# Copyright (c) 2025 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 .._utils.cli import ( add_simple_inference_args, get_subcommand_args, perform_simple_inference, str2bool, ) from .base import PaddleXPipelineWrapper, PipelineCLISubcommandExecutor from .utils import create_config_from_structure class TableRecognitionPipelineV2(PaddleXPipelineWrapper): def __init__( self, layout_detection_model_name=None, layout_detection_model_dir=None, table_classification_model_name=None, table_classification_model_dir=None, wired_table_structure_recognition_model_name=None, wired_table_structure_recognition_model_dir=None, wireless_table_structure_recognition_model_name=None, wireless_table_structure_recognition_model_dir=None, wired_table_cells_detection_model_name=None, wired_table_cells_detection_model_dir=None, wireless_table_cells_detection_model_name=None, wireless_table_cells_detection_model_dir=None, doc_orientation_classify_model_name=None, doc_orientation_classify_model_dir=None, doc_unwarping_model_name=None, doc_unwarping_model_dir=None, text_detection_model_name=None, text_detection_model_dir=None, text_det_limit_side_len=None, text_det_limit_type=None, text_det_thresh=None, text_det_box_thresh=None, text_det_unclip_ratio=None, text_recognition_model_name=None, text_recognition_model_dir=None, text_recognition_batch_size=None, text_rec_score_thresh=None, use_doc_orientation_classify=None, use_doc_unwarping=None, use_layout_detection=None, use_ocr_model=None, **kwargs, ): params = locals().copy() params.pop("self") params.pop("kwargs") self._params = params super().__init__(**kwargs) @property def _paddlex_pipeline_name(self): return "table_recognition_v2" def predict_iter( self, input, *, use_doc_orientation_classify=None, use_doc_unwarping=None, use_layout_detection=None, use_ocr_model=None, overall_ocr_res=None, layout_det_res=None, text_det_limit_side_len=None, text_det_limit_type=None, text_det_thresh=None, text_det_box_thresh=None, text_det_unclip_ratio=None, text_rec_score_thresh=None, use_e2e_wired_table_rec_model=False, use_e2e_wireless_table_rec_model=False, use_wired_table_cells_trans_to_html=False, use_wireless_table_cells_trans_to_html=False, use_table_orientation_classify=True, use_ocr_results_with_table_cells=True, **kwargs, ): return self.paddlex_pipeline.predict( input, use_doc_orientation_classify=use_doc_orientation_classify, use_doc_unwarping=use_doc_unwarping, use_layout_detection=use_layout_detection, use_ocr_model=use_ocr_model, overall_ocr_res=overall_ocr_res, layout_det_res=layout_det_res, 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, use_e2e_wired_table_rec_model=use_e2e_wired_table_rec_model, use_e2e_wireless_table_rec_model=use_e2e_wireless_table_rec_model, use_wired_table_cells_trans_to_html=use_wired_table_cells_trans_to_html, use_wireless_table_cells_trans_to_html=use_wireless_table_cells_trans_to_html, use_table_orientation_classify=use_table_orientation_classify, use_ocr_results_with_table_cells=use_ocr_results_with_table_cells, **kwargs, ) def predict( self, input, *, use_doc_orientation_classify=None, use_doc_unwarping=None, use_layout_detection=None, use_ocr_model=None, overall_ocr_res=None, layout_det_res=None, text_det_limit_side_len=None, text_det_limit_type=None, text_det_thresh=None, text_det_box_thresh=None, text_det_unclip_ratio=None, text_rec_score_thresh=None, use_e2e_wired_table_rec_model=False, use_e2e_wireless_table_rec_model=False, use_wired_table_cells_trans_to_html=False, use_wireless_table_cells_trans_to_html=False, use_table_orientation_classify=True, use_ocr_results_with_table_cells=True, **kwargs, ): return list( self.predict_iter( input, use_doc_orientation_classify=use_doc_orientation_classify, use_doc_unwarping=use_doc_unwarping, use_layout_detection=use_layout_detection, use_ocr_model=use_ocr_model, overall_ocr_res=overall_ocr_res, layout_det_res=layout_det_res, 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, use_e2e_wired_table_rec_model=use_e2e_wired_table_rec_model, use_e2e_wireless_table_rec_model=use_e2e_wireless_table_rec_model, use_wired_table_cells_trans_to_html=use_wired_table_cells_trans_to_html, use_wireless_table_cells_trans_to_html=use_wireless_table_cells_trans_to_html, use_table_orientation_classify=use_table_orientation_classify, use_ocr_results_with_table_cells=use_ocr_results_with_table_cells, **kwargs, ) ) @classmethod def get_cli_subcommand_executor(cls): return TableRecognitionPipelineV2CLISubcommandExecutor() def _get_paddlex_config_overrides(self): STRUCTURE = { "SubPipelines.DocPreprocessor.use_doc_orientation_classify": self._params[ "use_doc_orientation_classify" ], "SubPipelines.DocPreprocessor.use_doc_unwarping": self._params[ "use_doc_unwarping" ], "use_doc_preprocessor": self._params["use_doc_orientation_classify"] or self._params["use_doc_unwarping"], "use_layout_detection": self._params["use_layout_detection"], "use_ocr_model": self._params["use_ocr_model"], "SubModules.LayoutDetection.model_name": self._params[ "layout_detection_model_name" ], "SubModules.LayoutDetection.model_dir": self._params[ "layout_detection_model_dir" ], "SubModules.TableClassification.model_name": self._params[ "table_classification_model_name" ], "SubModules.TableClassification.model_dir": self._params[ "table_classification_model_dir" ], "SubModules.WiredTableStructureRecognition.model_name": self._params[ "wired_table_structure_recognition_model_name" ], "SubModules.WiredTableStructureRecognition.model_dir": self._params[ "wired_table_structure_recognition_model_dir" ], "SubModules.WirelessTableStructureRecognition.model_name": self._params[ "wireless_table_structure_recognition_model_name" ], "SubModules.WirelessTableStructureRecognition.model_dir": self._params[ "wireless_table_structure_recognition_model_dir" ], "SubModules.WiredTableCellsDetection.model_name": self._params[ "wired_table_cells_detection_model_name" ], "SubModules.WiredTableCellsDetection.model_dir": self._params[ "wired_table_cells_detection_model_dir" ], "SubModules.WirelessTableCellsDetection.model_name": self._params[ "wireless_table_cells_detection_model_name" ], "SubModules.WirelessTableCellsDetection.model_dir": self._params[ "wireless_table_cells_detection_model_dir" ], "SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_name": self._params[ "doc_orientation_classify_model_name" ], "SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_dir": self._params[ "doc_orientation_classify_model_dir" ], "SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_name": self._params[ "doc_unwarping_model_name" ], "SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_dir": self._params[ "doc_unwarping_model_dir" ], "SubPipelines.GeneralOCR.SubModules.TextDetection.model_name": self._params[ "text_detection_model_name" ], "SubPipelines.GeneralOCR.SubModules.TextDetection.model_dir": self._params[ "text_detection_model_dir" ], "SubPipelines.GeneralOCR.SubModules.TextDetection.limit_side_len": self._params[ "text_det_limit_side_len" ], "SubPipelines.GeneralOCR.SubModules.TextDetection.limit_type": self._params[ "text_det_limit_type" ], "SubPipelines.GeneralOCR.SubModules.TextDetection.thresh": self._params[ "text_det_thresh" ], "SubPipelines.GeneralOCR.SubModules.TextDetection.box_thresh": self._params[ "text_det_box_thresh" ], "SubPipelines.GeneralOCR.SubModules.TextDetection.unclip_ratio": self._params[ "text_det_unclip_ratio" ], "SubPipelines.GeneralOCR.SubModules.TextRecognition.model_name": self._params[ "text_recognition_model_name" ], "SubPipelines.GeneralOCR.SubModules.TextRecognition.model_dir": self._params[ "text_recognition_model_dir" ], "SubPipelines.GeneralOCR.SubModules.TextRecognition.batch_size": self._params[ "text_recognition_batch_size" ], "SubPipelines.GeneralOCR.SubModules.TextRecognition.score_thresh": self._params[ "text_rec_score_thresh" ], } return create_config_from_structure(STRUCTURE) class TableRecognitionPipelineV2CLISubcommandExecutor(PipelineCLISubcommandExecutor): @property def subparser_name(self): return "table_recognition_v2" def _update_subparser(self, subparser): add_simple_inference_args(subparser) subparser.add_argument( "--layout_detection_model_name", type=str, help="Name of the layout detection model.", ) subparser.add_argument( "--layout_detection_model_dir", type=str, help="Path to the layout detection model directory.", ) subparser.add_argument( "--table_classification_model_name", type=str, help="Name of the table classification model.", ) subparser.add_argument( "--table_classification_model_dir", type=str, help="Path to the table classification model directory.", ) subparser.add_argument( "--wired_table_structure_recognition_model_name", type=str, help="Name of the wired table structure recognition model.", ) subparser.add_argument( "--wired_table_structure_recognition_model_dir", type=str, help="Path to the wired table structure recognition model directory.", ) subparser.add_argument( "--wireless_table_structure_recognition_model_name", type=str, help="Name of the wireless table structure recognition model.", ) subparser.add_argument( "--wireless_table_structure_recognition_model_dir", type=str, help="Path to the wired table structure recognition model directory.", ) subparser.add_argument( "--wired_table_cells_detection_model_name", type=str, help="Name of the wired table cells detection model.", ) subparser.add_argument( "--wired_table_cells_detection_model_dir", type=str, help="Path to the wired table cells detection model directory.", ) subparser.add_argument( "--wireless_table_cells_detection_model_name", type=str, help="Name of the wireless table cells detection model.", ) subparser.add_argument( "--wireless_table_cells_detection_model_dir", type=str, help="Path to the wireless table cells detection model directory.", ) subparser.add_argument( "--doc_orientation_classify_model_name", type=str, help="Name of the document image orientation classification model.", ) subparser.add_argument( "--doc_orientation_classify_model_dir", type=str, help="Path to the document image orientation classification model directory.", ) subparser.add_argument( "--doc_unwarping_model_name", type=str, help="Name of the text image unwarping model.", ) subparser.add_argument( "--doc_unwarping_model_dir", type=str, help="Path to the image unwarping model directory.", ) subparser.add_argument( "--text_detection_model_name", type=str, help="Name of the text detection model.", ) subparser.add_argument( "--text_detection_model_dir", type=str, help="Path to the text detection model directory.", ) subparser.add_argument( "--text_det_limit_side_len", type=int, help="This sets a limit on the side length of the input image for the text detection model.", ) subparser.add_argument( "--text_det_limit_type", type=str, help="This determines how the side length limit is applied to the input image before feeding it into the text deteciton model.", ) subparser.add_argument( "--text_det_thresh", type=float, help="Detection pixel threshold for the text detection model. Pixels with scores greater than this threshold in the output probability map are considered text pixels.", ) subparser.add_argument( "--text_det_box_thresh", type=float, help="Detection box threshold for the text detection model. A detection result is considered a text region if the average score of all pixels within the border of the result is greater than this threshold.", ) subparser.add_argument( "--text_det_unclip_ratio", type=float, help="Text detection expansion coefficient, which expands the text region using this method. The larger the value, the larger the expansion area.", ) subparser.add_argument( "--text_recognition_model_name", type=str, help="Name of the text recognition model.", ) subparser.add_argument( "--text_recognition_model_dir", type=str, help="Path to the text recognition model directory.", ) subparser.add_argument( "--text_recognition_batch_size", type=int, help="Batch size for the text recognition model.", ) subparser.add_argument( "--text_rec_score_thresh", type=float, help="Text recognition threshold used in general OCR. Text results with scores greater than this threshold are retained.", ) subparser.add_argument( "--use_doc_orientation_classify", type=str2bool, help="Whether to use document image orientation classification.", ) subparser.add_argument( "--use_doc_unwarping", type=str2bool, help="Whether to use text image unwarping.", ) subparser.add_argument( "--use_layout_detection", type=str2bool, help="Whether to use layout detection.", ) subparser.add_argument( "--use_ocr_model", type=str2bool, help="Whether to use OCR models.", ) def execute_with_args(self, args): params = get_subcommand_args(args) perform_simple_inference(TableRecognitionPipelineV2, params)