src/transformers/models/deformable_detr/image_processing_deformable_detr.py/DeformableDetrImageProcessor/__init__
class DeformableDetrImageProcessor:    def __init__(
        self,
        format: Union[str, AnnotionFormat] = AnnotionFormat.COCO_DETECTION,
        do_resize: bool = True,
        size: Dict[str, int] = None,
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        do_rescale: bool = True,
        rescale_factor: Union[int, float] = 1 / 255,
        do_normalize: bool = True,
        image_mean: Union[float, List[float]] = None,
        image_std: Union[float, List[float]] = None,
        do_pad: bool = True,
        **kwargs,
    ) -> None:
        if "pad_and_return_pixel_mask" in kwargs:
            do_pad = kwargs.pop("pad_and_return_pixel_mask")

        if "max_size" in kwargs:
            warnings.warn(
                "The `max_size` parameter is deprecated and will be removed in v4.26. "
                "Please specify in `size['longest_edge'] instead`.",
                FutureWarning,
            )
            max_size = kwargs.pop("max_size")
        else:
            max_size = None if size is None else 1333

        size = size if size is not None else {"shortest_edge": 800, "longest_edge": 1333}
        size = get_size_dict(size, max_size=max_size, default_to_square=False)

        super().__init__(**kwargs)
        self.format = format
        self.do_resize = do_resize
        self.size = size
        self.resample = resample
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
        self.do_pad = do_pad