AhmedSamara · January 22, 2016 05:11 · Jan 22, 2016
diff --git a/matches.py b/matches.py
@@ -0,0 +1,103 @@
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+
+#http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_matcher/py_matcher.html
+
+def drawMatches(img1, kp1, img2, kp2, matches):
+    """
+    source:
+    http://stackoverflow.com/questions/20259025/module-object-has-no-attribute-drawmatches-opencv-python
+    My own implementation of cv2.drawMatches as OpenCV 2.4.9
+    does not have this function available but it's supported in
+    OpenCV 3.0.0
+
+    This function takes in two images with their associated 
+    keypoints, as well as a list of DMatch data structure (matches) 
+    that contains which keypoints matched in which images.
+
+    An image will be produced where a montage is shown with
+    the first image followed by the second image beside it.
+
+    Keypoints are delineated with circles, while lines are connected
+    between matching keypoints.
+
+    img1,img2 - Grayscale images
+    kp1,kp2 - Detected list of keypoints through any of the OpenCV keypoint 
+              detection algorithms
+    matches - A list of matches of corresponding keypoints through any
+              OpenCV keypoint matching algorithm
+    """
+
+    # Create a new output image that concatenates the two images together
+    # (a.k.a) a montage
+    rows1 = img1.shape[0]
+    cols1 = img1.shape[1]
+    rows2 = img2.shape[0]
+    cols2 = img2.shape[1]
+
+    out = np.zeros((max([rows1,rows2]),cols1+cols2,3), dtype='uint8')
+
+    # Place the first image to the left
+    out[:rows1,:cols1] = np.dstack([img1, img1, img1])
+
+    # Place the next image to the right of it
+    out[:rows2,cols1:] = np.dstack([img2, img2, img2])
+
+    # For each pair of points we have between both images
+    # draw circles, then connect a line between them
+    for mat in matches:
+
+        # Get the matching keypoints for each of the images
+        img1_idx = mat.queryIdx
+        img2_idx = mat.trainIdx
+
+        # x - columns
+        # y - rows
+        (x1,y1) = kp1[img1_idx].pt
+        (x2,y2) = kp2[img2_idx].pt
+
+        # Draw a small circle at both co-ordinates
+        # radius 4
+        # colour blue
+        # thickness = 1
+        cv2.circle(out, (int(x1),int(y1)), 4, (255, 0, 0), 1)   
+        cv2.circle(out, (int(x2)+cols1,int(y2)), 4, (255, 0, 0), 1)
+
+        # Draw a line in between the two points
+        # thickness = 1
+        # colour blue
+        cv2.line(out, (int(x1),int(y1)), (int(x2)+cols1,int(y2)), (255, 0, 0), 1)
+
+
+    # Show the image
+    cv2.imshow('Matched Features', out)
+    cv2.waitKey(0)
+    cv2.destroyWindow('Matched Features')
+
+    # Also return the image if you'd like a copy
+    return out
+
+
+img1 = cv2.imread('blue.png',0)          # queryImage
+img2 = cv2.imread('all.png',0) # trainImage
+
+surf = cv2.SURF(400)
+
+# find the keypoints and descriptors with SIFT
+kp1, des1 = surf.detectAndCompute(img1,None)
+kp2, des2 = surf.detectAndCompute(img2,None)
+
+# create BFMatcher object
+bf = cv2.BFMatcher()
+
+# Match descriptors.
+matches = bf.match(des1,des2)
+
+
+#matches = sorted(matches, key = lambda x:x.distance)
+
+
+drawMatches(img1, kp1, img2, kp2, matches)
+
+