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 )