Class PILWriter

monai/data/image_writer.py:675–862

Write image data into files on disk using pillow. It's based on the Image module in PIL library: https://pillow.readthedocs.io/en/stable/reference/Image.html .. code-block:: python import numpy as np from monai.data import PILWriter np_data = np.arange(48

@require_pkg(pkg_name="PIL")
class PILWriter(ImageWriter):
    """
    Write image data into files on disk using pillow.

    It's based on the Image module in PIL library:
    https://pillow.readthedocs.io/en/stable/reference/Image.html

    .. code-block:: python

        import numpy as np
        from monai.data import PILWriter

        np_data = np.arange(48).reshape(3, 4, 4)
        writer = PILWriter(np.uint8)
        writer.set_data_array(np_data, channel_dim=0)
        writer.write("test1.png", verbose=True)
    """

    output_dtype: DtypeLike
    channel_dim: int | None
    scale: int | None

    def __init__(
        self, output_dtype: DtypeLike = np.float32, channel_dim: int | None = 0, scale: int | None = 255, **kwargs
    ):
        """
        Args:
            output_dtype: output data type.
            channel_dim: channel dimension of the data array. Defaults to 0.
                ``None`` indicates data without any channel dimension.
            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.
            kwargs: keyword arguments passed to ``ImageWriter``.
        """
        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,