copied the latest tutorials & find_obj.py sample from trunk to 2.3 branch

doc/tutorials/features2d/feature_description/feature_description.rst

@@ -0,0 +1,104 @@
+.. _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 a :brute_force_matcher:`BruteForceMatcher<>`	to match the features vector
+     * 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/feature_flann_matcher/feature_flann_matcher.rst

@@ -0,0 +1,132 @@
+.. _feature_flann_matcher:
+
+Feature Matching with FLANN
+****************************
+
+Goal
+=====
+
+In this tutorial you will learn how to:
+
+.. container:: enumeratevisibleitemswithsquare
+
+   * Use the :flann_based_matcher:`FlannBasedMatcher<>` interface in order to perform a quick and efficient matching by using the :flann:`FLANN<>` ( *Fast Approximate Nearest Neighbor Search Library* )
+
+
+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_FlannMatcher.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 );
+
+     //-- 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 using FLANN matcher
+     FlannBasedMatcher matcher;
+     std::vector< DMatch > matches;
+     matcher.match( descriptors_1, descriptors_2, matches );
+
+     double max_dist = 0; double min_dist = 100;
+
+     //-- Quick calculation of max and min distances between keypoints
+     for( int i = 0; i < descriptors_1.rows; i++ )
+     { double dist = matches[i].distance;
+       if( dist < min_dist ) min_dist = dist;
+       if( dist > max_dist ) max_dist = dist;
+     }
+
+     printf("-- Max dist : %f \n", max_dist );
+     printf("-- Min dist : %f \n", min_dist );
+  
+     //-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist )
+     //-- PS.- radiusMatch can also be used here.
+     std::vector< DMatch > good_matches;
+
+     for( int i = 0; i < descriptors_1.rows; i++ )
+     { if( matches[i].distance < 2*min_dist )
+       { good_matches.push_back( matches[i]); }
+     }  
+
+     //-- Draw only "good" matches
+     Mat img_matches;
+     drawMatches( img_1, keypoints_1, img_2, keypoints_2, 
+                  good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), 
+                  vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS ); 
+
+     //-- Show detected matches
+     imshow( "Good Matches", img_matches );
+
+     for( int i = 0; i < good_matches.size(); i++ )
+     { printf( "-- Good Match [%d] Keypoint 1: %d  -- Keypoint 2: %d  \n", i, good_matches[i].queryIdx, good_matches[i].trainIdx ); }
+
+     waitKey(0);
+
+     return 0;
+    }
+
+    /** @function readme */
+    void readme()
+    { std::cout << " Usage: ./SURF_FlannMatcher <img1> <img2>" << std::endl; }
+
+Explanation
+============
+
+Result
+======
+ 
+#. Here is the result of the feature detection applied to the first image:
+ 
+   .. image:: images/Featur_FlannMatcher_Result.jpg
+      :align: center
+      :height: 250pt
+
+#. Additionally, we get as console output the keypoints filtered:
+
+   .. image:: images/Feature_FlannMatcher_Keypoints_Result.jpg
+      :align: center
+      :height: 250pt
+
+
+

doc/tutorials/features2d/feature_homography/feature_homography.rst

@@ -0,0 +1,148 @@
+.. _feature_homography:
+
+Features2D + Homography to find a known object
+**********************************************
+
+Goal
+=====
+
+In this tutorial you will learn how to:
+
+.. container:: enumeratevisibleitemswithsquare
+
+   * Use the function :find_homography:`findHomography<>` to find the transform between matched keypoints.
+   * Use the function :perspective_transform:`perspectiveTransform<>` to map the points.
+     
+
+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_Homography.cpp>`_
+
+.. code-block:: cpp 
+
+   #include <stdio.h>
+   #include <iostream>
+   #include "opencv2/core/core.hpp"
+   #include "opencv2/features2d/features2d.hpp"
+   #include "opencv2/highgui/highgui.hpp"
+   #include "opencv2/calib3d/calib3d.hpp"
+
+   using namespace cv;
+
+   void readme();
+
+   /** @function main */
+   int main( int argc, char** argv )
+   {
+     if( argc != 3 )
+     { readme(); return -1; }
+
+     Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
+     Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
+  
+     if( !img_object.data || !img_scene.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_object, keypoints_scene;
+
+     detector.detect( img_object, keypoints_object );
+     detector.detect( img_scene, keypoints_scene );
+
+     //-- Step 2: Calculate descriptors (feature vectors)
+     SurfDescriptorExtractor extractor;
+
+     Mat descriptors_object, descriptors_scene;
+
+     extractor.compute( img_object, keypoints_object, descriptors_object );
+     extractor.compute( img_scene, keypoints_scene, descriptors_scene );
+
+     //-- Step 3: Matching descriptor vectors using FLANN matcher
+     FlannBasedMatcher matcher;
+     std::vector< DMatch > matches;
+     matcher.match( descriptors_object, descriptors_scene, matches );
+
+     double max_dist = 0; double min_dist = 100;
+
+     //-- Quick calculation of max and min distances between keypoints
+     for( int i = 0; i < descriptors_object.rows; i++ )
+     { double dist = matches[i].distance;
+       if( dist < min_dist ) min_dist = dist;
+       if( dist > max_dist ) max_dist = dist;
+     }
+
+     printf("-- Max dist : %f \n", max_dist );
+     printf("-- Min dist : %f \n", min_dist );
+  
+     //-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
+     std::vector< DMatch > good_matches;
+
+     for( int i = 0; i < descriptors_object.rows; i++ )
+     { if( matches[i].distance < 3*min_dist )
+        { good_matches.push_back( matches[i]); }
+     }  
+
+     Mat img_matches;
+     drawMatches( img_object, keypoints_object, img_scene, keypoints_scene, 
+                  good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), 
+                  vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS ); 
+
+     //-- Localize the object 
+     std::vector<Point2f> obj;
+     std::vector<Point2f> scene;
+
+     for( int i = 0; i < good_matches.size(); i++ )
+     {
+       //-- Get the keypoints from the good matches
+       obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
+       scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt ); 
+     }
+
+     Mat H = findHomography( obj, scene, CV_RANSAC );
+
+     //-- Get the corners from the image_1 ( the object to be "detected" )
+     std::vector<Point2f> obj_corners(4);
+     obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
+     obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
+     std::vector<Point2f> scene_corners(4);
+
+     perspectiveTransform( obj_corners, scene_corners, H);
+
+     //-- Draw lines between the corners (the mapped object in the scene - image_2 )
+     line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
+     line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
+     line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
+     line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
+
+     //-- Show detected matches
+     imshow( "Good Matches & Object detection", img_matches );
+
+     waitKey(0);
+     return 0;
+     }
+
+     /** @function readme */
+     void readme()
+     { std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }
+
+Explanation
+============
+
+Result
+======
+
+
+#. And here is the result for the detected object (highlighted in green)
+
+   .. image:: images/Feature_Homography_Result.jpg
+      :align: center  
+      :height: 200pt 
+

doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.rst

@@ -72,7 +72,7 @@ Learn about how to use the feature points  detectors, descriptors and matching f
   .. cssclass:: toctableopencv
 
   ===================== ==============================================
-   |Subpixel|             **Title:** :ref:`corner_subpixeles`
+   |Subpixel|           **Title:** :ref:`corner_subpixeles`
   
                         *Compatibility:* > OpenCV 2.0
                         
@@ -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 the FLANN library to make a fast matching.
+  
+  ===================== ==============================================
+  
+  .. |FeatureFlann| image:: images/Feature_Flann_Matcher_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* and *calib3d* to detect an object in a scene.
+  
+  ===================== ==============================================
+  
+  .. |FeatureHomo| image:: images/Feature_Homography_Tutorial_Cover.jpg
+                   :height: 90pt
+                   :width:  90pt
+
+
 + 
   .. tabularcolumns:: m{100pt} m{300pt}
   .. cssclass:: toctableopencv
@@ -112,8 +190,17 @@ Learn about how to use the feature points  detectors, descriptors and matching f
 .. toctree::
    :hidden:
 
+   ../feature_description/feature_description
+   ../feature_detection/feature_detection
    ../trackingmotion/harris_detector/harris_detector
+   ../feature_flann_matcher/feature_flann_matcher
+   ../feature_homography/feature_homography
    ../trackingmotion/good_features_to_track/good_features_to_track.rst
    ../trackingmotion/generic_corner_detector/generic_corner_detector
    ../trackingmotion/corner_subpixeles/corner_subpixeles
-   ../detection_of_planar_objects/detection_of_planar_objects
+   ../feature_detection/feature_detection
+   ../feature_detection/feature_description
+   ../feature_flann_matcher/feature_flann_matcher
+   ../feature_homography/feature_homography
+   ../detection_of_planar_objects/detection_of_planar_objects
+

doc/tutorials/features2d/trackingmotion/good_features_to_track/good_features_to_track.rst

@@ -88,14 +88,14 @@ This tutorial code's is shown lines below. You can also download it from `here <
 
      /// Apply corner detection
      goodFeaturesToTrack( src_gray, 
-	   	          corners,
-		          maxCorners,
-		          qualityLevel,
-		          minDistance,
-		          Mat(),
-		          blockSize,
-		          useHarrisDetector,
-		          k );
+                  corners,
+                  maxCorners,
+                  qualityLevel,
+                  minDistance,
+                  Mat(),
+                  blockSize,
+                  useHarrisDetector,
+                  k );
   
 
      /// Draw corners detected
@@ -116,7 +116,7 @@ Explanation
 Result
 ======
 
-.. image:: images/Shi_Tomasi_Detector_Result.jpg
+.. image:: images/Feature_Detection_Result_a.jpg
               :align: center   
 
 

doc/tutorials/features2d/trackingmotion/harris_detector/harris_detector.rst

@@ -6,15 +6,46 @@ Harris corner detector
 Goal
 =====
 
-In this tutorial you will learn how to:
+In this tutorial you will learn:
 
 .. container:: enumeratevisibleitemswithsquare
 
+   * What features are and why they are important    
    * Use the function :corner_harris:`cornerHarris <>` to detect corners using the Harris-Stephens method.
 
 Theory
 ======
 
+What is a feature?
+-------------------
+
+.. container:: enumeratevisibleitemswithsquare
+
+   * In computer vision, usually we need to find matching points between different frames of an environment. Why? If we know how two images relate to each other, we can use *both* images to extract information of them.
+
+   * When we say **matching points** we are referring, in a general sense, to *characteristics* in the scene that we can recognize easily. We call these characteristics **features**.
+
+   * **So, what characteristics should a feature have?**
+
+     * It must be *uniquely recognizable*
+
+
+Types of Image Features
+------------------------
+
+To mention a few:
+
+.. container:: enumeratevisibleitemswithsquare
+
+   * Edges
+   * Corner (also known as interest points)
+   * Blobs (also known as regions of interest )
+
+In this tutorial we will study the *corner* features, specifically.
+
+Why is a corner so special?
+----------------------------
+
 Code
 ====