def resample(source, target, interpolation="continuous"):
source, source_affine = source
target, target_affine = target
input_image = VolumeImg(source[:], source_affine, "arbitrary", interpolation=interpolation)
resampled_image = input_image.as_volume_img(target_affine, target.shape)
return resampled_image.get_data()
def resample(source, target, interpolation='continuous'):
source, source_affine = source
target, target_affine = target
input_image = VolumeImg(source[:],
source_affine,
'arbitrary',
interpolation=interpolation)
resampled_image = input_image.as_volume_img(target_affine, target.shape)
return resampled_image.get_data()
def resample(source, target, interpolation='continuous', return_affine=False):
source, source_affine = source
target, target_affine = target
input_image = VolumeImg(source[:],
source_affine,
'arbitrary',
interpolation=interpolation
)
resampled_image = input_image.as_volume_img(target_affine, target.shape)
if return_affine:
return resampled_image.get_data(), resampled_image.get_affine()
else:
return resampled_image.get_data()
def resample(self, target, interpolation="continuous"):
""" resample to target - st after
inputs:
target: (shape, affine) tuple
data: numpy array, affine: numpy array
interpolation: 'nearest' or 'continuous'
returns:
interpolated_data
"""
target_shape, target_affine = target
input_image = VolumeImg(
self._data[:], self.affine, "arbitrary", interpolation=interpolation # why do we need self.data[:]?
)
resampled_img = input_image.as_volume_img(target_affine, target_shape)
assert np.all(resampled_img.affine == target_affine), "resampled_img.affine != target_affine"
return resampled_img.get_data()