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