Add Java and Python code for AKAZE local features matching tutorial. Fix incorrect uses of Mat.mul() in Java code.

Uniform Lowe's ratio test in the code.

samples/cpp/tutorial_code/features2D/AKAZE_match.cpp

@@ -6,11 +6,12 @@
 using namespace std;
 using namespace cv;
 
-const float inlier_threshold = 2.5f; // Distance threshold to identify inliers
+const float inlier_threshold = 2.5f; // Distance threshold to identify inliers with homography check
 const float nn_match_ratio = 0.8f;   // Nearest neighbor matching ratio
 
 int main(int argc, char* argv[])
 {
+    //! [load]
     CommandLineParser parser(argc, argv,
                              "{@img1 | ../data/graf1.png | input image 1}"
                              "{@img2 | ../data/graf3.png | input image 2}"
@@ -21,20 +22,25 @@ int main(int argc, char* argv[])
     Mat homography;
     FileStorage fs(parser.get<String>("@homography"), FileStorage::READ);
     fs.getFirstTopLevelNode() >> homography;
+    //! [load]
 
+    //! [AKAZE]
     vector<KeyPoint> kpts1, kpts2;
     Mat desc1, desc2;
 
     Ptr<AKAZE> akaze = AKAZE::create();
     akaze->detectAndCompute(img1, noArray(), kpts1, desc1);
     akaze->detectAndCompute(img2, noArray(), kpts2, desc2);
+    //! [AKAZE]
 
+    //! [2-nn matching]
     BFMatcher matcher(NORM_HAMMING);
     vector< vector<DMatch> > nn_matches;
     matcher.knnMatch(desc1, desc2, nn_matches, 2);
+    //! [2-nn matching]
 
-    vector<KeyPoint> matched1, matched2, inliers1, inliers2;
-    vector<DMatch> good_matches;
+    //! [ratio test filtering]
+    vector<KeyPoint> matched1, matched2;
     for(size_t i = 0; i < nn_matches.size(); i++) {
         DMatch first = nn_matches[i][0];
         float dist1 = nn_matches[i][0].distance;
@@ -45,8 +51,12 @@ int main(int argc, char* argv[])
             matched2.push_back(kpts2[first.trainIdx]);
         }
     }
+    //! [ratio test filtering]
 
-    for(unsigned i = 0; i < matched1.size(); i++) {
+    //! [homography check]
+    vector<DMatch> good_matches;
+    vector<KeyPoint> inliers1, inliers2;
+    for(size_t 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;
@@ -63,12 +73,14 @@ int main(int argc, char* argv[])
             good_matches.push_back(DMatch(new_i, new_i, 0));
         }
     }
+    //! [homography check]
 
+    //! [draw final matches]
     Mat res;
     drawMatches(img1, inliers1, img2, inliers2, good_matches, res);
     imwrite("akaze_result.png", res);
 
-    double inlier_ratio = inliers1.size() * 1.0 / matched1.size();
+    double inlier_ratio = inliers1.size() / (double) matched1.size();
     cout << "A-KAZE Matching Results" << endl;
     cout << "*******************************" << endl;
     cout << "# Keypoints 1:                        \t" << kpts1.size() << endl;
@@ -80,6 +92,7 @@ int main(int argc, char* argv[])
 
     imshow("result", res);
     waitKey();
+    //! [draw final matches]
 
     return 0;
 }

samples/cpp/tutorial_code/features2D/feature_flann_matcher/SURF_FLANN_matching_Demo.cpp

@@ -46,7 +46,7 @@ int main( int argc, char* argv[] )
     std::vector<DMatch> good_matches;
     for (size_t i = 0; i < knn_matches.size(); i++)
     {
-        if (knn_matches[i].size() > 1 && knn_matches[i][0].distance / knn_matches[i][1].distance <= ratio_thresh)
+        if (knn_matches[i][0].distance < ratio_thresh * knn_matches[i][1].distance)
         {
             good_matches.push_back(knn_matches[i][0]);
         }

samples/cpp/tutorial_code/features2D/feature_homography/SURF_FLANN_matching_homography_Demo.cpp

@@ -48,7 +48,7 @@ int main( int argc, char* argv[] )
     std::vector<DMatch> good_matches;
     for (size_t i = 0; i < knn_matches.size(); i++)
     {
-        if (knn_matches[i].size() > 1 && knn_matches[i][0].distance / knn_matches[i][1].distance <= ratio_thresh)
+        if (knn_matches[i][0].distance < ratio_thresh * knn_matches[i][1].distance)
         {
             good_matches.push_back(knn_matches[i][0]);
         }