Class NibabelWriter

monai/data/image_writer.py:534–671

Write data and metadata into files on disk using Nibabel. .. code-block:: python import numpy as np from monai.data import NibabelWriter np_data = np.arange(48).reshape(3, 4, 4) writer = NibabelWriter() writer.set_data_array(np_data, channel_dim=No

@require_pkg(pkg_name="nibabel")
class NibabelWriter(ImageWriter):
    """
    Write data and metadata into files on disk using Nibabel.

    .. code-block:: python

        import numpy as np
        from monai.data import NibabelWriter

        np_data = np.arange(48).reshape(3, 4, 4)
        writer = NibabelWriter()
        writer.set_data_array(np_data, channel_dim=None)
        writer.set_metadata({"affine": np.eye(4), "original_affine": np.eye(4)})
        writer.write("test1.nii.gz", verbose=True)

    """

    output_dtype: DtypeLike
    affine: Any

    def __init__(self, output_dtype: DtypeLike = np.float32, **kwargs):
        """
        Args:
            output_dtype: output data type.
            kwargs: keyword arguments passed to ``ImageWriter``.

        The constructor will create ``self.output_dtype`` internally.
        ``affine`` is initialized as instance members (default ``None``),
        user-specified ``affine`` should be set in ``set_metadata``.
        """
        super().__init__(output_dtype=output_dtype, affine=None, **kwargs)

    def set_data_array(
        self, data_array: NdarrayOrTensor, channel_dim: int | None = 0, squeeze_end_dims: bool = True, **kwargs
    ):
        """
        Convert ``data_array`` into 'channel-last' numpy ndarray.

        Args:
            data_array: input data array with the channel dimension specified by ``channel_dim``.
            channel_dim: channel dimension of the data array. Defaults to 0.
                ``None`` indicates data without any channel dimension.
            squeeze_end_dims: if ``True``, any trailing singleton dimensions will be removed.
            kwargs: keyword arguments passed to ``self.convert_to_channel_last``,
                currently support ``spatial_ndim``, defaulting to ``3``.
        """
        self.data_obj = self.convert_to_channel_last(
            data=data_array,
            channel_dim=channel_dim,
            squeeze_end_dims=squeeze_end_dims,
            spatial_ndim=kwargs.pop("spatial_ndim", 3),
        )

    def set_metadata(self, meta_dict: Mapping | None, resample: bool = True, **options):
        """
        Resample ``self.data_obj`` if needed.  This method assumes ``self.data_obj`` is a 'channel-last' ndarray.

        Args: