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116 lines
3.7 KiB
C++
116 lines
3.7 KiB
C++
#include <iostream>
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#include "opencv2/opencv_modules.hpp"
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#ifdef HAVE_OPENCV_XFEATURES2D
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#include <opencv2/core.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/features2d.hpp>
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#include <opencv2/xfeatures2d.hpp>
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#include <opencv2/imgcodecs.hpp>
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#include <vector>
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// If you find this code useful, please add a reference to the following paper in your work:
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// Gil Levi and Tal Hassner, "LATCH: Learned Arrangements of Three Patch Codes", arXiv preprint arXiv:1501.03719, 15 Jan. 2015
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using namespace std;
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using namespace cv;
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const float inlier_threshold = 2.5f; // Distance threshold to identify inliers
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const float nn_match_ratio = 0.8f; // Nearest neighbor matching ratio
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int main(int argc, char* argv[])
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{
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CommandLineParser parser(argc, argv,
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"{@img1 | graf1.png | input image 1}"
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"{@img2 | graf3.png | input image 2}"
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"{@homography | H1to3p.xml | homography matrix}");
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Mat img1 = imread( samples::findFile( parser.get<String>("@img1") ), IMREAD_GRAYSCALE);
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Mat img2 = imread( samples::findFile( parser.get<String>("@img2") ), IMREAD_GRAYSCALE);
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Mat homography;
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FileStorage fs( samples::findFile( parser.get<String>("@homography") ), FileStorage::READ);
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fs.getFirstTopLevelNode() >> homography;
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vector<KeyPoint> kpts1, kpts2;
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Mat desc1, desc2;
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Ptr<cv::ORB> orb_detector = cv::ORB::create(10000);
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Ptr<xfeatures2d::LATCH> latch = xfeatures2d::LATCH::create();
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orb_detector->detect(img1, kpts1);
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latch->compute(img1, kpts1, desc1);
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orb_detector->detect(img2, kpts2);
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latch->compute(img2, kpts2, desc2);
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BFMatcher matcher(NORM_HAMMING);
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vector< vector<DMatch> > nn_matches;
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matcher.knnMatch(desc1, desc2, nn_matches, 2);
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vector<KeyPoint> matched1, matched2, inliers1, inliers2;
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vector<DMatch> good_matches;
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for (size_t i = 0; i < nn_matches.size(); i++) {
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DMatch first = nn_matches[i][0];
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float dist1 = nn_matches[i][0].distance;
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float dist2 = nn_matches[i][1].distance;
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if (dist1 < nn_match_ratio * dist2) {
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matched1.push_back(kpts1[first.queryIdx]);
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matched2.push_back(kpts2[first.trainIdx]);
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}
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}
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for (unsigned i = 0; i < matched1.size(); i++) {
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Mat col = Mat::ones(3, 1, CV_64F);
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col.at<double>(0) = matched1[i].pt.x;
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col.at<double>(1) = matched1[i].pt.y;
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col = homography * col;
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col /= col.at<double>(2);
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double dist = sqrt(pow(col.at<double>(0) - matched2[i].pt.x, 2) +
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pow(col.at<double>(1) - matched2[i].pt.y, 2));
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if (dist < inlier_threshold) {
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int new_i = static_cast<int>(inliers1.size());
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inliers1.push_back(matched1[i]);
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inliers2.push_back(matched2[i]);
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good_matches.push_back(DMatch(new_i, new_i, 0));
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}
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}
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Mat res;
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drawMatches(img1, inliers1, img2, inliers2, good_matches, res);
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imwrite("latch_result.png", res);
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double inlier_ratio = inliers1.size() * 1.0 / matched1.size();
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cout << "LATCH Matching Results" << endl;
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cout << "*******************************" << endl;
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cout << "# Keypoints 1: \t" << kpts1.size() << endl;
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cout << "# Keypoints 2: \t" << kpts2.size() << endl;
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cout << "# Matches: \t" << matched1.size() << endl;
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cout << "# Inliers: \t" << inliers1.size() << endl;
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cout << "# Inliers Ratio: \t" << inlier_ratio << endl;
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cout << endl;
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imshow("result", res);
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waitKey();
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return 0;
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}
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#else
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int main()
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{
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std::cerr << "OpenCV was built without xfeatures2d module" << std::endl;
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return 0;
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}
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#endif
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