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): 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 _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 _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
def _run_interface(self, runtime): if not self._have_nipy: raise RuntimeError('nipy is not installed') 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