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from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import torchio as tio
import pickle
import numpy as np
import os
import torch
import SimpleITK as sitk
from prefetch_generator import BackgroundGenerator
from monai.transforms import (
Compose,
RandCropByPosNegLabeld,
ScaleIntensityRanged,
NormalizeIntensityd,
RandShiftIntensityd,
RandZoomd,
)
import cc3d, math
class Dataset_promise(Dataset):
def __init__(self, data, data_dir, split='train', image_size=128, transform=None, pcc=False, args=None):
self.args = args
self.data = data
self.paths = data_dir
self._set_file_paths(self.paths, split)
self._set_dataset_stat()
self.image_size = (image_size, image_size, image_size)
self.transform = transform
self.threshold = 0
self.split = split
self.pcc = pcc
self.monai_transforms = self._get_transforms(split=split)
self.cc = 1
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())
if sitk_image.GetSpacing() != sitk_label.GetSpacing():
sitk_label.SetSpacing(sitk_image.GetSpacing())
subject = tio.Subject(
image=tio.ScalarImage.from_sitk(sitk_image),
label=tio.LabelMap.from_sitk(sitk_label),
)
subject_save = tio.Subject(
image=tio.ScalarImage.from_sitk(sitk_image),
label=tio.LabelMap.from_sitk(sitk_label),
)
if self.data == 'lits':
b = subject.label.data
a = tio.CropOrPad._bbox_mask(b[0].cpu().numpy())
w, h, d = a[1][0] - a[0][0], a[1][1] - a[0][1], a[1][2] - a[0][2]
w, h, d = max(w + 20, 128), max(h + 20, 128), max(d + 20, 128)
crop_transform = tio.CropOrPad(mask_name='label', target_shape=(w, h, d))
subject = crop_transform(subject)
subject_save = crop_transform(subject_save)
if self.target_label != 0:
subject = self._binary_label(subject)
subject_save = self._binary_label(subject_save)
if self.transform:
try:
subject = self.transform(subject)
subject_save = self.transform(subject_save)
except:
print(self.image_paths[index])
if (self.pcc):
subject = self._pcc(subject)
if subject.label.data.sum() <= self.threshold:
print(self.image_paths[index], 'label volume too small')
if self.split == 'train':
return self.__getitem__(np.random.randint(self.__len__()))
#return self.__getitem__(0)
else:
if self.data == 'lits':
return subject, self.image_paths[index]
else:
return subject.image.data.clone().detach(), subject.label.data.clone().detach(), self.image_paths[index]
if self.split == "train":
trans_dict = self.monai_transforms({"image": subject.image.data.clone().detach(),
"label": subject.label.data.clone().detach()})[0]
img_aug, seg_aug = trans_dict["image"], trans_dict["label"]
return img_aug.float(), seg_aug.float(), self.image_paths[index]
else:
if self.data == 'lits':
trans_dict = self.monai_transforms({"image": subject.image.data.clone().detach()})
subject.image.data = trans_dict["image"]
return subject, self.image_paths[index], subject_save
if self.data == 'kits':
subject = self._separate_crop(subject)
crop_transform = tio.CropOrPad(mask_name='label', target_shape=self.image_size)
subject = crop_transform(subject)
subject_save = crop_transform(subject_save)
trans_dict = self.monai_transforms({"image": subject.image.data.clone().detach()})
img_aug = trans_dict["image"]
return img_aug, subject.label.data.clone().detach(), self.image_paths[index], subject_save
def _separate_crop(self, subject):
label = subject.label.data
labels_out, N = cc3d.connected_components(label[0].cpu().numpy(), return_N=True)
crop_transform = tio.CropOrPad(mask_name='label', target_shape=self.image_size)
mid_cut = 0
if N > 1:
label_1, label_2 = torch.zeros_like(label), torch.zeros_like(label)
# left, right
mid_cut = math.ceil(label.size(1) / 2)
label_1[0, 0: mid_cut, :], label_2[0, mid_cut: -1, :] = label[0, 0: mid_cut, :], label[0, mid_cut: -1, :] # left, right
image_1, image_2 = subject.image.data, subject.image.data
subject_1 = tio.Subject(image=tio.ScalarImage(tensor=image_1), label=tio.LabelMap(tensor=label_1))
subject_2 = tio.Subject(image=tio.ScalarImage(tensor=image_2), label=tio.LabelMap(tensor=label_2))
subject_1, subject_2 = crop_transform(subject_1), crop_transform(subject_2)
# found 2 connected components for some cases (e.g. case 289), use below to eliminate
# however, this will bring warnings, but it's okay
if torch.unique(subject_2.label.data).size(0) == 1:
subject.image.data, subject.label.data = subject_1.image.data, subject_1.label.data
elif torch.unique(subject_1.label.data).size(0) == 1:
subject.image.data, subject.label.data = subject_2.image.data, subject_2.label.data
else:
subject.image.data = torch.cat([subject_1.image.data, subject_2.image.data], dim=0)
subject.label.data = torch.cat([subject_1.label.data, subject_2.label.data], dim=0)
else:
subject = crop_transform(subject)
return subject
def _set_file_paths(self, data_dir, split):
self.image_paths = []
self.label_paths = []
split_file = "split.pkl"
dataset_split = os.path.join(data_dir, split_file)
if not os.path.exists(dataset_split):
alt_dir = os.path.join(data_dir, "Task01_LITS17")
alt_split = os.path.join(alt_dir, split_file)
if os.path.exists(alt_split):
data_dir = alt_dir
dataset_split = alt_split
if not os.path.exists(dataset_split):
raise FileNotFoundError(f"split.pkl not found under {data_dir}")
with open(dataset_split, "rb") as f:
d = pickle.load(f)[0][split]
self.image_paths = [os.path.join(data_dir, d[i][0].strip("/")) for i in list(d.keys())]
self.label_paths = [os.path.join(data_dir, d[i][1].strip("/")) for i in list(d.keys())]
def _set_dataset_stat(self):
self.target_label = 0
if self.data == 'colon':
self.intensity_range, self.global_mean, self.global_std = (-57, 175), 65.175035, 32.651197
elif self.data == 'pancreas':
self.intensity_range, self.global_mean, self.global_std = (-39, 204), 68.45214, 63.422806
self.target_label = 2
elif self.data == 'lits':
self.intensity_range, self.global_mean, self.global_std = (-48, 163), 60.057533, 40.198017
self.target_label = 2
elif self.data == 'kits':
self.intensity_range, self.global_mean, self.global_std = (-54, 247), 59.53867, 55.457336
self.target_label = 2
def _get_transforms(self, split):
if split == "train":
transforms = Compose(
[
ScaleIntensityRanged(
keys=["image"],
a_min=self.intensity_range[0],
a_max=self.intensity_range[1],
b_min=self.intensity_range[0],
b_max=self.intensity_range[1],
clip=True,
),
RandCropByPosNegLabeld(
keys=["image", "label"],
spatial_size=(128, 128, 128),
label_key="label",
pos=2,
neg=0,
num_samples=1,
),
RandShiftIntensityd(keys=["image"], offsets=20, prob=0.5),
NormalizeIntensityd(keys=["image"], subtrahend=self.global_mean, divisor=self.global_std),
RandZoomd(keys=["image", "label"], prob=0.8, min_zoom=0.85, max_zoom=1.25,
mode=["trilinear", "nearest"]),
])
else:
transforms = Compose(
[
ScaleIntensityRanged(
keys=["image"],
a_min=self.intensity_range[0],
a_max=self.intensity_range[1],
b_min=self.intensity_range[0],
b_max=self.intensity_range[1],
clip=True,
),
NormalizeIntensityd(keys=["image"], subtrahend=self.global_mean, divisor=self.global_std),
]
)
return transforms
def _binary_label(self, subject):
label = subject.label.data
label = (label == self.target_label)
subject.label.data = label.float()
return subject
def _pcc(self, subject):
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)(subject)
return subject
class Dataloader_promise(DataLoader):
def __iter__(self):
return BackgroundGenerator(super().__iter__())
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