Merge pull request spmallick#55 from vaibhawchandel/master

Python code for Feature Based Image Alignment.

Author: spmallick

Diff for: ImageAlignment-FeatureBased/align.py

@@ -0,0 +1,59 @@
+import cv2
+import numpy as np
+
+
+MAX_MATCHES = 500
+GOOD_MATCH_PERCENT = 0.15
+
+
+def alignImages(im1, im2):
+
+  # Convert images to grayscale
+  im1Gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
+  im2Gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
+  
+  # Detect ORB features and compute descriptors.
+  orb = cv2.ORB_create(MAX_MATCHES)
+  keypoints1, descriptors1 = orb.detectAndCompute(im1Gray, None)
+  keypoints2, descriptors2 = orb.detectAndCompute(im2Gray, None)
+  
+  # Match features.
+  matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
+  matches = matcher.match(descriptors1, descriptors2, None)
+  
+  # Sort matches by score
+  matches.sort(key=lambda x: x.distance, reverse=False)
+
+  # Remove not so good matches
+  numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)
+  matches = matches[:numGoodMatches]
+
+  # Draw top matches
+  imMatches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None)
+  cv2.imwrite("matches.jpg", imMatches)
+  
+  # Extract location of good matches
+  points1 = np.zeros((len(matches), 2), dtype=np.float32)
+  points2 = np.zeros((len(matches), 2), dtype=np.float32)
+
+  for i, match in enumerate(matches):
+    points1[i, :] = keypoints1[match.queryIdx].pt
+    points2[i, :] = keypoints2[match.trainIdx].pt
+  
+  # Find homography
+  h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
+
+  # Use homography
+  height, width, channels = im2.shape
+  im1Reg = cv2.warpPerspective(im1, h, (width, height))
+  
+  return im1Reg, h
+
+
+if __name__ == '__main__':
+  im = cv2.imread("scanned-form.jpg", cv2.IMREAD_COLOR)
+  imReference = cv2.imread("form.jpg", cv2.IMREAD_COLOR)
+  
+  imReg, h = alignImages(im, imReference)
+  cv2.imwrite("aligned.jpg", imReg)
+