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from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import torchio as tio
from torchio.data.io import sitk_to_nib
import torch
import numpy as np
import os
import torch
import SimpleITK as sitk
from prefetch_generator import BackgroundGenerator
class Dataset_Union_ALL(Dataset):
def __init__(
self,
paths,
mode="train",
data_type="Tr",
image_size=128,
transform=None,
threshold=500,
split_num=1,
split_idx=0,
pcc=False,
get_all_meta_info=False,
):
self.paths = paths
self.data_type = data_type
self.split_num = split_num
self.split_idx = split_idx
self._set_file_paths(self.paths)
self.image_size = image_size
self.transform = transform
self.threshold = threshold
self.mode = mode
self.pcc = pcc
self.get_all_meta_info = get_all_meta_info
def __len__(self):
return len(self.label_paths)
def __getitem__(self, index):
sitk_image = sitk.ReadImage(self.image_paths[index])
sitk_label = sitk.ReadImage(self.label_paths[index])
if sitk_image.GetOrigin() != sitk_label.GetOrigin():
sitk_image.SetOrigin(sitk_label.GetOrigin())
if sitk_image.GetDirection() != sitk_label.GetDirection():
sitk_image.SetDirection(sitk_label.GetDirection())
sitk_image_arr, _ = sitk_to_nib(sitk_image)
sitk_label_arr, _ = sitk_to_nib(sitk_label)
subject = tio.Subject(
image=tio.ScalarImage(tensor=sitk_image_arr),
label=tio.LabelMap(tensor=sitk_label_arr),
)
if "/ct_" in self.image_paths[index]:
subject = tio.Clamp(-1000, 1000)(subject)
if self.transform:
try:
subject = self.transform(subject)
except:
print(self.image_paths[index])
if self.pcc:
print("using pcc setting")
# crop from random click point
random_index = torch.argwhere(subject.label.data == 1)
if len(random_index) >= 1:
random_index = random_index[np.random.randint(0, len(random_index))]
# print(random_index)
crop_mask = torch.zeros_like(subject.label.data)
# print(crop_mask.shape)
crop_mask[random_index[0]][random_index[1]][random_index[2]][
random_index[3]
] = 1
subject.add_image(
tio.LabelMap(tensor=crop_mask, affine=subject.label.affine),
image_name="crop_mask",
)
subject = tio.CropOrPad(
mask_name="crop_mask",
target_shape=(self.image_size, self.image_size, self.image_size),
)(subject)
if subject.label.data.sum() <= self.threshold:
return self.__getitem__(np.random.randint(self.__len__()))
if self.mode == "train" and self.data_type == "Tr":
return {
"image": subject.image.data.clone().detach(),
"label": subject.label.data.clone().detach(),
}
elif self.get_all_meta_info:
meta_info = {
"image_path": self.image_paths[index],
"origin": sitk_label.GetOrigin(),
"direction": sitk_label.GetDirection(),
"spacing": sitk_label.GetSpacing(),
}
return {
"image": subject.image.data.clone().detach(),
"label": subject.label.data.clone().detach(),
"meta_info": meta_info
}
else:
return {
"image": subject.image.data.clone().detach(),
"label": subject.label.data.clone().detach(),
"path": self.image_paths[index],
}
def _set_file_paths(self, paths):
self.image_paths = []
self.label_paths = []
# if ${path}/labelsTr exists, search all .nii.gz
for path in paths:
d = os.path.join(path, f"labels{self.data_type}")
if os.path.exists(d):
for name in os.listdir(d):
base = os.path.basename(name).split(".nii.gz")[0]
label_path = os.path.join(
path, f"labels{self.data_type}", f"{base}.nii.gz"
)
self.image_paths.append(label_path.replace("labels", "images"))
self.label_paths.append(label_path)
class Dataset_Union_ALL_Val(Dataset_Union_ALL):
def _set_file_paths(self, paths):
self.image_paths = []
self.label_paths = []
# if ${path}/labelsTr exists, search all .nii.gz
for path in paths:
for dt in ["Tr", "Val", "Ts"]:
d = os.path.join(path, f"labels{dt}")
if os.path.exists(d):
for name in os.listdir(d):
base = os.path.basename(name).split(".nii.gz")[0]
label_path = os.path.join(path, f"labels{dt}", f"{base}.nii.gz")
self.image_paths.append(label_path.replace("labels", "images"))
self.label_paths.append(label_path)
self.image_paths = self.image_paths[self.split_idx :: self.split_num]
self.label_paths = self.label_paths[self.split_idx :: self.split_num]
class Dataset_Union_ALL_Infer(Dataset):
"""Only for inference, no label is returned from __getitem__."""
def __init__(
self,
paths,
data_type="infer",
image_size=128,
transform=None,
split_num=1,
split_idx=0,
pcc=False,
get_all_meta_info=False,
):
self.paths = paths
self.data_type = data_type
self.split_num = split_num
self.split_idx = split_idx
self._set_file_paths(self.paths)
self.image_size = image_size
self.transform = transform
self.pcc = pcc
self.get_all_meta_info = get_all_meta_info
def __len__(self):
return len(self.image_paths)
def __getitem__(self, index):
sitk_image = sitk.ReadImage(self.image_paths[index])
sitk_image_arr, _ = sitk_to_nib(sitk_image)
subject = tio.Subject(
image=tio.ScalarImage(tensor=sitk_image_arr),
)
if "/ct_" in self.image_paths[index]:
subject = tio.Clamp(-1000, 1000)(subject)
if self.transform:
try:
subject = self.transform(subject)
except:
print("Could not transform", self.image_paths[index])
if self.pcc:
print("using pcc setting")
# crop from random click point
random_index = torch.argwhere(subject.label.data == 1)
if len(random_index) >= 1:
random_index = random_index[np.random.randint(0, len(random_index))]
crop_mask = torch.zeros_like(subject.label.data)
crop_mask[random_index[0]][random_index[1]][random_index[2]][
random_index[3]
] = 1
subject.add_image(
tio.LabelMap(tensor=crop_mask, affine=subject.label.affine),
image_name="crop_mask",
)
subject = tio.CropOrPad(
mask_name="crop_mask",
target_shape=(self.image_size, self.image_size, self.image_size),
)(subject)
elif self.get_all_meta_info:
meta_info = {
"image_path": self.image_paths[index],
"direction": sitk_image.GetDirection(),
"origin": sitk_image.GetOrigin(),
"spacing": sitk_image.GetSpacing(),
}
return subject.image.data.clone().detach(), meta_info
else:
return subject.image.data.clone().detach(), self.image_paths[index]
def _set_file_paths(self, paths):
self.image_paths = []
# if ${path}/infer exists, search all .nii.gz
for path in paths:
d = os.path.join(path, f"{self.data_type}")
if os.path.exists(d):
for name in os.listdir(d):
base = os.path.basename(name).split(".nii.gz")[0]
image_path = os.path.join(
path, f"{self.data_type}", f"{base}.nii.gz"
)
self.image_paths.append(image_path)
self.image_paths = self.image_paths[self.split_idx :: self.split_num]
class Union_Dataloader(tio.SubjectsLoader):
def __iter__(self):
return BackgroundGenerator(super().__iter__())
class Test_Single(Dataset):
def __init__(self, paths, image_size=128, transform=None, threshold=500):
self.paths = paths
self._set_file_paths(self.paths)
self.image_size = image_size
self.transform = transform
self.threshold = threshold
def __len__(self):
return len(self.label_paths)
def __getitem__(self, index):
sitk_image = sitk.ReadImage(self.image_paths[index])
sitk_label = sitk.ReadImage(self.label_paths[index])
if sitk_image.GetOrigin() != sitk_label.GetOrigin():
sitk_image.SetOrigin(sitk_label.GetOrigin())
if sitk_image.GetDirection() != sitk_label.GetDirection():
sitk_image.SetDirection(sitk_label.GetDirection())
subject = tio.Subject(
image=tio.ScalarImage.from_sitk(sitk_image),
label=tio.LabelMap.from_sitk(sitk_label),
)
if "/ct_" in self.image_paths[index]:
subject = tio.Clamp(-1000, 1000)(subject)
if self.transform:
try:
subject = self.transform(subject)
except:
print(self.image_paths[index])
if subject.label.data.sum() <= self.threshold:
return self.__getitem__(np.random.randint(self.__len__()))
return (
subject.image.data.clone().detach(),
subject.label.data.clone().detach(),
self.image_paths[index],
)
def _set_file_paths(self, paths):
self.image_paths = []
self.label_paths = []
self.image_paths.append(paths)
self.label_paths.append(paths.replace("images", "labels"))
if __name__ == "__main__":
test_dataset = Dataset_Union_ALL_Infer(
paths=['./data/inference/heart/hearts/',],
data_type='infer',
transform=tio.Compose([
tio.ToCanonical(),
tio.CropOrPad(target_shape=(128,128,128)),
]),
pcc=False,
get_all_meta_info=True,
split_idx = 0,
split_num = 1,
)
# test_dataset = Dataset_Union_ALL_Val(
# paths=["./data/validation/experimental/heart/hearts"],
# mode="Val",
# transform=tio.Compose(
# [
# tio.ToCanonical(),
# tio.CropOrPad(target_shape=(128, 128, 128)),
# ]
# ),
# threshold=0,
# pcc=False,
# get_all_meta_info=True,
# )
test_dataloader = DataLoader(
dataset=test_dataset, sampler=None, batch_size=1, shuffle=True
)
print(len(test_dataset))
# for i, j, n in test_dataloader:
for i, j in test_dataloader:
print(i.shape)
# print(j.shape)
# print(n)
print(j)
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