masks.py 文件源码

def _run_interface(self, runtime):
        orig_file_nii = nb.load(self.inputs.in_file)
        in_file_data = orig_file_nii.get_data()

        # pad the data to avoid the mask estimation running into edge effects
        in_file_data_padded = np.pad(in_file_data, (1, 1), 'constant',
                                     constant_values=(0, 0))

        padded_nii = nb.Nifti1Image(in_file_data_padded, orig_file_nii.affine,
                                    orig_file_nii.header)

        mask_nii = compute_epi_mask(padded_nii, exclude_zeros=True)

        mask_data = mask_nii.get_data()
        if isdefined(self.inputs.dilation):
            mask_data = nd.morphology.binary_dilation(mask_data).astype(np.uint8)

        # reverse image padding
        mask_data = mask_data[1:-1, 1:-1, 1:-1]

        # exclude zero and NaN voxels
        mask_data[in_file_data == 0] = 0
        mask_data[np.isnan(in_file_data)] = 0

        better_mask = nb.Nifti1Image(mask_data, orig_file_nii.affine,
                                     orig_file_nii.header)
        better_mask.set_data_dtype(np.uint8)
        better_mask.to_filename("mask_file.nii.gz")

        self._mask_file = os.path.abspath("mask_file.nii.gz")

        runtime.returncode = 0
        return super(ComputeEPIMask, self)._run_interface(runtime)