-
Notifications
You must be signed in to change notification settings - Fork 25
/
demo.py
83 lines (62 loc) · 2.56 KB
/
demo.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
import torch
import torchvision.transforms.functional as TF
from PIL import Image
import os
from skimage import img_as_ubyte
from collections import OrderedDict
from natsort import natsorted
from glob import glob
import cv2
import argparse
from model.SUNet import SUNet_model
import yaml
with open('training.yaml', 'r') as config:
opt = yaml.safe_load(config)
parser = argparse.ArgumentParser(description='Demo Image Restoration')
parser.add_argument('--input_dir', default='C:/Users/Lab722 BX/Desktop/CBSD68_test/CBSD68_50_crop/', type=str, help='Input images')
parser.add_argument('--window_size', default=8, type=int, help='window size')
parser.add_argument('--result_dir', default='C:/Users/Lab722 BX/Desktop/CBSD68_test/SUNet_50_crop/', type=str, help='Directory for results')
parser.add_argument('--weights',
default='./pretrain-model/model_bestPSNR.pth', type=str,
help='Path to weights')
args = parser.parse_args()
def save_img(filepath, img):
cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
def load_checkpoint(model, weights):
checkpoint = torch.load(weights)
try:
model.load_state_dict(checkpoint["state_dict"])
except:
state_dict = checkpoint["state_dict"]
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
inp_dir = args.input_dir
out_dir = args.result_dir
os.makedirs(out_dir, exist_ok=True)
files = natsorted(glob(os.path.join(inp_dir, '*.jpg'))
+ glob(os.path.join(inp_dir, '*.JPG'))
+ glob(os.path.join(inp_dir, '*.png'))
+ glob(os.path.join(inp_dir, '*.PNG')))
if len(files) == 0:
raise Exception(f"No files found at {inp_dir}")
# Load corresponding model architecture and weights
model = SUNet_model(opt)
model.cuda()
load_checkpoint(model, args.weights)
model.eval()
print('restoring images......')
for file_ in files:
img = Image.open(file_).convert('RGB')
input_ = TF.to_tensor(img).unsqueeze(0).cuda()
with torch.no_grad():
restored = model(input_)
restored = torch.clamp(restored, 0, 1)
restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()
restored = img_as_ubyte(restored[0])
f = os.path.splitext(os.path.split(file_)[-1])[0]
save_img((os.path.join(out_dir, f + '.png')), restored)
print(f"Files saved at {out_dir}")
print('finish !')