Feature Description


In this tutorial you will learn how to:

  • Use the cv::DescriptorExtractor interface in order to find the feature vector correspondent to the keypoints. Specifically:
    • Use cv::xfeatures2d::SURF and its function cv::xfeatures2d::SURF::compute to perform the required calculations.
    • Use a cv::BFMatcher to match the features vector
    • Use the function cv::drawMatches to draw the detected matches.



This tutorial code’s is shown lines below.

#include <stdio.h>
#include <iostream>
#include "opencv2/core.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/xfeatures2d.hpp"

using namespace cv;
using namespace cv::xfeatures2d;

void readme();

/* @function main */
int main( int argc, char** argv )
  if( argc != 3 )
   { return -1; }

  Mat img_1 = imread( argv[1], IMREAD_GRAYSCALE );
  Mat img_2 = imread( argv[2], IMREAD_GRAYSCALE );

  if( !img_1.data || !img_2.data )
   { return -1; }

  //-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
  int minHessian = 400;

  Ptr<SURF> detector = SURF::create();

  std::vector<KeyPoint> keypoints_1, keypoints_2;
  Mat descriptors_1, descriptors_2;

  detector->detectAndCompute( img_1, Mat(), keypoints_1, descriptors_1 );
  detector->detectAndCompute( img_2, Mat(), keypoints_2, descriptors_2 );

  //-- Step 2: Matching descriptor vectors with a brute force matcher
  BFMatcher matcher(NORM_L2);
  std::vector< DMatch > matches;
  matcher.match( descriptors_1, descriptors_2, matches );

  //-- Draw matches
  Mat img_matches;
  drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );

  //-- Show detected matches
  imshow("Matches", img_matches );


  return 0;

 /* @function readme */
 void readme()
 { std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }



Here is the result after applying the BruteForce matcher between the two original images: