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      1 Feature Description {#tutorial_feature_description}
      2 ===================
      3 
      4 Goal
      5 ----
      6 
      7 In this tutorial you will learn how to:
      8 
      9 -   Use the @ref cv::DescriptorExtractor interface in order to find the feature vector correspondent
     10     to the keypoints. Specifically:
     11     -   Use cv::xfeatures2d::SURF and its function cv::xfeatures2d::SURF::compute to perform the
     12         required calculations.
     13     -   Use a @ref cv::BFMatcher to match the features vector
     14     -   Use the function @ref cv::drawMatches to draw the detected matches.
     15 
     16 Theory
     17 ------
     18 
     19 Code
     20 ----
     21 
     22 This tutorial code's is shown lines below.
     23 @code{.cpp}
     24 #include <stdio.h>
     25 #include <iostream>
     26 #include "opencv2/core.hpp"
     27 #include "opencv2/features2d.hpp"
     28 #include "opencv2/highgui.hpp"
     29 #include "opencv2/xfeatures2d.hpp"
     30 
     31 using namespace cv;
     32 using namespace cv::xfeatures2d;
     33 
     34 void readme();
     35 
     36 /* @function main */
     37 int main( int argc, char** argv )
     38 {
     39   if( argc != 3 )
     40    { return -1; }
     41 
     42   Mat img_1 = imread( argv[1], IMREAD_GRAYSCALE );
     43   Mat img_2 = imread( argv[2], IMREAD_GRAYSCALE );
     44 
     45   if( !img_1.data || !img_2.data )
     46    { return -1; }
     47 
     48   //-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
     49   int minHessian = 400;
     50 
     51   Ptr<SURF> detector = SURF::create();
     52   detector->setHessianThreshold(minHessian);
     53 
     54   std::vector<KeyPoint> keypoints_1, keypoints_2;
     55   Mat descriptors_1, descriptors_2;
     56 
     57   detector->detectAndCompute( img_1, Mat(), keypoints_1, descriptors_1 );
     58   detector->detectAndCompute( img_2, Mat(), keypoints_2, descriptors_2 );
     59 
     60   //-- Step 2: Matching descriptor vectors with a brute force matcher
     61   BFMatcher matcher(NORM_L2);
     62   std::vector< DMatch > matches;
     63   matcher.match( descriptors_1, descriptors_2, matches );
     64 
     65   //-- Draw matches
     66   Mat img_matches;
     67   drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );
     68 
     69   //-- Show detected matches
     70   imshow("Matches", img_matches );
     71 
     72   waitKey(0);
     73 
     74   return 0;
     75   }
     76 
     77  /* @function readme */
     78  void readme()
     79  { std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }
     80 @endcode
     81 
     82 Explanation
     83 -----------
     84 
     85 Result
     86 ------
     87 
     88 Here is the result after applying the BruteForce matcher between the two original images:
     89 
     90 ![](images/Feature_Description_BruteForce_Result.jpg)
     91