1 #include <iostream> 2 3 #include "opencv2/opencv_modules.hpp" 4 5 #ifdef HAVE_OPENCV_XFEATURES2D 6 7 #include <opencv2/features2d.hpp> 8 #include <opencv2/xfeatures2d.hpp> 9 #include <opencv2/imgcodecs.hpp> 10 #include <opencv2/opencv.hpp> 11 #include <vector> 12 13 // If you find this code useful, please add a reference to the following paper in your work: 14 // Gil Levi and Tal Hassner, "LATCH: Learned Arrangements of Three Patch Codes", arXiv preprint arXiv:1501.03719, 15 Jan. 2015 15 16 using namespace std; 17 using namespace cv; 18 19 const float inlier_threshold = 2.5f; // Distance threshold to identify inliers 20 const float nn_match_ratio = 0.8f; // Nearest neighbor matching ratio 21 22 int main(void) 23 { 24 Mat img1 = imread("../data/graf1.png", IMREAD_GRAYSCALE); 25 Mat img2 = imread("../data/graf3.png", IMREAD_GRAYSCALE); 26 27 28 Mat homography; 29 FileStorage fs("../data/H1to3p.xml", FileStorage::READ); 30 31 fs.getFirstTopLevelNode() >> homography; 32 33 vector<KeyPoint> kpts1, kpts2; 34 Mat desc1, desc2; 35 36 Ptr<cv::ORB> orb_detector = cv::ORB::create(10000); 37 38 Ptr<xfeatures2d::LATCH> latch = xfeatures2d::LATCH::create(); 39 40 41 orb_detector->detect(img1, kpts1); 42 latch->compute(img1, kpts1, desc1); 43 44 orb_detector->detect(img2, kpts2); 45 latch->compute(img2, kpts2, desc2); 46 47 BFMatcher matcher(NORM_HAMMING); 48 vector< vector<DMatch> > nn_matches; 49 matcher.knnMatch(desc1, desc2, nn_matches, 2); 50 51 vector<KeyPoint> matched1, matched2, inliers1, inliers2; 52 vector<DMatch> good_matches; 53 for (size_t i = 0; i < nn_matches.size(); i++) { 54 DMatch first = nn_matches[i][0]; 55 float dist1 = nn_matches[i][0].distance; 56 float dist2 = nn_matches[i][1].distance; 57 58 if (dist1 < nn_match_ratio * dist2) { 59 matched1.push_back(kpts1[first.queryIdx]); 60 matched2.push_back(kpts2[first.trainIdx]); 61 } 62 } 63 64 for (unsigned i = 0; i < matched1.size(); i++) { 65 Mat col = Mat::ones(3, 1, CV_64F); 66 col.at<double>(0) = matched1[i].pt.x; 67 col.at<double>(1) = matched1[i].pt.y; 68 69 col = homography * col; 70 col /= col.at<double>(2); 71 double dist = sqrt(pow(col.at<double>(0) - matched2[i].pt.x, 2) + 72 pow(col.at<double>(1) - matched2[i].pt.y, 2)); 73 74 if (dist < inlier_threshold) { 75 int new_i = static_cast<int>(inliers1.size()); 76 inliers1.push_back(matched1[i]); 77 inliers2.push_back(matched2[i]); 78 good_matches.push_back(DMatch(new_i, new_i, 0)); 79 } 80 } 81 82 Mat res; 83 drawMatches(img1, inliers1, img2, inliers2, good_matches, res); 84 imwrite("../../samples/data/latch_res.png", res); 85 86 87 double inlier_ratio = inliers1.size() * 1.0 / matched1.size(); 88 cout << "LATCH Matching Results" << endl; 89 cout << "*******************************" << endl; 90 cout << "# Keypoints 1: \t" << kpts1.size() << endl; 91 cout << "# Keypoints 2: \t" << kpts2.size() << endl; 92 cout << "# Matches: \t" << matched1.size() << endl; 93 cout << "# Inliers: \t" << inliers1.size() << endl; 94 cout << "# Inliers Ratio: \t" << inlier_ratio << endl; 95 cout << endl; 96 return 0; 97 } 98 99 #else 100 101 int main() 102 { 103 std::cerr << "OpenCV was built without xfeatures2d module" << std::endl; 104 return 0; 105 } 106 107 #endif 108
