-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathdataset.py
More file actions
191 lines (135 loc) · 7.67 KB
/
Copy pathdataset.py
File metadata and controls
191 lines (135 loc) · 7.67 KB
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
from torch.utils.data import Dataset
import nibabel as nib
from os.path import join
import numpy as np
import pandas as pd
import torch
class niiDataset(Dataset):
# Pour les modèles ne prenant en entier que les matrices de doses (format nii)
def __init__(self, annotations_file, path_to_dir, min_card_age,
training=False, validation=False, transform=None, target_transform=None):
# annotations_file : nom du fichier contenant les labels (0 ou 1)
# path_to_dir : chemin vers le dossier contenant les images (dans le sous-dossier nii), les labels (annotations_file)
# min_card_age : entier de valeur 40 ou 50, correspond au temps de censure minimal
# training, validation : booleans
# transform, feature_transform : if needed.
assert min_card_age == 40 or min_card_age == 50
self.labels_csv = pd.read_csv(join(path_to_dir, annotations_file), sep=',')
if training:
self.img_labels = self.labels_csv[self.labels_csv[f'train_{min_card_age}'] == 1]
elif validation:
self.img_labels = self.labels_csv[self.labels_csv[f'val_{min_card_age}'] == 1]
self.path_to_dir = path_to_dir
self.img_dir = join(self.path_to_dir, 'nii')
self.transform = transform
self.target_transform = target_transform
def __len__(self):
return len(self.img_labels)
def __getitem__(self, idx):
img_path = join(self.img_dir, self.img_labels.iloc[idx]["file_name"])
image = np.squeeze(np.asanyarray(nib.load(img_path).dataobj))[np.newaxis]
image_cropped = image[0:1, 2:66, 3:67, 3:67]
label = self.img_labels.iloc[idx]['Pathologie_cardiaque_3_new']
if self.transform:
image = self.transform(image)
if self.target_transform:
label = self.target_transform(label)
return image_cropped, label
class NiiFeatureDataset(Dataset):
# Pour les modèles prenants en entrée des matrices de doses et des variables cliniques
def __init__(self, annotations_file, feature_file, path_to_dir, min_card_age,
training=False, validation=False, transform=None, feature_transform=None):
# annotations_file : nom du fichier contenant les labels (0 ou 1)
# feature_file : nom du ficher contenant les variable cliniques de la chimiothérapie. Ce fichier est situé dans le
# même dossier que "annotations_file", accessible grâce à "path_to_dir"
# path_to_dir : chemin vers le dossier contenant les images (dans le sous-dossier nii), les labels (annotations_file)
# et les features cliniques (feature_file)
# min_card_age : entier de valeur 40 ou 50, correspond au temps de censure minimal
# training, validation : booleans
# transform, feature_transform : if needed.
assert min_card_age == 40 or min_card_age == 50
self.labels_csv = pd.read_csv(join(path_to_dir, annotations_file), sep=',')
self.feature_csv = pd.read_csv(join(path_to_dir, feature_file), sep=',', index_col="file_name")
#self.feature_list = self.feature_csv.columns[3:] ### all features
self.feature_list = ['do_ANTHRA', 'do_ALKYL','do_VINCA'] ### only anthra, alkyl and vinca
if training:
self.labels_csv = self.labels_csv[self.labels_csv[f'train_{min_card_age}'] == 1]
elif validation:
self.labels_csv = self.labels_csv[self.labels_csv[f'val_{min_card_age}'] == 1]
self.path_to_dir = path_to_dir
self.img_dir = join(self.path_to_dir, 'nii')
self.transform = transform
self.feature_transform = feature_transform
def __len__(self):
return len(self.labels_csv)
def __getitem__(self, idx):
img_name = self.labels_csv.iloc[idx]["file_name"]
image = np.squeeze(np.asanyarray(nib.load(join(self.img_dir, img_name)).dataobj))[np.newaxis]
image = torch.tensor(image[0:1, 2:66, 3:67, 3:67])
label = self.labels_csv.iloc[idx]['Pathologie_cardiaque_3_new']
features = torch.tensor(self.labels_csv.iloc[idx][self.feature_list]) ### Only vinca, alkyl et anthra
#features = torch.tensor(self.feature_csv.iloc[idx][self.feature_list]) ### all features
if self.transform:
image = self.transform(image)
if self.feature_transform:
features = self.feature_transform(features)
return (image, features), label
class TestDataset(Dataset):
# Pour tester les modèles ne prenant en entrée que les matrices de doses
def __init__(self, annotations_file, path_to_dir, min_card_age,
testing=True, transform=None, target_transform=None):
# annotations_file : nom du fichier contenant les labels (0 ou 1)
# path_to_dir : chemin vers le dossier contenant les images (dans le sous-dossier nii), les labels (annotations_file)
# min_card_age : entier de valeur 40 ou 50, correspond au temps de censure minimal
# training, validation : booleans
# transform, feature_transform : if needed.
assert min_card_age == 40 or min_card_age == 50
self.labels_csv = pd.read_csv(join(path_to_dir, annotations_file), sep=',')
if testing:
self.img_labels = self.labels_csv[self.labels_csv[f'test_{min_card_age}'] == 1]
self.path_to_dir = path_to_dir
self.img_dir = join(self.path_to_dir, 'nii')
self.transform = transform
self.target_transform = target_transform
def __len__(self):
return len(self.img_labels)
def __getitem__(self, idx):
img_path = join(self.img_dir, self.img_labels.iloc[idx]["file_name"])
image = np.squeeze(np.asanyarray(nib.load(img_path).dataobj))[np.newaxis]
image_cropped = image[0:1, 2:66, 3:67, 3:67]
label = self.img_labels.iloc[idx]['Pathologie_cardiaque_3_new']
if self.transform:
image = self.transform(image)
if self.target_transform:
label = self.target_transform(label)
return image_cropped, label
class FeatTestDataset(Dataset):
# Pour tester les modèles prenant en entrée les matrices de doses et les variables cliniques
def __init__(self, annotations_file, path_to_dir, min_card_age,
testing=True, transform=None, target_transform=None):
# annotations_file : nom du fichier contenant les labels (0 ou 1)
# feature_file : nom du ficher contenant les variable cliniques de la chimiothérapie. Ce fichier est situé dans le
# même dossier que "annotations_file", accessible grâce à "path_to_dir"
# path_to_dir : chemin vers le dossier contenant les images (dans le sous-dossier nii), les labels (annotations_file)
# et les features cliniques (feature_file)
# min_card_age : entier de valeur 40 ou 50, correspond au temps de censure minimal
# training, validation : booleans
# transform, feature_transform : if needed.
assert min_card_age == 40 or min_card_age == 50
self.labels_csv = pd.read_csv(join(path_to_dir, annotations_file), sep=',')
self.feature_list = ['do_ANTHRA', 'do_ALKYL','do_VINCA'] ### only anthra, alkyl and vinca
if testing:
self.img_labels = self.labels_csv[self.labels_csv[f'test_{min_card_age}'] == 1]
self.path_to_dir = path_to_dir
self.img_dir = join(self.path_to_dir, 'nii')
self.transform = transform
self.target_transform = target_transform
def __len__(self):
return len(self.img_labels)
def __getitem__(self, idx):
img_path = join(self.img_dir, self.img_labels.iloc[idx]["file_name"])
image = np.squeeze(np.asanyarray(nib.load(img_path).dataobj))[np.newaxis]
image = image[0:1, 2:66, 3:67, 3:67]
label = self.img_labels.iloc[idx]['Pathologie_cardiaque_3_new']
features = torch.tensor(self.labels_csv.iloc[idx][self.feature_list]) ### Only vinca, alkyl et anthra
return (image, features), label