# coding=utf-8 # Copyright 2023 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 RADIO.""" import math from typing import Dict, List, Optional, Tuple, Union import numpy as np from PIL import Image from transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from transformers.image_transforms import convert_to_rgb, pad, resize, to_channel_dimension_format from transformers.image_utils import ( IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images, ) from transformers.utils import ( TensorType, is_tf_available, is_torch_available, is_torchvision_available, logging, ) if is_torch_available(): import torch if is_torchvision_available(): pass if is_tf_available(): pass logger = logging.get_logger(__name__) def rank_print(s): rank = torch.distributed.get_rank() if torch.distributed.is_initialized() else 0 print(f"[Rank {rank}] {s}") class ImageProcessor(BaseImageProcessor): r""" Constructs an 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`, *optional*, defaults to `{"longest_edge": 1024}`): Size of the output image after resizing. If "longest_edge" is specified, resizes the longest edge of the image to match `size["longest_edge"]` while maintaining the aspect ratio. If "width" and "height" are specified, resizes the image to that size, possibly changing the aspect ratio. 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`): Wwhether 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`): Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be overridden by the `rescale_factor` 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. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_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. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_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. Can be overridden by the `image_std` parameter in the `preprocess` method. do_pad (`bool`, *optional*, defaults to `True`): Whether to pad the image to the specified `pad_size`. Can be overridden by the `do_pad` parameter in the `preprocess` method. pad_size (`dict`, *optional*, defaults to `{"height": 1024, "width": 1024}`): Size of the output image after padding. Can be overridden by the `pad_size` parameter in the `preprocess` method. pad_value (`float` or `Iterable[float]`, *optional*, defaults to `0.`): Value of padded pixels. pad_multiple (`int`, *optional*, defaults to `None`): Pad to a multiple of specified number. do_convert_rgb (`bool`, *optional*, defaults to `True`): Whether to convert the image to RGB. """ 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_pad: bool = True, pad_size: int = None, pad_multiple: int = None, pad_value: Optional[Union[float, List[float]]] = 0., do_convert_rgb: bool = True, **kwargs, ) -> None: super().__init__(**kwargs) x = 0 size = size if size is not None else {"longest_edge": 1024} size = get_size_dict(max_size=size, default_to_square=False) if not isinstance(size, dict) else size if pad_size is not None and pad_multiple is not None: raise ValueError("pad_size and pad_multiple should not be set at the same time.") pad_size = pad_size if pad_size is not None else {"height": 1024, "width": 1024} if pad_multiple is not None else None if do_pad: pad_size = get_size_dict(pad_size, default_to_square=True) 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 self.pad_multiple = pad_multiple self.pad_size = pad_size self.pad_value = tuple(pad_value) if isinstance(pad_value, list) else pad_value self.do_convert_rgb = do_convert_rgb self._valid_processor_keys = [ "images", "segmentation_maps", "do_resize", "size", "resample", "do_rescale", "rescale_factor", "do_normalize", "image_mean", "image_std", "do_pad", "pad_size", "do_convert_rgb", "return_tensors", "data_format", "input_data_format", ] def pad_image( self, image: np.ndarray, pad_size: Dict[str, int], data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> np.ndarray: """ Pad an image to `(pad_size["height"], pad_size["width"])` to the right and bottom. Args: image (`np.ndarray`): Image to pad. pad_size (`Dict[str, int]`): Size of the output image after padding. data_format (`str` or `ChannelDimension`, *optional*): The data format of the image. Can be either "channels_first" or "channels_last". If `None`, the `data_format` of the `image` will be used. input_data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. """ output_height, output_width = pad_size["height"], pad_size["width"] input_height, input_width = get_image_size(image, channel_dim=input_data_format) pad_width = output_width - input_width pad_height = output_height - input_height padded_image = pad( image, ((0, pad_height), (0, pad_width)), data_format=data_format, input_data_format=input_data_format, constant_values=self.pad_value, **kwargs, ) return padded_image def _get_preprocess_shape(self, old_shape: Tuple[int, int], longest_edge: int): """ Compute the output size given input size and target long side length. """ oldh, oldw = old_shape scale = longest_edge * 1.0 / max(oldh, oldw) newh, neww = oldh * scale, oldw * scale newh = int(newh + 0.5) neww = int(neww + 0.5) return (newh, neww) 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 to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Dictionary in the format `{"longest_edge": int}` or `{"width": int, "height": int}` specifying the size of the output image. If "longest_edge" is specified, resizes the longest edge of the image to match `size["longest_edge"]` while maintaining the aspect ratio. If "width" and "height" are specified, resizes the image to that size, possibly changing the aspect ratio. resample: `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. 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. 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. Returns: `np.ndarray`: The resized image. """ size = get_size_dict(size) if "longest_edge" not in size: if "width" not in size or "height" not in size: raise ValueError(f"The `size` dictionary must contain the key `longest_edge`, or `width` and `height`. Got {size.keys()}") input_size = get_image_size(image, channel_dim=input_data_format) if "longest_edge" in size: output_height, output_width = self._get_preprocess_shape(input_size, size["longest_edge"]) else: output_height, output_width = size["height"], size["width"] return resize( image, size=(output_height, output_width), resample=resample, data_format=data_format, input_data_format=input_data_format, **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, do_pad: Optional[bool] = None, pad_size: Optional[Dict[str, int]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ): if do_resize: image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format) reshaped_input_size = get_image_size(image, channel_dim=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) if do_pad: if self.pad_multiple: h, w = get_image_size(image, channel_dim=input_data_format) pad_size = { "height": math.ceil(h / self.pad_multiple) * self.pad_multiple, "width": math.ceil(w / self.pad_multiple) * self.pad_multiple, } image = self.pad_image(image=image, pad_size=pad_size, input_data_format=input_data_format) return image, reshaped_input_size def _preprocess_image( self, image: ImageInput, do_resize: Optional[bool] = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_rescale: 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_pad: Optional[bool] = None, pad_size: Optional[Dict[str, int]] = None, do_convert_rgb: Optional[bool] = None, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> Tuple[np.ndarray, Tuple[int, int], Tuple[int, int]]: #image = to_numpy_array(image) # import time # if int(time.time()*1000) % 10 == 0: # # create an PIL image of size 1x1 # image = PIL.Image.new('RGB', (1, 1)) if isinstance(image, Image.Image): # PIL always uses Channels Last. input_data_format = ChannelDimension.LAST # PIL RGBA images are converted to RGB #mode_before = image.mode if do_convert_rgb: image = convert_to_rgb(image) # All transformations expect numpy arrays. image_ = image image = to_numpy_array(image) # if isinstance(image_, np.ndarray): # rank_print(f"preprocess image type={type(image_)} shape={image_.shape} array shape={image.shape}") # elif isinstance(image_, Image.Image): # rank_print(f"preprocessimage type={type(image_)} size={image_.size} mode={image_.mode} array shape={image.shape}") # else: # rank_print(f"preprocess unknown image type={type(image_)} array shape={image.shape}") if len(image.shape) == 2: h, w = image.shape ret = np.empty((h, w, 3), dtype=np.uint8) ret[:, :, 0] = image ret[:, :, 1] = image ret[:, :, 2] = image image = ret rank_print(f"preprocess new image shape={image.shape}") elif len(image.shape) == 3 and image.shape[-1] == 1: ret = np.empty((h, w, 3), dtype=np.uint8) ret[:, :, 0] = image[:, :, 0] ret[:, :, 1] = image[:, :, 0] ret[:, :, 2] = image[:, :, 0] image = ret rank_print(f"preprocess new image shape={image.shape}") if is_scaled_image(image) 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: input_data_format = infer_channel_dimension_format(image) original_size = get_image_size(image, channel_dim=input_data_format) image, reshaped_input_size = 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, do_pad=do_pad, pad_size=pad_size, 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) # rank_print(f"preprocess original_size={original_size} reshaped_input_size={reshaped_input_size} image shape={image.shape} type={type(image)}") # if image is a single channel convert to rgb if do_convert_rgb and image.shape[0] == 1: c, h, w = image.shape ret = np.empty((3, h, w), dtype=np.uint8) ret[0, :, :] = image[0, :, :] ret[1, :, :] = image[0, :, :] ret[2, :, :] = image[0, :, :] image = ret rank_print(f"preprocess final: {image.shape}") return image, original_size, reshaped_input_size def preprocess( self, images: ImageInput, do_resize: Optional[bool] = None, size: Optional[Dict[str, int]] = None, resample: Optional["PILImageResampling"] = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[Union[int, float]] = None, do_normalize: Optional[bool] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_pad: Optional[bool] = None, pad_size: Optional[Dict[str, int]] = None, do_convert_rgb: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: ChannelDimension = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ): """ 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_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Controls the size of the image after `resize`. The longest edge of the image is resized to `size["longest_edge"]` whilst preserving the aspect ratio. resample (`PILImageResampling`, *optional*, defaults to `self.resample`): `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image pixel values by rescaling factor. rescale_factor (`int` or `float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to apply to the image pixel values. 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 normalize the image by if `do_normalize` is set to `True`. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to normalize the image by if `do_normalize` is set to `True`. do_pad (`bool`, *optional*, defaults to `self.do_pad`): Whether to pad the image. pad_size (`Dict[str, int]`, *optional*, defaults to `self.pad_size`): Controls the size of the padding applied to the image. The image is padded to `pad_size["height"]` and `pad_size["width"]` if `do_pad` 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_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(max_size=size, default_to_square=False) if not isinstance(size, dict) else size resample = resample if resample is not None else self.resample 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_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 if do_pad: pad_size = get_size_dict(pad_size, default_to_square=True) do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb 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." ) images, original_sizes, reshaped_input_sizes = zip( *( self._preprocess_image( image=img, 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, do_pad=do_pad, pad_size=pad_size, do_convert_rgb=do_convert_rgb, data_format=data_format, input_data_format=input_data_format, ) for img in images ) ) data = { "pixel_values": images, "original_sizes": original_sizes, "reshaped_input_sizes": reshaped_input_sizes, } return BatchFeature(data=data, tensor_type=return_tensors)