AKAZE fixes, tests and tutorial

samples/cpp/H1to3p.xml

@@ -0,0 +1,11 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<H13 type_id="opencv-matrix">
+  <rows>3</rows>
+  <cols>3</cols>
+  <dt>d</dt>
+  <data>
+	7.6285898e-01  -2.9922929e-01   2.2567123e+02
+	3.3443473e-01   1.0143901e+00  -7.6999973e+01
+	3.4663091e-04  -1.4364524e-05   1.0000000e+00 </data></H13>
+</opencv_storage>

samples/cpp/tutorial_code/features2D/AKAZE_match.cpp

@@ -0,0 +1,79 @@
+#include <opencv2/features2d.hpp>
+#include <opencv2/imgcodecs.hpp>
+#include <opencv2/opencv.hpp>
+#include <vector>
+#include <iostream>
+
+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("graf1.png", IMREAD_GRAYSCALE);
+    Mat img2 = imread("graf3.png", IMREAD_GRAYSCALE);
+
+    Mat homography;
+    FileStorage fs("H1to3p.xml", FileStorage::READ);
+    fs.getFirstTopLevelNode() >> homography;
+
+    vector<KeyPoint> kpts1, kpts2;
+    Mat desc1, desc2;
+
+    AKAZE akaze;
+    akaze(img1, noArray(), kpts1, desc1);
+    akaze(img2, noArray(), 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("res.png", res);
+
+    double inlier_ratio = inliers1.size() * 1.0 / matched1.size();
+    cout << "A-KAZE 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;
+}