-
Notifications
You must be signed in to change notification settings - Fork 49
/
Load_Dataset.py
184 lines (161 loc) · 6.78 KB
/
Load_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
# -*- coding: utf-8 -*-
# @Time : 2021/6/19 11:30 上午
# @Author : Haonan Wang
# @File : Load_Dataset.py
# @Software: PyCharm
import numpy as np
import torch
import random
from scipy.ndimage.interpolation import zoom
from torch.utils.data import Dataset
from torchvision import transforms as T
from torchvision.transforms import functional as F
from typing import Callable
import os
import cv2
from scipy import ndimage
def random_rot_flip(image, label):
k = np.random.randint(0, 4)
image = np.rot90(image, k)
label = np.rot90(label, k)
axis = np.random.randint(0, 2)
image = np.flip(image, axis=axis).copy()
label = np.flip(label, axis=axis).copy()
return image, label
def random_rotate(image, label):
angle = np.random.randint(-20, 20)
image = ndimage.rotate(image, angle, order=0, reshape=False)
label = ndimage.rotate(label, angle, order=0, reshape=False)
return image, label
class RandomGenerator(object):
def __init__(self, output_size):
self.output_size = output_size
def __call__(self, sample):
image, label = sample['image'], sample['label']
image, label = F.to_pil_image(image), F.to_pil_image(label)
x, y = image.size
if random.random() > 0.5:
image, label = random_rot_flip(image, label)
elif random.random() < 0.5:
image, label = random_rotate(image, label)
if x != self.output_size[0] or y != self.output_size[1]:
image = zoom(image, (self.output_size[0] / x, self.output_size[1] / y), order=3) # why not 3?
label = zoom(label, (self.output_size[0] / x, self.output_size[1] / y), order=0)
image = F.to_tensor(image)
label = to_long_tensor(label)
sample = {'image': image, 'label': label}
return sample
class ValGenerator(object):
def __init__(self, output_size):
self.output_size = output_size
def __call__(self, sample):
image, label = sample['image'], sample['label']
image, label = F.to_pil_image(image), F.to_pil_image(label)
x, y = image.size
if x != self.output_size[0] or y != self.output_size[1]:
image = zoom(image, (self.output_size[0] / x, self.output_size[1] / y), order=3) # why not 3?
label = zoom(label, (self.output_size[0] / x, self.output_size[1] / y), order=0)
image = F.to_tensor(image)
label = to_long_tensor(label)
sample = {'image': image, 'label': label}
return sample
def to_long_tensor(pic):
# handle numpy array
img = torch.from_numpy(np.array(pic, np.uint8))
# backward compatibility
return img.long()
def correct_dims(*images):
corr_images = []
# print(images)
for img in images:
if len(img.shape) == 2:
corr_images.append(np.expand_dims(img, axis=2))
else:
corr_images.append(img)
if len(corr_images) == 1:
return corr_images[0]
else:
return corr_images
class ImageToImage2D(Dataset):
"""
Reads the images and applies the augmentation transform on them.
Usage:
1. If used without the unet.model.Model wrapper, an instance of this object should be passed to
torch.utils.data.DataLoader. Iterating through this returns the tuple of image, mask and image
filename.
2. With unet.model.Model wrapper, an instance of this object should be passed as train or validation
datasets.
Args:
dataset_path: path to the dataset. Structure of the dataset should be:
dataset_path
|-- images
|-- img001.png
|-- img002.png
|-- ...
|-- masks
|-- img001.png
|-- img002.png
|-- ...
joint_transform: augmentation transform, an instance of JointTransform2D. If bool(joint_transform)
evaluates to False, torchvision.transforms.ToTensor will be used on both image and mask.
one_hot_mask: bool, if True, returns the mask in one-hot encoded form.
"""
def __init__(self, dataset_path: str, joint_transform: Callable = None, one_hot_mask: int = False, image_size: int =224) -> None:
self.dataset_path = dataset_path
self.image_size = image_size
self.input_path = os.path.join(dataset_path, 'img')
self.output_path = os.path.join(dataset_path, 'labelcol')
self.images_list = os.listdir(self.input_path)
self.one_hot_mask = one_hot_mask
if joint_transform:
self.joint_transform = joint_transform
else:
to_tensor = T.ToTensor()
self.joint_transform = lambda x, y: (to_tensor(x), to_tensor(y))
def __len__(self):
return len(os.listdir(self.input_path))
def __getitem__(self, idx):
image_filename = self.images_list[idx]
#print(image_filename[: -3])
# read image
# print(os.path.join(self.input_path, image_filename))
# print(os.path.join(self.output_path, image_filename[: -3] + "png"))
# print(os.path.join(self.input_path, image_filename))
image = cv2.imread(os.path.join(self.input_path, image_filename))
# print("img",image_filename)
# print("1",image.shape)
image = cv2.resize(image,(self.image_size,self.image_size))
# print(np.max(image), np.min(image))
# print("2",image.shape)
# read mask image
mask = cv2.imread(os.path.join(self.output_path, image_filename[: -3] + "png"),0)
# print("mask",image_filename[: -3] + "png")
# print(np.max(mask), np.min(mask))
mask = cv2.resize(mask,(self.image_size,self.image_size))
# print(np.max(mask), np.min(mask))
mask[mask<=0] = 0
# (mask == 35).astype(int)
mask[mask>0] = 1
# print("11111",np.max(mask), np.min(mask))
# correct dimensions if needed
image, mask = correct_dims(image, mask)
# image, mask = F.to_pil_image(image), F.to_pil_image(mask)
# print("11",image.shape)
# print("22",mask.shape)
sample = {'image': image, 'label': mask}
if self.joint_transform:
sample = self.joint_transform(sample)
# sample = {'image': image, 'label': mask}
# print("2222",np.max(mask), np.min(mask))
if self.one_hot_mask:
assert self.one_hot_mask > 0, 'one_hot_mask must be nonnegative'
mask = torch.zeros((self.one_hot_mask, mask.shape[1], mask.shape[2])).scatter_(0, mask.long(), 1)
# mask = np.swapaxes(mask,2,0)
# print(image.shape)
# print("mask",mask)
# mask = np.transpose(mask,(2,0,1))
# image = np.transpose(image,(2,0,1))
# print(image.shape)
# print(mask.shape)
# print(sample['image'].shape)
return sample, image_filename