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from argparse import Namespace
import os
import torch
import cv2
from .base import Viz
from src.utils.metrics import compute_symmetrical_epipolar_errors, compute_pose_errors
from third_party.loftr.src.loftr import LoFTR, default_cfg
class VizLoFTR(Viz):
def __init__(self, args):
super().__init__()
if type(args) == dict:
args = Namespace(**args)
self.match_threshold = args.match_threshold
# Load model
conf = dict(default_cfg)
conf["match_coarse"]["thr"] = self.match_threshold
print(conf)
self.model = LoFTR(config=conf)
ckpt_dict = torch.load(args.ckpt)
self.model.load_state_dict(ckpt_dict["state_dict"])
self.model = self.model.eval().to(self.device)
# Name the method
# self.ckpt_name = args.ckpt.split('/')[-1].split('.')[0]
self.name = "LoFTR"
print(f"Initialize {self.name}")
def match_and_draw(
self,
data_dict,
root_dir=None,
ground_truth=False,
measure_time=False,
viz_matches=True,
):
if measure_time:
torch.cuda.synchronize()
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
self.model(data_dict)
if measure_time:
torch.cuda.synchronize()
end.record()
torch.cuda.synchronize()
self.time_stats.append(start.elapsed_time(end))
kpts0 = data_dict["mkpts0_f"].cpu().numpy()
kpts1 = data_dict["mkpts1_f"].cpu().numpy()
img_name0, img_name1 = list(zip(*data_dict["pair_names"]))[0]
img0 = cv2.imread(os.path.join(root_dir, img_name0))
img1 = cv2.imread(os.path.join(root_dir, img_name1))
if str(data_dict["dataset_name"][0]).lower() == "scannet":
img0 = cv2.resize(img0, (640, 480))
img1 = cv2.resize(img1, (640, 480))
if viz_matches:
saved_name = "_".join(
[
img_name0.split("/")[-1].split(".")[0],
img_name1.split("/")[-1].split(".")[0],
]
)
folder_matches = os.path.join(root_dir, "{}_viz_matches".format(self.name))
if not os.path.exists(folder_matches):
os.makedirs(folder_matches)
path_to_save_matches = os.path.join(
folder_matches, "{}.png".format(saved_name)
)
if ground_truth:
compute_symmetrical_epipolar_errors(
data_dict
) # compute epi_errs for each match
compute_pose_errors(
data_dict
) # compute R_errs, t_errs, pose_errs for each pair
epi_errors = data_dict["epi_errs"].cpu().numpy()
R_errors, t_errors = data_dict["R_errs"][0], data_dict["t_errs"][0]
self.draw_matches(
kpts0,
kpts1,
img0,
img1,
epi_errors,
path=path_to_save_matches,
R_errs=R_errors,
t_errs=t_errors,
)
rel_pair_names = list(zip(*data_dict["pair_names"]))
bs = data_dict["image0"].size(0)
metrics = {
# to filter duplicate pairs caused by DistributedSampler
"identifiers": ["#".join(rel_pair_names[b]) for b in range(bs)],
"epi_errs": [
data_dict["epi_errs"][data_dict["m_bids"] == b].cpu().numpy()
for b in range(bs)
],
"R_errs": data_dict["R_errs"],
"t_errs": data_dict["t_errs"],
"inliers": data_dict["inliers"],
}
self.eval_stats.append({"metrics": metrics})
else:
m_conf = 1 - data_dict["mconf"].cpu().numpy()
self.draw_matches(
kpts0,
kpts1,
img0,
img1,
m_conf,
path=path_to_save_matches,
conf_thr=0.4,
)
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