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import os
import sys
ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.insert(0, ROOT_DIR)
from src.ASpanFormer.aspanformer import ASpanFormer
from src.config.default import get_cfg_defaults
from src.utils.misc import lower_config
import demo_utils
import cv2
import torch
import numpy as np
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--config_path",
type=str,
default="../configs/aspan/outdoor/aspan_test.py",
help="path for config file.",
)
parser.add_argument(
"--img0_path",
type=str,
default="../assets/phototourism_sample_images/piazza_san_marco_06795901_3725050516.jpg",
help="path for image0.",
)
parser.add_argument(
"--img1_path",
type=str,
default="../assets/phototourism_sample_images/piazza_san_marco_15148634_5228701572.jpg",
help="path for image1.",
)
parser.add_argument(
"--weights_path",
type=str,
default="../weights/outdoor.ckpt",
help="path for model weights.",
)
parser.add_argument(
"--long_dim0", type=int, default=1024, help="resize for longest dim of image0."
)
parser.add_argument(
"--long_dim1", type=int, default=1024, help="resize for longest dim of image1."
)
args = parser.parse_args()
if __name__ == "__main__":
config = get_cfg_defaults()
config.merge_from_file(args.config_path)
_config = lower_config(config)
matcher = ASpanFormer(config=_config["aspan"])
state_dict = torch.load(args.weights_path, map_location="cpu")["state_dict"]
matcher.load_state_dict(state_dict, strict=False)
matcher.cuda(), matcher.eval()
img0, img1 = cv2.imread(args.img0_path), cv2.imread(args.img1_path)
img0_g, img1_g = cv2.imread(args.img0_path, 0), cv2.imread(args.img1_path, 0)
img0, img1 = demo_utils.resize(img0, args.long_dim0), demo_utils.resize(
img1, args.long_dim1
)
img0_g, img1_g = demo_utils.resize(img0_g, args.long_dim0), demo_utils.resize(
img1_g, args.long_dim1
)
data = {
"image0": torch.from_numpy(img0_g / 255.0)[None, None].cuda().float(),
"image1": torch.from_numpy(img1_g / 255.0)[None, None].cuda().float(),
}
with torch.no_grad():
matcher(data, online_resize=True)
corr0, corr1 = data["mkpts0_f"].cpu().numpy(), data["mkpts1_f"].cpu().numpy()
F_hat, mask_F = cv2.findFundamentalMat(
corr0, corr1, method=cv2.FM_RANSAC, ransacReprojThreshold=1
)
if mask_F is not None:
mask_F = mask_F[:, 0].astype(bool)
else:
mask_F = np.zeros_like(corr0[:, 0]).astype(bool)
# visualize match
display = demo_utils.draw_match(img0, img1, corr0, corr1)
display_ransac = demo_utils.draw_match(img0, img1, corr0[mask_F], corr1[mask_F])
cv2.imwrite("match.png", display)
cv2.imwrite("match_ransac.png", display_ransac)
print(len(corr1), len(corr1[mask_F]))
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