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from PIL import Image
import torch
import cv2
from roma import roma_outdoor
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if __name__ == "__main__":
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("--im_A_path", default="assets/sacre_coeur_A.jpg", type=str)
parser.add_argument("--im_B_path", default="assets/sacre_coeur_B.jpg", type=str)
args, _ = parser.parse_known_args()
im1_path = args.im_A_path
im2_path = args.im_B_path
# Create model
roma_model = roma_outdoor(device=device)
W_A, H_A = Image.open(im1_path).size
W_B, H_B = Image.open(im2_path).size
# Match
warp, certainty = roma_model.match(im1_path, im2_path, device=device)
# Sample matches for estimation
matches, certainty = roma_model.sample(warp, certainty)
kpts1, kpts2 = roma_model.to_pixel_coordinates(matches, H_A, W_A, H_B, W_B)
F, mask = cv2.findFundamentalMat(
kpts1.cpu().numpy(), kpts2.cpu().numpy(), ransacReprojThreshold=0.2, method=cv2.USAC_MAGSAC, confidence=0.999999, maxIters=10000
) |