Method create_backend_obj

monai/data/image_writer.py:479–530

Create an ITK object from ``data_array``. This method assumes a 'channel-last' ``data_array``. Args: data_array: input data array. channel_dim: channel dimension of the data array. This is used to create a Vector Image if it is not ``None``. affi

(
        cls,
        data_array: NdarrayOrTensor,
        channel_dim: int | None = 0,
        affine: NdarrayOrTensor | None = None,
        dtype: DtypeLike = np.float32,
        affine_lps_to_ras: bool | None = True,
        **kwargs,
    )

    @classmethod
    def create_backend_obj(
        cls,
        data_array: NdarrayOrTensor,
        channel_dim: int | None = 0,
        affine: NdarrayOrTensor | None = None,
        dtype: DtypeLike = np.float32,
        affine_lps_to_ras: bool | None = True,
        **kwargs,
    ):
        """
        Create an ITK object from ``data_array``. This method assumes a 'channel-last' ``data_array``.

        Args:
            data_array: input data array.
            channel_dim: channel dimension of the data array. This is used to create a Vector Image if it is not ``None``.
            affine: affine matrix of the data array. This is used to compute `spacing`, `direction` and `origin`.
            dtype: output data type.
            affine_lps_to_ras: whether to convert the affine matrix from "LPS" to "RAS". Defaults to ``True``.
                Set to ``True`` to be consistent with ``NibabelWriter``,
                otherwise the affine matrix is assumed already in the ITK convention.
                Set to ``None`` to use ``data_array.meta[MetaKeys.SPACE]`` to determine the flag.
            kwargs: keyword arguments. Current `itk.GetImageFromArray` will read ``ttype`` from this dictionary.

        see also:

            - https://github.com/InsightSoftwareConsortium/ITK/blob/v5.2.1/Wrapping/Generators/Python/itk/support/extras.py#L389

        """
        if isinstance(data_array, MetaTensor) and affine_lps_to_ras is None:
            affine_lps_to_ras = (
                data_array.meta.get(MetaKeys.SPACE, SpaceKeys.LPS) != SpaceKeys.LPS
            )  # do the converting from LPS to RAS only if the space type is currently LPS.
        data_array = super().create_backend_obj(data_array)
        _is_vec = channel_dim is not None
        if _is_vec:
            data_array = np.moveaxis(data_array, -1, 0)  # from channel last to channel first
        data_array = data_array.T.astype(get_equivalent_dtype(dtype, np.ndarray), copy=True, order="C")
        itk_obj = itk.GetImageFromArray(data_array, is_vector=_is_vec, ttype=kwargs.pop("ttype", None))

        d = len(itk.size(itk_obj))
        if affine is None:
            affine = np.eye(d + 1, dtype=np.float64)
        _affine = convert_data_type(affine, np.ndarray)[0]
        if affine_lps_to_ras:
            _affine = orientation_ras_lps(to_affine_nd(d, _affine))
        spacing = affine_to_spacing(_affine, r=d)
        _direction: np.ndarray = np.diag(1 / spacing)
        _direction = _affine[:d, :d] @ _direction
        itk_obj.SetSpacing(spacing.tolist())
        itk_obj.SetOrigin(_affine[:d, -1].tolist())
        itk_obj.SetDirection(itk.GetMatrixFromArray(_direction))
        return itk_obj


@require_pkg(pkg_name="nibabel")