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Fixed documentation: corrected parameter names

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This commit is contained in:
Andrey Kamaev
2012-05-29 10:36:19 +00:00
parent 67b718f1cb
commit 05de6302fd
39 changed files with 554 additions and 509 deletions
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modules/imgproc/doc/feature_detection.rst
+7 -7
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@@ -52,7 +52,7 @@ Calculates eigenvalues and eigenvectors of image blocks for corner detection.
:param blockSize: Neighborhood size (see details below).
:param apertureSize: Aperture parameter for the :ocv:func:`Sobel` operator.
:param ksize: Aperture parameter for the :ocv:func:`Sobel` operator.
:param borderType: Pixel extrapolation method. See :ocv:func:`borderInterpolate` .
@@ -105,7 +105,7 @@ Harris edge detector.
:param blockSize: Neighborhood size (see the details on :ocv:func:`cornerEigenValsAndVecs` ).
:param apertureSize: Aperture parameter for the :ocv:func:`Sobel` operator.
:param ksize: Aperture parameter for the :ocv:func:`Sobel` operator.
:param k: Harris detector free parameter. See the formula below.
@@ -145,7 +145,7 @@ Calculates the minimal eigenvalue of gradient matrices for corner detection.
:param blockSize: Neighborhood size (see the details on :ocv:func:`cornerEigenValsAndVecs` ).
:param apertureSize: Aperture parameter for the :ocv:func:`Sobel` operator.
:param ksize: Aperture parameter for the :ocv:func:`Sobel` operator.
:param borderType: Pixel extrapolation method. See :ocv:func:`borderInterpolate` .
@@ -232,9 +232,9 @@ Determines strong corners on an image.
:param image: Input 8-bit or floating-point 32-bit, single-channel image.
:param eigImage: The parameter is ignored.
:param eig_image: The parameter is ignored.
:param tempImage: The parameter is ignored.
:param temp_image: The parameter is ignored.
:param corners: Output vector of detected corners.
@@ -298,7 +298,7 @@ Finds circles in a grayscale image using the Hough transform.
:param circles: Output vector of found circles. Each vector is encoded as a 3-element floating-point vector :math:`(x, y, radius)` .
:param circleStorage: In C function this is a memory storage that will contain the output sequence of found circles.
:param circle_storage: In C function this is a memory storage that will contain the output sequence of found circles.
:param method: Detection method to use. Currently, the only implemented method is ``CV_HOUGH_GRADIENT`` , which is basically *21HT* , described in [Yuen90]_.
@@ -516,7 +516,7 @@ Calculates a feature map for corner detection.
:param dst: Output image that has the type ``CV_32F`` and the same size as ``src`` .
:param apertureSize: Aperture size of the :ocv:func:`Sobel` .
:param ksize: Aperture size of the :ocv:func:`Sobel` .
:param borderType: Pixel extrapolation method. See :ocv:func:`borderInterpolate` .
modules/imgproc/doc/filtering.rst
+26 -26
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@@ -678,9 +678,9 @@ Returns an engine for smoothing images with the Gaussian filter.
:param ksize: Aperture size. See :ocv:func:`getGaussianKernel` .
:param sigmaX: Gaussian sigma in the horizontal direction. See :ocv:func:`getGaussianKernel` .
:param sigma1: Gaussian sigma in the horizontal direction. See :ocv:func:`getGaussianKernel` .
:param sigmaY: Gaussian sigma in the vertical direction. If 0, then :math:`\texttt{sigmaY}\leftarrow\texttt{sigmaX}` .
:param sigma2: Gaussian sigma in the vertical direction. If 0, then :math:`\texttt{sigma2}\leftarrow\texttt{sigma1}` .
:param borderType: Border type to use. See :ocv:func:`borderInterpolate` .
@@ -701,7 +701,7 @@ createLinearFilter
----------------------
Creates a non-separable linear filter engine.
.. ocv:function:: Ptr<FilterEngine> createLinearFilter( int srcType, int dstType, InputArray kernel, Point _anchor=Point(-1,-1), double delta=0, int _rowBorderType=BORDER_DEFAULT, int _columnBorderType=-1, const Scalar& _borderValue=Scalar() )
.. ocv:function:: Ptr<FilterEngine> createLinearFilter( int srcType, int dstType, InputArray kernel, Point _anchor=Point(-1,-1), double delta=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, const Scalar& borderValue=Scalar() )
.. ocv:function:: Ptr<BaseFilter> getLinearFilter(int srcType, int dstType, InputArray kernel, Point anchor=Point(-1,-1), double delta=0, int bits=0)
@@ -737,7 +737,7 @@ createMorphologyFilter
--------------------------
Creates an engine for non-separable morphological operations.
.. ocv:function:: Ptr<FilterEngine> createMorphologyFilter( int op, int type, InputArray kernel, Point anchor=Point(-1,-1), int _rowBorderType=BORDER_CONSTANT, int _columnBorderType=-1, const Scalar& _borderValue=morphologyDefaultBorderValue() )
.. ocv:function:: Ptr<FilterEngine> createMorphologyFilter( int op, int type, InputArray kernel, Point anchor=Point(-1,-1), int rowBorderType=BORDER_CONSTANT, int columnBorderType=-1, const Scalar& borderValue=morphologyDefaultBorderValue() )
.. ocv:function:: Ptr<BaseFilter> getMorphologyFilter( int op, int type, InputArray kernel, Point anchor=Point(-1,-1) )
@@ -751,9 +751,9 @@ Creates an engine for non-separable morphological operations.
:param type: Input/output image type. The number of channels can be arbitrary. The depth should be one of ``CV_8U``, ``CV_16U``, ``CV_16S``, ``CV_32F` or ``CV_64F``.
:param element: 2D 8-bit structuring element for a morphological operation. Non-zero elements indicate the pixels that belong to the element.
:param kernel: 2D 8-bit structuring element for a morphological operation. Non-zero elements indicate the pixels that belong to the element.
:param esize: Horizontal or vertical structuring element size for separable morphological operations.
:param ksize: Horizontal or vertical structuring element size for separable morphological operations.
:param anchor: Anchor position within the structuring element. Negative values mean that the anchor is at the kernel center.
@@ -783,7 +783,7 @@ createSeparableLinearFilter
-------------------------------
Creates an engine for a separable linear filter.
.. ocv:function:: Ptr<FilterEngine> createSeparableLinearFilter( int srcType, int dstType, InputArray rowKernel, InputArray columnKernel, Point _anchor=Point(-1,-1), double delta=0, int _rowBorderType=BORDER_DEFAULT, int _columnBorderType=-1, const Scalar& _borderValue=Scalar() )
.. ocv:function:: Ptr<FilterEngine> createSeparableLinearFilter( int srcType, int dstType, InputArray rowKernel, InputArray columnKernel, Point anchor=Point(-1,-1), double delta=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, const Scalar& borderValue=Scalar() )
.. ocv:function:: Ptr<BaseColumnFilter> getLinearColumnFilter( int bufType, int dstType, InputArray kernel, int anchor, int symmetryType, double delta=0, int bits=0 )
@@ -1126,9 +1126,9 @@ Returns a structuring element of the specified size and shape for morphological
: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.
:param anchorX: x-coordinate of the anchor
:param anchor_x: x-coordinate of the anchor
:param anchorY: y-coordinate of the anchor
:param anchor_y: y-coordinate of the anchor
:param values: integer array of ``cols``*``rows`` elements that specifies the custom shape of the structuring element, when ``shape=CV_SHAPE_CUSTOM``.
@@ -1418,9 +1418,9 @@ Applies a separable linear filter to an image.
when ``ddepth=-1``, the destination image will have the same depth as the source.
:param rowKernel: Coefficients for filtering each row.
:param kernelX: Coefficients for filtering each row.
:param columnKernel: Coefficients for filtering each column.
:param kernelY: Coefficients for filtering each column.
:param anchor: Anchor position within the kernel. The default value :math:`(-1, 1)` means that the anchor is at the kernel center.
@@ -1428,7 +1428,7 @@ Applies a separable linear filter to an image.
:param borderType: Pixel extrapolation method. See :ocv: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`` .
The function applies a separable linear filter to the image. That is, first, every row of ``src`` is filtered with the 1D kernel ``kernelX`` . Then, every column of the result is filtered with the 1D kernel ``kernelY`` . The final result shifted by ``delta`` is stored in ``dst`` .
.. seealso::
@@ -1454,32 +1454,32 @@ Smooths the image in one of several ways.
:param smoothtype: Type of the smoothing:
* **CV_BLUR_NO_SCALE** linear convolution with :math:`\texttt{param1}\times\texttt{param2}` box kernel (all 1's). If you want to smooth different pixels with different-size box kernels, you can use the integral image that is computed using :ocv:func:`integral`
* **CV_BLUR_NO_SCALE** linear convolution with :math:`\texttt{size1}\times\texttt{size2}` box kernel (all 1's). If you want to smooth different pixels with different-size box kernels, you can use the integral image that is computed using :ocv:func:`integral`
* **CV_BLUR** linear convolution with :math:`\texttt{param1}\times\texttt{param2}` box kernel (all 1's) with subsequent scaling by :math:`1/(\texttt{param1}\cdot\texttt{param2})`
* **CV_BLUR** linear convolution with :math:`\texttt{size1}\times\texttt{size2}` box kernel (all 1's) with subsequent scaling by :math:`1/(\texttt{size1}\cdot\texttt{size2})`
* **CV_GAUSSIAN** linear convolution with a :math:`\texttt{param1}\times\texttt{param2}` Gaussian kernel
* **CV_GAUSSIAN** linear convolution with a :math:`\texttt{size1}\times\texttt{size2}` Gaussian kernel
* **CV_MEDIAN** median filter with a :math:`\texttt{param1}\times\texttt{param1}` square aperture
* **CV_MEDIAN** median filter with a :math:`\texttt{size1}\times\texttt{size1}` square aperture
* **CV_BILATERAL** bilateral filter with a :math:`\texttt{param1}\times\texttt{param1}` square aperture, color sigma= ``param3`` and spatial sigma= ``param4`` . If ``param1=0`` , the aperture square side is set to ``cvRound(param4*1.5)*2+1`` . Information about bilateral filtering can be found at http://www.dai.ed.ac.uk/CVonline/LOCAL\_COPIES/MANDUCHI1/Bilateral\_Filtering.html
* **CV_BILATERAL** bilateral filter with a :math:`\texttt{size1}\times\texttt{size1}` square aperture, color sigma= ``sigma1`` and spatial sigma= ``sigma2`` . If ``size1=0`` , the aperture square side is set to ``cvRound(sigma2*1.5)*2+1`` . Information about bilateral filtering can be found at http://www.dai.ed.ac.uk/CVonline/LOCAL\_COPIES/MANDUCHI1/Bilateral\_Filtering.html
:param param1: The first parameter of the smoothing operation, the aperture width. Must be a positive odd number (1, 3, 5, ...)
:param size1: The first parameter of the smoothing operation, the aperture width. Must be a positive odd number (1, 3, 5, ...)
:param param2: The second parameter of the smoothing operation, the aperture height. Ignored by ``CV_MEDIAN`` and ``CV_BILATERAL`` methods. In the case of simple scaled/non-scaled and Gaussian blur if ``param2`` is zero, it is set to ``param1`` . Otherwise it must be a positive odd number.
:param size2: The second parameter of the smoothing operation, the aperture height. Ignored by ``CV_MEDIAN`` and ``CV_BILATERAL`` methods. In the case of simple scaled/non-scaled and Gaussian blur if ``size2`` is zero, it is set to ``size1`` . Otherwise it must be a positive odd number.
:param param3: In the case of a Gaussian parameter this parameter may specify Gaussian :math:`\sigma` (standard deviation). If it is zero, it is calculated from the kernel size:
:param sigma1: In the case of a Gaussian parameter this parameter may specify Gaussian :math:`\sigma` (standard deviation). If it is zero, it is calculated from the kernel size:
.. math::
\sigma = 0.3 (n/2 - 1) + 0.8 \quad \text{where} \quad n= \begin{array}{l l} \mbox{\texttt{param1} for horizontal kernel} \\ \mbox{\texttt{param2} for vertical kernel} \end{array}
\sigma = 0.3 (n/2 - 1) + 0.8 \quad \text{where} \quad n= \begin{array}{l l} \mbox{\texttt{size1} for horizontal kernel} \\ \mbox{\texttt{size2} for vertical kernel} \end{array}
Using standard sigma for small kernels ( :math:`3\times 3` to :math:`7\times 7` ) gives better speed. If ``param3`` is not zero, while ``param1`` and ``param2`` are zeros, the kernel size is calculated from the sigma (to provide accurate enough operation).
Using standard sigma for small kernels ( :math:`3\times 3` to :math:`7\times 7` ) gives better speed. If ``sigma1`` is not zero, while ``size1`` and ``size2`` are zeros, the kernel size is calculated from the sigma (to provide accurate enough operation).
The function smooths an image using one of several methods. Every of the methods has some features and restrictions listed below:
@@ -1593,9 +1593,9 @@ Calculates the first x- or y- image derivative using Scharr operator.
:param ddepth: Destination image depth. See :ocv:func:`Sobel` for the list of supported combination of ``src.depth()`` and ``ddepth``.
:param xorder: Order of the derivative x.
:param dx: Order of the derivative x.
:param yorder: Order of the derivative y.
:param dy: Order of the derivative y.
:param scale: Optional scale factor for the computed derivative values. By default, no scaling is applied. See :ocv:func:`getDerivKernels` for details.
@@ -1607,13 +1607,13 @@ The function computes the first x- or y- spatial image derivative using the Scha
.. math::
\texttt{Scharr(src, dst, ddepth, xorder, yorder, scale, delta, borderType)}
\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}
is equivalent to
.. math::
\texttt{Sobel(src, dst, ddepth, xorder, yorder, CV\_SCHARR, scale, delta, borderType)} .
\texttt{Sobel(src, dst, ddepth, dx, dy, CV\_SCHARR, scale, delta, borderType)} .
.. seealso::
modules/imgproc/doc/geometric_transformations.rst
+1 -1
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@@ -210,7 +210,7 @@ Calculates an affine matrix of 2D rotation.
:param scale: Isotropic scale factor.
:param mapMatrix: The output affine transformation, 2x3 floating-point matrix.
:param map_matrix: The output affine transformation, 2x3 floating-point matrix.
The function calculates the following matrix:
modules/imgproc/doc/histograms.rst
+10 -10
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@@ -18,13 +18,13 @@ Calculates a histogram of a set of arrays.
.. ocv:cfunction:: void cvCalcHist( IplImage** image, CvHistogram* hist, int accumulate=0, const CvArr* mask=NULL )
.. ocv:pyoldfunction:: cv.CalcHist(image, hist, accumulate=0, mask=None)-> None
:param arrays: Source arrays. They all should have the same depth, ``CV_8U`` or ``CV_32F`` , and the same size. Each of them can have an arbitrary number of channels.
:param images: Source arrays. They all should have the same depth, ``CV_8U`` or ``CV_32F`` , and the same size. Each of them can have an arbitrary number of channels.
:param narrays: Number of source arrays.
:param nimages: Number of source images.
:param channels: List of the ``dims`` channels used to compute the histogram. The first array channels are numerated from 0 to ``arrays[0].channels()-1`` , the second array channels are counted from ``arrays[0].channels()`` to ``arrays[0].channels() + arrays[1].channels()-1``, and so on.
:param channels: List of the ``dims`` channels used to compute the histogram. The first array channels are numerated from 0 to ``images[0].channels()-1`` , the second array channels are counted from ``images[0].channels()`` to ``images[0].channels() + images[1].channels()-1``, and so on.
:param mask: Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size as ``arrays[i]`` . The non-zero mask elements mark the array elements counted in the histogram.
:param mask: Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size as ``images[i]`` . The non-zero mask elements mark the array elements counted in the histogram.
:param hist: Output histogram, which is a dense or sparse ``dims`` -dimensional array.
@@ -115,15 +115,15 @@ Calculates the back projection of a histogram.
.. ocv:cfunction:: void cvCalcBackProject( IplImage** image, CvArr* backProject, const CvHistogram* hist )
.. ocv:pyoldfunction:: cv.CalcBackProject(image, back_project, hist) -> None
:param arrays: Source arrays. They all should have the same depth, ``CV_8U`` or ``CV_32F`` , and the same size. Each of them can have an arbitrary number of channels.
:param images: Source arrays. They all should have the same depth, ``CV_8U`` or ``CV_32F`` , and the same size. Each of them can have an arbitrary number of channels.
:param narrays: Number of source arrays.
:param nimages: Number of source images.
:param channels: The list of channels used to compute the back projection. The number of channels must match the histogram dimensionality. The first array channels are numerated from 0 to ``arrays[0].channels()-1`` , the second array channels are counted from ``arrays[0].channels()`` to ``arrays[0].channels() + arrays[1].channels()-1``, and so on.
:param channels: The list of channels used to compute the back projection. The number of channels must match the histogram dimensionality. The first array channels are numerated from 0 to ``images[0].channels()-1`` , the second array channels are counted from ``images[0].channels()`` to ``images[0].channels() + images[1].channels()-1``, and so on.
:param hist: Input histogram that can be dense or sparse.
:param backProject: Destination back projection array that is a single-channel array of the same size and depth as ``arrays[0]`` .
:param backProject: Destination back projection array that is a single-channel array of the same size and depth as ``images[0]`` .
:param ranges: Array of arrays of the histogram bin boundaries in each dimension. See :ocv:func:`calcHist` .
@@ -235,7 +235,7 @@ Computes the "minimal work" distance between two weighted point configurations.
:param distType: Used metric. ``CV_DIST_L1, CV_DIST_L2`` , and ``CV_DIST_C`` stand for one of the standard metrics. ``CV_DIST_USER`` means that a pre-calculated cost matrix ``cost`` is used.
:param distFunc: Custom distance function supported by the old interface. ``CvDistanceFunction`` is defined as: ::
:param distance_func: Custom distance function supported by the old interface. ``CvDistanceFunction`` is defined as: ::
typedef float (CV_CDECL * CvDistanceFunction)( const float* a,
const float* b, void* userdata );
@@ -333,7 +333,7 @@ Divides one histogram by another.
:param hist2: Second histogram.
:param dsthist: Destination histogram.
:param dst_hist: Destination histogram.
:param scale: Scale factor for the destination histogram.
modules/imgproc/doc/miscellaneous_transformations.rst
+11 -11
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@@ -499,7 +499,7 @@ Fills a connected component with the given color.
.. note:: Since the mask is larger than the filled image, a pixel :math:`(x, y)` in ``image`` corresponds to the pixel :math:`(x+1, y+1)` in the ``mask`` .
:param seed: Starting point.
:param seedPoint: Starting point.
:param newVal: New value of the repainted domain pixels.
@@ -529,7 +529,7 @@ The functions ``floodFill`` fill a connected component starting from the seed po
.. math::
\texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)+ \texttt{upDiff}
\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}
in case of a grayscale image and fixed range
@@ -556,17 +556,17 @@ The functions ``floodFill`` fill a connected component starting from the seed po
.. math::
\texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)_r+ \texttt{upDiff} _r,
\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,
.. math::
\texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)_g+ \texttt{upDiff} _g
\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g
and
.. math::
\texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seed} .x, \texttt{seed} .y)_b+ \texttt{upDiff} _b
\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b
in case of a color image and fixed range
@@ -663,9 +663,9 @@ Applies a fixed-level threshold to each array element.
:param thresh: Threshold value.
:param maxVal: Maximum value to use with the ``THRESH_BINARY`` and ``THRESH_BINARY_INV`` thresholding types.
:param maxval: Maximum value to use with the ``THRESH_BINARY`` and ``THRESH_BINARY_INV`` thresholding types.
:param thresholdType: Thresholding type (see the details below).
:param type: Thresholding type (see the details below).
The function applies fixed-level thresholding
to a single-channel array. The function is typically used to get a
@@ -673,19 +673,19 @@ bi-level (binary) image out of a grayscale image (
:ocv: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`` :
types of thresholding supported by the function. They are determined by ``type`` :
* **THRESH_BINARY**
.. math::
\texttt{dst} (x,y) = \fork{\texttt{maxVal}}{if $\texttt{src}(x,y) > \texttt{thresh}$}{0}{otherwise}
\texttt{dst} (x,y) = \fork{\texttt{maxval}}{if $\texttt{src}(x,y) > \texttt{thresh}$}{0}{otherwise}
* **THRESH_BINARY_INV**
.. math::
\texttt{dst} (x,y) = \fork{0}{if $\texttt{src}(x,y) > \texttt{thresh}$}{\texttt{maxVal}}{otherwise}
\texttt{dst} (x,y) = \fork{0}{if $\texttt{src}(x,y) > \texttt{thresh}$}{\texttt{maxval}}{otherwise}
* **THRESH_TRUNC**
@@ -755,7 +755,7 @@ Runs the GrabCut algorithm.
.. ocv:pyfunction:: cv2.grabCut(img, mask, rect, bgdModel, fgdModel, iterCount[, mode]) -> None
:param image: Input 8-bit 3-channel image.
:param img: Input 8-bit 3-channel image.
:param mask: Input/output 8-bit single-channel mask. The mask is initialized by the function when ``mode`` is set to ``GC_INIT_WITH_RECT``. Its elements may have one of following values:
modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst
+5 -5
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@@ -175,7 +175,7 @@ Approximates a polygonal curve(s) with the specified precision.
.. ocv:pyfunction:: cv2.approxPolyDP(curve, epsilon, closed[, approxCurve]) -> approxCurve
.. ocv:cfunction:: CvSeq* cvApproxPoly( const void* src_seq, int header_size, CvMemStorage* storage, int method, double parameter, int parameter2=0 )
.. ocv:cfunction:: CvSeq* cvApproxPoly( const void* src_seq, int header_size, CvMemStorage* storage, int method, double eps, int recursive=0 )
:param curve: Input vector of a 2D point stored in:
@@ -191,7 +191,7 @@ Approximates a polygonal curve(s) with the specified precision.
:param closed: If true, the approximated curve is closed (its first and last vertices are connected). Otherwise, it is not closed.
:param headerSize: Header size of the approximated curve. Normally, ``sizeof(CvContour)`` is used.
:param header_size: Header size of the approximated curve. Normally, ``sizeof(CvContour)`` is used.
:param storage: Memory storage where the approximated curve is stored.
@@ -213,7 +213,7 @@ Approximates Freeman chain(s) with a polygonal curve.
.. ocv:pyoldfunction:: cv.ApproxChains(src_seq, storage, method=CV_CHAIN_APPROX_SIMPLE, parameter=0, minimal_perimeter=0, recursive=0)-> contours
:param chain: Pointer to the approximated Freeman chain that can refer to other chains.
:param src_seq: Pointer to the approximated Freeman chain that can refer to other chains.
:param storage: Storage location for the resulting polylines.
@@ -221,7 +221,7 @@ Approximates Freeman chain(s) with a polygonal curve.
:param parameter: Method parameter (not used now).
:param minimalPerimeter: Approximates only those contours whose perimeters are not less than ``minimal_perimeter`` . Other chains are removed from the resulting structure.
:param minimal_perimeter: Approximates only those contours whose perimeters are not less than ``minimal_perimeter`` . Other chains are removed from the resulting structure.
:param recursive: Recursion flag. If it is non-zero, the function approximates all chains that can be obtained from ``chain`` by using the ``h_next`` or ``v_next`` links. Otherwise, the single input chain is approximated.
@@ -323,7 +323,7 @@ Finds the convex hull of a 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 are the convex hull points themselves.
:param storage: Output memory storage in the old API (``cvConvexHull2`` returns a sequence containing the convex hull points or their indices).
:param hull_storage: Output memory storage in the old API (``cvConvexHull2`` returns a sequence containing the convex hull points or their indices).
:param clockwise: Orientation flag. If it is true, the output convex hull is oriented clockwise. Otherwise, it is oriented counter-clockwise. The usual screen coordinate system is assumed so that the origin is at the top-left corner, x axis is oriented to the right, and y axis is oriented downwards.
modules/imgproc/include/opencv2/imgproc/imgproc.hpp
+117 -117
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@@ -60,20 +60,20 @@ namespace cv
//! various border interpolation methods
enum { BORDER_REPLICATE=IPL_BORDER_REPLICATE, BORDER_CONSTANT=IPL_BORDER_CONSTANT,
BORDER_REFLECT=IPL_BORDER_REFLECT, BORDER_WRAP=IPL_BORDER_WRAP,
BORDER_REFLECT=IPL_BORDER_REFLECT, BORDER_WRAP=IPL_BORDER_WRAP,
BORDER_REFLECT_101=IPL_BORDER_REFLECT_101, BORDER_REFLECT101=BORDER_REFLECT_101,
BORDER_TRANSPARENT=IPL_BORDER_TRANSPARENT,
BORDER_DEFAULT=BORDER_REFLECT_101, BORDER_ISOLATED=16 };
//! 1D interpolation function: returns coordinate of the "donor" pixel for the specified location p.
//! 1D interpolation function: returns coordinate of the "donor" pixel for the specified location p.
CV_EXPORTS_W int borderInterpolate( int p, int len, int borderType );
/*!
The Base Class for 1D or Row-wise Filters
This is the base class for linear or non-linear filters that process 1D data.
In particular, such filters are used for the "horizontal" filtering parts in separable filters.
Several functions in OpenCV return Ptr<BaseRowFilter> for the specific types of filters,
and those pointers can be used directly or within cv::FilterEngine.
*/
@@ -93,17 +93,17 @@ public:
/*!
The Base Class for Column-wise Filters
This is the base class for linear or non-linear filters that process columns of 2D arrays.
Such filters are used for the "vertical" filtering parts in separable filters.
Several functions in OpenCV return Ptr<BaseColumnFilter> for the specific types of filters,
and those pointers can be used directly or within cv::FilterEngine.
Unlike cv::BaseRowFilter, cv::BaseColumnFilter may have some context information,
i.e. box filter keeps the sliding sum of elements. To reset the state BaseColumnFilter::reset()
must be called (e.g. the method is called by cv::FilterEngine)
*/
*/
class CV_EXPORTS BaseColumnFilter
{
public:
@@ -121,15 +121,15 @@ public:
/*!
The Base Class for Non-Separable 2D Filters.
This is the base class for linear or non-linear 2D filters.
Several functions in OpenCV return Ptr<BaseFilter> for the specific types of filters,
and those pointers can be used directly or within cv::FilterEngine.
Similar to cv::BaseColumnFilter, the class may have some context information,
that should be reset using BaseFilter::reset() method before processing the new array.
*/
*/
class CV_EXPORTS BaseFilter
{
public:
@@ -148,7 +148,7 @@ public:
/*!
The Main Class for Image Filtering.
The class can be used to apply an arbitrary filtering operation to an image.
It contains all the necessary intermediate buffers, it computes extrapolated values
of the "virtual" pixels outside of the image etc.
@@ -156,45 +156,45 @@ public:
are returned by various OpenCV functions, such as cv::createSeparableLinearFilter(),
cv::createLinearFilter(), cv::createGaussianFilter(), cv::createDerivFilter(),
cv::createBoxFilter() and cv::createMorphologyFilter().
Using the class you can process large images by parts and build complex pipelines
that include filtering as some of the stages. If all you need is to apply some pre-defined
filtering operation, you may use cv::filter2D(), cv::erode(), cv::dilate() etc.
functions that create FilterEngine internally.
Here is the example on how to use the class to implement Laplacian operator, which is the sum of
second-order derivatives. More complex variant for different types is implemented in cv::Laplacian().
\code
void laplace_f(const Mat& src, Mat& dst)
{
CV_Assert( src.type() == CV_32F );
// make sure the destination array has the proper size and type
dst.create(src.size(), src.type());
// get the derivative and smooth kernels for d2I/dx2.
// for d2I/dy2 we could use the same kernels, just swapped
Mat kd, ks;
getSobelKernels( kd, ks, 2, 0, ksize, false, ktype );
// let's process 10 source rows at once
int DELTA = std::min(10, src.rows);
Ptr<FilterEngine> Fxx = createSeparableLinearFilter(src.type(),
dst.type(), kd, ks, Point(-1,-1), 0, borderType, borderType, Scalar() );
dst.type(), kd, ks, Point(-1,-1), 0, borderType, borderType, Scalar() );
Ptr<FilterEngine> Fyy = createSeparableLinearFilter(src.type(),
dst.type(), ks, kd, Point(-1,-1), 0, borderType, borderType, Scalar() );
int y = Fxx->start(src), dsty = 0, dy = 0;
Fyy->start(src);
const uchar* sptr = src.data + y*src.step;
// allocate the buffers for the spatial image derivatives;
// the buffers need to have more than DELTA rows, because at the
// last iteration the output may take max(kd.rows-1,ks.rows-1)
// rows more than the input.
Mat Ixx( DELTA + kd.rows - 1, src.cols, dst.type() );
Mat Iyy( DELTA + kd.rows - 1, src.cols, dst.type() );
// inside the loop we always pass DELTA rows to the filter
// (note that the "proceed" method takes care of possibe overflow, since
// it was given the actual image height in the "start" method)
@@ -241,7 +241,7 @@ public:
int srcType, int dstType, int bufType,
int _rowBorderType=BORDER_REPLICATE, int _columnBorderType=-1,
const Scalar& _borderValue=Scalar());
//! starts filtering of the specified ROI of an image of size wholeSize.
//! starts filtering of the specified ROI of an image of size wholeSize.
virtual int start(Size wholeSize, Rect roi, int maxBufRows=-1);
//! starts filtering of the specified ROI of the specified image.
virtual int start(const Mat& src, const Rect& srcRoi=Rect(0,0,-1,-1),
@@ -256,10 +256,10 @@ public:
bool isolated=false);
//! returns true if the filter is separable
bool isSeparable() const { return (const BaseFilter*)filter2D == 0; }
//! returns the number
//! returns the number
int remainingInputRows() const;
int remainingOutputRows() const;
int srcType, dstType, bufType;
Size ksize;
Point anchor;
@@ -276,7 +276,7 @@ public:
vector<uchar> constBorderRow;
int bufStep, startY, startY0, endY, rowCount, dstY;
vector<uchar*> rows;
Ptr<BaseFilter> filter2D;
Ptr<BaseRowFilter> rowFilter;
Ptr<BaseColumnFilter> columnFilter;
@@ -309,16 +309,16 @@ CV_EXPORTS Ptr<BaseFilter> getLinearFilter(int srcType, int dstType,
//! returns the separable linear filter engine
CV_EXPORTS 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());
Point anchor=Point(-1,-1), double delta=0,
int rowBorderType=BORDER_DEFAULT,
int columnBorderType=-1,
const Scalar& borderValue=Scalar());
//! returns the non-separable linear filter engine
CV_EXPORTS 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());
double delta=0, int rowBorderType=BORDER_DEFAULT,
int columnBorderType=-1, const Scalar& borderValue=Scalar());
//! returns the Gaussian kernel with the specified parameters
CV_EXPORTS_W Mat getGaussianKernel( int ksize, double sigma, int ktype=CV_64F );
@@ -335,7 +335,7 @@ CV_EXPORTS_W void getDerivKernels( OutputArray kx, OutputArray ky,
CV_EXPORTS Ptr<FilterEngine> createDerivFilter( int srcType, int dstType,
int dx, int dy, int ksize,
int borderType=BORDER_DEFAULT );
//! returns horizontal 1D box filter
//! returns horizontal 1D box filter
CV_EXPORTS Ptr<BaseRowFilter> getRowSumFilter(int srcType, int sumType,
int ksize, int anchor=-1);
//! returns vertical 1D box filter
@@ -347,11 +347,11 @@ CV_EXPORTS Ptr<FilterEngine> createBoxFilter( int srcType, int dstType, Size ksi
Point anchor=Point(-1,-1),
bool normalize=true,
int borderType=BORDER_DEFAULT);
//! returns the Gabor kernel with the specified parameters
CV_EXPORTS_W Mat getGaborKernel( Size ksize, double sigma, double theta, double lambd,
double gamma, double psi=CV_PI*0.5, int ktype=CV_64F );
//! type of morphological operation
enum { MORPH_ERODE=CV_MOP_ERODE, MORPH_DILATE=CV_MOP_DILATE,
MORPH_OPEN=CV_MOP_OPEN, MORPH_CLOSE=CV_MOP_CLOSE,
@@ -365,15 +365,15 @@ CV_EXPORTS Ptr<BaseColumnFilter> getMorphologyColumnFilter(int op, int type, int
//! returns 2D morphological filter
CV_EXPORTS Ptr<BaseFilter> getMorphologyFilter(int op, int type, InputArray kernel,
Point anchor=Point(-1,-1));
//! returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation.
static inline Scalar morphologyDefaultBorderValue() { return Scalar::all(DBL_MAX); }
//! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
CV_EXPORTS Ptr<FilterEngine> createMorphologyFilter(int op, int type, InputArray kernel,
Point anchor=Point(-1,-1), int _rowBorderType=BORDER_CONSTANT,
int _columnBorderType=-1,
const Scalar& _borderValue=morphologyDefaultBorderValue());
Point anchor=Point(-1,-1), int rowBorderType=BORDER_CONSTANT,
int columnBorderType=-1,
const Scalar& borderValue=morphologyDefaultBorderValue());
//! shape of the structuring element
enum { MORPH_RECT=0, MORPH_CROSS=1, MORPH_ELLIPSE=2 };
@@ -382,7 +382,7 @@ CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor=Point
template<> CV_EXPORTS void Ptr<IplConvKernel>::delete_obj();
//! copies 2D array to a larger destination array with extrapolation of the outer part of src using the specified border mode
//! copies 2D array to a larger destination array with extrapolation of the outer part of src using the specified border mode
CV_EXPORTS_W void copyMakeBorder( InputArray src, OutputArray dst,
int top, int bottom, int left, int right,
int borderType, const Scalar& value=Scalar() );
@@ -418,7 +418,7 @@ CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth,
InputArray kernelX, InputArray kernelY,
Point anchor=Point(-1,-1),
double delta=0, int borderType=BORDER_DEFAULT );
//! applies generalized Sobel operator to the image
CV_EXPORTS_W void Sobel( InputArray src, OutputArray dst, int ddepth,
int dx, int dy, int ksize=3,
@@ -452,7 +452,7 @@ CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize,
// low-level function for computing eigenvalues and eigenvectors of 2x2 matrices
CV_EXPORTS void eigen2x2( const float* a, float* e, int n );
//! computes both eigenvalues and the eigenvectors of 2x2 derivative covariation matrix at each pixel. The output is stored as 6-channel matrix.
CV_EXPORTS_W void cornerEigenValsAndVecs( InputArray src, OutputArray dst,
int blockSize, int ksize,
@@ -483,7 +483,7 @@ CV_EXPORTS_W void HoughLinesP( InputArray image, OutputArray lines,
double rho, double theta, int threshold,
double minLineLength=0, double maxLineGap=0 );
//! finds circles in the grayscale image using 2+1 gradient Hough transform
//! finds circles in the grayscale image using 2+1 gradient Hough transform
CV_EXPORTS_W void HoughCircles( InputArray image, OutputArray circles,
int method, double dp, double minDist,
double param1=100, double param2=100,
@@ -494,13 +494,13 @@ CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel,
Point anchor=Point(-1,-1), int iterations=1,
int borderType=BORDER_CONSTANT,
const Scalar& borderValue=morphologyDefaultBorderValue() );
//! dilates the image (applies the local maximum operator)
CV_EXPORTS_W void dilate( InputArray src, OutputArray dst, InputArray kernel,
Point anchor=Point(-1,-1), int iterations=1,
int borderType=BORDER_CONSTANT,
const Scalar& borderValue=morphologyDefaultBorderValue() );
//! applies an advanced morphological operation to the image
CV_EXPORTS_W void morphologyEx( InputArray src, OutputArray dst,
int op, InputArray kernel,
@@ -531,7 +531,7 @@ CV_EXPORTS_W void warpAffine( InputArray src, OutputArray dst,
int flags=INTER_LINEAR,
int borderMode=BORDER_CONSTANT,
const Scalar& borderValue=Scalar());
//! warps the image using perspective transformation
CV_EXPORTS_W void warpPerspective( InputArray src, OutputArray dst,
InputArray M, Size dsize,
@@ -556,7 +556,7 @@ CV_EXPORTS_W void remap( InputArray src, OutputArray dst,
CV_EXPORTS_W void convertMaps( InputArray map1, InputArray map2,
OutputArray dstmap1, OutputArray dstmap2,
int dstmap1type, bool nninterpolation=false );
//! returns 2x3 affine transformation matrix for the planar rotation.
CV_EXPORTS_W Mat getRotationMatrix2D( Point2f center, double angle, double scale );
//! returns 3x3 perspective transformation for the corresponding 4 point pairs.
@@ -597,12 +597,12 @@ CV_EXPORTS_W void accumulateProduct( InputArray src1, InputArray src2,
CV_EXPORTS_W void accumulateWeighted( InputArray src, InputOutputArray dst,
double alpha, InputArray mask=noArray() );
//! computes PSNR image/video quality metric
//! computes PSNR image/video quality metric
CV_EXPORTS_W double PSNR(InputArray src1, InputArray src2);
CV_EXPORTS_W Point2d phaseCorrelate(InputArray src1, InputArray src2, InputArray window = noArray());
CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type);
//! type of the threshold operation
enum { THRESH_BINARY=CV_THRESH_BINARY, THRESH_BINARY_INV=CV_THRESH_BINARY_INV,
THRESH_TRUNC=CV_THRESH_TRUNC, THRESH_TOZERO=CV_THRESH_TOZERO,
@@ -637,7 +637,7 @@ CV_EXPORTS_W void undistort( InputArray src, OutputArray dst,
InputArray cameraMatrix,
InputArray distCoeffs,
InputArray newCameraMatrix=noArray() );
//! initializes maps for cv::remap() to correct lens distortion and optionally rectify the image
CV_EXPORTS_W void initUndistortRectifyMap( InputArray cameraMatrix, InputArray distCoeffs,
InputArray R, InputArray newCameraMatrix,
@@ -647,25 +647,25 @@ enum
{
PROJ_SPHERICAL_ORTHO = 0,
PROJ_SPHERICAL_EQRECT = 1
};
};
//! initializes maps for cv::remap() for wide-angle
CV_EXPORTS_W float initWideAngleProjMap( InputArray cameraMatrix, InputArray distCoeffs,
Size imageSize, int destImageWidth,
int m1type, OutputArray map1, OutputArray map2,
int projType=PROJ_SPHERICAL_EQRECT, double alpha=0);
//! returns the default new camera matrix (by default it is the same as cameraMatrix unless centerPricipalPoint=true)
CV_EXPORTS_W Mat getDefaultNewCameraMatrix( InputArray cameraMatrix, Size imgsize=Size(),
bool centerPrincipalPoint=false );
//! returns points' coordinates after lens distortion correction
CV_EXPORTS_W void undistortPoints( InputArray src, OutputArray dst,
InputArray cameraMatrix, InputArray distCoeffs,
InputArray R=noArray(), InputArray P=noArray());
template<> CV_EXPORTS void Ptr<CvHistogram>::delete_obj();
//! computes the joint dense histogram for a set of images.
CV_EXPORTS void calcHist( const Mat* images, int nimages,
const int* channels, InputArray mask,
@@ -678,7 +678,7 @@ CV_EXPORTS void calcHist( const Mat* images, int nimages,
SparseMat& hist, int dims,
const int* histSize, const float** ranges,
bool uniform=true, bool accumulate=false );
CV_EXPORTS_W void calcHist( InputArrayOfArrays images,
const vector<int>& channels,
InputArray mask, OutputArray hist,
@@ -694,7 +694,7 @@ CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
//! computes back projection for the set of images
CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
const int* channels, const SparseMat& hist,
const int* channels, const SparseMat& hist,
OutputArray backProject, const float** ranges,
double scale=1, bool uniform=true );
@@ -705,8 +705,8 @@ CV_EXPORTS_W void calcBackProject( InputArrayOfArrays images, const vector<int>&
/*CV_EXPORTS void calcBackProjectPatch( const Mat* images, int nimages, const int* channels,
InputArray hist, OutputArray dst, Size patchSize,
int method, double factor=1 );
int method, double factor=1 );
CV_EXPORTS_W void calcBackProjectPatch( InputArrayOfArrays images, const vector<int>& channels,
InputArray hist, OutputArray dst, Size patchSize,
int method, double factor=1 );*/
@@ -719,7 +719,7 @@ CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int met
//! normalizes the grayscale image brightness and contrast by normalizing its histogram
CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst );
CV_EXPORTS float EMD( InputArray signature1, InputArray signature2,
int distType, InputArray cost=noArray(),
float* lowerBound=0, OutputArray flow=noArray() );
@@ -739,7 +739,7 @@ enum
GC_BGD = 0, //!< background
GC_FGD = 1, //!< foreground
GC_PR_BGD = 2, //!< most probably background
GC_PR_FGD = 3 //!< most probably foreground
GC_PR_FGD = 3 //!< most probably foreground
};
//! GrabCut algorithm flags
@@ -751,7 +751,7 @@ enum
};
//! segments the image using GrabCut algorithm
CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect,
CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect,
InputOutputArray bgdModel, InputOutputArray fgdModel,
int iterCount, int mode = GC_EVAL );
@@ -760,7 +760,7 @@ enum
DIST_LABEL_CCOMP = 0,
DIST_LABEL_PIXEL = 1
};
//! builds the discrete Voronoi diagram
CV_EXPORTS_AS(distanceTransformWithLabels) void distanceTransform( InputArray src, OutputArray dst,
OutputArray labels, int distanceType, int maskSize,
@@ -784,27 +784,27 @@ CV_EXPORTS_W int floodFill( InputOutputArray image, InputOutputArray mask,
Scalar loDiff=Scalar(), Scalar upDiff=Scalar(),
int flags=4 );
enum
{
COLOR_BGR2BGRA =0,
COLOR_RGB2RGBA =COLOR_BGR2BGRA,
COLOR_BGRA2BGR =1,
COLOR_RGBA2RGB =COLOR_BGRA2BGR,
COLOR_BGR2RGBA =2,
COLOR_RGB2BGRA =COLOR_BGR2RGBA,
COLOR_RGBA2BGR =3,
COLOR_BGRA2RGB =COLOR_RGBA2BGR,
COLOR_BGR2RGB =4,
COLOR_RGB2BGR =COLOR_BGR2RGB,
COLOR_BGRA2RGBA =5,
COLOR_RGBA2BGRA =COLOR_BGRA2RGBA,
COLOR_BGR2GRAY =6,
COLOR_RGB2GRAY =7,
COLOR_GRAY2BGR =8,
@@ -813,7 +813,7 @@ enum
COLOR_GRAY2RGBA =COLOR_GRAY2BGRA,
COLOR_BGRA2GRAY =10,
COLOR_RGBA2GRAY =11,
COLOR_BGR2BGR565 =12,
COLOR_RGB2BGR565 =13,
COLOR_BGR5652BGR =14,
@@ -822,10 +822,10 @@ enum
COLOR_RGBA2BGR565 =17,
COLOR_BGR5652BGRA =18,
COLOR_BGR5652RGBA =19,
COLOR_GRAY2BGR565 =20,
COLOR_BGR5652GRAY =21,
COLOR_BGR2BGR555 =22,
COLOR_RGB2BGR555 =23,
COLOR_BGR5552BGR =24,
@@ -834,86 +834,86 @@ enum
COLOR_RGBA2BGR555 =27,
COLOR_BGR5552BGRA =28,
COLOR_BGR5552RGBA =29,
COLOR_GRAY2BGR555 =30,
COLOR_BGR5552GRAY =31,
COLOR_BGR2XYZ =32,
COLOR_RGB2XYZ =33,
COLOR_XYZ2BGR =34,
COLOR_XYZ2RGB =35,
COLOR_BGR2YCrCb =36,
COLOR_RGB2YCrCb =37,
COLOR_YCrCb2BGR =38,
COLOR_YCrCb2RGB =39,
COLOR_BGR2HSV =40,
COLOR_RGB2HSV =41,
COLOR_BGR2Lab =44,
COLOR_RGB2Lab =45,
COLOR_BayerBG2BGR =46,
COLOR_BayerGB2BGR =47,
COLOR_BayerRG2BGR =48,
COLOR_BayerGR2BGR =49,
COLOR_BayerBG2RGB =COLOR_BayerRG2BGR,
COLOR_BayerGB2RGB =COLOR_BayerGR2BGR,
COLOR_BayerRG2RGB =COLOR_BayerBG2BGR,
COLOR_BayerGR2RGB =COLOR_BayerGB2BGR,
COLOR_BGR2Luv =50,
COLOR_RGB2Luv =51,
COLOR_BGR2HLS =52,
COLOR_RGB2HLS =53,
COLOR_HSV2BGR =54,
COLOR_HSV2RGB =55,
COLOR_Lab2BGR =56,
COLOR_Lab2RGB =57,
COLOR_Luv2BGR =58,
COLOR_Luv2RGB =59,
COLOR_HLS2BGR =60,
COLOR_HLS2RGB =61,
COLOR_BayerBG2BGR_VNG =62,
COLOR_BayerGB2BGR_VNG =63,
COLOR_BayerRG2BGR_VNG =64,
COLOR_BayerGR2BGR_VNG =65,
COLOR_BayerBG2RGB_VNG =COLOR_BayerRG2BGR_VNG,
COLOR_BayerGB2RGB_VNG =COLOR_BayerGR2BGR_VNG,
COLOR_BayerRG2RGB_VNG =COLOR_BayerBG2BGR_VNG,
COLOR_BayerGR2RGB_VNG =COLOR_BayerGB2BGR_VNG,
COLOR_BGR2HSV_FULL = 66,
COLOR_RGB2HSV_FULL = 67,
COLOR_BGR2HLS_FULL = 68,
COLOR_RGB2HLS_FULL = 69,
COLOR_HSV2BGR_FULL = 70,
COLOR_HSV2RGB_FULL = 71,
COLOR_HLS2BGR_FULL = 72,
COLOR_HLS2RGB_FULL = 73,
COLOR_LBGR2Lab = 74,
COLOR_LRGB2Lab = 75,
COLOR_LBGR2Luv = 76,
COLOR_LRGB2Luv = 77,
COLOR_Lab2LBGR = 78,
COLOR_Lab2LRGB = 79,
COLOR_Luv2LBGR = 80,
COLOR_Luv2LRGB = 81,
COLOR_BGR2YUV = 82,
COLOR_RGB2YUV = 83,
COLOR_YUV2BGR = 84,
COLOR_YUV2RGB = 85,
COLOR_BayerBG2GRAY = 86,
COLOR_BayerGB2GRAY = 87,
COLOR_BayerRG2GRAY = 88,
@@ -921,7 +921,7 @@ enum
//YUV 4:2:0 formats family
COLOR_YUV2RGB_NV12 = 90,
COLOR_YUV2BGR_NV12 = 91,
COLOR_YUV2BGR_NV12 = 91,
COLOR_YUV2RGB_NV21 = 92,
COLOR_YUV2BGR_NV21 = 93,
COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21,
@@ -933,7 +933,7 @@ enum
COLOR_YUV2BGRA_NV21 = 97,
COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21,
COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21,
COLOR_YUV2RGB_YV12 = 98,
COLOR_YUV2BGR_YV12 = 99,
COLOR_YUV2RGB_IYUV = 100,
@@ -942,7 +942,7 @@ enum
COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV,
COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12,
COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12,
COLOR_YUV2RGBA_YV12 = 102,
COLOR_YUV2BGRA_YV12 = 103,
COLOR_YUV2RGBA_IYUV = 104,
@@ -951,7 +951,7 @@ enum
COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV,
COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12,
COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12,
COLOR_YUV2GRAY_420 = 106,
COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420,
COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420,
@@ -960,7 +960,7 @@ enum
COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420,
COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420,
COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420,
//YUV 4:2:2 formats family
COLOR_YUV2RGB_UYVY = 107,
COLOR_YUV2BGR_UYVY = 108,
@@ -970,7 +970,7 @@ enum
COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY,
COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY,
COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY,
COLOR_YUV2RGBA_UYVY = 111,
COLOR_YUV2BGRA_UYVY = 112,
//COLOR_YUV2RGBA_VYUY = 113,
@@ -979,7 +979,7 @@ enum
COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY,
COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY,
COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY,
COLOR_YUV2RGB_YUY2 = 115,
COLOR_YUV2BGR_YUY2 = 116,
COLOR_YUV2RGB_YVYU = 117,
@@ -988,7 +988,7 @@ enum
COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2,
COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2,
COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2,
COLOR_YUV2RGBA_YUY2 = 119,
COLOR_YUV2BGRA_YUY2 = 120,
COLOR_YUV2RGBA_YVYU = 121,
@@ -997,7 +997,7 @@ enum
COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2,
COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2,
COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2,
COLOR_YUV2GRAY_UYVY = 123,
COLOR_YUV2GRAY_YUY2 = 124,
//COLOR_YUV2GRAY_VYUY = COLOR_YUV2GRAY_UYVY,
@@ -1006,11 +1006,11 @@ enum
COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2,
COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2,
COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2,
COLOR_COLORCVT_MAX = 125
};
//! converts image from one color space to another
CV_EXPORTS_W void cvtColor( InputArray src, OutputArray dst, int code, int dstCn=0 );
@@ -1027,7 +1027,7 @@ public:
Moments( const CvMoments& moments );
//! the conversion to CvMoments
operator CvMoments() const;
//! spatial moments
CV_PROP_RW double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03;
//! central moments
@@ -1093,7 +1093,7 @@ CV_EXPORTS_W double contourArea( InputArray contour, bool oriented=false );
CV_EXPORTS_W RotatedRect minAreaRect( InputArray points );
//! computes the minimal enclosing circle for a set of points
CV_EXPORTS_W void minEnclosingCircle( InputArray points,
CV_OUT Point2f& center, CV_OUT float& radius );
CV_OUT Point2f& center, CV_OUT float& radius );
//! matches two contours using one of the available algorithms
CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2,
int method, double parameter );
@@ -1118,7 +1118,7 @@ CV_EXPORTS_W void fitLine( InputArray points, OutputArray line, int distType,
double param, double reps, double aeps );
//! checks if the point is inside the contour. Optionally computes the signed distance from the point to the contour boundary
CV_EXPORTS_W double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist );
class CV_EXPORTS_W Subdiv2D
{
@@ -1131,7 +1131,7 @@ public:
PTLOC_VERTEX = 1,
PTLOC_ON_EDGE = 2
};
enum
{
NEXT_AROUND_ORG = 0x00,
@@ -1143,30 +1143,30 @@ public:
PREV_AROUND_LEFT = 0x20,
PREV_AROUND_RIGHT = 0x02
};
CV_WRAP Subdiv2D();
CV_WRAP Subdiv2D(Rect rect);
CV_WRAP void initDelaunay(Rect rect);
CV_WRAP int insert(Point2f pt);
CV_WRAP void insert(const vector<Point2f>& ptvec);
CV_WRAP int locate(Point2f pt, CV_OUT int& edge, CV_OUT int& vertex);
CV_WRAP int findNearest(Point2f pt, CV_OUT Point2f* nearestPt=0);
CV_WRAP void getEdgeList(CV_OUT vector<Vec4f>& edgeList) const;
CV_WRAP void getTriangleList(CV_OUT vector<Vec6f>& triangleList) const;
CV_WRAP void getVoronoiFacetList(const vector<int>& idx, CV_OUT vector<vector<Point2f> >& facetList,
CV_OUT vector<Point2f>& facetCenters);
CV_WRAP Point2f getVertex(int vertex, CV_OUT int* firstEdge=0) const;
CV_WRAP int getEdge( int edge, int nextEdgeType ) const;
CV_WRAP int nextEdge(int edge) const;
CV_WRAP int rotateEdge(int edge, int rotate) const;
CV_WRAP int symEdge(int edge) const;
CV_WRAP int edgeOrg(int edge, CV_OUT Point2f* orgpt=0) const;
CV_WRAP int edgeDst(int edge, CV_OUT Point2f* dstpt=0) const;
protected:
int newEdge();
void deleteEdge(int edge);
@@ -1180,7 +1180,7 @@ protected:
void calcVoronoi();
void clearVoronoi();
void checkSubdiv() const;
struct CV_EXPORTS Vertex
{
Vertex();
@@ -1199,13 +1199,13 @@ protected:
int next[4];
int pt[4];
};
vector<Vertex> vtx;
vector<QuadEdge> qedges;
int freeQEdge;
int freePoint;
bool validGeometry;
int recentEdge;
Point2f topLeft;
Point2f bottomRight;
modules/imgproc/include/opencv2/imgproc/imgproc_c.h
+2 -2
View File
@@ -351,8 +351,8 @@ CVAPI(CvPoint) cvReadChainPoint( CvChainPtReader* reader );
a tree of polygonal curves (contours) */
CVAPI(CvSeq*) cvApproxPoly( const void* src_seq,
int header_size, CvMemStorage* storage,
int method, double parameter,
int parameter2 CV_DEFAULT(0));
int method, double eps,
int recursive CV_DEFAULT(0));
/* Calculates perimeter of a contour or length of a part of contour */
CVAPI(double) cvArcLength( const void* curve,
modules/imgproc/src/approx.cpp
+15 -15
View File
@@ -70,15 +70,15 @@ CvSeq* icvApproximateChainTC89( CvChain* chain, int header_size,
CvChainPtReader reader;
CvSeqWriter writer;
CvPoint pt = chain->origin;
CV_Assert( CV_IS_SEQ_CHAIN_CONTOUR( chain ));
CV_Assert( header_size >= (int)sizeof(CvContour) );
cvStartWriteSeq( (chain->flags & ~CV_SEQ_ELTYPE_MASK) | CV_SEQ_ELTYPE_POINT,
header_size, sizeof( CvPoint ), storage, &writer );
if( chain->total == 0 )
{
{
CV_WRITE_SEQ_ELEM( pt, writer );
return cvEndWriteSeq( &writer );
}
@@ -380,13 +380,13 @@ CV_IMPL CvSeq*
cvApproxChains( CvSeq* src_seq,
CvMemStorage* storage,
int method,
double /*parameter*/,
int minimal_perimeter,
double /*parameter*/,
int minimal_perimeter,
int recursive )
{
CvSeq *prev_contour = 0, *parent = 0;
CvSeq *dst_seq = 0;
if( !src_seq || !storage )
CV_Error( CV_StsNullPtr, "" );
if( method > CV_CHAIN_APPROX_TC89_KCOS || method <= 0 || minimal_perimeter < 0 )
@@ -399,7 +399,7 @@ cvApproxChains( CvSeq* src_seq,
if( len >= minimal_perimeter )
{
CvSeq *contour = 0;
switch( method )
{
case CV_CHAIN_APPROX_NONE:
@@ -471,7 +471,7 @@ cvApproxChains( CvSeq* src_seq,
/* the version for integer point coordinates */
template<typename T> static CvSeq*
icvApproxPolyDP( CvSeq* src_contour, int header_size,
icvApproxPolyDP( CvSeq* src_contour, int header_size,
CvMemStorage* storage, double eps )
{
typedef cv::Point_<T> PT;
@@ -486,7 +486,7 @@ icvApproxPolyDP( CvSeq* src_contour, int header_size,
CvMemStorage* temp_storage = 0;
CvSeq* stack = 0;
CvSeq* dst_contour;
assert( CV_SEQ_ELTYPE(src_contour) == cv::DataType<PT>::type );
cvStartWriteSeq( src_contour->flags, header_size, sizeof(pt), storage, &writer );
@@ -518,7 +518,7 @@ icvApproxPolyDP( CvSeq* src_contour, int header_size,
init_iters = 1;
}
}
if( is_closed )
{
/* 1. Find approximately two farthest points of the contour */
@@ -629,10 +629,10 @@ icvApproxPolyDP( CvSeq* src_contour, int header_size,
CV_WRITE_SEQ_ELEM( end_pt, writer );
dst_contour = cvEndWriteSeq( &writer );
// last stage: do final clean-up of the approximated contour -
// remove extra points on the [almost] stright lines.
// remove extra points on the [almost] stright lines.
cvStartReadSeq( dst_contour, &reader, is_closed );
CV_READ_SEQ_ELEM( start_pt, reader );
@@ -675,7 +675,7 @@ icvApproxPolyDP( CvSeq* src_contour, int header_size,
CV_IMPL CvSeq*
cvApproxPoly( const void* array, int header_size,
CvMemStorage* storage, int method,
CvMemStorage* storage, int method,
double parameter, int parameter2 )
{
CvSeq* dst_seq = 0;