Update app.py

app.py

@@ -12,6 +12,47 @@ import os
 import gradio as gr
 
 
+def align_images(image, template, maxFeatures=500, keepPercent=0.2,debug=False):
+	# convert both the input image and template to grayscale
+  imageGray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+  templateGray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
+  orb = cv2.ORB_create(maxFeatures)
+  (kpsA, descsA) = orb.detectAndCompute(imageGray, None)
+  (kpsB, descsB) = orb.detectAndCompute(templateGray, None)
+	# match the features
+  method = cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING
+  matcher = cv2.DescriptorMatcher_create(method)
+  matches = matcher.match(descsA, descsB, None)
+  matches = sorted(matches, key=lambda x:x.distance)
+	# keep only the top matches
+  keep = int(len(matches) * keepPercent)
+  matches = matches[:keep]
+	# check to see if we should visualize the matched keypoints
+  if debug:
+    matchedVis = cv2.drawMatches(image, kpsA, template, kpsB,matches, None)
+    matchedVis = imutils.resize(matchedVis, width=1000)
+    cv2_imshow(matchedVis)
+    cv2.waitKey(0)
+    # allocate memory for the keypoints (x, y)-coordinates from the
+	# top matches -- we'll use these coordinates to compute our
+	# homography matrix
+  ptsA = np.zeros((len(matches), 2), dtype="float")
+  ptsB = np.zeros((len(matches), 2), dtype="float")
+	# loop over the top matches
+  for (i, m) in enumerate(matches):
+		# indicate that the two keypoints in the respective images
+		# map to each other
+    ptsA[i] = kpsA[m.queryIdx].pt
+    ptsB[i] = kpsB[m.trainIdx].pt
+    # compute the homography matrix between the two sets of matched
+	# points
+  (H, mask) = cv2.findHomography(ptsA, ptsB, method=cv2.RANSAC)
+	# use the homography matrix to align the images
+  (h, w) = template.shape[:2]
+  aligned = cv2.warpPerspective(image, H, (w, h))
+	# return the aligned image
+  return aligned
+
 def cleanup_text(text):
 	return "".join([c if ord(c) < 128 else "" for c in text]).strip()