# coding=utf-8 # Copyright 2024 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Image processor class for LLaVa.""" from typing import Dict, List, Optional, Tuple, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( convert_to_rgb, get_resize_output_image_size, resize, to_channel_dimension_format, ) from ...image_utils import ( OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images, validate_kwargs, validate_preprocess_arguments, ) from ...utils import TensorType, is_vision_available, logging logger = logging.get_logger(__name__) if is_vision_available(): import PIL class LlavaImageProcessor(BaseImageProcessor): r""" Constructs a LLaVa image processor. Args: do_pad (`bool`, *optional*, defaults to `False`): Whether to pad the image to a square based on the longest edge. The padding value is determined by the `image_mean` parameter. Can be overridden by `do_pad` in the `preprocess` method. do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by `do_resize` in the `preprocess` method. size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`): Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method. do_center_crop (`bool`, *optional*, defaults to `True`): Whether to center crop the image to the specified `crop_size`. Can be overridden by `do_center_crop` in the `preprocess` method. crop_size (`Dict[str, int]` *optional*, defaults to 224): Size of the output image after applying `center_crop`. Can be overridden by `crop_size` 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 `do_rescale` in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`): 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 `[0.26862954, 0.26130258, 0.27577711]`): 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. Can be overridden by the `image_std` parameter in the `preprocess` method. do_convert_rgb (`bool`, *optional*, defaults to `True`): Whether to convert the image to RGB. """ model_input_names = ["pixel_values"] def __init__( self, do_pad: bool = False, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImageResampling = PILImageResampling.BICUBIC, do_center_crop: bool = True, crop_size: Dict[str, int] = None, 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_convert_rgb: bool = True, **kwargs, ) -> None: super().__init__(**kwargs) size = size if size is not None else {"shortest_edge": 224} size = get_size_dict(size, default_to_square=False) crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224} crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size") self.do_pad = do_pad self.do_resize = do_resize self.size = size self.resample = resample self.do_center_crop = do_center_crop self.crop_size = crop_size 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 OPENAI_CLIP_MEAN self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD self.do_convert_rgb = do_convert_rgb self._valid_processor_keys = [ "images", "do_pad", "do_resize", "size", "resample", "do_center_crop", "crop_size", "do_rescale", "rescale_factor", "do_normalize", "image_mean", "image_std", "do_convert_rgb", "return_tensors", "data_format", "input_data_format", ] def pad_to_square( self, image: np.ndarray, background_color: Union[int, Tuple[int, int, int]] = 0, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> np.array: """ Pads an image to a square based on the longest edge. Args: image (`np.ndarray`): The image to pad. background_color (`int` or `Tuple[int, int, int]`, *optional*, defaults to 0): The color to use for the padding. Can be an integer for single channel or a tuple of integers representing for multi-channel images. If passed as integer in mutli-channel mode, it will default to `0` in subsequent channels. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the output 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. If unset, will use same as the input image. input_data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for 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. If unset, will use the inferred format of the input image. Returns: `np.ndarray`: The padded image. """ height, width = get_image_size(image, input_data_format) num_channels = image.shape[0] if input_data_format == ChannelDimension.FIRST else image.shape[-1] if height == width: image = ( to_channel_dimension_format(image, data_format, input_data_format) if data_format is not None else image ) return image max_dim = max(height, width) # Ensure background_color is the correct shape if isinstance(background_color, int): background_color = [background_color] elif len(background_color) != num_channels: raise ValueError( f"background_color must have no more than {num_channels} elements to match the number of channels" ) if input_data_format == ChannelDimension.FIRST: result = np.zeros((num_channels, max_dim, max_dim), dtype=image.dtype) for i, color in enumerate(background_color): result[i, :, :] = color if width > height: start = (max_dim - height) // 2 result[:, start : start + height, :] = image else: start = (max_dim - width) // 2 result[:, :, start : start + width] = image else: result = np.zeros((max_dim, max_dim, num_channels), dtype=image.dtype) for i, color in enumerate(background_color): result[:, :, i] = color if width > height: start = (max_dim - height) // 2 result[start : start + height, :, :] = image else: start = (max_dim - width) // 2 result[:, start : start + width, :] = image image = ( to_channel_dimension_format(result, data_format, input_data_format) if data_format is not None else result ) return image # Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize def resize( self, image: np.ndarray, size: Dict[str, int], resample: PILImageResampling = PILImageResampling.BICUBIC, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> np.ndarray: """ Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. """ default_to_square = True if "shortest_edge" in size: size = size["shortest_edge"] default_to_square = False elif "height" in size and "width" in size: size = (size["height"], size["width"]) else: raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.") output_size = get_resize_output_image_size( image, size=size, default_to_square=default_to_square, input_data_format=input_data_format, ) return resize( image, size=output_size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs, ) def preprocess( self, images: ImageInput, do_pad: Optional[bool] = None, do_resize: Optional[bool] = None, size: Optional[Dict[str, int]] = None, resample: Optional[PILImageResampling] = None, do_center_crop: Optional[bool] = None, crop_size: Optional[int] = 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_convert_rgb: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: Optional[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`. do_pad (`bool`, *optional*, defaults to `self.do_pad`): Whether to pad the image to a square based on the longest edge. The padding value is determined by the `image_mean` parameter. 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 resizing. Shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. 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_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image. crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): Size of the center crop. Only has an effect if `do_center_crop` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image. 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 to use for normalization. Only has an effect if `do_normalize` is set to `True`. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`. do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to convert the image to RGB. 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: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: Use the channel dimension format of the input image. 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_pad = do_pad if do_pad is not None else self.do_pad do_resize = do_resize if do_resize is not None else self.do_resize size = size if size is not None else self.size size = get_size_dict(size, param_name="size", default_to_square=False) resample = resample if resample is not None else self.resample do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop crop_size = crop_size if crop_size is not None else self.crop_size crop_size = get_size_dict(crop_size, param_name="crop_size", default_to_square=True) do_rescale = do_rescale if do_rescale is not None else self.do_rescale rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor do_normalize = do_normalize if do_normalize is not None else self.do_normalize 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 do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys) images = make_list_of_images(images) if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) # we don't pass `do_pad` here since LLaVa uses a custom padding to a square validate_preprocess_arguments( do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, do_center_crop=do_center_crop, crop_size=crop_size, do_resize=do_resize, size=size, resample=resample, ) if do_convert_rgb: images = [convert_to_rgb(image) for image in images] # All transformations expect numpy arrays. images = [to_numpy_array(image) for image in images] if is_scaled_image(images[0]) and do_rescale: logger.warning_once( "It looks like you are trying to rescale already rescaled images. If the input" " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." ) if input_data_format is None: # We assume that all images have the same channel dimension format. input_data_format = infer_channel_dimension_format(images[0]) processed_images = [] for image in images: if do_pad: image = self.pad_to_square( image=image, background_color=tuple(int(x * 255) for x in self.image_mean), input_data_format=input_data_format, ) if do_resize: image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format) if do_center_crop: image = self.center_crop(image=image, size=crop_size, input_data_format=input_data_format) 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 ) image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) processed_images.append(image) return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) __all__ = ["LlavaImageProcessor"]