# Copyright (c) 2024 PaddlePaddle Authors. 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. from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np from ....utils import logging from ....utils.deps import pipeline_requires_extra from ...common.batch_sampler import ImageBatchSampler from ...common.reader import ReadImage from ...models.object_detection.result import DetResult from ...utils.benchmark import benchmark from ...utils.hpi import HPIConfig from ...utils.pp_option import PaddlePredictorOption from .._parallel import AutoParallelImageSimpleInferencePipeline from ..base import BasePipeline from ..components import CropByBoxes from .result import SealRecognitionResult @benchmark.time_methods class _SealRecognitionPipeline(BasePipeline): """Seal Recognition Pipeline""" def __init__( self, config: Dict, device: str = None, pp_option: PaddlePredictorOption = None, use_hpip: bool = False, hpi_config: Optional[Union[Dict[str, Any], HPIConfig]] = None, ) -> None: """Initializes the seal 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 the high-performance inference plugin (HPIP) by default. Defaults to False. hpi_config (Optional[Union[Dict[str, Any], HPIConfig]], optional): The default high-performance inference configuration dictionary. Defaults to None. """ super().__init__( device=device, pp_option=pp_option, use_hpip=use_hpip, hpi_config=hpi_config ) 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 ) seal_ocr_config = config.get("SubPipelines", {}).get( "SealOCR", {"pipeline_config_error": "config error for seal_ocr_pipeline!"} ) self.seal_ocr_pipeline = self.create_pipeline(seal_ocr_config) self._crop_by_boxes = CropByBoxes() self.batch_sampler = ImageBatchSampler(batch_size=config.get("batch_size", 1)) self.img_reader = ReadImage(format="BGR") 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): 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 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 predict( self, input: Union[str, List[str], np.ndarray, List[np.ndarray]], use_doc_orientation_classify: Optional[bool] = None, use_doc_unwarping: Optional[bool] = None, use_layout_detection: Optional[bool] = None, layout_det_res: Optional[Union[DetResult, List[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, seal_det_limit_side_len: Optional[int] = None, seal_det_limit_type: Optional[str] = None, seal_det_thresh: Optional[float] = None, seal_det_box_thresh: Optional[float] = None, seal_det_unclip_ratio: Optional[float] = None, seal_rec_score_thresh: Optional[float] = None, **kwargs, ) -> SealRecognitionResult: 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!"} external_layout_det_results = layout_det_res if external_layout_det_results is not None: if not isinstance(external_layout_det_results, list): external_layout_det_results = [external_layout_det_results] external_layout_det_results = iter(external_layout_det_results) for _, batch_data in enumerate(self.batch_sampler(input)): image_arrays = self.img_reader(batch_data.instances) if model_settings["use_doc_preprocessor"]: doc_preprocessor_results = list( self.doc_preprocessor_pipeline( image_arrays, use_doc_orientation_classify=use_doc_orientation_classify, use_doc_unwarping=use_doc_unwarping, ) ) else: doc_preprocessor_results = [{"output_img": arr} for arr in image_arrays] doc_preprocessor_images = [ item["output_img"] for item in doc_preprocessor_results ] if ( not model_settings["use_layout_detection"] and external_layout_det_results is None ): layout_det_results = [{} for _ in doc_preprocessor_images] flat_seal_results = list( self.seal_ocr_pipeline( doc_preprocessor_images, text_det_limit_side_len=seal_det_limit_side_len, text_det_limit_type=seal_det_limit_type, text_det_thresh=seal_det_thresh, text_det_box_thresh=seal_det_box_thresh, text_det_unclip_ratio=seal_det_unclip_ratio, text_rec_score_thresh=seal_rec_score_thresh, ) ) for seal_res in flat_seal_results: seal_res["seal_region_id"] = 1 seal_results = [[item] for item in flat_seal_results] else: if model_settings["use_layout_detection"]: layout_det_results = list( self.layout_det_model( doc_preprocessor_images, threshold=layout_threshold, layout_nms=layout_nms, layout_unclip_ratio=layout_unclip_ratio, layout_merge_bboxes_mode=layout_merge_bboxes_mode, ) ) else: layout_det_results = [] for _ in doc_preprocessor_images: try: layout_det_res = next(external_layout_det_results) except StopIteration: raise ValueError("No more layout det results") layout_det_results.append(layout_det_res) cropped_imgs = [] chunk_indices = [0] for doc_preprocessor_image, layout_det_res in zip( doc_preprocessor_images, layout_det_results ): for box_info in layout_det_res["boxes"]: if box_info["label"].lower() in ["seal"]: crop_img_info = self._crop_by_boxes( doc_preprocessor_image, [box_info] ) crop_img_info = crop_img_info[0] cropped_imgs.append(crop_img_info["img"]) chunk_indices.append(len(cropped_imgs)) flat_seal_results = list( self.seal_ocr_pipeline( cropped_imgs, text_det_limit_side_len=seal_det_limit_side_len, text_det_limit_type=seal_det_limit_type, text_det_thresh=seal_det_thresh, text_det_box_thresh=seal_det_box_thresh, text_det_unclip_ratio=seal_det_unclip_ratio, text_rec_score_thresh=seal_rec_score_thresh, ) ) seal_results = [ flat_seal_results[i:j] for i, j in zip(chunk_indices[:-1], chunk_indices[1:]) ] for seal_results_for_img in seal_results: seal_region_id = 1 for seal_res in seal_results_for_img: seal_res["seal_region_id"] = seal_region_id seal_region_id += 1 for ( input_path, page_index, doc_preprocessor_res, layout_det_res, seal_results_for_img, ) in zip( batch_data.input_paths, batch_data.page_indexes, doc_preprocessor_results, layout_det_results, seal_results, ): single_img_res = { "input_path": input_path, "page_index": page_index, "doc_preprocessor_res": doc_preprocessor_res, "layout_det_res": layout_det_res, "seal_res_list": seal_results_for_img, "model_settings": model_settings, } yield SealRecognitionResult(single_img_res) @pipeline_requires_extra("ocr") class SealRecognitionPipeline(AutoParallelImageSimpleInferencePipeline): entities = ["seal_recognition"] @property def _pipeline_cls(self): return _SealRecognitionPipeline def _get_batch_size(self, config): return config.get("batch_size", 1)