Added Features2D descriptor tutorial + drawMatches in rst

doc/conf.py

@@ -358,7 +358,15 @@ extlinks = {'cvt_color': ('http://opencv.willowgarage.com/documentation/cpp/imgp
             'xmlymlpers':('http://opencv.itseez.com/modules/core/doc/xml_yaml_persistence.html#%s', None),
 			'huivideo' : ('http://opencv.itseez.com/modules/highgui/doc/reading_and_writing_images_and_video.html#%s', None),
             'filtering':('http://opencv.itseez.com/modules/imgproc/doc/filtering.html#%s', None),
-            'point_polygon_test' : ('http://opencv.willowgarage.com/documentation/cpp/imgproc_structural_analysis_and_shape_descriptors.html#cv-pointpolygontest%s', None)
+            'point_polygon_test' : ('http://opencv.willowgarage.com/documentation/cpp/imgproc_structural_analysis_and_shape_descriptors.html#cv-pointpolygontest%s', None),
+            'feature_detector' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_feature_detectors.html#featuredetector%s', None),
+	    'feature_detector_detect' : ('http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_feature_detectors.html#cv-featuredetector-detect%s', None ),
+	    'surf_feature_detector' : ('http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_feature_detectors.html#surffeaturedetector%s', None ),
+	    'draw_keypoints' : ('http://opencv.willowgarage.com/documentation/cpp/features2d_drawing_function_of_keypoints_and_matches.html#cv-drawkeypoints%s', None ),
+	    'descriptor_extractor': ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_extractors.html#descriptorextractor%s', None ),
+	    'descriptor_extractor_compute' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_extractors.html#cv-descriptorextractor-compute%s', None ),
+	    'surf_descriptor_extractor' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_extractors.html#surfdescriptorextractor%s', None ),
+	    'draw_matches' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_drawing_function_of_keypoints_and_matches.html#cv-drawmatches%s', None )		 		 
            }
 
 

doc/tutorials/features2d/feature_description/feature_description.rst

@@ -0,0 +1,103 @@
+.. _feature_description:
+
+Feature Description
+*******************
+
+Goal
+=====
+
+In this tutorial you will learn how to:
+
+.. container:: enumeratevisibleitemswithsquare
+
+   * Use the :descriptor_extractor:`DescriptorExtractor<>` interface in order to find the feature vector correspondent to the keypoints. Specifically:
+
+     * Use :surf_descriptor_extractor:`SurfDescriptorExtractor<>` and its function :descriptor_extractor:`compute<>` to perform the required calculations.
+     * Use the function :draw_matches:`drawMatches<>` to draw the detected matches.
+     
+
+Theory
+======
+
+Code
+====
+
+This tutorial code's is shown lines below. You can also download it from `here <https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/features2D/SURF_descriptor.cpp>`_
+
+.. code-block:: cpp 
+
+   #include <stdio.h>
+   #include <iostream>
+   #include "opencv2/core/core.hpp"
+   #include "opencv2/features2d/features2d.hpp"
+   #include "opencv2/highgui/highgui.hpp"
+
+   using namespace cv;
+
+   void readme();
+
+   /** @function main */
+   int main( int argc, char** argv )
+   {
+     if( argc != 3 )
+      { return -1; }
+
+     Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
+     Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
+  
+     if( !img_1.data || !img_2.data )
+      { return -1; }
+
+     //-- Step 1: Detect the keypoints using SURF Detector
+     int minHessian = 400;
+
+     SurfFeatureDetector detector( minHessian );
+
+     std::vector<KeyPoint> keypoints_1, keypoints_2;
+
+     detector.detect( img_1, keypoints_1 );
+     detector.detect( img_2, keypoints_2 );
+
+     //-- Step 2: Calculate descriptors (feature vectors)
+     SurfDescriptorExtractor extractor;
+
+     Mat descriptors_1, descriptors_2;
+
+     extractor.compute( img_1, keypoints_1, descriptors_1 );
+     extractor.compute( img_2, keypoints_2, descriptors_2 );
+
+     //-- Step 3: Matching descriptor vectors with a brute force matcher
+     BruteForceMatcher< L2<float> > matcher;
+     std::vector< DMatch > matches;
+     matcher.match( descriptors_1, descriptors_2, matches );
+
+     //-- Draw matches
+     Mat img_matches;
+     drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches ); 
+
+     //-- Show detected matches
+     imshow("Matches", img_matches );
+
+     waitKey(0);
+
+     return 0;
+     }
+
+    /** @function readme */
+    void readme()
+    { std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }
+
+Explanation
+============
+
+Result
+======
+ 
+#. Here is the result after applying the BruteForce matcher between the two original images:
+ 
+   .. image:: images/Feature_Description_BruteForce_Result.jpg
+      :align: center
+      :height: 200pt
+
+
+

doc/tutorials/features2d/feature_detection/feature_detection.rst

@@ -0,0 +1,97 @@
+.. _feature_detection:
+
+Feature Detection
+******************
+
+Goal
+=====
+
+In this tutorial you will learn how to:
+
+.. container:: enumeratevisibleitemswithsquare
+
+   * Use the :feature_detector:`FeatureDetector<>` interface in order to find interest points. Specifically:
+
+     * Use the :surf_feature_detector:`SurfFeatureDetector<>` and its function :feature_detector_detect:`detect<>` to perform the detection process
+     * Use the function :draw_keypoints:`drawKeypoints<>` to draw the detected keypoints
+     
+
+Theory
+======
+
+Code
+====
+
+This tutorial code's is shown lines below. You can also download it from `here <https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/features2D/SURF_detector.cpp>`_
+
+.. code-block:: cpp 
+
+   #include <stdio.h>
+   #include <iostream>
+   #include "opencv2/core/core.hpp"
+   #include "opencv2/features2d/features2d.hpp"	
+   #include "opencv2/highgui/highgui.hpp"
+
+   using namespace cv;
+
+   void readme();
+
+   /** @function main */
+   int main( int argc, char** argv )
+   {
+     if( argc != 3 )
+     { readme(); return -1; }
+
+     Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
+     Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
+  
+     if( !img_1.data || !img_2.data )
+     { std::cout<< " --(!) Error reading images " << std::endl; return -1; }
+
+     //-- Step 1: Detect the keypoints using SURF Detector
+     int minHessian = 400;
+
+     SurfFeatureDetector detector( minHessian );
+
+     std::vector<KeyPoint> keypoints_1, keypoints_2;
+
+     detector.detect( img_1, keypoints_1 );
+     detector.detect( img_2, keypoints_2 );
+
+     //-- Draw keypoints
+     Mat img_keypoints_1; Mat img_keypoints_2;
+
+     drawKeypoints( img_1, keypoints_1, img_keypoints_1, Scalar::all(-1), DrawMatchesFlags::DEFAULT ); 
+     drawKeypoints( img_2, keypoints_2, img_keypoints_2, Scalar::all(-1), DrawMatchesFlags::DEFAULT ); 
+
+     //-- Show detected (drawn) keypoints
+     imshow("Keypoints 1", img_keypoints_1 );
+     imshow("Keypoints 2", img_keypoints_2 );
+
+     waitKey(0);
+
+     return 0;
+     }
+
+     /** @function readme */
+     void readme()
+     { std::cout << " Usage: ./SURF_detector <img1> <img2>" << std::endl; }
+
+Explanation
+============
+
+Result
+======
+ 
+#. Here is the result of the feature detection applied to the first image:
+ 
+   .. image:: images/Feature_Detection_Result_a.jpg
+      :align: center
+      :height: 125pt
+
+#. And here is the result for the second image:
+
+   .. image:: images/Feature_Detection_Result_b.jpg
+      :align: center  
+      :height: 200pt 
+

doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.rst

@@ -3,7 +3,7 @@
 *feature2d* module. 2D Features framework
 -----------------------------------------------------------
 
-Learn about how to use the feature points  detectors, descriptors and matching framework found inside OpenCV.
+Learn about how to use the feature points  detectors, descriptors and matching framework found inside OpenCV.ddddddd
 
 .. include:: ../../definitions/tocDefinitions.rst 
 
@@ -86,6 +86,84 @@ Learn about how to use the feature points  detectors, descriptors and matching f
                      :height: 90pt
                      :width:  90pt
 
++ 
+  .. tabularcolumns:: m{100pt} m{300pt}
+  .. cssclass:: toctableopencv
+
+  ===================== ==============================================
+   |FeatureDetect|      **Title:** :ref:`feature_detection`
+  
+                        *Compatibility:* > OpenCV 2.0
+                        
+                        *Author:* |Author_AnaH|
+                        
+                        In this tutorial, you will use *features2d* to detect interest points.
+  
+  ===================== ==============================================
+  
+  .. |FeatureDetect| image:: images/Feature_Detection_Tutorial_Cover.jpg
+                     :height: 90pt
+                     :width:  90pt
+
+
++ 
+  .. tabularcolumns:: m{100pt} m{300pt}
+  .. cssclass:: toctableopencv
+
+  ===================== ==============================================
+   |FeatureDescript|    **Title:** :ref:`feature_description`
+  
+                        *Compatibility:* > OpenCV 2.0
+                        
+                        *Author:* |Author_AnaH|
+                        
+                        In this tutorial, you will use *features2d* to calculate feature vectors.
+  
+  ===================== ==============================================
+  
+  .. |FeatureDescript| image:: images/Feature_Description_Tutorial_Cover.jpg
+                       :height: 90pt
+                       :width:  90pt
+
++ 
+  .. tabularcolumns:: m{100pt} m{300pt}
+  .. cssclass:: toctableopencv
+
+  ===================== ==============================================
+   |FeatureFlann|       **Title:** :ref:`feature_flann_matcher`
+  
+                        *Compatibility:* > OpenCV 2.0
+                        
+                        *Author:* |Author_AnaH|
+                        
+                        In this tutorial, you will use *features2d* to detect interest points.
+  
+  ===================== ==============================================
+  
+  .. |FeatureFlann| image:: images/Feature_Detection_Tutorial_Cover.jpg
+                    :height: 90pt
+                    :width:  90pt
+
++ 
+  .. tabularcolumns:: m{100pt} m{300pt}
+  .. cssclass:: toctableopencv
+
+  ===================== ==============================================
+   |FeatureHomo|        **Title:** :ref:`feature_homography`
+  
+                        *Compatibility:* > OpenCV 2.0
+                        
+                        *Author:* |Author_AnaH|
+                        
+                        In this tutorial, you will use *features2d* to detect interest points.
+  
+  ===================== ==============================================
+  
+  .. |FeatureHomo| image:: images/Feature_Detection_Tutorial_Cover.jpg
+                   :height: 90pt
+                   :width:  90pt
+
+
 + 
   .. tabularcolumns:: m{100pt} m{300pt}
   .. cssclass:: toctableopencv