM();
virtual void operator()(const Mat& left, const Mat& right, Mat& disp);
virtual void operator()(InputArray left, InputArray right, OutputArray disp);
int minDisparity;
int numberOfDisparities;
@ -1113,9 +1009,9 @@ The class implements the modified H. Hirschmuller algorithm HH08 that differs fr
StereoSGBM::StereoSGBM
--------------------------
.. c:function:: StereoSGBM::StereoSGBM()
.. cpp:function:: StereoSGBM::StereoSGBM()
.. c: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)
.. cpp: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)
The constructor.
@ -1148,7 +1044,7 @@ The first constructor initializes ``StereoSGBM`` with all the default parameters
StereoSGBM::operator ()
-----------------------
.. c:function:: void StereoSGBM::operator()(const Mat& left, const Mat& right, Mat& disp)
.. cpp:function:: void StereoSGBM::operator()(InputArray left, InputArray right, OutputArray disp)
Computes disparity using the SGBM algorithm for a rectified stereo pair.
@ -1170,7 +1066,7 @@ The method is not constant, so you should not use the same ``StereoSGBM`` instan
stereoCalibrate
-------------------
.. c:function:: double stereoCalibrate( const vector<vector<Point3f> >& objectPoints, const vector<vector<Point2f> >& imagePoints1, const vector<vector<Point2f> >& imagePoints2, Mat& cameraMatrix1, Mat& distCoeffs1, Mat& cameraMatrix2, Mat& distCoeffs2, Size imageSize, Mat& R, Mat& T, Mat& E, Mat& F, TermCriteria term_crit = TermCriteria(TermCriteria::COUNT+ TermCriteria::EPS, 30, 1e-6), int flags=CALIB_FIX_INTRINSIC )
.. cpp: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 )
Calibrates the stereo camera.
@ -1254,13 +1150,10 @@ Similarly to :ref:`calibrateCamera` , the function minimizes the total re-projec
.. index:: stereoRectify
.. _stereoRectify:
stereoRectify
-----------------
.. c:function:: void stereoRectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, const Mat& cameraMatrix2, const Mat& distCoeffs2, Size imageSize, const Mat& R, const Mat& T, Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, int flags=CALIB_ZERO_DISPARITY )
.. c:function:: void stereoRectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, const Mat& cameraMatrix2, const Mat& distCoeffs2, Size imageSize, const Mat& R, const Mat& T, Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, double alpha, Size newImageSize=Size(), Rect* roi1=0, Rect* roi2=0, int flags=CALIB_ZERO_DISPARITY )
.. cpp: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 )
Computes rectification transforms for each head of a calibrated stereo camera.
@ -1339,7 +1232,7 @@ See below the screenshot from the ``stereo_calib.cpp`` sample. Some red horizont
stereoRectifyUncalibrated
-----------------------------
.. c:function:: bool stereoRectifyUncalibrated( const Mat& points1, const Mat& points2, const Mat& F, Size imgSize, Mat& H1, Mat& H2, double threshold=5 )
.. cpp:function:: bool stereoRectifyUncalibrated( InputArray points1, InputArray points2, InputArray F, Size imgSize, OutputArray H1, OutputArray H2, double threshold=5 )
Computes a rectification transform for an uncalibrated stereo camera.
@ -1364,85 +1257,3 @@ While the algorithm does not need to know the intrinsic parameters of the camera
:ref:`calibrateCamera` . Then, the images can be corrected using
:ref:`undistort` , or just the point coordinates can be corrected with
:ref:`undistortPoints` .
.. index:: undistort
.. _undistort:
undistort
-------------
.. c:function:: void undistort( const Mat& src, Mat& dst, const Mat& cameraMatrix, const Mat& distCoeffs, const Mat& newCameraMatrix=Mat() )
Transforms an image to compensate for lens distortion.
:param src: Input (distorted) image.
:param dst: Output (corrected) image that has the same size and type as ``src`` .
:param cameraMatrix: Input camera matrix :math:`A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}` .
:param distCoeffs: Input vector of distortion coefficients :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])` of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
:param newCameraMatrix: Camera matrix of the distorted image. By default, it is the same as ``cameraMatrix`` but you may additionally scale and shift the result by using a different matrix.
The function transforms an image to compensate radial and tangential lens distortion.
The function is simply a combination of
:ref:`InitUndistortRectifyMap` (with unity ``R`` ) and
:ref:`Remap` (with bilinear interpolation). See the former function for details of the transformation being performed.
Those pixels in the destination image, for which there is no correspondent pixels in the source image, are filled with zeros (black color).
A particular subset of the source image that will be visible in the corrected image can be regulated by ``newCameraMatrix`` . You can use
:ref:`GetOptimalNewCameraMatrix` to compute the appropriate ``newCameraMatrix`` depending on your requirements.
The camera matrix and the distortion parameters can be determined using
:ref:`calibrateCamera` . If the resolution of images is different from the resolution used at the calibration stage,
:math:`f_x, f_y, c_x` and
:math:`c_y` need to be scaled accordingly, while the distortion coefficients remain the same.
.. index:: undistortPoints
.. _undistortPoints:
undistortPoints
-------------------
.. c:function:: void undistortPoints( const Mat& src, vector<Point2f>& dst, const Mat& cameraMatrix, const Mat& distCoeffs, const Mat& R=Mat(), const Mat& P=Mat())
.. c:function:: void undistortPoints( const Mat& src, Mat& dst, const Mat& cameraMatrix, const Mat& distCoeffs, const Mat& R=Mat(), const Mat& P=Mat())
Computes the ideal point coordinates from the observed point coordinates.
:param src: Observed point coordinates, 1xN or Nx1 2-channel (CV _ 32FC2 or CV _ 64FC2).
:param dst: Output ideal point coordinates after undistortion and reverse perspective transformation.
:param cameraMatrix: Camera matrix :math:`\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}` .
:param distCoeffs: Input vector of distortion coefficients :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])` of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
:param R: Rectification transformation in the object space (3x3 matrix). ``R1`` or ``R2`` computed by :ref:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is used.
:param P: New camera matrix (3x3) or new projection matrix (3x4). ``P1`` or ``P2`` computed by :ref:`StereoRectify` can be passed here. If the matrix is empty, the identity new camera matrix is used.
The function is similar to
:ref:`undistort` and
:ref:`initUndistortRectifyMap` but it operates on a sparse set of points instead of a raster image. Also the function performs a reverse transformation to
:ref:`projectPoints` . In case of a 3D object, it does not reconstruct its 3D coordinates, but for a planar object, it does, up to a translation vector, if the proper ``R`` is specified. ::
// (u,v) is the input point, (u', v') is the output point
// camera_matrix=[fx 0 cx; 0 fy cy; 0 0 1]
// P=[fx' 0 cx' tx; 0 fy' cy' ty; 0 0 1 tz]
x" = (u - cx)/fx
y" = (v - cy)/fy
(x',y') = undistort(x",y",dist_coeffs)
[X,Y,W]T = R*[x' y' 1]T
x = X/W, y = Y/W
u' = x*fx' + cx'
v' = y*fy' + cy',
where ``undistort()`` is an approximate iterative algorithm that estimates the normalized original point coordinates out of the normalized distorted point coordinates ("normalized" means that the coordinates do not depend on the camera matrix).
The function can be used for both a stereo camera head or a monocular camera (when R is
empty
).

205
modules/core/doc/basic_structures.rst
View File

@ -10,6 +10,8 @@ Basic Structures
DataType
--------
.. cpp:class:: DataType
Template "trait" class for other OpenCV primitive data types ::
template<typename _Tp> class DataType
@ -36,7 +38,7 @@ Template "trait" class for other OpenCV primitive data types ::
};
};
The template class ``DataType`` is a descriptive class for OpenCV primitive data types and other types that comply with the following definition. A primitive OpenCV data type is one of ``unsigned char``, ``bool``, ``signed char``, ``unsigned short``, ``signed short``, ``int``, ``float``, ``double`` or a tuple of values of one of these types, where all the values in the tuple have the same type. Any primitive type from the list can be defined by an identifier in the form ``CV_<bit-depth>{U|S|F}C<number_of_channels>``, for example: ``uchar`` ~ ``CV_8UC1``, 3-element floating-point tuple ~ ``CV_32FC3``, and so on. A universal OpenCV structure that is able to store a single instance of such a primitive data type is
The template class ``DataType`` is a descriptive class for OpenCV primitive data types and other types that comply with the following definition. A primitive OpenCV data type is one of ``unsigned char``, ``bool``, ``signed char``, ``unsigned short``, ``signed short``, ``int``, ``float``, ``double`` or a tuple of values of one of these types, where all the values in the tuple have the same type. Any primitive type from the list can be defined by an identifier in the form ``CV_<bit-depth>{U|S|F}C(<number_of_channels>)``, for example: ``uchar`` ~ ``CV_8UC1``, 3-element floating-point tuple ~ ``CV_32FC3``, and so on. A universal OpenCV structure that is able to store a single instance of such a primitive data type is
:ref:`Vec`. Multiple instances of such a type can be stored in a ``std::vector``, ``Mat``, ``Mat_``, ``SparseMat``, ``SparseMat_``, or any other container that is able to store ``Vec`` instances.
The ``DataType`` class is basically used to provide a description of such primitive data types without adding any fields or methods to the corresponding classes (and it is actually impossible to add anything to primitive C/C++ data types). This technique is known in C++ as class traits. It is not ``DataType`` itself that is used but its specialized versions, such as: ::
@ -78,6 +80,8 @@ So, such traits are used to tell OpenCV which data type you are working with, ev
Point\_
-------
.. cpp:class:: Point_
Template class for 2D points ::
template<typename _Tp> class Point_
@ -143,6 +147,8 @@ Example: ::
Point3\_
--------
.. cpp:class:: Point3_
Template class for 3D points ::
template<typename _Tp> class Point3_
@ -174,7 +180,7 @@ The class represents a 3D point specified by its coordinates
:math:`z` .
An instance of the class is interchangeable with the C structure ``CvPoint2D32f`` . Similarly to ``Point_`` , the coordinates of 3D points can be converted to another type. The vector arithmetic and comparison operations are also supported.
The following types of?? aliases are available: ::
The following ``Point3_<>`` aliases are available: ::
typedef Point3_<int> Point3i;
typedef Point3_<float> Point3f;
@ -185,6 +191,8 @@ The following types of?? aliases are available: ::
Size\_
------
.. cpp:class:: Size_
Template class for specfying an image or rectangle size ::
template<typename _Tp> class Size_
@ -214,7 +222,7 @@ Template class for specfying an image or rectangle size ::
The class ``Size_`` is similar to ``Point_`` except that the two members are called ``width`` and ``height`` instead of ``x`` and ``y`` . The structure can be converted to and from the old OpenCV structures
``CvSize`` and ``CvSize2D32f`` . The same set of arithmetic and comparison operations as for ``Point_`` is available.
OpenCV defines the following types of?? aliases: ::
OpenCV defines the following ``Size_<>`` aliases: ::
typedef Size_<int> Size2i;
typedef Size2i Size;
@ -225,6 +233,8 @@ OpenCV defines the following types of?? aliases: ::
Rect\_
------
.. cpp:class:: Rect_
Template class for 2D rectangles ::
template<typename _Tp> class Rect_
@ -314,17 +324,19 @@ This is an example how the partial ordering on rectangles can be established (re
}
For your convenience, the following type of aliases?? is available: ::
For your convenience, the ``Rect_<>`` alias is available: ::
typedef Rect_<int> Rect;
.. index:: _RotatedRect
.. _RotatedRect:
RotatedRect
-----------
.. cpp:class:: RotatedRect
Template class for rotated rectangles ::
class RotatedRect
@ -356,7 +368,7 @@ The class ``RotatedRect`` replaces the old ``CvBox2D`` and is fully compatible w
TermCriteria
------------
.. c:type:: TermCriteria
.. cpp:class:: TermCriteria
Template class defining termination criteria for iterative algorithms ::
@ -393,6 +405,8 @@ The class ``TermCriteria`` replaces the old ``CvTermCriteria`` and is fully comp
Matx
----
.. cpp:class:: Matx
Template class for small matrices ::
template<typename T, int m, int n> class Matx
@ -428,9 +442,9 @@ Template class for small matrices ::
The class represents small matrices whose type and size are known at compilation time. If you need a more flexible type, use
:ref:`Mat` . The elements of the matrix ``M`` are accessible using the ``M(i,j)`` notation. Most of the common matrix operations (see also
:cpp:class:`Mat` . The elements of the matrix ``M`` are accessible using the ``M(i,j)`` notation. Most of the common matrix operations (see also
:ref:`MatrixExpressions` ) are available. To do an operation on ``Matx`` that is not implemented, you can easily convert the matrix to
:ref:`Mat` and backwards. ::
:cpp:class:`Mat` and backwards. ::
Matx33f m(1, 2, 3,
4, 5, 6,
@ -438,12 +452,14 @@ The class represents small matrices whose type and size are known at compilation
cout << sum(Mat(m*m.t())) << endl;
.. index:: Vec
.. _Vec:
Vec
---
.. cpp:class:: Vec
Template class for short numerical vectors ::
template<typename T, int cn> class Vec : public Matx<T, cn, 1>
@ -487,7 +503,7 @@ Template class for short numerical vectors ::
* ``v1 == v2, v1 != v2`` * ``norm(v1)`` (:math:`L_2`-norm)
The ``Vec`` class is commonly used to describe pixel types of multi-channel arrays. See
:ref:`Mat_`?? for details.
:ref:`Mat_` for details.
.. index:: Scalar
@ -496,6 +512,8 @@ The ``Vec`` class is commonly used to describe pixel types of multi-channel arra
Scalar\_
--------
.. cpp:class:: Scalar_
Template class for a 4-element vector ::
template<typename _Tp> class Scalar_ : public Vec<_Tp, 4>
@ -527,6 +545,8 @@ The template class ``Scalar_`` and its double-precision instantiation ``Scalar``
Range
-----
.. cpp:class:: Range
Template class specifying a continuous subsequence (slice) of a sequence ::
class Range
@ -568,6 +588,8 @@ The static method ``Range::all()`` returns a special variable that means "the wh
Ptr
---
.. cpp:class:: Ptr
Template class for smart reference-counting pointers ::
template<typename _Tp> class Ptr
@ -623,7 +645,7 @@ This class provides the following options:
Default constructor, copy constructor, and assignment operator for an arbitrary C++ class or a C structure. For some objects, like files, windows, mutexes, sockets, and others, a copy constructor or an assignment operator are difficult to define. For some other objects, like complex classifiers in OpenCV, copy constructors are absent and not easy to implement. Finally, some of complex OpenCV and your own data structures may be written in C. However, copy constructors and default constructors can simplify programming a lot. Besides, they are often required (for example, by STL containers). By wrapping a pointer to such a complex object ``TObj`` to ``Ptr<TObj>`` , you automatically get all of the necessary constructors and the assignment operator.
*
Speed-up for the above-mentioned operations regardless of the data size, similar to "O(1)" operations.?? Indeed, while some structures, like ``std::vector`` , provide a copy constructor and an assignment operator, the operations may take a considerable amount of time if the data structures are large. But if the structures are put into ``Ptr<>`` , the overhead is small and independent of the data size.
*O(1)* complexity of the above-mentioned operations. Indeed, while some structures, like ``std::vector``, provide a copy constructor and an assignment operator, the operations may take a considerable amount of time if the data structures are large. But if the structures are put into ``Ptr<>`` , the overhead is small and independent of the data size.
*
Automatic destruction, even for C structures. See the example below with ``FILE*`` .
@ -654,12 +676,10 @@ However, if the object is deallocated in a different way, the specialized method
.. index:: Mat
.. _Mat:
Mat
---
.. c:type:: Mat
.. cpp:class:: Mat
OpenCV C++ n-dimensional dense array class ::
@ -818,9 +838,9 @@ There are many different ways to create a ``Mat`` object. The most popular optio
..
??is the indent required here? does it apply to step 2 but not to the whole bulleted item??Partial yet very common cases of this *user-allocated data* case are conversions from ``CvMat`` and ``IplImage`` to ``Mat``. For this purpose, there are special constructors taking pointers to ``CvMat`` or ``IplImage`` and the optional flag indicating whether to copy the data or not.
Partial yet very common cases of this *user-allocated data* case are conversions from ``CvMat`` and ``IplImage`` to ``Mat``. For this purpose, there are special constructors taking pointers to ``CvMat`` or ``IplImage`` and the optional flag indicating whether to copy the data or not.
Backward conversion from ``Mat`` to ``CvMat`` or ``IplImage`` is provided via cast operators ``Mat::operator CvMat() const`` and ``Mat::operator IplImage()``. The operators do NOT copy the data.
Backward conversion from ``Mat`` to ``CvMat`` or ``IplImage`` is provided via cast operators ``Mat::operator CvMat() const`` and ``Mat::operator IplImage()``. The operators do NOT copy the data.
::
@ -967,8 +987,6 @@ Below is the formal description of the ``Mat`` methods.
.. index:: Mat::Mat
.. _Mat::Mat:
Mat::Mat
------------
.. cpp:function:: Mat::Mat()
@ -1078,7 +1096,7 @@ Mat::operator =
:param m: The assigned, right-hand-side matrix. Matrix assignment is O(1) operation, that is, no data is copied. Instead, the data is shared and the reference counter, if any, is incremented. Before assigning new data, the old data is de-referenced via :ref:`Mat::release` .
:param expr: The assigned matrix expression object. As opposite to the first form of assignment operation, the second form can reuse already allocated matrix if it has the right size and type to fit the matrix expression result. It is automatically handled by the real function that the matrix expressions is expanded to. For example, ``C=A+B`` is expanded to ``add(A, B, C)`` , and :func:`add` takes care of automatic ``C`` reallocation.
:param expr: The assigned matrix expression object. As opposite to the first form of assignment operation, the second form can reuse already allocated matrix if it has the right size and type to fit the matrix expression result. It is automatically handled by the real function that the matrix expressions is expanded to. For example, ``C=A+B`` is expanded to ``add(A, B, C)`` , and :cpp:func:`add` takes care of automatic ``C`` reallocation.
:param s: The scalar assigned to each matrix element. The matrix size or type is not changed.
@ -1097,8 +1115,6 @@ The cast operator should not be called explicitly. It is used internally by the
.. index:: Mat::row
.. _Mat::row:
Mat::row
------------
.. cpp:function:: Mat Mat::row(int i) const
@ -1138,8 +1154,6 @@ This is because ``A.row(i)`` forms a temporary header, which is further assigned
.. index:: Mat::col
.. _Mat::col:
Mat::col
------------
.. cpp:function:: Mat Mat::col(int j) const
@ -1153,8 +1167,6 @@ The method makes a new header for the specified matrix column and returns it. Th
.. index:: Mat::rowRange
.. _Mat::rowRange:
Mat::rowRange
-----------------
.. cpp:function:: Mat Mat::rowRange(int startrow, int endrow) const
@ -1167,16 +1179,14 @@ Mat::rowRange
:param endrow: A 0-based ending index of the row span.
:param r: The :func:`Range` structure containing both the start and the end indices.
:param r: The :cpp:func:`Range` structure containing both the start and the end indices.
The method makes a new header for the specified row span of the matrix. Similarly to
:func:`Mat::row` and
:func:`Mat::col` , this is an O(1) operation.
:cpp:func:`Mat::row` and
:cpp:func:`Mat::col` , this is an O(1) operation.
.. index:: Mat::colRange
.. _Mat::colRange:
Mat::colRange
-----------------
.. cpp:function:: Mat Mat::colRange(int startcol, int endcol) const
@ -1189,16 +1199,14 @@ Mat::colRange
:param endcol: A 0-based ending index of the column span.
:param r: The :func:`Range` structure containing both the start and the end indices.
:param r: The :cpp:func:`Range` structure containing both the start and the end indices.
The method makes a new header for the specified column span of the matrix. Similarly to
:func:`Mat::row` and
:func:`Mat::col` , this is an O(1) operation.
:cpp:func:`Mat::row` and
:cpp:func:`Mat::col` , this is an O(1) operation.
.. index:: Mat::diag
.. _Mat::diag:
Mat::diag
-------------
.. cpp:function:: Mat Mat::diag(int d) const
@ -1218,13 +1226,11 @@ Mat::diag
:param matD: A single-column matrix that forms the diagonal matrix.
The method makes a new header for the specified matrix diagonal. The new matrix is represented as a single-column matrix. Similarly to
:func:`Mat::row` and
:func:`Mat::col` , this is an O(1) operation.
:cpp:func:`Mat::row` and
:cpp:func:`Mat::col` , this is an O(1) operation.
.. index:: Mat::clone
.. _Mat::clone:
Mat::clone
--------------
.. cpp:function:: Mat Mat::clone() const
@ -1259,8 +1265,6 @@ When the operation mask is specified, and the ``Mat::create`` call shown above r
.. index:: Mat::convertTo
.. _Mat::convertTo:
Mat::convertTo
------------------
.. cpp:function:: void Mat::convertTo( Mat& m, int rtype, double alpha=1, double beta=0 ) const
@ -1300,7 +1304,7 @@ This is an internally used method called by the
Mat::setTo
--------------
.. c:function:: Mat& Mat::setTo(const Scalar& s, const Mat& mask=Mat())
.. cpp:function:: Mat& Mat::setTo(const Scalar& s, const Mat& mask=Mat())
Sets all or some of the array elements to the specified value.
@ -1327,7 +1331,7 @@ The method makes a new matrix header for ``*this`` elements. The new matrix may
*
No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of rows, or the operation changes the indices of elements' row in some other way, the matrix must be continuous. See
:func:`Mat::isContinuous` .
:cpp:func:`Mat::isContinuous` .
For example, if there is a set of 3D points stored as an STL vector, and you want to represent the points as a ``3xN`` matrix, do the following: ::
@ -1471,7 +1475,7 @@ Mat::ones
:param type: Created matrix type.
The method returns a Matlab-style 1's array initializer, similarly to
:func:`Mat::zeros` . Note that using this method you can initialize an array with an arbitrary value, using the following Matlab idiom: ::
:cpp:func:`Mat::zeros` . Note that using this method you can initialize an array with an arbitrary value, using the following Matlab idiom: ::
Mat A = Mat::ones(100, 100, CV_8U)*3; // make 100x100 matrix filled with 3.
@ -1496,7 +1500,7 @@ Mat::eye
:param type: Created matrix type.
The method returns a Matlab-style identity matrix initializer, similarly to
:func:`Mat::zeros` . Similarly to ``Mat::ones`` , you can use a scale operation to create a scaled identity matrix efficiently: ::
:cpp:func:`Mat::zeros` . Similarly to ``Mat::ones`` , you can use a scale operation to create a scaled identity matrix efficiently: ::
// make a 4x4 diagonal matrix with 0.1's on the diagonal.
Mat A = Mat::eye(4, 4, CV_32F)*0.1;
@ -1535,7 +1539,7 @@ This is one of the key ``Mat`` methods. Most new-style OpenCV functions and meth
#.
Otherwise, de-reference the previous data by calling
:func:`Mat::release` #.
:cpp:func:`Mat::release` #.
initialize the new header
#.
@ -1564,8 +1568,6 @@ because ``cvtColor`` , as well as the most of OpenCV functions, calls ``Mat::cre
.. index:: Mat::addref
.. _Mat::addref:
Mat::addref
---------------
.. cpp:function:: void Mat::addref()
@ -1573,12 +1575,10 @@ Mat::addref
Increments the reference counter.
The method increments the reference counter associated with the matrix data. If the matrix header points to an external data set (see
:func:`Mat::Mat` ), the reference counter is NULL, and the method has no effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It is called implicitly by the matrix assignment operator. The reference counter increment is an atomic operation on the platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in different threads.
:cpp:func:`Mat::Mat` ), the reference counter is NULL, and the method has no effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It is called implicitly by the matrix assignment operator. The reference counter increment is an atomic operation on the platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in different threads.
.. index:: Mat::release
.. _Mat::release:
Mat::release
----------------
.. cpp:function:: void Mat::release()
@ -1586,14 +1586,12 @@ Mat::release
Decrements the reference counter and deallocates the matrix if needed.
The method decrements the reference counter associated with the matrix data. When the reference counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers are set to NULL's. If the matrix header points to an external data set (see
:func:`Mat::Mat` ), the reference counter is NULL, and the method has no effect in this case.
:cpp:func:`Mat::Mat` ), the reference counter is NULL, and the method has no effect in this case.
This method can be called manually to force the matrix data deallocation. But since this method is automatically called in the destructor, or by any other method that changes the data pointer, it is usually not needed. The reference counter decrement and check for 0 is an atomic operation on the platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in different threads.
.. index:: Mat::resize
.. _Mat::resize:
Mat::resize
---------------
.. cpp:function:: void Mat::resize( size_t sz ) const
@ -1606,12 +1604,10 @@ The method changes the number of matrix rows. If the matrix is reallocated, the
.. index:: Mat::push_back
.. _Mat::push_back:
Mat::push_back
--------------
.. c:function:: template<typename T> void Mat::push_back(const T& elem)
.. c:function:: template<typename T> void Mat::push_back(const Mat_<T>& elem)
.. cpp:function:: template<typename T> void Mat::push_back(const T& elem)
.. cpp:function:: template<typename T> void Mat::push_back(const Mat_<T>& elem)
Adds elements to the bottom of the matrix.
@ -1625,7 +1621,7 @@ The methods add one or more elements to the bottom of the matrix. They emulate t
Mat::pop_back
-------------
.. c:function:: template<typename T> void Mat::pop_back(size_t nelems=1)
.. cpp:function:: template<typename T> void Mat::pop_back(size_t nelems=1)
Removes elements from the bottom of the matrix.
@ -1648,7 +1644,7 @@ Mat::locateROI
:param ofs: An output parameter that contains an offset of ``*this`` inside the whole matrix.
After you extracted a submatrix from a matrix using
:func:`Mat::row`,:func:`Mat::col`,:func:`Mat::rowRange`,:func:`Mat::colRange` , and others, the resultant submatrix will point just to the part of the original big matrix. However, each submatrix contains some information (represented by ``datastart`` and ``dataend`` fields) that helps reconstruct the original matrix size and the position of the extracted submatrix within the original matrix. The method ``locateROI`` does exactly that.
:cpp:func:`Mat::row`,:cpp:func:`Mat::col`,:cpp:func:`Mat::rowRange`,:cpp:func:`Mat::colRange` , and others, the resultant submatrix will point just to the part of the original big matrix. However, each submatrix contains some information (represented by ``datastart`` and ``dataend`` fields) that helps reconstruct the original matrix size and the position of the extracted submatrix within the original matrix. The method ``locateROI`` does exactly that.
.. index:: Mat::adjustROI
@ -1669,7 +1665,7 @@ Mat::adjustROI
:param dright: The shift of the right submatrix boundary to the right.
The method is complimentary to
:func:`Mat::locateROI` . Indeed, the typical use of these functions is to determine the submatrix position within the parent matrix and then shift the position somehow. Typically, it can be required for filtering operations when pixels outside of the ROI should be taken into account. When all the method parameters are positive, the ROI needs to grow in all directions by the specified amount, for example: ::
:cpp:func:`Mat::locateROI` . Indeed, the typical use of these functions is to determine the submatrix position within the parent matrix and then shift the position somehow. Typically, it can be required for filtering operations when pixels outside of the ROI should be taken into account. When all the method parameters are positive, the ROI needs to grow in all directions by the specified amount, for example: ::
A.adjustROI(2, 2, 2, 2);
@ -1679,10 +1675,10 @@ In this example, the matrix size is increased by 4 elements in each direction. T
It is your responsibility to make sure ``adjustROI`` does not cross the parent matrix boundary. If it does, the function signals an error.
The function is used internally by the OpenCV filtering functions, like
:func:`filter2D` , morphological operations, and so on.
:cpp:func:`filter2D` , morphological operations, and so on.
See Also
:func:`copyMakeBorder`
:cpp:func:`copyMakeBorder`
.. index:: Mat::operator()
@ -1707,14 +1703,14 @@ Mat::operator()
:param ranges: The array of selected ranges along each array dimension.
The operators make a new header for the specified sub-array of ``*this`` . They are the most generalized forms of
:func:`Mat::row`,:func:`Mat::col`,:func:`Mat::rowRange`, and
:func:`Mat::colRange` . For example, ``A(Range(0, 10), Range::all())`` is equivalent to ``A.rowRange(0, 10)`` . Similarly to all of the above, the operators are O(1) operations, that is, no matrix data is copied.
:cpp:func:`Mat::row`,:cpp:func:`Mat::col`,:cpp:func:`Mat::rowRange`, and
:cpp:func:`Mat::colRange` . For example, ``A(Range(0, 10), Range::all())`` is equivalent to ``A.rowRange(0, 10)`` . Similarly to all of the above, the operators are O(1) operations, that is, no matrix data is copied.
.. index:: Mat::operator CvMat
Mat::operator CvMat
-----------------------
.. cpp:function:: Mat::operator CvMat(void) const
.. cpp:function:: Mat::operator CvMat() const
Creates the ``CvMat`` header for the matrix.
@ -1733,7 +1729,7 @@ where ``mycvOldFunc`` is a function written to work with OpenCV 1.x data structu
Mat::operator IplImage
--------------------------
.. cpp:function:: Mat::operator IplImage(void) const
.. cpp:function:: Mat::operator IplImage() const
Creates the ``IplImage`` header for the matrix.
@ -1745,7 +1741,7 @@ The operator creates the ``IplImage`` header for the matrix without copying the
Mat::total
--------------
.. cpp:function:: size_t Mat::total(void) const
.. cpp:function:: size_t Mat::total() const
Returns the total number of array elements.
@ -1757,13 +1753,13 @@ The method returns the number of array elements (a number of pixels if the array
Mat::isContinuous
---------------------
.. cpp:function:: bool Mat::isContinuous(void) const
.. cpp:function:: bool Mat::isContinuous() const
Reports whether the matrix is continuous or not.
The method returns ``true`` if the matrix elements are stored continuously - without gaps in the end of each row. Otherwise, it returns ``false``. Obviously, ``1x1`` or ``1xN`` matrices are always continuous. Matrices created with
:func:`Mat::create` are always continuous. But if you extract a part of the matrix using
:func:`Mat::col`,:func:`Mat::diag` , and so on, or constructed a matrix header for externally allocated data, such matrices may no longer have this property.
:cpp:func:`Mat::create` are always continuous. But if you extract a part of the matrix using
:cpp:func:`Mat::col`,:cpp:func:`Mat::diag` , and so on, or constructed a matrix header for externally allocated data, such matrices may no longer have this property.
The continuity flag is stored as a bit in the ``Mat::flags`` field and is computed automatically when you construct a matrix header. Thus, the continuity check is a very fast operation, though it could be, in theory, done as following: ::
@ -1822,8 +1818,8 @@ The method is used in quite a few of OpenCV functions. The point is that element
This trick, while being very simple, can boost performance of a simple element-operation by 10-20 percents, especially if the image is rather small and the operation is quite simple.
Also, note that there is another OpenCV idiom in this function: a call of
:func:`Mat::create` for the destination array instead of checking that it already has the proper size and type. And while the newly allocated arrays are always continuous, we still check the destination array, because
:func:`create` does not always allocate a new matrix.
:cpp:func:`Mat::create` for the destination array instead of checking that it already has the proper size and type. And while the newly allocated arrays are always continuous, we still check the destination array, because
:cpp:func:`create` does not always allocate a new matrix.
.. index:: Mat::elemSize
@ -1831,7 +1827,7 @@ Also, note that there is another OpenCV idiom in this function: a call of
Mat::elemSize
-----------------
.. cpp:function:: size_t Mat::elemSize(void) const
.. cpp:function:: size_t Mat::elemSize() const
Returns the matrix element size in bytes.
@ -1843,7 +1839,7 @@ The method returns the matrix element size in bytes. For example, if the matrix
Mat::elemSize1
------------------
.. cpp:function:: size_t Mat::elemSize1(void) const
.. cpp:function:: size_t Mat::elemSize1() const
Returns the size of each matrix element channel in bytes.
@ -1855,7 +1851,7 @@ The method returns the matrix element channel size in bytes, that is, it ignores
Mat::type
-------------
.. cpp:function:: int Mat::type(void) const
.. cpp:function:: int Mat::type() const
Returns a matrix element type.
@ -1867,7 +1863,7 @@ The method returns a matrix element type. This is an id, compatible with the ``C
Mat::depth
--------------
.. cpp:function:: int Mat::depth(void) const
.. cpp:function:: int Mat::depth() const
Returns the matrix element depth.
@ -1893,7 +1889,7 @@ The method returns the matrix element depth id (the type of each individual chan
Mat::channels
-----------------
.. cpp:function:: int Mat::channels(void) const
.. cpp:function:: int Mat::channels() const
Returns the number of matrix channels.
@ -1905,20 +1901,18 @@ The method returns the number of matrix channels.
Mat::step1
--------------
.. cpp:function:: size_t Mat::step1(void) const
.. cpp:function:: size_t Mat::step1() const
Returns a normalized step.
The method returns a matrix step divided by
:func:`Mat::elemSize1()` . It can be useful to quickly access an arbitrary matrix element.
:cpp:func:`Mat::elemSize1()` . It can be useful to quickly access an arbitrary matrix element.
.. index:: Mat::size
.. _Mat::size:
Mat::size
-------------
.. cpp:function:: Size Mat::size(void) const
.. cpp:function:: Size Mat::size() const
Returns a matrix size.
@ -1930,7 +1924,7 @@ The method returns a matrix size: ``Size(cols, rows)`` .
Mat::empty
--------------
.. cpp:function:: bool Mat::empty(void) const
.. cpp:function:: bool Mat::empty() const
Returns ``true`` if the array has no elemens.
@ -1942,20 +1936,20 @@ The method returns ``true`` if ``Mat::total()`` is 0 or if ``Mat::data`` is NULL
Mat::ptr
------------
.. c:function:: uchar* Mat::ptr(int i=0)
.. cpp:function:: uchar* Mat::ptr(int i=0)
.. c:function:: const uchar* Mat::ptr(int i=0) const
.. cpp:function:: const uchar* Mat::ptr(int i=0) const
.. c:function:: template<typename _Tp> _Tp* Mat::ptr(int i=0)
.. cpp:function:: template<typename _Tp> _Tp* Mat::ptr(int i=0)
.. c:function:: template<typename _Tp> const _Tp* Mat::ptr(int i=0) const
.. cpp:function:: template<typename _Tp> const _Tp* Mat::ptr(int i=0) const
Returns a pointer to the specified matrix row.
:param i: A 0-based row index.
The methods return ``uchar*`` or typed pointer to the specified matrix row. See the sample in
:func:`Mat::isContinuous` () to know how to use these methods.
:cpp:func:`Mat::isContinuous` () to know how to use these methods.
.. index:: Mat::at
@ -1963,25 +1957,25 @@ The methods return ``uchar*`` or typed pointer to the specified matrix row. See
Mat::at
-----------
.. c:function:: template<typename T> T& Mat::at(int i) const
.. cpp:function:: template<typename T> T& Mat::at(int i) const
.. c:function:: template<typename T> const T& Mat::at(int i) const
.. cpp:function:: template<typename T> const T& Mat::at(int i) const
.. c:function:: template<typename T> T& Mat::at(int i, int j)
.. cpp:function:: template<typename T> T& Mat::at(int i, int j)
.. c:function:: template<typename T> const T& Mat::at(int i, int j) const
.. cpp:function:: template<typename T> const T& Mat::at(int i, int j) const
.. c:function:: template<typename T> T& Mat::at(Point pt)
.. cpp:function:: template<typename T> T& Mat::at(Point pt)
.. c:function:: template<typename T> const T& Mat::at(Point pt) const
.. cpp:function:: template<typename T> const T& Mat::at(Point pt) const
.. c:function:: template<typename T> T& Mat::at(int i, int j, int k)
.. cpp:function:: template<typename T> T& Mat::at(int i, int j, int k)
.. c:function:: template<typename T> const T& Mat::at(int i, int j, int k) const
.. cpp:function:: template<typename T> const T& Mat::at(int i, int j, int k) const
.. c:function:: template<typename T> T& Mat::at(const int* idx)
.. cpp:function:: template<typename T> T& Mat::at(const int* idx)
.. c:function:: template<typename T> const T& Mat::at(const int* idx) const
.. cpp:function:: template<typename T> const T& Mat::at(const int* idx) const
Returns a reference to the specified array element.
@ -2009,7 +2003,7 @@ Here is an example of initialization of a Hilbert matrix: ::
Mat::begin
--------------
.. c:function:: template<typename _Tp> MatIterator_<_Tp> Mat::begin() template<typename _Tp> MatConstIterator_<_Tp> Mat::begin() const
.. cpp:function:: template<typename _Tp> MatIterator_<_Tp> Mat::begin() template<typename _Tp> MatConstIterator_<_Tp> Mat::begin() const
Returns the matrix iterator and sets it to the first matrix element..
@ -2051,7 +2045,8 @@ The methods return the matrix read-only or read-write iterators. The use of matr
Mat::end
------------
.. c:function:: template<typename _Tp> MatIterator_<_Tp> Mat::end() template<typename _Tp> MatConstIterator_<_Tp> Mat::end() const
.. cpp:function:: template<typename _Tp> MatIterator_<_Tp> Mat::end()
.. cpp:function:: template<typename _Tp> MatConstIterator_<_Tp> Mat::end() const
Returns the matrix iterator and sets it to the after-last matrix element.
@ -2178,11 +2173,13 @@ Here is an example of how you can compute a normalized and thresholded 3D color
}
.. _SparseMat:
.. index:: SparseMat
SparseMat
---------
.. cpp:class:: SparseMat
Sparse n-dimensional array. ::
class SparseMat
@ -2459,9 +2456,13 @@ The class ``SparseMat`` represents multi-dimensional sparse numerical arrays. Su
..
.. index:: SparseMat\_
SparseMat\_
-----------
.. cpp:class:: SparseMat
Template sparse n-dimensional array class derived from
:ref:`SparseMat` ::

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

@ -10,7 +10,7 @@ Clustering
kmeans
------
.. c:function:: double kmeans( const Mat& samples, int clusterCount, Mat& labels, TermCriteria termcrit, int attempts, int flags, Mat* centers )
.. cpp:function:: double kmeans( InputArray samples, int clusterCount, InputOutputArray labels, TermCriteria termcrit, int attempts, int flags, OutputArray centers=None() )
Finds centers of clusters and groups input samples around the clusters.
@ -57,9 +57,9 @@ attempts to 1, initialize labels each time using a custom algorithm, pass them w
partition
-------------
.. c:function:: template<typename _Tp, class _EqPredicate> int
.. cpp:function:: template<typename _Tp, class _EqPredicate> int
.. c:function:: partition( const vector<_Tp>& vec, vector<int>& labels, _EqPredicate predicate=_EqPredicate())
.. cpp:function:: partition( const vector<_Tp>& vec, vector<int>& labels, _EqPredicate predicate=_EqPredicate())
Splits an element set into equivalency classes.

68
modules/core/doc/drawing_functions.rst
View File

@ -10,7 +10,7 @@ with ``CV_RGB`` or the :ref:`Scalar` constructor
) for color
images and brightness for grayscale images. For color images, the channel ordering
is normally *Blue, Green, Red*.
This is what :func:`imshow`, :func:`imread`, and :func:`imwrite` expect.
This is what :cpp:func:`imshow`, :cpp:func:`imread`, and :cpp:func:`imwrite` expect.
So, if you form a color using the
:ref:`Scalar` constructor, it should look like:
@ -19,7 +19,7 @@ So, if you form a color using the
\texttt{Scalar} (blue \_ component, green \_ component, red \_ component[, alpha \_ component])
If you are using your own image rendering and I/O functions, you can use any channel ordering. The drawing functions process each channel independently and do not depend on the channel order or even on the used color space. The whole image can be converted from BGR to RGB or to a different color space using
:func:`cvtColor` .
:cpp:func:`cvtColor` .
If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means that the coordinates can be passed as fixed-point numbers encoded as integers. The number of fractional bits is specified by the ``shift`` parameter and the real point coordinates are calculated as
:math:`\texttt{Point}(x,y)\rightarrow\texttt{Point2f}(x*2^{-shift},y*2^{-shift})` . This feature is especially effective when rendering antialiased shapes.
@ -32,7 +32,7 @@ The functions do not support alpha-transparency when the target image is 4-chann
circle
----------
.. c:function:: void circle(Mat& img, Point center, int radius, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
.. cpp:function:: void circle(Mat& img, Point center, int radius, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
Draws a circle.
@ -46,7 +46,7 @@ circle
:param thickness: Thickness of the circle outline if positive. Negative thickness means that a filled circle is to be drawn.
:param lineType: Type of the circle boundary. See :func:`line` description.
:param lineType: Type of the circle boundary. See :cpp:func:`line` description.
:param shift: Number of fractional bits in the center's coordinates and in the radius value.
@ -56,9 +56,9 @@ The function ``circle`` draws a simple or filled circle with a given center and
clipLine
------------
.. c:function:: bool clipLine(Size imgSize, Point& pt1, Point& pt2)
.. cpp:function:: bool clipLine(Size imgSize, Point& pt1, Point& pt2)
.. c:function:: bool clipLine(Rect imgRect, Point& pt1, Point& pt2)
.. cpp:function:: bool clipLine(Rect imgRect, Point& pt1, Point& pt2)
Clips the line against the image rectangle.
@ -77,9 +77,9 @@ They return ``false`` if the line segment is completely outside the rectangle. O
ellipse
-----------
.. c:function:: void ellipse(Mat& img, Point center, Size axes, double angle, double startAngle, double endAngle, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
.. cpp:function:: void ellipse(Mat& img, Point center, Size axes, double angle, double startAngle, double endAngle, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
.. c:function:: void ellipse(Mat& img, const RotatedRect& box, const Scalar& color, int thickness=1, int lineType=8)
.. cpp:function:: void ellipse(Mat& img, const RotatedRect& box, const Scalar& color, int thickness=1, int lineType=8)
Draws a simple or thick elliptic arc or fills an ellipse sector.
@ -101,15 +101,15 @@ ellipse
:param thickness: Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
:param lineType: Type of the ellipse boundary. See :func:`line` description.
:param lineType: Type of the ellipse boundary. See :cpp:func:`line` description.
:param shift: Number of fractional bits in the center's coordinates and axes' values.
The functions ``ellipse`` with less parameters draw an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector.
A piecewise-linear curve is used to approximate the elliptic arc boundary. If you need more control of the ellipse rendering, you can retrieve the curve using
:func:`ellipse2Poly` and then render it with
:func:`polylines` or fill it with
:func:`fillPoly` . If you use the first variant of the function and want to draw the whole ellipse, not an arc, pass ``startAngle=0`` and ``endAngle=360`` . The picture below explains the meaning of the parameters.
:cpp:func:`ellipse2Poly` and then render it with
:cpp:func:`polylines` or fill it with
:cpp:func:`fillPoly` . If you use the first variant of the function and want to draw the whole ellipse, not an arc, pass ``startAngle=0`` and ``endAngle=360`` . The picture below explains the meaning of the parameters.
**Figure 1. Parameters of Elliptic Arc**
@ -119,15 +119,15 @@ A piecewise-linear curve is used to approximate the elliptic arc boundary. If yo
ellipse2Poly
----------------
.. c:function:: void ellipse2Poly( Point center, Size axes, int angle, int startAngle, int endAngle, int delta, vector<Point>& pts )
.. cpp:function:: void ellipse2Poly( Point center, Size axes, int angle, int startAngle, int endAngle, int delta, vector<Point>& pts )
Approximates an elliptic arc with a polyline.
:param center: Center of the arc.
:param axes: Half-sizes of the arc. See :func:`ellipse` for details.
:param axes: Half-sizes of the arc. See :cpp:func:`ellipse` for details.
:param angle: Rotation angle of the ellipse in degrees. See :func:`ellipse` for details.
:param angle: Rotation angle of the ellipse in degrees. See :cpp:func:`ellipse` for details.
:param startAngle: Starting angle of the elliptic arc in degrees.
@ -138,13 +138,13 @@ ellipse2Poly
:param pts: Output vector of polyline vertices.
The function ``ellipse2Poly`` computes the vertices of a polyline that approximates the specified elliptic arc. It is used by
:func:`ellipse` .
:cpp:func:`ellipse` .
.. index:: fillConvexPoly
fillConvexPoly
------------------
.. c:function:: void fillConvexPoly(Mat& img, const Point* pts, int npts, const Scalar& color, int lineType=8, int shift=0)
.. cpp:function:: void fillConvexPoly(Mat& img, const Point* pts, int npts, const Scalar& color, int lineType=8, int shift=0)
Fills a convex polygon.
@ -156,7 +156,7 @@ fillConvexPoly
:param color: Polygon color.
:param lineType: Type of the polygon boundaries. See :func:`line` description.
:param lineType: Type of the polygon boundaries. See :cpp:func:`line` description.
:param shift: Number of fractional bits in the vertex coordinates.
@ -168,7 +168,7 @@ that is, a polygon whose contour intersects every horizontal line (scan line) tw
fillPoly
------------
.. c:function:: void fillPoly(Mat& img, const Point** pts, const int* npts, int ncontours, const Scalar& color, int lineType=8, int shift=0, Point offset=Point() )
.. cpp:function:: void fillPoly(Mat& img, const Point** pts, const int* npts, int ncontours, const Scalar& color, int lineType=8, int shift=0, Point offset=Point() )
Fills the area bounded by one or more polygons.
@ -182,7 +182,7 @@ fillPoly
:param color: Polygon color.
:param lineType: Type of the polygon boundaries. See :func:`line` description.
:param lineType: Type of the polygon boundaries. See :cpp:func:`line` description.
:param shift: Number of fractional bits in the vertex coordinates.
@ -193,17 +193,17 @@ areas with holes, contours with self-intersections (some of thier parts), and so
getTextSize
---------------
.. c:function:: Size getTextSize(const string& text, int fontFace, double fontScale, int thickness, int* baseLine)
.. cpp:function:: Size getTextSize(const string& text, int fontFace, double fontScale, int thickness, int* baseLine)
Calculates the width and height of a text string.
:param text: Input text string.
:param fontFace: Font to use. See :func:`putText` for details.
:param fontFace: Font to use. See :cpp:func:`putText` for details.
:param fontScale: Font scale. See :func:`putText` for details.
:param fontScale: Font scale. See :cpp:func:`putText` for details.
:param thickness: Thickness of lines used to render the text. See :func:`putText` for details.
:param thickness: Thickness of lines used to render the text. See :cpp:func:`putText` for details.
:param baseLine: Output parameter - y-coordinate of the baseline relative to the bottom-most text point.
@ -244,7 +244,7 @@ That is, the following code renders some text, the tight box surrounding it, and
line
--------
.. c:function:: void line(Mat& img, Point pt1, Point pt2, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
.. cpp:function:: void line(Mat& img, Point pt1, Point pt2, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
Draws a line segment connecting two points.
@ -321,7 +321,9 @@ The number of pixels along the line is stored in ``LineIterator::count`` . ::
rectangle
-------------
.. c:function:: void rectangle(Mat& img, Point pt1, Point pt2, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
.. cpp:function:: void rectangle(Mat& img, Point pt1, Point pt2, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
.. cpp:function:: void rectangle(Mat& img, Rect r, const Scalar& color, int thickness=1, int lineType=8, int shift=0)
Draws a simple, thick, or filled up-right rectangle.
@ -330,22 +332,24 @@ rectangle
:param pt1: One of the rectangle's vertices.
:param pt2: Opposite to ``pt1`` rectangle vertex.
:param r: Alternative specification of the drawn rectangle
:param color: Rectangle color or brightness (grayscale image).
:param thickness: Thickness of lines that make up the rectangle. Negative values, like ``CV_FILLED`` , mean that the function has to draw a filled rectangle.
:param lineType: Type of the line. See :func:`line` description.
:param lineType: Type of the line. See :cpp:func:`line` description.
:param shift: Number of fractional bits in the point coordinates.
The function ``rectangle`` draws a rectangle outline or a filled rectangle whose two opposite corners are ``pt1`` and ``pt2`` .
The function ``rectangle`` draws a rectangle outline or a filled rectangle whose two opposite corners are ``pt1`` and ``pt2``, or ``r.tl()`` and ``r.br()-Point(1,1)``.
.. index:: polylines
polylines
-------------
.. c:function:: void polylines(Mat& img, const Point** pts, const int* npts, int ncontours, bool isClosed, const Scalar& color, int thickness=1, int lineType=8, int shift=0 )
.. cpp:function:: void polylines(Mat& img, const Point** pts, const int* npts, int ncontours, bool isClosed, const Scalar& color, int thickness=1, int lineType=8, int shift=0 )
Draws several polygonal curves.
@ -363,7 +367,7 @@ polylines
:param thickness: Thickness of the polyline edges.
:param lineType: Type of the line segments. See :func:`line` description.
:param lineType: Type of the line segments. See :cpp:func:`line` description.
:param shift: Number of fractional bits in the vertex coordinates.
@ -373,7 +377,7 @@ The function ``polylines`` draws one or more polygonal curves.
putText
-----------
.. c: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 )
.. cpp: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 )
Draws a text string.
@ -399,5 +403,5 @@ putText
The function ``putText`` renders the specified text string in the image.
Symbols that cannot be rendered using the specified font are
replaced by question marks. See
:func:`getTextSize` for a text rendering code example.
:cpp:func:`getTextSize` for a text rendering code example.

23
modules/core/doc/intro.rst
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@ -9,12 +9,12 @@ OpenCV (Open Source Computer Vision Library: http://opencv.willowgarage.com/wiki
OpenCV has a modular structure, which means that the package includes several shared or static libraries. The following modules are available:
* **core** - a compact module defining basic data structures, including the dense multi-dimensional array ``Mat`` and basic functions used by all other modules.
* **imgproc** - an image processing module that includes linear and non-linear image filtering, geometrical image transformations (resize, affine and perspective wraping, generic table-based remapping), color space conversion, histograms, and so on.
* **imgproc** - an image processing module that includes linear and non-linear image filtering, geometrical image transformations (resize, affine and perspective warping, generic table-based remapping), color space conversion, histograms, and so on.
* **video** - a video analysis module that includes motion estimation, background subtraction, and object tracking algorithms.
* **calib3d** - basic multiple-view geometry algorithms, single and stereo camera calibration, object pose estimation, stereo correspondence algorithms, and elements of 3D reconstruction.
* **features2d** - salient feature detectors, descriptors, and descriptor matchers.
* **objdetect** - detection of objects and instances of the predefined classes (for example, faces, eyes, mugs, people, cars, and so on).
* **highgui** - an easy-to-use interface to video capturing, image and video codecs APIs, as well as simple UI capabilities.
* **highgui** - an easy-to-use interface to video capturing, image and video codecs, as well as simple UI capabilities.
* **gpu** - GPU-accelerated algorithms from different OpenCV modules.
* ... some other helper modules, such as FLANN and Google test wrappers, Python bindings, and others.
@ -56,7 +56,7 @@ Automatic Memory Management
OpenCV handles all the memory automatically.
First of all, ``std::vector``, ``Mat``, and other data structures used by the functions and methods have destructors that deallocate the underlying memory buffers when needed. This means that the destructors do not always deallocate the buffers as in case of ``Mat``. They take into account possible data sharing. A destructor decrements the reference counter associated with the matrix data buffer. The buffer is deallocated if and only if the reference counter reaches zero, that is, when no other structures refer to the same buffer. Similarly, when a ``Mat`` instance is copied, no actual data is really copied. Instead, the counter associated with its reference is incremented to memorize that there is another owner of the same data. There is also the ``Mat::clone`` method that creates a full copy of the matrix data. See the example below: ::
First of all, ``std::vector``, ``Mat``, and other data structures used by the functions and methods have destructors that deallocate the underlying memory buffers when needed. This means that the destructors do not always deallocate the buffers as in case of ``Mat``. They take into account possible data sharing. A destructor decrements the reference counter associated with the matrix data buffer. The buffer is deallocated if and only if the reference counter reaches zero, that is, when no other structures refer to the same buffer. Similarly, when a ``Mat`` instance is copied, no actual data is really copied. Instead, the reference counter is incremented to memorize that there is another owner of the same data. There is also the ``Mat::clone`` method that creates a full copy of the matrix data. See the example below: ::
// create a big 8Mb matrix
Mat A(1000, 1000, CV_64F);
@ -100,7 +100,7 @@ description for details.
Automatic Allocation of the Output Data
---------------------------------------
OpenCV deallocates the memory automatically, as well as automatically allocates the memory for output function parameters most of the time. So, if a function has one or more input arrays (``cv::Mat`` instances) and some output arrays, the output arrays are automatically allocated or reallocated. The size and type of the output arrays are determined from the size and type of input arrays. If needed, the functions take extra parameters that help to figure out the output array properties.
OpenCV deallocates the memory automatically, as well as automatically allocates the memory for output function parameters most of the time. So, if a function has one or more input arrays (``cv::Mat`` instances) and some output arrays, the output arrays are automatically allocated or reallocated. The size and type of the output arrays are determined from the size and type of input arrays. If needed, the functions take extra parameters that help to figure out the output array properties.
Example: ::
@ -152,9 +152,9 @@ where ``cv::uchar`` is an OpenCV 8-bit unsigned integer type. In the optimized S
Fixed Pixel Types. Limited Use of Templates
-------------------------------------------
Templates is a great feature of C++ that enables implementation of very powerful, efficient and yet safe data structures and algorithms. However, the extensive use of templates may dramatically increase compilation time and code size. Besides, it is difficult to separate an interface and implementation when templates are used exclusively. This could be fine for basic algorithms but not good for computer vision libraries where a single algorithm may span a thousand lines of code. Because of this and also to simplify development of bindings for other languages, like Python*, Java*, Matlab* that do not have templates at all or have limited template capabilities, the current OpenCV implementation is based on polymorphism and runtime dispatching over templates. In those places where runtime dispatching would be too slow (like pixel access operators), impossible (generic ``Ptr<>`` implementation), or just very inconvenient (``saturate_cast<>()``) the current implementation introduces small template classes, methods, and functions. Anywhere else in this implementation templates are not used.
Templates is a great feature of C++ that enables implementation of very powerful, efficient and yet safe data structures and algorithms. However, the extensive use of templates may dramatically increase compilation time and code size. Besides, it is difficult to separate an interface and implementation when templates are used exclusively. This could be fine for basic algorithms but not good for computer vision libraries where a single algorithm may span thousands lines of code. Because of this and also to simplify development of bindings for other languages, like Python, Java, Matlab that do not have templates at all or have limited template capabilities, the current OpenCV implementation is based on polymorphism and runtime dispatching over templates. In those places where runtime dispatching would be too slow (like pixel access operators), impossible (generic ``Ptr<>`` implementation), or just very inconvenient (``saturate_cast<>()``) the current implementation introduces small template classes, methods, and functions. Anywhere else in the current OpenCV version the use of templates is limited.
There is a limited fixed set of primitive data types the library can operate on. That is, array elements should have one of the following types:
Consequently, there is a limited fixed set of primitive data types the library can operate on. That is, array elements should have one of the following types:
* 8-bit unsigned integer (uchar)
* 8-bit signed integer (schar)
@ -163,7 +163,7 @@ There is a limited fixed set of primitive data types the library can operate on.
* 32-bit signed integer (int)
* 32-bit floating-point number (float)
* 64-bit floating-point number (double)
* a tuple of several elements where all elements have the same type (one of the above). An array whose elements are such tuples, are called multi-channel arrays, as opposite to the single-channel arrays, whose elements are scalar values. The maximum possible number of channels is defined by the ``CV_CN_MAX`` constant, which is not smaller than 32.
* a tuple of several elements where all elements have the same type (one of the above). An array whose elements are such tuples, are called multi-channel arrays, as opposite to the single-channel arrays, whose elements are scalar values. The maximum possible number of channels is defined by the ``CV_CN_MAX`` constant, which is currently set to 512.
For these basic types, the following enumeration is applied::
@ -190,12 +190,17 @@ Arrays with more complex elements cannot be constructed or processed using OpenC
* The face detection algorithm only works with 8-bit grayscale or color images.
* Linear algebra functions and most of the machine learning algorithms work with floating-point arrays only.
* Basic functions, such as ``cv::add``, support all types, except for ``CV_8SC(n)``.
* Basic functions, such as ``cv::add``, support all types.
* Color space conversion functions support 8-bit unsigned, 16-bit unsigned, and 32-bit floating-point types.
The subset of supported types for each functions has been defined from practical needs. All this information about supported types can be put together into a special table. In different implementations of the standard, the tables may look differently. For example, on embedded platforms the double-precision floating-point type (``CV_64F``) may be unavailable.
The subset of supported types for each function has been defined from practical needs and could be extended in future based on user requests.
InputArray and OutputArray
--------------------------
Many OpenCV functions process dense 2-dimensional or multi-dimensional numerical arrays. Usually, such functions take cpp:class:`Mat` as parameters, but in some cases it's more convenient to use ``std::vector<>`` (for a point set, for example) or ``Matx<>`` (for 3x3 homography matrix and such). To avoid many duplicates in the API, special "proxy" classes have been introduced. The base "proxy" class is ``InputArray``. It is used for passing read-only arrays on a function input. The derived from ``InputArray`` class ``OutputArray`` is used to specify an output array for a function. Normally, you should not care of those intermediate types (and you should not declare variables of those types explicitly) - it will all just work automatically. You can assume that instead of ``InputArray``/``OutputArray`` you can always use ``Mat``, ``std::vector<>``, ``Matx<>``, ``Vec<>`` or ``Scalar``. When a function has an optional input or output array, and you do not have or do not want one, pass ``cv::None()``.
Error Handling
--------------

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58
modules/core/doc/utility_and_system_functions_and_macros.rst
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@ -7,7 +7,7 @@ Utility and System Functions and Macros
alignPtr
------------
.. c:function:: template<typename _Tp> _Tp* alignPtr(_Tp* ptr, int n=sizeof(_Tp))
.. cpp:function:: template<typename _Tp> _Tp* alignPtr(_Tp* ptr, int n=sizeof(_Tp))
Aligns a pointer to the specified number of bytes.
@ -25,7 +25,7 @@ The function returns the aligned pointer of the same type as the input pointer:
alignSize
-------------
.. c:function:: size_t alignSize(size_t sz, int n)
.. cpp:function:: size_t alignSize(size_t sz, int n)
Aligns a buffer size to the specified number of bytes.
@ -43,7 +43,7 @@ The function returns the minimum number that is greater or equal to ``sz`` and i
allocate
------------
.. c:function:: template<typename _Tp> _Tp* allocate(size_t n)
.. cpp:function:: template<typename _Tp> _Tp* allocate(size_t n)
Allocates an array of elements.
@ -55,7 +55,7 @@ The generic function ``allocate`` allocates a buffer for the specified number of
deallocate
--------------
.. c:function:: template<typename _Tp> void deallocate(_Tp* ptr, size_t n)
.. cpp:function:: template<typename _Tp> void deallocate(_Tp* ptr, size_t n)
Deallocates an array of elements.
@ -64,8 +64,8 @@ deallocate
:param n: Number of elements in the buffer.
The generic function ``deallocate`` deallocates the buffer allocated with
:func:`allocate` . The number of elements must match the number passed to
:func:`allocate` .
:cpp:func:`allocate` . The number of elements must match the number passed to
:cpp:func:`allocate` .
.. index:: CV_Assert
@ -73,7 +73,7 @@ The generic function ``deallocate`` deallocates the buffer allocated with
CV_Assert
---------
.. c:function:: CV_Assert(expr)
.. cpp:function:: CV_Assert(expr)
Checks a condition at runtime. ::
@ -84,17 +84,17 @@ CV_Assert
:param expr: Expression to check.
The macros ``CV_Assert`` and ``CV_DbgAssert`` evaluate the specified expression. If it is 0, the macros raise an error (see
:func:`error` ). The macro ``CV_Assert`` checks the condition in both Debug and Release configurations, while ``CV_DbgAssert`` is only retained in the Debug configuration.
:cpp:func:`error` ). The macro ``CV_Assert`` checks the condition in both Debug and Release configurations, while ``CV_DbgAssert`` is only retained in the Debug configuration.
.. index:: error
error
---------
.. c:function:: void error( const Exception\& exc )
.. cpp:function:: void error( const Exception\& exc )
.. c:function:: \#define CV_Error( code, msg ) <...>
.. cpp:function:: \#define CV_Error( code, msg ) <...>
.. c:function:: \#define CV_Error_( code, args ) <...>
.. cpp:function:: \#define CV_Error_( code, args ) <...>
Signals an error and raises an exception.
@ -148,13 +148,13 @@ Exception class passed to error ::
};
The class ``Exception`` encapsulates all or almost all the necessary information about the error happened in the program. The exception is usually constructed and thrown implicitly via ``CV_Error`` and ``CV_Error_`` macros. See
:func:`error` .
:cpp:func:`error` .
.. index:: fastMalloc
fastMalloc
--------------
.. c:function:: void* fastMalloc(size_t size)
.. cpp:function:: void* fastMalloc(size_t size)
Allocates an aligned memory buffer.
@ -166,74 +166,74 @@ The function allocates the buffer of the specified size and returns it. When the
fastFree
------------
.. c:function:: void fastFree(void* ptr)
.. cpp:function:: void fastFree(void* ptr)
Deallocates a memory buffer.
:param ptr: Pointer to the allocated buffer.
The function deallocates the buffer allocated with
:func:`fastMalloc` .
:cpp:func:`fastMalloc` .
If NULL pointer is passed, the function does nothing.
.. index:: format
format
----------
.. c:function:: string format( const char* fmt, ... )
.. cpp:function:: string format( const char* fmt, ... )
Returns a text string formatted using the ``printf`` -like expression.
:param fmt: ``printf`` -compatible formatting specifiers.
The function acts like ``sprintf`` but forms and returns an STL string. It can be used to form an error message in the
:func:`Exception` constructor.
:cpp:func:`Exception` constructor.
.. index:: getNumThreads
getNumThreads
-----------------
.. c:function:: int getNumThreads()
.. cpp:function:: int getNumThreads()
Returns the number of threads used by OpenCV.
The function returns the number of threads that is used by OpenCV.
See Also:
:func:`setNumThreads`,
:func:`getThreadNum`
:cpp:func:`setNumThreads`,
:cpp:func:`getThreadNum`
.. index:: getThreadNum
getThreadNum
----------------
.. c:function:: int getThreadNum()
.. cpp:function:: int getThreadNum()
Returns the index of the currently executed thread.
The function returns a 0-based index of the currently executed thread. The function is only valid inside a parallel OpenMP region. When OpenCV is built without OpenMP support, the function always returns 0.
See Also:
:func:`setNumThreads`,
:func:`getNumThreads` .
:cpp:func:`setNumThreads`,
:cpp:func:`getNumThreads` .
.. index:: getTickCount
getTickCount
----------------
.. c:function:: int64 getTickCount()
.. cpp:function:: int64 getTickCount()
Returns the number of ticks.
The function returns the number of ticks after the certain event (for example, when the machine was turned on).
It can be used to initialize
:func:`RNG` or to measure a function execution time by reading the tick count before and after the function call. See also the tick frequency.
:cpp:func:`RNG` or to measure a function execution time by reading the tick count before and after the function call. See also the tick frequency.
.. index:: getTickFrequency
getTickFrequency
--------------------
.. c:function:: double getTickFrequency()
.. cpp:function:: double getTickFrequency()
Returns the number of ticks per second.
@ -248,7 +248,7 @@ That is, the following code computes the execution time in seconds: ::
setNumThreads
-----------------
.. c:function:: void setNumThreads(int nthreads)
.. cpp:function:: void setNumThreads(int nthreads)
Sets the number of threads used by OpenCV.
@ -257,5 +257,5 @@ setNumThreads
The function sets the number of threads used by OpenCV in parallel OpenMP regions. If ``nthreads=0`` , the function uses the default number of threads that is usually equal to the number of the processing cores.
See Also:
:func:`getNumThreads`,
:func:`getThreadNum`
:cpp:func:`getNumThreads`,
:cpp:func:`getThreadNum`

4
modules/core/include/opencv2/core/core.hpp
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@ -2340,10 +2340,10 @@ CV_EXPORTS RNG& theRNG();
template<typename _Tp> static inline _Tp randu() { return (_Tp)theRNG(); }
//! fills array with uniformly-distributed random numbers from the range [low, high)
CV_EXPORTS_W void randu(CV_IN_OUT OutputArray dst, InputArray low, InputArray high);
CV_EXPORTS_W void randu(InputOutputArray dst, InputArray low, InputArray high);
//! fills array with normally-distributed random numbers with the specified mean and the standard deviation
CV_EXPORTS_W void randn(CV_IN_OUT OutputArray dst, InputArray mean, InputArray stddev);
CV_EXPORTS_W void randn(InputOutputArray dst, InputArray mean, InputArray stddev);
//! shuffles the input array elements
CV_EXPORTS void randShuffle(InputOutputArray dst, double iterFactor=1., RNG* rng=0);

10
modules/features2d/doc/common_interfaces_of_descriptor_extractors.rst
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@ -51,7 +51,7 @@ descriptors is represented as
DescriptorExtractor::compute
--------------------------------
.. c:function:: void DescriptorExtractor::compute( const Mat\& image, vector<KeyPoint>\& keypoints, Mat\& descriptors ) const
.. cpp:function:: void DescriptorExtractor::compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const
Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).
@ -61,7 +61,7 @@ DescriptorExtractor::compute
:param descriptors: Descriptors. Row i is the descriptor for keypoint i.
.. c:function:: void DescriptorExtractor::compute( const vector<Mat>\& images, vector<vector<KeyPoint> >\& keypoints, vector<Mat>\& descriptors ) const
.. cpp:function:: void DescriptorExtractor::compute( const vector<Mat>& images, vector<vector<KeyPoint> >& keypoints, vector<Mat>& descriptors ) const
:param images: Image set.
@ -75,7 +75,7 @@ DescriptorExtractor::compute
DescriptorExtractor::read
-----------------------------
.. c:function:: void DescriptorExtractor::read( const FileNode\& fn )
.. cpp:function:: void DescriptorExtractor::read( const FileNode& fn )
Reads the object of a descriptor extractor from a file node.
@ -85,7 +85,7 @@ DescriptorExtractor::read
DescriptorExtractor::write
------------------------------
.. c:function:: void DescriptorExtractor::write( FileStorage\& fs ) const
.. cpp:function:: void DescriptorExtractor::write( FileStorage& fs ) const
Writes the object of a descriptor extractor to a file storage.
@ -95,7 +95,7 @@ DescriptorExtractor::write
DescriptorExtractor::create
-------------------------------
.. c:function:: Ptr<DescriptorExtractor> DescriptorExtractor::create( const string& descriptorExtractorType )
.. cpp:function:: Ptr<DescriptorExtractor> DescriptorExtractor::create( const string& descriptorExtractorType )
Creates a descriptor extractor by name.

38
modules/features2d/doc/feature_detection_and_description.rst
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@ -7,7 +7,7 @@ Feature Detection and Description
FAST
--------
.. c:function:: void FAST( const Mat& image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true )
.. cpp:function:: void FAST( const Mat& image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true )
Detects corners using the FAST algorithm by E. Rosten (*Machine learning for high-speed corner detection*, 2006).
@ -355,11 +355,11 @@ Class containing a base structure for ``RTreeClassifier`` ::
RandomizedTree::train
-------------------------
.. c:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
.. cpp:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
Trains a randomized tree using an input set of keypoints.
.. c:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
.. cpp:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
:param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training.
@ -379,9 +379,9 @@ RandomizedTree::train
RandomizedTree::read
------------------------
.. c:function:: read(const char* file_name, int num_quant_bits)
.. cpp:function:: read(const char* file_name, int num_quant_bits)
.. c:function:: read(std::istream &is, int num_quant_bits)
.. cpp:function:: read(std::istream &is, int num_quant_bits)
Reads a pre-saved randomized tree from a file or stream.
@ -395,11 +395,11 @@ RandomizedTree::read
RandomizedTree::write
-------------------------
.. c:function:: void write(const char* file_name) const
.. cpp:function:: void write(const char* file_name) const
Writes the current randomized tree to a file or stream.
.. c:function:: void write(std::ostream \&os) const
.. cpp:function:: void write(std::ostream \&os) const
:param file_name: Name of the file where randomized tree data is stored.
@ -409,7 +409,7 @@ RandomizedTree::write
RandomizedTree::applyQuantization
-------------------------------------
.. c:function:: void applyQuantization(int num_quant_bits)
.. cpp:function:: void applyQuantization(int num_quant_bits)
Applies quantization to the current randomized tree.
@ -519,11 +519,11 @@ Class containing ``RTreeClassifier``. It represents the Calonder descriptor that
RTreeClassifier::train
--------------------------
.. c:function:: void train(vector<BaseKeypoint> const& base_set, RNG& rng, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
.. cpp:function:: void train(vector<BaseKeypoint> const& base_set, RNG& rng, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
Trains a randomized tree classifier using an input set of keypoints.
.. c:function:: void train(vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
.. cpp:function:: void train(vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
:param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training.
@ -547,11 +547,11 @@ RTreeClassifier::train
RTreeClassifier::getSignature
---------------------------------
.. c:function:: void getSignature(IplImage *patch, uchar *sig)
.. cpp:function:: void getSignature(IplImage *patch, uchar *sig)
Returns a signature for an image patch.
.. c:function:: void getSignature(IplImage *patch, float *sig)
.. cpp:function:: void getSignature(IplImage *patch, float *sig)
:param patch: Image patch to calculate the signature for.
:param sig: Output signature (array dimension is ``reduced_num_dim)`` .
@ -561,7 +561,7 @@ RTreeClassifier::getSignature
RTreeClassifier::getSparseSignature
---------------------------------------
.. c:function:: void getSparseSignature(IplImage *patch, float *sig, float thresh)
.. cpp:function:: void getSparseSignature(IplImage *patch, float *sig, float thresh)
Returns a signature for an image patch similarly to ``getSignature`` but uses a threshold for removing all signature elements below the threshold so that the signature is compressed.
@ -575,7 +575,7 @@ RTreeClassifier::getSparseSignature
RTreeClassifier::countNonZeroElements
-----------------------------------------
.. c:function:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)
.. cpp:function:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)
Returns the number of non-zero elements in an input array.
@ -589,11 +589,11 @@ RTreeClassifier::countNonZeroElements
RTreeClassifier::read
-------------------------
.. c:function:: read(const char* file_name)
.. cpp:function:: read(const char* file_name)
Reads a pre-saved ``RTreeClassifier`` from a file or stream.
.. c:function:: read(std::istream& is)
.. cpp:function:: read(std::istream& is)
:param file_name: Name of the file that contains randomized tree data.
@ -603,11 +603,11 @@ RTreeClassifier::read
RTreeClassifier::write
--------------------------
.. c:function:: void write(const char* file_name) const
.. cpp:function:: void write(const char* file_name) const
Writes the current ``RTreeClassifier`` to a file or stream.
.. c:function:: void write(std::ostream &os) const
.. cpp:function:: void write(std::ostream &os) const
:param file_name: Name of the file where randomized tree data is stored.
@ -617,7 +617,7 @@ RTreeClassifier::write
RTreeClassifier::setQuantization
------------------------------------
.. c:function:: void setQuantization(int num_quant_bits)
.. cpp:function:: void setQuantization(int num_quant_bits)
Applies quantization to the current randomized tree.

22
modules/features2d/doc/object_categorization.rst
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@ -41,7 +41,7 @@ Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. ::
BOWTrainer::add
-------------------
.. c:function:: void BOWTrainer::add( const Mat& descriptors )
.. cpp:function:: void BOWTrainer::add( const Mat& descriptors )
Adds descriptors to a training set. The training set is clustered using ``clustermethod`` to construct the vocabulary.
@ -51,7 +51,7 @@ BOWTrainer::add
BOWTrainer::getDescriptors
------------------------------
.. c:function:: const vector<Mat>& BOWTrainer::getDescriptors() const
.. cpp:function:: const vector<Mat>& BOWTrainer::getDescriptors() const
Returns a training set of descriptors.
@ -59,7 +59,7 @@ BOWTrainer::getDescriptors
BOWTrainer::descripotorsCount
---------------------------------
.. c:function:: const vector<Mat>& BOWTrainer::descripotorsCount() const
.. cpp:function:: const vector<Mat>& BOWTrainer::descripotorsCount() const
Returns the count of all descriptors stored in the training set.
@ -67,11 +67,11 @@ BOWTrainer::descripotorsCount
BOWTrainer::cluster
-----------------------
.. c:function:: Mat BOWTrainer::cluster() const
.. cpp:function:: Mat BOWTrainer::cluster() const
Clusters train descriptors. The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first variant of the method, train descriptors stored in the object are clustered. In the second variant, input descriptors are clustered.
.. c:function:: Mat BOWTrainer::cluster( const Mat& descriptors ) const
.. cpp:function:: Mat BOWTrainer::cluster( const Mat& descriptors ) const
:param descriptors: Descriptors to cluster. Each row of the ``descriptors`` matrix is a descriptor. Descriptors are not added to the inner train descriptor set.
@ -146,7 +146,7 @@ Here is the class declaration ::
BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
--------------------------------------------------------
.. c:function:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor, const Ptr<DescriptorMatcher>& dmatcher )
.. cpp:function:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor, const Ptr<DescriptorMatcher>& dmatcher )
The class constructor.
@ -158,7 +158,7 @@ BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
BOWImgDescriptorExtractor::setVocabulary
--------------------------------------------
.. c:function:: void BOWImgDescriptorExtractor::setVocabulary( const Mat& vocabulary )
.. cpp:function:: void BOWImgDescriptorExtractor::setVocabulary( const Mat& vocabulary )
Sets a visual vocabulary.
@ -168,7 +168,7 @@ BOWImgDescriptorExtractor::setVocabulary
BOWImgDescriptorExtractor::getVocabulary
--------------------------------------------
.. c:function:: const Mat& BOWImgDescriptorExtractor::getVocabulary() const
.. cpp:function:: const Mat& BOWImgDescriptorExtractor::getVocabulary() const
Returns the set vocabulary.
@ -176,7 +176,7 @@ BOWImgDescriptorExtractor::getVocabulary
BOWImgDescriptorExtractor::compute
--------------------------------------
.. c:function:: void BOWImgDescriptorExtractor::compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& imgDescriptor, vector<vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 )
.. cpp:function:: void BOWImgDescriptorExtractor::compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& imgDescriptor, vector<vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 )
Computes an image descriptor using the set visual vocabulary.
@ -194,7 +194,7 @@ BOWImgDescriptorExtractor::compute
BOWImgDescriptorExtractor::descriptorSize
---------------------------------------------
.. c:function:: int BOWImgDescriptorExtractor::descriptorSize() const
.. cpp:function:: int BOWImgDescriptorExtractor::descriptorSize() const
Returns an image discriptor size if the vocabulary is set. Otherwise, it returns 0.
@ -202,7 +202,7 @@ BOWImgDescriptorExtractor::descriptorSize
BOWImgDescriptorExtractor::descriptorType
---------------------------------------------
.. c:function:: int BOWImgDescriptorExtractor::descriptorType() const
.. cpp:function:: int BOWImgDescriptorExtractor::descriptorType() const
Returns an image descriptor type.

6
modules/gpu/doc/camera_calibration_and_3d_reconstruction.rst
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@ -490,7 +490,7 @@ gpu::reprojectImageTo3D
:param stream: Stream for the asynchronous version.
See Also: :c:func:`reprojectImageTo3D` .
See Also: :c:cpp:func:`reprojectImageTo3D` .
.. index:: gpu::solvePnPRansac
@ -507,7 +507,7 @@ gpu::solvePnPRansac
:param camera_mat: 3x3 matrix of intrinsic camera parameters.
:param dist_coef: Distortion coefficients. See :c:func:`undistortPoints` for details.
:param dist_coef: Distortion coefficients. See :c:cpp:func:`undistortPoints` for details.
:param rvec: Output 3D rotation vector.
@ -523,5 +523,5 @@ gpu::solvePnPRansac
:param inliers: Output vector of inlier indices.
See Also :c:func:`solvePnPRansac`.
See Also :c:cpp:func:`solvePnPRansac`.

4
modules/gpu/doc/data_structures.rst
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@ -148,7 +148,7 @@ In contrast with :c:type:`Mat`, in most cases ``GpuMat::isContinuous() == false`
You are not recommended to leave static or global ``GpuMat`` variables allocated, that is to rely on its destructor. The destruction order of such variables and CUDA context is undefined. GPU memory release function returns error if the CUDA context has been destroyed before.
See Also:
:func:`Mat`
:cpp:func:`Mat`
.. index:: gpu::CudaMem
@ -157,7 +157,7 @@ gpu::CudaMem
.. cpp:class:: gpu::CudaMem
This class with reference counting wraps special memory type allocation functions from CUDA. Its interface is also
:func:`Mat`-like but with additional memory type parameters.
:cpp:func:`Mat`-like but with additional memory type parameters.
*
``ALLOC_PAGE_LOCKED``: Sets a page locked memory type, used commonly for fast and asynchronous uploading/downloading data from/to GPU.

8
modules/gpu/doc/feature_detection_and_description.rst
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@ -195,7 +195,7 @@ gpu::BruteForceMatcher_GPU::match
Finds the best match for each descriptor from a query set with train descriptors.
See Also:
:c:func:`DescriptorMatcher::match`
:c:cpp:func:`DescriptorMatcher::match`
.. index:: gpu::BruteForceMatcher_GPU::matchSingle
@ -264,10 +264,10 @@ gpu::BruteForceMatcher_GPU::knnMatch
Finds the k best matches for each descriptor from a query set with train descriptors. The function returns detected k (or less if not possible) matches in the increasing order by distance.
.. c:function:: void knnMatch(const GpuMat& queryDescs, std::vector< std::vector<DMatch> >&matches, int k, const std::vector<GpuMat>&masks = std::vector<GpuMat>(), bool compactResult = false )
.. cpp:function:: void knnMatch(const GpuMat& queryDescs, std::vector< std::vector<DMatch> >&matches, int k, const std::vector<GpuMat>&masks = std::vector<GpuMat>(), bool compactResult = false )
See Also:
:func:`DescriptorMatcher::knnMatch`
:cpp:func:`DescriptorMatcher::knnMatch`
.. index:: gpu::BruteForceMatcher_GPU::knnMatch
@ -308,7 +308,7 @@ gpu::BruteForceMatcher_GPU::radiusMatch
This function works only on devices with the compute capability :math:`>=` 1.1.
See Also:
:func:`DescriptorMatcher::radiusMatch`
:cpp:func:`DescriptorMatcher::radiusMatch`
.. index:: gpu::BruteForceMatcher_GPU::radiusMatch

74
modules/gpu/doc/image_filtering.rst
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@ -93,7 +93,7 @@ This is a base class for Filter Engine. ::
The class can be used to apply an arbitrary filtering operation to an image. It contains all the necessary intermediate buffers. Pointers to the initialized ``FilterEngine_GPU`` instances are returned by various ``create*Filter_GPU`` functions (see below), and they are used inside high-level functions such as
:func:`gpu::filter2D`, :func:`gpu::erode`, :func:`gpu::Sobel` , and others.
:cpp:func:`gpu::filter2D`, :cpp:func:`gpu::erode`, :cpp:func:`gpu::Sobel` , and others.
By using ``FilterEngine_GPU`` instead of functions you can avoid unnecessary memory allocation for intermediate buffers and get much better performance:
::
@ -224,7 +224,7 @@ gpu::createBoxFilter_GPU
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`boxFilter`
See Also: :c:cpp:func:`boxFilter`
.. index:: gpu::boxFilter
@ -248,7 +248,7 @@ gpu::boxFilter
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`boxFilter`
See Also: :c:cpp:func:`boxFilter`
.. index:: gpu::blur
@ -270,7 +270,7 @@ gpu::blur
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`blur`, :cpp:func:`gpu::boxFilter`
See Also: :c:cpp:func:`blur`, :cpp:func:`gpu::boxFilter`
.. index:: gpu::createMorphologyFilter_GPU
@ -296,7 +296,7 @@ gpu::createMorphologyFilter_GPU
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`createMorphologyFilter`
See Also: :c:cpp:func:`createMorphologyFilter`
.. index:: gpu::erode
@ -320,7 +320,7 @@ gpu::erode
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`erode`
See Also: :c:cpp:func:`erode`
.. index:: gpu::dilate
@ -344,7 +344,7 @@ gpu::dilate
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`dilate`
See Also: :c:cpp:func:`dilate`
.. index:: gpu::morphologyEx
@ -381,7 +381,7 @@ gpu::morphologyEx
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`morphologyEx`
See Also: :c:cpp:func:`morphologyEx`
.. index:: gpu::createLinearFilter_GPU
@ -407,7 +407,7 @@ gpu::createLinearFilter_GPU
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`createLinearFilter`
See Also: :c:cpp:func:`createLinearFilter`
.. index:: gpu::filter2D
@ -431,7 +431,7 @@ gpu::filter2D
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`filter2D`
See Also: :c:cpp:func:`filter2D`
.. index:: gpu::Laplacian
@ -447,15 +447,15 @@ gpu::Laplacian
:param ddepth: Desired depth of the destination image. It supports only the same depth as the source image depth.
:param ksize: Aperture size used to compute the second-derivative filters (see :c:func:`getDerivKernels`). It must be positive and odd. Only ``ksize`` = 1 and ``ksize`` = 3 are supported.
:param ksize: Aperture size used to compute the second-derivative filters (see :c:cpp:func:`getDerivKernels`). It must be positive and odd. Only ``ksize`` = 1 and ``ksize`` = 3 are supported.
:param scale: Optional scale factor for the computed Laplacian values. By default, no scaling is applied (see :c:func:`getDerivKernels` ).
:param scale: Optional scale factor for the computed Laplacian values. By default, no scaling is applied (see :c:cpp:func:`getDerivKernels` ).
**Note:**
This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
See Also: :c:func:`Laplacian`,:func:`gpu::filter2D` .
See Also: :c:cpp:func:`Laplacian`,:cpp:func:`gpu::filter2D` .
.. index:: gpu::getLinearRowFilter_GPU
@ -473,13 +473,13 @@ gpu::getLinearRowFilter_GPU
:param anchor: Anchor position within the kernel. Negative values mean that the anchor is positioned at the aperture center.
:param borderType: Pixel extrapolation method. For details, see :c:func:`borderInterpolate`. For details on limitations, see below.
:param borderType: Pixel extrapolation method. For details, see :c:cpp:func:`borderInterpolate`. For details on limitations, see below.
There are two versions of the algorithm: NPP and OpenCV.
* NPP version is called when ``srcType == CV_8UC1`` or ``srcType == CV_8UC4`` and ``bufType == srcType`` . Otherwise, the OpenCV version is called. NPP supports only ``BORDER_CONSTANT`` border type and does not check indices outside the image.
* OpenCV version supports only ``CV_32F`` buffer depth and ``BORDER_REFLECT101``,``BORDER_REPLICATE``, and ``BORDER_CONSTANT`` border types. It checks indices outside the image.
See Also:,:func:`createSeparableLinearFilter` .
See Also:,:cpp:func:`createSeparableLinearFilter` .
.. index:: gpu::getLinearColumnFilter_GPU
@ -497,13 +497,13 @@ gpu::getLinearColumnFilter_GPU
:param anchor: Anchor position within the kernel. Negative values mean that the anchor is positioned at the aperture center.
:param borderType: Pixel extrapolation method. For details, see :c:func:`borderInterpolate` . For details on limitations, see below.
:param borderType: Pixel extrapolation method. For details, see :c:cpp:func:`borderInterpolate` . For details on limitations, see below.
There are two versions of the algorithm: NPP and OpenCV.
* NPP version is called when ``dstType == CV_8UC1`` or ``dstType == CV_8UC4`` and ``bufType == dstType`` . Otherwise, the OpenCV version is called. NPP supports only ``BORDER_CONSTANT`` border type and does not check indices outside the image.
* OpenCV version supports only ``CV_32F`` buffer depth and ``BORDER_REFLECT101``, ``BORDER_REPLICATE``, and ``BORDER_CONSTANT`` border types. It checks indices outside image.
See Also: :cpp:func:`gpu::getLinearRowFilter_GPU`, :c:func:`createSeparableLinearFilter`
See Also: :cpp:func:`gpu::getLinearRowFilter_GPU`, :c:cpp:func:`createSeparableLinearFilter`
.. index:: gpu::createSeparableLinearFilter_GPU
@ -521,10 +521,10 @@ gpu::createSeparableLinearFilter_GPU
:param anchor: Anchor position within the kernel. Negative values mean that anchor is positioned at the aperture center.
:param rowBorderType, columnBorderType: Pixel extrapolation method in the horizontal and vertical directions For details, see :c:func:`borderInterpolate`. For details on limitations, see :cpp:func:`gpu::getLinearRowFilter_GPU`, cpp:func:`gpu::getLinearColumnFilter_GPU`.
:param rowBorderType, columnBorderType: Pixel extrapolation method in the horizontal and vertical directions For details, see :c:cpp:func:`borderInterpolate`. For details on limitations, see :cpp:func:`gpu::getLinearRowFilter_GPU`, cpp:cpp:func:`gpu::getLinearColumnFilter_GPU`.
See Also: :cpp:func:`gpu::getLinearRowFilter_GPU`, :cpp:func:`gpu::getLinearColumnFilter_GPU`, :c:func:`createSeparableLinearFilter`
See Also: :cpp:func:`gpu::getLinearRowFilter_GPU`, :cpp:func:`gpu::getLinearColumnFilter_GPU`, :c:cpp:func:`createSeparableLinearFilter`
.. index:: gpu::sepFilter2D
@ -544,9 +544,9 @@ gpu::sepFilter2D
:param anchor: Anchor position within the kernel. The default value ``(-1, 1)`` means that the anchor is at the kernel center.
:param rowBorderType, columnBorderType: Pixel extrapolation method. For details, see :c:func:`borderInterpolate`.
:param rowBorderType, columnBorderType: Pixel extrapolation method. For details, see :c:cpp:func:`borderInterpolate`.
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:func:`sepFilter2D`
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:cpp:func:`sepFilter2D`
.. index:: gpu::createDerivFilter_GPU
@ -564,11 +564,11 @@ gpu::createDerivFilter_GPU
:param dy: Derivative order in respect of y.
:param ksize: Aperture size. See :c:func:`getDerivKernels` for details.
:param ksize: Aperture size. See :c:cpp:func:`getDerivKernels` for details.
:param rowBorderType, columnBorderType: Pixel extrapolation method. See :c:func:`borderInterpolate` for details.
:param rowBorderType, columnBorderType: Pixel extrapolation method. See :c:cpp:func:`borderInterpolate` for details.
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:func:`createDerivFilter`
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:cpp:func:`createDerivFilter`
.. index:: gpu::Sobel
@ -590,11 +590,11 @@ gpu::Sobel
:param ksize: Size of the extended Sobel kernel. Possible valies are 1, 3, 5 or 7.
:param scale: Optional scale factor for the computed derivative values. By default, no scaling is applied. For details, see :c:func:`getDerivKernels` .
:param scale: Optional scale factor for the computed derivative values. By default, no scaling is applied. For details, see :c:cpp:func:`getDerivKernels` .
:param rowBorderType, columnBorderType: Pixel extrapolation method. See :c:func:`borderInterpolate` for details.
:param rowBorderType, columnBorderType: Pixel extrapolation method. See :c:cpp:func:`borderInterpolate` for details.
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:func:`Sobel`
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:cpp:func:`Sobel`
.. index:: gpu::Scharr
@ -614,11 +614,11 @@ gpu::Scharr
:param yorder: Order of the derivative y.
:param scale: Optional scale factor for the computed derivative values. By default, no scaling is applied. See :c:func:`getDerivKernels` for details.
:param scale: Optional scale factor for the computed derivative values. By default, no scaling is applied. See :c:cpp:func:`getDerivKernels` for details.
:param rowBorderType, columnBorderType: Pixel extrapolation method. For details, see :c:func:`borderInterpolate` and :c:func:`Scharr` .
:param rowBorderType, columnBorderType: Pixel extrapolation method. For details, see :c:cpp:func:`borderInterpolate` and :c:cpp:func:`Scharr` .
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:func:`Scharr`
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:cpp:func:`Scharr`
.. index:: gpu::createGaussianFilter_GPU
@ -630,15 +630,15 @@ gpu::createGaussianFilter_GPU
:param type: Source and destination image type. ``CV_8UC1``, ``CV_8UC4``, ``CV_16SC1``, ``CV_16SC2``, ``CV_32SC1``, ``CV_32FC1`` are supported.
:param ksize: Aperture size. See :c:func:`getGaussianKernel` for details.
:param ksize: Aperture size. See :c:cpp:func:`getGaussianKernel` for details.
:param sigmaX: Gaussian sigma in the horizontal direction. See :c:func:`getGaussianKernel` for details.
:param sigmaX: Gaussian sigma in the horizontal direction. See :c:cpp:func:`getGaussianKernel` for details.
:param sigmaY: Gaussian sigma in the vertical direction. If 0, then :math:`\texttt{sigmaY}\leftarrow\texttt{sigmaX}` .
:param rowBorderType, columnBorderType: Border type to use. See :c:func:`borderInterpolate` for details.
:param rowBorderType, columnBorderType: Border type to use. See :c:cpp:func:`borderInterpolate` for details.
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:func:`createGaussianFilter`
See Also: :cpp:func:`gpu::createSeparableLinearFilter_GPU`, :c:cpp:func:`createGaussianFilter`
.. index:: gpu::GaussianBlur
@ -654,11 +654,11 @@ gpu::GaussianBlur
:param ksize: Gaussian kernel size. ``ksize.width`` and ``ksize.height`` can differ but they both must be positive and odd. If they are zeros, they are computed from ``sigmaX`` and ``sigmaY`` .
:param sigmaX, sigmaY: Gaussian kernel standard deviations in X and Y direction. If ``sigmaY`` is zero, it is set to be equal to ``sigmaX`` . If they are both zeros, they are computed from ``ksize.width`` and ``ksize.height``, respectively. See :c:func:`getGaussianKernel` for details. To fully control the result regardless of possible future modification of all this semantics, you are recommended to specify all of ``ksize``, ``sigmaX``, and ``sigmaY`` .
:param sigmaX, sigmaY: Gaussian kernel standard deviations in X and Y direction. If ``sigmaY`` is zero, it is set to be equal to ``sigmaX`` . If they are both zeros, they are computed from ``ksize.width`` and ``ksize.height``, respectively. See :c:cpp:func:`getGaussianKernel` for details. To fully control the result regardless of possible future modification of all this semantics, you are recommended to specify all of ``ksize``, ``sigmaX``, and ``sigmaY`` .
:param rowBorderType, columnBorderType: Pixel extrapolation method. See :c:func:`borderInterpolate` for details.
:param rowBorderType, columnBorderType: Pixel extrapolation method. See :c:cpp:func:`borderInterpolate` for details.
See Also: :cpp:func:`gpu::createGaussianFilter_GPU`, :c:func:`GaussianBlur`
See Also: :cpp:func:`gpu::createGaussianFilter_GPU`, :c:cpp:func:`GaussianBlur`
.. index:: gpu::getMaxFilter_GPU

46
modules/gpu/doc/image_processing.rst
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@ -42,7 +42,7 @@ gpu::meanShiftProc
:param criteria: Termination criteria. See :cpp:class:`TermCriteria`.
See Also:
:c:func:`gpu::meanShiftFiltering`
:c:cpp:func:`gpu::meanShiftFiltering`
.. index:: gpu::meanShiftSegmentation
@ -81,7 +81,7 @@ gpu::integral
:param sqsum: Squared integral image of the ``CV_32FC1`` type.
See Also:
:c:func:`integral`
:c:cpp:func:`integral`
.. index:: gpu::sqrIntegral
@ -128,7 +128,7 @@ gpu::cornerHarris
:param borderType: Pixel extrapolation method. Only ``BORDER_REFLECT101`` and ``BORDER_REPLICATE`` are supported for now.
See Also:
:c:func:`cornerHarris`
:c:cpp:func:`cornerHarris`
.. index:: gpu::cornerMinEigenVal
@ -150,7 +150,7 @@ gpu::cornerMinEigenVal
:param borderType: Pixel extrapolation method. Only ``BORDER_REFLECT101`` and ``BORDER_REPLICATE`` are supported for now.
See also: :c:func:`cornerMinEigenVal`
See also: :c:cpp:func:`cornerMinEigenVal`
.. index:: gpu::mulSpectrums
@ -173,7 +173,7 @@ gpu::mulSpectrums
Only full (not packed) ``CV_32FC2`` complex spectrums in the interleaved format are supported for now.
See Also:
:c:func:`mulSpectrums`
:c:cpp:func:`mulSpectrums`
.. index:: gpu::mulAndScaleSpectrums
@ -198,7 +198,7 @@ gpu::mulAndScaleSpectrums
Only full (not packed) ``CV_32FC2`` complex spectrums in the interleaved format are supported for now.
See Also:
:c:func:`mulSpectrums`
:c:cpp:func:`mulSpectrums`
.. index:: gpu::dft
@ -237,7 +237,7 @@ gpu::dft
If the source matrix is real (its type is ``CV_32FC1`` ), forward DFT is performed. The result of the DFT is packed into complex ( ``CV_32FC2`` ) matrix. So, the width of the destination matrix is ``dft_size.width / 2 + 1`` . But if the source is a single column, the height is reduced instead of the width.
See Also:
:c:func:`dft`
:c:cpp:func:`dft`
.. index:: gpu::convolve
@ -268,7 +268,7 @@ gpu::ConvolveBuf
.. cpp:class:: gpu::ConvolveBuf
This class provides a memory buffer for the
:c:func:`gpu::convolve` function.
:c:cpp:func:`gpu::convolve` function.
::
struct CV_EXPORTS ConvolveBuf
@ -290,12 +290,12 @@ gpu::ConvolveBuf::ConvolveBuf
.. cpp:function:: ConvolveBuf::ConvolveBuf()
Constructs an empty buffer that is properly resized after the first call of the
:c:func:`convolve` function.
:c:cpp:func:`convolve` function.
.. cpp:function:: ConvolveBuf::ConvolveBuf(Size image_size, Size templ_size)
Constructs a buffer for the
:c:func:`convolve` function with respective arguments.
:c:cpp:func:`convolve` function with respective arguments.
.. index:: gpu::matchTemplate
@ -328,7 +328,7 @@ gpu::matchTemplate
* ``CV_TM_CCORR``
See Also:
:c:func:`matchTemplate`
:c:cpp:func:`matchTemplate`
.. index:: gpu::remap
@ -354,7 +354,7 @@ gpu::remap
Values of pixels with non-integer coordinates are computed using bilinear the interpolation.
See Also: :c:func:`remap`
See Also: :c:cpp:func:`remap`
.. index:: gpu::cvtColor
@ -370,7 +370,7 @@ gpu::cvtColor
:param dst: Destination image with the same size and depth as ``src`` .
:param code: Color space conversion code. For details, see :func:`cvtColor` . Conversion to/from Luv and Bayer color spaces is not supported.
:param code: Color space conversion code. For details, see :cpp:func:`cvtColor` . Conversion to/from Luv and Bayer color spaces is not supported.
:param dcn: Number of channels in the destination image. If the parameter is 0, the number of the channels is derived automatically from ``src`` and the ``code`` .
@ -379,7 +379,7 @@ gpu::cvtColor
3-channel color spaces (like ``HSV``, ``XYZ``, and so on) can be stored in a 4-channel image for better perfomance.
See Also:
:func:`cvtColor`
:cpp:func:`cvtColor`
.. index:: gpu::threshold
@ -399,12 +399,12 @@ gpu::threshold
:param maxVal: Maximum value to use with ``THRESH_BINARY`` and ``THRESH_BINARY_INV`` threshold types.
:param thresholdType: Threshold type. For details, see :func:`threshold` . The ``THRESH_OTSU`` threshold type is not supported.
:param thresholdType: Threshold type. For details, see :cpp:func:`threshold` . The ``THRESH_OTSU`` threshold type is not supported.
:param stream: Stream for the asynchronous version.
See Also:
:func:`threshold`
:cpp:func:`threshold`
.. index:: gpu::resize
@ -439,7 +439,7 @@ gpu::resize
:param interpolation: Interpolation method. Only ``INTER_NEAREST`` and ``INTER_LINEAR`` are supported.
See Also: :func:`resize`
See Also: :cpp:func:`resize`
.. index:: gpu::warpAffine
@ -457,10 +457,10 @@ gpu::warpAffine
:param dsize: Size of the destination image.
:param flags: Combination of interpolation methods (see :func:`resize`) and the optional flag ``WARP_INVERSE_MAP`` specifying that ``M`` is an inverse transformation (``dst=>src``). Only ``INTER_NEAREST``, ``INTER_LINEAR``, and ``INTER_CUBIC`` interpolation methods are supported.
:param flags: Combination of interpolation methods (see :cpp:func:`resize`) and the optional flag ``WARP_INVERSE_MAP`` specifying that ``M`` is an inverse transformation (``dst=>src``). Only ``INTER_NEAREST``, ``INTER_LINEAR``, and ``INTER_CUBIC`` interpolation methods are supported.
See Also:
:func:`warpAffine`
:cpp:func:`warpAffine`
.. index:: gpu::warpPerspective
@ -478,10 +478,10 @@ gpu::warpPerspective
:param dsize: Size of the destination image.
:param flags: Combination of interpolation methods (see :func:`resize` ) and the optional flag ``WARP_INVERSE_MAP`` specifying that ``M`` is the inverse transformation (``dst => src``). Only ``INTER_NEAREST``, ``INTER_LINEAR``, and ``INTER_CUBIC`` interpolation methods are supported.
:param flags: Combination of interpolation methods (see :cpp:func:`resize` ) and the optional flag ``WARP_INVERSE_MAP`` specifying that ``M`` is the inverse transformation (``dst => src``). Only ``INTER_NEAREST``, ``INTER_LINEAR``, and ``INTER_CUBIC`` interpolation methods are supported.
See Also:
:func:`warpPerspective`
:cpp:func:`warpPerspective`
.. index:: gpu::rotate
@ -506,7 +506,7 @@ gpu::rotate
:param interpolation: Interpolation method. Only ``INTER_NEAREST``, ``INTER_LINEAR``, and ``INTER_CUBIC`` are supported.
See Also:
:func:`gpu::warpAffine`
:cpp:func:`gpu::warpAffine`
.. index:: gpu::copyMakeBorder
@ -525,7 +525,7 @@ gpu::copyMakeBorder
:param value: Border value.
See Also:
:func:`copyMakeBorder`
:cpp:func:`copyMakeBorder`
.. index:: gpu::rectStdDev

12
modules/gpu/doc/introduction.rst
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@ -15,9 +15,9 @@ The GPU module depends on the CUDA Toolkit and NVIDIA Performance Primitives lib
The OpenCV GPU module is designed for ease of use and does not require any knowledge of CUDA. Though, such a knowledge will certainly be useful to handle non-trivial cases or achieve the highest performance. It is helpful to understand the cost of various operations, what the GPU does, what the preferred data formats are, and so on. The GPU module is an effective instrument for quick implementation of GPU-accelerated computer vision algorithms. However, if your algorithm involves many simple operations, then, for the best possible performance, you may still need to write your own kernels to avoid extra write and read operations on the intermediate results.
To enable CUDA support, configure OpenCV using ``CMake`` with ``WITH_CUDA=ON`` . When the flag is set and if CUDA is installed, the full-featured OpenCV GPU module is built. Otherwise, the module is still built, but at runtime all functions from the module throw
:func:`Exception` with ``CV_GpuNotSupported`` error code, except for
:func:`gpu::getCudaEnabledDeviceCount()`. The latter function returns zero GPU count in this case. Building OpenCV without CUDA support does not perform device code compilation, so it does not require the CUDA Toolkit installed. Therefore, using the
:func:`gpu::getCudaEnabledDeviceCount()` function, you can implement a high-level algorithm that will detect GPU presence at runtime and choose an appropriate implementation (CPU or GPU) accordingly.
:cpp:func:`Exception` with ``CV_GpuNotSupported`` error code, except for
:cpp:func:`gpu::getCudaEnabledDeviceCount()`. The latter function returns zero GPU count in this case. Building OpenCV without CUDA support does not perform device code compilation, so it does not require the CUDA Toolkit installed. Therefore, using the
:cpp:func:`gpu::getCudaEnabledDeviceCount()` function, you can implement a high-level algorithm that will detect GPU presence at runtime and choose an appropriate implementation (CPU or GPU) accordingly.
Compilation for Different NVIDIA* Platforms
-------------------------------------------
@ -34,12 +34,12 @@ By default, the OpenCV GPU module includes:
PTX code for compute capabilities 1.1 and 1.3 (controlled by ``CUDA_ARCH_PTX`` in ``CMake``)
This means that for devices with CC 1.3 and 2.0 binary images are ready to run. For all newer platforms, the PTX code for 1.3 is JIT'ed to a binary image. For devices with CC 1.1 and 1.2, the PTX for 1.1 is JIT'ed. For devices with CC 1.0, no code is available and the functions throw
:func:`Exception`. For platforms where JIT compilation is performed first, the run is slow.
:cpp:func:`Exception`. For platforms where JIT compilation is performed first, the run is slow.
On a GPU with CC 1.0, you can still compile the GPU module and most of the functions will run flawlessly. To achieve this, add "1.0" to the list of binaries, for example, ``CUDA_ARCH_BIN="1.0 1.3 2.0"`` . The functions that cannot be run on CC 1.0 GPUs throw an exception.
You can always determine at runtime whether the OpenCV GPU-built binaries (or PTX code) are compatible with your GPU. The function
:func:`gpu::DeviceInfo::isCompatible` returns the compatibility status (true/false).
:cpp:func:`gpu::DeviceInfo::isCompatible` returns the compatibility status (true/false).
Threading and Multi-threading
------------------------------
@ -57,7 +57,7 @@ In the current version, each of the OpenCV GPU algorithms can use only a single
*
If you use only synchronous functions, create several CPU threads (one per each GPU) and from within each thread create a CUDA context for the corresponding GPU using
:func:`gpu::setDevice()` or Driver API. Each of the threads will use the associated GPU.
:cpp:func:`gpu::setDevice()` or Driver API. Each of the threads will use the associated GPU.
*
If you use asynchronous functions, you can use the Driver API to create several CUDA contexts associated with different GPUs but attached to one CPU thread. Within the thread you can switch from one GPU to another by making the corresponding context "current". With non-blocking GPU calls, managing algorithm is clear.

12
modules/gpu/doc/matrix_reductions.rst
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@ -17,7 +17,7 @@ gpu::meanStdDev
:param stddev: Standard deviation value.
See Also: :c:func:`meanStdDev`
See Also: :c:cpp:func:`meanStdDev`
.. index:: gpu::norm
@ -37,7 +37,7 @@ gpu::norm
:param buf: Optional buffer to avoid extra memory allocations. It is resized automatically.
See Also: :c:func:`norm`
See Also: :c:cpp:func:`norm`
.. index:: gpu::sum
@ -53,7 +53,7 @@ gpu::sum
:param buf: Optional buffer to avoid extra memory allocations. It is resized automatically.
See Also: :c:func:`sum`
See Also: :c:cpp:func:`sum`
.. index:: gpu::absSum
@ -105,7 +105,7 @@ gpu::minMax
The function does not work with ``CV_64F`` images on GPUs with the compute capability < 1.3.
See Also: :c:func:`minMaxLoc`
See Also: :c:cpp:func:`minMaxLoc`
.. index:: gpu::minMaxLoc
@ -135,7 +135,7 @@ gpu::minMaxLoc
The function does not work with ``CV_64F`` images on GPU with the compute capability < 1.3.
See Also: :c:func:`minMaxLoc`
See Also: :c:cpp:func:`minMaxLoc`
.. index:: gpu::countNonZero
@ -153,4 +153,4 @@ gpu::countNonZero
The function does not work with ``CV_64F`` images on GPUs with the compute capability < 1.3.
See Also: :c:func:`countNonZero`
See Also: :c:cpp:func:`countNonZero`

10
modules/gpu/doc/object_detection.rst
View File

@ -173,11 +173,11 @@ gpu::HOGDescriptor::detectMultiScale
Performs object detection with a multi-scale window.
:param img: Source image. See :func:`gpu::HOGDescriptor::detect` for type limitations.
:param img: Source image. See :cpp:func:`gpu::HOGDescriptor::detect` for type limitations.
:param found_locations: Detected objects boundaries.
:param hit_threshold: Threshold for the distance between features and SVM classifying plane. See :func:`gpu::HOGDescriptor::detect` for details.
:param hit_threshold: Threshold for the distance between features and SVM classifying plane. See :cpp:func:`gpu::HOGDescriptor::detect` for details.
:param win_stride: Window stride. It must be a multiple of block stride.
@ -185,7 +185,7 @@ gpu::HOGDescriptor::detectMultiScale
:param scale0: Coefficient of the detection window increase.
:param group_threshold: Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping. See :func:`groupRectangles` .
:param group_threshold: Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping. See :cpp:func:`groupRectangles` .
.. index:: gpu::HOGDescriptor::getDescriptors
@ -197,7 +197,7 @@ gpu::HOGDescriptor::getDescriptors
Returns block descriptors computed for the whole image. The function is mainly used to learn the classifier.
:param img: Source image. See :func:`gpu::HOGDescriptor::detect` for type limitations.
:param img: Source image. See :cpp:func:`gpu::HOGDescriptor::detect` for type limitations.
:param win_stride: Window stride. It must be a multiple of block stride.
@ -324,5 +324,5 @@ gpu::CascadeClassifier_GPU::detectMultiScale
imshow("Faces", image_cpu);
See Also: :c:func:`CascadeClassifier::detectMultiScale`
See Also: :c:cpp:func:`CascadeClassifier::detectMultiScale`

18
modules/gpu/doc/operations_on_matrices.rst
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@ -16,7 +16,7 @@ gpu::transpose
:param dst: Destination matrix.
See Also:
:c:func:`transpose`
:c:cpp:func:`transpose`
.. index:: gpu::flip
@ -40,7 +40,7 @@ gpu::flip
See Also:
:c:func:`flip`
:c:cpp:func:`flip`
.. index:: gpu::LUT
@ -57,7 +57,7 @@ gpu::LUT
:param dst: Destination matrix with the same depth as ``lut`` and the same number of channels as ``src``.
See Also: :c:func:`LUT`
See Also: :c:cpp:func:`LUT`
.. index:: gpu::merge
@ -81,7 +81,7 @@ gpu::merge
:param stream: Stream for the asynchronous version.
See Also: :c:func:`merge`
See Also: :c:cpp:func:`merge`
.. index:: gpu::split
@ -103,7 +103,7 @@ gpu::split
:param stream: Stream for the asynchronous version.
See Also: :c:func:`split`
See Also: :c:cpp:func:`split`
.. index:: gpu::magnitude
@ -128,7 +128,7 @@ gpu::magnitude
:param stream: Stream for the asynchronous version.
See Also:
:c:func:`magnitude`
:c:cpp:func:`magnitude`
.. index:: gpu::magnitudeSqr
@ -173,7 +173,7 @@ gpu::phase
:param stream: Stream for the asynchronous version.
See Also:
:c:func:`phase`
:c:cpp:func:`phase`
.. index:: gpu::cartToPolar
@ -198,7 +198,7 @@ gpu::cartToPolar
:param stream: Stream for the asynchronous version.
See Also:
:c:func:`cartToPolar`
:c:cpp:func:`cartToPolar`
.. index:: gpu::polarToCart
@ -223,4 +223,4 @@ gpu::polarToCart
:param stream: Stream for the asynchronous version.
See Also:
:c:func:`polarToCart`
:c:cpp:func:`polarToCart`

22
modules/gpu/doc/per_element_operations.rst
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@ -21,7 +21,7 @@ gpu::add
:param dst: Destination matrix with the same size and type as ``src1``.
See Also: :c:func:`add`
See Also: :c:cpp:func:`add`
.. index:: gpu::subtract
@ -39,7 +39,7 @@ gpu::subtract
:param dst: Destination matrix with the same size and type as ``src1``.
See Also: :c:func:`subtract`
See Also: :c:cpp:func:`subtract`
@ -59,7 +59,7 @@ gpu::multiply
:param dst: Destination matrix with the same size and type as ``src1``.
See Also: :c:func:`multiply`
See Also: :c:cpp:func:`multiply`
.. index:: gpu::divide
@ -78,9 +78,9 @@ gpu::divide
:param dst: Destination matrix with the same size and type as ``src1``.
This function, in contrast to :c:func:`divide`, uses a round-down rounding mode.
This function, in contrast to :c:cpp:func:`divide`, uses a round-down rounding mode.
See Also: :c:func:`divide`
See Also: :c:cpp:func:`divide`
@ -96,7 +96,7 @@ gpu::exp
:param dst: Destination matrix with the same size and type as ``src``.
See Also: :c:func:`exp`
See Also: :c:cpp:func:`exp`
@ -112,7 +112,7 @@ gpu::log
:param dst: Destination matrix with the same size and type as ``src``.
See Also: :c:func:`log`
See Also: :c:cpp:func:`log`
@ -132,7 +132,7 @@ gpu::absdiff
:param dst: Destination matrix with the same size and type as ``src1``.
See Also: :c:func:`absdiff`
See Also: :c:cpp:func:`absdiff`
.. index:: gpu::compare
@ -157,7 +157,7 @@ gpu::compare
* **CMP_LE:** ``src1(.) <= src2(.)``
* **CMP_NE:** ``src1(.) != src2(.)``
See Also: :c:func:`compare`
See Also: :c:cpp:func:`compare`
.. index:: gpu::bitwise_not
@ -268,7 +268,7 @@ gpu::min
:param stream: Stream for the asynchronous version.
See Also: :c:func:`min`
See Also: :c:cpp:func:`min`
@ -294,4 +294,4 @@ gpu::max
:param stream: Stream for the asynchronous version.
See Also: :c:func:`max`
See Also: :c:cpp:func:`max`

20
modules/highgui/doc/qt_new_functions.rst
View File

@ -62,7 +62,7 @@ The following code is an example used to generate the figure. ::
setWindowProperty
---------------------
.. c:function:: void setWindowProperty(const string& name, int prop_id, double prop_value)
.. cpp:function:: void setWindowProperty(const string& name, int prop_id, double prop_value)
Changes parameters of a window dynamically.
@ -96,7 +96,7 @@ The function ``setWindowProperty`` enables changing properties of a window.
getWindowProperty
---------------------
.. c:function:: void getWindowProperty(const char* name, int prop_id)
.. cpp:function:: void getWindowProperty(const string& name, int prop_id)
Provides parameters of a window.
@ -122,7 +122,7 @@ The function ``getWindowProperty`` returns properties of a window.
fontQt
----------
.. c:function:: CvFont fontQt(const string& nameFont, int pointSize = -1, Scalar color = Scalar::all(0), int weight = CV_FONT_NORMAL, int style = CV_STYLE_NORMAL, int spacing = 0)
.. cpp:function:: CvFont fontQt(const string& nameFont, int pointSize = -1, Scalar color = Scalar::all(0), int weight = CV_FONT_NORMAL, int style = CV_STYLE_NORMAL, int spacing = 0)
Creates the font to draw a text on an image.
@ -167,7 +167,7 @@ A basic usage of this function is the following: ::
addText
-----------
.. c:function:: void addText(const Mat& img, const string& text, Point location, CvFont *font)
.. cpp:function:: void addText(const Mat& img, const string& text, Point location, CvFont *font)
Creates the font to draw a text on an image.
@ -192,7 +192,7 @@ using a specific font
displayOverlay
------------------
.. c:function:: void displayOverlay(const string& name, const string& text, int delay)
.. cpp:function:: void displayOverlay(const string& name, const string& text, int delay)
Displays a text on a window image as an overlay for a specified duration.
@ -208,7 +208,7 @@ The function ``displayOverlay`` displays useful information/tips on top of the w
displayStatusBar
--------------------
.. c:function:: void displayStatusBar(const string& name, const string& text, int delay)
.. cpp:function:: void displayStatusBar(const string& name, const string& text, int delay)
Displays a text on the window statusbar during the specified period of time.
@ -227,7 +227,7 @@ The function ``displayOverlay`` displays useful information/tips on top of the w
createOpenGLCallback
------------------------
.. c:function:: void createOpenGLCallback( const string& window_name, OpenGLCallback callbackOpenGL, void* userdata CV_DEFAULT(NULL), double angle CV_DEFAULT(-1), double zmin CV_DEFAULT(-1), double zmax CV_DEFAULT(-1)
.. cpp:function:: void createOpenGLCallback( const string& window_name, OpenGLCallback callbackOpenGL, void* userdata CV_DEFAULT(NULL), double angle CV_DEFAULT(-1), double zmin CV_DEFAULT(-1), double zmax CV_DEFAULT(-1)
Creates a callback function called to draw OpenGL on top the the image display by ``windowname``.
@ -279,7 +279,7 @@ The function ``createOpenGLCallback`` can be used to draw 3D data on the window.
saveWindowParameters
------------------------
.. c:function:: void saveWindowParameters(const string& name)
.. cpp:function:: void saveWindowParameters(const string& name)
Saves parameters of the window ``windowname`` .
@ -293,7 +293,7 @@ The function ``saveWindowParameters`` saves size, location, flags, trackbars va
loadWindowParameters
------------------------
.. c:function:: void loadWindowParameters(const string& name)
.. cpp:function:: void loadWindowParameters(const string& name)
Loads parameters of the window ``windowname`` .
@ -307,7 +307,7 @@ The function ``loadWindowParameters`` loads size, location, flags, trackbars val
createButton
----------------
.. c:function:: createButton( const string& button_name CV_DEFAULT(NULL),ButtonCallback on_change CV_DEFAULT(NULL), void* userdata CV_DEFAULT(NULL), int button_type CV_DEFAULT(CV_PUSH_BUTTON), int initial_button_state CV_DEFAULT(0))
.. cpp:function:: createButton( const string& button_name CV_DEFAULT(NULL),ButtonCallback on_change CV_DEFAULT(NULL), void* userdata CV_DEFAULT(NULL), int button_type CV_DEFAULT(CV_PUSH_BUTTON), int initial_button_state CV_DEFAULT(0))
Creates a callback function called to draw OpenGL on top of the image display by ``windowname`` .

18
modules/highgui/doc/reading_and_writing_images_and_video.rst
View File

@ -9,7 +9,7 @@ Reading and Writing Images and Video
imdecode
------------
.. c:function:: Mat imdecode( const Mat\& buf, int flags )
.. cpp:function:: Mat imdecode( InputArray buf, int flags )
Reads an image from a buffer in memory.
@ -29,7 +29,7 @@ See
imencode
------------
.. c:function:: bool imencode( const string\& ext, const Mat\& img, vector<uchar>\& buf, const vector<int>\& params=vector<int>())
.. cpp:function:: bool imencode( const string& ext, InputArray img, vector<uchar>& buf, const vector<int>& params=vector<int>())
Encode an image into a memory buffer.
@ -51,7 +51,7 @@ See
imread
----------
.. c:function:: Mat imread( const string\& filename, int flags=1 )
.. cpp:function:: Mat imread( const string& filename, int flags=1 )
Loads an image from a file.
@ -95,7 +95,7 @@ The function ``imread`` loads an image from the specified file and returns it. I
imwrite
-----------
.. c:function:: bool imwrite( const string\& filename, const Mat\& img, const vector<int>\& params=vector<int>())
.. cpp:function:: bool imwrite( const string& filename, InputArray img, const vector<int>& params=vector<int>())
Saves an image to a specified file.
@ -208,11 +208,11 @@ The class provides C++ video capturing API. Here is how the class can be used: :
VideoCapture::VideoCapture
------------------------------
.. c:function:: VideoCapture::VideoCapture()
.. cpp:function:: VideoCapture::VideoCapture()
.. c:function:: VideoCapture::VideoCapture(const string& filename)
.. cpp:function:: VideoCapture::VideoCapture(const string& filename)
.. c:function:: VideoCapture::VideoCapture(int device)
.. cpp:function:: VideoCapture::VideoCapture(int device)
VideoCapture constructors.
@ -226,7 +226,7 @@ VideoCapture constructors.
VideoCapture::get
---------------------
.. c:function:: double VideoCapture::get(int property_id)
.. cpp:function:: double VideoCapture::get(int property_id)
:param property_id: Property identifier. It can be one of the following:
@ -277,7 +277,7 @@ VideoCapture::get
VideoCapture::set
---------------------
.. c:function:: bool VideoCapture::set(int property_id, double value)
.. cpp:function:: bool VideoCapture::set(int property_id, double value)
Sets a property in the VideoCapture backend.

12
modules/highgui/doc/user_interface.rst
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@ -9,7 +9,7 @@ User Interface
createTrackbar
------------------
.. c:function:: int createTrackbar( const string& trackbarname, const string& winname, int* value, int count, TrackbarCallback onChange CV_DEFAULT(0), void* userdata CV_DEFAULT(0))
.. cpp:function:: int createTrackbar( const string& trackbarname, const string& winname, int* value, int count, TrackbarCallback onChange=0, void* userdata=0)
Creates a trackbar and attaches it to the specified window.
@ -45,7 +45,7 @@ Clicking the label of each trackbar enables editing the trackbar values manually
getTrackbarPos
------------------
.. c:function:: int getTrackbarPos( const string& trackbarname, const string& winname )
.. cpp:function:: int getTrackbarPos( const string& trackbarname, const string& winname )
Returns the trackbar position.
@ -67,7 +67,7 @@ Qt-specific details:
imshow
----------
.. c:function:: void imshow( const string& winname, const Mat& image )
.. cpp:function:: void imshow( const string& winname, InputArray image )
Displays an image in the specified window.
@ -92,7 +92,7 @@ The function ``imshow`` displays an image in the specified window. If the window
namedWindow
---------------
.. c:function:: void namedWindow( const string& winname, int flags )
.. cpp:function:: void namedWindow( const string& winname, int flags )
Creates a window.
@ -132,7 +132,7 @@ Qt-specific details:
setTrackbarPos
------------------
.. c:function:: void setTrackbarPos( const string& trackbarname, const string& winname, int pos )
.. cpp:function:: void setTrackbarPos( const string& trackbarname, const string& winname, int pos )
Sets the trackbar position.
@ -156,7 +156,7 @@ Qt-specific details:
waitKey
-----------
.. c:function:: int waitKey(int delay=0)
.. cpp:function:: int waitKey(int delay=0)
Waits for a pressed key.

18
modules/highgui/include/opencv2/highgui/highgui.hpp
View File

@ -56,7 +56,7 @@ namespace cv
enum { WINDOW_AUTOSIZE=1 };
CV_EXPORTS_W void namedWindow( const string& winname, int flags CV_DEFAULT(WINDOW_AUTOSIZE) );
CV_EXPORTS_W void namedWindow( const string& winname, int flags=WINDOW_AUTOSIZE );
CV_EXPORTS_W void destroyWindow( const string& winname );
CV_EXPORTS_W int startWindowThread();
@ -66,16 +66,16 @@ CV_EXPORTS_W double getWindowProperty(const string& winname, int prop_id);//YV
//Only for Qt
//------------------------
CV_EXPORTS CvFont fontQt(const string& nameFont, int pointSize CV_DEFAULT(-1),
Scalar color CV_DEFAULT(Scalar::all(0)), int weight CV_DEFAULT(CV_FONT_NORMAL),
int style CV_DEFAULT(CV_STYLE_NORMAL), int spacing CV_DEFAULT(0));
CV_EXPORTS CvFont fontQt(const string& nameFont, int pointSize=-1,
Scalar color=Scalar::all(0), int weight=CV_FONT_NORMAL,
int style=CV_STYLE_NORMAL, int spacing=0);
CV_EXPORTS void addText( const Mat& img, const string& text, Point org, CvFont font);
CV_EXPORTS void displayOverlay(const string& winname, const string& text, int delayms);
CV_EXPORTS void displayStatusBar(const string& winname, const string& text, int delayms);
typedef void (CV_CDECL *OpenGLCallback)(void* userdata);
CV_EXPORTS void createOpenGLCallback(const string& winname, CvOpenGLCallback callbackOpenGL, void* userdata CV_DEFAULT(0));
CV_EXPORTS void createOpenGLCallback(const string& winname, CvOpenGLCallback callbackOpenGL, void* userdata=0);
CV_EXPORTS void saveWindowParameters(const string& windowName);
CV_EXPORTS void loadWindowParameters(const string& windowName);
@ -84,8 +84,8 @@ CV_EXPORTS void stopLoop();
typedef void (CV_CDECL *ButtonCallback)(int state, void* userdata);
CV_EXPORTS int createButton( const string& bar_name, ButtonCallback on_change,
void* userdata CV_DEFAULT(NULL), int type CV_DEFAULT(CV_PUSH_BUTTON),
bool initial_button_state CV_DEFAULT(0));
void* userdata=NULL, int type=CV_PUSH_BUTTON,
bool initial_button_state=0);
//-------------------------
CV_EXPORTS_W void imshow( const string& winname, InputArray mat );
@ -94,8 +94,8 @@ typedef void (CV_CDECL *TrackbarCallback)(int pos, void* userdata);
CV_EXPORTS int createTrackbar( const string& trackbarname, const string& winname,
int* value, int count,
TrackbarCallback onChange CV_DEFAULT(0),
void* userdata CV_DEFAULT(0));
TrackbarCallback onChange=0,
void* userdata=0);
CV_EXPORTS_W int getTrackbarPos( const string& trackbarname, const string& winname );
CV_EXPORTS_W void setTrackbarPos( const string& trackbarname, const string& winname, int pos );

108
modules/imgproc/doc/feature_detection.rst
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@ -5,11 +5,9 @@ Feature Detection
.. index:: Canny
.. _Canny:
Canny
---------
.. c:function:: void Canny( const Mat& image, Mat& edges, double threshold1, double threshold2, int apertureSize=3, bool L2gradient=false )
.. cpp:function:: void Canny( InputArray image, OutputArray edges, double threshold1, double threshold2, int apertureSize=3, bool L2gradient=false )
Finds edges in an image using the Canny algorithm.
@ -21,7 +19,7 @@ Canny
:param threshold2: The second threshold for the hysteresis procedure.
:param apertureSize: Aperture size for the :func:`Sobel` operator.
:param apertureSize: Aperture size for the :cpp:func:`Sobel` operator.
:param L2gradient: Flag indicating whether a more accurate :math:`L_2` norm :math:`=\sqrt{(dI/dx)^2 + (dI/dy)^2}` should be used to compute the image gradient magnitude ( ``L2gradient=true`` ), or a faster default :math:`L_1` norm :math:`=|dI/dx|+|dI/dy|` is enough ( ``L2gradient=false`` ).
@ -30,12 +28,10 @@ http://en.wikipedia.org/wiki/Canny_edge_detector
.. index:: cornerEigenValsAndVecs
.. _cornerEigenValsAndVecs:
cornerEigenValsAndVecs
----------------------
.. c:function:: void cornerEigenValsAndVecs( const Mat& src, Mat& dst, int blockSize, int apertureSize, int borderType=BORDER_DEFAULT )
.. cpp:function:: void cornerEigenValsAndVecs( InputArray src, OutputArray dst, int blockSize, int apertureSize, int borderType=BORDER_DEFAULT )
Calculates eigenvalues and eigenvectors of image blocks for corner detection.
@ -45,9 +41,9 @@ cornerEigenValsAndVecs
:param blockSize: Neighborhood size (see details below).
:param apertureSize: Aperture parameter for the :func:`Sobel` operator.
:param apertureSize: Aperture parameter for the :cpp:func:`Sobel` operator.
:param boderType: Pixel extrapolation method. See :func:`borderInterpolate` .
:param boderType: Pixel extrapolation method. See :cpp:func:`borderInterpolate` .
For every pixel
:math:`p` , the function ``cornerEigenValsAndVecs`` considers a ``blockSize`` :math:`\times` ``blockSize`` neigborhood
@ -58,7 +54,7 @@ For every pixel
M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}(dI/dx dI/dy)^2 \\ \sum _{S(p)}(dI/dx dI/dy)^2 & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}
where the derivatives are computed using the
:func:`Sobel` operator.
:cpp:func:`Sobel` operator.
After that it finds eigenvectors and eigenvalues of
:math:`M` and stores them in the destination image as
@ -73,18 +69,16 @@ After that it finds eigenvectors and eigenvalues of
The output of the function can be used for robust edge or corner detection.
See Also:
:func:`cornerMinEigenVal`,
:func:`cornerHarris`,
:func:`preCornerDetect`
:cpp:func:`cornerMinEigenVal`,
:cpp:func:`cornerHarris`,
:cpp:func:`preCornerDetect`
.. index:: cornerHarris
.. _cornerHarris:
cornerHarris
------------
.. c:function:: void cornerHarris( const Mat& src, Mat& dst, int blockSize, int apertureSize, double k, int borderType=BORDER_DEFAULT )
.. cpp:function:: void cornerHarris( InputArray src, OutputArray dst, int blockSize, int apertureSize, double k, int borderType=BORDER_DEFAULT )
Harris edge detector.
@ -92,17 +86,17 @@ cornerHarris
:param dst: Image to store the Harris detector responses. It has the type ``CV_32FC1`` and the same size as ``src`` .
:param blockSize: Neighborhood size (see the details on :func:`cornerEigenValsAndVecs` ).
:param blockSize: Neighborhood size (see the details on :cpp:func:`cornerEigenValsAndVecs` ).
:param apertureSize: Aperture parameter for the :func:`Sobel` operator.
:param apertureSize: Aperture parameter for the :cpp:func:`Sobel` operator.
:param k: Harris detector free parameter. See the formula below.
:param boderType: Pixel extrapolation method. See :func:`borderInterpolate` .
:param boderType: Pixel extrapolation method. See :cpp:func:`borderInterpolate` .
The function runs the Harris edge detector on the image. Similarly to
:func:`cornerMinEigenVal` and
:func:`cornerEigenValsAndVecs` , for each pixel
:cpp:func:`cornerMinEigenVal` and
:cpp:func:`cornerEigenValsAndVecs` , for each pixel
:math:`(x, y)` it calculates a
:math:`2\times2` gradient covariation matrix
:math:`M^{(x,y)}` over a
@ -116,12 +110,10 @@ Corners in the image can be found as the local maxima of this response map.
.. index:: cornerMinEigenVal
.. _cornerMinEigenVal:
cornerMinEigenVal
-----------------
.. c:function:: void cornerMinEigenVal( const Mat& src, Mat& dst, int blockSize, int apertureSize=3, int borderType=BORDER_DEFAULT )
.. cpp:function:: void cornerMinEigenVal( InputArray src, OutputArray dst, int blockSize, int apertureSize=3, int borderType=BORDER_DEFAULT )
Calculates the minimal eigenvalue of gradient matrices for corner detection.
@ -129,24 +121,22 @@ cornerMinEigenVal
:param dst: Image to store the minimal eigenvalues. It has the type ``CV_32FC1`` and the same size as ``src`` .
:param blockSize: Neighborhood size (see the details on :func:`cornerEigenValsAndVecs` ).
:param blockSize: Neighborhood size (see the details on :cpp:func:`cornerEigenValsAndVecs` ).
:param apertureSize: Aperture parameter for the :func:`Sobel` operator.
:param apertureSize: Aperture parameter for the :cpp:func:`Sobel` operator.
:param boderType: Pixel extrapolation method. See :func:`borderInterpolate` .
:param boderType: Pixel extrapolation method. See :cpp:func:`borderInterpolate` .
The function is similar to
:func:`cornerEigenValsAndVecs` but it calculates and stores only the minimal eigenvalue of the covariation matrix of derivatives, that is,
:cpp:func:`cornerEigenValsAndVecs` but it calculates and stores only the minimal eigenvalue of the covariation matrix of derivatives, that is,
:math:`\min(\lambda_1, \lambda_2)` in terms of the formulae in the
:func:`cornerEigenValsAndVecs` description.
:cpp:func:`cornerEigenValsAndVecs` description.
.. index:: cornerSubPix
.. _cornerSubPix:
cornerSubPix
----------------
.. c:function:: void cornerSubPix( const Mat& image, vector<Point2f>& corners, Size winSize, Size zeroZone, TermCriteria criteria )
.. cpp:function:: void cornerSubPix( InputArray image, InputOutputArray corners, Size winSize, Size zeroZone, TermCriteria criteria )
Refines the corner locations.
@ -200,12 +190,10 @@ The algorithm sets the center of the neighborhood window at this new center
.. index:: goodFeaturesToTrack
.. _goodFeaturesToTrack:
goodFeaturesToTrack
-------------------
.. c:function:: void goodFeaturesToTrack( const Mat& image, vector<Point2f>& corners, int maxCorners, double qualityLevel, double minDistance, const Mat& mask=Mat(), int blockSize=3, bool useHarrisDetector=false, double k=0.04 )
.. cpp:function:: void goodFeaturesToTrack( InputArray image, OutputArray corners, int maxCorners, double qualityLevel, double minDistance, InputArray mask=None(), int blockSize=3, bool useHarrisDetector=false, double k=0.04 )
Determines strong corners on an image.
@ -215,15 +203,15 @@ goodFeaturesToTrack
:param maxCorners: Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.
:param qualityLevel: Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see :func:`cornerMinEigenVal` ) or the Harris function response (see :func:`cornerHarris` ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the ``qualityLevel=0.01`` , then all the corners with the quality measure less than 15 are rejected.
:param qualityLevel: Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see :cpp:func:`cornerMinEigenVal` ) or the Harris function response (see :cpp:func:`cornerHarris` ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the ``qualityLevel=0.01`` , then all the corners with the quality measure less than 15 are rejected.
:param minDistance: Minimum possible Euclidean distance between the returned corners.
:param mask: Optional region of interest. If the image is not empty (it needs to have the type ``CV_8UC1`` and the same size as ``image`` ), it specifies the region in which the corners are detected.
:param blockSize: Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See :func:`cornerEigenValsAndVecs` .
:param blockSize: Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See :cpp:func:`cornerEigenValsAndVecs` .
:param useHarrisDetector: Parameter indicating whether to use a Harris detector (see :func:`cornerHarris`) or :func:`cornerMinEigenVal`.
:param useHarrisDetector: Parameter indicating whether to use a Harris detector (see :cpp:func:`cornerHarris`) or :cpp:func:`cornerMinEigenVal`.
:param k: Free parameter of the Harris detector.
@ -231,8 +219,8 @@ The function finds the most prominent corners in the image or in the specified i
#.
Function calculates the corner quality measure at every source image pixel using the
:func:`cornerMinEigenVal` or
:func:`cornerHarris` .
:cpp:func:`cornerMinEigenVal` or
:cpp:func:`cornerHarris` .
#.
Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are retained).
@ -251,21 +239,19 @@ The function can be used to initialize a point-based tracker of an object.
**Note**: If the function is called with different values ``A`` and ``B`` of the parameter ``qualityLevel`` , and ``A`` > {B}, the vector of returned corners with ``qualityLevel=A`` will be the prefix of the output vector with ``qualityLevel=B`` .
See Also: :func:`cornerMinEigenVal`,
:func:`cornerHarris`,
:func:`calcOpticalFlowPyrLK`,
:func:`estimateRigidMotion`,
:func:`PlanarObjectDetector`,
:func:`OneWayDescriptor`
See Also: :cpp:func:`cornerMinEigenVal`,
:cpp:func:`cornerHarris`,
:cpp:func:`calcOpticalFlowPyrLK`,
:cpp:func:`estimateRigidMotion`,
:cpp:func:`PlanarObjectDetector`,
:cpp:func:`OneWayDescriptor`
.. index:: HoughCircles
.. _HoughCircles:
HoughCircles
------------
.. c:function:: void HoughCircles( Mat& image, vector<Vec3f>& circles, int method, double dp, double minDist, double param1=100, double param2=100, int minRadius=0, int maxRadius=0 )
.. cpp: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 )
Finds circles in a grayscale image using the Hough transform.
@ -279,7 +265,7 @@ HoughCircles
:param minDist: Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.
:param param1: The first method-specific parameter. In case of ``CV_HOUGH_GRADIENT`` , it is the higher threshold of the two passed to the :func:`Canny` edge detector (the lower one is twice smaller).
:param param1: The first method-specific parameter. In case of ``CV_HOUGH_GRADIENT`` , it is the higher threshold of the two passed to the :cpp:func:`Canny` edge detector (the lower one is twice smaller).
:param param2: The second method-specific parameter. In case of ``CV_HOUGH_GRADIENT`` , it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first
@ -323,17 +309,15 @@ The function finds circles in a grayscale image using a modification of the Houg
**Note**: Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( ``minRadius`` and ``maxRadius`` ) if you know it. Or, you may ignore the returned radius, use only the center, and find the correct radius using an additional procedure.
See Also:
:func:`fitEllipse`,
:func:`minEnclosingCircle`
:cpp:func:`fitEllipse`,
:cpp:func:`minEnclosingCircle`
.. index:: HoughLines
.. _HoughLines:
HoughLines
----------
.. c:function:: void HoughLines( Mat& image, vector<Vec2f>& lines, double rho, double theta, int threshold, double srn=0, double stn=0 )
.. cpp:function:: void HoughLines( InputArray image, OutputArray lines, double rho, double theta, int threshold, double srn=0, double stn=0 )
Finds lines in a binary image using the standard Hough transform.
@ -352,16 +336,14 @@ HoughLines
:param stn: For the multi-scale Hough transform, it is a divisor for the distance resolution ``theta`` .
The function implements the standard or standard multi-scale Hough transform algorithm for line detection. See
:func:`HoughLinesP` for the code example.
:cpp:func:`HoughLinesP` for the code example.
.. index:: HoughLinesP
.. _HoughLinesP:
HoughLinesP
-----------
.. c:function:: void HoughLinesP( Mat& image, vector<Vec4i>& lines, double rho, double theta, int threshold, double minLineLength=0, double maxLineGap=0 )
.. cpp:function:: void HoughLinesP( InputArray image, OutputArray lines, double rho, double theta, int threshold, double minLineLength=0, double maxLineGap=0 )
Finds line segments in a binary image using the probabilistic Hough transform.
@ -446,12 +428,10 @@ And this is the output of the above program in case of the probabilistic Hough t
.. index:: preCornerDetect
.. _preCornerDetect:
preCornerDetect
---------------
.. c:function:: void preCornerDetect( const Mat& src, Mat& dst, int apertureSize, int borderType=BORDER_DEFAULT )
.. cpp:function:: void preCornerDetect( InputArray src, OutputArray dst, int apertureSize, int borderType=BORDER_DEFAULT )
Calculates a feature map for corner detection.
@ -459,9 +439,9 @@ preCornerDetect
:param dst: Output image that has the type ``CV_32F`` and the same size as ``src`` .
:param apertureSize: Aperture size of the :func:`Sobel` .
:param apertureSize: Aperture size of the :cpp:func:`Sobel` .
:param borderType: Pixel extrapolation method. See :func:`borderInterpolate` .
:param borderType: Pixel extrapolation method. See :cpp:func:`borderInterpolate` .
The function calculates the complex spatial derivative-based function of the source image

458
modules/imgproc/doc/filtering.rst
View File

@ -6,21 +6,19 @@ Image Filtering
===============
Functions and classes described in this section are used to perform various linear or non-linear filtering operations on 2D images (represented as
:func:`Mat`'s). It means that for each pixel location
:cpp:func:`Mat`'s). It means that for each pixel location
:math:`(x,y)` in the source image (normally, rectangular), its neighborhood is considered and used to compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of morphological operations, it is the minimum or maximum values, and so on. The computed response is stored in the destination image at the same location
:math:`(x,y)` . It means that the output image will be of the same size as the input image. Normally, the functions support multi-channel arrays, in which case every channel is processed independently. Therefore, the output image will also have the same number of channels as the input one.
Another common feature of the functions and classes described in this section is that, unlike simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For example, if you want to smooth an image using a Gaussian
:math:`3 \times 3` filter, then, when processing the left-most pixels in each row, you need pixels to the left of them, that is, outside of the image. You can let these pixels be the same as the left-most image pixels ("replicated border" extrapolation method), or assume that all the non-existing pixels are zeros ("contant border" extrapolation method), and so on.
OpenCV enables you to specify the extrapolation method. For details, see the function :func:`borderInterpolate` and discussion of the ``borderType`` parameter in various functions below.
OpenCV enables you to specify the extrapolation method. For details, see the function :cpp:func:`borderInterpolate` and discussion of the ``borderType`` parameter in various functions below.
.. index:: BaseColumnFilter
.. _BaseColumnFilter:
BaseColumnFilter
----------------
.. c:type:: BaseColumnFilter
.. cpp:class:: BaseColumnFilter
Base class for filters with single-column kernels ::
@ -55,23 +53,21 @@ The class ``BaseColumnFilter`` is a base class for filtering data using single-c
where
:math:`F` is a filtering function but, as it is represented as a class, it can produce any side effects, memorize previously processed data, and so on. The class only defines an interface and is not used directly. Instead, there are several functions in OpenCV (and you can add more) that return pointers to the derived classes that implement specific filtering operations. Those pointers are then passed to the
:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
:cpp:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
See Also:
:func:`BaseRowFilter`,
:func:`BaseFilter`,
:func:`FilterEngine`,
:func:`getColumnSumFilter`,
:func:`getLinearColumnFilter`,
:func:`getMorphologyColumnFilter`
:cpp:func:`BaseRowFilter`,
:cpp:func:`BaseFilter`,
:cpp:func:`FilterEngine`,
:cpp:func:`getColumnSumFilter`,
:cpp:func:`getLinearColumnFilter`,
:cpp:func:`getMorphologyColumnFilter`
.. index:: BaseFilter
.. _BaseFilter:
BaseFilter
----------
.. c:type:: BaseFilter
.. cpp:class:: BaseFilter
Base class for 2D image filters ::
@ -107,22 +103,20 @@ The class ``BaseFilter`` is a base class for filtering data using 2D kernels. Fi
where
:math:`F` is a filtering function. The class only defines an interface and is not used directly. Instead, there are several functions in OpenCV (and you can add more) that return pointers to the derived classes that implement specific filtering operations. Those pointers are then passed to the
:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
:cpp:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
See Also:
:func:`BaseColumnFilter`,
:func:`BaseRowFilter`,
:func:`FilterEngine`,
:func:`getLinearFilter`,
:func:`getMorphologyFilter`
:cpp:func:`BaseColumnFilter`,
:cpp:func:`BaseRowFilter`,
:cpp:func:`FilterEngine`,
:cpp:func:`getLinearFilter`,
:cpp:func:`getMorphologyFilter`
.. index:: BaseRowFilter
.. _BaseRowFilter:
BaseRowFilter
-------------
.. c:type:: BaseRowFilter
.. cpp:class:: BaseRowFilter
Base class for filters with single-row kernels ::
@ -150,23 +144,21 @@ The class ``BaseRowFilter`` is a base class for filtering data using single-row
where
:math:`F` is a filtering function. The class only defines an interface and is not used directly. Instead, there are several functions in OpenCV (and you can add more) that return pointers to the derived classes that implement specific filtering operations. Those pointers are then passed to the
:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
:cpp:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
See Also:
:func:`BaseColumnFilter`,
:func:`Filter`,
:func:`FilterEngine`,
:func:`getLinearRowFilter`,
:func:`getMorphologyRowFilter`,
:func:`getRowSumFilter`
:cpp:func:`BaseColumnFilter`,
:cpp:func:`Filter`,
:cpp:func:`FilterEngine`,
:cpp:func:`getLinearRowFilter`,
:cpp:func:`getMorphologyRowFilter`,
:cpp:func:`getRowSumFilter`
.. index:: FilterEngine
.. _FilterEngine:
FilterEngine
------------
.. c:type:: FilterEngine
.. cpp:class:: FilterEngine
Generic image filtering class ::
@ -239,12 +231,12 @@ The class ``FilterEngine`` can be used to apply an arbitrary filtering operation
It contains all the necessary intermediate buffers, computes extrapolated values
of the "virtual" pixels outside of the image, and so on. Pointers to the initialized ``FilterEngine`` instances
are returned by various ``create*Filter`` functions (see below) and they are used inside high-level functions such as
:func:`filter2D`,
:func:`erode`,
:func:`dilate`, and others. Thus, the class plays a key role in many of OpenCV filtering functions.
:cpp:func:`filter2D`,
:cpp:func:`erode`,
:cpp:func:`dilate`, and others. Thus, the class plays a key role in many of OpenCV filtering functions.
This class makes it easier to combine filtering operations with other operations, such as color space conversions, thresholding, arithmetic operations, and others. By combining several operations together you can get much better performance because your data will stay in cache. For example, see below the implementation of the Laplace operator for floating-point images, which is a simplified implementation of
:func:`Laplacian` : ::
:cpp:func:`Laplacian` : ::
void laplace_f(const Mat& src, Mat& dst)
{
@ -355,7 +347,7 @@ Unlike the earlier versions of OpenCV, now the filtering operations fully suppor
Explore the data types. As it was mentioned in the
:func:`BaseFilter` description, the specific filters can process data of any type, despite that ``Base*Filter::operator()`` only takes ``uchar`` pointers and no information about the actual types. To make it all work, the following rules are used:
:cpp:func:`BaseFilter` description, the specific filters can process data of any type, despite that ``Base*Filter::operator()`` only takes ``uchar`` pointers and no information about the actual types. To make it all work, the following rules are used:
*
In case of separable filtering, ``FilterEngine::rowFilter`` is applied first. It transforms the input image data (of type ``srcType`` ) to the intermediate results stored in the internal buffers (of type ``bufType`` ). Then, these intermediate results are processed as
@ -366,21 +358,21 @@ Explore the data types. As it was mentioned in the
In case of non-separable filtering, ``bufType`` must be the same as ``srcType`` . The source data is copied to the temporary buffer, if needed, and then just passed to ``FilterEngine::filter2D`` . That is, the input type for ``filter2D`` is ``srcType`` (= ``bufType`` ) and the output type is ``dstType`` .
See Also:
:func:`BaseColumnFilter`,
:func:`BaseFilter`,
:func:`BaseRowFilter`,
:func:`createBoxFilter`,
:func:`createDerivFilter`,
:func:`createGaussianFilter`,
:func:`createLinearFilter`,
:func:`createMorphologyFilter`,
:func:`createSeparableLinearFilter`
:cpp:func:`BaseColumnFilter`,
:cpp:func:`BaseFilter`,
:cpp:func:`BaseRowFilter`,
:cpp:func:`createBoxFilter`,
:cpp:func:`createDerivFilter`,
:cpp:func:`createGaussianFilter`,
:cpp:func:`createLinearFilter`,
:cpp:func:`createMorphologyFilter`,
:cpp:func:`createSeparableLinearFilter`
.. index:: bilateralFilter
bilateralFilter
-------------------
.. c:function:: void bilateralFilter( const Mat& src, Mat& dst, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT )
.. cpp:function:: void bilateralFilter( InputArray src, OutputArray dst, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT )
Applies the bilateral filter to an image.
@ -401,7 +393,7 @@ http://www.dai.ed.ac.uk/CVonline/LOCAL\_COPIES/MANDUCHI1/Bilateral\_Filtering.ht
blur
--------
.. c:function:: void blur( const Mat& src, Mat& dst, Size ksize, Point anchor=Point(-1,-1), int borderType=BORDER_DEFAULT )
.. cpp:function:: void blur( InputArray src, OutputArray dst, Size ksize, Point anchor=Point(-1,-1), int borderType=BORDER_DEFAULT )
Smoothes an image using the normalized box filter.
@ -424,16 +416,16 @@ The function smoothes an image using the kernel:
The call ``blur(src, dst, ksize, anchor, borderType)`` is equivalent to ``boxFilter(src, dst, src.type(), anchor, true, borderType)`` .
See Also:
:func:`boxFilter`,
:func:`bilateralFilter`,
:func:`GaussianBlur`,
:func:`medianBlur`
:cpp:func:`boxFilter`,
:cpp:func:`bilateralFilter`,
:cpp:func:`GaussianBlur`,
:cpp:func:`medianBlur`
.. index:: borderInterpolate
borderInterpolate
---------------------
.. c:function:: int borderInterpolate( int p, int len, int borderType )
.. cpp:function:: int borderInterpolate( int p, int len, int borderType )
Computes the source location of an extrapolated pixel.
@ -450,18 +442,18 @@ The function computes and returns the coordinate of the donor pixel, correspondi
Normally, the function is not called directly. It is used inside
:func:`FilterEngine` and
:func:`copyMakeBorder` to compute tables for quick extrapolation.
:cpp:func:`FilterEngine` and
:cpp:func:`copyMakeBorder` to compute tables for quick extrapolation.
See Also:
:func:`FilterEngine`,
:func:`copyMakeBorder`
:cpp:func:`FilterEngine`,
:cpp:func:`copyMakeBorder`
.. index:: boxFilter
boxFilter
-------------
.. c:function:: void boxFilter( const Mat& src, Mat& dst, int ddepth, Size ksize, Point anchor=Point(-1,-1), bool normalize=true, int borderType=BORDER_DEFAULT )
.. cpp:function:: void boxFilter( InputArray src, OutputArray dst, int ddepth, Size ksize, Point anchor=Point(-1,-1), bool normalize=true, int borderType=BORDER_DEFAULT )
Smoothes an image using the box filter.
@ -491,24 +483,24 @@ where
Unnormalized box filter is useful for computing various integral characteristics over each pixel neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms,
and so on). If you need to compute pixel sums over variable-size windows, use
:func:`integral` .
:cpp:func:`integral` .
See Also:
:func:`boxFilter`,
:func:`bilateralFilter`,
:func:`GaussianBlur`,
:func:`medianBlur`,
:func:`integral`
:cpp:func:`boxFilter`,
:cpp:func:`bilateralFilter`,
:cpp:func:`GaussianBlur`,
:cpp:func:`medianBlur`,
:cpp:func:`integral`
.. index:: buildPyramid
buildPyramid
----------------
.. c:function:: void buildPyramid( const Mat& src, vector<Mat>& dst, int maxlevel )
.. cpp:function:: void buildPyramid( InputArray src, OutputArrayOfArrays dst, int maxlevel )
Constructs the Gaussian pyramid for an image.
:param src: Source image. Check :func:`pyrDown` for the list of supported types.
:param src: Source image. Check :cpp:func:`pyrDown` for the list of supported types.
:param dst: Destination vector of ``maxlevel+1`` images of the same type as ``src`` . ``dst[0]`` will be the same as ``src`` . ``dst[1]`` is the next pyramid layer,
a smoothed and down-sized ``src`` , and so on.
@ -516,13 +508,13 @@ buildPyramid
:param maxlevel: 0-based index of the last (the smallest) pyramid layer. It must be non-negative.
The function constructs a vector of images and builds the Gaussian pyramid by recursively applying
:func:`pyrDown` to the previously built pyramid layers, starting from ``dst[0]==src`` .
:cpp:func:`pyrDown` to the previously built pyramid layers, starting from ``dst[0]==src`` .
.. index:: copyMakeBorder
copyMakeBorder
------------------
.. c:function:: void copyMakeBorder( const Mat& src, Mat& dst, int top, int bottom, int left, int right, int borderType, const Scalar& value=Scalar() )
.. cpp:function:: void copyMakeBorder( InputArray src, OutputArray dst, int top, int bottom, int left, int right, int borderType, const Scalar& value=Scalar() )
Forms a border around an image.
@ -532,12 +524,12 @@ copyMakeBorder
:param top, bottom, left, right: Parameter specifying how many pixels in each direction from the source image rectangle to extrapolate. For example, ``top=1, bottom=1, left=1, right=1`` mean that 1 pixel-wide border needs to be built.
:param borderType: Border type. See :func:`borderInterpolate` for details.
:param borderType: Border type. See :cpp:func:`borderInterpolate` for details.
:param value: Border value if ``borderType==BORDER_CONSTANT`` .
The function copies the source image into the middle of the destination image. The areas to the left, to the right, above and below the copied source image will be filled with extrapolated pixels. This is not what
:func:`FilterEngine` or filtering functions based on it do (they extrapolate pixels on-fly), but what other more complex functions, including your own, may do to simplify image boundary handling.
:cpp:func:`FilterEngine` or filtering functions based on it do (they extrapolate pixels on-fly), but what other more complex functions, including your own, may do to simplify image boundary handling.
The function supports the mode when ``src`` is already in the middle of ``dst`` . In this case, the function does not copy ``src`` itself but simply constructs the border, for example: ::
@ -557,16 +549,16 @@ The function supports the mode when ``src`` is already in the middle of ``dst``
See Also:
:func:`borderInterpolate`
:cpp:func:`borderInterpolate`
.. index:: createBoxFilter
createBoxFilter
-------------------
.. c:function:: Ptr<FilterEngine> createBoxFilter( int srcType, int dstType, Size ksize, Point anchor=Point(-1,-1), bool normalize=true, int borderType=BORDER_DEFAULT)
.. cpp:function:: Ptr<FilterEngine> createBoxFilter( int srcType, int dstType, Size ksize, Point anchor=Point(-1,-1), bool normalize=true, int borderType=BORDER_DEFAULT)
.. c:function:: Ptr<BaseRowFilter> getRowSumFilter(int srcType, int sumType, int ksize, int anchor=-1)
.. cpp:function:: Ptr<BaseRowFilter> getRowSumFilter(int srcType, int sumType, int ksize, int anchor=-1)
.. c:function:: Ptr<BaseColumnFilter> getColumnSumFilter(int sumType, int dstType, int ksize, int anchor=-1, double scale=1)
.. cpp:function:: Ptr<BaseColumnFilter> getColumnSumFilter(int sumType, int dstType, int ksize, int anchor=-1, double scale=1)
Returns a box filter engine.
@ -580,31 +572,31 @@ createBoxFilter
:param anchor: Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
:param normalize: Flag specifying whether the sums are normalized or not. See :func:`boxFilter` for details.
:param normalize: Flag specifying whether the sums are normalized or not. See :cpp:func:`boxFilter` for details.
:param scale: Another way to specify normalization in lower-level ``getColumnSumFilter`` .
:param borderType: Border type to use. See :func:`borderInterpolate` .
:param borderType: Border type to use. See :cpp:func:`borderInterpolate` .
The function is a convenience function that retrieves the horizontal sum primitive filter with
:func:`getRowSumFilter` , vertical sum filter with
:func:`getColumnSumFilter` , constructs new
:func:`FilterEngine` , and passes both of the primitive filters there. The constructed filter engine can be used for image filtering with normalized or unnormalized box filter.
:cpp:func:`getRowSumFilter` , vertical sum filter with
:cpp:func:`getColumnSumFilter` , constructs new
:cpp:func:`FilterEngine` , and passes both of the primitive filters there. The constructed filter engine can be used for image filtering with normalized or unnormalized box filter.
The function itself is used by
:func:`blur` and
:func:`boxFilter` .
:cpp:func:`blur` and
:cpp:func:`boxFilter` .
See Also:
:func:`FilterEngine`,
:func:`blur`,
:func:`boxFilter`
:cpp:func:`FilterEngine`,
:cpp:func:`blur`,
:cpp:func:`boxFilter`
.. index:: createDerivFilter
createDerivFilter
---------------------
.. c:function:: Ptr<FilterEngine> createDerivFilter( int srcType, int dstType, int dx, int dy, int ksize, int borderType=BORDER_DEFAULT )
.. cpp:function:: Ptr<FilterEngine> createDerivFilter( int srcType, int dstType, int dx, int dy, int ksize, int borderType=BORDER_DEFAULT )
Returns an engine for computing image derivatives.
@ -616,57 +608,57 @@ createDerivFilter
:param dy: Derivative order in respect of y.
:param ksize: Aperture size See :func:`getDerivKernels` .
:param ksize: Aperture size See :cpp:func:`getDerivKernels` .
:param borderType: Border type to use. See :func:`borderInterpolate` .
:param borderType: Border type to use. See :cpp:func:`borderInterpolate` .
The function :func:`createDerivFilter` is a small convenience function that retrieves linear filter coefficients for computing image derivatives using
:func:`getDerivKernels` and then creates a separable linear filter with
:func:`createSeparableLinearFilter` . The function is used by
:func:`Sobel` and
:func:`Scharr` .
The function :cpp:func:`createDerivFilter` is a small convenience function that retrieves linear filter coefficients for computing image derivatives using
:cpp:func:`getDerivKernels` and then creates a separable linear filter with
:cpp:func:`createSeparableLinearFilter` . The function is used by
:cpp:func:`Sobel` and
:cpp:func:`Scharr` .
See Also:
:func:`createSeparableLinearFilter`,
:func:`getDerivKernels`,
:func:`Scharr`,
:func:`Sobel`
:cpp:func:`createSeparableLinearFilter`,
:cpp:func:`getDerivKernels`,
:cpp:func:`Scharr`,
:cpp:func:`Sobel`
.. index:: createGaussianFilter
createGaussianFilter
------------------------
.. c:function:: Ptr<FilterEngine> createGaussianFilter( int type, Size ksize, double sigmaX, double sigmaY=0, int borderType=BORDER_DEFAULT)
.. cpp:function:: Ptr<FilterEngine> createGaussianFilter( int type, Size ksize, double sigmaX, double sigmaY=0, int borderType=BORDER_DEFAULT)
Returns an engine for smoothing images with the Gaussian filter.
:param type: Source and destination image type.
:param ksize: Aperture size. See :func:`getGaussianKernel` .
:param ksize: Aperture size. See :cpp:func:`getGaussianKernel` .
:param sigmaX: Gaussian sigma in the horizontal direction. See :func:`getGaussianKernel` .
:param sigmaX: Gaussian sigma in the horizontal direction. See :cpp:func:`getGaussianKernel` .
:param sigmaY: Gaussian sigma in the vertical direction. If 0, then :math:`\texttt{sigmaY}\leftarrow\texttt{sigmaX}` .
:param borderType: Border type to use. See :func:`borderInterpolate` .
:param borderType: Border type to use. See :cpp:func:`borderInterpolate` .
The function :func:`createGaussianFilter` computes Gaussian kernel coefficients and then returns a separable linear filter for that kernel. The function is used by
:func:`GaussianBlur` . Note that while the function takes just one data type, both for input and output, you can pass this limitation by calling
:func:`getGaussianKernel` and then
:func:`createSeparableFilter` directly.
The function :cpp:func:`createGaussianFilter` computes Gaussian kernel coefficients and then returns a separable linear filter for that kernel. The function is used by
:cpp:func:`GaussianBlur` . Note that while the function takes just one data type, both for input and output, you can pass this limitation by calling
:cpp:func:`getGaussianKernel` and then
:cpp:func:`createSeparableFilter` directly.
See Also:
:func:`createSeparableLinearFilter`,
:func:`getGaussianKernel`,
:func:`GaussianBlur`
:cpp:func:`createSeparableLinearFilter`,
:cpp:func:`getGaussianKernel`,
:cpp:func:`GaussianBlur`
.. index:: createLinearFilter
createLinearFilter
----------------------
.. c:function:: Ptr<FilterEngine> createLinearFilter(int srcType, int dstType, const Mat& kernel, Point _anchor=Point(-1,-1), double delta=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, const Scalar& borderValue=Scalar())
.. cpp: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())
.. c:function:: Ptr<BaseFilter> getLinearFilter(int srcType, int dstType, const Mat& kernel, Point anchor=Point(-1,-1), double delta=0, int bits=0)
.. cpp:function:: Ptr<BaseFilter> getLinearFilter(int srcType, int dstType, InputArray kernel, Point anchor=Point(-1,-1), double delta=0, int bits=0)
Creates a non-separable linear filter engine.
@ -682,30 +674,30 @@ createLinearFilter
:param bits: Number of the fractional bits. the parameter is used when the kernel is an integer matrix representing fixed-point filter coefficients.
:param rowBorderType, columnBorderType: Pixel extrapolation methods in the horizontal and vertical directions. See :func:`borderInterpolate` for details.
:param rowBorderType, columnBorderType: Pixel extrapolation methods in the horizontal and vertical directions. See :cpp:func:`borderInterpolate` for details.
:param borderValue: Border vaule used in case of a constant border.
The function returns a pointer to a 2D linear filter for the specified kernel, the source array type, and the destination array type. The function is a higher-level function that calls ``getLinearFilter`` and passes the retrieved 2D filter to the
:func:`FilterEngine` constructor.
:cpp:func:`FilterEngine` constructor.
See Also:
:func:`createSeparableLinearFilter`,
:func:`FilterEngine`,
:func:`filter2D`
:cpp:func:`createSeparableLinearFilter`,
:cpp:func:`FilterEngine`,
:cpp:func:`filter2D`
.. index:: createMorphologyFilter
createMorphologyFilter
--------------------------
.. c:function:: Ptr<FilterEngine> createMorphologyFilter(int op, int type, const Mat& element, Point anchor=Point(-1,-1), int rowBorderType=BORDER_CONSTANT, int columnBorderType=-1, const Scalar& borderValue=morphologyDefaultBorderValue())
.. cpp: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())
.. c:function:: Ptr<BaseFilter> getMorphologyFilter(int op, int type, const Mat& element, Point anchor=Point(-1,-1))
.. cpp:function:: Ptr<BaseFilter> getMorphologyFilter(int op, int type, InputArray element, Point anchor=Point(-1,-1))
.. c:function:: Ptr<BaseRowFilter> getMorphologyRowFilter(int op, int type, int esize, int anchor=-1)
.. cpp:function:: Ptr<BaseRowFilter> getMorphologyRowFilter(int op, int type, int esize, int anchor=-1)
.. c:function:: Ptr<BaseColumnFilter> getMorphologyColumnFilter(int op, int type, int esize, int anchor=-1)
.. cpp:function:: Ptr<BaseColumnFilter> getMorphologyColumnFilter(int op, int type, int esize, int anchor=-1)
.. c:function:: static inline Scalar morphologyDefaultBorderValue(){ return Scalar::all(DBL_MAX) }
.. cpp:function:: static inline Scalar morphologyDefaultBorderValue(){ return Scalar::all(DBL_MAX) }
Creates an engine for non-separable morphological operations.
@ -719,32 +711,32 @@ createMorphologyFilter
:param anchor: Anchor position within the structuring element. Negative values mean that the anchor is at the kernel center.
:param rowBorderType, columnBorderType: Pixel extrapolation methods in the horizontal and vertical directions. See :func:`borderInterpolate` for details.
:param rowBorderType, columnBorderType: Pixel extrapolation methods in the horizontal and vertical directions. See :cpp:func:`borderInterpolate` for details.
:param borderValue: Border value in case of a constant border. The default value, \ ``morphologyDefaultBorderValue`` , has a special meaning. It is transformed :math:`+\inf` for the erosion and to :math:`-\inf` for the dilation, which means that the minimum (maximum) is effectively computed only over the pixels that are inside the image.
The functions construct primitive morphological filtering operations or a filter engine based on them. Normally it is enough to use
:func:`createMorphologyFilter` or even higher-level
:func:`erode`,
:func:`dilate` , or
:func:`morphologyEx` .
:cpp:func:`createMorphologyFilter` or even higher-level
:cpp:func:`erode`,
:cpp:func:`dilate` , or
:cpp:func:`morphologyEx` .
Note that
:func:`createMorphologyFilter` analyzes the structuring element shape and builds a separable morphological filter engine when the structuring element is square.
:cpp:func:`createMorphologyFilter` analyzes the structuring element shape and builds a separable morphological filter engine when the structuring element is square.
See Also:
:func:`erode`,
:func:`dilate`,
:func:`morphologyEx`,
:func:`FilterEngine`
:cpp:func:`erode`,
:cpp:func:`dilate`,
:cpp:func:`morphologyEx`,
:cpp:func:`FilterEngine`
.. index:: createSeparableLinearFilter
createSeparableLinearFilter
-------------------------------
.. c:function:: Ptr<FilterEngine> createSeparableLinearFilter(int srcType, int dstType, const Mat& rowKernel, const Mat& columnKernel, Point anchor=Point(-1,-1), double delta=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, const Scalar& borderValue=Scalar())
.. cpp: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())
.. c:function:: Ptr<BaseColumnFilter> getLinearColumnFilter(int bufType, int dstType, const Mat& columnKernel, int anchor, int symmetryType, double delta=0, int bits=0)
.. cpp:function:: Ptr<BaseColumnFilter> getLinearColumnFilter(int bufType, int dstType, InputArray columnKernel, int anchor, int symmetryType, double delta=0, int bits=0)
.. c:function:: Ptr<BaseRowFilter> getLinearRowFilter(int srcType, int bufType, const Mat& rowKernel, int anchor, int symmetryType)
.. cpp:function:: Ptr<BaseRowFilter> getLinearRowFilter(int srcType, int bufType, InputArray rowKernel, int anchor, int symmetryType)
Creates an engine for a separable linear filter.
@ -764,28 +756,28 @@ createSeparableLinearFilter
:param bits: Number of the fractional bits. The parameter is used when the kernel is an integer matrix representing fixed-point filter coefficients.
:param rowBorderType, columnBorderType: Pixel extrapolation methods in the horizontal and vertical directions. See :func:`borderInterpolate` for details.
:param rowBorderType, columnBorderType: Pixel extrapolation methods in the horizontal and vertical directions. See :cpp:func:`borderInterpolate` for details.
:param borderValue: Border value used in case of a constant border.
:param symmetryType: Type of each row and column kernel. See :func:`getKernelType` .
:param symmetryType: Type of each row and column kernel. See :cpp:func:`getKernelType` .
The functions construct primitive separable linear filtering operations or a filter engine based on them. Normally it is enough to use
:func:`createSeparableLinearFilter` or even higher-level
:func:`sepFilter2D` . The function
:func:`createMorphologyFilter` is smart enough to figure out the ``symmetryType`` for each of the two kernels, the intermediate ``bufType`` and, if filtering can be done in integer arithmetics, the number of ``bits`` to encode the filter coefficients. If it does not work for you, it is possible to call ``getLinearColumnFilter``,``getLinearRowFilter`` directly and then pass them to the
:func:`FilterEngine` constructor.
:cpp:func:`createSeparableLinearFilter` or even higher-level
:cpp:func:`sepFilter2D` . The function
:cpp:func:`createMorphologyFilter` is smart enough to figure out the ``symmetryType`` for each of the two kernels, the intermediate ``bufType`` and, if filtering can be done in integer arithmetics, the number of ``bits`` to encode the filter coefficients. If it does not work for you, it is possible to call ``getLinearColumnFilter``,``getLinearRowFilter`` directly and then pass them to the
:cpp:func:`FilterEngine` constructor.
See Also:
:func:`sepFilter2D`,
:func:`createLinearFilter`,
:func:`FilterEngine`,
:func:`getKernelType`
:cpp:func:`sepFilter2D`,
:cpp:func:`createLinearFilter`,
:cpp:func:`FilterEngine`,
:cpp:func:`getKernelType`
.. index:: dilate
dilate
----------
.. c:function:: void dilate( const Mat& src, Mat& dst, const Mat& element, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
.. cpp: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() )
Dilates an image by using a specific structuring element.
@ -799,9 +791,9 @@ dilate
:param iterations: Number of times dilation is applied.
:param borderType: Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType: Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
:param borderValue: Border value in case of a constant border. The default value has a special meaning. See :func:`createMorphologyFilter` for details.
:param borderValue: Border value in case of a constant border. The default value has a special meaning. See :cpp:func:`createMorphologyFilter` for details.
The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken:
@ -812,14 +804,14 @@ The function dilates the source image using the specified structuring element th
The function supports the in-place mode. Dilation can be applied several ( ``iterations`` ) times. In case of multi-channel images, each channel is processed independently.
See Also:
:func:`erode`,
:func:`morphologyEx`,
:func:`createMorphologyFilter`
:cpp:func:`erode`,
:cpp:func:`morphologyEx`,
:cpp:func:`createMorphologyFilter`
.. index:: erode
erode
---------
.. c:function:: void erode( const Mat& src, Mat& dst, const Mat& element, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
.. cpp: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() )
Erodes an image by using a specific structuring element.
@ -833,9 +825,9 @@ erode
:param iterations: Number of times erosion is applied.
:param borderType: Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType: Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
:param borderValue: Border value in case of a constant border. The default value has a special meaning. See :func:`createMorphoogyFilter` for details.
:param borderValue: Border value in case of a constant border. The default value has a special meaning. See :cpp:func:`createMorphoogyFilter` for details.
The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken:
@ -846,15 +838,15 @@ The function erodes the source image using the specified structuring element tha
The function supports the in-place mode. Erosion can be applied several ( ``iterations`` ) times. In case of multi-channel images, each channel is processed independently.
See Also:
:func:`dilate`,
:func:`morphologyEx`,
:func:`createMorphologyFilter`
:cpp:func:`dilate`,
:cpp:func:`morphologyEx`,
:cpp:func:`createMorphologyFilter`
.. index:: filter2D
filter2D
------------
.. c:function:: void filter2D( const Mat& src, Mat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1), double delta=0, int borderType=BORDER_DEFAULT )
.. cpp:function:: void filter2D( InputArray src, OutputArray dst, int ddepth, InputArray kernel, Point anchor=Point(-1,-1), double delta=0, int borderType=BORDER_DEFAULT )
Convolves an image with the kernel.
@ -864,13 +856,13 @@ filter2D
:param ddepth: Desired depth of the destination image. If it is negative, it will be the same as ``src.depth()`` .
:param kernel: Convolution kernel (or rather a correlation kernel), a single-channel floating point matrix. If you want to apply different kernels to different channels, split the image into separate color planes using :func:`split` and process them individually.
:param kernel: Convolution kernel (or rather a correlation kernel), a single-channel floating point matrix. If you want to apply different kernels to different channels, split the image into separate color planes using :cpp:func:`split` and process them individually.
:param anchor: Anchor of the kernel that indicates the relative position of a filtered point within the kernel. The anchor should lie within the kernel. The special default value (-1,-1) means that the anchor is at the kernel center.
:param delta: Optional value added to the filtered pixels before storing them in ``dst`` .
:param borderType: Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType: Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
The function applies an arbitrary linear filter to an image. In-place operation is supported. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode.
@ -881,21 +873,21 @@ The function does actually compute correlation, not the convolution:
\texttt{dst} (x,y) = \sum _{ \stackrel{0\leq x' < \texttt{kernel.cols},}{0\leq y' < \texttt{kernel.rows}} } \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )
That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using
:func:`flip` and set the new anchor to ``(kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1)`` .
:cpp:func:`flip` and set the new anchor to ``(kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1)`` .
The function uses the DFT-based algorithm in case of sufficiently large kernels (~``11 x 11`` or larger) and the direct algorithm (that uses the engine retrieved by :func:`createLinearFilter` ) for small kernels.
The function uses the DFT-based algorithm in case of sufficiently large kernels (~``11 x 11`` or larger) and the direct algorithm (that uses the engine retrieved by :cpp:func:`createLinearFilter` ) for small kernels.
See Also:
:func:`sepFilter2D`,
:func:`createLinearFilter`,
:func:`dft`,
:func:`matchTemplate`
:cpp:func:`sepFilter2D`,
:cpp:func:`createLinearFilter`,
:cpp:func:`dft`,
:cpp:func:`matchTemplate`
.. index:: GaussianBlur
GaussianBlur
----------------
.. c:function:: void GaussianBlur( const Mat& src, Mat& dst, Size ksize, double sigmaX, double sigmaY=0, int borderType=BORDER_DEFAULT )
.. cpp:function:: void GaussianBlur( InputArray src, OutputArray dst, Size ksize, double sigmaX, double sigmaY=0, int borderType=BORDER_DEFAULT )
Smoothes an image using a Gaussian filter.
@ -905,24 +897,24 @@ GaussianBlur
:param ksize: Gaussian kernel size. ``ksize.width`` and ``ksize.height`` can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from ``sigma*`` .
:param sigmaX, sigmaY: Gaussian kernel standard deviations in X and Y direction. If ``sigmaY`` is zero, it is set to be equal to ``sigmaX`` . If they are both zeros, they are computed from ``ksize.width`` and ``ksize.height`` , respectively. See :func:`getGaussianKernel` for details. To fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ``ksize`` , ``sigmaX`` , and ``sigmaY`` .
:param sigmaX, sigmaY: Gaussian kernel standard deviations in X and Y direction. If ``sigmaY`` is zero, it is set to be equal to ``sigmaX`` . If they are both zeros, they are computed from ``ksize.width`` and ``ksize.height`` , respectively. See :cpp:func:`getGaussianKernel` for details. To fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ``ksize`` , ``sigmaX`` , and ``sigmaY`` .
:param borderType: Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType: Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.
See Also:
:func:`sepFilter2D`,
:func:`filter2D`,
:func:`blur`,
:func:`boxFilter`,
:func:`bilateralFilter`,
:func:`medianBlur`
:cpp:func:`sepFilter2D`,
:cpp:func:`filter2D`,
:cpp:func:`blur`,
:cpp:func:`boxFilter`,
:cpp:func:`bilateralFilter`,
:cpp:func:`medianBlur`
.. index:: getDerivKernels
getDerivKernels
-------------------
.. c:function:: void getDerivKernels( Mat& kx, Mat& ky, int dx, int dy, int ksize, bool normalize=false, int ktype=CV_32F )
.. cpp:function:: void getDerivKernels( OutputArray kx, OutputArray ky, int dx, int dy, int ksize, bool normalize=false, int ktype=CV_32F )
Returns filter coefficients for computing spatial image derivatives.
@ -942,16 +934,16 @@ getDerivKernels
The function computes and returns the filter coefficients for spatial image derivatives. When ``ksize=CV_SCHARR`` , the Scharr
:math:`3 \times 3` kernels are generated (see
:func:`Scharr` ). Otherwise, Sobel kernels are generated (see
:func:`Sobel` ). The filters are normally passed to
:func:`sepFilter2D` or to
:func:`createSeparableLinearFilter` .
:cpp:func:`Scharr` ). Otherwise, Sobel kernels are generated (see
:cpp:func:`Sobel` ). The filters are normally passed to
:cpp:func:`sepFilter2D` or to
:cpp:func:`createSeparableLinearFilter` .
.. index:: getGaussianKernel
getGaussianKernel
---------------------
.. c:function:: Mat getGaussianKernel( int ksize, double sigma, int ktype=CV_64F )
.. cpp:function:: Mat getGaussianKernel( int ksize, double sigma, int ktype=CV_64F )
Returns Gaussian filter coefficients.
@ -973,22 +965,22 @@ where
:math:`\sum_i G_i=1`.
Two of such generated kernels can be passed to
:func:`sepFilter2D` or to
:func:`createSeparableLinearFilter`. Those functions automatically recognize smoothing kernels (i.e. symmetrical kernel with sum of weights = 1) and handle them accordingly. You may also use the higher-level
:func:`GaussianBlur`.
:cpp:func:`sepFilter2D` or to
:cpp:func:`createSeparableLinearFilter`. Those functions automatically recognize smoothing kernels (i.e. symmetrical kernel with sum of weights = 1) and handle them accordingly. You may also use the higher-level
:cpp:func:`GaussianBlur`.
See Also:
:func:`sepFilter2D`,
:func:`createSeparableLinearFilter`,
:func:`getDerivKernels`,
:func:`getStructuringElement`,
:func:`GaussianBlur`
:cpp:func:`sepFilter2D`,
:cpp:func:`createSeparableLinearFilter`,
:cpp:func:`getDerivKernels`,
:cpp:func:`getStructuringElement`,
:cpp:func:`GaussianBlur`
.. index:: getKernelType
getKernelType
-----------------
.. c:function:: int getKernelType(const Mat& kernel, Point anchor)
.. cpp:function:: int getKernelType(InputArray kernel, Point anchor)
Returns the kernel type.
@ -1011,7 +1003,7 @@ The function analyzes the kernel coefficients and returns the corresponding kern
getStructuringElement
-------------------------
.. c:function:: Mat getStructuringElement(int shape, Size esize, Point anchor=Point(-1,-1))
.. cpp:function:: Mat getStructuringElement(int shape, Size esize, Point anchor=Point(-1,-1))
Returns a structuring element of the specified size and shape for morphological operations.
@ -1036,16 +1028,16 @@ getStructuringElement
:param anchor: Anchor position within the element. The default value :math:`(-1, -1)` means that the anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted.
The function constructs and returns the structuring element that can be then passed to
:func:`createMorphologyFilter`,
:func:`erode`,
:func:`dilate` or
:func:`morphologyEx` . But you can also construct an arbitrary binary mask yourself and use it as the structuring element.
:cpp:func:`createMorphologyFilter`,
:cpp:func:`erode`,
:cpp:func:`dilate` or
:cpp:func:`morphologyEx` . But you can also construct an arbitrary binary mask yourself and use it as the structuring element.
.. index:: medianBlur
medianBlur
--------------
.. c:function:: void medianBlur( const Mat& src, Mat& dst, int ksize )
.. cpp:function:: void medianBlur( InputArray src, OutputArray dst, int ksize )
Smoothes an image using the median filter.
@ -1059,16 +1051,16 @@ The function smoothes an image using the median filter with the
:math:`\texttt{ksize} \times \texttt{ksize}` aperture. Each channel of a multi-channel image is processed independently. In-place operation is supported.
See Also:
:func:`bilateralFilter`,
:func:`blur`,
:func:`boxFilter`,
:func:`GaussianBlur`
:cpp:func:`bilateralFilter`,
:cpp:func:`blur`,
:cpp:func:`boxFilter`,
:cpp:func:`GaussianBlur`
.. index:: morphologyEx
morphologyEx
----------------
.. c:function:: void morphologyEx( const Mat& src, Mat& dst, int op, const Mat& element, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar& borderValue=morphologyDefaultBorderValue() )
.. cpp: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() )
Performs advanced morphological transformations.
@ -1092,9 +1084,9 @@ morphologyEx
:param iterations: Number of times erosion and dilation are applied.
:param borderType: Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType: Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
:param borderValue: Border value in case of a constant border. The default value has a special meaning. See :func:`createMorphoogyFilter` for details.
:param borderValue: Border value in case of a constant border. The default value has a special meaning. See :cpp:func:`createMorphoogyFilter` for details.
The function can perform advanced morphological transformations using an erosion and dilation as basic operations.
@ -1131,15 +1123,15 @@ Morphological gradient:
Any of the operations can be done in-place.
See Also:
:func:`dilate`,
:func:`erode`,
:func:`createMorphologyFilter`
:cpp:func:`dilate`,
:cpp:func:`erode`,
:cpp:func:`createMorphologyFilter`
.. index:: Laplacian
Laplacian
-------------
.. c:function:: void Laplacian( const Mat& src, Mat& dst, int ddepth, int ksize=1, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
.. cpp:function:: void Laplacian( InputArray src, OutputArray dst, int ddepth, int ksize=1, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
Calculates the Laplacian of an image.
@ -1149,13 +1141,13 @@ Laplacian
:param ddepth: Desired depth of the destination image.
:param ksize: Aperture size used to compute the second-derivative filters. See :func:`getDerivKernels` for details. The size must be positive and odd.
:param ksize: Aperture size used to compute the second-derivative filters. See :cpp:func:`getDerivKernels` for details. The size must be positive and odd.
:param scale: Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See :func:`getDerivKernels` for details.
:param scale: Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See :cpp:func:`getDerivKernels` for details.
:param delta: Optional delta value that is added to the results prior to storing them in ``dst`` .
:param borderType: Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType: Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator:
@ -1171,14 +1163,14 @@ This is done when ``ksize > 1`` . When ``ksize == 1`` , the Laplacian is compute
\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}
See Also:
:func:`Sobel`,
:func:`Scharr`
:cpp:func:`Sobel`,
:cpp:func:`Scharr`
.. index:: pyrDown
pyrDown
-----------
.. c:function:: void pyrDown( const Mat& src, Mat& dst, const Size& dstsize=Size())
.. cpp:function:: void pyrDown( InputArray src, OutputArray dst, const Size& dstsize=Size())
Smoothes an image and downsamples it.
@ -1205,7 +1197,7 @@ Then, it downsamples the image by rejecting even rows and columns.
pyrUp
---------
.. c:function:: void pyrUp( const Mat& src, Mat& dst, const Size& dstsize=Size())
.. cpp:function:: void pyrUp( InputArray src, OutputArray dst, const Size& dstsize=Size())
Upsamples an image and then smoothes it.
@ -1221,13 +1213,13 @@ pyrUp
| \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}
The function performs the upsampling step of the Gaussian pyramid construction though it can actually be used to construct the Laplacian pyramid. First, it upsamples the source image by injecting even zero rows and columns and then convolves the result with the same kernel as in
:func:`pyrDown` multiplied by 4.
:cpp:func:`pyrDown` multiplied by 4.
.. index:: sepFilter2D
sepFilter2D
---------------
.. c:function:: void sepFilter2D( const Mat& src, Mat& dst, int ddepth, const Mat& rowKernel, const Mat& columnKernel, Point anchor=Point(-1,-1), double delta=0, int borderType=BORDER_DEFAULT )
.. cpp: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 )
Applies a separable linear filter to an image.
@ -1245,23 +1237,23 @@ sepFilter2D
:param delta: Value added to the filtered results before storing them.
:param borderType: Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType: Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
The function applies a separable linear filter to the image. That is, first, every row of ``src`` is filtered with the 1D kernel ``rowKernel`` . Then, every column of the result is filtered with the 1D kernel ``columnKernel`` . The final result shifted by ``delta`` is stored in ``dst`` .
See Also:
:func:`createSeparableLinearFilter`,
:func:`filter2D`,
:func:`Sobel`,
:func:`GaussianBlur`,
:func:`boxFilter`,
:func:`blur`
:cpp:func:`createSeparableLinearFilter`,
:cpp:func:`filter2D`,
:cpp:func:`Sobel`,
:cpp:func:`GaussianBlur`,
:cpp:func:`boxFilter`,
:cpp:func:`blur`
.. index:: Sobel
Sobel
---------
.. c:function:: void Sobel( const Mat& src, Mat& dst, int ddepth, int xorder, int yorder, int ksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
.. cpp: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 )
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
@ -1277,11 +1269,11 @@ Sobel
:param ksize: Size of the extended Sobel kernel. It must be 1, 3, 5, or 7.
:param scale: Optional scale factor for the computed derivative values. By default, no scaling is applied. See :func:`getDerivKernels` for details.
:param scale: Optional scale factor for the computed derivative values. By default, no scaling is applied. See :cpp:func:`getDerivKernels` for details.
:param delta: Optional delta value that is added to the results prior to storing them in ``dst`` .
:param borderType: Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType: Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
In all cases except one, the
:math:`\texttt{ksize} \times
@ -1324,17 +1316,17 @@ The second case corresponds to a kernel of:
\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}
See Also:
:func:`Scharr`,
:func:`Lapacian`,
:func:`sepFilter2D`,
:func:`filter2D`,
:func:`GaussianBlur`
:cpp:func:`Scharr`,
:cpp:func:`Lapacian`,
:cpp:func:`sepFilter2D`,
:cpp:func:`filter2D`,
:cpp:func:`GaussianBlur`
.. index:: Scharr
Scharr
----------
.. c:function:: void Scharr( const Mat& src, Mat& dst, int ddepth, int xorder, int yorder, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
.. cpp:function:: void Scharr( InputArray src, OutputArray dst, int ddepth, int xorder, int yorder, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )
Calculates the first x- or y- image derivative using Scharr operator.
@ -1348,11 +1340,11 @@ Scharr
:param yorder: Order of the derivative y.
:param scale: Optional scale factor for the computed derivative values. By default, no scaling is applied. See :func:`getDerivKernels` for details.
:param scale: Optional scale factor for the computed derivative values. By default, no scaling is applied. See :cpp:func:`getDerivKernels` for details.
:param delta: Optional delta value that is added to the results prior to storing them in ``dst`` .
:param borderType: Pixel extrapolation method. See :func:`borderInterpolate` for details.
:param borderType: Pixel extrapolation method. See :cpp:func:`borderInterpolate` for details.
The function computes the first x- or y- spatial image derivative using the Scharr operator. The call

267
modules/imgproc/doc/geometric_transformations.rst
View File

@ -39,7 +39,7 @@ The actual implementations of the geometrical transformations, from the most gen
convertMaps
-----------
.. c:function:: void convertMaps( const Mat& map1, const Mat& map2, Mat& dstmap1, Mat& dstmap2, int dstmap1type, bool nninterpolation=false )
.. cpp:function:: void convertMaps( InputArray map1, InputArray map2, OutputArray dstmap1, OutputArray dstmap2, int dstmap1type, bool nninterpolation=false )
Converts image transformation maps from one representation to another.
@ -56,11 +56,11 @@ convertMaps
:param nninterpolation: Flag indicating whether the fixed-point maps are used for the nearest-neighbor or for a more complex interpolation.
The function converts a pair of maps for
:func:`remap` from one representation to another. The following options ( ``(map1.type(), map2.type())`` :math:`\rightarrow` ``(dstmap1.type(), dstmap2.type())`` ) are supported:
:cpp:func:`remap` from one representation to another. The following options ( ``(map1.type(), map2.type())`` :math:`\rightarrow` ``(dstmap1.type(), dstmap2.type())`` ) are supported:
*
:math:`\texttt{(CV\_32FC1, CV\_32FC1)} \rightarrow \texttt{(CV\_16SC2, CV\_16UC1)}` . This is the most frequently used conversion operation, in which the original floating-point maps (see
:func:`remap` ) are converted to a more compact and much faster fixed-point representation. The first output array contains the rounded coordinates and the second array (created only when ``nninterpolation=false`` ) contains indices in the interpolation tables.
:cpp:func:`remap` ) are converted to a more compact and much faster fixed-point representation. The first output array contains the rounded coordinates and the second array (created only when ``nninterpolation=false`` ) contains indices in the interpolation tables.
*
:math:`\texttt{(CV\_32FC2)} \rightarrow \texttt{(CV\_16SC2, CV\_16UC1)}` . The same as above but the original maps are stored in one 2-channel matrix.
@ -69,17 +69,15 @@ The function converts a pair of maps for
Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same as the originals.
See Also:
:func:`remap`,
:func:`undisort`,
:func:`initUndistortRectifyMap`
:cpp:func:`remap`,
:cpp:func:`undisort`,
:cpp:func:`initUndistortRectifyMap`
.. index:: getAffineTransform
.. _getAffineTransform:
getAffineTransform
----------------------
.. c:function:: Mat getAffineTransform( const Point2f src[], const Point2f dst[] )
.. cpp:function:: Mat getAffineTransform( const Point2f src[], const Point2f dst[] )
Calculates an affine transform from three pairs of the corresponding points.
@ -102,8 +100,8 @@ where
i=0,1,2
See Also:
:func:`warpAffine`,
:func:`transform`
:cpp:func:`warpAffine`,
:cpp:func:`transform`
.. index:: getPerspectiveTransform
@ -112,7 +110,7 @@ See Also:
getPerspectiveTransform
---------------------------
.. c:function:: Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] )
.. cpp:function:: Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] )
Calculates a perspective transform from four pairs of the corresponding points.
@ -135,9 +133,9 @@ where
i=0,1,2
See Also:
:func:`findHomography`,
:func:`warpPerspective`,
:func:`perspectiveTransform`
:cpp:func:`findHomography`,
:cpp:func:`warpPerspective`,
:cpp:func:`perspectiveTransform`
.. index:: getRectSubPix
@ -145,7 +143,7 @@ See Also:
getRectSubPix
-----------------
.. c:function:: void getRectSubPix( const Mat& image, Size patchSize, Point2f center, Mat& dst, int patchType=-1 )
.. cpp:function:: void getRectSubPix( InputArray image, Size patchSize, Point2f center, OutputArray dst, int patchType=-1 )
Retrieves a pixel rectangle from an image with sub-pixel accuracy.
@ -170,12 +168,12 @@ using bilinear interpolation. Every channel of multi-channel
images is processed independently. While the center of the rectangle
must be inside the image, parts of the rectangle may be
outside. In this case, the replication border mode (see
:func:`borderInterpolate` ) is used to extrapolate
:cpp:func:`borderInterpolate` ) is used to extrapolate
the pixel values outside of the image.
See Also:
:func:`warpAffine`,
:func:`warpPerspective`
:cpp:func:`warpAffine`,
:cpp:func:`warpPerspective`
.. index:: getRotationMatrix2D
@ -183,7 +181,7 @@ See Also:
getRotationMatrix2D
-----------------------
.. c:function:: Mat getRotationMatrix2D( Point2f center, double angle, double scale )
.. cpp:function:: Mat getRotationMatrix2D( Point2f center, double angle, double scale )
Calculates an affine matrix of 2D rotation.
@ -208,9 +206,9 @@ where
The transformation maps the rotation center to itself. If this is not the target, adjust the shift.
See Also:
:func:`getAffineTransform`,
:func:`warpAffine`,
:func:`transform`
:cpp:func:`getAffineTransform`,
:cpp:func:`warpAffine`,
:cpp:func:`transform`
.. index:: invertAffineTransform
@ -218,7 +216,7 @@ See Also:
invertAffineTransform
-------------------------
.. c:function:: void invertAffineTransform(const Mat& M, Mat& iM)
.. cpp:function:: void invertAffineTransform(InputArray M, OutputArray iM)
Inverts an affine transformation.
@ -243,20 +241,20 @@ The result is also a
remap
-----
.. c:function:: void remap( const Mat& src, Mat& dst, const Mat& map1, const Mat& map2, int interpolation, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar())
.. cpp:function:: void remap( InputArray src, OutputArray dst, InputArray map1, InputArray map2, int interpolation, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar())
Applies a generic geometrical transformation to an image.
:param src: Source image.
:param dst: Destination image. It has the same size as ``map1`` and the same type as ``src`` .
:param map1: The first map of either ``(x,y)`` points or just ``x`` values having the type ``CV_16SC2`` , ``CV_32FC1`` , or ``CV_32FC2`` . See :func:`convertMaps` for details on converting a floating point representation to fixed-point for speed.
:param map1: The first map of either ``(x,y)`` points or just ``x`` values having the type ``CV_16SC2`` , ``CV_32FC1`` , or ``CV_32FC2`` . See :cpp:func:`convertMaps` for details on converting a floating point representation to fixed-point for speed.
:param map2: The second map of ``y`` values having the type ``CV_16UC1`` , ``CV_32FC1`` , or none (empty map if ``map1`` is ``(x,y)`` points), respectively.
:param interpolation: Interpolation method (see :func:`resize` ). The method ``INTER_AREA`` is not supported by this function.
:param interpolation: Interpolation method (see :cpp:func:`resize` ). The method ``INTER_AREA`` is not supported by this function.
:param borderMode: Pixel extrapolation method (see :func:`borderInterpolate` ). When \ ``borderMode=BORDER_TRANSPARENT`` , it means that the pixels in the destination image that corresponds to the "outliers" in the source image are not modified by the function.
:param borderMode: Pixel extrapolation method (see :cpp:func:`borderInterpolate` ). When \ ``borderMode=BORDER_TRANSPARENT`` , it means that the pixels in the destination image that corresponds to the "outliers" in the source image are not modified by the function.
:param borderValue: Value used in case of a constant border. By default, it is 0.
@ -274,7 +272,7 @@ where values of pixels with non-integer coordinates are computed using one of av
:math:`(x,y)` in
:math:`map_1` , or
fixed-point maps created by using
:func:`convertMaps` . The reason you might want to convert from floating to fixed-point
:cpp:func:`convertMaps` . The reason you might want to convert from floating to fixed-point
representations of a map is that they can yield much faster (~2x) remapping operations. In the converted case,
:math:`map_1` contains pairs ``(cvFloor(x), cvFloor(y))`` and
:math:`map_2` contains indices in a table of interpolation coefficients.
@ -288,7 +286,7 @@ This function cannot operate in-place.
resize
----------
.. c:function:: void resize( const Mat& src, Mat& dst, Size dsize, double fx=0, double fy=0, int interpolation=INTER_LINEAR )
.. cpp:function:: void resize( InputArray src, OutputArray dst, Size dsize, double fx=0, double fy=0, int interpolation=INTER_LINEAR )
Resizes an image.
@ -343,9 +341,9 @@ If you want to decimate the image by factor of 2 in each direction, you can call
See Also:
:func:`warpAffine`,
:func:`warpPerspective`,
:func:`remap`
:cpp:func:`warpAffine`,
:cpp:func:`warpPerspective`,
:cpp:func:`remap`
.. index:: warpAffine
@ -353,7 +351,7 @@ See Also:
warpAffine
--------------
.. c:function:: void warpAffine( const Mat& src, Mat& dst, const Mat& M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar())
.. cpp:function:: void warpAffine( InputArray src, OutputArray dst, InputArray M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar())
Applies an affine transformation to an image.
@ -365,9 +363,9 @@ warpAffine
:param dsize: Size of the destination image.
:param flags: Combination of interpolation methods (see :func:`resize` ) and the optional flag ``WARP_INVERSE_MAP`` that means that ``M`` is the inverse transformation ( :math:`\texttt{dst}\rightarrow\texttt{src}` ).
:param flags: Combination of interpolation methods (see :cpp:func:`resize` ) and the optional flag ``WARP_INVERSE_MAP`` that means that ``M`` is the inverse transformation ( :math:`\texttt{dst}\rightarrow\texttt{src}` ).
:param borderMode: Pixel extrapolation method (see :func:`borderInterpolate` ). When \ ``borderMode=BORDER_TRANSPARENT`` , it means that the pixels in the destination image corresponding to the "outliers" in the source image are not modified by the function.
:param borderMode: Pixel extrapolation method (see :cpp:func:`borderInterpolate` ). When \ ``borderMode=BORDER_TRANSPARENT`` , it means that the pixels in the destination image corresponding to the "outliers" in the source image are not modified by the function.
:param borderValue: Value used in case of a constant border. By default, it is 0.
@ -378,23 +376,21 @@ The function ``warpAffine`` transforms the source image using the specified matr
\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})
when the flag ``WARP_INVERSE_MAP`` is set. Otherwise, the transformation is first inverted with
:func:`invertAffineTransform` and then put in the formula above instead of ``M`` .
:cpp:func:`invertAffineTransform` and then put in the formula above instead of ``M`` .
The function cannot operate in-place.
See Also:
:func:`warpPerspective`,
:func:`resize`,
:func:`remap`,
:func:`getRectSubPix`,
:func:`transform`
:cpp:func:`warpPerspective`,
:cpp:func:`resize`,
:cpp:func:`remap`,
:cpp:func:`getRectSubPix`,
:cpp:func:`transform`
.. index:: warpPerspective
.. _warpPerspective:
warpPerspective
-------------------
.. c:function:: void warpPerspective( const Mat& src, Mat& dst, const Mat& M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar())
.. cpp:function:: void warpPerspective( InputArray src, OutputArray dst, InputArray M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar())
Applies a perspective transformation to an image.
@ -406,9 +402,9 @@ warpPerspective
:param dsize: Size of the destination image.
:param flags: Combination of interpolation methods (see :func:`resize` ) and the optional flag ``WARP_INVERSE_MAP`` that means that ``M`` is the inverse transformation ( :math:`\texttt{dst}\rightarrow\texttt{src}` ).
:param flags: Combination of interpolation methods (see :cpp:func:`resize` ) and the optional flag ``WARP_INVERSE_MAP`` that means that ``M`` is the inverse transformation ( :math:`\texttt{dst}\rightarrow\texttt{src}` ).
:param borderMode: Pixel extrapolation method (see :func:`borderInterpolate` ). When \ ``borderMode=BORDER_TRANSPARENT`` , it means that the pixels in the destination image that corresponds to the "outliers" in the source image are not modified by the function.
:param borderMode: Pixel extrapolation method (see :cpp:func:`borderInterpolate` ). When \ ``borderMode=BORDER_TRANSPARENT`` , it means that the pixels in the destination image that corresponds to the "outliers" in the source image are not modified by the function.
:param borderValue: Value used in case of a constant border. By default, it is 0.
@ -420,13 +416,178 @@ The function ``warpPerspective`` transforms the source image using the specified
\frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )
when the flag ``WARP_INVERSE_MAP`` is set. Otherwise, the transformation is first inverted with
:func:`invert` and then put in the formula above instead of ``M`` .
:cpp:func:`invert` and then put in the formula above instead of ``M`` .
The function cannot operate in-place.
See Also:
:func:`warpAffine`,
:func:`resize`,
:func:`remap`,
:func:`getRectSubPix`,
:func:`perspectiveTransform`
:cpp:func:`warpAffine`,
:cpp:func:`resize`,
:cpp:func:`remap`,
:cpp:func:`getRectSubPix`,
:cpp:func:`perspectiveTransform`
.. index:: initUndistortRectifyMap
initUndistortRectifyMap
---------------------------
.. cpp:function:: void initUndistortRectifyMap( InputArray cameraMatrix, InputArray distCoeffs, InputArray R, InputArray newCameraMatrix, Size size, int m1type, OutputArray map1, OutputArray map2 )
Computes the undistortion and rectification transformation map.
:param cameraMatrix: Input camera matrix :math:`A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}` .
:param distCoeffs: Input vector of distortion coefficients :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])` of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
:param R: Optional rectification transformation in the object space (3x3 matrix). ``R1`` or ``R2`` , computed by :ref:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is assumed.
:param newCameraMatrix: New camera matrix :math:`A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}` .
:param size: Undistorted image size.
:param m1type: Type of the first output map that can be ``CV_32FC1`` or ``CV_16SC2`` . See :ref:`convertMaps` for details.
:param map1: The first output map.
:param map2: The second output map.
The function computes the joint undistortion and rectification transformation and represents the result in the form of maps for
:ref:`Remap` . The undistorted image looks like original, as if it is captured with a camera using the camera matrix ``=newCameraMatrix`` and zero distortion. In case of a monocular camera, ``newCameraMatrix`` is usually equal to ``cameraMatrix`` , or it can be computed by
:ref:`GetOptimalNewCameraMatrix` for a better control over scaling. In case of a stereo camera, ``newCameraMatrix`` is normally set to ``P1`` or ``P2`` computed by
:ref:`StereoRectify` .
Also, this new camera is oriented differently in the coordinate space, according to ``R`` . That, for example, helps to align two heads of a stereo camera so that the epipolar lines on both images become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).
The function actually builds the maps for the inverse mapping algorithm that is used by
:ref:`Remap` . That is, for each pixel
:math:`(u, v)` in the destination (corrected and rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original image from camera). The following process is applied:
.. math::
\begin{array}{l} x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\{[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ x' \leftarrow X/W \\ y' \leftarrow Y/W \\ x" \leftarrow x' (1 + k_1 r^2 + k_2 r^4 + k_3 r^6) + 2p_1 x' y' + p_2(r^2 + 2 x'^2) \\ y" \leftarrow y' (1 + k_1 r^2 + k_2 r^4 + k_3 r^6) + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' \\ map_x(u,v) \leftarrow x" f_x + c_x \\ map_y(u,v) \leftarrow y" f_y + c_y \end{array}
where
:math:`(k_1, k_2, p_1, p_2[, k_3])` are the distortion coefficients.
In case of a stereo camera, this function is called twice: once for each camera head, after
:ref:`StereoRectify` , which in its turn is called after
:ref:`StereoCalibrate` . But if the stereo camera was not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using
:ref:`StereoRectifyUncalibrated` . For each camera, the function computes homography ``H`` as the rectification transformation in a pixel domain, not a rotation matrix ``R`` in 3D space. ``R`` can be computed from ``H`` as
.. math::
\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}
where ``cameraMatrix`` can be chosen arbitrarily.
.. index:: getDefaultNewCameraMatrix
getDefaultNewCameraMatrix
-----------------------------
.. cpp:function:: Mat getDefaultNewCameraMatrix(InputArray cameraMatrix, Size imgSize=Size(), bool centerPrincipalPoint=false )
Returns the default new camera matrix.
:param cameraMatrix: Input camera matrix.
:param imageSize: Camera view image size in pixels.
:param centerPrincipalPoint: Location of the principal point in the new camera matrix. The parameter indicates whether this location should be at the image center or not.
The function returns the camera matrix that is either an exact copy of the input ``cameraMatrix`` (when ``centerPrinicipalPoint=false`` ), or the modified one (when ``centerPrincipalPoint`` =true).
In the latter case, the new camera matrix will be:
.. math::
\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,
where
:math:`f_x` and
:math:`f_y` are
:math:`(0,0)` and
:math:`(1,1)` elements of ``cameraMatrix`` , respectively.
By default, the undistortion functions in OpenCV (see
:ref:`initUndistortRectifyMap`,
:ref:`undistort`) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.
.. index:: undistort
undistort
-------------
.. cpp:function:: void undistort( InputArray src, OutputArray dst, InputArray cameraMatrix, InputArray distCoeffs, InputArray newCameraMatrix=None() )
Transforms an image to compensate for lens distortion.
:param src: Input (distorted) image.
:param dst: Output (corrected) image that has the same size and type as ``src`` .
:param cameraMatrix: Input camera matrix :math:`A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}` .
:param distCoeffs: Input vector of distortion coefficients :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])` of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
:param newCameraMatrix: Camera matrix of the distorted image. By default, it is the same as ``cameraMatrix`` but you may additionally scale and shift the result by using a different matrix.
The function transforms an image to compensate radial and tangential lens distortion.
The function is simply a combination of
:ref:`InitUndistortRectifyMap` (with unity ``R`` ) and
:ref:`Remap` (with bilinear interpolation). See the former function for details of the transformation being performed.
Those pixels in the destination image, for which there is no correspondent pixels in the source image, are filled with zeros (black color).
A particular subset of the source image that will be visible in the corrected image can be regulated by ``newCameraMatrix`` . You can use
:ref:`GetOptimalNewCameraMatrix` to compute the appropriate ``newCameraMatrix`` depending on your requirements.
The camera matrix and the distortion parameters can be determined using
:ref:`calibrateCamera` . If the resolution of images is different from the resolution used at the calibration stage,
:math:`f_x, f_y, c_x` and
:math:`c_y` need to be scaled accordingly, while the distortion coefficients remain the same.
.. index:: undistortPoints
undistortPoints
-------------------
.. cpp:function:: void undistortPoints( InputArray src, OutputArray dst, InputArray cameraMatrix, InputArray distCoeffs, InputArray R=None(), InputArray P=None())
Computes the ideal point coordinates from the observed point coordinates.
:param src: Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2).
:param dst: Output ideal point coordinates after undistortion and reverse perspective transformation.
:param cameraMatrix: Camera matrix :math:`\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}` .
:param distCoeffs: Input vector of distortion coefficients :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])` of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
:param R: Rectification transformation in the object space (3x3 matrix). ``R1`` or ``R2`` computed by :ref:`StereoRectify` can be passed here. If the matrix is empty, the identity transformation is used.
:param P: New camera matrix (3x3) or new projection matrix (3x4). ``P1`` or ``P2`` computed by :ref:`StereoRectify` can be passed here. If the matrix is empty, the identity new camera matrix is used.
The function is similar to
:ref:`undistort` and
:ref:`initUndistortRectifyMap` but it operates on a sparse set of points instead of a raster image. Also the function performs a reverse transformation to
:ref:`projectPoints` . In case of a 3D object, it does not reconstruct its 3D coordinates, but for a planar object, it does, up to a translation vector, if the proper ``R`` is specified. ::
// (u,v) is the input point, (u', v') is the output point
// camera_matrix=[fx 0 cx; 0 fy cy; 0 0 1]
// P=[fx' 0 cx' tx; 0 fy' cy' ty; 0 0 1 tz]
x" = (u - cx)/fx
y" = (v - cy)/fy
(x',y') = undistort(x",y",dist_coeffs)
[X,Y,W]T = R*[x' y' 1]T
x = X/W, y = Y/W
u' = x*fx' + cx'
v' = y*fy' + cy',
where ``undistort()`` is an approximate iterative algorithm that estimates the normalized original point coordinates out of the normalized distorted point coordinates ("normalized" means that the coordinates do not depend on the camera matrix).
The function can be used for both a stereo camera head or a monocular camera (when R is empty).

30
modules/imgproc/doc/histograms.rst
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@ -5,13 +5,11 @@ Histograms
.. index:: calcHist
.. _calcHist:
calcHist
------------
.. c:function:: void calcHist( const Mat* arrays, int narrays, const int* channels, const Mat\& mask, MatND\& hist, int dims, const int* histSize, const float** ranges, bool uniform=true, bool accumulate=false )
.. cpp: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 )
.. c:function:: void calcHist( const Mat* arrays, int narrays, const int* channels, const Mat\& mask, SparseMat\& hist, int dims, const int* histSize, const float** ranges, bool uniform=true, bool accumulate=false )
.. cpp: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 )
Calculates a histogram of a set of arrays.
@ -99,13 +97,11 @@ input arrays at the same location. The sample below shows how to compute a 2D Hu
.. index:: calcBackProject
.. _calcBackProject:
calcBackProject
-------------------
.. c:function:: void calcBackProject( const Mat* arrays, int narrays, const int* channels, const MatND\& hist, Mat\& backProject, const float** ranges, double scale=1, bool uniform=true )
.. cpp:function:: void calcBackProject( const Mat* arrays, int narrays, const int* channels, InputArray hist, OutputArray backProject, const float** ranges, double scale=1, bool uniform=true )
.. c:function:: void calcBackProject( const Mat* arrays, int narrays, const int* channels, const SparseMat\& hist, Mat\& backProject, const float** ranges, double scale=1, bool uniform=true )
.. cpp: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 )
Calculates the back projection of a histogram.
@ -119,7 +115,7 @@ calcBackProject
:param backProject: Destination back projection aray that is a single-channel array of the same size and depth as ``arrays[0]`` .
:param ranges: Array of arrays of the histogram bin boundaries in each dimension. See :func:`calcHist` .
:param ranges: Array of arrays of the histogram bin boundaries in each dimension. See :cpp:func:`calcHist` .
:param scale: Optional scale factor for the output back projection.
@ -137,21 +133,19 @@ The functions ``calcBackProject`` calculate the back project of the histogram. T
Find connected components in the resulting picture and choose, for example, the largest component.
This is an approximate algorithm of the
:func:`CAMShift` color object tracker.
:cpp:func:`CAMShift` color object tracker.
See Also:
:func:`calcHist`
:cpp:func:`calcHist`
.. index:: compareHist
.. _compareHist:
compareHist
-----------
.. c:function:: double compareHist( const MatND\& H1, const MatND\& H2, int method )
.. cpp:function:: double compareHist( InputArray H1, InputArray H2, int method )
.. c:function:: double compareHist( const SparseMat\& H1, const SparseMat\& H2, int method )
.. cpp:function:: double compareHist( const SparseMat& H1, const SparseMat& H2, int method )
Compares two histograms.
@ -207,15 +201,13 @@ The function returns
:math:`d(H_1, H_2)` .
While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms or more general sparse configurations of weighted points, consider using the
:func:`calcEMD` function.
:cpp:func:`calcEMD` function.
.. index:: equalizeHist
.. _equalizeHist:
equalizeHist
----------------
.. c:function:: void equalizeHist( const Mat\& src, Mat\& dst )
.. cpp:function:: void equalizeHist( InputArray src, OutputArray dst )
Equalizes the histogram of a grayscale image.

62
modules/imgproc/doc/miscellaneous_transformations.rst
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@ -9,7 +9,7 @@ Miscellaneous Image Transformations
adaptiveThreshold
---------------------
.. c:function:: void adaptiveThreshold( const Mat& src, Mat& dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C )
.. cpp:function:: void adaptiveThreshold( InputArray src, OutputArray dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C )
Applies an adaptive threshold to an array.
@ -54,14 +54,14 @@ where
:math:`T(x, y)` is a weighted sum (cross-correlation with a Gaussian window) of the
:math:`\texttt{blockSize} \times \texttt{blockSize}` neighborhood of
:math:`(x, y)` minus ``C`` . The default sigma (standard deviation) is used for the specified ``blockSize`` . See
:func:`getGaussianKernel` .
:cpp:func:`getGaussianKernel` .
The function can process the image in-place.
See Also:
:func:`threshold`,
:func:`blur`,
:func:`GaussianBlur`
:cpp:func:`threshold`,
:cpp:func:`blur`,
:cpp:func:`GaussianBlur`
.. index:: cvtColor
@ -70,7 +70,7 @@ See Also:
cvtColor
------------
.. c:function:: void cvtColor( const Mat& src, Mat& dst, int code, int dstCn=0 )
.. cpp:function:: void cvtColor( InputArray src, OutputArray dst, int code, int dstCn=0 )
Converts an image from one color space to another.
@ -127,7 +127,7 @@ The function can do the following transformations:
..
More advanced channel reordering can also be done with
:func:`mixChannels` .
:cpp:func:`mixChannels` .
*
RGB
@ -404,9 +404,9 @@ The function can do the following transformations:
distanceTransform
---------------------
.. c:function:: void distanceTransform( const Mat& src, Mat& dst, int distanceType, int maskSize )
.. cpp:function:: void distanceTransform( InputArray src, OutputArray dst, int distanceType, int maskSize )
.. c:function:: void distanceTransform( const Mat& src, Mat& dst, Mat& labels, int distanceType, int maskSize )
.. cpp:function:: void distanceTransform( InputArray src, OutputArray dst, OutputArray labels, int distanceType, int maskSize )
Calculates the distance to the closest zero pixel for each pixel of the source image.
@ -472,9 +472,9 @@ Currently, the second variant can use only the approximate distance transform al
floodFill
-------------
.. c:function:: int floodFill( Mat& image, Point seed, Scalar newVal, Rect* rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4 )
.. cpp:function:: int floodFill( InputOutputArray image, Point seed, Scalar newVal, Rect* rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4 )
.. c:function:: int floodFill( Mat& image, Mat& mask, Point seed, Scalar newVal, Rect* rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4 )
.. cpp:function:: int floodFill( InputOutputArray image, InputOutputArray mask, Point seed, Scalar newVal, Rect* rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4 )
Fills a connected component with the given color.
@ -566,7 +566,7 @@ where
Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on. Various modes of the function are demonstrated in the ``floodfill.cpp`` sample.
See Also:
:func:`findContours`
:cpp:func:`findContours`
.. index:: inpaint
@ -574,7 +574,7 @@ See Also:
inpaint
-----------
.. c:function:: void inpaint( const Mat& src, const Mat& inpaintMask, Mat& dst, double inpaintRadius, int flags )
.. cpp:function:: void inpaint( InputArray src, InputArray inpaintMask, OutputArray dst, double inpaintRadius, int flags )
Restores the selected region in an image using the region neighborhood.
@ -598,15 +598,13 @@ for more details.
.. index:: integral
.. _integral:
integral
------------
.. c:function:: void integral( const Mat& image, Mat& sum, int sdepth=-1 )
.. cpp:function:: void integral( InputArray image, OutputArray sum, int sdepth=-1 )
.. c:function:: void integral( const Mat& image, Mat& sum, Mat& sqsum, int sdepth=-1 )
.. cpp:function:: void integral( InputArray image, OutputArray sum, OutputArray sqsum, int sdepth=-1 )
.. c:function:: void integral( const Mat& image, Mat& sum, Mat& sqsum, Mat& tilted, int sdepth=-1 )
.. cpp:function:: void integral( InputArray image, OutputArray sum, OutputArray sqsum, OutputArray tilted, int sdepth=-1 )
Calculates the integral of an image.
@ -656,7 +654,7 @@ As a practical example, the next figure shows the calculation of the integral of
threshold
-------------
.. c:function:: double threshold( const Mat& src, Mat& dst, double thresh, double maxVal, int thresholdType )
.. cpp:function:: double threshold( InputArray src, OutputArray dst, double thresh, double maxVal, int thresholdType )
Applies a fixed-level threshold to each array element.
@ -673,7 +671,7 @@ threshold
The function applies fixed-level thresholding
to a single-channel array. The function is typically used to get a
bi-level (binary) image out of a grayscale image (
:func:`compare` could
:cpp:func:`compare` could
be also used for this purpose) or for removing a noise, that is, filtering
out pixels with too small or too large values. There are several
types of thresholding supported by the function. They are determined by ``thresholdType`` :
@ -717,19 +715,17 @@ Currently, Otsu's method is implemented only for 8-bit images.
.. image:: pics/threshold.png
See Also:
:func:`adaptiveThreshold`,
:func:`findContours`,
:func:`compare`,
:func:`min`,
:func:`max`
:cpp:func:`adaptiveThreshold`,
:cpp:func:`findContours`,
:cpp:func:`compare`,
:cpp:func:`min`,
:cpp:func:`max`
.. index:: watershed
.. _watershed:
watershed
-------------
.. c:function:: void watershed( const Mat& image, Mat& markers )
.. cpp:function:: void watershed( InputArray image, InputOutputArray markers )
Performs a marker-based image segmentation using the watershed algrorithm.
@ -745,8 +741,8 @@ function, you have to roughly outline the desired regions in the image ``markers
represented as one or more connected components with the pixel values
1, 2, 3, and so on. Such markers can be retrieved from a binary mask
using
:func:`findContours` and
:func:`drawContours` (see the ``watershed.cpp`` demo).
:cpp:func:`findContours` and
:cpp:func:`drawContours` (see the ``watershed.cpp`` demo).
The markers are "seeds" of the future image
regions. All the other pixels in ``markers`` , whose relation to the
outlined regions is not known and should be defined by the algorithm,
@ -761,16 +757,14 @@ marker image. Visual demonstration and usage example of the function
can be found in the OpenCV samples directory (see the ``watershed.cpp`` demo).
See Also:
:func:`findContours`
:cpp:func:`findContours`
.. index:: grabCut
.. _grabCut:
grabCut
-------
.. c:function:: void grabCut(const Mat& image, Mat& mask, Rect rect, Mat& bgdModel, Mat& fgdModel, int iterCount, int mode )
.. cpp:function:: void grabCut(InputArray image, InputOutputArray mask, Rect rect, InputOutputArray bgdModel, InputOutputArray fgdModel, int iterCount, int mode )
Runs the GrabCut algorithm.

32
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@ -7,7 +7,7 @@ Motion Analysis and Object Tracking
accumulate
--------------
.. c:function:: void accumulate( const Mat\& src, Mat\& dst, const Mat\& mask=Mat() )
.. cpp:function:: void accumulate( InputArray src, InputOutputArray dst, InputArray mask=None() )
Adds an image to the accumulator.
@ -28,15 +28,15 @@ The function supports multi-channel images. Each channel is processed independen
The functions ``accumulate*`` can be used, for example, to collect statistics of a scene background viewed by a still camera and for the further foreground-background segmentation.
See Also:
:func:`accumulateSquare`,
:func:`accumulateProduct`,
:func:`accumulateWeighted`
:cpp:func:`accumulateSquare`,
:cpp:func:`accumulateProduct`,
:cpp:func:`accumulateWeighted`
.. index:: accumulateSquare
accumulateSquare
--------------------
.. c:function:: void accumulateSquare( const Mat\& src, Mat\& dst, const Mat\& mask=Mat() )
.. cpp:function:: void accumulateSquare( InputArray src, InputOutputArray dst, InputArray mask=None() )
Adds the square of a source image to the accumulator.
@ -55,15 +55,15 @@ The function adds the input image ``src`` or its selected region, raised to powe
The function supports multi-channel images Each channel is processed independently.
See Also:
:func:`accumulateSquare`,
:func:`accumulateProduct`,
:func:`accumulateWeighted`
:cpp:func:`accumulateSquare`,
:cpp:func:`accumulateProduct`,
:cpp:func:`accumulateWeighted`
.. index:: accumulateProduct
accumulateProduct
---------------------
.. c:function:: void accumulateProduct( const Mat\& src1, const Mat\& src2, Mat\& dst, const Mat\& mask=Mat() )
.. cpp:function:: void accumulateProduct( InputArray src1, InputArray src2, InputOutputArray dst, InputArray mask=None() )
Adds the per-element product of two input images to the accumulator.
@ -84,15 +84,15 @@ The function adds the product of 2 images or their selected regions to the accum
The function supports multi-channel images. Each channel is processed independently.
See Also:
:func:`accumulate`,
:func:`accumulateSquare`,
:func:`accumulateWeighted`
:cpp:func:`accumulate`,
:cpp:func:`accumulateSquare`,
:cpp:func:`accumulateWeighted`
.. index:: accumulateWeighted
accumulateWeighted
----------------------
.. c:function:: void accumulateWeighted( const Mat\& src, Mat\& dst, double alpha, const Mat\& mask=Mat() )
.. cpp:function:: void accumulateWeighted( InputArray src, InputOutputArray dst, double alpha, InputArray mask=None() )
Updates a running average.
@ -114,6 +114,6 @@ That is, ``alpha`` regulates the update speed (how fast the accumulator "forgets
The function supports multi-channel images. Each channel is processed independently.
See Also:
:func:`accumulate`,
:func:`accumulateSquare`,
:func:`accumulateProduct`
:cpp:func:`accumulate`,
:cpp:func:`accumulateSquare`,
:cpp:func:`accumulateProduct`

4
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@ -7,7 +7,7 @@ Object Detection
matchTemplate
-----------------
.. c:function:: void matchTemplate( const Mat& image, const Mat& temp, Mat& result, int method )
.. cpp:function:: void matchTemplate( InputArray image, InputArray temp, OutputArray result, int method )
Compares a template against overlapped image regions.
@ -69,5 +69,5 @@ image patch:
R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }
After the function finishes the comparison, the best matches can be found as global minimums (when ``CV_TM_SQDIFF`` was used) or maximums (when ``CV_TM_CCORR`` or ``CV_TM_CCOEFF`` was used) using the
:func:`minMaxLoc` function. In case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels and separate mean values are used for each channel. That is, the function can take a color template and a color image. The result will still be a single-channel image, which is easier to analyze.
:cpp:func:`minMaxLoc` function. In case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels and separate mean values are used for each channel. That is, the function can take a color template and a color image. The result will still be a single-channel image, which is easier to analyze.

115
modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst
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@ -7,7 +7,7 @@ Structural Analysis and Shape Descriptors
moments
-----------
.. c:function:: Moments moments( const Mat& array, bool binaryImage=false )
.. cpp:function:: Moments moments( InputArray array, bool binaryImage=false )
Calculates all of the moments up to the third order of a polygon or rasterized shape where the class ``Moments`` is defined as: ::
@ -72,18 +72,18 @@ http://en.wikipedia.org/wiki/Green_theorem
). So, due to a limited raster resolution, the moments computed for a contour are slightly different from the moments computed for the same rasterized contour.
See Also:
:func:`contourArea`,
:func:`arcLength`
:cpp:func:`contourArea`,
:cpp:func:`arcLength`
.. index:: HuMoments
HuMoments
-------------
.. c:function:: void HuMoments( const Moments& moments, double h[7] )
.. cpp:function:: void HuMoments( const Moments& moments, double h[7] )
Calculates the seven Hu invariants.
:param moments: Input moments computed with :func:`moments` .
:param moments: Input moments computed with :cpp:func:`moments` .
:param h: Output Hu invariants.
The function calculates the seven Hu invariants (see
@ -101,19 +101,19 @@ where
These values are proved to be invariants to the image scale, rotation, and reflection except the seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of infinite image resolution. In case of raster images, the computed Hu invariants for the original and transformed images are a bit different.
See Also:
:func:`matchShapes`
:cpp:func:`matchShapes`
.. index:: findContours
findContours
----------------
.. c:function:: void findContours( const Mat& image, vector<vector<Point> >& contours, vector<Vec4i>& hierarchy, int mode, int method, Point offset=Point())
.. cpp:function:: void findContours( InputArray image, OutputArrayOfArrays contours, OutputArray hierarchy, int mode, int method, Point offset=Point())
.. c:function:: void findContours( const Mat& image, vector<vector<Point> >& contours, int mode, int method, Point offset=Point())
.. cpp:function:: void findContours( InputArray image, OutputArrayOfArrays contours, int mode, int method, Point offset=Point())
Finds contours in a binary image.
: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 :func:`compare` , :func:`inRange` , :func:`threshold` , :func:`adaptiveThreshold` , :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.
: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 :cpp:func:`compare` , :cpp:func:`inRange` , :cpp:func:`threshold` , :cpp:func:`adaptiveThreshold` , :cpp: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.
:param contours: Detected contours. Each contour is stored as a vector of points.
@ -150,7 +150,7 @@ Source ``image`` is modified by this function.
drawContours
----------------
.. c:function:: void drawContours( Mat& image, const vector<vector<Point> >& contours, int contourIdx, const Scalar& color, int thickness=1, int lineType=8, const vector<Vec4i>& hierarchy=vector<Vec4i>(), int maxLevel=INT_MAX, Point offset=Point() )
.. cpp:function:: void drawContours( InputOutputArray image, InputArrayOfArrays contours, int contourIdx, const Scalar& color, int thickness=1, int lineType=8, InputArray hierarchy=None(), int maxLevel=INT_MAX, Point offset=Point() )
Draws contours outlines or filled contours.
@ -165,7 +165,7 @@ drawContours
:param thickness: Thickness of lines the contours are drawn with. If it is negative (for example, ``thickness=CV_FILLED`` ), the contour interiors are
drawn.
:param lineType: Line connectivity. See :func:`line` for details.
:param lineType: Line connectivity. See :cpp:func:`line` for details.
:param hierarchy: Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see ``maxLevel`` ).
@ -221,13 +221,11 @@ The function draws contour outlines in the image if
approxPolyDP
----------------
.. c:function:: void approxPolyDP( const Mat& curve, vector<Point>& approxCurve, double epsilon, bool closed )
.. c:function:: void approxPolyDP( const Mat& curve, vector<Point2f>& approxCurve, double epsilon, bool closed )
.. cpp:function:: void approxPolyDP( InputArray curve, OutputArray approxCurve, double epsilon, bool closed )
Approximates a polygonal curve(s) with the specified precision.
:param curve: Polygon or curve to approximate. It must be :math:`1 \times N` or :math:`N \times 1` matrix of type ``CV_32SC2`` or ``CV_32FC2`` . You can also convert ``vector<Point>`` or ``vector<Point2f>`` to the matrix by calling the ``Mat(const vector<T>&)`` constructor.
:param curve: Input vector of 2d point, stored in ``std::vector`` or ``Mat``.
:param approxCurve: Result of the approximation. The type should match the type of the input curve.
@ -238,15 +236,17 @@ approxPolyDP
The functions ``approxPolyDP`` approximate a curve or a polygon with another curve/polygon with less vertices, so that the distance between them is less or equal to the specified precision. It uses the Douglas-Peucker algorithm
http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm
See http://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/contours.cpp on how to use the function.
.. index:: arcLength
arcLength
-------------
.. c:function:: double arcLength( const Mat& curve, bool closed )
.. cpp:function:: double arcLength( InputArray curve, bool closed )
Calculates a contour perimeter or a curve length.
:param curve: Input vector of 2D points represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>`` /``vector<Point2f>`` converted to a matrix with the ``Mat(const vector<T>&)`` constructor.
:param curve: Input vector of 2D points, stored in ``std::vector`` or ``Mat``.
:param closed: Flag indicating whether the curve is closed or not.
@ -256,11 +256,11 @@ The function computes a curve length or a closed contour perimeter.
boundingRect
----------------
.. c:function:: Rect boundingRect( const Mat& points )
.. cpp:function:: Rect boundingRect( InputArray points )
Calculates the up-right bounding rectangle of a point set.
:param points: Input 2D point set represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>`` /``vector<Point2f>`` converted to a matrix using the ``Mat(const vector<T>&)`` constructor.
:param points: Input 2D point set, stored in ``std::vector`` or ``Mat``.
The function calculates and returns the minimal up-right bounding rectangle for the specified point set.
@ -268,14 +268,14 @@ The function calculates and returns the minimal up-right bounding rectangle for
estimateRigidTransform
--------------------------
.. c:function:: Mat estimateRigidTransform( const Mat& srcpt, const Mat& dstpt, bool fullAffine )
.. cpp:function:: Mat estimateRigidTransform( InputArray srcpt, InputArray dstpt, bool fullAffine )
Computes an optimal affine transformation between two 2D point sets.
:param srcpt: The first input 2D point set.
:param srcpt: The first input 2D point set, stored in ``std::vector`` or ``Mat``.
:param dst: The second input 2D point set of the same size and the same type as ``A`` .
:param fullAffine: If true, the function finds an optimal affine transformation with no additional resrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is limited to combinations of translation, rotation, and uniform scaling (5 degrees of freedom).
The function finds an optimal affine transform
@ -298,15 +298,15 @@ where
when ``fullAffine=false`` .
See Also:
:func:`getAffineTransform`,
:func:`getPerspectiveTransform`,
:func:`findHomography`
:cpp:func:`getAffineTransform`,
:cpp:func:`getPerspectiveTransform`,
:cpp:func:`findHomography`
.. index:: estimateAffine3D
estimateAffine3D
--------------------
.. c:function:: int estimateAffine3D(const Mat& srcpt, const Mat& dstpt, Mat& out, vector<uchar>& outliers, double ransacThreshold = 3.0, double confidence = 0.99)
.. cpp:function:: int estimateAffine3D(InputArray srcpt, InputArray dstpt, OutputArray out, OutputArray outliers, double ransacThreshold = 3.0, double confidence = 0.99)
Computes an optimal affine transformation between two 3D point sets.
@ -328,16 +328,16 @@ The function estimates an optimal 3D affine transformation between two 3D point
contourArea
---------------
.. c:function:: double contourArea( const Mat& contour )
.. cpp:function:: double contourArea( InputArray contour )
Calculates a contour area.
:param contour: Contour vertices represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>`` /``vector<Point2f>`` converted to a matrix using the ``Mat(const vector<T>&)`` constructor.
:param contour: Input vector of 2d points (contour vertices), stored in ``std::vector`` or ``Mat``.
The function computes a contour area. Similarly to
:func:`moments` , the area is computed using the Green formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using
:func:`drawContours` or
:func:`fillPoly` , can be different.
:cpp:func:`moments` , the area is computed using the Green formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using
:cpp:func:`drawContours` or
:cpp:func:`fillPoly` , can be different.
Here is a short example: ::
vector<Point> contour;
@ -351,45 +351,40 @@ Here is a short example: ::
approxPolyDP(contour, approx, 5, true);
double area1 = contourArea(approx);
cout << "area0 = " << area0 << endl <<
"area1 = " << area1 << endl <<
"approx poly vertices = " << approx.size() << endl;
cout << "area0 =" << area0 << endl <<
"area1 =" << area1 << endl <<
"approx poly vertices" << approx.size() << endl;
.. index:: convexHull
convexHull
--------------
.. c:function:: void convexHull( const Mat& points, vector<int>& hull, bool clockwise=false )
.. c:function:: void convexHull( const Mat& points, vector<Point>& hull, bool clockwise=false )
.. c:function:: void convexHull( const Mat& points, vector<Point2f>& hull, bool clockwise=false )
.. cpp:function:: void convexHull( InputArray points, OutputArray hull, bool clockwise=false, bool returnPoints=true )
Finds the convex hull of a point set.
:param points: Input 2D point set represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>`` /``vector<Point2f>`` converted to a matrix using the ``Mat(const vector<T>&)`` constructor.
:param points: Input 2D point set, stored in ``std::vector`` or ``Mat``.
:param hull: Output convex hull. It is either a vector of points that form the hull (must have the same type as the input points), or a vector of 0-based point indices of the hull points in the original array (since the set of convex hull points is a subset of the original point set).
:param hull: Output convex hull. It is either an integer vector of indices or vector of points. In the first case the ``hull`` elements are 0-based indices of the convex hull points in the original array (since the set of convex hull points is a subset of the original point set). In the second case ``hull`` elements will be the convex hull points themselves.
:param clockwise: If true, the output convex hull will be oriented clockwise. Otherwise, it will be oriented counter-clockwise. The usual screen coordinate system is assumed where the origin is at the top-left corner, x axis is oriented to the right, and y axis is oriented downwards.
:param clockwise: Orientation flag. If true, the output convex hull will be oriented clockwise. Otherwise, it will be oriented counter-clockwise. The usual screen coordinate system is assumed where the origin is at the top-left corner, x axis is oriented to the right, and y axis is oriented downwards.
:param returnPoints: Operation flag. In the case of matrix, when the flag is true, the function will return convex hull points, otherwise it will return indices of the convex hull points. When the output array is ``std::vector``, the flag is ignored, and the output depends on the type of the vector - ``std::vector<int>`` implies ``returnPoints=true``, ``std::vector<Point>`` implies ``returnPoints=false``.
The functions find the convex hull of a 2D point set using the Sklansky's algorithm
Sklansky82
that has
:math:`O(N logN)` or
:math:`O(N)` complexity (where
:math:`N` is the number of input points), depending on how the initial sorting is implemented (currently it is
:math:`O(N logN)` . See the OpenCV sample ``convexhull.c`` that demonstrates the usage of different function variants.
*O(N logN)* complexity in the current implementation. See the OpenCV sample ``convexhull.cpp`` that demonstrates the usage of different function variants.
.. index:: fitEllipse
fitEllipse
--------------
.. c:function:: RotatedRect fitEllipse( const InputArray& points )
.. cpp:function:: RotatedRect fitEllipse( InputArray points )
Fits an ellipse around a set of 2D points.
:param points: Input 2D point set represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>`` or ``vector<Point2f>``.
:param points: Input vector of 2D points, stored in ``std::vector<>`` or ``Mat``.
The function calculates the ellipse that fits (in least-squares sense) a set of 2D points best of all. It returns the rotated rectangle in which the ellipse is inscribed.
@ -397,13 +392,13 @@ The function calculates the ellipse that fits (in least-squares sense) a set of
fitLine
-----------
.. c:function:: void fitLine( const InputArray& points, OutputArray& line, int distType, double param, double reps, double aeps )
.. cpp:function:: void fitLine( InputArray points, OutputArray line, int distType, double param, double reps, double aeps )
Fits a line to a 2D or 3D point set.
:param points: Input 2D or 3D point set represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>``, ``vector<Point2f>``, ``vector<Point3i>`` or ``vector<Point3f>``.
:param points: Input vector of 2D or 3D points, stored in ``std::vector<>`` or ``Mat``.
:param line: Output line parameters. In case of 2D fitting it should be ``Vec4f``, a vector of 4 floats ``(vx, vy, x0, y0)``, where ``(vx, vy)`` is a normalized vector collinear to the line and ``(x0, y0)`` is a point on the line. In case of 3D fitting, it should be ``Vec6f``, a vector of 6 floats ``(vx, vy, vz, x0, y0, z0)``, where ``(vx, vy, vz)`` is a normalized vector collinear to the line and ``(x0, y0, z0)`` is a point on the line.
:param line: Output line parameters. In case of 2D fitting it should be a vector of 4 elements (like ``Vec4f``) - ``(vx, vy, x0, y0)``, where ``(vx, vy)`` is a normalized vector collinear to the line and ``(x0, y0)`` is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like ``Vec6f``) - ``(vx, vy, vz, x0, y0, z0)``, where ``(vx, vy, vz)`` is a normalized vector collinear to the line and ``(x0, y0, z0)`` is a point on the line.
:param distType: Distance used by the M-estimator (see the discussion).
@ -463,11 +458,11 @@ http://en.wikipedia.org/wiki/M-estimator
isContourConvex
-------------------
.. c:function:: bool isContourConvex( const InputArray& contour )
.. cpp:function:: bool isContourConvex( InputArray contour )
Tests a contour convexity.
:param contour: Tested contour, a matrix of type ``CV_32SC2`` or ``CV_32FC2`` , or ``vector<Point>`` or ``vector<Point2f>``.
:param contour: The input vector of 2D points, stored in ``std::vector<>`` or ``Mat``.
The function tests whether the input contour is convex or not. The contour must be simple, that is, without self-intersections. Otherwise, the function output is undefined.
@ -475,11 +470,11 @@ The function tests whether the input contour is convex or not. The contour must
minAreaRect
---------------
.. c:function:: RotatedRect minAreaRect( const InputArray& points )
.. cpp:function:: RotatedRect minAreaRect( InputArray points )
Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
:param points: Input 2D point set represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>`` or ``vector<Point2f>``.
:param points: The input vector of 2D points, stored in ``std::vector<>`` or ``Mat``.
The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a specified point set. See the OpenCV sample ``minarea.cpp`` .
@ -487,11 +482,11 @@ The function calculates and returns the minimum-area bounding rectangle (possibl
minEnclosingCircle
----------------------
.. c:function:: void minEnclosingCircle( const InputArray& points, Point2f& center, float& radius )
.. cpp:function:: void minEnclosingCircle( InputArray points, Point2f& center, float& radius )
Finds a circle of the minimum area enclosing a 2D point set.
:param points: Input 2D point set represented either by ``CV_32SC2`` or ``CV_32FC2`` matrix, or by ``vector<Point>`` or ``vector<Point2f>``.
:param points: The input vector of 2D points, stored in ``std::vector<>`` or ``Mat``.
:param center: Output center of the circle.
@ -503,7 +498,7 @@ The function finds the minimal enclosing circle of a 2D point set using an itera
matchShapes
---------------
.. c:function:: double matchShapes( const InputArray& object1, const InputArray& object2, int method, double parameter=0 )
.. cpp:function:: double matchShapes( InputArray object1, InputArray object2, int method, double parameter=0 )
Compares two shapes.
@ -517,7 +512,7 @@ matchShapes
:param parameter: Method-specific parameter (not supported now).
The function compares two shapes. All three implemented methods use the Hu invariants (see
:func:`HuMoments` ) as follows (
:cpp:func:`HuMoments` ) as follows (
:math:`A` denotes ``object1``,:math:`B` denotes ``object2`` ):
* method=CV\_CONTOUR\_MATCH\_I1
@ -553,7 +548,7 @@ and
pointPolygonTest
--------------------
.. c:function:: double pointPolygonTest( const InputArray& contour, Point2f pt, bool measureDist )
.. cpp:function:: double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist )
Performs a point-in-contour test.

8
modules/ml/doc/boosting.rst
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@ -221,7 +221,7 @@ Boosted tree classifier ::
CvBoost::train
--------------
.. c:function:: bool CvBoost::train( const CvMat* _train_data, int _tflag, const CvMat* _responses, const CvMat* _var_idx=0, const CvMat* _sample_idx=0, const CvMat* _var_type=0, const CvMat* _missing_mask=0, CvBoostParams params=CvBoostParams(), bool update=false )
.. cpp:function:: bool CvBoost::train( const CvMat* _train_data, int _tflag, const CvMat* _responses, const CvMat* _var_idx=0, const CvMat* _sample_idx=0, const CvMat* _var_type=0, const CvMat* _missing_mask=0, CvBoostParams params=CvBoostParams(), bool update=false )
Trains a boosted tree classifier.
@ -233,7 +233,7 @@ The train method follows the common template. The last parameter ``update`` spec
CvBoost::predict
----------------
.. c:function:: float CvBoost::predict( const CvMat* sample, const CvMat* missing=0, CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ, bool raw_mode=false ) const
.. cpp:function:: float CvBoost::predict( const CvMat* sample, const CvMat* missing=0, CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ, bool raw_mode=false ) const
Predicts a response for an input sample.
@ -245,7 +245,7 @@ The method ``CvBoost::predict`` runs the sample through the trees in the ensembl
CvBoost::prune
--------------
.. c:function:: void CvBoost::prune( CvSlice slice )
.. cpp:function:: void CvBoost::prune( CvSlice slice )
Removes the specified weak classifiers.
@ -261,7 +261,7 @@ Do not confuse this method with the pruning of individual decision trees, which
CvBoost::get_weak_predictors
----------------------------
.. c:function:: CvSeq* CvBoost::get_weak_predictors()
.. cpp:function:: CvSeq* CvBoost::get_weak_predictors()
Returns the sequence of weak tree classifiers.

6
modules/ml/doc/decision_trees.rst
View File

@ -371,9 +371,9 @@ Decision tree ::
CvDTree::train
--------------
.. c:function:: bool CvDTree::train( const CvMat* _train_data, int _tflag, const CvMat* _responses, const CvMat* _var_idx=0, const CvMat* _sample_idx=0, const CvMat* _var_type=0, const CvMat* _missing_mask=0, CvDTreeParams params=CvDTreeParams() )
.. cpp:function:: bool CvDTree::train( const CvMat* _train_data, int _tflag, const CvMat* _responses, const CvMat* _var_idx=0, const CvMat* _sample_idx=0, const CvMat* _var_type=0, const CvMat* _missing_mask=0, CvDTreeParams params=CvDTreeParams() )
.. c:function:: bool CvDTree::train( CvDTreeTrainData* _train_data, const CvMat* _subsample_idx )
.. cpp:function:: bool CvDTree::train( CvDTreeTrainData* _train_data, const CvMat* _subsample_idx )
Trains a decision tree.
@ -391,7 +391,7 @@ There are two ``train`` methods in ``CvDTree`` :
CvDTree::predict
----------------
.. c:function:: CvDTreeNode* CvDTree::predict( const CvMat* _sample, const CvMat* _missing_data_mask=0, bool raw_mode=false ) const
.. cpp:function:: CvDTreeNode* CvDTree::predict( const CvMat* _sample, const CvMat* _missing_data_mask=0, bool raw_mode=false ) const
Returns the leaf node of a decision tree corresponding to the input vector.

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

@ -195,7 +195,7 @@ EM model ::
CvEM::train
-----------
.. c:function:: void CvEM::train( const CvMat* samples, const CvMat* sample_idx=0, CvEMParams params=CvEMParams(), CvMat* labels=0 )
.. cpp:function:: void CvEM::train( const CvMat* samples, const CvMat* sample_idx=0, CvEMParams params=CvEMParams(), CvMat* labels=0 )
Estimates the Gaussian mixture parameters from a sample set.

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

@ -49,7 +49,7 @@ K-Nearest Neighbors model ::
CvKNearest::train
-----------------
.. c:function:: bool CvKNearest::train( const CvMat* _train_data, const CvMat* _responses, const CvMat* _sample_idx=0, bool is_regression=false, int _max_k=32, bool _update_base=false )
.. cpp:function:: bool CvKNearest::train( const CvMat* _train_data, const CvMat* _responses, const CvMat* _sample_idx=0, bool is_regression=false, int _max_k=32, bool _update_base=false )
Trains the model.
@ -70,7 +70,7 @@ The parameter ``_update_base`` specifies whether the model is trained from scrat
CvKNearest::find_nearest
------------------------
.. c:function:: float CvKNearest::find_nearest( const CvMat* _samples, int k, CvMat* results=0, const float** neighbors=0, CvMat* neighbor_responses=0, CvMat* dist=0 ) const
.. cpp:function:: float CvKNearest::find_nearest( const CvMat* _samples, int k, CvMat* results=0, const float** neighbors=0, CvMat* neighbor_responses=0, CvMat* dist=0 ) const
Finds the neighbors for input vectors.

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

@ -225,7 +225,7 @@ Unlike many other models in ML that are constructed and trained at once, in the
CvANN_MLP::create
-----------------
.. c:function:: void CvANN_MLP::create( const CvMat* _layer_sizes, int _activ_func=SIGMOID_SYM, double _f_param1=0, double _f_param2=0 )
.. cpp:function:: void CvANN_MLP::create( const CvMat* _layer_sizes, int _activ_func=SIGMOID_SYM, double _f_param1=0, double _f_param2=0 )
Constructs MLP with the specified topology.
@ -243,7 +243,7 @@ The method creates an MLP network with the specified topology and assigns the sa
CvANN_MLP::train
----------------
.. c:function:: int CvANN_MLP::train( const CvMat* _inputs, const CvMat* _outputs, const CvMat* _sample_weights, const CvMat* _sample_idx=0, CvANN_MLP_TrainParams _params = CvANN_MLP_TrainParams(), int flags=0 )
.. cpp:function:: int CvANN_MLP::train( const CvMat* _inputs, const CvMat* _outputs, const CvMat* _sample_weights, const CvMat* _sample_idx=0, CvANN_MLP_TrainParams _params = CvANN_MLP_TrainParams(), int flags=0 )
Trains/updates MLP.

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

@ -46,7 +46,7 @@ Bayes classifier for normally distributed data ::
CvNormalBayesClassifier::train
------------------------------
.. c:function:: bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _responses, const CvMat* _var_idx =0, const CvMat* _sample_idx=0, bool update=false )
.. cpp:function:: bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _responses, const CvMat* _var_idx =0, const CvMat* _sample_idx=0, bool update=false )
Trains the model.
@ -65,7 +65,7 @@ In addition, there is an ``update`` flag that identifies whether the model shoul
CvNormalBayesClassifier::predict
--------------------------------
.. c:function:: float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results=0 ) const
.. cpp:function:: float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results=0 ) const
Predicts the response for sample(s).

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

@ -136,7 +136,7 @@ Random trees ::
CvRTrees::train
---------------
.. c:function:: bool CvRTrees::train( const CvMat* train_data, int tflag, const CvMat* responses, const CvMat* comp_idx=0, const CvMat* sample_idx=0, const CvMat* var_type=0, const CvMat* missing_mask=0, CvRTParams params=CvRTParams() )
.. cpp:function:: bool CvRTrees::train( const CvMat* train_data, int tflag, const CvMat* responses, const CvMat* comp_idx=0, const CvMat* sample_idx=0, const CvMat* var_type=0, const CvMat* missing_mask=0, CvRTParams params=CvRTParams() )
Trains the Random Tree model.
@ -149,7 +149,7 @@ The method ``CvRTrees::train`` is very similar to the first form of ``CvDTree::t
CvRTrees::predict
-----------------
.. c:function:: double CvRTrees::predict( const CvMat* sample, const CvMat* missing=0 ) const
.. cpp:function:: double CvRTrees::predict( const CvMat* sample, const CvMat* missing=0 ) const
Predicts the output for an input sample.
@ -161,7 +161,7 @@ The input parameters of the prediction method are the same as in ``CvDTree::pred
CvRTrees::get_var_importance
----------------------------
.. c:function:: const CvMat* CvRTrees::get_var_importance() const
.. cpp:function:: const CvMat* CvRTrees::get_var_importance() const
Retrieves the variable importance array.
@ -173,7 +173,7 @@ The method returns the variable importance vector, computed at the training stag
CvRTrees::get_proximity
-----------------------
.. c:function:: float CvRTrees::get_proximity( const CvMat* sample_1, const CvMat* sample_2 ) const
.. cpp:function:: float CvRTrees::get_proximity( const CvMat* sample_1, const CvMat* sample_2 ) const
Retrieves the proximity measure between two training samples.

20
modules/ml/doc/statistical_models.rst
View File

@ -46,7 +46,7 @@ In this declaration, some methods are commented off. These are methods for which
CvStatModel::CvStatModel
------------------------
.. c:function:: CvStatModel::CvStatModel()
.. cpp:function:: CvStatModel::CvStatModel()
Serves as a default constructor.
@ -58,7 +58,7 @@ Each statistical model class in ML has a default constructor without parameters.
CvStatModel::CvStatModel(...)
-----------------------------
.. c:function:: CvStatModel::CvStatModel( const CvMat* train_data ... )
.. cpp:function:: CvStatModel::CvStatModel( const CvMat* train_data ... )
Serves as a training constructor.
@ -70,7 +70,7 @@ Most ML classes provide a single-step constructor and train constructors. This c
CvStatModel::~CvStatModel
-------------------------
.. c:function:: CvStatModel::~CvStatModel()
.. cpp:function:: CvStatModel::~CvStatModel()
Serves as a virtual destructor.
@ -93,7 +93,7 @@ Normally, the destructor of each derived class does nothing. But in this instanc
CvStatModel::clear
------------------
.. c:function:: void CvStatModel::clear()
.. cpp:function:: void CvStatModel::clear()
Deallocates memory and resets the model state.
@ -105,7 +105,7 @@ The method ``clear`` does the same job as the destructor: it deallocates all the
CvStatModel::save
-----------------
.. c:function:: void CvStatModel::save( const char* filename, const char* name=0 )
.. cpp:function:: void CvStatModel::save( const char* filename, const char* name=0 )
Saves the model to a file.
@ -117,7 +117,7 @@ The method ``save`` saves the complete model state to the specified XML or YAML
CvStatModel::load
-----------------
.. c:function:: void CvStatModel::load( const char* filename, const char* name=0 )
.. cpp:function:: void CvStatModel::load( const char* filename, const char* name=0 )
Loads the model from a file.
@ -134,7 +134,7 @@ The method is virtual, so any model can be loaded using this virtual method. How
CvStatModel::write
------------------
.. c:function:: void CvStatModel::write( CvFileStorage* storage, const char* name )
.. cpp:function:: void CvStatModel::write( CvFileStorage* storage, const char* name )
Writes the model to the file storage.
@ -146,7 +146,7 @@ The method ``write`` stores the complete model state in the file storage with th
CvStatModel::read
-----------------
.. c:function:: void CvStatMode::read( CvFileStorage* storage, CvFileNode* node )
.. cpp:function:: void CvStatMode::read( CvFileStorage* storage, CvFileNode* node )
Reads the model from the file storage.
@ -161,7 +161,7 @@ The previous model state is cleared by ``clear()`` .
CvStatModel::train
------------------
.. c:function:: bool CvStatMode::train( const CvMat* train_data, [int tflag,] ..., const CvMat* responses, ..., [const CvMat* var_idx,] ..., [const CvMat* sample_idx,] ... [const CvMat* var_type,] ..., [const CvMat* missing_mask,] <misc_training_alg_params> ... )
.. cpp:function:: bool CvStatMode::train( const CvMat* train_data, [int tflag,] ..., const CvMat* responses, ..., [const CvMat* var_idx,] ..., [const CvMat* sample_idx,] ... [const CvMat* var_type,] ..., [const CvMat* missing_mask,] <misc_training_alg_params> ... )
Trains the model.
@ -193,7 +193,7 @@ Usually, the previous model state is cleared by ``clear()`` before running the t
CvStatModel::predict
--------------------
.. c:function:: float CvStatMode::predict( const CvMat* sample[, <prediction_params>] ) const
.. cpp:function:: float CvStatMode::predict( const CvMat* sample[, <prediction_params>] ) const
Predicts the response for a sample.

12
modules/ml/doc/support_vector_machines.rst
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@ -125,7 +125,7 @@ The structure must be initialized and passed to the training method of
CvSVM::train
------------
.. c:function:: bool CvSVM::train( const CvMat* _train_data, const CvMat* _responses, const CvMat* _var_idx=0, const CvMat* _sample_idx=0, CvSVMParams _params=CvSVMParams() )
.. cpp:function:: bool CvSVM::train( const CvMat* _train_data, const CvMat* _responses, const CvMat* _var_idx=0, const CvMat* _sample_idx=0, CvSVMParams _params=CvSVMParams() )
Trains SVM.
@ -145,7 +145,7 @@ All the other parameters are gathered in the
CvSVM::train_auto
-----------------
.. c:function:: train_auto( const CvMat* _train_data, const CvMat* _responses, const CvMat* _var_idx, const CvMat* _sample_idx, CvSVMParams params, int k_fold = 10, CvParamGrid C_grid = get_default_grid(CvSVM::C), CvParamGrid gamma_grid = get_default_grid(CvSVM::GAMMA), CvParamGrid p_grid = get_default_grid(CvSVM::P), CvParamGrid nu_grid = get_default_grid(CvSVM::NU), CvParamGrid coef_grid = get_default_grid(CvSVM::COEF), CvParamGrid degree_grid = get_default_grid(CvSVM::DEGREE) )
.. cpp:function:: train_auto( const CvMat* _train_data, const CvMat* _responses, const CvMat* _var_idx, const CvMat* _sample_idx, CvSVMParams params, int k_fold = 10, CvParamGrid C_grid = get_default_grid(CvSVM::C), CvParamGrid gamma_grid = get_default_grid(CvSVM::GAMMA), CvParamGrid p_grid = get_default_grid(CvSVM::P), CvParamGrid nu_grid = get_default_grid(CvSVM::NU), CvParamGrid coef_grid = get_default_grid(CvSVM::COEF), CvParamGrid degree_grid = get_default_grid(CvSVM::DEGREE) )
Trains SVM with optimal parameters.
@ -189,7 +189,7 @@ as well as for the regression
CvSVM::get_default_grid
-----------------------
.. c:function:: CvParamGrid CvSVM::get_default_grid( int param_id )
.. cpp:function:: CvParamGrid CvSVM::get_default_grid( int param_id )
Generates a grid for SVM parameters.
@ -217,7 +217,7 @@ The function generates a grid for the specified parameter of the SVM algorithm.
CvSVM::get_params
-----------------
.. c:function:: CvSVMParams CvSVM::get_params() const
.. cpp:function:: CvSVMParams CvSVM::get_params() const
Returns the current SVM parameters.
@ -229,9 +229,9 @@ This function may be used to get the optimal parameters obtained while automatic
CvSVM::get_support_vector*
--------------------------
.. c:function:: int CvSVM::get_support_vector_count() const
.. cpp:function:: int CvSVM::get_support_vector_count() const
.. c:function:: const float* CvSVM::get_support_vector(int i) const
.. cpp:function:: const float* CvSVM::get_support_vector(int i) const
Retrieves a number of support vectors and the particular vector.

32
modules/objdetect/doc/cascade_classification.rst
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@ -34,7 +34,7 @@ Base class for computing feature values in cascade classifiers ::
FeatureEvaluator::read
--------------------------
.. c:function:: bool FeatureEvaluator::read(const FileNode\& node)
.. cpp:function:: bool FeatureEvaluator::read(const FileNode\& node)
Reads parameters of features from the ``FileStorage`` node.
@ -44,7 +44,7 @@ FeatureEvaluator::read
FeatureEvaluator::clone
---------------------------
.. c:function:: Ptr<FeatureEvaluator> FeatureEvaluator::clone() const
.. cpp:function:: Ptr<FeatureEvaluator> FeatureEvaluator::clone() const
Returns a full copy of the feature evaluator.
@ -52,7 +52,7 @@ FeatureEvaluator::clone
FeatureEvaluator::getFeatureType
------------------------------------
.. c:function:: int FeatureEvaluator::getFeatureType() const
.. cpp:function:: int FeatureEvaluator::getFeatureType() const
Returns the feature type (``HAAR`` or ``LBP`` for now).
@ -60,7 +60,7 @@ FeatureEvaluator::getFeatureType
FeatureEvaluator::setImage
------------------------------
.. c:function:: bool FeatureEvaluator::setImage(const Mat\& img, Size origWinSize)
.. cpp:function:: bool FeatureEvaluator::setImage(const Mat\& img, Size origWinSize)
Sets an image where the features are computed??.
@ -72,7 +72,7 @@ FeatureEvaluator::setImage
FeatureEvaluator::setWindow
-------------------------------
.. c:function:: bool FeatureEvaluator::setWindow(Point p)
.. cpp:function:: bool FeatureEvaluator::setWindow(Point p)
Sets a window in the current image where the features are computed (called by ??).
@ -82,7 +82,7 @@ FeatureEvaluator::setWindow
FeatureEvaluator::calcOrd
-----------------------------
.. c:function:: double FeatureEvaluator::calcOrd(int featureIdx) const
.. cpp:function:: double FeatureEvaluator::calcOrd(int featureIdx) const
Computes the value of an ordered (numerical) feature.
@ -94,7 +94,7 @@ The function returns the computed value of an ordered feature.
FeatureEvaluator::calcCat
-----------------------------
.. c:function:: int FeatureEvaluator::calcCat(int featureIdx) const
.. cpp:function:: int FeatureEvaluator::calcCat(int featureIdx) const
Computes the value of a categorical feature.
@ -106,7 +106,7 @@ The function returns the computed label of a categorical feature, that is, the v
FeatureEvaluator::create
----------------------------
.. c:function:: static Ptr<FeatureEvaluator> FeatureEvaluator::create(int type)
.. cpp:function:: static Ptr<FeatureEvaluator> FeatureEvaluator::create(int type)
Constructs the feature evaluator.
@ -193,7 +193,7 @@ The cascade classifier class for object detection ::
CascadeClassifier::CascadeClassifier
----------------------------------------
.. c:function:: CascadeClassifier::CascadeClassifier(const string\& filename)
.. cpp:function:: CascadeClassifier::CascadeClassifier(const string\& filename)
Loads a classifier from a file.
@ -203,7 +203,7 @@ CascadeClassifier::CascadeClassifier
CascadeClassifier::empty
----------------------------
.. c:function:: bool CascadeClassifier::empty() const
.. cpp:function:: bool CascadeClassifier::empty() const
Checks if the classifier has been loaded or not.
@ -211,7 +211,7 @@ CascadeClassifier::empty
CascadeClassifier::load
---------------------------
.. c:function:: bool CascadeClassifier::load(const string\& filename)
.. cpp:function:: bool CascadeClassifier::load(const string\& filename)
Loads a classifier from a file. The previous content is destroyed.
@ -221,7 +221,7 @@ CascadeClassifier::load
CascadeClassifier::read
---------------------------
.. c:function:: bool CascadeClassifier::read(const FileNode\& node)
.. cpp:function:: bool CascadeClassifier::read(const FileNode\& node)
Reads a classifier from a FileStorage node. The file may contain a new cascade classifier (trained traincascade application) only.
@ -229,7 +229,7 @@ CascadeClassifier::read
CascadeClassifier::detectMultiScale
---------------------------------------
.. c:function:: void CascadeClassifier::detectMultiScale( const Mat\& image, vector<Rect>\& objects, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size())
.. cpp:function:: void CascadeClassifier::detectMultiScale( const Mat\& image, vector<Rect>\& objects, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size())
Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
@ -249,7 +249,7 @@ CascadeClassifier::detectMultiScale
CascadeClassifier::setImage
-------------------------------
.. c:function:: bool CascadeClassifier::setImage( Ptr<FeatureEvaluator>\& feval, const Mat\& image )
.. cpp:function:: bool CascadeClassifier::setImage( Ptr<FeatureEvaluator>\& feval, const Mat\& image )
Sets an image for detection, which is called by ``detectMultiScale`` at each image level.
@ -261,7 +261,7 @@ CascadeClassifier::setImage
CascadeClassifier::runAt
----------------------------
.. c:function:: int CascadeClassifier::runAt( Ptr<FeatureEvaluator>\& feval, Point pt )
.. cpp:function:: int CascadeClassifier::runAt( Ptr<FeatureEvaluator>\& feval, Point pt )
Runs the detector at the specified point. Use ``setImage`` to set the image that the detector is working with.
@ -276,7 +276,7 @@ Otherwise, it returns ``si``, which is an index of the stage that first predicte
groupRectangles
-------------------
.. c:function:: void groupRectangles(vector<Rect>\& rectList, int groupThreshold, double eps=0.2)
.. cpp:function:: void groupRectangles(vector<Rect>\& rectList, int groupThreshold, double eps=0.2)
Groups the object candidate rectangles.

32
modules/python/src2/hdr_parser.py
View File

@ -1,4 +1,4 @@
import os, sys, re
import os, sys, re, string
# the list only for debugging. The real list, used in the real OpenCV build, is specified in CMakeLists.txt
opencv_hdr_list = [
@ -253,7 +253,25 @@ class CppHeaderParser(object):
fnpos = 0
fname = fname[fnpos:].strip()
rettype = fdecl[:fnpos].strip()
args0 = fdecl[fdecl.find("(")+1:fdecl.rfind(")")].strip().split(",")
if rettype.endswith("operator"):
fname = ("operator " + fname).strip()
rettype = rettype[:rettype.rfind("operator")].strip()
if rettype.endswith("::"):
rpos = rettype.rfind(" ")
if rpos >= 0:
fname = rettype[rpos+1:].strip() + fname
rettype = rettype[:rpos].strip()
else:
fname = rettype + fname
rettype = ""
apos = fdecl.find("(")
if fname.endswith("operator"):
fname += "()"
apos = fdecl.find("(", apos+1)
args0 = fdecl[apos+1:fdecl.rfind(")")].strip().split(",")
args = []
narg = ""
for arg in args0:
@ -265,6 +283,7 @@ class CppHeaderParser(object):
narg = ""
fname = "cv." + fname.replace("::", ".")
decl = [fname, rettype, [], []]
for arg in args:
dfpos = arg.find("=")
defval = ""
@ -274,7 +293,8 @@ class CppHeaderParser(object):
pos = arg.rfind(" ")
aname = arg[pos+1:]
atype = arg[:pos]
decl[3].append([atype, aname, defval, []])
decl[3].append([atype, aname, defval, []])
return decl
def parse_func_decl(self, decl_str):
@ -293,7 +313,11 @@ class CppHeaderParser(object):
if not (("CV_EXPORTS_AS" in decl_str) or ("CV_EXPORTS_W" in decl_str) or \
("CV_WRAP" in decl_str) or ("CV_WRAP_AS" in decl_str)):
return []
# ignore old API in the documentation check (for now)
if "CVAPI(" in decl_str:
return []
top = self.block_stack[-1]
func_modlist = []

60
modules/video/doc/motion_analysis_and_object_tracking.rst
View File

@ -7,14 +7,14 @@ Motion Analysis and Object Tracking
calcOpticalFlowPyrLK
------------------------
.. c:function:: void calcOpticalFlowPyrLK( const Mat\& prevImg, const Mat\& nextImg, const vector<Point2f>\& prevPts, vector<Point2f>\& nextPts, vector<uchar>\& status, vector<float>\& err, Size winSize=Size(15,15), int maxLevel=3, TermCriteria criteria=TermCriteria( TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), double derivLambda=0.5, int flags=0 )
.. cpp: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), double derivLambda=0.5, int flags=0 )
Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.
:param prevImg: The first 8-bit single-channel or 3-channel input image.
:param nextImg: The second input image of the same size and the same type as ``prevImg`` .
:param prevPts: Vector of points for which the flow needs to be found.
:param nextPts: Output vector of points containing the calculated new positions of input features in the second image.
@ -43,16 +43,16 @@ Bouguet00
calcOpticalFlowFarneback
----------------------------
.. c:function:: void calcOpticalFlowFarneback( const Mat\& prevImg, const Mat\& nextImg, Mat\& flow, double pyrScale, int levels, int winsize, int iterations, int polyN, double polySigma, int flags )
.. cpp:function:: void calcOpticalFlowFarneback( InputArray prevImg, InputArray nextImg, InputOutputArray flow, double pyrScale, int levels, int winsize, int iterations, int polyN, double polySigma, int flags )
Computes a dense optical flow using the Gunnar Farneback's algorithm.
:param prevImg: The first 8-bit single-channel input image.
:param nextImg: The second input image of the same size and the same type as ``prevImg`` .
:param flow: Computed flow image that has the same size as ``prevImg`` and type ``CV_32FC2`` .
:param pyrScale: Parameter specifying the image scale (<1) to build pyramids for each image. ``pyrScale=0.5`` means a classical pyramid, where each next layer is twice smaller than the previous one.
:param levels: Number of pyramid layers including the initial image. ``levels=1`` means that no extra layers are created and only the original images are used.
@ -81,7 +81,7 @@ The function finds an optical flow for each ``prevImg`` pixel using the alorithm
updateMotionHistory
-----------------------
.. c:function:: void updateMotionHistory( const Mat\& silhouette, Mat\& mhi, double timestamp, double duration )
.. cpp:function:: void updateMotionHistory( InputArray silhouette, InputOutputArray mhi, double timestamp, double duration )
Updates the motion history image by a moving silhouette.
@ -102,8 +102,8 @@ The function updates the motion history image as follows:
That is, MHI pixels where the motion occurs are set to the current ``timestamp`` , while the pixels where the motion happened last time a long time ago are cleared.
The function, together with
:func:`calcMotionGradient` and
:func:`calcGlobalOrientation` , implements a motion templates technique described in
:cpp:func:`calcMotionGradient` and
:cpp:func:`calcGlobalOrientation` , implements a motion templates technique described in
Davis97
and
Bradski00
@ -114,7 +114,7 @@ See also the OpenCV sample ``motempl.c`` that demonstrates the use of all the mo
calcMotionGradient
----------------------
.. c:function:: void calcMotionGradient( const Mat\& mhi, Mat\& mask, Mat\& orientation, double delta1, double delta2, int apertureSize=3 )
.. cpp:function:: void calcMotionGradient( InputArray mhi, OutputArray mask, OutputArray orientation, double delta1, double delta2, int apertureSize=3 )
Calculates a gradient orientation of a motion history image.
@ -130,7 +130,7 @@ calcMotionGradient
\min ( \texttt{delta1} , \texttt{delta2} ) \le M(x,y)-m(x,y) \le \max ( \texttt{delta1} , \texttt{delta2} ).
:param apertureSize: Aperture size of the :func:`Sobel` operator.
:param apertureSize: Aperture size of the :cpp:func:`Sobel` operator.
The function calculates a gradient orientation at each pixel
:math:`(x, y)` as:
@ -140,26 +140,26 @@ The function calculates a gradient orientation at each pixel
\texttt{orientation} (x,y)= \arctan{\frac{d\texttt{mhi}/dy}{d\texttt{mhi}/dx}}
In fact,
:func:`fastArctan` and
:func:`phase` are used so that the computed angle is measured in degrees and covers the full range 0..360. Also, the ``mask`` is filled to indicate pixels where the computed angle is valid.
:cpp:func:`fastArctan` and
:cpp:func:`phase` are used so that the computed angle is measured in degrees and covers the full range 0..360. Also, the ``mask`` is filled to indicate pixels where the computed angle is valid.
.. index:: calcGlobalOrientation
calcGlobalOrientation
-------------------------
.. c:function:: double calcGlobalOrientation( const Mat\& orientation, const Mat\& mask, const Mat\& mhi, double timestamp, double duration )
.. cpp:function:: double calcGlobalOrientation( InputArray orientation, InputArray mask, InputArray mhi, double timestamp, double duration )
Calculates a global motion orientation in a selected region.
:param orientation: Motion gradient orientation image calculated by the function :func:`calcMotionGradient` .
:param orientation: Motion gradient orientation image calculated by the function :cpp:func:`calcMotionGradient` .
:param mask: Mask image. It may be a conjunction of a valid gradient mask, also calculated by :func:`calcMotionGradient` , and the mask of a region whose direction needs to be calculated.
:param mask: Mask image. It may be a conjunction of a valid gradient mask, also calculated by :cpp:func:`calcMotionGradient` , and the mask of a region whose direction needs to be calculated.
:param mhi: Motion history image calculated by :func:`updateMotionHistory` .
:param mhi: Motion history image calculated by :cpp:func:`updateMotionHistory` .
:param timestamp: Timestamp passed to :func:`updateMotionHistory` .
:param timestamp: Timestamp passed to :cpp:func:`updateMotionHistory` .
:param duration: Maximum duration of a motion track in milliseconds, passed to :func:`updateMotionHistory` .
:param duration: Maximum duration of a motion track in milliseconds, passed to :cpp:func:`updateMotionHistory` .
The function calculates an average
motion direction in the selected region and returns the angle between
@ -171,21 +171,21 @@ weight and the motion occurred in the past has a smaller weight, as recorded in
CamShift
------------
.. c:function:: RotatedRect CamShift( const Mat\& probImage, Rect\& window, TermCriteria criteria )
.. cpp:function:: RotatedRect CamShift( InputArray probImage, Rect& window, TermCriteria criteria )
Finds an object center, size, and orientation.
:param probImage: Back projection of the object histogram. See :func:`calcBackProject` .
:param probImage: Back projection of the object histogram. See :cpp:func:`calcBackProject` .
:param window: Initial search window.
:param criteria: Stop criteria for the underlying :func:`meanShift` .
:param criteria: Stop criteria for the underlying :cpp:func:`meanShift` .
The function implements the CAMSHIFT object tracking algrorithm
Bradski98
.
First, it finds an object center using
:func:`meanShift` and then adjusts the window size and finds the optimal rotation. The function returns the rotated rectangle structure that includes the object position, size, and orientation. The next position of the search window can be obtained with ``RotatedRect::boundingRect()`` .
:cpp:func:`meanShift` and then adjusts the window size and finds the optimal rotation. The function returns the rotated rectangle structure that includes the object position, size, and orientation. The next position of the search window can be obtained with ``RotatedRect::boundingRect()`` .
See the OpenCV sample ``camshiftdemo.c`` that tracks colored objects.
@ -193,23 +193,23 @@ See the OpenCV sample ``camshiftdemo.c`` that tracks colored objects.
meanShift
-------------
.. c:function:: int meanShift( const Mat\& probImage, Rect\& window, TermCriteria criteria )
.. cpp:function:: int meanShift( InputArray probImage, Rect& window, TermCriteria criteria )
Finds an object on a back projection image.
:param probImage: Back projection of the object histogram. See :func:`calcBackProject` for details.
:param probImage: Back projection of the object histogram. See :cpp:func:`calcBackProject` for details.
:param window: Initial search window.
:param criteria: Stop criteria for the iterative search algorithm.
The function implements the iterative object search algorithm. It takes the input back projection of an object and the initial position. The mass center in ``window`` of the back projection image is computed and the search window center shifts to the mass center. The procedure is repeated until the specified number of iterations ``criteria.maxCount`` is done or until the window center shifts by less than ``criteria.epsilon`` . The algorithm is used inside
:func:`CamShift` and, unlike
:func:`CamShift` , the search window size or orientation do not change during the search. You can simply pass the output of
:func:`calcBackProject` to this function. But better results can be obtained if you pre-filter the back projection and remove the noise (for example, by retrieving connected components with
:func:`findContours` , throwing away contours with small area (
:func:`contourArea` ), and rendering the remaining contours with
:func:`drawContours` ).
:cpp:func:`CamShift` and, unlike
:cpp:func:`CamShift` , the search window size or orientation do not change during the search. You can simply pass the output of
:cpp:func:`calcBackProject` to this function. But better results can be obtained if you pre-filter the back projection and remove the noise (for example, by retrieving connected components with
:cpp:func:`findContours` , throwing away contours with small area (
:cpp:func:`contourArea` ), and rendering the remaining contours with
:cpp:func:`drawContours` ).
.. index:: KalmanFilter