Add Java and Python code for the following features2d tutorials: Harris corner detector, Shi-Tomasi corner detector, Creating your own corner detector, Detecting corners location in subpixels, Feature Detection, Feature Description, Feature Matching with FLANN, Features2D + Homography to find a known object. Use Lowe's ratio test to filter the matches.

samples/python/tutorial_code/features2D/feature_description/SURF_matching_Demo.py

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+from __future__ import print_function
+import cv2 as cv
+import numpy as np
+import argparse
+
+parser = argparse.ArgumentParser(description='Code for Feature Detection tutorial.')
+parser.add_argument('--input1', help='Path to input image 1.', default='../data/box.png')
+parser.add_argument('--input2', help='Path to input image 2.', default='../data/box_in_scene.png')
+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)
+
+#-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
+minHessian = 400
+detector = cv.xfeatures2d_SURF.create(hessianThreshold=minHessian)
+keypoints1, descriptors1 = detector.detectAndCompute(img1, None)
+keypoints2, descriptors2 = detector.detectAndCompute(img2, None)
+
+#-- Step 2: Matching descriptor vectors with a brute force matcher
+# Since SURF is a floating-point descriptor NORM_L2 is used
+matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_BRUTEFORCE)
+matches = matcher.match(descriptors1, descriptors2)
+
+#-- Draw matches
+img_matches = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], 3), dtype=np.uint8)
+cv.drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches)
+
+#-- Show detected matches
+cv.imshow('Matches', img_matches)
+
+cv.waitKey()