def __getitem__(self, i):
# Index the fname and slice using the list created in __init__
fname, slice = self.examples[i]
t1_path = os.path.join(self.t1_dir, fname)
flair_path = os.path.join(self.flair_dir, fname)
with h5py.File(flair_path, 'r') as data:
flair_img = data[self.key_img][:, :, slice]
flair_kspace = data[self.key_kspace][:, :, slice]
flair_kspace = npComplexToTorch(flair_kspace)
flair_target = data['volfs'][:, :, slice].astype(np.float64)
with h5py.File(t1_path, 'r') as data:
t1_img = data[self.key_img][:, :, slice]
t1_kspace = data[self.key_kspace][:, :, slice]
t1_kspace = npComplexToTorch(t1_kspace)
t1_target = data['volfs'][:, :, slice].astype(np.float64)
return torch.from_numpy(flair_img), flair_kspace, torch.from_numpy(
t1_img), t1_kspace, torch.from_numpy(
flair_target), torch.from_numpy(t1_target), fname, slice
def __getitem__(self, i):
# Index the fname and slice using the list created in __init__
fname, slice = self.examples[i]
#print (fname)
# Print statements
#print (type(fname),slice)
with h5py.File(fname, 'r') as data:
input_img = data[self.key_img][:,:,slice]
input_kspace = data[self.key_kspace][:,:,slice]
input_kspace = npComplexToTorch(input_kspace)
target = data['volfs'][:,:,slice]
#kspace_cmplx = np.fft.fft2(target,norm='ortho')
#uskspace_cmplx = kspace_cmplx * self.mask
#zf_img = np.abs(np.fft.ifft2(uskspace_cmplx,norm='ortho'))
#if self.dataset_type == 'cardiac':
if False:
# Cardiac dataset should be padded,150 becomes 160. # this can be commented for kirby brain
input_img = np.pad(input_img,(5,5),'constant',constant_values=(0,0))
target = np.pad(target,(5,5),'constant',constant_values=(0,0))
# Print statements
#print (input.shape,target.shape)
return torch.from_numpy(input_img), input_kspace, torch.from_numpy(target),str(fname.name),slice
def __getitem__(self, i):
fname = self.examples[i]
with h5py.File(fname, 'r') as data:
input_img = data[self.key_img].value
input_kspace = data[self.key_kspace].value
input_kspace = npComplexToTorch(input_kspace)
target = data['volfs'].value
return torch.from_numpy(input_img), input_kspace, torch.from_numpy(target),str(fname.name)
def __getitem__(self, i):
fpath, slice, context = self.examples[i]
with h5py.File(fpath, 'r') as data:
img = data[self.key_img][:, :, slice]
kspace = data[self.key_kspace][:, :, slice]
kspace = npComplexToTorch(kspace)
target = data['volfs'][:, :, slice].astype(np.float64)
return torch.from_numpy(img), kspace, torch.from_numpy(
target), fpath, slice, context
def __getitem__(self, i):
# Index the fname and slice using the list created in __init__
fname, slice = self.examples[i]
data_path = os.path.join(self.data_dir, fname)
with h5py.File(data_path, 'r') as data:
img = data[self.key_img][:, :, slice]
kspace = data[self.key_kspace][:, :, slice]
kspace = npComplexToTorch(kspace)
target = data['volfs'][:, :, slice].astype(np.float64)
return torch.from_numpy(img), kspace, torch.from_numpy(
target), fname, slice