# 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. from __future__ import annotations from typing import Optional, Union, Tuple import numpy as np from ....utils import logging from ...common.batch_sampler import ImageBatchSampler from ...common.reader import ReadImage from ...models.object_detection.result import DetResult from ...utils.pp_option import PaddlePredictorOption from ..base import BasePipeline from ..ocr.result import OCRResult from .result_v2 import LayoutParsingResultV2 from .utils import get_single_block_parsing_res from .utils import get_sub_regions_ocr_res class LayoutParsingPipelineV2(BasePipeline): """Layout Parsing Pipeline V2""" entities = ["layout_parsing_v2"] def __init__( self, config: dict, device: str = None, pp_option: PaddlePredictorOption = None, use_hpip: bool = False, ) -> None: """Initializes the layout parsing 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.inintial_predictor(config) self.batch_sampler = ImageBatchSampler(batch_size=1) self.img_reader = ReadImage(format="BGR") def inintial_predictor(self, config: dict) -> None: """Initializes the predictor based on the provided configuration. Args: config (Dict): A dictionary containing the configuration for the predictor. Returns: None """ self.use_doc_preprocessor = config.get("use_doc_preprocessor", True) self.use_general_ocr = config.get("use_general_ocr", True) self.use_table_recognition = config.get("use_table_recognition", True) self.use_seal_recognition = config.get("use_seal_recognition", True) self.use_formula_recognition = config.get( "use_formula_recognition", 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, ) 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) if self.use_general_ocr or self.use_table_recognition: general_ocr_config = config.get("SubPipelines", {}).get( "GeneralOCR", {"pipeline_config_error": "config error for general_ocr_pipeline!"}, ) self.general_ocr_pipeline = self.create_pipeline( general_ocr_config, ) if self.use_seal_recognition: seal_recognition_config = config.get("SubPipelines", {}).get( "SealRecognition", { "pipeline_config_error": "config error for seal_recognition_pipeline!", }, ) self.seal_recognition_pipeline = self.create_pipeline( seal_recognition_config, ) if self.use_table_recognition: table_recognition_config = config.get("SubPipelines", {}).get( "TableRecognition", { "pipeline_config_error": "config error for table_recognition_pipeline!", }, ) self.table_recognition_pipeline = self.create_pipeline( table_recognition_config, ) if self.use_formula_recognition: formula_recognition_config = config.get("SubPipelines", {}).get( "FormulaRecognition", { "pipeline_config_error": "config error for formula_recognition_pipeline!", }, ) self.formula_recognition_pipeline = self.create_pipeline( formula_recognition_config, ) return def get_text_paragraphs_ocr_res( self, overall_ocr_res: OCRResult, layout_det_res: DetResult, ) -> OCRResult: """ Retrieves the OCR results for text paragraphs, excluding those of formulas, tables, and seals. Args: overall_ocr_res (OCRResult): The overall OCR result containing text information. layout_det_res (DetResult): The detection result containing the layout information of the document. Returns: OCRResult: The OCR result for text paragraphs after excluding formulas, tables, and seals. """ object_boxes = [] for box_info in layout_det_res["boxes"]: if box_info["label"].lower() in ["formula", "table", "seal"]: object_boxes.append(box_info["coordinate"]) object_boxes = np.array(object_boxes) sub_regions_ocr_res = get_sub_regions_ocr_res( overall_ocr_res, object_boxes, flag_within=False ) return sub_regions_ocr_res def check_model_settings_valid(self, input_params: dict) -> bool: """ Check if the input parameters are valid based on the initialized models. Args: input_params (Dict): A dictionary containing input parameters. Returns: bool: True if all required models are initialized according to input parameters, False otherwise. """ if input_params["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 input_params["use_general_ocr"] and not self.use_general_ocr: logging.error( "Set use_general_ocr, but the models for general OCR are not initialized.", ) return False if input_params["use_seal_recognition"] and not self.use_seal_recognition: logging.error( "Set use_seal_recognition, but the models for seal recognition are not initialized.", ) return False if input_params["use_table_recognition"] and not self.use_table_recognition: logging.error( "Set use_table_recognition, but the models for table recognition are not initialized.", ) return False return True def get_layout_parsing_res( self, image: list, layout_det_res: DetResult, overall_ocr_res: OCRResult, table_res_list: list, seal_res_list: list, formula_res_list: list, text_det_limit_side_len: Optional[int] = None, text_det_limit_type: Optional[str] = None, text_det_thresh: Optional[float] = None, text_det_box_thresh: Optional[float] = None, text_det_unclip_ratio: Optional[float] = None, text_rec_score_thresh: Optional[float] = None, ) -> list: """ Retrieves the layout parsing result based on the layout detection result, OCR result, and other recognition results. Args: image (list): The input image. layout_det_res (DetResult): The detection result containing the layout information of the document. overall_ocr_res (OCRResult): The overall OCR result containing text information. table_res_list (list): A list of table recognition results. seal_res_list (list): A list of seal recognition results. formula_res_list (list): A list of formula recognition results. text_det_limit_side_len (Optional[int], optional): The maximum side length of the text detection region. Defaults to None. text_det_limit_type (Optional[str], optional): The type of limit for the text detection region. Defaults to None. text_det_thresh (Optional[float], optional): The confidence threshold for text detection. Defaults to None. text_det_box_thresh (Optional[float], optional): The confidence threshold for text detection bounding boxes. Defaults to None text_det_unclip_ratio (Optional[float], optional): The unclip ratio for text detection. Defaults to None. text_rec_score_thresh (Optional[float], optional): The score threshold for text recognition. Defaults to None. Returns: list: A list of dictionaries representing the layout parsing result. """ matched_ocr_dict = {} image = np.array(image) object_boxes = [] for object_box_idx, box_info in enumerate(layout_det_res["boxes"]): box = box_info["coordinate"] label = box_info["label"].lower() object_boxes.append(box) if label not in ["formula", "table", "seal"]: _, matched_idxs = get_sub_regions_ocr_res( overall_ocr_res, [box], return_match_idx=True ) for matched_idx in matched_idxs: if matched_ocr_dict.get(matched_idx, None) is None: matched_ocr_dict[matched_idx] = [object_box_idx] else: matched_ocr_dict[matched_idx].append(object_box_idx) already_processed = set() for matched_idx, layout_box_ids in matched_ocr_dict.items(): if len(layout_box_ids) <= 1: continue # one ocr is matched to multiple layout boxes, split the text into multiple lines for idx in layout_box_ids: if idx in already_processed: continue already_processed.add(idx) wht_im = np.ones(image.shape, dtype=image.dtype) * 255 box = object_boxes[idx] x1, y1, x2, y2 = [int(i) for i in box] wht_im[y1:y2, x1:x2, :] = image[y1:y2, x1:x2, :] sub_ocr_res = next( self.general_ocr_pipeline( wht_im, text_det_limit_side_len=text_det_limit_side_len, text_det_limit_type=text_det_limit_type, text_det_thresh=text_det_thresh, text_det_box_thresh=text_det_box_thresh, text_det_unclip_ratio=text_det_unclip_ratio, text_rec_score_thresh=text_rec_score_thresh, ) ) _, matched_idxs = get_sub_regions_ocr_res( overall_ocr_res, [box], return_match_idx=True ) for matched_idx in sorted(matched_idxs, reverse=True): del overall_ocr_res["dt_polys"][matched_idx] del overall_ocr_res["rec_texts"][matched_idx] overall_ocr_res["rec_boxes"] = np.delete( overall_ocr_res["rec_boxes"], matched_idx, axis=0 ) del overall_ocr_res["rec_polys"][matched_idx] del overall_ocr_res["rec_scores"][matched_idx] if sub_ocr_res["rec_boxes"] != []: overall_ocr_res["dt_polys"].extend(sub_ocr_res["dt_polys"]) overall_ocr_res["rec_texts"].extend(sub_ocr_res["rec_texts"]) overall_ocr_res["rec_boxes"] = np.concatenate( [overall_ocr_res["rec_boxes"], sub_ocr_res["rec_boxes"]], axis=0 ) overall_ocr_res["rec_polys"].extend(sub_ocr_res["rec_polys"]) overall_ocr_res["rec_scores"].extend(sub_ocr_res["rec_scores"]) for formula_res in formula_res_list: x_min, y_min, x_max, y_max = list(map(int, formula_res["dt_polys"])) poly_points = [ (x_min, y_min), (x_max, y_min), (x_max, y_max), (x_min, y_max), ] overall_ocr_res["dt_polys"].append(poly_points) overall_ocr_res["rec_texts"].append(f"${formula_res['rec_formula']}$") overall_ocr_res["rec_boxes"] = np.vstack( (overall_ocr_res["rec_boxes"], [formula_res["dt_polys"]]) ) overall_ocr_res["rec_polys"].append(poly_points) overall_ocr_res["rec_scores"].append(1) parsing_res_list = get_single_block_parsing_res( overall_ocr_res=overall_ocr_res, layout_det_res=layout_det_res, table_res_list=table_res_list, seal_res_list=seal_res_list, ) return parsing_res_list def get_model_settings( self, use_doc_orientation_classify: Union[bool, None], use_doc_unwarping: Union[bool, None], use_general_ocr: Union[bool, None], use_seal_recognition: Union[bool, None], use_table_recognition: Union[bool, None], use_formula_recognition: Union[bool, None], ) -> dict: """ Get the model settings based on the provided parameters or default values. Args: use_doc_orientation_classify (Union[bool, None]): Enables document orientation classification if True. Defaults to system setting if None. use_doc_unwarping (Union[bool, None]): Enables document unwarping if True. Defaults to system setting if None. use_general_ocr (Union[bool, None]): Enables general OCR if True. Defaults to system setting if None. use_seal_recognition (Union[bool, None]): Enables seal recognition if True. Defaults to system setting if None. use_table_recognition (Union[bool, None]): Enables table recognition if True. Defaults to system setting if None. use_formula_recognition (Union[bool, None]): Enables formula recognition if True. Defaults to system setting if None. 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_general_ocr is None: use_general_ocr = self.use_general_ocr if use_seal_recognition is None: use_seal_recognition = self.use_seal_recognition if use_table_recognition is None: use_table_recognition = self.use_table_recognition if use_formula_recognition is None: use_formula_recognition = self.use_formula_recognition return dict( use_doc_preprocessor=use_doc_preprocessor, use_general_ocr=use_general_ocr, use_seal_recognition=use_seal_recognition, use_table_recognition=use_table_recognition, use_formula_recognition=use_formula_recognition, ) def predict( self, input: Union[str, list[str], np.ndarray, list[np.ndarray]], use_doc_orientation_classify: Union[bool, None] = None, use_doc_unwarping: Union[bool, None] = None, use_textline_orientation: Optional[bool] = None, use_general_ocr: Union[bool, None] = None, use_seal_recognition: Union[bool, None] = None, use_table_recognition: Union[bool, None] = None, use_formula_recognition: Union[bool, None] = 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, text_det_limit_side_len: Union[int, None] = None, text_det_limit_type: Union[str, None] = None, text_det_thresh: Union[float, None] = None, text_det_box_thresh: Union[float, None] = None, text_det_unclip_ratio: Union[float, None] = None, text_rec_score_thresh: Union[float, None] = None, seal_det_limit_side_len: Union[int, None] = None, seal_det_limit_type: Union[str, None] = None, seal_det_thresh: Union[float, None] = None, seal_det_box_thresh: Union[float, None] = None, seal_det_unclip_ratio: Union[float, None] = None, seal_rec_score_thresh: Union[float, None] = None, **kwargs, ) -> LayoutParsingResultV2: """ Predicts the layout parsing result for the given input. Args: use_doc_orientation_classify (Optional[bool]): Whether to use document orientation classification. use_doc_unwarping (Optional[bool]): Whether to use document unwarping. use_textline_orientation (Optional[bool]): Whether to use textline orientation prediction. use_general_ocr (Optional[bool]): Whether to use general OCR. use_seal_recognition (Optional[bool]): Whether to use seal recognition. use_table_recognition (Optional[bool]): Whether to use table recognition. use_formula_recognition (Optional[bool]): Whether to use formula recognition. layout_threshold (Optional[float]): The threshold value to filter out low-confidence predictions. Default is None. layout_nms (bool, optional): Whether to use layout-aware NMS. Defaults to False. layout_unclip_ratio (Optional[Union[float, Tuple[float, float]]], optional): The ratio of unclipping the bounding box. Defaults to None. If it's a single number, then both width and height are used. If it's a tuple of two numbers, then they are used separately for width and height respectively. If it's None, then no unclipping will be performed. layout_merge_bboxes_mode (Optional[str], optional): The mode for merging bounding boxes. Defaults to None. text_det_limit_side_len (Optional[int]): Maximum side length for text detection. text_det_limit_type (Optional[str]): Type of limit to apply for text detection. text_det_thresh (Optional[float]): Threshold for text detection. text_det_box_thresh (Optional[float]): Threshold for text detection boxes. text_det_unclip_ratio (Optional[float]): Ratio for unclipping text detection boxes. text_rec_score_thresh (Optional[float]): Score threshold for text recognition. seal_det_limit_side_len (Optional[int]): Maximum side length for seal detection. seal_det_limit_type (Optional[str]): Type of limit to apply for seal detection. seal_det_thresh (Optional[float]): Threshold for seal detection. seal_det_box_thresh (Optional[float]): Threshold for seal detection boxes. seal_det_unclip_ratio (Optional[float]): Ratio for unclipping seal detection boxes. seal_rec_score_thresh (Optional[float]): Score threshold for seal recognition. **kwargs (Any): Additional settings to extend functionality. Returns: LayoutParsingResultV2: The predicted layout parsing result. """ model_settings = self.get_model_settings( use_doc_orientation_classify, use_doc_unwarping, use_general_ocr, use_seal_recognition, use_table_recognition, use_formula_recognition, ) if not self.check_model_settings_valid(model_settings): 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"] 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, ) ) if model_settings["use_formula_recognition"]: formula_res_all = next( self.formula_recognition_pipeline( doc_preprocessor_image, use_layout_detection=False, use_doc_orientation_classify=False, use_doc_unwarping=False, layout_det_res=layout_det_res, ), ) formula_res_list = formula_res_all["formula_res_list"] else: formula_res_list = [] for formula_res in formula_res_list: x_min, y_min, x_max, y_max = list(map(int, formula_res["dt_polys"])) doc_preprocessor_image[y_min:y_max, x_min:x_max, :] = 255.0 if ( model_settings["use_general_ocr"] or model_settings["use_table_recognition"] ): overall_ocr_res = next( self.general_ocr_pipeline( doc_preprocessor_image, use_textline_orientation=use_textline_orientation, text_det_limit_side_len=text_det_limit_side_len, text_det_limit_type=text_det_limit_type, text_det_thresh=text_det_thresh, text_det_box_thresh=text_det_box_thresh, text_det_unclip_ratio=text_det_unclip_ratio, text_rec_score_thresh=text_rec_score_thresh, ), ) else: overall_ocr_res = {} if model_settings["use_table_recognition"]: table_res_all = next( self.table_recognition_pipeline( doc_preprocessor_image, use_doc_orientation_classify=False, use_doc_unwarping=False, use_layout_detection=False, use_ocr_model=False, overall_ocr_res=overall_ocr_res, layout_det_res=layout_det_res, cell_sort_by_y_projection=True, ), ) table_res_list = table_res_all["table_res_list"] else: table_res_list = [] if model_settings["use_seal_recognition"]: seal_res_all = next( self.seal_recognition_pipeline( doc_preprocessor_image, use_doc_orientation_classify=False, use_doc_unwarping=False, use_layout_detection=False, layout_det_res=layout_det_res, seal_det_limit_side_len=seal_det_limit_side_len, seal_det_limit_type=seal_det_limit_type, seal_det_thresh=seal_det_thresh, seal_det_box_thresh=seal_det_box_thresh, seal_det_unclip_ratio=seal_det_unclip_ratio, seal_rec_score_thresh=seal_rec_score_thresh, ), ) seal_res_list = seal_res_all["seal_res_list"] else: seal_res_list = [] parsing_res_list = self.get_layout_parsing_res( doc_preprocessor_image, layout_det_res=layout_det_res, overall_ocr_res=overall_ocr_res, table_res_list=table_res_list, seal_res_list=seal_res_list, formula_res_list=formula_res_list, text_det_limit_side_len=text_det_limit_side_len, text_det_limit_type=text_det_limit_type, text_det_thresh=text_det_thresh, text_det_box_thresh=text_det_box_thresh, text_det_unclip_ratio=text_det_unclip_ratio, text_rec_score_thresh=text_rec_score_thresh, ) for formula_res in formula_res_list: x_min, y_min, x_max, y_max = list(map(int, formula_res["dt_polys"])) doc_preprocessor_image[y_min:y_max, x_min:x_max, :] = formula_res[ "input_img" ] single_img_res = { "input_path": batch_data.input_paths[0], "page_index": batch_data.page_indexes[0], "doc_preprocessor_res": doc_preprocessor_res, "layout_det_res": layout_det_res, "overall_ocr_res": overall_ocr_res, "table_res_list": table_res_list, "seal_res_list": seal_res_list, "formula_res_list": formula_res_list, "parsing_res_list": parsing_res_list, "model_settings": model_settings, } yield LayoutParsingResultV2(single_img_res)