-
Notifications
You must be signed in to change notification settings - Fork 3
/
dataset_loader.py
224 lines (185 loc) · 6.27 KB
/
dataset_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
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
import os
import numpy as np
import PIL.Image
import torch
from torch.utils import data
class MyTrainData(data.Dataset):
"""
load data in a folder
"""
mean_rgb = np.array([0.447, 0.407, 0.386])
std_rgb = np.array([0.244, 0.250, 0.253])
def __init__(self, root, stage, transform=False):
super(MyTrainData, self).__init__()
self.root = root
self.stage = stage
self._transform = transform
img_root = os.path.join(self.root, 'DUTS-TR-Image')
lbl_root = os.path.join(self.root, stage)
file_names = os.listdir(img_root)
self.img_names = []
self.lbl_names = []
for i, name in enumerate(file_names):
if not name.endswith('.jpg'):
continue
self.lbl_names.append(
os.path.join(lbl_root, name[:-4]+'.png')
)
self.img_names.append(
os.path.join(img_root, name)
)
def __len__(self):
return len(self.img_names)
def __getitem__(self, index):
# load image
img_file = self.img_names[index]
img = PIL.Image.open(img_file)
img = img.convert('RGB')
img = img.resize((256, 256))
img = np.array(img, dtype=np.uint8)
lbl_file = self.lbl_names[index]
lbl = PIL.Image.open(lbl_file)
lbl = lbl.convert('L')
lbl = lbl.resize((256, 256))
lbl = np.array(lbl)
lbl = lbl / 255
if self._transform:
return self.transform(img, lbl)
else:
return img, lbl
# Translating numpy_array into format that pytorch can use on Code.
def transform(self, img, lbl):
img = img.astype(np.float64)/255.0
lbl = lbl.astype(np.float32)
# lbl[lbl != 0] = 1
img = img - self.mean_rgb
img = img / self.std_rgb
img = img.transpose(2, 0, 1) # to verify #256*256*3 to 3*256*256
img = torch.from_numpy(img).float()
lbl = torch.from_numpy(lbl).float()
return img, lbl
class MyPseudoIterData(data.Dataset):
"""
load data in a folder
"""
mean_rgb = np.array([0.447, 0.407, 0.386])
std_rgb = np.array([0.244, 0.250, 0.253])
def __init__(self, root, transform=False):
super(MyPseudoIterData, self).__init__()
self.root = root
self._transform = transform
img_root = os.path.join(self.root, 'DUTS-TR-Image')
file_names = os.listdir(img_root)
self.img_names = []
self.names = []
for i, name in enumerate(file_names):
if not name.endswith('.jpg'):
continue
self.img_names.append(
os.path.join(img_root, name)
)
self.names.append(name[:-4])
def __len__(self):
return len(self.img_names)
def __getitem__(self, index):
# load image
img_file = self.img_names[index]
img = PIL.Image.open(img_file).convert('RGB')
img_size = img.size
img = img.resize((256, 256))
img = np.array(img, dtype=np.uint8)
if self._transform:
img = self.transform(img)
return img, self.names[index], img_size
else:
return img, self.names[index], img_size
def transform(self, img):
img = img.astype(np.float64)/255.0
img -= self.mean_rgb
img /= self.std_rgb
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).float()
return img
class MyPostData(data.Dataset):
"""
load data in a folder
"""
mean_rgb = np.array([0.447, 0.407, 0.386])
std_rgb = np.array([0.244, 0.250, 0.253])
def __init__(self, root):
super(MyPostData, self).__init__()
self.root = root
img_root = os.path.join(self.root, 'DUTS-TR-Image')
file_names = os.listdir(img_root)
self.img_names = []
self.names = []
for i, name in enumerate(file_names):
if not name.endswith('.jpg'):
continue
self.img_names.append(
os.path.join(img_root, name)
)
self.names.append(name[:-4])
def __len__(self):
return len(self.img_names)
def __getitem__(self, index):
# load image
img_file = self.img_names[index]
img = PIL.Image.open(img_file).convert('RGB')
img_size = img.size
img = img.resize((256, 256))
img = np.array(img, dtype=np.uint8)
if self._transform:
img = self.transform(img)
return img, self.names[index], img_size
else:
return img, self.names[index], img_size
def transform(self, img):
img = img.astype(np.float64)/255.0
img -= self.mean_rgb
img /= self.std_rgb
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).float()
return img
class MyTestData(data.Dataset):
"""
load data in a folder
"""
mean_rgb = np.array([0.447, 0.407, 0.386])
std_rgb = np.array([0.244, 0.250, 0.253])
def __init__(self, root, transform=False):
super(MyTestData, self).__init__()
self.root = root
self._transform = transform
img_root = os.path.join(self.root, 'ECSSD-image')#test dataset document
file_names = os.listdir(img_root)
self.img_names = []
self.names = []
for i, name in enumerate(file_names):
if not name.endswith('.jpg'):
continue
self.img_names.append(
os.path.join(img_root, name)
)
self.names.append(name[:-4])
def __len__(self):
return len(self.img_names)
def __getitem__(self, index):
# load image
img_file = self.img_names[index]
img = PIL.Image.open(img_file).convert('RGB')
img_size = img.size
img = img.resize((256, 256))
img = np.array(img, dtype=np.uint8)
if self._transform:
img = self.transform(img)
return img, self.names[index], img_size
else:
return img, self.names[index], img_size
def transform(self, img):
img = img.astype(np.float64)/255.0
img -= self.mean_rgb
img /= self.std_rgb
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).float()
return img