Method create_backend_obj

monai/data/image_writer.py:822–862

Create a PIL object from ``data_array``. Args: data_array: input data array. dtype: output data type. scale: {``255``, ``65535``} postprocess data by clipping to [0, 1] and scaling [0, 255] (uint8) or [0, 65535] (uint16). Default

(
        cls,
        data_array: NdarrayOrTensor,
        dtype: DtypeLike = None,
        scale: int | None = 255,
        reverse_indexing: bool = True,
        **kwargs,
    )

    @classmethod
    def create_backend_obj(
        cls,
        data_array: NdarrayOrTensor,
        dtype: DtypeLike = None,
        scale: int | None = 255,
        reverse_indexing: bool = True,
        **kwargs,
    ):
        """
        Create a PIL object from ``data_array``.

        Args:
            data_array: input data array.
            dtype: output data type.
            scale: {``255``, ``65535``} postprocess data by clipping to [0, 1] and scaling
                [0, 255] (uint8) or [0, 65535] (uint16). Default is None to disable scaling.
            reverse_indexing: if ``True``, the data array's first two dimensions will be swapped.
            kwargs: keyword arguments. Currently ``PILImage.fromarray`` will read
                ``image_mode`` from this dictionary, defaults to ``None``.

        See also:

            - https://pillow.readthedocs.io/en/stable/reference/Image.html
        """
        data: np.ndarray = super().create_backend_obj(data_array)
        if scale:
            # scale the data to be in an integer range
            data = np.clip(data, 0.0, 1.0)  # png writer only can scale data in range [0, 1]

            if scale == np.iinfo(np.uint8).max:
                data = (scale * data).astype(np.uint8, copy=False)
            elif scale == np.iinfo(np.uint16).max:
                data = (scale * data).astype(np.uint16, copy=False)
            else:
                raise ValueError(f"Unsupported scale: {scale}, available options are [255, 65535].")
        if dtype is not None:
            data = data.astype(get_equivalent_dtype(dtype, np.ndarray), copy=False)
        if reverse_indexing:
            data = np.moveaxis(data, 0, 1)

        return PILImage.fromarray(data, mode=kwargs.pop("image_mode", None))


def init():