Suya OCR API — vLLM-backed, OpenAI-compatible OCR service
FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM: legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint, a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual example outputs, and quantization/concurrency benchmarks. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Fu Dai
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# coding=utf-8
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# Copyright 2022 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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"""Modified image processor class for Segformer based on transformers"""
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import warnings
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from typing import Any, Dict, List, Optional, Union
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import numpy as np
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from transformers.image_processing_utils import (
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BaseImageProcessor,
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BatchFeature,
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get_size_dict,
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)
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from transformers.image_transforms import to_channel_dimension_format
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from transformers.image_utils import (
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IMAGENET_DEFAULT_MEAN,
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IMAGENET_DEFAULT_STD,
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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infer_channel_dimension_format,
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make_list_of_images,
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)
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from transformers.utils import TensorType
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import PIL.Image
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from surya.common.s3 import S3DownloaderMixin
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class SegformerImageProcessor(S3DownloaderMixin, BaseImageProcessor):
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r"""
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Constructs a Segformer 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["height"],
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size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
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size (`Dict[str, int]` *optional*, defaults to `{"height": 512, "width": 512}`):
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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.BILINEAR`):
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Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
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`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 `do_rescale`
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parameter in the `preprocess` method.
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rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
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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.
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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.
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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_reduce_labels (`bool`, *optional*, defaults to `False`):
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Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is
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used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The
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background label will be replaced by 255. Can be overridden by the `do_reduce_labels` parameter in the
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`preprocess` method.
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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: Dict[str, int] = None,
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resample: PILImageResampling = PILImageResampling.BILINEAR,
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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_reduce_labels: bool = False,
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**kwargs,
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) -> None:
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if "reduce_labels" in kwargs:
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warnings.warn(
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"The `reduce_labels` parameter is deprecated and will be removed in a future version. Please use "
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"`do_reduce_labels` instead.",
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FutureWarning,
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)
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do_reduce_labels = kwargs.pop("reduce_labels")
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super().__init__(**kwargs)
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size = size if size is not None else {"height": 512, "width": 512}
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size = get_size_dict(size)
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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 = (
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image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
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)
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self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
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self.do_reduce_labels = do_reduce_labels
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self._valid_processor_keys = [
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"images",
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"segmentation_maps",
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"do_resize",
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"size",
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"resample",
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"do_rescale",
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"rescale_factor",
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"do_normalize",
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"image_mean",
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"image_std",
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"do_reduce_labels",
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"return_tensors",
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"data_format",
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"input_data_format",
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]
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@classmethod
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def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
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"""
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Overrides the `from_dict` method from the base class to make sure `do_reduce_labels` is updated if image
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processor is created using from_dict and kwargs e.g. `SegformerImageProcessor.from_pretrained(checkpoint,
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reduce_labels=True)`
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"""
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image_processor_dict = image_processor_dict.copy()
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if "reduce_labels" in kwargs:
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image_processor_dict["reduce_labels"] = kwargs.pop("reduce_labels")
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return super().from_dict(image_processor_dict, **kwargs)
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def _preprocess(
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self,
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image: ImageInput,
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do_resize: bool,
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do_rescale: bool,
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do_normalize: bool,
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size: Optional[Dict[str, int]] = None,
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resample: PILImageResampling = None,
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rescale_factor: Optional[float] = 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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input_data_format: Optional[Union[str, ChannelDimension]] = None,
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):
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if do_rescale:
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image = self.rescale(
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image=image, scale=rescale_factor, input_data_format=input_data_format
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)
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if do_normalize:
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image = self.normalize(
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image=image,
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mean=image_mean,
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std=image_std,
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input_data_format=input_data_format,
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)
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return image
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def _preprocess_image(
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self,
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image: ImageInput,
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do_resize: bool = None,
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size: Dict[str, int] = None,
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resample: PILImageResampling = None,
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do_rescale: bool = None,
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rescale_factor: float = None,
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do_normalize: 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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data_format: Optional[Union[str, ChannelDimension]] = None,
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input_data_format: Optional[Union[str, ChannelDimension]] = None,
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) -> np.ndarray:
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"""Preprocesses a single image."""
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# All transformations expect numpy arrays.
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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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image = self._preprocess(
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image=image,
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do_resize=do_resize,
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size=size,
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resample=resample,
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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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input_data_format=input_data_format,
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)
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if data_format is not None:
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image = to_channel_dimension_format(
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image, data_format, input_channel_dim=input_data_format
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)
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return image
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def __call__(self, images, segmentation_maps=None, **kwargs):
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"""
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Preprocesses a batch of images and optionally segmentation maps.
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Overrides the `__call__` method of the `Preprocessor` class so that both images and segmentation maps can be
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passed in as positional arguments.
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"""
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return super().__call__(images, segmentation_maps=segmentation_maps, **kwargs)
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def preprocess(
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self,
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images: ImageInput,
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segmentation_maps: Optional[ImageInput] = None,
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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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do_reduce_labels: Optional[bool] = None,
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return_tensors: Optional[Union[str, TensorType]] = 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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**kwargs,
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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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segmentation_maps (`ImageInput`, *optional*):
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Segmentation map to preprocess.
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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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Size of the image after `resize` is applied.
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resample (`int`, *optional*, defaults to `self.resample`):
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Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
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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.
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image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
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Image standard deviation.
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do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`):
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Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0
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is used for background, and background itself is not included in all classes of a dataset (e.g.
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ADE20k). The background label will be replaced by 255.
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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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- `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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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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do_rescale = do_rescale if do_rescale is not None else self.do_rescale
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do_normalize = do_normalize if do_normalize is not None else self.do_normalize
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resample = resample if resample is not None else self.resample
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size = size if size is not None else self.size
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rescale_factor = (
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rescale_factor if rescale_factor is not None else self.rescale_factor
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)
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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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images = make_list_of_images(images)
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images = [
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self._preprocess_image(
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image=img,
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do_resize=do_resize,
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resample=resample,
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size=size,
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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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data_format=data_format,
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input_data_format=input_data_format,
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)
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for img in images
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]
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data = {"pixel_values": images}
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return BatchFeature(data=data, tensor_type=return_tensors)
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