doc/tutorials/features2d/feature_homography/feature_homography.rst

   1 .. _feature_homography:
   2
   3 Features2D + Homography to find a known object
   4 **********************************************
   5
   6 Goal
   7 =====
   8
   9 In this tutorial you will learn how to:
  10
  11 .. container:: enumeratevisibleitemswithsquare
  12
  13    * Use the function :find_homography:`findHomography<>` to find the transform between matched keypoints.
  14    * Use the function :perspective_transform:`perspectiveTransform<>` to map the points.
  15
  16
  17 Theory
  18 ======
  19
  20 Code
  21 ====
  22
  23 This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/features2D/SURF_Homography.cpp>`_
  24
  25 .. code-block:: cpp
  26
  27    #include <stdio.h>
  28    #include <iostream>
  29    #include "opencv2/core/core.hpp"
  30    #include "opencv2/features2d/features2d.hpp"
  31    #include "opencv2/highgui/highgui.hpp"
  32    #include "opencv2/calib3d/calib3d.hpp"
  33
  34    using namespace cv;
  35
  36    void readme();
  37
  38    /** @function main */
  39    int main( int argc, char** argv )
  40    {
  41      if( argc != 3 )
  42      { readme(); return -1; }
  43
  44      Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
  45      Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
  46
  47      if( !img_object.data || !img_scene.data )
  48      { std::cout<< " --(!) Error reading images " << std::endl; return -1; }
  49
  50      //-- Step 1: Detect the keypoints using SURF Detector
  51      int minHessian = 400;
  52
  53      SurfFeatureDetector detector( minHessian );
  54
  55      std::vector<KeyPoint> keypoints_object, keypoints_scene;
  56
  57      detector.detect( img_object, keypoints_object );
  58      detector.detect( img_scene, keypoints_scene );
  59
  60      //-- Step 2: Calculate descriptors (feature vectors)
  61      SurfDescriptorExtractor extractor;
  62
  63      Mat descriptors_object, descriptors_scene;
  64
  65      extractor.compute( img_object, keypoints_object, descriptors_object );
  66      extractor.compute( img_scene, keypoints_scene, descriptors_scene );
  67
  68      //-- Step 3: Matching descriptor vectors using FLANN matcher
  69      FlannBasedMatcher matcher;
  70      std::vector< DMatch > matches;
  71      matcher.match( descriptors_object, descriptors_scene, matches );
  72
  73      double max_dist = 0; double min_dist = 100;
  74
  75      //-- Quick calculation of max and min distances between keypoints
  76      for( int i = 0; i < descriptors_object.rows; i++ )
  77      { double dist = matches[i].distance;
  78        if( dist < min_dist ) min_dist = dist;
  79        if( dist > max_dist ) max_dist = dist;
  80      }
  81
  82      printf("-- Max dist : %f \n", max_dist );
  83      printf("-- Min dist : %f \n", min_dist );
  84
  85      //-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
  86      std::vector< DMatch > good_matches;
  87
  88      for( int i = 0; i < descriptors_object.rows; i++ )
  89      { if( matches[i].distance < 3*min_dist )
  90         { good_matches.push_back( matches[i]); }
  91      }
  92
  93      Mat img_matches;
  94      drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
  95                   good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
  96                   vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
  97
  98      //-- Localize the object
  99      std::vector<Point2f> obj;
 100      std::vector<Point2f> scene;
 101
 102      for( int i = 0; i < good_matches.size(); i++ )
 103      {
 104        //-- Get the keypoints from the good matches
 105        obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
 106        scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
 107      }
 108
 109      Mat H = findHomography( obj, scene, CV_RANSAC );
 110
 111      //-- Get the corners from the image_1 ( the object to be "detected" )
 112      std::vector<Point2f> obj_corners(4);
 113      obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
 114      obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
 115      std::vector<Point2f> scene_corners(4);
 116
 117      perspectiveTransform( obj_corners, scene_corners, H);
 118
 119      //-- Draw lines between the corners (the mapped object in the scene - image_2 )
 120      line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
 121      line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
 122      line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
 123      line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
 124
 125      //-- Show detected matches
 126      imshow( "Good Matches & Object detection", img_matches );
 127
 128      waitKey(0);
 129      return 0;
 130      }
 131
 132      /** @function readme */
 133      void readme()
 134      { std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }
 135
 136 Explanation
 137 ============
 138
 139 Result
 140 ======
 141
 142
 143 #. And here is the result for the detected object (highlighted in green)
 144
 145    .. image:: images/Feature_Homography_Result.jpg
 146       :align: center
 147       :height: 200pt
 148