# coding=utf-8 # Copyright 2023 The Intel AIA Team Authors, and 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 TVP.""" from typing import Dict, Iterable, List, Optional, Tuple, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( PaddingMode, flip_channel_order, pad, resize, to_channel_dimension_format, ) from ...image_utils import ( IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageInput, PILImageResampling, get_image_size, is_valid_image, to_numpy_array, valid_images, validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging if is_vision_available(): import PIL logger = logging.get_logger(__name__) # Copied from transformers.models.vivit.image_processing_vivit.make_batched def make_batched(videos) -> List[List[ImageInput]]: if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]): return videos elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]): return [videos] elif is_valid_image(videos): return [[videos]] raise ValueError(f"Could not make batched video from {videos}") def get_resize_output_image_size( input_image: np.ndarray, max_size: int = 448, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> Tuple[int, int]: height, width = get_image_size(input_image, input_data_format) if height >= width: ratio = width * 1.0 / height new_height = max_size new_width = new_height * ratio else: ratio = height * 1.0 / width new_width = max_size new_height = new_width * ratio size = (int(new_height), int(new_width)) return size class TvpImageProcessor(BaseImageProcessor): r""" Constructs a Tvp image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`Dict[str, int]` *optional*, defaults to `{"longest_edge": 448}`): Size of the output image after resizing. The longest edge of the image will be resized to `size["longest_edge"]` while maintaining the aspect ratio of the original image. Can be overriden by `size` 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_center_crop (`bool`, *optional*, defaults to `True`): Whether to center crop the image to the specified `crop_size`. Can be overridden by the `do_center_crop` parameter in the `preprocess` method. crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 448, "width": 448}`): Size of the image after applying the center crop. Can be overridden by the `crop_size` 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`): Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_pad (`bool`, *optional*, defaults to `True`): Whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess` method. pad_size (`Dict[str, int]`, *optional*, defaults to `{"height": 448, "width": 448}`): Size of the image after applying the padding. Can be overridden by the `pad_size` parameter in the `preprocess` method. constant_values (`Union[float, Iterable[float]]`, *optional*, defaults to 0): The fill value to use when padding the image. pad_mode (`PaddingMode`, *optional*, defaults to `PaddingMode.CONSTANT`): Use what kind of mode in padding. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. do_flip_channel_order (`bool`, *optional*, defaults to `True`): Whether to flip the color channels from RGB to BGR. Can be overridden by the `do_flip_channel_order` 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. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImageResampling = PILImageResampling.BILINEAR, do_center_crop: bool = True, crop_size: Dict[str, int] = None, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_pad: bool = True, pad_size: Dict[str, int] = None, constant_values: Union[float, Iterable[float]] = 0, pad_mode: PaddingMode = PaddingMode.CONSTANT, do_normalize: bool = True, do_flip_channel_order: bool = True, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, **kwargs, ) -> None: super().__init__(**kwargs) size = size if size is not None else {"longest_edge": 448} crop_size = crop_size if crop_size is not None else {"height": 448, "width": 448} pad_size = pad_size if pad_size is not None else {"height": 448, "width": 448} self.do_resize = do_resize self.size = size self.do_center_crop = do_center_crop self.crop_size = crop_size self.resample = resample self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_pad = do_pad self.pad_size = pad_size self.constant_values = constant_values self.pad_mode = pad_mode self.do_normalize = do_normalize self.do_flip_channel_order = do_flip_channel_order self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD def resize( self, image: np.ndarray, size: Dict[str, int], resample: PILImageResampling = PILImageResampling.BILINEAR, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> np.ndarray: """ Resize an image. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Size of the output image. If `size` is of the form `{"height": h, "width": w}`, the output image will have the size `(h, w)`. If `size` is of the form `{"longest_edge": s}`, the output image will have its longest edge of length `s` while keeping the aspect ratio of the original image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): 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 (`str` or `ChannelDimension`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. """ size = get_size_dict(size, default_to_square=False) if "height" in size and "width" in size: output_size = (size["height"], size["width"]) elif "longest_edge" in size: output_size = get_resize_output_image_size(image, size["longest_edge"], input_data_format) else: raise ValueError(f"Size must have 'height' and 'width' or 'longest_edge' as keys. Got {size.keys()}") return resize( image, size=output_size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs, ) def pad_image( self, image: np.ndarray, pad_size: Dict[str, int] = None, constant_values: Union[float, Iterable[float]] = 0, pad_mode: PaddingMode = PaddingMode.CONSTANT, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ): """ Pad an image with zeros to the given size. Args: image (`np.ndarray`): Image to pad. pad_size (`Dict[str, int]`) Size of the output image with pad. constant_values (`Union[float, Iterable[float]]`) The fill value to use when padding the image. pad_mode (`PaddingMode`) The pad mode, default to PaddingMode.CONSTANT data_format (`ChannelDimension` or `str`, *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. """ height, width = get_image_size(image, channel_dim=input_data_format) max_height = pad_size.get("height", height) max_width = pad_size.get("width", width) pad_right, pad_bottom = max_width - width, max_height - height if pad_right < 0 or pad_bottom < 0: raise ValueError("The padding size must be greater than image size") padding = ((0, pad_bottom), (0, pad_right)) padded_image = pad( image, padding, mode=pad_mode, constant_values=constant_values, data_format=data_format, input_data_format=input_data_format, ) return padded_image def _preprocess_image( self, image: ImageInput, do_resize: Optional[bool] = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_center_crop: Optional[bool] = None, crop_size: Dict[str, int] = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_pad: bool = True, pad_size: Dict[str, int] = None, constant_values: Union[float, Iterable[float]] = None, pad_mode: PaddingMode = None, do_normalize: Optional[bool] = None, do_flip_channel_order: Optional[bool] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> np.ndarray: """Preprocesses a single image.""" validate_preprocess_arguments( do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, do_pad=do_pad, size_divisibility=pad_size, # here the pad() method simply requires the pad_size argument. do_center_crop=do_center_crop, crop_size=crop_size, do_resize=do_resize, size=size, resample=resample, ) # All transformations expect numpy arrays. image = to_numpy_array(image) 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, 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.astype(np.float32), mean=image_mean, std=image_std, input_data_format=input_data_format ) if do_pad: image = self.pad_image( image=image, pad_size=pad_size, constant_values=constant_values, pad_mode=pad_mode, input_data_format=input_data_format, ) # the pretrained checkpoints assume images are BGR, not RGB if do_flip_channel_order: image = flip_channel_order(image=image, input_data_format=input_data_format) image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) return image @filter_out_non_signature_kwargs() def preprocess( self, videos: Union[ImageInput, List[ImageInput], List[List[ImageInput]]], do_resize: Optional[bool] = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_center_crop: Optional[bool] = None, crop_size: Dict[str, int] = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_pad: Optional[bool] = None, pad_size: Dict[str, int] = None, constant_values: Union[float, Iterable[float]] = None, pad_mode: PaddingMode = None, do_normalize: Optional[bool] = None, do_flip_channel_order: Optional[bool] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: ChannelDimension = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> PIL.Image.Image: """ Preprocess an image or batch of images. Args: videos (`ImageInput` or `List[ImageInput]` or `List[List[ImageInput]]`): Frames 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 applying resize. resample (`PILImageResampling`, *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_centre_crop`): Whether to centre crop the image. crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): Size of the image after applying the centre crop. 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_pad (`bool`, *optional*, defaults to `True`): Whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess` method. pad_size (`Dict[str, int]`, *optional*, defaults to `{"height": 448, "width": 448}`): Size of the image after applying the padding. Can be overridden by the `pad_size` parameter in the `preprocess` method. constant_values (`Union[float, Iterable[float]]`, *optional*, defaults to 0): The fill value to use when padding the image. pad_mode (`PaddingMode`, *optional*, defaults to "PaddingMode.CONSTANT"): Use what kind of mode in padding. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. do_flip_channel_order (`bool`, *optional*, defaults to `self.do_flip_channel_order`): Whether to flip the channel order of 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. 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. - Unset: Use the inferred 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_resize = do_resize if do_resize is not None else self.do_resize 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 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_pad = do_pad if do_pad is not None else self.do_pad pad_size = pad_size if pad_size is not None else self.pad_size constant_values = constant_values if constant_values is not None else self.constant_values pad_mode = pad_mode if pad_mode else self.pad_mode do_normalize = do_normalize if do_normalize is not None else self.do_normalize do_flip_channel_order = ( do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order ) 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 size = size if size is not None else self.size size = get_size_dict(size, default_to_square=False) 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") if not valid_images(videos): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) videos = make_batched(videos) videos = [ np.array( [ self._preprocess_image( image=img, do_resize=do_resize, size=size, resample=resample, do_center_crop=do_center_crop, crop_size=crop_size, do_rescale=do_rescale, rescale_factor=rescale_factor, do_pad=do_pad, pad_size=pad_size, constant_values=constant_values, pad_mode=pad_mode, do_normalize=do_normalize, do_flip_channel_order=do_flip_channel_order, image_mean=image_mean, image_std=image_std, data_format=data_format, input_data_format=input_data_format, ) for img in video ] ) for video in videos ] data = {"pixel_values": videos} return BatchFeature(data=data, tensor_type=return_tensors) __all__ = ["TvpImageProcessor"]