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Image Processor
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src/transformers/models/bagel/image_processing_bagel.py
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371
src/transformers/models/bagel/image_processing_bagel.py
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# coding=utf-8
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Image processor class for Bagel."""
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from typing import Optional, Union
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import numpy as np
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from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
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from ...image_transforms import convert_to_rgb, resize, to_channel_dimension_format
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from ...image_utils import (
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IMAGENET_STANDARD_MEAN,
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IMAGENET_STANDARD_STD,
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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get_image_size,
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infer_channel_dimension_format,
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is_scaled_image,
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make_flat_list_of_images,
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to_numpy_array,
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valid_images,
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validate_preprocess_arguments,
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)
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from ...utils import (
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TensorType,
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filter_out_non_signature_kwargs,
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is_vision_available,
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logging,
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)
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if is_vision_available():
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import PIL
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logger = logging.get_logger(__name__)
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class BagelImageProcessor(BaseImageProcessor):
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r"""
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Constructs a Bagel image processor.
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Args:
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do_resize (`bool`, *optional*, defaults to `True`):
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Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
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`do_resize` parameter in the `preprocess` method.
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size (`dict`, *optional*, defaults to `{"height": 384, "width": 384}`):
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Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
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method.
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resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
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Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
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overridden by the `resample` parameter in the `preprocess` method.
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do_rescale (`bool`, *optional*, defaults to `True`):
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Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
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`do_rescale` parameter in the `preprocess` method.
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rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
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Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
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overridden by the `rescale_factor` parameter in the `preprocess` method.
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do_normalize (`bool`, *optional*, defaults to `True`):
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Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
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method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
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image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
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Mean to use if normalizing the image. This is a float or list of floats the length of the number of
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channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
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image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
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Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
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number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
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do_convert_rgb (`bool`, *optional*, defaults to `True`):
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Whether to convert the image to RGB.
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"""
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model_input_names = ["pixel_values"]
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def __init__(
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self,
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do_resize: bool = True,
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size: Optional[dict[str, int]] = None,
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resample: PILImageResampling = PILImageResampling.BICUBIC,
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do_rescale: bool = True,
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rescale_factor: Union[int, float] = 1 / 255,
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do_normalize: bool = True,
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image_mean: Optional[Union[float, list[float]]] = None,
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image_std: Optional[Union[float, list[float]]] = None,
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do_convert_rgb: Optional[bool] = None,
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**kwargs,
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) -> None:
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super().__init__(**kwargs)
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size = size if size is not None else {"height": 384, "width": 384}
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size = get_size_dict(size, default_to_square=True)
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self.do_resize = do_resize
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self.size = size
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self.resample = resample
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self.do_rescale = do_rescale
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self.rescale_factor = rescale_factor
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self.do_normalize = do_normalize
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self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
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self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
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self.do_convert_rgb = do_convert_rgb
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self.background_color = tuple([int(x * 255) for x in self.image_mean])
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def resize(
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self,
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image: np.ndarray,
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size: dict[str, int],
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resample: PILImageResampling = PILImageResampling.BICUBIC,
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data_format: Optional[Union[str, ChannelDimension]] = None,
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input_data_format: Optional[Union[str, ChannelDimension]] = None,
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**kwargs,
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) -> np.ndarray:
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"""
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Resize and pad an image to a square based on the longest edge in `size`.
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Args:
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image (`np.ndarray`):
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Image to resize.
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resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
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`PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.
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data_format (`ChannelDimension` or `str`, *optional*):
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The channel dimension format for the output image. If unset, the channel dimension format of the input
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image is used. Can be one of:
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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- `None`: will be inferred from input
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input_data_format (`ChannelDimension` or `str`, *optional*):
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The channel dimension format for the input image. If unset, the channel dimension format is inferred
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from the input image. Can be one of:
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
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Returns:
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`np.ndarray`: The resized image.
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"""
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if input_data_format is None:
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input_data_format = infer_channel_dimension_format(image)
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height, width = get_image_size(image, input_data_format)
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max_size = max(height, width)
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size = get_size_dict(size, default_to_square=True)
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if size["height"] != size["width"]:
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raise ValueError(
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f"Output height and width must be the same. Got height={size['height']} and width={size['width']}"
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)
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size = size["height"]
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delta = size / max_size
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# Largest side becomes `size` and the other side is scaled according to the aspect ratio.
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output_size_nonpadded = [int(height * delta), int(width * delta)]
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image = resize(
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image,
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size=output_size_nonpadded,
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resample=resample,
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data_format=data_format,
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input_data_format=input_data_format,
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return_numpy=True,
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**kwargs,
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)
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# Expand and pad the images to obtain a square image of dimensions `size x size`
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image = self.pad_to_square(
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image=image,
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input_data_format=input_data_format,
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)
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return image
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@filter_out_non_signature_kwargs()
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def preprocess(
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self,
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images: ImageInput,
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do_resize: Optional[bool] = None,
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size: Optional[dict[str, int]] = None,
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resample: PILImageResampling = None,
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do_rescale: Optional[bool] = None,
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rescale_factor: Optional[float] = None,
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do_normalize: Optional[bool] = None,
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image_mean: Optional[Union[float, list[float]]] = None,
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image_std: Optional[Union[float, list[float]]] = None,
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return_tensors: Optional[Union[str, TensorType]] = None,
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do_convert_rgb: Optional[bool] = None,
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data_format: ChannelDimension = ChannelDimension.FIRST,
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input_data_format: Optional[Union[str, ChannelDimension]] = None,
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) -> PIL.Image.Image:
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"""
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Preprocess an image or batch of images.
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Args:
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images (`ImageInput`):
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Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
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passing in images with pixel values between 0 and 1, set `do_rescale=False`.
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do_resize (`bool`, *optional*, defaults to `self.do_resize`):
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Whether to resize the image.
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size (`Dict[str, int]`, *optional*, defaults to `self.size`):
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Controls the size of the image after `resize`. The shortest edge of the image is resized to
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`size["shortest_edge"]` whilst preserving the aspect ratio. If the longest edge of this resized image
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is > `int(size["shortest_edge"] * (1333 / 800))`, then the image is resized again to make the longest
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edge equal to `int(size["shortest_edge"] * (1333 / 800))`.
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resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
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Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`.
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do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
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Whether to rescale the image values between [0 - 1].
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rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
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Rescale factor to rescale the image by if `do_rescale` is set to `True`.
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do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
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Whether to normalize the image.
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image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
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Image mean to normalize the image by if `do_normalize` is set to `True`.
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image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
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Image standard deviation to normalize the image by if `do_normalize` is set to `True`.
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do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
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Whether to convert the image to RGB.
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return_tensors (`str` or `TensorType`, *optional*):
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The type of tensors to return. Can be one of:
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- Unset: Return a list of `np.ndarray`.
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- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
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- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
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- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
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- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
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data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
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The channel dimension format for the output image. Can be one of:
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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- Unset: Use the channel dimension format of the input image.
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input_data_format (`ChannelDimension` or `str`, *optional*):
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The channel dimension format for the input image. If unset, the channel dimension format is inferred
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from the input image. Can be one of:
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
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"""
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do_resize = do_resize if do_resize is not None else self.do_resize
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resample = resample if resample is not None else self.resample
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do_rescale = do_rescale if do_rescale is not None else self.do_rescale
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rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
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do_normalize = do_normalize if do_normalize is not None else self.do_normalize
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image_mean = image_mean if image_mean is not None else self.image_mean
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image_std = image_std if image_std is not None else self.image_std
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do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
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size = size if size is not None else self.size
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size = get_size_dict(size, default_to_square=False)
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images = make_flat_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
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"torch.Tensor, tf.Tensor or jax.ndarray."
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)
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validate_preprocess_arguments(
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do_rescale=do_rescale,
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rescale_factor=rescale_factor,
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do_normalize=do_normalize,
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image_mean=image_mean,
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image_std=image_std,
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do_resize=do_resize,
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size=size,
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resample=resample,
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)
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# PIL RGBA images are converted to RGB
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if do_convert_rgb:
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images = [convert_to_rgb(image) for image in images]
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# All transformations expect numpy arrays.
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images = [to_numpy_array(image) for image in images]
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if do_rescale and is_scaled_image(images[0]):
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logger.warning_once(
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"It looks like you are trying to rescale already rescaled images. If the input"
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" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
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)
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if input_data_format is None:
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# We assume that all images have the same channel dimension format.
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input_data_format = infer_channel_dimension_format(images[0])
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all_images = []
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for image in images:
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if do_resize:
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image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
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if do_rescale:
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image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
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if do_normalize:
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image = self.normalize(
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image=image, mean=image_mean, std=image_std, input_data_format=input_data_format
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)
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image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
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all_images.append(image)
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data = {"pixel_values": all_images}
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return BatchFeature(data=data, tensor_type=return_tensors)
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def pad_to_square(
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self,
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image: np.ndarray,
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data_format: Optional[Union[str, ChannelDimension]] = None,
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input_data_format: Optional[Union[str, ChannelDimension]] = None,
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) -> np.array:
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"""
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Pads an image to a square based on the longest edge.
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Args:
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image (`np.ndarray`):
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The image to pad.
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data_format (`str` or `ChannelDimension`, *optional*):
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The channel dimension format for the output image. Can be one of:
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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If unset, will use same as the input image.
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input_data_format (`str` or `ChannelDimension`, *optional*):
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The channel dimension format for the input image. Can be one of:
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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Returns:
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`np.ndarray`: The padded image.
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"""
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height, width = get_image_size(image, input_data_format)
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num_channels = image.shape[0] if input_data_format == ChannelDimension.FIRST else image.shape[-1]
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if height == width:
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image = (
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to_channel_dimension_format(image, data_format, input_data_format)
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if data_format is not None
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else image
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)
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return image
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max_dim = max(height, width)
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if input_data_format == ChannelDimension.FIRST:
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result = np.zeros((num_channels, max_dim, max_dim), dtype=image.dtype)
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for i, color in enumerate(self.background_color):
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result[i, :, :] = color
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if width > height:
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start = (max_dim - height) // 2
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result[:, start : start + height, :] = image
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else:
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start = (max_dim - width) // 2
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result[:, :, start : start + width] = image
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else:
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result = np.zeros((max_dim, max_dim, num_channels), dtype=image.dtype)
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for i, color in enumerate(self.background_color):
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result[:, :, i] = color
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if width > height:
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start = (max_dim - height) // 2
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result[start : start + height, :, :] = image
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else:
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start = (max_dim - width) // 2
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result[:, start : start + width, :] = image
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return result
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__all__ = ["BagelImageProcessor"]
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