# coding=utf-8 # Copyright 2022 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 LayoutLMv2.""" from typing import Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image from ...image_utils import ( ChannelDimension, ImageInput, PILImageResampling, infer_channel_dimension_format, make_flat_list_of_images, to_numpy_array, valid_images, validate_preprocess_arguments, ) from ...utils import ( TensorType, filter_out_non_signature_kwargs, is_pytesseract_available, is_vision_available, logging, requires_backends, ) from ...utils.import_utils import requires if is_vision_available(): import PIL # soft dependency if is_pytesseract_available(): import pytesseract logger = logging.get_logger(__name__) def normalize_box(box, width, height): return [ int(1000 * (box[0] / width)), int(1000 * (box[1] / height)), int(1000 * (box[2] / width)), int(1000 * (box[3] / height)), ] def apply_tesseract( image: np.ndarray, lang: Optional[str], tesseract_config: Optional[str] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ): """Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes.""" tesseract_config = tesseract_config if tesseract_config is not None else "" # apply OCR pil_image = to_pil_image(image, input_data_format=input_data_format) image_width, image_height = pil_image.size data = pytesseract.image_to_data(pil_image, lang=lang, output_type="dict", config=tesseract_config) words, left, top, width, height = data["text"], data["left"], data["top"], data["width"], data["height"] # filter empty words and corresponding coordinates irrelevant_indices = [idx for idx, word in enumerate(words) if not word.strip()] words = [word for idx, word in enumerate(words) if idx not in irrelevant_indices] left = [coord for idx, coord in enumerate(left) if idx not in irrelevant_indices] top = [coord for idx, coord in enumerate(top) if idx not in irrelevant_indices] width = [coord for idx, coord in enumerate(width) if idx not in irrelevant_indices] height = [coord for idx, coord in enumerate(height) if idx not in irrelevant_indices] # turn coordinates into (left, top, left+width, top+height) format actual_boxes = [] for x, y, w, h in zip(left, top, width, height): actual_box = [x, y, x + w, y + h] actual_boxes.append(actual_box) # finally, normalize the bounding boxes normalized_boxes = [] for box in actual_boxes: normalized_boxes.append(normalize_box(box, image_width, image_height)) assert len(words) == len(normalized_boxes), "Not as many words as there are bounding boxes" return words, normalized_boxes @requires(backends=("vision",)) class LayoutLMv2ImageProcessor(BaseImageProcessor): r""" Constructs a LayoutLMv2 image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to `(size["height"], size["width"])`. Can be overridden by `do_resize` in `preprocess`. size (`dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`): Size of the image after resizing. Can be overridden by `size` in `preprocess`. 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. apply_ocr (`bool`, *optional*, defaults to `True`): Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes. Can be overridden by `apply_ocr` in `preprocess`. ocr_lang (`str`, *optional*): The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is used. Can be overridden by `ocr_lang` in `preprocess`. tesseract_config (`str`, *optional*, defaults to `""`): Any additional custom configuration flags that are forwarded to the `config` parameter when calling Tesseract. For example: '--psm 6'. Can be overridden by `tesseract_config` in `preprocess`. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size: Optional[dict[str, int]] = None, resample: PILImageResampling = PILImageResampling.BILINEAR, apply_ocr: bool = True, ocr_lang: Optional[str] = None, tesseract_config: Optional[str] = "", **kwargs, ) -> None: super().__init__(**kwargs) size = size if size is not None else {"height": 224, "width": 224} size = get_size_dict(size) self.do_resize = do_resize self.size = size self.resample = resample self.apply_ocr = apply_ocr self.ocr_lang = ocr_lang self.tesseract_config = tesseract_config # Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize 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 to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): `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. - `"none"` or `ChannelDimension.NONE`: image in (height, width) 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. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. Returns: `np.ndarray`: The resized image. """ size = get_size_dict(size) if "height" not in size or "width" not in size: raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}") output_size = (size["height"], size["width"]) return resize( image, size=output_size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs, ) @filter_out_non_signature_kwargs() def preprocess( self, images: ImageInput, do_resize: Optional[bool] = None, size: Optional[dict[str, int]] = None, resample: Optional[PILImageResampling] = None, apply_ocr: Optional[bool] = None, ocr_lang: Optional[str] = None, tesseract_config: Optional[str] = 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: images (`ImageInput`): Image 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`): Desired size of the output image after resizing. resample (`PILImageResampling`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PIL.Image` resampling filter. Only has an effect if `do_resize` is set to `True`. apply_ocr (`bool`, *optional*, defaults to `self.apply_ocr`): Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes. ocr_lang (`str`, *optional*, defaults to `self.ocr_lang`): The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is used. tesseract_config (`str`, *optional*, defaults to `self.tesseract_config`): Any additional custom configuration flags that are forwarded to the `config` parameter when calling Tesseract. 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. """ do_resize = do_resize if do_resize is not None else self.do_resize size = size if size is not None else self.size size = get_size_dict(size) resample = resample if resample is not None else self.resample apply_ocr = apply_ocr if apply_ocr is not None else self.apply_ocr ocr_lang = ocr_lang if ocr_lang is not None else self.ocr_lang tesseract_config = tesseract_config if tesseract_config is not None else self.tesseract_config images = make_flat_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." ) validate_preprocess_arguments( do_resize=do_resize, size=size, resample=resample, ) # All transformations expect numpy arrays. images = [to_numpy_array(image) for image in images] if input_data_format is None: # We assume that all images have the same channel dimension format. input_data_format = infer_channel_dimension_format(images[0]) if apply_ocr: requires_backends(self, "pytesseract") words_batch = [] boxes_batch = [] for image in images: words, boxes = apply_tesseract(image, ocr_lang, tesseract_config, input_data_format=input_data_format) words_batch.append(words) boxes_batch.append(boxes) if do_resize: images = [ self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format) for image in images ] # flip color channels from RGB to BGR (as Detectron2 requires this) images = [flip_channel_order(image, input_data_format=input_data_format) for image in images] images = [ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images ] data = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors) if apply_ocr: data["words"] = words_batch data["boxes"] = boxes_batch return data __all__ = ["LayoutLMv2ImageProcessor"]