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from __future__ import print_function
import cv2
import numpy as np
MAX_MATCHES = 500
GOOD_MATCH_PERCENT = 0.15
def align_images(im1, im2):
im1_gra = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
im2_gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
orb = cv2.ORB_create(MAX_MATCHES)
keypoints1, descriptors1 = orb.detectAndCompute(im1_gra, None)
keypoints2, descriptors2 = orb.detectAndCompute(im2_gray, None)
matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
matches = matcher.match(descriptors1, descriptors2, None)
matches.sort(key=lambda x: x.distance, reverse=False)
num_good_matches = int(len(matches) * GOOD_MATCH_PERCENT)
matches = matches[:num_good_matches]
im_matches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None)
cv2.imwrite("matches.jpg", im_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
h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
height, width, channels = im2.shape
im1_reg = cv2.warpPerspective(im1, h, (width, height))
return im1_reg, h
if __name__ == '__main__':
ref_filename = "mask.jpg"
print("Reading reference image : ", ref_filename)
im_reference = cv2.imread(ref_filename, cv2.IMREAD_COLOR)
im_filename = "example.jpg"
print("Reading image to align : ", im_filename)
im = cv2.imread(im_filename, cv2.IMREAD_COLOR)
print("Aligning images ...")
im_req, h = align_images(im, im_reference)
out_filename = "aligned.jpg"
print("Saving aligned image : ", out_filename)
cv2.imwrite(out_filename, im_req)
print("Estimated homography : \n", h)
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