135 lines
4.2 KiB
C++
135 lines
4.2 KiB
C++
/*
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* matching_test.cpp
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*
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* Created on: Oct 17, 2010
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* Author: ethan
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*/
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#include <opencv2/calib3d/calib3d.hpp>
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#include <opencv2/features2d/features2d.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <vector>
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#include <iostream>
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using namespace cv;
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using std::cout;
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using std::cerr;
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using std::endl;
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using std::vector;
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void help(char **av)
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{
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cerr << "usage: " << av[0] << " im1.jpg im2.jpg"
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<< "\n"
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<< "This program shows how to use BRIEF descriptor to match points in features2d\n"
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<< "It takes in two images, finds keypoints and matches them displaying matches and final homography warped results\n"
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<< endl;
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}
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//Copy (x,y) location of descriptor matches found from KeyPoint data structures into Point2f vectors
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void matches2points(const vector<DMatch>& matches, const vector<KeyPoint>& kpts_train,
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const vector<KeyPoint>& kpts_query, vector<Point2f>& pts_train, vector<Point2f>& pts_query)
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{
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pts_train.clear();
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pts_query.clear();
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pts_train.reserve(matches.size());
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pts_query.reserve(matches.size());
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for (size_t i = 0; i < matches.size(); i++)
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{
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const DMatch& match = matches[i];
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pts_query.push_back(kpts_query[match.queryIdx].pt);
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pts_train.push_back(kpts_train[match.trainIdx].pt);
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}
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}
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double match(const vector<KeyPoint>& /*kpts_train*/, const vector<KeyPoint>& /*kpts_query*/, DescriptorMatcher& matcher,
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const Mat& train, const Mat& query, vector<DMatch>& matches)
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{
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double t = (double)getTickCount();
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matcher.match(query, train, matches); //Using features2d
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return ((double)getTickCount() - t) / getTickFrequency();
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}
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int main(int ac, char ** av)
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{
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if (ac != 3)
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{
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help(av);
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return 1;
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}
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string im1_name, im2_name;
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im1_name = av[1];
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im2_name = av[2];
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Mat im1 = imread(im1_name, CV_LOAD_IMAGE_GRAYSCALE);
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Mat im2 = imread(im2_name, CV_LOAD_IMAGE_GRAYSCALE);
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if (im1.empty() || im2.empty())
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{
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cerr << "could not open one of the images..." << endl;
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return 1;
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}
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double t = (double)getTickCount();
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FastFeatureDetector detector(50);
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BriefDescriptorExtractor extractor(32); //this is really 32 x 8 matches since they are binary matches packed into bytes
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vector<KeyPoint> kpts_1, kpts_2;
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detector.detect(im1, kpts_1);
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detector.detect(im2, kpts_2);
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t = ((double)getTickCount() - t) / getTickFrequency();
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cout << "found " << kpts_1.size() << " keypoints in " << im1_name << endl << "fount " << kpts_2.size()
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<< " keypoints in " << im2_name << endl << "took " << t << " seconds." << endl;
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Mat desc_1, desc_2;
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cout << "computing descriptors..." << endl;
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t = (double)getTickCount();
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extractor.compute(im1, kpts_1, desc_1);
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extractor.compute(im2, kpts_2, desc_2);
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t = ((double)getTickCount() - t) / getTickFrequency();
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cout << "done computing descriptors... took " << t << " seconds" << endl;
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//Do matching with 2 methods using features2d
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cout << "matching with BruteForceMatcher<HammingLUT>" << endl;
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BruteForceMatcher<HammingLUT> matcher;
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vector<DMatch> matches_lut;
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float lut_time = (float)match(kpts_1, kpts_2, matcher, desc_1, desc_2, matches_lut);
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cout << "done BruteForceMatcher<HammingLUT> matching. took " << lut_time << " seconds" << endl;
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cout << "matching with BruteForceMatcher<Hamming>" << endl;
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BruteForceMatcher<Hamming> matcher_popcount;
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vector<DMatch> matches_popcount;
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double pop_time = match(kpts_1, kpts_2, matcher_popcount, desc_1, desc_2, matches_popcount);
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cout << "done BruteForceMatcher<Hamming> matching. took " << pop_time << " seconds" << endl;
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vector<Point2f> mpts_1, mpts_2;
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matches2points(matches_popcount, kpts_1, kpts_2, mpts_1, mpts_2); //Extract a list of the (x,y) location of the matches
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vector<uchar> outlier_mask;
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Mat H = findHomography(Mat(mpts_2), Mat(mpts_1), outlier_mask, RANSAC, 1);
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Mat outimg;
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drawMatches(im2, kpts_2, im1, kpts_1, matches_popcount, outimg, Scalar::all(-1), Scalar::all(-1),
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reinterpret_cast<const vector<char>&> (outlier_mask));
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imshow("matches - popcount - outliers removed", outimg);
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Mat warped;
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warpPerspective(im2, warped, H, im1.size());
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imshow("warped", warped);
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imshow("diff", im1 - warped);
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waitKey();
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return 0;
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}
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