opencv/doc/tutorials/features2d/feature_homography/feature_homography.rst

.. _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