# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve. # # 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. import os, sys from typing import Any, Dict, Optional, Union, List, Tuple import numpy as np import cv2 from ..base import BasePipeline from ..components import CropByBoxes, convert_points_to_boxes from .result import FormulaRecognitionResult from ...models.formula_recognition.result import ( FormulaRecResult as SingleFormulaRecognitionResult, ) from ....utils import logging from ...utils.pp_option import PaddlePredictorOption from ...common.reader import ReadImage from ...common.batch_sampler import ImageBatchSampler from ..ocr.result import OCRResult from ..doc_preprocessor.result import DocPreprocessorResult from ...models.object_detection.result import DetResult class FormulaRecognitionPipeline(BasePipeline): """Formula Recognition Pipeline""" entities = ["formula_recognition"] def __init__( self, config: Dict, device: str = None, pp_option: PaddlePredictorOption = None, use_hpip: bool = False, ) -> None: """Initializes the formula recognition pipeline. Args: config (Dict): Configuration dictionary containing various settings. device (str, optional): Device to run the predictions on. Defaults to None. pp_option (PaddlePredictorOption, optional): PaddlePredictor options. Defaults to None. use_hpip (bool, optional): Whether to use high-performance inference (hpip) for prediction. Defaults to False. """ super().__init__(device=device, pp_option=pp_option, use_hpip=use_hpip) self.use_doc_preprocessor = config.get("use_doc_preprocessor", True) if self.use_doc_preprocessor: doc_preprocessor_config = config.get("SubPipelines", {}).get( "DocPreprocessor", { "pipeline_config_error": "config error for doc_preprocessor_pipeline!" }, ) self.doc_preprocessor_pipeline = self.create_pipeline( doc_preprocessor_config ) self.use_layout_detection = config.get("use_layout_detection", True) if self.use_layout_detection: layout_det_config = config.get("SubModules", {}).get( "LayoutDetection", {"model_config_error": "config error for layout_det_model!"}, ) layout_kwargs = {} if (threshold := layout_det_config.get("threshold", None)) is not None: layout_kwargs["threshold"] = threshold if (layout_nms := layout_det_config.get("layout_nms", None)) is not None: layout_kwargs["layout_nms"] = layout_nms if ( layout_unclip_ratio := layout_det_config.get( "layout_unclip_ratio", None ) ) is not None: layout_kwargs["layout_unclip_ratio"] = layout_unclip_ratio if ( layout_merge_bboxes_mode := layout_det_config.get( "layout_merge_bboxes_mode", None ) ) is not None: layout_kwargs["layout_merge_bboxes_mode"] = layout_merge_bboxes_mode self.layout_det_model = self.create_model( layout_det_config, **layout_kwargs ) formula_recognition_config = config.get("SubModules", {}).get( "FormulaRecognition", {"model_config_error": "config error for formula_rec_model!"}, ) self.formula_recognition_model = self.create_model(formula_recognition_config) self._crop_by_boxes = CropByBoxes() self.batch_sampler = ImageBatchSampler(batch_size=1) self.img_reader = ReadImage(format="BGR") def get_model_settings( self, use_doc_orientation_classify: Optional[bool], use_doc_unwarping: Optional[bool], use_layout_detection: Optional[bool], ) -> dict: """ Get the model settings based on the provided parameters or default values. Args: use_doc_orientation_classify (Optional[bool]): Whether to use document orientation classification. use_doc_unwarping (Optional[bool]): Whether to use document unwarping. use_layout_detection (Optional[bool]): Whether to use layout detection. Returns: dict: A dictionary containing the model settings. """ if use_doc_orientation_classify is None and use_doc_unwarping is None: use_doc_preprocessor = self.use_doc_preprocessor else: if use_doc_orientation_classify is True or use_doc_unwarping is True: use_doc_preprocessor = True else: use_doc_preprocessor = False if use_layout_detection is None: use_layout_detection = self.use_layout_detection return dict( use_doc_preprocessor=use_doc_preprocessor, use_layout_detection=use_layout_detection, ) def check_model_settings_valid( self, model_settings: Dict, layout_det_res: DetResult ) -> bool: """ Check if the input parameters are valid based on the initialized models. Args: model_settings (Dict): A dictionary containing input parameters. layout_det_res (DetResult): The layout detection result. Returns: bool: True if all required models are initialized according to input parameters, False otherwise. """ if model_settings["use_doc_preprocessor"] and not self.use_doc_preprocessor: logging.error( "Set use_doc_preprocessor, but the models for doc preprocessor are not initialized." ) return False if model_settings["use_layout_detection"]: if layout_det_res is not None: logging.error( "The layout detection model has already been initialized, please set use_layout_detection=False" ) return False if not self.use_layout_detection: logging.error( "Set use_layout_detection, but the models for layout detection are not initialized." ) return False return True def predict_single_formula_recognition_res( self, image_array: np.ndarray, ) -> SingleFormulaRecognitionResult: """ Predict formula recognition results from an image array, layout detection results. Args: image_array (np.ndarray): The input image represented as a numpy array. formula_box (list): The formula box coordinates. flag_find_nei_text (bool): Whether to find neighboring text. Returns: SingleFormulaRecognitionResult: single formula recognition result. """ formula_recognition_pred = next(self.formula_recognition_model(image_array)) return formula_recognition_pred def predict( self, input: Union[str, List[str], np.ndarray, List[np.ndarray]], use_layout_detection: Optional[bool] = None, use_doc_orientation_classify: Optional[bool] = None, use_doc_unwarping: Optional[bool] = None, layout_det_res: Optional[DetResult] = None, layout_threshold: Optional[Union[float, dict]] = None, layout_nms: Optional[bool] = None, layout_unclip_ratio: Optional[Union[float, Tuple[float, float]]] = None, layout_merge_bboxes_mode: Optional[str] = None, **kwargs, ) -> FormulaRecognitionResult: """ This function predicts the layout parsing result for the given input. Args: input (Union[str, list[str], np.ndarray, list[np.ndarray]]): The input image(s) of pdf(s) to be processed. use_layout_detection (Optional[bool]): Whether to use layout detection. use_doc_orientation_classify (Optional[bool]): Whether to use document orientation classification. use_doc_unwarping (Optional[bool]): Whether to use document unwarping. layout_det_res (Optional[DetResult]): The layout detection result. It will be used if it is not None and use_layout_detection is False. **kwargs: Additional keyword arguments. Returns: formulaRecognitionResult: The predicted formula recognition result. """ model_settings = self.get_model_settings( use_doc_orientation_classify, use_doc_unwarping, use_layout_detection, ) if not self.check_model_settings_valid(model_settings, layout_det_res): yield {"error": "the input params for model settings are invalid!"} for img_id, batch_data in enumerate(self.batch_sampler(input)): image_array = self.img_reader(batch_data.instances)[0] if model_settings["use_doc_preprocessor"]: doc_preprocessor_res = next( self.doc_preprocessor_pipeline( image_array, use_doc_orientation_classify=use_doc_orientation_classify, use_doc_unwarping=use_doc_unwarping, ) ) else: doc_preprocessor_res = {"output_img": image_array} doc_preprocessor_image = doc_preprocessor_res["output_img"] formula_res_list = [] formula_region_id = 1 if not model_settings["use_layout_detection"] and layout_det_res is None: layout_det_res = {} img_height, img_width = doc_preprocessor_image.shape[:2] single_formula_rec_res = self.predict_single_formula_recognition_res( doc_preprocessor_image, ) single_formula_rec_res["formula_region_id"] = formula_region_id formula_res_list.append(single_formula_rec_res) formula_region_id += 1 else: if model_settings["use_layout_detection"]: layout_det_res = next( self.layout_det_model( doc_preprocessor_image, threshold=layout_threshold, layout_nms=layout_nms, layout_unclip_ratio=layout_unclip_ratio, layout_merge_bboxes_mode=layout_merge_bboxes_mode, ) ) formula_crop_img = [] for box_info in layout_det_res["boxes"]: if box_info["label"].lower() in ["formula"]: crop_img_info = self._crop_by_boxes( doc_preprocessor_image, [box_info] ) crop_img_info = crop_img_info[0] formula_crop_img.append(crop_img_info["img"]) single_formula_rec_res = {} single_formula_rec_res["formula_region_id"] = formula_region_id single_formula_rec_res["dt_polys"] = box_info["coordinate"] formula_res_list.append(single_formula_rec_res) formula_region_id += 1 for idx, formula_rec_res in enumerate( self.formula_recognition_model(formula_crop_img) ): formula_region_id = formula_res_list[idx]["formula_region_id"] dt_polys = formula_res_list[idx]["dt_polys"] formula_rec_res["formula_region_id"] = formula_region_id formula_rec_res["dt_polys"] = dt_polys formula_res_list[idx] = formula_rec_res single_img_res = { "input_path": batch_data.input_paths[0], "page_index": batch_data.page_indexes[0], "layout_det_res": layout_det_res, "doc_preprocessor_res": doc_preprocessor_res, "formula_res_list": formula_res_list, "model_settings": model_settings, } yield FormulaRecognitionResult(single_img_res)