-
Notifications
You must be signed in to change notification settings - Fork 0
/
dataset.py
186 lines (169 loc) · 7 KB
/
dataset.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
import os
import glob
import scipy
import torch
import random
import numpy as np
import torchvision.transforms.functional as F
from PIL import Image
# from scipy.misc import imread
from imageio import imread
import cv2
class Dataset(torch.utils.data.Dataset):
def __init__(self, image_path, mask_path, mask_mode, target_size, augment=True, training=True, mask_reverse = False):
super(Dataset, self).__init__()
self.augment = augment
self.training = training
self.data = self.load_list(image_path)
self.mask_data = self.load_list(mask_path)
self.target_size = target_size
self.mask_type = mask_mode
self.mask_reverse = mask_reverse
# in test mode, there's a one-to-one relationship between mask and image
# masks are loaded non random
def __len__(self):
return len(self.data)
def __getitem__(self, index):
try:
item = self.load_item(index)
except:
print('loading error: ' + self.data[index])
item = self.load_item(0)
return item
def load_item(self, index):
img = imread(self.data[index])
if self.training:
img = self.resize(img)
else:
img = self.resize(img, True, True, True)
# load mask
mask = self.load_mask(img, index)
# augment data
if self.training:
if self.augment and np.random.binomial(1, 0.5) > 0:
img = img[:, ::-1, ...]
if self.augment and np.random.binomial(1, 0.5) > 0:
mask = mask[:, ::-1, ...]
return self.to_tensor(img), self.to_tensor(mask)
def load_mask(self, img, index):
imgh, imgw = img.shape[0:2]
#external mask, random order
if self.mask_type == 0:
mask_index = random.randint(0, len(self.mask_data) - 1)
mask = imread(self.mask_data[mask_index])
mask = (mask > 0).astype(np.uint8) # threshold due to interpolation
mask = self.resize(mask, False)
if self.mask_reverse:
return (1 - mask) * 255
else:
return mask * 255
#generate random mask
if self.mask_type == 1:
mask = 1 - generate_stroke_mask([self.target_size, self.target_size])
mask = (mask>0).astype(np.uint8)* 255
mask = self.resize(mask,False)
return mask
#external mask, fixed order
if self.mask_type == 2:
mask_index = index
mask = imread(self.mask_data[mask_index])
mask = (mask > 0).astype(np.uint8) # threshold due to interpolation
mask = self.resize(mask, False)
if self.mask_reverse:
return (1 - mask) * 255
else:
return mask * 255
def resize(self, img, aspect_ratio_kept = True, fixed_size = False, centerCrop=False):
if aspect_ratio_kept:
imgh, imgw = img.shape[0:2]
side = np.minimum(imgh, imgw)
if fixed_size:
if centerCrop:
# center crop
j = (imgh - side) // 2
i = (imgw - side) // 2
img = img[j:j + side, i:i + side, ...]
else:
j = (imgh - side)
i = (imgw - side)
h_start = 0
w_start = 0
if j != 0:
h_start = random.randrange(0, j)
if i != 0:
w_start = random.randrange(0, i)
img = img[h_start:h_start + side, w_start:w_start + side, ...]
else:
if side <= self.target_size:
j = (imgh - side)
i = (imgw - side)
h_start = 0
w_start = 0
if j != 0:
h_start = random.randrange(0, j)
if i != 0:
w_start = random.randrange(0, i)
img = img[h_start:h_start + side, w_start:w_start + side, ...]
else:
side = random.randrange(self.target_size, side)
j = (imgh - side)
i = (imgw - side)
h_start = random.randrange(0, j)
w_start = random.randrange(0, i)
img = img[h_start:h_start + side, w_start:w_start + side, ...]
# img = scipy.misc.imresize(img, [self.target_size, self.target_size])
img = np.array(Image.fromarray(img).resize(size=(self.target_size, self.target_size)))
return img
def to_tensor(self, img):
img = Image.fromarray(img)
img_t = F.to_tensor(img).float()
return img_t
def load_list(self, path):
if isinstance(path, str):
if path[-3:] == "txt":
line = open(path,"r")
lines = line.readlines()
file_names = []
for line in lines:
file_names.append("../../Dataset/Places2/train/data_256"+line.split(" ")[0])
return file_names
if os.path.isdir(path):
path = list(glob.glob(path + '/*.jpg')) + list(glob.glob(path + '/*.png'))
path.sort()
return path
if os.path.isfile(path):
try:
return np.genfromtxt(path, dtype=np.str, encoding='utf-8')
except:
return [path]
return []
def generate_stroke_mask(im_size, max_parts=15, maxVertex=25, maxLength=100, maxBrushWidth=24, maxAngle=360):
mask = np.zeros((im_size[0], im_size[1], 1), dtype=np.float32)
parts = random.randint(1, max_parts)
for i in range(parts):
mask = mask + np_free_form_mask(maxVertex, maxLength, maxBrushWidth, maxAngle, im_size[0], im_size[1])
mask = np.minimum(mask, 1.0)
mask = np.concatenate([mask, mask, mask], axis = 2)
return mask
def np_free_form_mask(maxVertex, maxLength, maxBrushWidth, maxAngle, h, w):
mask = np.zeros((h, w, 1), np.float32)
numVertex = np.random.randint(maxVertex + 1)
startY = np.random.randint(h)
startX = np.random.randint(w)
brushWidth = 0
for i in range(numVertex):
angle = np.random.randint(maxAngle + 1)
angle = angle / 360.0 * 2 * np.pi
if i % 2 == 0:
angle = 2 * np.pi - angle
length = np.random.randint(maxLength + 1)
brushWidth = np.random.randint(10, maxBrushWidth + 1) // 2 * 2
nextY = startY + length * np.cos(angle)
nextX = startX + length * np.sin(angle)
nextY = np.maximum(np.minimum(nextY, h - 1), 0).astype(np.int)
nextX = np.maximum(np.minimum(nextX, w - 1), 0).astype(np.int)
cv2.line(mask, (startY, startX), (nextY, nextX), 1, brushWidth)
cv2.circle(mask, (startY, startX), brushWidth // 2, 2)
startY, startX = nextY, nextX
cv2.circle(mask, (startY, startX), brushWidth // 2, 2)
return mask