Merge pull request #4015 from GilLevi:AddingLATCH

samples/cpp/tutorial_code/xfeatures2D/LATCH_match.cpp

+#include <opencv2/features2d.hpp>
+#include <opencv2/xfeatures2d.hpp>
+#include <opencv2/imgcodecs.hpp>
+#include <opencv2/opencv.hpp>
+#include <vector>
+#include <iostream>
+
+// 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(void)
+{
+    Mat img1 = imread("../data/graf1.png", IMREAD_GRAYSCALE);
+    Mat img2 = imread("../data/graf3.png", IMREAD_GRAYSCALE);
+
+
+    Mat homography;
+    FileStorage fs("../data/H1to3p.xml", 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("../../samples/data/latch_res.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;
+    return 0;
+}