Added Fernandos first tutorial regarding SVMs. Extended the global links…

Added Fernandos first tutorial regarding SVMs. Extended the global links (conf.py). Corrected the author names. Minor modification to the dft tutorial. 

doc/conf.py

@@ -348,10 +348,12 @@ extlinks = {'cvt_color': ('http://opencv.willowgarage.com/documentation/cpp/imgp
             'contour_area' : ('http://opencv.willowgarage.com/documentation/cpp/imgproc_structural_analysis_and_shape_descriptors.html#cv-contourarea%s', None),
             'arc_length' : ('http://opencv.willowgarage.com/documentation/cpp/imgproc_structural_analysis_and_shape_descriptors.html#cv-arclength%s', None),
             'basicstructures' : ('http://opencv.itseez.com/modules/core/doc/basic_structures.html#%s', None),
+            'oldbasicstructures' : ('http://opencv.itseez.com/modules/core/doc/old_basic_structures.html#%s', None),
             'readwriteimagevideo' : ('http://opencv.itseez.com/modules/highgui/doc/reading_and_writing_images_and_video.html#%s', None),
             'operationsonarrays' : ('http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html#%s', None),
             'utilitysystemfunctions':('http://opencv.itseez.com/modules/core/doc/utility_and_system_functions_and_macros.html#%s', None),
             'imgprocfilter':('http://opencv.itseez.com/modules/imgproc/doc/filtering.html#%s', None),
+            'svms':('http://opencv.itseez.com/modules/ml/doc/support_vector_machines.html#%s', None),
             'point_polygon_test' : ('http://opencv.willowgarage.com/documentation/cpp/imgproc_structural_analysis_and_shape_descriptors.html#cv-pointpolygontest%s', None)
            }
 

doc/tutorials/core/discrete_fourier_transform/discrete_fourier_transform.rst

@@ -13,3 +13,26 @@ We'll seek answers for the following questions:
    + What is a Fourier transform and why use it? 
    + How to do it in OpenCV? 
    + Usage of functions such as: :imgprocfilter:`copyMakeBorder() <copymakeborder>`, :operationsonarrays:`merge() <merge>`, :operationsonarrays:`dft() <dft>`, :operationsonarrays:`getOptimalDFTSize() <getoptimaldftsize>`, :operationsonarrays:`log() <log>` and :operationsonarrays:`normalize() <normalize>` .
+
+Source code
+===========
+
+Here's a sample usage of :operationsonarrays:`dft() <dft>` : 
+
+.. literalinclude:: ../../../../samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp
+   :language: cpp
+   :linenos:
+   :tab-width: 4
+   :lines: 1-3, 5, 19-20, 23-78
+
+Explanation
+===========
+
+The Fourier Transform will decompose an image into its sinus and cosines components. In other words, it will transform an image from its spatial domain to its frequency domain. The idea is that any function may be approximated exactly with the sum of infinite sinus and cosines functions. The Fourier Transform is a way how to do this. Mathematically a two dimensional images Fourier transform is: 
+
+.. math::
+   
+   F(k,l) = \displaystyle\sum\limits_{i=0}^{N-1}\sum\limits_{j=0}^{N-1} f(i,j)e^{-i2\pi(\frac{ki}{N}+\frac{lj}{N})} 
+   
+   e^{ix} = \cos{x} + i\sin {x}
+

doc/tutorials/definitions/tocDefinitions.rst

 .. |Author_AnaH| unicode:: Ana U+0020 Huam U+00E1 n
 .. |Author_BernatG| unicode:: Bern U+00E1 t U+0020 G U+00E1 bor
 .. |Author_AndreyK| unicode:: Andrey U+0020 Kamaev
-.. |Author_VictorE| unicode:: Victor Eruhimov 
-
+.. |Author_VictorE| unicode:: Victor U+0020 Eruhimov 
+.. |Author_FernandoI| unicode:: Fernando U+0020 Iglesias U+0020 Garc U+00ED a 
 
 

doc/tutorials/ml/table_of_content_ml/table_of_content_ml.rst

@@ -5,8 +5,32 @@
 
 Use the powerfull machine learning classes for statistical classification, regression and clustering of data.
 
-.. include:: ../../definitions/noContent.rst
+.. include:: ../../definitions/tocDefinitions.rst 
+
++ 
+  .. tabularcolumns:: m{100pt} m{300pt}
+  .. cssclass:: toctableopencv
+
+  ============ ==============================================
+  |IntroSVM|   **Title:** :ref:`introductiontosvms`
+
+               *Compatibility:* > OpenCV 2.0
+
+               *Author:* |Author_FernandoI|
+
+               Learn what a Suport Vector Machine is. 
+
+  ============ ==============================================
+
+  .. |IntroSVM| image:: images/introduction_to_svm.png
+     :height: 90pt
+     :width:  90pt
 
 .. raw:: latex
 
    \pagebreak
+
+.. toctree::
+   :hidden:
+   
+   ../introduction_to_svm/introduction_to_svm

samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp

@@ -75,5 +75,4 @@ int main(int argc, char ** argv)
     waitKey();
 
     return 0;
-}
-
+}
\ No newline at end of file

samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp

+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/ml/ml.hpp>
+
+using namespace cv;
+
+int main()
+{
+    // Data for visual representation
+    int width = 512, height = 512;
+    Mat image = Mat::zeros(height, width, CV_8UC3);
+
+    // Set up training data
+    float labels[4] = {1.0, -1.0, -1.0, -1.0};
+    Mat labelsMat(3, 1, CV_32FC1, labels);
+
+    float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
+    Mat trainingDataMat(3, 2, CV_32FC1, trainingData);
+
+    // Set up SVM's parameters
+    CvSVMParams params;
+    params.svm_type    = CvSVM::C_SVC;
+    params.kernel_type = CvSVM::LINEAR;
+    params.term_crit   = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
+
+    // Train the SVM
+    CvSVM SVM;
+    SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params);
+    
+    Vec3b green(0,255,0), blue (255,0,0);
+    // Show the decision regions given by the SVM
+    for (int i = 0; i < image.rows; ++i)
+        for (int j = 0; j < image.cols; ++j)
+        {
+            Mat sampleMat = (Mat_<float>(1,2) << i,j);
+            float response = SVM.predict(sampleMat);
+
+            if (response == 1)
+                image.at<Vec3b>(j, i)  = green;
+            else if (response == -1) 
+                 image.at<Vec3b>(j, i)  = blue;
+        }
+
+    // Show the training data
+    int thickness = -1;
+    int lineType = 8;
+    circle(	image, Point(501,  10), 5, Scalar(  0,   0,   0), thickness, lineType);
+    circle(	image, Point(255,  10), 5, Scalar(255, 255, 255), thickness, lineType);
+    circle(	image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType);
+    circle(	image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness, lineType);
+
+    // Show support vectors
+    thickness = 2;
+    lineType  = 8;
+    int c     = SVM.get_support_vector_count();
+
+    for (int i = 0; i < c; ++i)
+    {
+        const float* v = SVM.get_support_vector(i);
+        circle(	image,  Point( (int) v[0], (int) v[1]),   6,  Scalar(128, 128, 128), thickness, lineType);
+    }
+
+    imwrite("result.png", image);        // save the image 
+
+    imshow("SVM Simple Example", image); // show it to the user
+    waitKey(0);
+
+}