目标

在这篇文章中你将学到：

• 使用  接口来执行快速高效的匹配，用的是  ( Fast Approximate Nearest Neighbor Search Library ) 算法

代码

完整代码可从这里 

/** * @file SURF_FlannMatcher * @brief SURF detector + descriptor + FLANN Matcher * @author A. Huaman */#include "opencv2/opencv_modules.hpp"#include 
       
        #ifndef HAVE_OPENCV_NONFREEint main(int, char**){    printf("The sample requires nonfree module that is not available in your OpenCV distribution.\n");    return -1;}#else# include "opencv2/core/core.hpp"# include "opencv2/features2d/features2d.hpp"# include "opencv2/highgui/highgui.hpp"# include "opencv2/nonfree/features2d.hpp"using namespace cv;void readme();/** * @function main * @brief Main function */int main( int argc, char** argv ){  if( argc != 3 )  { readme(); return -1; }  Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );  Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );  if( !img_1.data || !img_2.data )  { printf(" --(!) Error reading images \n"); return -1; }  //-- Step 1: Detect the keypoints using SURF Detector  int minHessian = 400;  SurfFeatureDetector detector( minHessian );  std::vector
        
          keypoints_1, keypoints_2;  detector.detect( img_1, keypoints_1 );  detector.detect( img_2, keypoints_2 );  //-- Step 2: Calculate descriptors (feature vectors)  SurfDescriptorExtractor extractor;  Mat descriptors_1, descriptors_2;  extractor.compute( img_1, keypoints_1, descriptors_1 );  extractor.compute( img_2, keypoints_2, descriptors_2 );  //-- Step 3: Matching descriptor vectors using FLANN matcher  FlannBasedMatcher matcher;  std::vector< DMatch > matches;  matcher.match( descriptors_1, descriptors_2, matches );  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 = matches[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( matches[i].distance <= max(2*min_dist, 0.02) )    { good_matches.push_back( matches[i]); }  }  //-- Draw only "good" matches  Mat img_matches;  drawMatches( img_1, keypoints_1, img_2, keypoints_2,               good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),               vector
         
          (), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );  //-- Show detected matches  imshow( "Good Matches", img_matches );  for( int i = 0; i < (int)good_matches.size(); i++ )  { printf( "-- Good Match [%d] Keypoint 1: %d  -- Keypoint 2: %d  \n", i, good_matches[i].queryIdx, good_matches[i].trainIdx ); }  waitKey(0);  return 0;}/** * @function readme */void readme(){ printf(" Usage: ./SURF_FlannMatcher 
           
           
            \n"); }#endif
           
          
         
        
       

结果

1. 这是对首张图片进行特征检测的结果

   

2. 下面是对关键点进行过滤过程中的控制台输出：