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/python/tutorial_code/features2D/akaze_matching/AKAZE_match.py

@@ -0,0 +1,81 @@
+from __future__ import print_function
+import cv2 as cv
+import numpy as np
+import argparse
+from math import sqrt
+
+## [load]
+parser = argparse.ArgumentParser(description='Code for AKAZE local features matching tutorial.')
+parser.add_argument('--input1', help='Path to input image 1.', default='../data/graf1.png')
+parser.add_argument('--input2', help='Path to input image 2.', default='../data/graf3.png')
+parser.add_argument('--homography', help='Path to the homography matrix.', default='../data/H1to3p.xml')
+args = parser.parse_args()
+
+img1 = cv.imread(args.input1, cv.IMREAD_GRAYSCALE)
+img2 = cv.imread(args.input2, cv.IMREAD_GRAYSCALE)
+if img1 is None or img2 is None:
+    print('Could not open or find the images!')
+    exit(0)
+
+fs = cv.FileStorage(args.homography, cv.FILE_STORAGE_READ)
+homography = fs.getFirstTopLevelNode().mat()
+## [load]
+
+## [AKAZE]
+akaze = cv.AKAZE_create()
+kpts1, desc1 = akaze.detectAndCompute(img1, None)
+kpts2, desc2 = akaze.detectAndCompute(img2, None)
+## [AKAZE]
+
+## [2-nn matching]
+matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_BRUTEFORCE_HAMMING)
+nn_matches = matcher.knnMatch(desc1, desc2, 2)
+## [2-nn matching]
+
+## [ratio test filtering]
+matched1 = []
+matched2 = []
+nn_match_ratio = 0.8 # Nearest neighbor matching ratio
+for m, n in nn_matches:
+    if m.distance < nn_match_ratio * n.distance:
+        matched1.append(kpts1[m.queryIdx])
+        matched2.append(kpts2[m.trainIdx])
+## [ratio test filtering]
+
+## [homography check]
+inliers1 = []
+inliers2 = []
+good_matches = []
+inlier_threshold = 2.5 # Distance threshold to identify inliers with homography check
+for i, m in enumerate(matched1):
+    col = np.ones((3,1), dtype=np.float64)
+    col[0:2,0] = m.pt
+
+    col = np.dot(homography, col)
+    col /= col[2,0]
+    dist = sqrt(pow(col[0,0] - matched2[i].pt[0], 2) +\
+                pow(col[1,0] - matched2[i].pt[1], 2))
+
+    if dist < inlier_threshold:
+        good_matches.append(cv.DMatch(len(inliers1), len(inliers2), 0))
+        inliers1.append(matched1[i])
+        inliers2.append(matched2[i])
+## [homography check]
+
+## [draw final matches]
+res = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], 3), dtype=np.uint8)
+cv.drawMatches(img1, inliers1, img2, inliers2, good_matches, res)
+cv.imwrite("akaze_result.png", res)
+
+inlier_ratio = len(inliers1) / float(len(matched1))
+print('A-KAZE Matching Results')
+print('*******************************')
+print('# Keypoints 1:                        \t', len(kpts1))
+print('# Keypoints 2:                        \t', len(kpts2))
+print('# Matches:                            \t', len(matched1))
+print('# Inliers:                            \t', len(inliers1))
+print('# Inliers Ratio:                      \t', inlier_ratio)
+
+cv.imshow('result', res)
+cv.waitKey()
+## [draw final matches]

samples/python/tutorial_code/features2D/feature_flann_matcher/SURF_FLANN_matching_Demo.py

@@ -29,7 +29,7 @@ knn_matches = matcher.knnMatch(descriptors1, descriptors2, 2)
 ratio_thresh = 0.7
 good_matches = []
 for m,n in knn_matches:
-    if m.distance / n.distance <= ratio_thresh:
+    if m.distance < ratio_thresh * n.distance:
         good_matches.append(m)
 
 #-- Draw matches

samples/python/tutorial_code/features2D/feature_homography/SURF_FLANN_matching_homography_Demo.py

@@ -29,7 +29,7 @@ knn_matches = matcher.knnMatch(descriptors_obj, descriptors_scene, 2)
 ratio_thresh = 0.75
 good_matches = []
 for m,n in knn_matches:
-    if m.distance / n.distance <= ratio_thresh:
+    if m.distance < ratio_thresh * n.distance:
         good_matches.append(m)
 
 #-- Draw matches