Method set_data_array

monai/data/image_writer.py:711–737

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 wit

(
        self,
        data_array: NdarrayOrTensor,
        channel_dim: int | None = 0,
        squeeze_end_dims: bool = True,
        contiguous: bool = False,
        **kwargs,
    )

        super().__init__(output_dtype=output_dtype, channel_dim=channel_dim, scale=scale, **kwargs)

    def set_data_array(
        self,
        data_array: NdarrayOrTensor,
        channel_dim: int | None = 0,
        squeeze_end_dims: bool = True,
        contiguous: bool = False,
        **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.
            contiguous: if ``True``, the data array will be converted to a contiguous array. Default is ``False``.
            kwargs: keyword arguments passed to ``self.convert_to_channel_last``,
                currently support ``spatial_ndim``, defaulting to ``2``.
        """
        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", 2),
            contiguous=contiguous,
        )

    def set_metadata(self, meta_dict: Mapping | None = None, resample: bool = True, **options):
        """