Added tutorial for features2d using homography to find a planar object (Based on the well known find_obj.cpp

doc/conf.py

@@ -366,7 +366,9 @@ extlinks = {'cvt_color': ('http://opencv.willowgarage.com/documentation/cpp/imgp
 	    '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 )		 		 
+	    'draw_matches' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_drawing_function_of_keypoints_and_matches.html#cv-drawmatches%s', None ),
+	    'find_homography' : ('http://opencv.willowgarage.com/documentation/cpp/calib3d_camera_calibration_and_3d_reconstruction.html?#findHomography%s', None),
+	    'perspective_transform' : ('http://opencv.willowgarage.com/documentation/cpp/core_operations_on_arrays.html?#perspectiveTransform%s', None )			 		 
            }
 
 

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

@@ -140,7 +140,7 @@ Learn about how to use the feature points  detectors, descriptors and matching f
   
   ===================== ==============================================
   
-  .. |FeatureFlann| image:: images/Feature_Detection_Tutorial_Cover.jpg
+  .. |FeatureFlann| image:: images/Feature_Flann_Matcher_Tutorial_Cover.jpg
                     :height: 90pt
                     :width:  90pt
 
@@ -155,11 +155,11 @@ Learn about how to use the feature points  detectors, descriptors and matching f
                         
                         *Author:* |Author_AnaH|
                         
-                        In this tutorial, you will use *features2d* to detect interest points.
+                        In this tutorial, you will use *features2d* and *calib3d* to detect an object in a scene.
   
   ===================== ==============================================
   
-  .. |FeatureHomo| image:: images/Feature_Detection_Tutorial_Cover.jpg
+  .. |FeatureHomo| image:: images/Feature_Homography_Tutorial_Cover.jpg
                    :height: 90pt
                    :width:  90pt
 

samples/cpp/tutorial_code/features2D/SURF_Homography.cpp

@@ -24,10 +24,10 @@ 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 );
+  Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
+  Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
   
-  if( !img_1.data || !img_2.data )
+  if( !img_object.data || !img_scene.data )
   { std::cout<< " --(!) Error reading images " << std::endl; return -1; }
 
   //-- Step 1: Detect the keypoints using SURF Detector
@@ -35,28 +35,28 @@ int main( int argc, char** argv )
 
   SurfFeatureDetector detector( minHessian );
 
-  std::vector<KeyPoint> keypoints_1, keypoints_2;
+  std::vector<KeyPoint> keypoints_object, keypoints_scene;
 
-  detector.detect( img_1, keypoints_1 );
-  detector.detect( img_2, keypoints_2 );
+  detector.detect( img_object, keypoints_object );
+  detector.detect( img_scene, keypoints_scene );
 
   //-- Step 2: Calculate descriptors (feature vectors)
   SurfDescriptorExtractor extractor;
 
-  Mat descriptors_1, descriptors_2;
+  Mat descriptors_object, descriptors_scene;
 
-  extractor.compute( img_1, keypoints_1, descriptors_1 );
-  extractor.compute( img_2, keypoints_2, descriptors_2 );
+  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_1, descriptors_2, 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_1.rows; i++ )
+  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;
@@ -68,13 +68,13 @@ int main( int argc, char** argv )
   //-- 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_1.rows; i++ )
+  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_1, keypoints_1, img_2, keypoints_2, 
+  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 ); 
 
@@ -86,33 +86,26 @@ int main( int argc, char** argv )
   for( int i = 0; i < good_matches.size(); i++ )
   {
     //-- Get the keypoints from the good matches
-    obj.push_back( keypoints_1[ good_matches[i].queryIdx ].pt );
-    scene.push_back( keypoints_2[ good_matches[i].trainIdx ].pt ); 
+    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" )
-  Point2f obj_corners[4] = { cvPoint(0,0), cvPoint( img_1.cols, 0 ), cvPoint( img_1.cols, img_1.rows ), cvPoint( 0, img_1.rows ) };
-  Point scene_corners[4];
+  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);
 
-  //-- Map these corners in the scene ( image_2)
-  for( int i = 0; i < 4; i++ )
-  {
-    double x = obj_corners[i].x; 
-    double y = obj_corners[i].y;
+  perspectiveTransform( obj_corners, scene_corners, H);
 
-    double Z = 1./( H.at<double>(2,0)*x + H.at<double>(2,1)*y + H.at<double>(2,2) );
-    double X = ( H.at<double>(0,0)*x + H.at<double>(0,1)*y + H.at<double>(0,2) )*Z;
-    double Y = ( H.at<double>(1,0)*x + H.at<double>(1,1)*y + H.at<double>(1,2) )*Z;
-    scene_corners[i] = cvPoint( cvRound(X) + img_1.cols, cvRound(Y) );
-  }  
    
   //-- Draw lines between the corners (the mapped object in the scene - image_2 )
-  line( img_matches, scene_corners[0], scene_corners[1], Scalar(0, 255, 0), 2 );
-  line( img_matches, scene_corners[1], scene_corners[2], Scalar( 0, 255, 0), 2 );
-  line( img_matches, scene_corners[2], scene_corners[3], Scalar( 0, 255, 0), 2 );
-  line( img_matches, scene_corners[3], scene_corners[0], Scalar( 0, 255, 0), 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 );