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#1205 fixed ~100 of ~700 parameters

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This commit is contained in:
Andrey Kamaev
2012-03-29 06:50:05 +00:00
parent e74be5a65d
commit 94c258cf15
25 changed files with 78 additions and 66 deletions
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7
modules/imgproc/doc/feature_detection.rst
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@@ -52,7 +52,7 @@ Calculates eigenvalues and eigenvectors of image blocks for corner detection.
:param apertureSize: Aperture parameter for the :ocv:func:`Sobel` operator.
:param boderType: Pixel extrapolation method. See :ocv:func:`borderInterpolate` .
:param borderType: Pixel extrapolation method. See :ocv:func:`borderInterpolate` .
For every pixel
:math:`p` , the function ``cornerEigenValsAndVecs`` considers a ``blockSize`` :math:`\times` ``blockSize`` neigborhood
@@ -106,7 +106,7 @@ Harris edge detector.
:param k: Harris detector free parameter. See the formula below.
:param boderType: Pixel extrapolation method. See :ocv:func:`borderInterpolate` .
:param borderType: Pixel extrapolation method. See :ocv:func:`borderInterpolate` .
The function runs the Harris edge detector on the image. Similarly to
:ocv:func:`cornerMinEigenVal` and
@@ -133,6 +133,7 @@ Calculates the minimal eigenvalue of gradient matrices for corner detection.
.. ocv:pyfunction:: cv2.cornerMinEigenVal(src, blockSize[, dst[, ksize[, borderType]]]) -> dst
.. ocv:cfunction:: void cvCornerMinEigenVal( const CvArr* image, CvArr* eigenval, int blockSize, int apertureSize=3 )
.. ocv:pyoldfunction:: cv.CornerMinEigenVal(image, eigenval, blockSize, apertureSize=3)-> None
:param src: Input single-channel 8-bit or floating-point image.
@@ -143,7 +144,7 @@ Calculates the minimal eigenvalue of gradient matrices for corner detection.
:param apertureSize: Aperture parameter for the :ocv:func:`Sobel` operator.
:param boderType: Pixel extrapolation method. See :ocv:func:`borderInterpolate` .
:param borderType: Pixel extrapolation method. See :ocv:func:`borderInterpolate` .
The function is similar to
:ocv:func:`cornerEigenValsAndVecs` but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is,

6
modules/imgproc/doc/geometric_transformations.rst
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@@ -581,16 +581,16 @@ where ``cameraMatrix`` can be chosen arbitrarily.
getDefaultNewCameraMatrix
-----------------------------
-------------------------
Returns the default new camera matrix.
.. ocv:function:: Mat getDefaultNewCameraMatrix(InputArray cameraMatrix, Size imgSize=Size(), bool centerPrincipalPoint=false )
.. ocv:function:: Mat getDefaultNewCameraMatrix(InputArray cameraMatrix, Size imgsize=Size(), bool centerPrincipalPoint=false )
.. ocv:pyfunction:: cv2.getDefaultNewCameraMatrix(cameraMatrix[, imgsize[, centerPrincipalPoint]]) -> retval
:param cameraMatrix: Input camera matrix.
:param imageSize: Camera view image size in pixels.
:param imgsize: 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.

8
modules/imgproc/doc/histograms.rst
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@@ -409,13 +409,13 @@ GetHistValue*D
--------------
Returns a pointer to the histogram bin.
.. ocv:cfunction:: float cvGetHistValue_1D(hist, idx0)
.. ocv:cfunction:: float cvGetHistValue_1D(CvHistogram hist, int idx0)
.. ocv:cfunction:: float cvGetHistValue_2D(hist, idx0, idx1)
.. ocv:cfunction:: float cvGetHistValue_2D(CvHistogram hist, int idx0, int idx1)
.. ocv:cfunction:: float cvGetHistValue_3D(hist, idx0, idx1, idx2)
.. ocv:cfunction:: float cvGetHistValue_3D(CvHistogram hist, int idx0, int idx1, int idx2)
.. ocv:cfunction:: float cvGetHistValue_nD(hist, idx)
.. ocv:cfunction:: float cvGetHistValue_nD(CvHistogram hist, int idx)
:param hist: Histogram.

7
modules/imgproc/doc/motion_analysis_and_object_tracking.rst
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@@ -143,17 +143,18 @@ The function supports multi-channel images. Each channel is processed independen
phaseCorrelate
-------------------------------
--------------
The function is used to detect translational shifts that occur between two images. The operation takes advantage of the Fourier shift theorem for detecting the translational shift in the frequency domain. It can be used for fast image registration as well as motion esitimation. For more information please see http://en.wikipedia.org/wiki/Phase\_correlation .
Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with ``getOptimalDFTSize`` .
Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with :ocv:func:`getOptimalDFTSize`.
.. ocv:function:: Point2d phaseCorrelate(InputArray src1, InputArray src2, InputArray window = noArray())
:param src1: Source floating point array (CV_32FC1 or CV_64FC1)
:param src2: Source floating point array (CV_32FC1 or CV_64FC1)
:param window: Floating point array with windowing coefficients to reduce edge effects (optional).
:param result: Detected phase shift (sub-pixel) between the two arrays.
Return value: detected phase shift (sub-pixel) between the two arrays.
The function performs the following equations

9
modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst
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@@ -108,18 +108,19 @@ findContours
----------------
Finds contours in a binary image.
.. ocv:function:: void findContours( InputOutputArray image, OutputArrayOfArrays contours, OutputArray hierarchy, int mode, int method, Point offset=Point())
.. ocv:function:: void findContours( InputOutputArray image, OutputArrayOfArrays contours, OutputArray hierarchy, int mode, int method, Point offset=Point())
.. ocv:function:: void findContours( InputOutputArray image, OutputArrayOfArrays contours, int mode, int method, Point offset=Point())
.. ocv:cfunction:: int cvFindContours( CvArr* image, CvMemStorage* storage, CvSeq** firstContour, int headerSize=sizeof(CvContour), int mode=CV_RETR_LIST, int method=CV_CHAIN_APPROX_SIMPLE, CvPoint offset=cvPoint(0, 0) )
.. ocv:pyoldfunction:: cv.FindContours(image, storage, mode=CV_RETR_LIST, method=CV_CHAIN_APPROX_SIMPLE, offset=(0, 0)) -> cvseq
:param image: Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as ``binary`` . You can use :ocv:func:`compare` , :ocv:func:`inRange` , :ocv:func:`threshold` , :ocv:func:`adaptiveThreshold` , :ocv:func:`Canny` , and others to create a binary image out of a grayscale or color one. The function modifies the ``image`` while extracting the contours.
:param contours: Detected contours. Each contour is stored as a vector of points.
:param hiararchy: Optional output vector containing information about the image topology. It has as many elements as the number of contours. For each contour ``contours[i]`` , the elements ``hierarchy[i][0]`` , ``hiearchy[i][1]`` , ``hiearchy[i][2]`` , and ``hiearchy[i][3]`` are set to 0-based indices in ``contours`` of the next and previous contours at the same hierarchical level: the first child contour and the parent contour, respectively. If for a contour ``i`` there are no next, previous, parent, or nested contours, the corresponding elements of ``hierarchy[i]`` will be negative.
:param hierarchy: Optional output vector containing information about the image topology. It has as many elements as the number of contours. For each contour ``contours[i]`` , the elements ``hierarchy[i][0]`` , ``hiearchy[i][1]`` , ``hiearchy[i][2]`` , and ``hiearchy[i][3]`` are set to 0-based indices in ``contours`` of the next and previous contours at the same hierarchical level: the first child contour and the parent contour, respectively. If for a contour ``i`` there are no next, previous, parent, or nested contours, the corresponding elements of ``hierarchy[i]`` will be negative.
:param mode: Contour retrieval mode.
@@ -330,10 +331,12 @@ Calculates a contour area.
.. ocv:pyfunction:: cv2.contourArea(contour[, oriented]) -> retval
.. ocv:cfunction:: double cvContourArea( const CvArr* contour, CvSlice slice=CV_WHOLE_SEQ )
.. ocv:pyoldfunction:: cv.ContourArea(contour, slice=CV_WHOLE_SEQ)-> double
:param contour: Input vector of 2D points (contour vertices), stored in ``std::vector`` or ``Mat``.
:param orientation: Oriented area flag. If it is true, the function returns a signed area value, depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can determine orientation of a contour by taking the sign of an area. By default, the parameter is ``false``, which means that the absolute value is returned.
:param oriented: Oriented area flag. If it is true, the function returns a signed area value, depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can determine orientation of a contour by taking the sign of an area. By default, the parameter is ``false``, which means that the absolute value is returned.
The function computes a contour area. Similarly to
:ocv: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