-
Notifications
You must be signed in to change notification settings - Fork 0
/
test_loader.py
85 lines (73 loc) · 3.14 KB
/
test_loader.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 os
from PIL import Image
import torch.utils.data as data
import torchvision.transforms as transforms
import numpy as np
class Medical_Dataset(data.Dataset):
def __init__(self, root, trainsize=512,mode='train',augmentation_prob=0.4):
self.trainsize = trainsize
self.image_root = root
self.gt_root = root[:-1]+'_GT/'
self.images = [self.image_root + f for f in os.listdir(self.image_root) if f.endswith('.tif') or f.endswith('.png')]
self.gts = [self.gt_root + f for f in os.listdir(self.gt_root) if f.endswith('.png')
or f.endswith('.tif')]
self.images = sorted(self.images)
self.gts = sorted(self.gts)
self.filter_files()
self.size = len(self.images)
self.img_transform = transforms.Compose([
transforms.Resize((self.trainsize,self.trainsize),Image.BILINEAR),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
self.gt_transform = transforms.Compose([
transforms.Resize((self.trainsize, self.trainsize), Image.NEAREST),
transforms.ToTensor()])
def __getitem__(self, index):
image = self.rgb_loader(self.images[index])
gt = self.binary_loader(self.gts[index])
#print(image.size)
#image,gt = self.resize(image,gt)
image = self.img_transform(image)
gt = self.gt_transform(gt)
file_name = self.images[index].split('/')[-1][:-len(".tif")]
return image, gt, file_name
def filter_files(self):
assert len(self.images) == len(self.gts)
images = []
gts = []
for img_path, gt_path in zip(self.images, self.gts):
img = Image.open(img_path)
gt = Image.open(gt_path)
if img.size == gt.size:
images.append(img_path)
gts.append(gt_path)
self.images = images
self.gts = gts
def rgb_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
def binary_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
# return img.convert('1')
return img.convert('L')
def resize(self, img, gt):
assert img.size == gt.size
w, h = img.size
if h < self.trainsize or w < self.trainsize:
h = max(h, self.trainsize)
w = max(w, self.trainsize)
return img.resize((w, h), Image.BILINEAR), gt.resize((w, h), Image.NEAREST)
else:
return img, gt
def __len__(self):
return self.size
def get_loader(root, batchsize, trainsize, num_workers=4, mode='train',augmentation_prob=0.4,shuffle=True, pin_memory=True):
dataset = Medical_Dataset(root= root, trainsize= trainsize,mode =mode, augmentation_prob=augmentation_prob)
data_loader = data.DataLoader(dataset=dataset,
batch_size=batchsize,
shuffle=shuffle,
num_workers=num_workers,
pin_memory=pin_memory)
return data_loader