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
16using namespace std;
17using namespace cv;
18
19const float inlier_threshold = 2.5f; // Distance threshold to identify inliers
20const float nn_match_ratio = 0.8f;   // Nearest neighbor matching ratio
21
22int 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
101int main()
102{
103    std::cerr << "OpenCV was built without xfeatures2d module" << std::endl;
104    return 0;
105}
106
107#endif
108