Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch between different algorithms solving the same problem.

DescriptorExtractor::compute

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant). 计算描述子

DescriptorExtractor::create

The current implementation supports the following types of a descriptor extractor:

• "SIFT" – SIFT
• "SURF" – SURF
• "BRIEF" – BriefDescriptorExtractor
• "BRISK" – BRISK
• "ORB" – ORB
• "FREAK" – FREAK

A combined format is also supported: descriptor extractor adapter name ( "Opponent" –OpponentColorDescriptorExtractor ) + descriptor extractor name (see above), for example: "OpponentSIFT" .

 

OpponentColorDescriptorExtractor

Class adapting a descriptor extractor to compute descriptors in the Opponent Color Space (refer to Van de Sande et al., CGIV 2008 Color Descriptors for Object Category Recognition). Input RGB image is transformed in the Opponent Color Space.

Then, an unadapted descriptor extractor (set in the constructor) computes descriptors on each of three channels and concatenates them into a single color descriptor.

检测子 —— 描述子 —— 匹配子

Code

#include "stdafx.h"#include <stdio.h>#include <iostream>#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 )  { std::cout<< " --(!) Error reading images " << std::endl; return -1; }  //-- Step 1: Detect the keypoints using SURF Detector  int minHessian = 400;  SurfFeatureDetector detector( minHessian );  std::vector<KeyPoint> keypoints_1, keypoints_2;  detector.detect( img_1, keypoints_1 );  detector.detect( img_2, keypoints_2 );  //-- Step 2: Calculate descriptors (feature vectors)  SurfDescriptorExtractor extractor;	// 描述子  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<char>(), 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(){ std::cout << " Usage: ./SURF_FlannMatcher <img1> <img2>" << std::endl; }

OpenCV Tutorials —— Feature Matching with FLANN