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import argparse
import glob
import math
import subprocess
import numpy as np
import os
import tqdm
import torch
import torch.nn as nn
import cv2
from darkfeat import DarkFeat
from utils import matching
def darkfeat_pre(img, cuda):
H, W = img.shape[0], img.shape[1]
inp = img.copy()
inp = inp.transpose(2, 0, 1)
inp = torch.from_numpy(inp)
inp = torch.autograd.Variable(inp).view(1, 3, H, W)
if cuda:
inp = inp.cuda()
return inp
if __name__ == "__main__":
# Parse command line arguments.
parser = argparse.ArgumentParser()
parser.add_argument("--H", type=int, default=int(640))
parser.add_argument("--W", type=int, default=int(960))
parser.add_argument("--histeq", action="store_true")
parser.add_argument("--model_path", type=str)
parser.add_argument("--dataset_dir", type=str, default="/data/hyz/MID/")
opt = parser.parse_args()
sizer = (opt.W, opt.H)
focallength_x = 4.504986436499113e03 / (6744 / sizer[0])
focallength_y = 4.513311442889859e03 / (4502 / sizer[1])
K = np.eye(3)
K[0, 0] = focallength_x
K[1, 1] = focallength_y
K[0, 2] = 3.363322177533149e03 / (6744 / sizer[0]) # * 0.5
K[1, 2] = 2.291824660547715e03 / (4502 / sizer[1]) # * 0.5
Kinv = np.linalg.inv(K)
Kinvt = np.transpose(Kinv)
cuda = True
if cuda:
darkfeat = DarkFeat(opt.model_path).cuda().eval()
for scene in ["Indoor", "Outdoor"]:
base_save = "./result/" + scene + "/"
dir_base = opt.dataset_dir + "/" + scene + "/"
pair_list = sorted(os.listdir(dir_base))
for pair in tqdm.tqdm(pair_list):
opention = 1
if scene == "Outdoor":
pass
else:
if int(pair[4::]) <= 17:
opention = 0
else:
pass
name = []
files = sorted(os.listdir(dir_base + pair))
for file_ in files:
if file_.endswith(".cr2"):
name.append(file_[0:9])
ISO = [
"00100",
"00200",
"00400",
"00800",
"01600",
"03200",
"06400",
"12800",
]
if opention == 1:
Shutter_speed = ["0.005", "0.01", "0.025", "0.05", "0.17", "0.5"]
else:
Shutter_speed = ["0.01", "0.02", "0.05", "0.1", "0.3", "1"]
E_GT = np.load(dir_base + pair + "/GT_Correspondence/" + "E_estimated.npy")
F_GT = np.dot(np.dot(Kinvt, E_GT), Kinv)
R_GT = np.load(dir_base + pair + "/GT_Correspondence/" + "R_GT.npy")
t_GT = np.load(dir_base + pair + "/GT_Correspondence/" + "T_GT.npy")
id0, id1 = sorted(
[int(i.split("/")[-1]) for i in glob.glob(f"{dir_base+pair}/?????")]
)
cnt = 0
for iso in ISO:
for ex in Shutter_speed:
dark_name1 = name[0] + iso + "_" + ex + "_" + scene + ".npy"
dark_name2 = name[1] + iso + "_" + ex + "_" + scene + ".npy"
if not opt.histeq:
dst_T1_None = (
f"{dir_base}{pair}/{id0:05d}-npy-nohisteq/{dark_name1}"
)
dst_T2_None = (
f"{dir_base}{pair}/{id1:05d}-npy-nohisteq/{dark_name2}"
)
img1_orig_None = np.load(dst_T1_None)
img2_orig_None = np.load(dst_T2_None)
dir_save = base_save + pair + "/None/"
img_input1 = darkfeat_pre(
img1_orig_None.astype("float32") / 255.0, cuda
)
img_input2 = darkfeat_pre(
img2_orig_None.astype("float32") / 255.0, cuda
)
else:
dst_T1_histeq = f"{dir_base}{pair}/{id0:05d}-npy/{dark_name1}"
dst_T2_histeq = f"{dir_base}{pair}/{id1:05d}-npy/{dark_name2}"
img1_orig_histeq = np.load(dst_T1_histeq)
img2_orig_histeq = np.load(dst_T2_histeq)
dir_save = base_save + pair + "/HistEQ/"
img_input1 = darkfeat_pre(
img1_orig_histeq.astype("float32") / 255.0, cuda
)
img_input2 = darkfeat_pre(
img2_orig_histeq.astype("float32") / 255.0, cuda
)
result1 = darkfeat({"image": img_input1})
result2 = darkfeat({"image": img_input2})
mkpts0, mkpts1, _ = matching.match_descriptors(
cv2.KeyPoint_convert(
result1["keypoints"].detach().cpu().float().numpy()
),
result1["descriptors"].detach().cpu().numpy(),
cv2.KeyPoint_convert(
result2["keypoints"].detach().cpu().float().numpy()
),
result2["descriptors"].detach().cpu().numpy(),
ORB=False,
)
POINT_1_dir = dir_save + f"DarkFeat/POINT_1/"
POINT_2_dir = dir_save + f"DarkFeat/POINT_2/"
subprocess.check_output(["mkdir", "-p", POINT_1_dir])
subprocess.check_output(["mkdir", "-p", POINT_2_dir])
np.save(POINT_1_dir + dark_name1[0:-3] + "npy", mkpts0)
np.save(POINT_2_dir + dark_name2[0:-3] + "npy", mkpts1)
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