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import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import nibabel as nib
from skimage.transform import rescale, resize, downscale_local_mean
from scipy.ndimage import zoom
import numpy as np
# import SimpleITK as sitk
# print(os.getcwd())
import sys
sys.path.append('./')
from Dataloader.dataloader_utils import *
EPS = 1e-7
def get_dataloader(data_name='cmr',mode='train'):
if data_name=='cmr':
if mode=='train':
dataloader=CMR_loader
elif mode =='aug':
dataloader=CMR_tgt_loader
else:
print('mode not exist')
elif data_name=='lct':
if mode=='train':
dataloader=LCT_loader
elif mode =='aug':
dataloader=LCT_tgt_loader
else:
print('mode not exist')
else:
print('dataloader not exist')
return dataloader
class LCT_loader(Dataset):
def __init__(self, data_root_path = f'Data/Src_data/CTLung_processed/', target_res = (256, 256),transforms = None, noise_scale=0.0, patient_index = None):
# def __init__(self, data_root_path = '/home/data/jzheng/CTLung_processed/', target_res = (256, 256),transforms = None, noise_scale=0.0, patient_index = None):
self.files = [data_root_path + f for f in os.listdir(data_root_path) if f.endswith('.npy')]
self.transforms = transforms
self.noise_scale=noise_scale
self.d_p = data_root_path
def __getitem__(self, item):
array = np.load(self.files[item])
if 'process' not in self.d_p:
array = (array - array.min()) / (array.max() - array.min() + EPS) # Normalize to 0 to 1
array = array[None,:,:,:] # add a channel to array make it (‘C’,H,W,Z)
if self.transforms != None:
array = self.transforms(array)
# print(array.shape)
return array, array, item # -> (B, C, H, W, Z)
# return array, array # -> (B, C, H, W, Z)
def __len__(self):
return len(self.files)
class LCT_tgt_loader(Dataset):
def __init__(self, data_root_path = "Data/Tgt_data/lct/",noise_scale=0.0, patient_index = None):
self.files_gt = [data_root_path + "Gt/" + f for f in os.listdir(data_root_path + "Gt/")]
self.files_tr = [data_root_path + 'Tr/' + f for f in os.listdir(data_root_path + "Tr/")]
self.files_tr.sort()
self.files_gt.sort()
self.transforms = transforms
self.noise_scale=noise_scale
def __getitem__(self, item):
img_nib = nib.load(self.files_tr[item])
mask_nib = nib.load(self.files_gt[item])
image = img_nib.get_fdata()
mask = mask_nib.get_fdata()
image = image[None,:,:,:]
mask = mask[None,:,:,:]
print(self.files_tr[item],self.files_gt[item])
return image, mask, item
def __len__(self):
assert len(self.files_gt) == len(self.files_tr)
return len(self.files_gt)
class LCT_seg(Dataset):
def __init__(self, data_root_path = "/home/data/jzheng/CTLung_processed/testset/modality_0001/",noise_scale=0.0, patient_index = None):
self.files_gt = [data_root_path + "Gt/" + f for f in os.listdir(data_root_path + "Gt/")]
self.files_tr = [data_root_path + 'Tr/' + f for f in os.listdir(data_root_path + "Tr/")]
self.files_tr.sort()
self.files_gt.sort()
self.transforms = transforms
self.noise_scale=noise_scale
def __getitem__(self, item):
img_nib = nib.load(self.files_tr[item])
mask_nib = nib.load(self.files_gt[item])
image = img_nib.get_fdata()
mask = mask_nib.get_fdata()
image = image[None,:,:,:]
mask = mask[None,:,:,:]
print(self.files_tr[item],self.files_gt[item])
return image, mask, item
def __len__(self):
assert len(self.files_gt) == len(self.files_tr)
return len(self.files_gt)
class CMR_loader_preprocess(Dataset):
# This is for pre_processing for CMR. not use for training model
def __init__(self, data_path = 'Data/CTLung_processed/', target_res = (256, 256), transforms = None, noise_scale=0.0):
# def __init__(self, data_path = '/home/data/jzheng/CMR_processed/', target_res = (256, 256), transforms = None, noise_scale=0.0):
self.d_p = data_path
self.target_res = target_res
self.files = [self.d_p + x for x in os.listdir(self.d_p)]
self.transforms = transforms
self.noise_scale=noise_scale
def __getitem__(self, item):
array = nib.load(self.files[item]).get_fdata()
array = resize(array, self.target_res, anti_aliasing = True, preserve_range = True)
array = array[None, :, :]
array = remove_background(array) # jzheng 20240228
array = (array - array.min()) / (array.max() - array.min() + EPS)
if self.noise_scale > 0:
array = thresh_img(array,[0,self.noise_scale])
array = array * (np.random.normal(1, self.noise_scale*2))
if self.transforms != None:
array = self.transforms(array)
return array, self.files[item]
def __len__(self):
return len(self.files)
class CMR_loader(Dataset):
# niff format size is (H,W) for CMR
# CMR_processed_rmbg_resize means the niif image has been gone throught rmbg and resize offline to make trainig fast
def __init__(self, data_path = f'Data/Src_data/CMR_processed_rmbg_resize/', target_res = (256, 256), transforms = None, noise_scale=0.0):
# def __init__(self, data_path = '/home/data/jzheng/CMR_processed_rmbg_resize/', target_res = (256, 256), transforms = None, noise_scale=0.0):
self.d_p = data_path
self.ndims = 2
self.target_res = target_res
self.files = [self.d_p + x for x in os.listdir(self.d_p)]
self.transforms = transforms
# self.get_transform()
self.noise_scale=noise_scale
self.preprocessed='resize' in data_path
def __getitem__(self, item):
array = nib.load(self.files[item]).get_fdata()
if not self.preprocessed:
array = resize(array, self.target_res, anti_aliasing = True, preserve_range = True)
array = array[None, :, :]
if not self.preprocessed:
array = remove_background(array) # jzheng 20240228
array = (array - array.min()) / (array.max() - array.min() + EPS)
# if self.noise_scale > 0:
# array = thresh_img(array,[0,self.noise_scale])
# array = array * (np.random.normal(1, self.noise_scale*2)) + np.random.normal(0, self.noise_scale*2)
if self.transforms != None:
array = self.transforms(array)
return array, array, item
def __len__(self):
return len(self.files)
def get_transform(self,degrees=np.pi,translate=0.125):
# self.transforms = torchvision.transforms.RandomAffine(degrees=degrees,translate=[translate]*self.ndims,interpolation=torchvision.transforms.InterpolationMode.BILINEAR)
self.transforms = torchvision.transforms.Compose([
# torchvision.transforms.Resize((hyp_parameters['img_size'], hyp_parameters['img_size'])),
torchvision.transforms.ToTensor(),
torchvision.transforms.RandomAffine(degrees=degrees,translate=[translate]*self.ndims,interpolation=torchvision.transforms.InterpolationMode.BILINEAR),
# torchvision.transforms.ToTensor(),
# torchvision.transforms.Normalize(0.5, 0.5)
# Lambda(lambda x: (x - 0.5) * 2)
])
return
class CMR_tgt_loader(Dataset):
def __init__(self,
data_path = 'Data/Tgt_data/cmr/',
# gt_path = '/home/data/jzheng/acdc/train_gt/',
target_res = (256,256),
is_3d = False,
patient_index = [],
):
# parameter initialize
self.d_p = os.path.join(data_path,'Tr','')
self.gt_p = os.path.join(data_path,'Gt','')
self.img_files = os.listdir(self.d_p)
self.gt_files = os.listdir(self.gt_p)
self.p_indice = patient_index
self.target_res_2d = target_res
self.img_files.sort()
self.gt_files.sort()
self.img_samples = []
self.gt_samples = []
self.p_id = []
if len(self.p_indice) == 0:
self.p_indice = [x for x in range(1,101)]
# build patient-to-file correspondence
p2f = {}
assert len(self.gt_files) == len(self.img_files)
print(self.p_indice)
for i in self.p_indice:
for gt_f, img_f in zip(self.gt_files, self.img_files):
pf_id = gt_f.split('_')[0]
pf_id = pf_id[-3:]
if i == int(pf_id):
img_volume = nib.load(self.d_p + img_f).get_fdata()
gt_volume = nib.load(self.gt_p + gt_f).get_fdata()
assert img_volume.shape == gt_volume.shape
depth = img_volume.shape[2]
for si in range(depth):
img = resize(img_volume[:, :, si], self.target_res_2d, anti_aliasing=True, preserve_range=True)
img = (img - img.min()) / (img.max() - img.min() + EPS)
gt = gt_volume[:, :, si]
gt_1_index = gt == 1
gt_2_index = gt == 2
gt_3_index = gt == 3
gt_4_index = gt == 4
gt_1 = gt * gt_1_index
gt_2 = gt * gt_2_index
gt_3 = gt * gt_3_index
gt_4 = gt * gt_4_index
gt_1 = resize(gt_1, self.target_res_2d, anti_aliasing=True, preserve_range=True)
gt_2 = resize(gt_2, self.target_res_2d, anti_aliasing=True, preserve_range=True)
gt_3 = resize(gt_3, self.target_res_2d, anti_aliasing=True, preserve_range=True)
gt_4 = resize(gt_4, self.target_res_2d, anti_aliasing=True, preserve_range=True)
self.img_samples.append(img[np.newaxis, :, :])
self.gt_samples.append(np.array([gt_1, gt_2, gt_3, gt_4]))
self.p_id.append(i)
def __getitem__(self, item):
return self.img_samples[item], self.gt_samples[item], self.p_id[item]
def __len__(self):
assert len(self.img_samples) == len(self.gt_samples)
return len(self.img_samples)
class acdc_seg(Dataset):
def __init__(self,
data_path = '/home/data/jzheng/acdc/train_images/',
gt_path = '/home/data/jzheng/acdc/train_gt/',
target_res = (256,256),
is_3d = False,
patient_index = [],
):
# parameter initialize
self.d_p = data_path
self.gt_p = gt_path
self.img_files = os.listdir(self.d_p)
self.gt_files = os.listdir(self.gt_p)
self.p_indice = patient_index
self.target_res_2d = target_res
self.img_files.sort()
self.gt_files.sort()
self.img_samples = []
self.gt_samples = []
self.p_id = []
if len(self.p_indice) == 0:
self.p_indice = [x for x in range(1,101)]
# build patient-to-file correspondence
p2f = {}
assert len(self.gt_files) == len(self.img_files)
print(self.p_indice)
for i in self.p_indice:
for gt_f, img_f in zip(self.gt_files, self.img_files):
pf_id = gt_f.split('_')[0]
pf_id = pf_id[-3:]
if i == int(pf_id):
img_volume = nib.load(self.d_p + img_f).get_fdata()
gt_volume = nib.load(self.gt_p + gt_f).get_fdata()
assert img_volume.shape == gt_volume.shape
depth = img_volume.shape[2]
for si in range(depth):
img = resize(img_volume[:, :, si], self.target_res_2d, anti_aliasing=True, preserve_range=True)
img = (img - img.min()) / (img.max() - img.min() + EPS)
gt = gt_volume[:, :, si]
gt_1_index = gt == 1
gt_2_index = gt == 2
gt_3_index = gt == 3
gt_4_index = gt == 4
gt_1 = gt * gt_1_index
gt_2 = gt * gt_2_index
gt_3 = gt * gt_3_index
gt_4 = gt * gt_4_index
gt_1 = resize(gt_1, self.target_res_2d, anti_aliasing=True, preserve_range=True)
gt_2 = resize(gt_2, self.target_res_2d, anti_aliasing=True, preserve_range=True)
gt_3 = resize(gt_3, self.target_res_2d, anti_aliasing=True, preserve_range=True)
gt_4 = resize(gt_4, self.target_res_2d, anti_aliasing=True, preserve_range=True)
self.img_samples.append(img[np.newaxis, :, :])
self.gt_samples.append(np.array([gt_1, gt_2, gt_3, gt_4]))
self.p_id.append(i)
def __getitem__(self, item):
return self.img_samples[item], self.gt_samples[item], self.p_id[item]
def __len__(self):
assert len(self.img_samples) == len(self.gt_samples)
return len(self.img_samples)
class acdc_gan(Dataset):
def __init__(self,
train_path = '/home/data/jzheng/acdc/images/',
target_res = (32, 256, 256),
is_3d = False,
transforms = None
):
self.t_p = train_path
self.files = os.listdir(self.t_p)
self.sample_list_2d = []
self.is_3d = is_3d
self.target_res = target_res
self.res_2d = (target_res[1], target_res[2])
self.transforms = transforms
if self.is_3d == False:
for f in self.files:
img = nib.load(self.t_p + f).get_fdata()
depth = img.shape[2]
f_i = int(round(depth*0.1))
b_i = int(round(depth*0.9))
interval_slice = img[:, :, f_i:b_i]
for ii in range(interval_slice.shape[2]):
single_slice = interval_slice[:,:,ii]
single_slice = resize(single_slice, self.res_2d, anti_aliasing=True, preserve_range=True)
single_slice = (single_slice - single_slice.min()) / ( single_slice.max() - single_slice.min() + EPS)
self.sample_list_2d.append(single_slice[None,:,:])
def __len__(self):
if self.is_3d == False:
return len(self.sample_list_2d)
else:
return len(self.files )
def __getitem__(self, index):
if self.is_3d == False:
return self.sample_list_2d[index], self.sample_list_2d[index]
for f in self.files:
img = nib.load(self.t_p + f).get_fdata()
target_d_ratio = self.target_res[0] / img.shape[2]
target_w_ratio = self.target_res[1] / img.shape[0]
target_h_ratio = self.target_res[2] / img.shape[1]
resize_img = zoom(img, (target_w_ratio, target_h_ratio, target_d_ratio))
resize_img = np.swapaxes(resize_img, 0, 2)
resize_img = np.swapaxes(resize_img, 1, 2)
resize_img = (resize_img - resize_img.min()) / (resize_img.max() - resize_img.min() + EPS)
if transforms != None:
resize_img = self.transforms(resize_img)
return resize_img, resize_img
class acdc_gan_single_slice(Dataset):
def __init__(self, train_path = '/well/papiez/shared/ACDC/clean_training/images/'):
self.t_p = train_path
self.files = os.listdir(self.t_p)
def __len__(self):
return len(self.files)
def __getitem__(self, index):
img = self.files[index]
img = nib.load(self.t_p + img).get_fdata()
depth = img.shape[2]
mid_d = int(depth/2)
mid_slice = img[:,:,mid_d]
mid_slice = resize(mid_slice, (128, 128), anti_aliasing=True, preserve_range=True)
mid_slice = (mid_slice-mid_slice.min())/(mid_slice.max()-mid_slice.min()+EPS)
# print(mid_slice.max(),mid_slice.min())
return mid_slice, mid_slice
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