import warnings from typing import Any, Dict, List, Optional, Union import numpy as np from transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from transformers.image_transforms import to_channel_dimension_format from transformers.image_utils import ( IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ChannelDimension, ImageInput, PILImageResampling, infer_channel_dimension_format, make_list_of_images, ) from transformers.utils import TensorType import PIL.Image import torch from surya.common.s3 import S3DownloaderMixin class SegformerImageProcessor(S3DownloaderMixin, BaseImageProcessor): r""" Constructs a Segformer image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `(size["height"], size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`Dict[str, int]` *optional*, defaults to `{"height": 512, "width": 512}`): Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. do_reduce_labels (`bool`, *optional*, defaults to `False`): Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255. Can be overridden by the `do_reduce_labels` parameter in the `preprocess` method. """ model_input_names = ["pixel_values"] def __init__( self, 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: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_reduce_labels: bool = False, **kwargs, ) -> None: if "reduce_labels" in kwargs: warnings.warn( "The `reduce_labels` parameter is deprecated and will be removed in a future version. Please use " "`do_reduce_labels` instead.", FutureWarning, ) do_reduce_labels = kwargs.pop("reduce_labels") super().__init__(**kwargs) size = size if size is not None else {"height": 512, "width": 512} size = get_size_dict(size) 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_reduce_labels = do_reduce_labels self._valid_processor_keys = [ "images", "segmentation_maps", "do_resize", "size", "resample", "do_rescale", "rescale_factor", "do_normalize", "image_mean", "image_std", "do_reduce_labels", "return_tensors", "data_format", "input_data_format", ] @classmethod def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs): """ Overrides the `from_dict` method from the base class to make sure `do_reduce_labels` is updated if image processor is created using from_dict and kwargs e.g. `SegformerImageProcessor.from_pretrained(checkpoint, reduce_labels=True)` """ image_processor_dict = image_processor_dict.copy() if "reduce_labels" in kwargs: image_processor_dict["reduce_labels"] = kwargs.pop("reduce_labels") return super().from_dict(image_processor_dict, **kwargs) def _preprocess( self, image: ImageInput, do_resize: bool, do_rescale: bool, do_normalize: bool, size: Optional[Dict[str, int]] = None, resample: PILImageResampling = None, rescale_factor: Optional[float] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ): if do_rescale: image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) if do_normalize: image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) return image def _preprocess_image( self, image: ImageInput, do_resize: bool = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_rescale: bool = None, rescale_factor: float = None, do_normalize: bool = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> np.ndarray: """Preprocesses a single image.""" # All transformations expect numpy arrays. if input_data_format is None: input_data_format = infer_channel_dimension_format(image) image = self._preprocess( image=image, do_resize=do_resize, size=size, resample=resample, do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, input_data_format=input_data_format, ) if data_format is not None: image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) return image def __call__(self, images, segmentation_maps=None, **kwargs): """ Preprocesses a batch of images and optionally segmentation maps. Overrides the `__call__` method of the `Preprocessor` class so that both images and segmentation maps can be passed in as positional arguments. """ return super().__call__(images, segmentation_maps=segmentation_maps, **kwargs) def preprocess( self, images: ImageInput, segmentation_maps: Optional[ImageInput] = None, do_resize: Optional[bool] = None, size: Optional[Dict[str, int]] = None, resample: PILImageResampling = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_normalize: Optional[bool] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_reduce_labels: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: ChannelDimension = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> PIL.Image.Image: """ Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. segmentation_maps (`ImageInput`, *optional*): Segmentation map to preprocess. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Size of the image after `resize` is applied. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only has an effect if `do_resize` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image values between [0 - 1]. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Image mean. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`): Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. """ do_resize = do_resize if do_resize is not None else self.do_resize do_rescale = do_rescale if do_rescale is not None else self.do_rescale do_normalize = do_normalize if do_normalize is not None else self.do_normalize resample = resample if resample is not None else self.resample size = size if size is not None else self.size rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std images = make_list_of_images(images) images = [ self._preprocess_image( image=img, do_resize=do_resize, resample=resample, size=size, do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, data_format=data_format, input_data_format=input_data_format, ) for img in images ] data = {"pixel_values": images} return BatchFeature(data=data, tensor_type=return_tensors)