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samples/python/tutorial_code/features2D/feature_homography/SURF_FLANN_matching_homography_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 Matching with FLANN tutorial.')
+parser.add_argument('--input1', help='Path to input image 1.', default='box.png')
+parser.add_argument('--input2', help='Path to input image 2.', default='box_in_scene.png')
+args = parser.parse_args()
+
+img_object = cv.imread(cv.samples.findFile(args.input1), cv.IMREAD_GRAYSCALE)
+img_scene = cv.imread(cv.samples.findFile(args.input2), cv.IMREAD_GRAYSCALE)
+if img_object is None or img_scene 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)
+keypoints_obj, descriptors_obj = detector.detectAndCompute(img_object, None)
+keypoints_scene, descriptors_scene = detector.detectAndCompute(img_scene, None)
+
+#-- Step 2: Matching descriptor vectors with a FLANN based matcher
+# Since SURF is a floating-point descriptor NORM_L2 is used
+matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_FLANNBASED)
+knn_matches = matcher.knnMatch(descriptors_obj, descriptors_scene, 2)
+
+#-- Filter matches using the Lowe's ratio test
+ratio_thresh = 0.75
+good_matches = []
+for m,n in knn_matches:
+    if m.distance < ratio_thresh * n.distance:
+        good_matches.append(m)
+
+#-- Draw matches
+img_matches = np.empty((max(img_object.shape[0], img_scene.shape[0]), img_object.shape[1]+img_scene.shape[1], 3), dtype=np.uint8)
+cv.drawMatches(img_object, keypoints_obj, img_scene, keypoints_scene, good_matches, img_matches, flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
+
+#-- Localize the object
+obj = np.empty((len(good_matches),2), dtype=np.float32)
+scene = np.empty((len(good_matches),2), dtype=np.float32)
+for i in range(len(good_matches)):
+    #-- Get the keypoints from the good matches
+    obj[i,0] = keypoints_obj[good_matches[i].queryIdx].pt[0]
+    obj[i,1] = keypoints_obj[good_matches[i].queryIdx].pt[1]
+    scene[i,0] = keypoints_scene[good_matches[i].trainIdx].pt[0]
+    scene[i,1] = keypoints_scene[good_matches[i].trainIdx].pt[1]
+
+H, _ =  cv.findHomography(obj, scene, cv.RANSAC)
+
+#-- Get the corners from the image_1 ( the object to be "detected" )
+obj_corners = np.empty((4,1,2), dtype=np.float32)
+obj_corners[0,0,0] = 0
+obj_corners[0,0,1] = 0
+obj_corners[1,0,0] = img_object.shape[1]
+obj_corners[1,0,1] = 0
+obj_corners[2,0,0] = img_object.shape[1]
+obj_corners[2,0,1] = img_object.shape[0]
+obj_corners[3,0,0] = 0
+obj_corners[3,0,1] = img_object.shape[0]
+
+scene_corners = cv.perspectiveTransform(obj_corners, H)
+
+#-- Draw lines between the corners (the mapped object in the scene - image_2 )
+cv.line(img_matches, (int(scene_corners[0,0,0] + img_object.shape[1]), int(scene_corners[0,0,1])),\
+    (int(scene_corners[1,0,0] + img_object.shape[1]), int(scene_corners[1,0,1])), (0,255,0), 4)
+cv.line(img_matches, (int(scene_corners[1,0,0] + img_object.shape[1]), int(scene_corners[1,0,1])),\
+    (int(scene_corners[2,0,0] + img_object.shape[1]), int(scene_corners[2,0,1])), (0,255,0), 4)
+cv.line(img_matches, (int(scene_corners[2,0,0] + img_object.shape[1]), int(scene_corners[2,0,1])),\
+    (int(scene_corners[3,0,0] + img_object.shape[1]), int(scene_corners[3,0,1])), (0,255,0), 4)
+cv.line(img_matches, (int(scene_corners[3,0,0] + img_object.shape[1]), int(scene_corners[3,0,1])),\
+    (int(scene_corners[0,0,0] + img_object.shape[1]), int(scene_corners[0,0,1])), (0,255,0), 4)
+
+#-- Show detected matches
+cv.imshow('Good Matches & Object detection', img_matches)
+
+cv.waitKey()