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# import sys
# import os
# import numpy as np
# import torch
# from torch.utils.data.dataset import Dataset
# import glob
# import cv2
# import torchio as tio
# import matplotlib.pyplot as plt
# from torch.utils.data import DataLoader
# from sdf import compute_sdf
# PREPROCESSING_TRANSORMS_CT = tio.Compose([
# tio.Clamp(out_min=-250, out_max=800),
# tio.RescaleIntensity(in_min_max=(-250, 800),
# out_min_max=(-1.0, 1.0)),
# tio.CropOrPad(target_shape=(64, 64, 64))
# ])
# PREPROCESSING_TRANSORMS_MRI = tio.Compose([
# tio.Clamp(out_min=0, out_max=1000),
# tio.RescaleIntensity(in_min_max=(0, 1000),
# out_min_max=(-1.0, 1.0)),
# tio.CropOrPad(target_shape=(64, 64, 64))
# ])
# PREPROCESSING_MASK_TRANSORMS = tio.Compose([
# tio.CropOrPad(target_shape=(64, 64, 64))
# ])
# class MMWHS_Dataset(Dataset):
# def __init__(self, root_dir='', data_type='', mode = ''):
# self.root_dir = root_dir
# self.data_type = data_type
# self.mode = mode
# self.file_names = self.get_file_names()
# self.preprocessing_img_ct = PREPROCESSING_TRANSORMS_CT
# self.preprocessing_img_mri = PREPROCESSING_TRANSORMS_MRI
# self.preprocessing_mask = PREPROCESSING_MASK_TRANSORMS
# @staticmethod
# def create_mask(shape):
# return torch.zeros(shape, dtype=torch.uint8)
# @staticmethod
# def project_to_2d(mask):
# projection = torch.max(mask, dim=0)[0]
# return projection.numpy()
# @staticmethod
# def min_enclosing_circle(projection):
# points = np.column_stack(np.where(projection > 0))
# points = points.astype(np.float32)
# print(points.shape)
# (x, y), radius = cv2.minEnclosingCircle(points.astype(np.float32))
# center = (int(x), int(y))
# radius = int(radius)
# return center, radius
# @staticmethod
# def create_circle_mask_2d(shape, center, radius):
# mask = np.zeros(shape, dtype=np.uint8)
# cv2.circle(mask, center, radius, 1, thickness=-1)
# return mask
# @staticmethod
# def apply_circle_mask_to_3d(mask, circle_mask_2d):
# for i in range(mask.shape[0]):
# mask[i] = torch.from_numpy(circle_mask_2d)
# return mask
# def train_transform(self, image, label, p):
# TRAIN_TRANSFORMS = tio.Compose([
# tio.RandomFlip(axes=(1), flip_probability=p),
# ])
# image = TRAIN_TRANSFORMS(image)
# label = TRAIN_TRANSFORMS(label)
# return image, label
# def get_file_names(self):
# all_img_names = glob.glob(os.path.join(self.root_dir, './*image.nii.gz'), recursive=True)
# return all_img_names
# def __len__(self):
# return len(self.file_names)
# def __getitem__(self, index):
# img_path = self.file_names[index]
# mask_path = img_path.replace("image.nii.gz", "label.nii.gz")
# sdf_path = img_path.split()
# img = tio.ScalarImage(img_path)
# mask = tio.LabelMap(mask_path)
# name = img_path.split('/')[-1]
# name = name.split('.nii')[0]
# if self.data_type.upper() == 'CT':
# img = self.preprocessing_img_ct(img)
# elif self.data_type.upper() == 'MRI':
# img = self.preprocessing_img_mri(img)
# else:
# raise ValueError("Wrong Data Type!")
# mask = self.preprocessing_mask(mask)
# # p = np.random.choice([0, 1])
# # if self.mode == 'train':
# # img, mask = self.train_transform(img, mask, p)
# affine = img.affine
# mask = mask.data
# img = img.data
# label_1 = (mask == 1).float()
# label_1_sdf = compute_sdf(label_1)
# label_2 = (mask == 2).float()
# label_2_sdf = compute_sdf(label_2)
# label_3 = (mask == 3).float()
# label_3_sdf = compute_sdf(label_3)
# label_4 = (mask == 4).float()
# label_4_sdf = compute_sdf(label_4)
# label_5 = (mask == 5).float()
# label_5_sdf = compute_sdf(label_5)
# label = torch.cat((label_1, label_2, label_3, label_4, label_5), dim=0)
# label_sdf = torch.cat((torch.tensor(label_1_sdf), torch.tensor(label_2_sdf), torch.tensor(label_3_sdf), torch.tensor(label_4_sdf), torch.tensor(label_5_sdf)), dim=0)
# sdf_to_save = tio.ScalarImage(tensor=label_sdf, channels_last=False, affine=affine)
# os.makedirs(self.root_dir, exist_ok=True)
# sdf_to_save.save(os.path.join(self.root_dir, f"{name.replace('image', 'sdf')}.nii.gz"))
# return {
# 'name': name,
# 'img': img,
# 'mask_sdf': label_sdf,
# 'mask': label,
# 'affine': affine
# }
# def get_MMWHS_dataloader(root_dir, data_type, mode, batch_size=1, drop_last=False):
# dataset = MMWHS_Dataset(root_dir=root_dir, data_type=data_type, mode=mode)
# if mode == 'train':
# shuffle = True
# return dataset
# elif mode == 'test':
# shuffle = False
# else:
# raise ValueError('No Such Mode')
# loader = DataLoader(
# dataset, batch_size=batch_size, shuffle=shuffle, num_workers=20, pin_memory=True, drop_last=drop_last
# )
# return loader
# def test_dataset():
# dl = get_MMWHS_dataloader(root_dir='/mnt/data2/data/store/MSN/data_v2/MMWHS-CT/MMWHS-CT-resize-try-tiny-train', data_type='CT', mode='test')
# for batch in iter(dl):
# print(batch['name'])
# print(len(dl))
# if __name__ == '__main__':
# test_dataset()
from concurrent.futures import ThreadPoolExecutor
import sys
import os
import numpy as np
import torch
from torch.utils.data.dataset import Dataset
import glob
import cv2
import torchio as tio
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from sdf import compute_sdf
PREPROCESSING_TRANSORMS_CT = tio.Compose([
tio.Clamp(out_min=-250, out_max=800),
tio.RescaleIntensity(in_min_max=(-250, 800),
out_min_max=(-1.0, 1.0)),
tio.CropOrPad(target_shape=(64, 64, 64))
])
PREPROCESSING_TRANSORMS_MRI = tio.Compose([
tio.Clamp(out_min=0, out_max=1000),
tio.RescaleIntensity(in_min_max=(0, 1000),
out_min_max=(-1.0, 1.0)),
tio.CropOrPad(target_shape=(64, 64, 64))
])
PREPROCESSING_MASK_TRANSORMS = tio.Compose([
tio.CropOrPad(target_shape=(64, 64, 64))
])
class MMWHS_Dataset(Dataset):
def __init__(self, root_dir='', data_type='', mode = ''):
self.root_dir = root_dir
self.data_type = data_type
self.mode = mode
self.file_names = self.get_file_names()
self.preprocessing_img_ct = PREPROCESSING_TRANSORMS_CT
self.preprocessing_img_mri = PREPROCESSING_TRANSORMS_MRI
self.preprocessing_mask = PREPROCESSING_MASK_TRANSORMS
@staticmethod
def create_mask(shape):
return torch.zeros(shape, dtype=torch.uint8)
@staticmethod
def project_to_2d(mask):
projection = torch.max(mask, dim=0)[0]
return projection.numpy()
@staticmethod
def min_enclosing_circle(projection):
points = np.column_stack(np.where(projection > 0))
points = points.astype(np.float32)
print(points.shape)
(x, y), radius = cv2.minEnclosingCircle(points.astype(np.float32))
center = (int(x), int(y))
radius = int(radius)
return center, radius
@staticmethod
def create_circle_mask_2d(shape, center, radius):
mask = np.zeros(shape, dtype=np.uint8)
cv2.circle(mask, center, radius, 1, thickness=-1)
return mask
@staticmethod
def apply_circle_mask_to_3d(mask, circle_mask_2d):
for i in range(mask.shape[0]):
mask[i] = torch.from_numpy(circle_mask_2d)
return mask
def train_transform(self, image, label, sdf, p):
TRAIN_TRANSFORMS = tio.Compose([
tio.RandomFlip(axes=(1), flip_probability=p),
])
image = TRAIN_TRANSFORMS(image)
label = TRAIN_TRANSFORMS(label)
sdf = TRAIN_TRANSFORMS(sdf)
return image, label, sdf
def get_file_names(self):
all_img_names = glob.glob(os.path.join(self.root_dir, './*image.nii.gz'), recursive=True)
return all_img_names
def __len__(self):
return len(self.file_names)
def __getitem__(self, index):
img_path = self.file_names[index]
mask_path = img_path.replace("image.nii.gz", "label.nii.gz")
sdf_path = img_path.replace("image.nii.gz", "sdf.nii.gz")
img = tio.ScalarImage(img_path)
mask = tio.LabelMap(mask_path)
sdf = tio.ScalarImage(sdf_path)
name = img_path.split('/')[-1]
name = name.split('.nii')[0]
if self.data_type.upper() == 'CT':
img = self.preprocessing_img_ct(img)
elif self.data_type.upper() == 'MRI':
img = self.preprocessing_img_mri(img)
else:
raise ValueError("Wrong Data Type!")
mask = self.preprocessing_mask(mask)
p = np.random.choice([0, 1])
if self.mode == 'train':
img, mask, sdf = self.train_transform(img, mask, sdf, p)
affine = img.affine
mask = mask.data
img = img.data
sdf = sdf.data
label_1 = (mask == 1).float()
# label_1_sdf = compute_sdf(label_1)
label_2 = (mask == 2).float()
# label_2_sdf = compute_sdf(label_2)
label_3 = (mask == 3).float()
# label_3_sdf = compute_sdf(label_3)
label_4 = (mask == 4).float()
# label_4_sdf = compute_sdf(label_4)
label_5 = (mask == 5).float()
# label_5_sdf = compute_sdf(label_5)
label = torch.cat((label_1, label_2, label_3, label_4, label_5), dim=0)
# label_sdf = torch.cat((torch.tensor(label_1_sdf), torch.tensor(label_2_sdf), torch.tensor(label_3_sdf), torch.tensor(label_4_sdf), torch.tensor(label_5_sdf)), dim=0)
label_sdf = sdf
# sdf_to_save = tio.LabelMap(tensor=label_sdf, channels_last=False, affine=affine)
# os.makedirs(self.root_dir, exist_ok=True)
# sdf_to_save.save(os.path.join(self.root_dir, f"{name.replace('image', 'sdf')}.nii.gz"))
return {
'name': name,
'img': img,
'mask_sdf': label_sdf,
'mask': label,
'affine': affine
}
def get_MMWHS_dataloader(root_dir, data_type, mode, batch_size=1, drop_last=False):
dataset = MMWHS_Dataset(root_dir=root_dir, data_type=data_type, mode=mode)
if mode == 'train':
shuffle = True
return dataset
elif mode == 'test':
shuffle = False
else:
raise ValueError('No Such Mode')
loader = DataLoader(
dataset, batch_size=batch_size, shuffle=shuffle, num_workers=20, pin_memory=True, drop_last=drop_last
)
return loader
# def test_dataset():
# dl = get_MMWHS_dataloader(root_dir='/mnt/data2/data/store/MSN/data/MMWHS-MRI/mr_train_clean_resize_to_64_train_10percent', data_type='MRI', mode='test')
# for batch in iter(dl):
# print(batch['name'])
# print(len(dl))
# if __name__ == '__main__':
# test_dataset()
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