1Feature Detection {#tutorial_feature_detection}
2=================
3
4Goal
5----
6
7In this tutorial you will learn how to:
8
9-   Use the @ref cv::FeatureDetector interface in order to find interest points. Specifically:
10    -   Use the cv::xfeatures2d::SURF and its function cv::xfeatures2d::SURF::detect to perform the
11        detection process
12    -   Use the function @ref cv::drawKeypoints to draw the detected keypoints
13
14Theory
15------
16
17Code
18----
19
20This tutorial code's is shown lines below.
21@code{.cpp}
22#include <stdio.h>
23#include <iostream>
24#include "opencv2/core.hpp"
25#include "opencv2/features2d.hpp"
26#include "opencv2/xfeatures2d.hpp"
27#include "opencv2/highgui.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_1 = imread( argv[1], IMREAD_GRAYSCALE );
41  Mat img_2 = imread( argv[2], IMREAD_GRAYSCALE );
42
43  if( !img_1.data || !img_2.data )
44  { std::cout<< " --(!) Error reading images " << std::endl; return -1; }
45
46  //-- Step 1: Detect the keypoints using SURF Detector
47  int minHessian = 400;
48
49  Ptr<SURF> detector = SURF::create( minHessian );
50
51  std::vector<KeyPoint> keypoints_1, keypoints_2;
52
53  detector->detect( img_1, keypoints_1 );
54  detector->detect( img_2, keypoints_2 );
55
56  //-- Draw keypoints
57  Mat img_keypoints_1; Mat img_keypoints_2;
58
59  drawKeypoints( img_1, keypoints_1, img_keypoints_1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
60  drawKeypoints( img_2, keypoints_2, img_keypoints_2, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
61
62  //-- Show detected (drawn) keypoints
63  imshow("Keypoints 1", img_keypoints_1 );
64  imshow("Keypoints 2", img_keypoints_2 );
65
66  waitKey(0);
67
68  return 0;
69  }
70
71  /* @function readme */
72  void readme()
73  { std::cout << " Usage: ./SURF_detector <img1> <img2>" << std::endl; }
74@endcode
75
76Explanation
77-----------
78
79Result
80------
81
82-#  Here is the result of the feature detection applied to the first image:
83
84    ![](images/Feature_Detection_Result_a.jpg)
85
86-#  And here is the result for the second image:
87
88    ![](images/Feature_Detection_Result_b.jpg)
89