added pictures for OpenCV 2.x reference manual; fixed some build problems and…

added pictures for OpenCV 2.x reference manual; fixed some build problems and done some more cleanup work

modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst

 Camera Calibration and 3D Reconstruction
 ========================================
 
-.. highlight:: cpp
-
 The functions in this section use the so-called pinhole camera model. That
 is, a scene view is formed by projecting 3D points into the image plane
 using a perspective transformation.
@@ -15,8 +13,7 @@ or
 
 .. math::
 
-    s  \vecthree{u}{v}{1} =  \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}
-    
+    s  \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}
     \begin{bmatrix}
     r_{11} & r_{12} & r_{13} & t_1  \\
     r_{21} & r_{22} & r_{23} & t_2  \\

modules/core/doc/core.rst

@@ -7,7 +7,6 @@ core. The Core Functionality
 
     basic_structures
     operations_on_arrays
-    dynamic_structures
     drawing_functions
     xml_yaml_persistence
     clustering

modules/core/doc/intro.rst

@@ -2,8 +2,6 @@
 Introduction
 ************
 
-.. highlight:: cpp
-
 OpenCV (Open Source Computer Vision Library: http://opencv.willowgarage.com/wiki/) is open-source BSD-licensed library that includes several hundreds computer vision algorithms. It is very popular in the Computer Vision community. Some people call it “de-facto standard” API. The document aims to specify the stable parts of the library, as well as some abstract interfaces for high-level interfaces, with the final goal to make it an official standard.
 
 API specifications in the document use the standard C++ (http://www.open-std.org/jtc1/sc22/wg21/) and the standard C++ library.

modules/features2d/doc/common_interfaces_of_feature_detectors.rst

 Common Interfaces of Feature Detectors
 ======================================
 
-.. highlight:: cpp
-
 Feature detectors in OpenCV have wrappers with common interface that enables to switch easily
 between different algorithms solving the same problem. All objects that implement keypoint detectors
 inherit
@@ -1005,11 +1003,10 @@ AdjusterAdapter::good
 
     Are params maxed out or still valid? Returns false if the parameters can't be adjusted any more. An example implementation of this is ::
 
-    bool FastAdjuster::good() const
-    {
-            return (thresh_ > 1) && (thresh_ < 200);
-    }
-
+        bool FastAdjuster::good() const
+        {
+            return (thresh > 1) && (thresh < 200);
+        }
 
 .. index:: FastAdjuster
 

modules/features2d/doc/common_interfaces_of_generic_descriptor_matchers.rst

@@ -14,21 +14,11 @@ There are descriptors such as One way descriptor and Ferns that have ``GenericDe
 
 .. index:: GenericDescriptorMatcher
 
-.. _GenericDescriptorMatcher:
-
 GenericDescriptorMatcher
 ------------------------
 .. c:type:: GenericDescriptorMatcher
 
-Abstract interface for a keypoint descriptor extracting and matching.
-There is
-:func:`DescriptorExtractor` and
-:func:`DescriptorMatcher` for these purposes too, but their interfaces are intended for descriptors
-represented as vectors in a multidimensional space. ``GenericDescriptorMatcher`` is a more generic interface for descriptors.
-
-:func:`DescriptorMatcher`,``GenericDescriptorMatcher`` has two groups
-of match methods: for matching keypoints of one image with other image or
-with image set. ::
+Abstract interface for a keypoint descriptor extracting and matching. There is :func:`DescriptorExtractor` and :func:`DescriptorMatcher` for these purposes too, but their interfaces are intended for descriptors represented as vectors in a multidimensional space. ``GenericDescriptorMatcher`` is a more generic interface for descriptors. :func:`DescriptorMatcher`,``GenericDescriptorMatcher`` has two groups of match methods: for matching keypoints of one image with other image or with image set. ::
 
     class GenericDescriptorMatcher
     {

modules/features2d/doc/drawing_function_of_keypoints_and_matches.rst

 Drawing Function of Keypoints and Matches
 =========================================
 
-.. highlight:: cpp
-
 .. index:: drawMatches
 
 drawMatches
 ---------------
-.. c:function:: void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1,          const Mat& img2, const vector<KeyPoint>& keypoints2,          const vector<DMatch>& matches1to2, Mat& outImg,          const Scalar& matchColor=Scalar::all(-1),           const Scalar& singlePointColor=Scalar::all(-1),          const vector<char>& matchesMask=vector<char>(),          int flags=DrawMatchesFlags::DEFAULT )
 
-    This function draws matches of keypints from two images on output image. Match is a line connecting two keypoints (circles).
+.. c:function:: void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1,          const Mat& img2, const vector<KeyPoint>& keypoints2,          const vector<DMatch>& matches1to2, Mat& outImg,          const Scalar& matchColor=Scalar::all(-1),           const Scalar& singlePointColor=Scalar::all(-1),          const vector<char>& matchesMask=vector<char>(),          int flags=DrawMatchesFlags::DEFAULT )
 
 .. c:function:: void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1,           const Mat& img2, const vector<KeyPoint>& keypoints2,           const vector<vector<DMatch> >& matches1to2, Mat& outImg,           const Scalar& matchColor=Scalar::all(-1),            const Scalar& singlePointColor=Scalar::all(-1),           const vector<vector<char>>& matchesMask=           vector<vector<char> >(),           int flags=DrawMatchesFlags::DEFAULT )
 
@@ -31,29 +28,34 @@ drawMatches
 
     :param matchesMask: Mask determining which matches will be drawn. If mask is empty all matches will be drawn.
 
-    :param flags: Each bit of  ``flags``  sets some feature of drawing. Possible  ``flags``  bit values is defined by  ``DrawMatchesFlags`` ::
-
-            struct DrawMatchesFlags
-            {
-                enum{ DEFAULT = 0, // Output image matrix will be created (Mat::create),
-                                   // i.e. existing memory of output image may be reused.
-                                   // Two source image, matches and single keypoints
-                                   // will be drawn.
-                                   // For each keypoint only the center point will be
-                                   // drawn (without the circle around keypoint with
-                                   // keypoint size and orientation).
-                      DRAW_OVER_OUTIMG = 1, // Output image matrix will not be
-                                   // created (Mat::create). Matches will be drawn
-                                   // on existing content of output image.
-                      NOT_DRAW_SINGLE_POINTS = 2, // Single keypoints will not be drawn.
-                      DRAW_RICH_KEYPOINTS = 4 // For each keypoint the circle around
-                                   // keypoint with keypoint size and orientation will
-                                   // be drawn.
-                    };
-            };
-            
-    ..
-
+    :param flags: Each bit of  ``flags``  sets some feature of drawing. Possible  ``flags``  bit values is defined by  ``DrawMatchesFlags``.
+    
+This function draws matches of keypints from two images on output image. Match is a line connecting two keypoints (circles). The structure ``DrawMatchesFlags`` is defined as follows:
+
+.. code-block:: cpp
+
+    struct DrawMatchesFlags
+    {
+        enum
+        {
+            DEFAULT = 0, // Output image matrix will be created (Mat::create),
+                         // i.e. existing memory of output image may be reused.
+                         // Two source image, matches and single keypoints
+                         // will be drawn.
+                         // For each keypoint only the center point will be
+                         // drawn (without the circle around keypoint with
+                         // keypoint size and orientation).
+            DRAW_OVER_OUTIMG = 1, // Output image matrix will not be
+                           // created (using Mat::create). Matches will be drawn
+                           // on existing content of output image.
+            NOT_DRAW_SINGLE_POINTS = 2, // Single keypoints will not be drawn.
+            DRAW_RICH_KEYPOINTS = 4 // For each keypoint the circle around
+                           // keypoint with keypoint size and orientation will
+                           // be drawn.
+        };
+    };
+
+..
 
 .. index:: drawKeypoints
 

modules/gpu/doc/gpu.rst

@@ -9,7 +9,7 @@ gpu. GPU-accelerated Computer Vision
     initalization_and_information
     data_structures
     operations_on_matrices
-    per_element_operations.
+    per_element_operations
     image_processing
     matrix_reductions
     object_detection

modules/highgui/doc/qt_new_functions.rst

 Qt new functions
 ================
 
-.. highlight:: cpp
-
-.. image:: ../../pics/Qt_GUI.png
+.. image:: pics/qtgui.png
 
 This figure explains the new functionalities implemented with Qt GUI. As we can see, the new GUI provides a statusbar, a toolbar, and a control panel. The control panel can have trackbars and buttonbars attached to it.
 

modules/imgproc/doc/feature_detection.rst

 Feature Detection
 =================
 
-.. highlight:: cpp
-
 .. index:: Canny
 
 Canny
@@ -146,7 +144,7 @@ cornerSubPix
 
 The function iterates to find the sub-pixel accurate location of corners, or radial saddle points, as shown in on the picture below.
 
-.. image:: ../../pics/cornersubpix.png
+.. image:: pics/cornersubpix.png
 
 Sub-pixel accurate corner locator is based on the observation that every vector from the center
 :math:`q` to a point
@@ -408,11 +406,11 @@ Matas00
 
 This is the sample picture the function parameters have been tuned for:
 
-.. image:: ../../pics/building.jpg
+.. image:: pics/building.jpg
 
 And this is the output of the above program in the case of probabilistic Hough transform
 
-.. image:: ../../pics/houghp.png
+.. image:: pics/houghp.png
 
 .. index:: preCornerDetect
 

modules/imgproc/doc/filtering.rst

 Image Filtering
 ===============
 
-.. highlight:: cpp
-
 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), 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

modules/imgproc/doc/geometric_transformations.rst

 Geometric Image Transformations
 ===============================
 
-.. highlight:: cpp
-
 The functions in this section perform various geometrical transformations of 2D images. That is, 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, that is:
 

modules/imgproc/doc/histograms.rst

 Histograms
 ==========
 
-.. highlight:: cpp
-
 .. index:: calcHist
 
 calcHist

modules/imgproc/doc/imgproc.rst

@@ -2,6 +2,8 @@
 imgproc. Image Processing
 *************************
 
+.. highlight:: cpp
+
 .. toctree::
     :maxdepth: 2
 
@@ -10,7 +12,6 @@ imgproc. Image Processing
     miscellaneous_transformations
     histograms
     structural_analysis_and_shape_descriptors
-    planar_subdivisions
     motion_analysis_and_object_tracking
     feature_detection
     object_detection

modules/imgproc/doc/miscellaneous_transformations.rst

 Miscellaneous Image Transformations
 ===================================
 
-.. highlight:: cpp
-
 .. index:: adaptiveThreshold
 
 adaptiveThreshold
@@ -616,7 +614,7 @@ As a practical example, the next figure shows the calculation of the integral of
 
 \begin{center}
 
-.. image:: ../../pics/integral.png
+.. image:: pics/integral.png
 
 \end{center}
 
@@ -682,7 +680,7 @@ value using Otsu's algorithm and uses it instead of the specified ``thresh`` .
 The function returns the computed threshold value.
 Currently, Otsu's method is implemented only for 8-bit images.
 
-.. image:: ../../pics/threshold.png
+.. image:: pics/threshold.png
 
 See also:
 :func:`adaptiveThreshold`,:func:`findContours`,:func:`compare`,:func:`min`,:func:`max`

modules/imgproc/doc/motion_analysis_and_object_tracking.rst

 Motion Analysis and Object Tracking
 ===================================
 
-.. highlight:: cpp
-
 .. index:: accumulate
 
 accumulate

modules/imgproc/doc/object_detection.rst

 Object Detection
 ================
 
-.. highlight:: cpp
-
 .. index:: matchTemplate
 
 matchTemplate

modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst

 Structural Analysis and Shape Descriptors
 =========================================
 
-.. highlight:: cpp
-
 .. index:: moments
 
 moments
@@ -572,5 +570,5 @@ edge.
 
 Here is the sample output of the function, where each image pixel is tested against the contour.
 
-.. image:: ../../pics/pointpolygon.png
+.. image:: pics/pointpolygon.png
 

modules/index.rst

@@ -8,6 +8,8 @@ Welcome to opencvstd's documentation!
 
 Contents:
 
+.. highlight:: cpp
+
 .. toctree::
    :maxdepth: 2
 

modules/ml/doc/boosting.rst

 Boosting
 ========
 
-.. highlight:: cpp
-
 A common machine learning task is supervised learning. In supervised learning, the goal is to learn the functional relationship
 :math:`F: y = F(x)` between the input
 :math:`x` and the output

modules/ml/doc/decision_trees.rst

 Decision Trees
 ==============
 
-.. highlight:: cpp
-
 The ML classes discussed in this section implement Classification And Regression Tree algorithms, which are described in `[Breiman84] <#paper_Breiman84>`_
 .
 

modules/ml/doc/expectation_maximization.rst

 Expectation-Maximization
 ========================
 
-.. highlight:: cpp
-
 The EM (Expectation-Maximization) algorithm estimates the parameters of the multivariate probability density function in the form of a Gaussian mixture distribution with a specified number of mixtures.
 
 Consider the set of the feature vectors

modules/ml/doc/k_nearest_neighbors.rst

 K Nearest Neighbors
 ===================
 
-.. highlight:: cpp
-
 The algorithm caches all of the training samples, and predicts the response for a new sample by analyzing a certain number (
 **K**
 ) of the nearest neighbors of the sample (using voting, calculating weighted sum etc.) The method is sometimes referred to as "learning by example", because for prediction it looks for the feature vector with a known response that is closest to the given vector.

modules/ml/doc/neural_networks.rst

 Neural Networks
 ===============
 
-.. highlight:: cpp
-
 ML implements feed-forward artificial neural networks, more particularly, multi-layer perceptrons (MLP), the most commonly used type of neural networks. MLP consists of the input layer, output layer and one or more hidden layers. Each layer of MLP includes one or more neurons that are directionally linked with the neurons from the previous and the next layer. Here is an example of a 3-layer perceptron with 3 inputs, 2 outputs and the hidden layer including 5 neurons:
 
-.. image:: ../../pics/mlp_.png
+.. image:: pics/mlp.png
 
 All the neurons in MLP are similar. Each of them has several input links (i.e. it takes the output values from several neurons in the previous layer on input) and several output links (i.e. it passes the response to several neurons in the next layer). The values retrieved from the previous layer are summed with certain weights, individual for each neuron, plus the bias term, and the sum is transformed using the activation function
 :math:`f` that may be also different for different neurons. Here is the picture:
 
-.. image:: ../../pics/neuron_model.png
+.. image:: pics/neuron_model.png
 
 In other words, given the outputs
 :math:`x_j` of the layer
@@ -36,7 +34,7 @@ Different activation functions may be used, ML implements 3 standard ones:
     :math:`f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x}`     ), the default choice for MLP; the standard sigmoid with
     :math:`\beta =1, \alpha =1`     is shown below:
 
-    .. image:: ../../pics/sigmoid_bipolar.png
+    .. image:: pics/sigmoid_bipolar.png
 
 *
     Gaussian function ( ``CvANN_MLP::GAUSSIAN``     ):

modules/ml/doc/normal_bayes_classifier.rst

@@ -9,8 +9,6 @@ This is a simple classification model assuming that feature vectors from each cl
 
 .. index:: CvNormalBayesClassifier
 
-.. _CvNormalBayesClassifier:
-
 CvNormalBayesClassifier
 -----------------------
 .. c:type:: CvNormalBayesClassifier

modules/ml/doc/statistical_models.rst

@@ -5,8 +5,6 @@ Statistical Models
 
 .. index:: CvStatModel
 
-.. _CvStatModel:
-
 CvStatModel
 -----------
 .. c:type:: CvStatModel

modules/objdetect/doc/cascade_classification.rst

@@ -5,8 +5,6 @@ Cascade Classification
 
 .. index:: FeatureEvaluator
 
-.. _FeatureEvaluator:
-
 FeatureEvaluator
 ----------------
 .. c:type:: FeatureEvaluator

modules/objdetect/doc/objdetect.rst

@@ -2,6 +2,8 @@
 objdetect. Object Detection
 ***************************
 
+.. highlight:: cpp
+
 .. toctree::
     :maxdepth: 2