def _run_interface(self, runtime):
vol1_nii = nb.load(self.inputs.volume1)
vol2_nii = nb.load(self.inputs.volume2)
if isdefined(self.inputs.mask1):
mask1_nii = nb.load(self.inputs.mask1)
mask1_nii = nb.Nifti1Image(
nb.load(self.inputs.mask1).get_data() == 1,
mask1_nii.get_affine(), mask1_nii.get_header())
else:
mask1_nii = None
if isdefined(self.inputs.mask2):
mask2_nii = nb.load(self.inputs.mask2)
mask2_nii = nb.Nifti1Image(
nb.load(self.inputs.mask2).get_data() == 1,
mask2_nii.get_affine(), mask2_nii.get_header())
else:
mask2_nii = None
histreg = HistogramRegistration(from_img=vol1_nii,
to_img=vol2_nii,
similarity=self.inputs.metric,
from_mask=mask1_nii,
to_mask=mask2_nii)
self._similarity = histreg.eval(Affine())
return runtime
def _run_interface(self, runtime):
from nipy.algorithms.registration.histogram_registration import (
HistogramRegistration, )
from nipy.algorithms.registration.affine import Affine
vol1_nii = nb.load(self.inputs.volume1)
vol2_nii = nb.load(self.inputs.volume2)
if isdefined(self.inputs.mask1):
mask1 = nb.load(self.inputs.mask1).get_data() == 1
else:
mask1 = None
if isdefined(self.inputs.mask2):
mask2 = nb.load(self.inputs.mask2).get_data() == 1
else:
mask2 = None
histreg = HistogramRegistration(
from_img=vol1_nii,
to_img=vol2_nii,
similarity=self.inputs.metric,
from_mask=mask1,
to_mask=mask2,
)
self._similarity = histreg.eval(Affine())
return runtime
def save_nifty(filename,
data,
origin=(0, 0, 0),
vox_scale=(0, 0, 0),
angles=(0, 0, 0)):
"""
save_nifty: write data to given file in nifty format, taking
care of the affine transform.
Inputs
------
filename : string
the file in which to put the output. If not given, the extension
.nii will be added.
data : 3d numpy array
the volume data to save. A single slice will be considered as
a 3d volume that is only one voxel thick in the through-plane
direction.
origin : 3-element sequence =(0,0,0)
the (x,y,z) physical coordinates of element (0,0,0) of the data.
vox_scale : 3-element sequence
the physical size of each voxel along the three axes, in the order
that the input data is given, i.e., the first value is the length
step along the first data axis. Typically in mm units.
angles : 3-element sequence
the (Euler) rotation angles about the three (data) axes, defining
the rotation data-coordinates->physical coordinates. See
nipy.algorithms.registration.affine.rotation_vec2mat for how
the calculation is performed.
If my assumption about what angles mean is wrong, alternative
explicit conversions are here:
http://nipy.org/nibabel/generated/nibabel.eulerangles.html
"""
aff = Affine()
aff.translation = origin
aff.scaling = vox_scale
aff.rotation = angles
nifti = nibabel.Nifti1Image(data, aff)
if not ('.nii' in filename):
filename = filename + '.nii'
nifti.to_filename(filename)
def save_nifty(filename, data, origin=(0,0,0), vox_scale=(0,0,0),
angles=(0,0,0) ):
"""
save_nifty: write data to given file in nifty format, taking
care of the affine transform.
Inputs
------
filename : string
the file in which to put the output. If not given, the extension
.nii will be added.
data : 3d numpy array
the volume data to save. A single slice will be considered as
a 3d volume that is only one voxel thick in the through-plane
direction.
origin : 3-element sequence =(0,0,0)
the (x,y,z) physical coordinates of element (0,0,0) of the data.
vox_scale : 3-element sequence
the physical size of each voxel along the three axes, in the order
that the input data is given, i.e., the first value is the length
step along the first data axis. Typically in mm units.
angles : 3-element sequence
the (Euler) rotation angles about the three (data) axes, defining
the rotation data-coordinates->physical coordinates. See
nipy.algorithms.registration.affine.rotation_vec2mat for how
the calculation is performed.
If my assumption about what angles mean is wrong, alternative
explicit conversions are here:
http://nipy.org/nibabel/generated/nibabel.eulerangles.html
"""
aff = Affine()
aff.translation = origin
aff.scaling = vox_scale
aff.rotation = angles
nifti = nibabel.Nifti1Image(data, aff)
if not('.nii' in filename):
filename = filename + '.nii'
nifti.to_filename(filename)
def _run_interface(self, runtime):
from nipy.algorithms.registration.histogram_registration import (
HistogramRegistration, )
from nipy.algorithms.registration.affine import Affine
vol1_nii = nb.load(self.inputs.volume1)
vol2_nii = nb.load(self.inputs.volume2)
dims = vol1_nii.get_data().ndim
if dims == 3 or dims == 2:
vols1 = [vol1_nii]
vols2 = [vol2_nii]
if dims == 4:
vols1 = nb.four_to_three(vol1_nii)
vols2 = nb.four_to_three(vol2_nii)
if dims < 2 or dims > 4:
raise RuntimeError(
"Image dimensions not supported (detected %dD file)" % dims)
if isdefined(self.inputs.mask1):
mask1 = nb.load(self.inputs.mask1).get_data() == 1
else:
mask1 = None
if isdefined(self.inputs.mask2):
mask2 = nb.load(self.inputs.mask2).get_data() == 1
else:
mask2 = None
self._similarity = []
for ts1, ts2 in zip(vols1, vols2):
histreg = HistogramRegistration(
from_img=ts1,
to_img=ts2,
similarity=self.inputs.metric,
from_mask=mask1,
to_mask=mask2,
)
self._similarity.append(histreg.eval(Affine()))
return runtime