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from PIL import Image
import torch
import torch.nn.functional as F
import numpy as np
from dkm.utils.utils import tensor_to_pil
import cv2
from dkm import DKMv3_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
dkm_model = DKMv3_outdoor(device=device)
W_A, H_A = Image.open(im1_path).size
W_B, H_B = Image.open(im2_path).size
# Match
warp, certainty = dkm_model.match(im1_path, im2_path, device=device)
# Sample matches for estimation
matches, certainty = dkm_model.sample(warp, certainty)
kpts1, kpts2 = dkm_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,
)
# TODO: some better visualization
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