forked from PaddlePaddle/PaddleSeg
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathprepare_msd_brain_seg.py
169 lines (156 loc) · 6.91 KB
/
prepare_msd_brain_seg.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
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import os.path as osp
import sys
import time
import numpy as np
import nibabel as nib
import SimpleITK as sitk
from tqdm import tqdm
sys.path.append(osp.join(osp.dirname(osp.realpath(__file__)), ""))
from prepare import Prep
tasks = {
1: {
"Task01_BrainTumour.tar":
"https://bj.bcebos.com/v1/ai-studio-online/netdisk/975fea1d4c8549b883b2b4bb7e6a82de84392a6edd054948b46ced0f117fd701?responseContentDisposition=attachment%3B%20filename%3DTask01_BrainTumour.tar&authorization=bce-auth-v1%2F0ef6765c1e494918bc0d4c3ca3e5c6d1%2F2022-01-21T18%3A50%3A30Z%2F-1%2F%2F283ea6f8700c129903e3278ea38a54eac2cf087e7f65197268739371898aa1b3"
}
}
class PrepMSDBrain(Prep):
def __init__(self, task_id):
task_name = list(tasks[task_id].keys())[0].split('.')[0]
print(f"Preparing task {task_id} {task_name}")
super().__init__(
dataset_root=f"data/{task_name}",
raw_dataset_dir=f"{task_name}_raw/",
images_dir=f"{task_name}/{task_name}/imagesTr",
labels_dir=f"{task_name}/{task_name}/labelsTr",
phase_dir=f"{task_name}_phase0/",
urls=tasks[task_id],
valid_suffix=("nii.gz", "nii.gz"),
filter_key=(None, None),
uncompress_params={"format": "tar",
"num_files": 1})
self.preprocess = {"images": [], "labels": []}
def generate_txt(self, train_split=0.8, test_split=0.95):
"""generate the train_list.txt and val_list.txt"""
txtname = [
osp.join(self.phase_path, 'train_list.txt'),
osp.join(self.phase_path, 'val_list.txt'),
osp.join(self.phase_path, 'test_list.txt')
]
image_files_npy = os.listdir(self.image_path)
label_files_npy = os.listdir(self.label_path)
self.split_files_txt(txtname[0], image_files_npy, label_files_npy,
train_split, test_split)
self.split_files_txt(txtname[1], image_files_npy, label_files_npy,
train_split, test_split)
self.split_files_txt(txtname[2], image_files_npy, label_files_npy,
train_split, test_split)
def split_files_txt(self,
txt,
image_files,
label_files=None,
split=None,
testsplit=None):
split = int(split * len(image_files))
testsplit = int(testsplit * len(image_files))
if "train" in txt:
image_names = image_files[:split]
label_names = label_files[:split]
elif "val" in txt:
# set the valset to 20% of images if all files need to be used in training
image_names = image_files[split:testsplit]
label_names = label_files[split:testsplit]
elif "test" in txt:
image_names = image_files[testsplit:]
label_names = label_files[testsplit:]
else:
raise NotImplementedError(
"The txt split except for train.txt, val.txt and test.txt is not implemented yet."
)
self.write_txt(txt, image_names, label_names)
@staticmethod
def load_medical_data(f):
"""
load data of different format into numpy array, return data is in xyz
f: the complete path to the file that you want to load
"""
filename = osp.basename(f).lower()
images = []
# validate nii.gz on lung and mri with correct spacing_resample
if filename.endswith((".nii", ".nii.gz", ".dcm")):
if "radiopaedia" in filename or "corona" in filename:
f_nps = [nib.load(f).get_fdata(dtype=np.float32)]
else:
itkimage = sitk.ReadImage(f)
if itkimage.GetDimension() == 4:
images = [itkimage]
else:
images = [itkimage]
f_nps = [sitk.GetArrayFromImage(img) for img in images]
return f_nps
def load_save(self):
"""
preprocess files, transfer to the correct type, and save it to the directory.
"""
print(
"Start convert images to numpy array using {}, please wait patiently"
.format(self.gpu_tag))
tic = time.time()
process_files = (self.image_files, self.label_files)
process_tuple = ("images", "labels")
save_tuple = (self.image_path, self.label_path)
for i, files in enumerate(process_files):
pre = self.preprocess[process_tuple[i]]
savepath = save_tuple[i]
for f in tqdm(
files,
total=len(files),
desc="preprocessing the {}".format(
["images", "labels", "images_test"][i])):
# load data will transpose the image from "zyx" to "xyz"
spacing = (1, 1, 1)
f_nps = self.load_medical_data(f)
for volume_idx, f_np in enumerate(f_nps):
for op in pre:
if op.__name__ == "resample":
f_np, new_spacing = op(
f_np,
spacing=spacing) # (960, 15, 960) if transpose
else:
f_np = op(f_np)
f_np = f_np.astype("float32") if i == 0 else f_np.astype(
"int32")
volume_idx = "" if len(f_nps) == 1 else f"-{volume_idx}"
np.save(
os.path.join(
savepath,
osp.basename(f).split(".")[0] + volume_idx), f_np)
print("The preprocess time on {} is {}".format(self.gpu_tag,
time.time() - tic))
if __name__ == "__main__":
if len(sys.argv) != 2:
print(
"Please provide task id. Example usage: \n\t python tools/prepare_msd.py 1 # for preparing MSD task 1"
)
try:
task_id = int(sys.argv[1])
except ValueError:
print(
f"Expecting number as command line argument, got {sys.argv[1]}. Example usage: \n\t python tools/prepare_msd.py 1 # for preparing MSD task 1"
)
prep = PrepMSDBrain(task_id)
prep.load_save()
prep.generate_txt()