Updated rst configuration to read OpenCV version from sources; fixed several sphinx' warnings

doc/conf.py

@@ -11,7 +11,7 @@
 # All configuration values have a default; values that are commented out
 # serve to show the default.
 
-import sys, os
+import sys, os, re
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
@@ -47,11 +47,16 @@ copyright = u'2011, opencv dev team'
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
 # built documents.
-#
+
+version_file = open("../modules/core/include/opencv2/core/version.hpp", "rt").read()
+version_major = re.search("^W*#\W*define\W+CV_MAJOR_VERSION\W+(\d+)\W*$", version_file, re.MULTILINE).group(1)
+version_minor = re.search("^W*#\W*define\W+CV_MINOR_VERSION\W+(\d+)\W*$", version_file, re.MULTILINE).group(1)
+version_patch = re.search("^W*#\W*define\W+CV_SUBMINOR_VERSION\W+(\d+)\W*$", version_file, re.MULTILINE).group(1)
+
 # The short X.Y version.
-version = '2.3'
+version = version_major + '.' + version_minor
 # The full version, including alpha/beta/rc tags.
-release = '2.3.2'
+release = version_major + '.' + version_minor + '.' + version_patch
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

doc/user_guide/ug_traincascade.rst

@@ -20,7 +20,7 @@ Note that ``opencv_traincascade`` application can use TBB for multi-threading. T
 
 Also there are some auxilary utilities related to the training. 
 
-    * ``opencv_createsamples`` is used to prepare a training dataset of positive and test samples. ``opencv_createsamples`` produces dataset of positive samples in a format that is supported by both ``opencv_haartraining`` and ``opencv_traincascade`` applications. The output is a file with *.vec extension, it is a binary format which contains images.
+    * ``opencv_createsamples`` is used to prepare a training dataset of positive and test samples. ``opencv_createsamples`` produces dataset of positive samples in a format that is supported by both ``opencv_haartraining`` and ``opencv_traincascade`` applications. The output is a file with \*.vec extension, it is a binary format which contains images.
     
     * ``opencv_performance`` may be used to evaluate the quality of classifiers, but for trained by ``opencv_haartraining`` only. It takes a collection of marked up images, runs the classifier and reports the performance, i.e. number of found objects, number of missed objects, number of false alarms and other information.
 

modules/gpu/doc/feature_detection_and_description.rst

@@ -606,7 +606,7 @@ Downloads matrices obtained via :ocv:func:`gpu::BruteForceMatcher_GPU::radiusMat
 
 .. ocv:function:: void gpu::BruteForceMatcher_GPU::radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, const GpuMat& nMatches, std::vector< std::vector<DMatch> >&matches, bool compactResult = false)
 
-.. ocv:function:: void gpu::BruteForceMatcher_GPU::radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& nMatches, std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
+.. ocv:function:: void gpu::BruteForceMatcher_GPU::radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& nMatches, std::vector< std::vector<DMatch> >& matches, bool compactResult = false)
 
 If ``compactResult`` is ``true`` , the ``matches`` vector does not contain matches for fully masked-out query descriptors.
 

modules/gpu/doc/image_processing.rst

@@ -324,7 +324,7 @@ gpu::remap
 --------------
 Applies a generic geometrical transformation to an image.
 
-.. ocv:function:: void gpu::remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap, int interpolation, int borderMode = BORDER_CONSTANT, const Scalar& borderValue = Scalar(), Stream& stream = Stream::Null()
+.. ocv:function:: void gpu::remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap, int interpolation, int borderMode = BORDER_CONSTANT, const Scalar& borderValue = Scalar(), Stream& stream = Stream::Null())
 
     :param src: Source image.
 
@@ -678,7 +678,7 @@ Equalizes the histogram of a grayscale image.
 
 
 gpu::buildWarpPlaneMaps
--------------------
+-----------------------
 Builds plane warping maps.
 
 .. ocv:function:: void gpu::buildWarpPlaneMaps(Size src_size, Rect dst_roi, const Mat& R, double f, double s, double dist, GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null())
@@ -688,7 +688,7 @@ Builds plane warping maps.
 
 
 gpu::buildWarpCylindricalMaps
--------------------
+-----------------------------
 Builds cylindrical warping maps.
 
 .. ocv:function:: void gpu::buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat& R, double f, double s, GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null())
@@ -698,7 +698,7 @@ Builds cylindrical warping maps.
 
 
 gpu::buildWarpSphericalMaps
--------------------
+---------------------------
 Builds spherical warping maps.
 
 .. ocv:function:: void gpu::buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat& R, double f, double s, GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null())

modules/gpu/doc/matrix_reductions.rst

@@ -10,7 +10,7 @@ gpu::meanStdDev
 Computes a mean value and a standard deviation of matrix elements.
 
 .. ocv:function:: void gpu::meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev)
-.. ocv:function:: void gpu::meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev, GpuMat& buf);
+.. ocv:function:: void gpu::meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev, GpuMat& buf)
 
     :param mtx: Source matrix.  ``CV_8UC1``  matrices are supported for now.
 
@@ -163,7 +163,7 @@ The function does not work with ``CV_64F`` images on GPUs with the compute capab
 
 
 gpu::reduce
-------
+-----------
 Reduces a matrix to a vector.
 
 .. ocv:function:: void gpu::reduce(const GpuMat& mtx, GpuMat& vec, int dim, int reduceOp, int dtype = -1, Stream& stream = Stream::Null())

modules/gpu/doc/per_element_operations.rst

@@ -109,7 +109,7 @@ addWeighted
 ---------------
 Computes the weighted sum of two arrays.
 
-.. ocv:function:: void gpu::addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst, int dtype = -1, Stream& stream = Stream::Null());
+.. ocv:function:: void gpu::addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst, int dtype = -1, Stream& stream = Stream::Null())
 
     :param src1: First source array.
 

modules/imgproc/doc/motion_analysis_and_object_tracking.rst

@@ -163,23 +163,28 @@ The function performs the following equations
 *
     Next it computes the forward DFTs of each source array:
     .. math::
+
         \mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}
+
     where
     :math:`\mathcal{F}` is the forward DFT.
 
 *
     It then computes the cross-power spectrum of each frequency domain array:
     .. math::
+
         R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}
 
 *
     Next the cross-correlation is converted back into the time domain via the inverse DFT:
     .. math::
+
         r = \mathcal{F}^{-1}\{R\}
 *
     Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.
     .. math::
-        (\Delta x, \Delta y) = \texttt{weighted_centroid}\{\arg \max_{(x, y)}\{r\}\}
+
+       (\Delta x, \Delta y) = \texttt{weighted_centroid}\{\arg \max_{(x, y)}\{r\}\}
 
 .. seealso::
     :ocv:func:`dft`,

modules/objdetect/doc/latent_svm.rst

@@ -212,7 +212,7 @@ Two types of constructors.
     :param classNames: A set of trained models names. If it's empty then the name of each model will be constructed from the name of file containing the model. E.g. the model stored in "/home/user/cat.xml" will get the name "cat".
 
 LatentSvmDetector::~LatentSvmDetector
-------------------------------------
+-------------------------------------
 Destructor.
 
 .. ocv:function:: LatentSvmDetector::~LatentSvmDetector()

modules/ts/misc/testlog_parser.py

@@ -8,7 +8,6 @@ class TestInfo(object):
         self.name = xmlnode.getAttribute("name")
         self.value_param = xmlnode.getAttribute("value_param")
         self.type_param = xmlnode.getAttribute("type_param")
-        self.name = xmlnode.getAttribute("name")
         if xmlnode.getElementsByTagName("failure"):
             self.status = "failed"
         else: