OpoenCV BRIEF特征提取

/* * matching_test.cpp * *  Created on: Oct 17, 2010 *      Author: ethan */#include "opencv2/core/core.hpp"#include "opencv2/calib3d/calib3d.hpp"#include "opencv2/features2d/features2d.hpp"#include "opencv2/imgproc/imgproc.hpp"#include "opencv2/highgui/highgui.hpp"#include <vector>#include <iostream>using namespace cv;using namespace std;//Copy (x,y) location of descriptor matches found from KeyPoint data structures into Point2f vectorsvoid matches2points(const vector<DMatch>& matches, const vector<KeyPoint>& kpts_train,                    const vector<KeyPoint>& kpts_query, vector<Point2f>& pts_train, vector<Point2f>& pts_query){  pts_train.clear();  pts_query.clear();  pts_train.reserve(matches.size());  pts_query.reserve(matches.size());  for (size_t i = 0; i < matches.size(); i++)  {    const DMatch& match = matches[i];    pts_query.push_back(kpts_query[match.queryIdx].pt);    pts_train.push_back(kpts_train[match.trainIdx].pt);  }}double match(const vector<KeyPoint>& /*kpts_train*/, const vector<KeyPoint>& /*kpts_query*/, DescriptorMatcher& matcher,            const Mat& train, const Mat& query, vector<DMatch>& matches){  double t = (double)getTickCount();  matcher.match(query, train, matches); //Using features2d  return ((double)getTickCount() - t) / getTickFrequency();}void help(){       cout << "This program shows how to use BRIEF descriptor to match points in features2d" << endl <<               "It takes in two images, finds keypoints and matches them displaying matches and final homography warped results" << endl <<                "Usage: " << endl <<                    "image1 image2 " << endl <<                "Example: " << endl <<                    "box.png box_in_scene.png " << endl;}const char* keys ={"{1|  |box.png               |the first image}""{2|  |box_in_scene.png|the second image}"};int main(int argc, const char ** argv){  help();  CommandLineParser parser(argc, argv, keys);  string im1_name = "/home/wdy/left01.jpg";  string im2_name = "/home/wdy/left02.jpg";  Mat im1 = imread(im1_name, CV_LOAD_IMAGE_GRAYSCALE);  Mat im2 = imread(im2_name, CV_LOAD_IMAGE_GRAYSCALE);  if (im1.empty() || im2.empty())  {    cout << "could not open one of the images..." << endl;    cout << "the cmd parameters have next current value: " << endl;parser.printParams();    return 1;  }  double t = (double)getTickCount();  FastFeatureDetector detector(50);  BriefDescriptorExtractor extractor(32); //this is really 32 x 8 matches since they are binary matches packed into bytes  vector<KeyPoint> kpts_1, kpts_2;  detector.detect(im1, kpts_1);  detector.detect(im2, kpts_2);  t = ((double)getTickCount() - t) / getTickFrequency();  cout << "found " << kpts_1.size() << " keypoints in " << im1_name << endl << "fount " << kpts_2.size()      << " keypoints in " << im2_name << endl << "took " << t << " seconds." << endl;  Mat desc_1, desc_2;  cout << "computing descriptors..." << endl;  t = (double)getTickCount();  extractor.compute(im1, kpts_1, desc_1);  extractor.compute(im2, kpts_2, desc_2);  t = ((double)getTickCount() - t) / getTickFrequency();  cout << "done computing descriptors... took " << t << " seconds" << endl;  //Do matching with 2 methods using features2d  cout << "matching with BruteForceMatcher<HammingLUT>" << endl;  BruteForceMatcher<HammingLUT> matcher;  vector<DMatch> matches_lut;  float lut_time = (float)match(kpts_1, kpts_2, matcher, desc_1, desc_2, matches_lut);  cout << "done BruteForceMatcher<HammingLUT> matching. took " << lut_time << " seconds" << endl;  cout << "matching with BruteForceMatcher<Hamming>" << endl;  BruteForceMatcher<Hamming> matcher_popcount;  vector<DMatch> matches_popcount;  double pop_time = match(kpts_1, kpts_2, matcher_popcount, desc_1, desc_2, matches_popcount);  cout << "done BruteForceMatcher<Hamming> matching. took " << pop_time << " seconds" << endl;  vector<Point2f> mpts_1, mpts_2;  matches2points(matches_popcount, kpts_1, kpts_2, mpts_1, mpts_2); //Extract a list of the (x,y) location of the matches  vector<uchar> outlier_mask;  Mat H = findHomography(mpts_2, mpts_1, RANSAC, 1, outlier_mask);  Mat outimg;  drawMatches(im2, kpts_2, im1, kpts_1, matches_popcount, outimg, Scalar::all(-1), Scalar::all(-1),              reinterpret_cast<const vector<char>&> (outlier_mask));  imshow("matches - popcount - outliers removed", outimg);  Mat warped;  Mat diff;  warpPerspective(im2, warped, H, im1.size());  imshow("warped", warped);  absdiff(im1,warped,diff);  imshow("diff", diff);  waitKey();  return 0;}

可以看出

  FastFeatureDetector detector(50);  BriefDescriptorExtractor extractor(32); //this is really 32 x 8 matches since they are binary matches packed into bytes

提取特征点用的FAST特征

描述采用的BRIEF