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from PIL import Image
import numpy as np
import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
if __name__ == "__main__":
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("--im_A_path", default="assets/toronto_A.jpg", type=str)
parser.add_argument("--im_B_path", default="assets/toronto_B.jpg", type=str)
parser.add_argument("--save_path", default="demo/roma_warp_toronto.jpg", type=str)
args, _ = parser.parse_known_args()
im1_path = args.im_A_path
im2_path = args.im_B_path
save_path = args.save_path
img1 = cv.imread(im1_path,cv.IMREAD_GRAYSCALE) # queryImage
img2 = cv.imread(im2_path,cv.IMREAD_GRAYSCALE) # trainImage
# Initiate SIFT detector
sift = cv.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
# BFMatcher with default params
bf = cv.BFMatcher()
matches = bf.knnMatch(des1,des2,k=2)
# Apply ratio test
good = []
for m,n in matches:
if m.distance < 0.75*n.distance:
good.append([m])
# cv.drawMatchesKnn expects list of lists as matches.
draw_params = dict(matchColor = (255,0,0), # draw matches in red color
singlePointColor = None,
flags = 2)
img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,good,None,**draw_params)
Image.fromarray(img3).save("demo/sift_matches.png")
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