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dataload.py
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from torch.utils.data import Dataset
import glob
import numpy as np
import SimpleITK as sitk
from skimage import transform
import torch
class LGE_TrainSet(Dataset):
def __init__(self,dir,sample_num):
self.imgdir=dir+'/LGE/'
self.imgsname = glob.glob(self.imgdir + '*LGE.nii*')
imgs = np.zeros((1,192,192))
self.info = []
self.times = int((35.0 / sample_num) * 4)
for img_num in range(sample_num):
itkimg = sitk.ReadImage(self.imgsname[img_num])
npimg = sitk.GetArrayFromImage(itkimg) # Z,Y,X,220*240*1
npimg = npimg.astype(np.float32)
imgs = np.concatenate((imgs,npimg),axis=0)
spacing = itkimg.GetSpacing()[2]
media_slice = int(npimg.shape[0] / 2)
for i in range(npimg.shape[0]):
a, _ = divmod((i - media_slice) * spacing, 20.0)
info = int(a) + 3
if info < 0:
info = 0
elif info > 5:
info = 5
self.info.append(info)
self.imgs = imgs[1:,:,:]
def __getitem__(self, item):
imgindex,crop_indice = divmod(item,self.times)
npimg = self.imgs[imgindex,:,:]
randx = np.random.randint(-16,16)
randy = np.random.randint(-16, 16)
npimg=npimg[96+randx-80:96+randx+80,96+randy-80:96+randy+80]
# npimg_o = transform.resize(npimg, (80, 80),
# order=3, mode='edge', preserve_range=True)
#npimg_resize = transform.resize(npimg, (96, 96), order=3,mode='edge', preserve_range=True)
npimg_down2 = transform.resize(npimg, (80,80 ), order=3,mode='edge', preserve_range=True)
npimg_down4 = transform.resize(npimg, (40,40 ), order=3,mode='edge', preserve_range=True)
return torch.from_numpy(npimg).unsqueeze(0).type(dtype=torch.FloatTensor),torch.from_numpy(npimg_down2).unsqueeze(0).type(dtype=torch.FloatTensor),torch.from_numpy(npimg_down4).unsqueeze(0).type(dtype=torch.FloatTensor),torch.tensor(self.info[imgindex]).type(dtype=torch.LongTensor)
def __len__(self):
return self.imgs.shape[0]*self.times
class C0_TrainSet(Dataset):
def __init__(self,dir,sample_num):
self.imgdir = dir+'/C0/'
self.imgsname = glob.glob(self.imgdir + '*C0.nii*')
imgs = np.zeros((1,192,192))
labs = np.zeros((1,192,192))
self.info = []
self.times = int((35.0 / sample_num) * 4)
for img_num in range(sample_num):
itkimg = sitk.ReadImage(self.imgsname[img_num])
npimg = sitk.GetArrayFromImage(itkimg) # Z,Y,X,220*240*1
imgs = np.concatenate((imgs,npimg),axis=0)
labname = self.imgsname[img_num].replace('.nii','_manual.nii')
itklab = sitk.ReadImage(labname)
nplab = sitk.GetArrayFromImage(itklab)
nplab = (nplab == 200) * 1 + (nplab == 500) * 2 + (nplab == 600) * 3
labs = np.concatenate((labs, nplab), axis=0)
spacing = itkimg.GetSpacing()[2]
media_slice = int(npimg.shape[0] / 2)
for i in range(npimg.shape[0]):
a, _ = divmod((i - media_slice) * spacing, 20.0)
info = int(a) + 3
if info < 0:
info = 0
elif info > 5:
info = 5
self.info.append(info)
self.imgs = imgs[1:,:,:]
self.labs = labs[1:,:,:]
self.imgs.astype(np.float32)
self.labs.astype(np.float32)
def __getitem__(self, item):
imgindex,crop_indice = divmod(item,self.times)
npimg = self.imgs[imgindex,:,:]
nplab = self.labs[imgindex,:,:]
# npimg = transform.resize(npimg, (96, 96), order=3,mode='edge', preserve_range=True)
# nplab = transform.resize(nplab, (96, 96), order=0,mode='edge', preserve_range=True)
randx = np.random.randint(-16,16)
randy = np.random.randint(-16, 16)
npimg=npimg[96+randx-80:96+randx+80,96+randy-80:96+randy+80]
nplab=nplab[96+randx-80:96+randx+80,96+randy-80:96+randy+80]
# npimg_o=transform.resize(npimg, (80,80 ), order=3,mode='edge', preserve_range=True)
# nplab_o=transform.resize(nplab, (80,80 ), order=0,mode='edge', preserve_range=True)
npimg_down2 = transform.resize(npimg, (80,80 ), order=3,mode='edge', preserve_range=True)
npimg_down4 = transform.resize(npimg, (40,40 ), order=3,mode='edge', preserve_range=True)
nplab_down2 = transform.resize(nplab, (80,80 ), order=0,mode='edge', preserve_range=True)
nplab_down4 = transform.resize(nplab, (40,40), order=0,mode='edge', preserve_range=True)
return torch.from_numpy(npimg).unsqueeze(0).type(dtype=torch.FloatTensor),torch.from_numpy(npimg_down2).unsqueeze(0).type(dtype=torch.FloatTensor),torch.from_numpy(npimg_down4).unsqueeze(0).type(dtype=torch.FloatTensor),torch.from_numpy(nplab).type(dtype=torch.LongTensor),torch.from_numpy(nplab_down2).type(dtype=torch.LongTensor),torch.from_numpy(nplab_down4).type(dtype=torch.LongTensor),torch.tensor(self.info[imgindex]).type(dtype=torch.LongTensor)
def __len__(self):
return self.imgs.shape[0]*self.times