def test_train_histogram(self):
paths = [sample['image']['path'] for sample in self.dataset]
HistogramStandardization.train(
paths,
masking_function=HistogramStandardization.mean,
output_path=(self.dir / 'landmarks.txt'),
)
HistogramStandardization.train(
paths,
mask_path=self.dataset[0]['label']['path'],
output_path=(self.dir / 'landmarks.npy'),
)
def create_normalization_file(use_controls, use_nofcd, mods):
"""
Creates t1_landmark.npy file using torchio library for brain normalizations.
"""
for j in range(1, mods + 1):
fcd_paths = sorted(glob.glob(FCD_FOLDER + f'fcd_*.{j}.nii.gz'))
nofcd_paths = sorted(glob.glob(FCD_FOLDER + f'nofcd_*.{j}.nii.gz'))
control_paths = sorted(
glob.glob(CONTROL_FOLDER + f'control_*.{j}.nii.gz'))
mri_paths = fcd_paths
if use_nofcd:
mri_paths += nofcd_paths
if use_controls:
mri_paths += control_paths
t1_landmarks_path = Path(f'./data/t1_landmarks_{j}.npy')
if t1_landmarks_path.is_file():
continue
# os.remove(f'./data/t1_landmarks_{j}.npy')
t1_landmarks = (t1_landmarks_path if t1_landmarks_path.is_file() else
HistogramStandardization.train(mri_paths))
np.save(t1_landmarks_path, t1_landmarks, allow_pickle=True)
def create_normalization_file(use_controls, use_nofcd):
"""
Creates t1_landmark.npy file using torchio library for brain normalizations.
"""
FCD_FOLDER = './data/fcd_brains/'
fcd_paths = sorted(list(filter(lambda x: 'nofcd' not in x, os.listdir(FCD_FOLDER))))
fcd_paths = list(map(lambda x: FCD_FOLDER + x, fcd_paths))
nofcd_paths = sorted(list(filter(lambda x: 'nofcd' in x, os.listdir(FCD_FOLDER))))
nofcd_paths = list(map(lambda x: FCD_FOLDER + x, nofcd_paths))
CONTROL_FOLDER = './data/control_brains/'
control_paths = sorted(os.listdir(CONTROL_FOLDER))
control_paths = list(map(lambda x: CONTROL_FOLDER + x, control_paths))
mri_paths = fcd_paths
if use_nofcd:
mri_paths += fcd_paths
if use_controls:
mri_paths += control_paths
t1_landmarks_path = Path('./data/t1_landmarks.npy')
if t1_landmarks_path.is_file():
os.remove('./data/t1_landmarks.npy')
t1_landmarks = (
t1_landmarks_path
if t1_landmarks_path.is_file()
else HistogramStandardization.train(mri_paths)
)
np.save(t1_landmarks_path, t1_landmarks, allow_pickle=True)
def get_hist_landmarks(img_path, lndmrk_path, names):
image_paths = list(map(lambda x: os.path.join(img_path, str(x), 'T1w',
'T1w_acpc_dc_restore_brain.nii.gz'),
names))
landmarks = HistogramStandardization.train(image_paths,
masking_function=cut_img_mask,
output_path=lndmrk_path)
return landmarks
def test_train_histogram(self):
paths = [subject.image.path for subject in self.dataset]
# Use a function to mask
HistogramStandardization.train(
paths,
masking_function=HistogramStandardization.mean,
output_path=(self.dir / 'landmarks.txt'),
)
# Use a file to mask
HistogramStandardization.train(
paths,
mask_path=self.dataset[0].label.path,
output_path=(self.dir / 'landmarks.npy'),
)
# Use files to mask
masks = [subject.label.path for subject in self.dataset]
HistogramStandardization.train(
paths,
mask_path=masks,
output_path=(self.dir / 'landmarks_masks.npy'),
)
def test_bad_paths_lengths(self):
with self.assertRaises(ValueError):
HistogramStandardization.train(
[1, 2],
mask_path=[1, 2, 3],
)