test.cpp

#include "test.h"

void find_feature_matches_surf ( const Mat& img_1, const Mat& img_2,
                            std::vector<KeyPoint>& keypoints_1,
                            std::vector<KeyPoint>& keypoints_2,
                            std::vector< DMatch >& matches )
{
  //-- frame frequency
  int64 time0 = getTickCount();
  
  //-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
  int minHessian = 400;
  Ptr<SURF> detector = SURF::create();
  detector->setHessianThreshold(minHessian);
  //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 using FLANN matcher
  FlannBasedMatcher matcher;
  std::vector< DMatch > matche;
  matcher.match( descriptors_1, descriptors_2, matche );
  double max_dist = 0; double min_dist = 100;
  
  //-- Quick calculation of max and min distances between keypoints
  for( int i = 0; i < descriptors_1.rows; i++ )
  { double dist = matche[i].distance;
    if( dist < min_dist ) min_dist = dist;
    if( dist > max_dist ) max_dist = dist;
  }
  printf("-- Max dist : %f \n", max_dist );
  printf("-- Min dist : %f \n", min_dist );
  
  //-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist,
  //-- or a small arbitary value ( 0.02 ) in the event that min_dist is very
  //-- small)
  //-- PS.- radiusMatch can also be used here.
//   std::vector< DMatch > good_matches;
  for( int i = 0; i < descriptors_1.rows; i++ )
  { if( matche[i].distance <= max(2*min_dist, 0.03) )
    { matches.push_back( matche[i]); }
  }
  
  cout << "当前帧率为： " << getTickFrequency() / (getTickCount() - time0) << endl;
}