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from argparse import Namespace
import os, sys
import torch
import cv2
from pathlib import Path
from .base import Viz
from src.utils.metrics import compute_symmetrical_epipolar_errors, compute_pose_errors
patch2pix_path = Path(__file__).parent / "../../third_party/patch2pix"
sys.path.append(str(patch2pix_path))
from third_party.patch2pix.utils.eval.model_helper import load_model, estimate_matches
class VizPatch2Pix(Viz):
def __init__(self, args):
super().__init__()
if type(args) == dict:
args = Namespace(**args)
self.imsize = args.imsize
self.match_threshold = args.match_threshold
self.ksize = args.ksize
self.model = load_model(args.ckpt, method="patch2pix")
self.name = "Patch2Pix"
print(f"Initialize {self.name} with image size {self.imsize}")
def match_and_draw(
self,
data_dict,
root_dir=None,
ground_truth=False,
measure_time=False,
viz_matches=True,
):
img_name0, img_name1 = list(zip(*data_dict["pair_names"]))[0]
path_img0 = os.path.join(root_dir, img_name0)
path_img1 = os.path.join(root_dir, img_name1)
img0, img1 = cv2.imread(path_img0), cv2.imread(path_img1)
return_m_upscale = True
if str(data_dict["dataset_name"][0]).lower() == "scannet":
# self.imsize = 640
img0 = cv2.resize(img0, tuple(self.imsize)) # (640, 480))
img1 = cv2.resize(img1, tuple(self.imsize)) # (640, 480))
return_m_upscale = False
outputs = estimate_matches(
self.model,
path_img0,
path_img1,
ksize=self.ksize,
io_thres=self.match_threshold,
eval_type="fine",
imsize=self.imsize,
return_upscale=return_m_upscale,
measure_time=measure_time,
)
if measure_time:
self.time_stats.append(outputs[-1])
matches, mconf = outputs[0], outputs[1]
kpts0 = matches[:, :2]
kpts1 = matches[:, 2:4]
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:
data_dict["mkpts0_f"] = (
torch.from_numpy(matches[:, :2]).float().to(self.device)
)
data_dict["mkpts1_f"] = (
torch.from_numpy(matches[:, 2:4]).float().to(self.device)
)
data_dict["m_bids"] = torch.zeros(
matches.shape[0], device=self.device, dtype=torch.float32
)
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 - mconf
self.draw_matches(
kpts0,
kpts1,
img0,
img1,
m_conf,
path=path_to_save_matches,
conf_thr=0.4,
)
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