def intensity(self, x):
transform = tio.OneOf([
tio.RandomMotion(translation=1),
tio.RandomBlur(),
tio.RandomGamma(),
tio.RandomSpike(intensity=[0.2, 0.5]),
tio.RandomBiasField()
])
x = transform(x.unsqueeze(0)).squeeze(0)
return x
def __init__(self, X, Y, mixup=0, aug=False):
self.X = X.astype('float32')
self.Y = Y.astype('float32')
self.aug = aug
if self.aug:
self.intensity = [
tio.RandomMotion(translation=1),
tio.RandomBlur(),
tio.RandomGamma(),
tio.RandomSpike(intensity=[0.2, 0.5]),
tio.RandomBiasField()
]
else:
self.intensity = None
self.mixup = mixup
$ torchio-transform ~/Dropbox/MRI/t1.nii.gz RandomMotion /tmp/t1_motion.nii.gz --seed 42 --kwargs "degrees=10 translation=10 num_transforms=3 proportion_to_augment=1"
"""
from pprint import pprint
from torchio import Image, ImagesDataset, transforms, INTENSITY, LABEL, Subject
subject = Subject(
Image('label', '~/Dropbox/MRI/t1_brain_seg.nii.gz', LABEL),
Image('t1', '~/Dropbox/MRI/t1.nii.gz', INTENSITY),
)
subjects_list = [subject]
dataset = ImagesDataset(subjects_list)
sample = dataset[0]
transform = transforms.RandomMotion(
seed=42,
degrees=10,
translation=10,
num_transforms=3,
)
transformed = transform(sample)
pprint(transformed['t1']['random_motion_times'])
pprint(transformed['t1']['random_motion_degrees'])
pprint(transformed['t1']['random_motion_translation'])
dataset.save_sample(transformed, dict(t1='/tmp/t1_motion.nii.gz'))
dataset.save_sample(transformed, dict(label='/tmp/t1_brain_seg_motion.nii.gz'))
subject = Subject(dic_suj)
subject = Subject(
t1 = Image('/data/romain/HCPdata/suj_100307/T1w_1mm.nii.gz', INTENSITY),
label = Image('/data/romain/HCPdata/suj_100307/T1w_1mm.nii.gz', LABEL),
)
subjects_list = [subject]
dataset = ImagesDataset(subjects_list)
sample = dataset[0]
transform = transforms.RandomMotion(
seed=2,
degrees=0,
translation=100,
num_transforms=1,
verbose=True,
proportion_to_augment=1,
)
transformed = transform(sample)
_, random_parameters = transformed.history[0]
pprint(random_parameters['t1']['times'])
pprint(random_parameters['t1']['degrees'])
pprint(random_parameters['t1']['translation'])
dataset.save_sample(transformed, dict(t1='/tmp/t1_motion.nii.gz'))
dataset.save_sample(transformed, dict(label='/tmp/t1_brain_seg_motion.nii.gz'))
$ torchio-transform ~/Dropbox/MRI/t1.nii.gz RandomMotion /tmp/t1_motion.nii.gz --seed 42 --kwargs "degrees=10 translation=10 num_transforms=3 proportion_to_augment=1"
"""
from pprint import pprint
from torchio import Image, ImagesDataset, transforms, INTENSITY, LABEL, Subject
subject = Subject(
Image('label', '~/Dropbox/MRI/t1_brain_seg.nii.gz', LABEL),
Image('t1', '~/Dropbox/MRI/t1.nii.gz', INTENSITY),
)
subjects_list = [subject]
dataset = ImagesDataset(subjects_list)
sample = dataset[0]
transform = transforms.RandomMotion(
seed=42,
degrees=10,
translation=10,
num_transforms=3,
proportion_to_augment=1,
)
transformed = transform(sample)
pprint(transformed['t1']['random_motion_times'])
pprint(transformed['t1']['random_motion_degrees'])
pprint(transformed['t1']['random_motion_translation'])
dataset.save_sample(transformed, dict(t1='/tmp/t1_motion.nii.gz'))
dataset.save_sample(transformed, dict(label='/tmp/t1_brain_seg_motion.nii.gz'))
from pprint import pprint
from torchio import ImagesDataset, transforms, INTENSITY
paths = [{
't1': dict(path='~/Dropbox/MRI/t1.nii.gz', type=INTENSITY),
'colin': dict(path='/tmp/colin27_t1_tal_lin.nii.gz', type=INTENSITY),
}]
dataset = ImagesDataset(paths)
sample = dataset[0]
transform = transforms.RandomMotion(
seed=42,
degrees=20,
translation=15,
num_transforms=3,
verbose=True,
)
transformed = transform(sample)
pprint(transformed['t1']['random_motion_times'])
dataset.save_sample(transformed, dict(t1='/tmp/t1_motion.nii.gz'))
dataset.save_sample(transformed, dict(colin='/tmp/colin_motion.nii.gz'))