Some reST syntax correction. Now the PDF documentation builds correctly under Windows using MIKTEX. Some update on the tutorial structure. Some CSS syntax correction. Windows Install Tutorial v0.4.

2011-06-30 00:41:41 +00:00
parent d8ea8ef98a
commit 762bc0f8af
59 changed files with 469 additions and 364 deletions
--- a/modules/imgproc/doc/filtering.rst
+++ b/modules/imgproc/doc/filtering.rst
@@ -54,12 +54,12 @@ where
 :ocv:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.

 .. seealso::
-:ocv:func:`BaseRowFilter`,
-:ocv:func:`BaseFilter`,
-:ocv:func:`FilterEngine`,
-:ocv:func:`getColumnSumFilter`,
-:ocv:func:`getLinearColumnFilter`,
-:ocv:func:`getMorphologyColumnFilter`
+   :ocv:func:`BaseRowFilter`,
+   :ocv:func:`BaseFilter`,
+   :ocv:func:`FilterEngine`,
+   :ocv:func:`getColumnSumFilter`,
+   :ocv:func:`getLinearColumnFilter`,
+   :ocv:func:`getMorphologyColumnFilter`



@@ -104,11 +104,11 @@ where
 :ocv:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.

 .. seealso::
-:ocv:func:`BaseColumnFilter`,
-:ocv:func:`BaseRowFilter`,
-:ocv:func:`FilterEngine`,
-:ocv:func:`getLinearFilter`,
-:ocv:func:`getMorphologyFilter`
+   :ocv:func:`BaseColumnFilter`,
+   :ocv:func:`BaseRowFilter`,
+   :ocv:func:`FilterEngine`,
+   :ocv:func:`getLinearFilter`,
+   :ocv:func:`getMorphologyFilter`



@@ -145,12 +145,12 @@ where
 :ocv:func:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.

 .. seealso::
-:ocv:func:`BaseColumnFilter`,
-:ocv:func:`Filter`,
-:ocv:func:`FilterEngine`,
-:ocv:func:`getLinearRowFilter`,
-:ocv:func:`getMorphologyRowFilter`,
-:ocv:func:`getRowSumFilter`
+   :ocv:func:`BaseColumnFilter`,
+   :ocv:func:`Filter`,
+   :ocv:func:`FilterEngine`,
+   :ocv:func:`getLinearRowFilter`,
+   :ocv:func:`getMorphologyRowFilter`,
+   :ocv:func:`getRowSumFilter`



@@ -356,15 +356,15 @@ 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``     .

 .. seealso::
-:ocv:func:`BaseColumnFilter`,
-:ocv:func:`BaseFilter`,
-:ocv:func:`BaseRowFilter`,
-:ocv:func:`createBoxFilter`,
-:ocv:func:`createDerivFilter`,
-:ocv:func:`createGaussianFilter`,
-:ocv:func:`createLinearFilter`,
-:ocv:func:`createMorphologyFilter`,
-:ocv:func:`createSeparableLinearFilter`
+   :ocv:func:`BaseColumnFilter`,
+   :ocv:func:`BaseFilter`,
+   :ocv:func:`BaseRowFilter`,
+   :ocv:func:`createBoxFilter`,
+   :ocv:func:`createDerivFilter`,
+   :ocv:func:`createGaussianFilter`,
+   :ocv:func:`createLinearFilter`,
+   :ocv:func:`createMorphologyFilter`,
+   :ocv:func:`createSeparableLinearFilter`



@@ -426,10 +426,10 @@ 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)`` .

 .. seealso::
-:ocv:func:`boxFilter`,
-:ocv:func:`bilateralFilter`,
-:ocv:func:`GaussianBlur`,
-:ocv:func:`medianBlur` 
+   :ocv:func:`boxFilter`,
+   :ocv:func:`bilateralFilter`,
+   :ocv:func:`GaussianBlur`,
+   :ocv:func:`medianBlur` 



@@ -458,8 +458,8 @@ Normally, the function is not called directly. It is used inside
 :ocv:func:`copyMakeBorder` to compute tables for quick extrapolation.

 .. seealso::
-:ocv:func:`FilterEngine`,
-:ocv:func:`copyMakeBorder`
+   :ocv:func:`FilterEngine`,
+   :ocv:func:`copyMakeBorder`



@@ -500,11 +500,11 @@ and so on). If you need to compute pixel sums over variable-size windows, use
 :ocv:func:`integral` .

 .. seealso::
-:ocv:func:`boxFilter`,
-:ocv:func:`bilateralFilter`,
-:ocv:func:`GaussianBlur`,
-:ocv:func:`medianBlur`,
-:ocv:func:`integral` 
+   :ocv:func:`boxFilter`,
+   :ocv:func:`bilateralFilter`,
+   :ocv:func:`GaussianBlur`,
+   :ocv:func:`medianBlur`,
+   :ocv:func:`integral` 



@@ -567,7 +567,7 @@ The function supports the mode when ``src`` is already in the middle of ``dst``


 .. seealso::
-:ocv:func:`borderInterpolate`
+   :ocv:func:`borderInterpolate`


 createBoxFilter
@@ -606,9 +606,9 @@ The function itself is used by
 :ocv:func:`boxFilter` .

 .. seealso::
-:ocv:func:`FilterEngine`,
-:ocv:func:`blur`,
-:ocv:func:`boxFilter` 
+   :ocv:func:`FilterEngine`,
+   :ocv:func:`blur`,
+   :ocv:func:`boxFilter` 



@@ -637,10 +637,10 @@ The function :ocv:func:`createDerivFilter` is a small convenience function that
 :ocv:func:`Scharr` .

 .. seealso::
-:ocv:func:`createSeparableLinearFilter`,
-:ocv:func:`getDerivKernels`,
-:ocv:func:`Scharr`,
-:ocv:func:`Sobel` 
+   :ocv:func:`createSeparableLinearFilter`,
+   :ocv:func:`getDerivKernels`,
+   :ocv:func:`Scharr`,
+   :ocv:func:`Sobel` 



@@ -666,9 +666,9 @@ The function :ocv:func:`createGaussianFilter` computes Gaussian kernel coefficie
 :ocv:func:`createSeparableFilter` directly.

 .. seealso::
-:ocv:func:`createSeparableLinearFilter`,
-:ocv:func:`getGaussianKernel`,
-:ocv:func:`GaussianBlur` 
+   :ocv:func:`createSeparableLinearFilter`,
+   :ocv:func:`getGaussianKernel`,
+   :ocv:func:`GaussianBlur` 



@@ -700,9 +700,9 @@ The function returns a pointer to a 2D linear filter for the specified kernel, t
 :ocv:func:`FilterEngine` constructor.

 .. seealso::
-:ocv:func:`createSeparableLinearFilter`,
-:ocv:func:`FilterEngine`,
-:ocv:func:`filter2D`
+  :ocv:func:`createSeparableLinearFilter`,
+  :ocv:func:`FilterEngine`,
+  :ocv:func:`filter2D`


 createMorphologyFilter
@@ -742,10 +742,10 @@ Note that
 :ocv:func:`createMorphologyFilter` analyzes the structuring element shape and builds a separable morphological filter engine when the structuring element is square.

 .. seealso::
-:ocv:func:`erode`,
-:ocv:func:`dilate`,
-:ocv:func:`morphologyEx`,
-:ocv:func:`FilterEngine`
+   :ocv:func:`erode`,
+   :ocv:func:`dilate`,
+   :ocv:func:`morphologyEx`,
+   :ocv:func:`FilterEngine`


 createSeparableLinearFilter
@@ -787,10 +787,10 @@ The functions construct primitive separable linear filtering operations or a fil
 :ocv:func:`FilterEngine` constructor.

 .. seealso::
-:ocv:func:`sepFilter2D`,
-:ocv:func:`createLinearFilter`,
-:ocv:func:`FilterEngine`,
-:ocv:func:`getKernelType`
+   :ocv:func:`sepFilter2D`,
+   :ocv:func:`createLinearFilter`,
+   :ocv:func:`FilterEngine`,
+   :ocv:func:`getKernelType`


 dilate
@@ -827,9 +827,9 @@ 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.

 .. seealso::
-:ocv:func:`erode`,
-:ocv:func:`morphologyEx`,
-:ocv:func:`createMorphologyFilter`
+   :ocv:func:`erode`,
+   :ocv:func:`morphologyEx`,
+   :ocv:func:`createMorphologyFilter`


 erode
@@ -866,9 +866,9 @@ 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.

 .. seealso::
-:ocv:func:`dilate`,
-:ocv:func:`morphologyEx`,
-:ocv:func:`createMorphologyFilter`
+   :ocv:func:`dilate`,
+   :ocv:func:`morphologyEx`,
+   :ocv:func:`createMorphologyFilter`



@@ -911,10 +911,10 @@ That is, the kernel is not mirrored around the anchor point. If you need a real
 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 :ocv:func:`createLinearFilter` ) for small kernels.

 .. seealso::
-:ocv:func:`sepFilter2D`,
-:ocv:func:`createLinearFilter`,
-:ocv:func:`dft`,
-:ocv:func:`matchTemplate`
+   :ocv:func:`sepFilter2D`,
+   :ocv:func:`createLinearFilter`,
+   :ocv:func:`dft`,
+   :ocv:func:`matchTemplate`



@@ -939,12 +939,12 @@ Smoothes an image using a Gaussian filter.
 The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.

 .. seealso::
-:ocv:func:`sepFilter2D`,
-:ocv:func:`filter2D`,
-:ocv:func:`blur`,
-:ocv:func:`boxFilter`,
-:ocv:func:`bilateralFilter`,
-:ocv:func:`medianBlur`
+   :ocv:func:`sepFilter2D`,
+   :ocv:func:`filter2D`,
+   :ocv:func:`blur`,
+   :ocv:func:`boxFilter`,
+   :ocv:func:`bilateralFilter`,
+   :ocv:func:`medianBlur`


 getDerivKernels
@@ -1009,11 +1009,11 @@ Two of such generated kernels can be passed to
 :ocv:func:`GaussianBlur`.

 .. seealso::
-:ocv:func:`sepFilter2D`,
-:ocv:func:`createSeparableLinearFilter`,
-:ocv:func:`getDerivKernels`,
-:ocv:func:`getStructuringElement`,
-:ocv:func:`GaussianBlur` 
+   :ocv:func:`sepFilter2D`,
+   :ocv:func:`createSeparableLinearFilter`,
+   :ocv:func:`getDerivKernels`,
+   :ocv:func:`getStructuringElement`,
+   :ocv:func:`GaussianBlur` 



@@ -1111,10 +1111,10 @@ 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.

 .. seealso::
-:ocv:func:`bilateralFilter`,
-:ocv:func:`blur`,
-:ocv:func:`boxFilter`,
-:ocv:func:`GaussianBlur`
+   :ocv:func:`bilateralFilter`,
+   :ocv:func:`blur`,
+   :ocv:func:`boxFilter`,
+   :ocv:func:`GaussianBlur`



@@ -1188,9 +1188,9 @@ Morphological gradient:
 Any of the operations can be done in-place.

 .. seealso::
-:ocv:func:`dilate`,
-:ocv:func:`erode`,
-:ocv:func:`createMorphologyFilter`
+   :ocv:func:`dilate`,
+   :ocv:func:`erode`,
+   :ocv:func:`createMorphologyFilter`



@@ -1234,8 +1234,8 @@ This is done when ``ksize > 1`` . When ``ksize == 1`` , the Laplacian is compute
    \vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}

 .. seealso::
-:ocv:func:`Sobel`,
-:ocv:func:`Scharr`
+   :ocv:func:`Sobel`,
+   :ocv:func:`Scharr`



@@ -1370,12 +1370,12 @@ Applies a separable linear filter to an image.
 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`` .

 .. seealso::
-:ocv:func:`createSeparableLinearFilter`,
-:ocv:func:`filter2D`,
-:ocv:func:`Sobel`,
-:ocv:func:`GaussianBlur`,
-:ocv:func:`boxFilter`,
-:ocv:func:`blur` 
+   :ocv:func:`createSeparableLinearFilter`,
+   :ocv:func:`filter2D`,
+   :ocv:func:`Sobel`,
+   :ocv:func:`GaussianBlur`,
+   :ocv:func:`boxFilter`,
+   :ocv:func:`blur` 


 Smooth
@@ -1500,11 +1500,11 @@ The second case corresponds to a kernel of:
    \vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}

 .. seealso::
-:ocv:func:`Scharr`,
-:ocv:func:`Lapacian`,
-:ocv:func:`sepFilter2D`,
-:ocv:func:`filter2D`,
-:ocv:func:`GaussianBlur`
+   :ocv:func:`Scharr`,
+   :ocv:func:`Lapacian`,
+   :ocv:func:`sepFilter2D`,
+   :ocv:func:`filter2D`,
+   :ocv:func:`GaussianBlur`



--- a/modules/imgproc/doc/geometric_transformations.rst
+++ b/modules/imgproc/doc/geometric_transformations.rst
@@ -5,6 +5,7 @@ Geometric Image Transformations
 The functions in this section perform various geometrical transformations of 2D images. They do not change the image content but deform the pixel grid and map this deformed grid to the destination image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from destination to the source. That is, for each pixel
 :math:`(x, y)` of the destination image, the functions compute coordinates of the corresponding "donor" pixel in the source image and copy the pixel value:

+The functions in this section perform various geometrical transformations of 2D images. They do not change the image content but deform the pixel grid and map this deformed grid to the destination image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from destination to the source. That is, for each pixel :math:`(x, y)` of the destination image, the functions compute    coordinates of the corresponding "donor" pixel in the source image and copy the pixel value:
 .. math::

    \texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))
--- a/modules/imgproc/doc/histograms.rst
+++ b/modules/imgproc/doc/histograms.rst
@@ -148,7 +148,7 @@ This is an approximate algorithm of the
 See Also:
 :ocv:func:`calcHist`

-
+.. _compareHist:

 compareHist
 -----------
@@ -368,6 +368,7 @@ Copies a histogram.
    
 The function makes a copy of the histogram. If the second histogram pointer ``*dst`` is NULL, a new histogram of the same size as  ``src`` is created. Otherwise, both histograms must have equal types and sizes. Then the function copies the source histogram's bin values to the destination histogram and sets the same bin value ranges as in ``src``.

+.. _createhist:

 CreateHist
 ----------
--- a/modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst
+++ b/modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst
@@ -31,13 +31,7 @@ The function computes moments, up to the 3rd order, of a vector shape or a raste
        Moments( const CvMoments& moments );
        operator CvMoments() const;

-        // spatial moments
-        double  m00, m10, m01, m20, m11, m02, m30, m21, m12, m03;
-        // central moments
-        double  mu20, mu11, mu02, mu30, mu21, mu12, mu03;
-        // central normalized moments
-        double  nu20, nu11, nu02, nu30, nu21, nu12, nu03;
-    };
+    ..


 In case of a raster image, the spatial moments :math:`\texttt{Moments::m}_{ji}` are computed as:
@@ -178,7 +172,7 @@ Draws contours outlines or filled contours.
    :param contourIdx: Parameter indicating a contour to draw. If it is negative, all the contours are drawn.

    :param color: Color of the contours.
-	
+    
    :param thickness: Thickness of lines the contours are drawn with. If it is negative (for example,  ``thickness=CV_FILLED`` ), the contour interiors are
        drawn.

@@ -680,12 +674,12 @@ Here is a sample output of the function where each image pixel is tested against

 .. image:: pics/pointpolygon.png

-.. [Fitzgibbon95] Andrew W. Fitzgibbon, R.B.Fisher. A Buyer’s Guide to Conic Fitting. Proc.5th British Machine Vision Conference, Birmingham, pp. 513-522, 1995.
+.. [Fitzgibbon95] Andrew W. Fitzgibbon, R.B.Fisher. A Buyer<EFBFBD>s Guide to Conic Fitting. Proc.5th British Machine Vision Conference, Birmingham, pp. 513-522, 1995.

 .. [Hu62] M. Hu. Visual Pattern Recognition by Moment Invariants, IRE Transactions on Information Theory, 8:2, pp. 179-187, 1962.

-.. [Sklansky82] Sklansky, J., “Finding the Convex Hull of a Simple Polygon”. PRL 1 $number, pp 79-83 (1982)
+.. [Sklansky82] Sklansky, J., <EFBFBD>Finding the Convex Hull of a Simple Polygon<EFBFBD>. PRL 1 $number, pp 79-83 (1982)

-.. [Suzuki85] Suzuki, S. and Abe, K., “Topological Structural Analysis of Digitized Binary Images by Border Following”. CVGIP 30 1, pp 32-46 (1985)
+.. [Suzuki85] Suzuki, S. and Abe, K., <EFBFBD>Topological Structural Analysis of Digitized Binary Images by Border Following<EFBFBD>. CVGIP 30 1, pp 32-46 (1985)

-.. [TehChin89] Teh, C.H. and Chin, R.T., “On the Detection of Dominant Points on Digital Curve”. PAMI 11 8, pp 859-872 (1989)
+.. [TehChin89] Teh, C.H. and Chin, R.T., <EFBFBD>On the Detection of Dominant Points on Digital Curve<EFBFBD>. PAMI 11 8, pp 859-872 (1989)