1Features2D + Homography to find a known object {#tutorial_feature_homography}
2==============================================
3
4Goal
5----
6
7In this tutorial you will learn how to:
8
9-   Use the function @ref cv::findHomography to find the transform between matched keypoints.
10-   Use the function @ref cv::perspectiveTransform to map the points.
11
12Theory
13------
14
15Code
16----
17
18This tutorial code's is shown lines below.
19@code{.cpp}
20#include <stdio.h>
21#include <iostream>
22#include "opencv2/core.hpp"
23#include "opencv2/imgproc.hpp"
24#include "opencv2/features2d.hpp"
25#include "opencv2/highgui.hpp"
26#include "opencv2/calib3d.hpp"
27#include "opencv2/xfeatures2d.hpp"
28
29using namespace cv;
30using namespace cv::xfeatures2d;
31
32void readme();
33
34/* @function main */
35int main( int argc, char** argv )
36{
37  if( argc != 3 )
38  { readme(); return -1; }
39
40  Mat img_object = imread( argv[1], IMREAD_GRAYSCALE );
41  Mat img_scene = imread( argv[2], IMREAD_GRAYSCALE );
42
43  if( !img_object.data || !img_scene.data )
44  { std::cout<< " --(!) Error reading images " << std::endl; return -1; }
45
46  //-- Step 1: Detect the keypoints and extract descriptors using SURF
47  int minHessian = 400;
48
49  Ptr<SURF> detector = SURF::create( minHessian );
50
51  std::vector<KeyPoint> keypoints_object, keypoints_scene;
52  Mat descriptors_object, descriptors_scene;
53
54  detector->detectAndCompute( img_object, Mat(), keypoints_object, descriptors_object );
55  detector->detectAndCompute( img_scene, Mat(), keypoints_scene, descriptors_scene );
56
57  //-- Step 2: Matching descriptor vectors using FLANN matcher
58  FlannBasedMatcher matcher;
59  std::vector< DMatch > matches;
60  matcher.match( descriptors_object, descriptors_scene, matches );
61
62  double max_dist = 0; double min_dist = 100;
63
64  //-- Quick calculation of max and min distances between keypoints
65  for( int i = 0; i < descriptors_object.rows; i++ )
66  { double dist = matches[i].distance;
67    if( dist < min_dist ) min_dist = dist;
68    if( dist > max_dist ) max_dist = dist;
69  }
70
71  printf("-- Max dist : %f \n", max_dist );
72  printf("-- Min dist : %f \n", min_dist );
73
74  //-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
75  std::vector< DMatch > good_matches;
76
77  for( int i = 0; i < descriptors_object.rows; i++ )
78  { if( matches[i].distance < 3*min_dist )
79     { good_matches.push_back( matches[i]); }
80  }
81
82  Mat img_matches;
83  drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
84               good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
85               std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
86
87  //-- Localize the object
88  std::vector<Point2f> obj;
89  std::vector<Point2f> scene;
90
91  for( size_t i = 0; i < good_matches.size(); i++ )
92  {
93    //-- Get the keypoints from the good matches
94    obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
95    scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
96  }
97
98  Mat H = findHomography( obj, scene, RANSAC );
99
100  //-- Get the corners from the image_1 ( the object to be "detected" )
101  std::vector<Point2f> obj_corners(4);
102  obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
103  obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
104  std::vector<Point2f> scene_corners(4);
105
106  perspectiveTransform( obj_corners, scene_corners, H);
107
108  //-- Draw lines between the corners (the mapped object in the scene - image_2 )
109  line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
110  line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
111  line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
112  line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
113
114  //-- Show detected matches
115  imshow( "Good Matches & Object detection", img_matches );
116
117  waitKey(0);
118  return 0;
119  }
120
121  /* @function readme */
122  void readme()
123  { std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }
124@endcode
125Explanation
126-----------
127
128Result
129------
130
131-#  And here is the result for the detected object (highlighted in green)
132
133    ![](images/Feature_Homography_Result.jpg)
134