converted user guide & tutorials from tex to rst; added them into the whole documentation tree; added html_docs target.

doc/tutorials/calib3d.rst

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+#######
+Calib3D
+#######
+
+.. highlight:: cpp
+
+Camera calibration
+==================
+
+The goal of this tutorial is to learn how to calibrate a camera given a set of chessboard images.
+
+*Test data*: use images in your data/chess folder. 
+
+#.
+    Compile opencv with samples by setting ``BUILD_EXAMPLES`` to ``ON`` in cmake configuration. 
+
+#.
+    Go to ``bin`` folder and use ``imagelist_creator`` to create an ``XML/YAML`` list of your images.
+    
+#.
+    Then, run ``calibration`` sample to get camera parameters. Use square size equal to 3cm. 
+
+Pose estimation
+===============
+
+Now, let us write a code that detects a chessboard in a new image and finds its distance from the camera. You can apply the same method to any object with known 3D geometry that you can detect in an image.
+
+*Test data*: use chess_test*.jpg images from your data folder.
+
+#.
+    Create an empty console project. Load a test image: ::
+
+        Mat img = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
+
+#.
+    Detect a chessboard in this image using findChessboard function. ::
+
+        bool found = findChessboardCorners( img, boardSize, ptvec, CV_CALIB_CB_ADAPTIVE_THRESH );
+
+#.
+    Now, write a function that generates a ``vector<Point3f>`` array of 3d coordinates of a chessboard in any coordinate system. For simplicity, let us choose a system such that one of the chessboard corners is in the origin and the board is in the plane *z = 0*.
+
+#.
+    Read camera parameters from XML/YAML file: ::
+
+        FileStorage fs(filename, FileStorage::READ);
+        Mat intrinsics, distortion;
+        fs["camera_matrix"] >> intrinsics;
+        fs["distortion_coefficients"] >> distortion;
+
+#.
+    Now we are ready to find chessboard pose by running ``solvePnP``: ::
+
+        vector<Point3f> boardPoints;
+        // fill the array
+        ...
+
+        solvePnP(Mat(boardPoints), Mat(foundBoardCorners), cameraMatrix,
+                             distCoeffs, rvec, tvec, false);
+
+#.
+    Calculate reprojection error like it is done in ``calibration`` sample (see ``opencv/samples/cpp/calibration.cpp``, function ``computeReprojectionErrors``). 
+
+Question: how to calculate the distance from the camera origin to any of the corners? 

doc/tutorials/calib3d.tex

@@ -1,56 +0,0 @@
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%                                                                                    %
-%                                        C++                                         %
-%                                                                                    %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-\ifCpp
-\section{Camera calibration}
-The goal of this tutorial is to learn how to calibrate a camera given a set of chessboard images. 
-
-\texttt{Test data}: use images in your data/chess folder. 
-
-Compile opencv with samples by setting BUILD\_EXAMPLES to ON in cmake configuration. 
-
-Go to bin folder and use \texttt{imagelist\_creator} to create an xml/yaml list of your images. Then, run \texttt{calibration} sample to get camera parameters. Use square size equal to 3cm. 
-
-\section{Pose estimation}
-Now, let us write a code that detects a chessboard in a new image and finds its distance from the camera. You can apply the same method to any object with knwon 3d geometry that you can detect in an image.
-
-\texttt{Test data}: use chess\_test*.jpg images from your data folder.
-
-Create an empty console project. Load a test image:
-\begin{lstlisting}
-Mat img = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
-\end{lstlisting}
-
-Detect a chessboard in this image using findChessboard function. 
-\begin{lstlisting}
-bool found = findChessboardCorners( img, boardSize, ptvec, CV_CALIB_CB_ADAPTIVE_THRESH );
-\end{lstlisting}
-
-Now, write a function that generates a \texttt{vector<Point3f>} array of 3d coordinates of a chessboard in any coordinate system. For simplicity, let us choose a system such that one of the chessboard corners is in the origin and the board is in the plane \(z = 0\).
-
-Read camera parameters from xml/yaml file:
-\begin{lstlisting}
-FileStorage fs(filename, FileStorage::READ);
-Mat intrinsics, distortion;
-fs["camera_matrix"] >> intrinsics;
-fs["distortion_coefficients"] >> distortion;
-\end{lstlisting}
-
-Now we are ready to find chessboard pose by running solvePnP:
-\begin{lstlisting}
-vector<Point3f> boardPoints;
-// fill the array
-...
-
-solvePnP(Mat(boardPoints), Mat(foundBoardCorners), cameraMatrix,
-                     distCoeffs, rvec, tvec, false);
-\end{lstlisting}
-
-Calculate reprojection error like it is done in \texttt{calibration} sample (see textttt{opencv/samples/cpp/calibration.cpp}, function \texttt{computeReprojectionErrors}). 
-
-How to calculate the distance from the camera origin to any of the corners? 
-\fi

doc/tutorials/features2d.rst

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+##########
+Features2D
+##########
+
+.. highlight:: cpp
+
+Detection of planar objects
+===========================
+
+The goal of this tutorial is to learn how to use *features2d* and *calib3d* modules for detecting known planar objects in scenes. 
+
+*Test data*: use images in your data folder, for instance, ``box.png`` and ``box_in_scene.png``. 
+
+#.
+    Create a new console project. Read two input images. ::
+
+        Mat img1 = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
+        Mat img2 = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
+
+#.
+    Detect keypoints in both images. ::
+
+        // detecting keypoints
+        FastFeatureDetector detector(15);
+        vector<KeyPoint> keypoints1;
+        detector.detect(img1, keypoints1);
+        
+        ... // do the same for the second image
+
+#.
+    Compute descriptors for each of the keypoints. ::
+
+        // computing descriptors
+        SurfDescriptorExtractor extractor;
+        Mat descriptors1;
+        extractor.compute(img1, keypoints1, descriptors1);
+        
+        ... // process keypoints from the second image as well
+
+#.
+    Now, find the closest matches between descriptors from the first image to the second: ::
+
+        // matching descriptors
+        BruteForceMatcher<L2<float> > matcher;
+        vector<DMatch> matches;
+        matcher.match(descriptors1, descriptors2, matches);
+
+#.
+    Visualize the results: ::
+
+        // drawing the results
+        namedWindow("matches", 1);
+        Mat img_matches;
+        drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches);
+        imshow("matches", img_matches);
+        waitKey(0);
+
+#.
+    Find the homography transformation between two sets of points: ::
+
+        vector<Point2f> points1, points2;
+        // fill the arrays with the points
+        ....
+        Mat H = findHomography(Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold);
+
+
+#.
+    Create a set of inlier matches and draw them. Use perspectiveTransform function to map points with homography:
+
+        Mat points1Projected;
+        perspectiveTransform(Mat(points1), points1Projected, H);
+
+#.
+    Use ``drawMatches`` for drawing inliers. 

doc/tutorials/features2d.tex

@@ -1,67 +0,0 @@
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%                                                                                    %
-%                                        C++                                         %
-%                                                                                    %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-\ifCpp
-\section{Detection of planar objects}
-The goal of this tutorial is to learn how to use features2d and calib3d modules for detecting known planar objects in scenes. 
-
-\texttt{Test data}: use images in your data folder, for instance, box.png and box\_in\_scene.png. 
-
-Create a new console project. Read two input images. Example:
-\begin{lstlisting}
-Mat img1 = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
-\end{lstlisting}
-
-Detect keypoints in both images. Example:
-\begin{lstlisting}
-// detecting keypoints
-FastFeatureDetector detector(15);
-vector<KeyPoint> keypoints1;
-detector.detect(img1, keypoints1);
-\end{lstlisting}
-
-Compute descriptors for each of the keypoints. Example:
-\begin{lstlisting}
-// computing descriptors
-SurfDescriptorExtractor extractor;
-Mat descriptors1;
-extractor.compute(img1, keypoints1, descriptors1);
-\end{lstlisting}
-
-Now, find the closest matches between descriptors from the first image to the second:
-\begin{lstlisting}
-// matching descriptors
-BruteForceMatcher<L2<float> > matcher;
-vector<DMatch> matches;
-matcher.match(descriptors1, descriptors2, matches);
-\end{lstlisting}
-
-Visualize the results:
-\begin{lstlisting}
-// drawing the results
-namedWindow("matches", 1);
-Mat img_matches;
-drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches);
-imshow("matches", img_matches);
-waitKey(0);
-\end{lstlisting}
-
-Find the homography transformation between two sets of points:
-\begin{lstlisting}
-vector<Point2f> points1, points2;
-// fill the arrays with the points
-....
-Mat H = findHomography(Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold);
-\end{lstlisting}
-
-Create a set of inlier matches and draw them. Use perspectiveTransform function to map points with homography:
-\begin{lstlisting}
-Mat points1Projected;
-perspectiveTransform(Mat(points1), points1Projected, H);
-\end{lstlisting}
-Use drawMatches for drawing inliers. 
-\fi

doc/tutorials/opencv_tutorials_body.tex

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-\chapter{Prerequisites}
-\renewcommand{\curModule}{Prerequisites}
-\input{tutorials/prerequisites}
-
-
-\chapter{Features2d}
-\renewcommand{\curModule}{Features2d}
-\input{tutorials/features2d}
-
-\chapter{Calib3d}
-\renewcommand{\curModule}{Calib3d}
-\input{tutorials/calib3d}

doc/tutorials/prerequisites.rst

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+#############
+Prerequisites
+#############
+
+Download the latest release of opencv from \url{http://sourceforge.net/projects/opencvlibrary/}. You will need cmake and your favorite compiler environment in order to build opencv from sources. Please refer to the installation guide \url{http://opencv.willowgarage.com/wiki/InstallGuide} for detailed instructions. 

doc/tutorials/prerequisites.tex

@@ -1,12 +0,0 @@
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%                                                                                    %
-%                                        C++                                         %
-%                                                                                    %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-\ifCpp
-\section{Prerequisites}
-Download the latest release of opencv from \url{http://sourceforge.net/projects/opencvlibrary/}. You will need cmake and your favorite compiler environment in order to build opencv from sources. Please refer to the installation guide \url{http://opencv.willowgarage.com/wiki/InstallGuide} for detailed instructions. 
-
-\fi

doc/tutorials/tutorials.rst

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+#################
+OpenCV Tutorials
+#################
+
+.. toctree::
+   :maxdepth: 2
+
+   prerequisites.rst
+   features2d.rst 
+   calib3d.rst