605 lines
25 KiB
Python
605 lines
25 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ..utils.cli import (
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add_simple_inference_args,
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get_subcommand_args,
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perform_simple_inference,
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str2bool,
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)
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from .base import PaddleXPipelineWrapper, PipelineCLISubcommandExecutor
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from .utils import create_config_from_structure
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class PPStructureV3(PaddleXPipelineWrapper):
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def __init__(
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self,
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layout_detection_model_name=None,
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layout_detection_model_dir=None,
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layout_threshold=None,
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layout_nms=None,
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layout_unclip_ratio=None,
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layout_merge_bboxes_mode=None,
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doc_orientation_classify_model_name=None,
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doc_orientation_classify_model_dir=None,
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doc_unwarping_model_name=None,
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doc_unwarping_model_dir=None,
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text_detection_model_name=None,
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text_detection_model_dir=None,
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text_det_limit_side_len=None,
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text_det_limit_type=None,
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text_det_thresh=None,
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text_det_box_thresh=None,
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text_det_unclip_ratio=None,
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textline_orientation_model_name=None,
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textline_orientation_model_dir=None,
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textline_orientation_batch_size=None,
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text_recognition_model_name=None,
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text_recognition_model_dir=None,
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text_recognition_batch_size=None,
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text_rec_score_thresh=None,
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table_classification_model_name=None,
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table_classification_model_dir=None,
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wired_table_structure_recognition_model_name=None,
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wired_table_structure_recognition_model_dir=None,
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wireless_table_structure_recognition_model_name=None,
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wireless_table_structure_recognition_model_dir=None,
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wired_table_cells_detection_model_name=None,
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wired_table_cells_detection_model_dir=None,
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wireless_table_cells_detection_model_name=None,
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wireless_table_cells_detection_model_dir=None,
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seal_text_detection_model_name=None,
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seal_text_detection_model_dir=None,
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seal_det_limit_side_len=None,
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seal_det_limit_type=None,
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seal_det_thresh=None,
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seal_det_box_thresh=None,
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seal_det_unclip_ratio=None,
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seal_text_recognition_model_name=None,
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seal_text_recognition_model_dir=None,
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seal_text_recognition_batch_size=None,
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seal_rec_score_thresh=None,
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formula_recognition_model_name=None,
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formula_recognition_model_dir=None,
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formula_recognition_batch_size=None,
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use_doc_orientation_classify=None,
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use_doc_unwarping=None,
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use_general_ocr=None,
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use_seal_recognition=None,
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use_table_recognition=None,
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use_formula_recognition=None,
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**kwargs,
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):
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params = locals().copy()
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params.pop("self")
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params.pop("kwargs")
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self._params = params
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super().__init__(**kwargs)
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@property
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def _paddlex_pipeline_name(self):
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return "PP-StructureV3"
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def predict(
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self,
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input,
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use_doc_orientation_classify=None,
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use_doc_unwarping=None,
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use_textline_orientation=None,
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use_general_ocr=None,
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use_seal_recognition=None,
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use_table_recognition=None,
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use_formula_recognition=None,
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layout_threshold=None,
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layout_nms=None,
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layout_unclip_ratio=None,
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layout_merge_bboxes_mode=None,
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text_det_limit_side_len=None,
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text_det_limit_type=None,
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text_det_thresh=None,
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text_det_box_thresh=None,
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text_det_unclip_ratio=None,
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text_rec_score_thresh=None,
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seal_det_limit_side_len=None,
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seal_det_limit_type=None,
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seal_det_thresh=None,
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seal_det_box_thresh=None,
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seal_det_unclip_ratio=None,
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seal_rec_score_thresh=None,
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use_table_cells_ocr_results=None,
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use_e2e_wired_table_rec_model=None,
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use_e2e_wireless_table_rec_model=None,
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**kwargs,
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):
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result = []
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for res in self.paddlex_pipeline.predict(
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input,
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use_doc_orientation_classify=use_doc_orientation_classify,
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use_doc_unwarping=use_doc_unwarping,
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use_textline_orientation=use_textline_orientation,
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use_general_ocr=use_general_ocr,
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use_seal_recognition=use_seal_recognition,
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use_table_recognition=use_table_recognition,
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use_formula_recognition=use_formula_recognition,
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layout_threshold=layout_threshold,
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layout_nms=layout_nms,
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layout_unclip_ratio=layout_unclip_ratio,
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layout_merge_bboxes_mode=layout_merge_bboxes_mode,
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text_det_limit_side_len=text_det_limit_side_len,
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text_det_limit_type=text_det_limit_type,
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text_det_thresh=text_det_thresh,
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text_det_box_thresh=text_det_box_thresh,
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text_det_unclip_ratio=text_det_unclip_ratio,
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text_rec_score_thresh=text_rec_score_thresh,
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seal_det_limit_side_len=seal_det_limit_side_len,
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seal_det_limit_type=seal_det_limit_type,
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seal_det_thresh=seal_det_thresh,
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seal_det_box_thresh=seal_det_box_thresh,
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seal_det_unclip_ratio=seal_det_unclip_ratio,
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seal_rec_score_thresh=seal_rec_score_thresh,
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use_table_cells_ocr_results=use_table_cells_ocr_results,
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use_e2e_wired_table_rec_model=use_e2e_wired_table_rec_model,
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use_e2e_wireless_table_rec_model=use_e2e_wireless_table_rec_model,
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**kwargs,
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):
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result.append(res)
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return result
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@classmethod
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def get_cli_subcommand_executor(cls):
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return PPStructureV3CLISubcommandExecutor()
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def _get_paddlex_config_overrides(self):
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STRUCTURE = {
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"SubPipelines.DocPreprocessor.use_doc_orientation_classify": self._params[
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"use_doc_orientation_classify"
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],
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"SubPipelines.DocPreprocessor.use_doc_unwarping": self._params[
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"use_doc_unwarping"
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],
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"use_general_ocr": self._params["use_general_ocr"],
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"use_seal_recognition": self._params["use_seal_recognition"],
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"use_table_recognition": self._params["use_table_recognition"],
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"use_formula_recognition": self._params["use_formula_recognition"],
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"SubModules.LayoutDetection.model_name": self._params[
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"layout_detection_model_name"
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],
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"SubModules.LayoutDetection.model_dir": self._params[
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"layout_detection_model_dir"
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],
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"SubModules.LayoutDetection.threshold": self._params["layout_threshold"],
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"SubModules.LayoutDetection.layout_nms": self._params["layout_nms"],
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"SubModules.LayoutDetection.layout_unclip_ratio": self._params[
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"layout_unclip_ratio"
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],
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"SubModules.LayoutDetection.layout_merge_bboxes_mode": self._params[
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"layout_merge_bboxes_mode"
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],
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"SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_name": self._params[
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"doc_orientation_classify_model_name"
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],
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"SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_dir": self._params[
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"doc_orientation_classify_model_dir"
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],
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"SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_name": self._params[
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"doc_unwarping_model_name"
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],
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"SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_dir": self._params[
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"doc_unwarping_model_dir"
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],
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"SubPipelines.GeneralOCR.SubModules.TextDetection.model_name": self._params[
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"text_detection_model_name"
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],
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"SubPipelines.GeneralOCR.SubModules.TextDetection.model_dir": self._params[
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"text_detection_model_dir"
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],
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"SubPipelines.GeneralOCR.SubModules.TextDetection.limit_side_len": self._params[
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"text_det_limit_side_len"
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],
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"SubPipelines.GeneralOCR.SubModules.TextDetection.limit_type": self._params[
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"text_det_limit_type"
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],
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"SubPipelines.GeneralOCR.SubModules.TextDetection.thresh": self._params[
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"text_det_thresh"
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],
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"SubPipelines.GeneralOCR.SubModules.TextDetection.box_thresh": self._params[
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"text_det_box_thresh"
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],
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"SubPipelines.GeneralOCR.SubModules.TextDetection.unclip_ratio": self._params[
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"text_det_unclip_ratio"
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],
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"SubPipelines.GeneralOCR.SubModules.TextLineOrientation.model_name": self._params[
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"textline_orientation_model_name"
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],
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"SubPipelines.GeneralOCR.SubModules.TextLineOrientation.model_dir": self._params[
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"textline_orientation_model_dir"
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],
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"SubPipelines.GeneralOCR.SubModules.TextLineOrientation.batch_size": self._params[
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"textline_orientation_batch_size"
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],
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"SubPipelines.GeneralOCR.SubModules.TextRecognition.model_name": self._params[
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"text_recognition_model_name"
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],
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"SubPipelines.GeneralOCR.SubModules.TextRecognition.model_dir": self._params[
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"text_recognition_model_dir"
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],
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"SubPipelines.GeneralOCR.SubModules.TextRecognition.batch_size": self._params[
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"text_recognition_batch_size"
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],
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"SubPipelines.GeneralOCR.SubModules.TextRecognition.score_thresh": self._params[
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"text_rec_score_thresh"
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],
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"SubPipelines.TableRecognition.SubModules.TableClassification.model_name": self._params[
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"table_classification_model_name"
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],
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"SubPipelines.TableRecognition.SubModules.TableClassification.model_dir": self._params[
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"table_classification_model_dir"
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],
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"SubPipelines.TableRecognition.SubModules.WiredTableStructureRecognition.model_name": self._params[
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"wired_table_structure_recognition_model_name"
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],
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"SubPipelines.TableRecognition.SubModules.WiredTableStructureRecognition.model_dir": self._params[
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"wired_table_structure_recognition_model_dir"
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],
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"SubPipelines.TableRecognition.SubModules.WirelessTableStructureRecognition.model_name": self._params[
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"wireless_table_structure_recognition_model_name"
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],
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"SubPipelines.TableRecognition.SubModules.WirelessTableStructureRecognition.model_dir": self._params[
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"wireless_table_structure_recognition_model_dir"
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],
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"SubPipelines.TableRecognition.SubModules.WiredTableCellsDetection.model_name": self._params[
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"wired_table_cells_detection_model_name"
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],
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"SubPipelines.TableRecognition.SubModules.WiredTableCellsDetection.model_dir": self._params[
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"wired_table_cells_detection_model_dir"
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],
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"SubPipelines.TableRecognition.SubModules.WirelessTableCellsDetection.model_name": self._params[
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"wireless_table_cells_detection_model_name"
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],
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"SubPipelines.TableRecognition.SubModules.WirelessTableCellsDetection.model_dir": self._params[
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"wireless_table_cells_detection_model_dir"
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],
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"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.model_name": self._params[
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"seal_text_detection_model_name"
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],
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"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.model_dir": self._params[
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"seal_text_detection_model_dir"
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],
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"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.limit_side_len": self._params[
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"text_det_limit_side_len"
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],
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"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.limit_type": self._params[
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"seal_det_limit_type"
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],
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"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.thresh": self._params[
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"seal_det_thresh"
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],
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"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.box_thresh": self._params[
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"seal_det_box_thresh"
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],
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"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.unclip_ratio": self._params[
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"seal_det_unclip_ratio"
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],
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"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextRecognition.model_name": self._params[
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"seal_text_recognition_model_name"
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],
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"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextRecognition.model_dir": self._params[
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"seal_text_recognition_model_dir"
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],
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"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextRecognition.batch_size": self._params[
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"seal_text_recognition_batch_size"
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],
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"SubPipelines.FormulaRecognition.SubModules.FormulaRecognition.model_name": self._params[
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"formula_recognition_model_name"
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],
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"SubPipelines.FormulaRecognition.SubModules.FormulaRecognition.model_dir": self._params[
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"formula_recognition_model_dir"
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],
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"SubPipelines.FormulaRecognition.SubModules.FormulaRecognition.batch_size": self._params[
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"formula_recognition_batch_size"
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],
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}
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return create_config_from_structure(STRUCTURE)
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class PPStructureV3CLISubcommandExecutor(PipelineCLISubcommandExecutor):
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@property
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def subparser_name(self):
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return "PP-StructureV3"
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def _update_subparser(self, subparser):
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add_simple_inference_args(subparser)
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subparser.add_argument(
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"--layout_detection_model_name",
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type=str,
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help="Name of the layout detection model.",
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)
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subparser.add_argument(
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"--layout_detection_model_dir",
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type=str,
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help="Path to the layout detection model directory.",
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)
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subparser.add_argument(
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"--layout_threshold",
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type=float,
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help="Score threshold for the layout detection model.",
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)
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subparser.add_argument(
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"--layout_nms",
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type=str2bool,
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help="Whether to use NMS in layout detection.",
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)
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subparser.add_argument(
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"--layout_unclip_ratio",
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type=float,
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help="Expansion coefficient for layout detection.",
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)
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subparser.add_argument(
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"--layout_merge_bboxes_mode",
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type=str,
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help="Overlapping box filtering method.",
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)
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subparser.add_argument(
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"--doc_orientation_classify_model_name",
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type=str,
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help="Name of the document image orientation classification model.",
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)
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subparser.add_argument(
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"--doc_orientation_classify_model_dir",
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type=str,
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help="Path to the document image orientation classification model directory.",
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)
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subparser.add_argument(
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"--doc_unwarping_model_name",
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type=str,
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help="Name of the text image unwarping model.",
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)
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subparser.add_argument(
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"--doc_unwarping_model_dir",
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type=str,
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help="Path to the image unwarping model directory.",
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)
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subparser.add_argument(
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"--text_detection_model_name",
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type=str,
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help="Name of the text detection model.",
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)
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subparser.add_argument(
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"--text_detection_model_dir",
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type=str,
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help="Path to the text detection model directory.",
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)
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subparser.add_argument(
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"--text_det_limit_side_len",
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type=int,
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help="This sets a limit on the side length of the input image for the text detection model.",
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)
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subparser.add_argument(
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"--text_det_limit_type",
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type=str,
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help="This determines how the side length limit is applied to the input image before feeding it into the text deteciton model.",
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)
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subparser.add_argument(
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"--text_det_thresh",
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type=float,
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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.",
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)
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subparser.add_argument(
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"--text_det_box_thresh",
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type=float,
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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.",
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)
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subparser.add_argument(
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"--text_det_unclip_ratio",
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type=float,
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help="Text detection expansion coefficient, which expands the text region using this method. The larger the value, the larger the expansion area.",
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)
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subparser.add_argument(
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"--textline_orientation_model_name",
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type=str,
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help="Name of the text tetextline orientation.",
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)
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subparser.add_argument(
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"--textline_orientation_model_dir",
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type=str,
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help="Path to the text tetextline orientation directory.",
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)
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subparser.add_argument(
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"--textline_orientation_batch_size",
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type=int,
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help="Batch size for the tetextline orientation model.",
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)
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subparser.add_argument(
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"--text_recognition_model_name",
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type=str,
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help="Name of the text recognition model.",
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)
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subparser.add_argument(
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"--text_recognition_model_dir",
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type=str,
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help="Path to the text recognition model directory.",
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)
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subparser.add_argument(
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"--text_recognition_batch_size",
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type=int,
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help="Batch size for the text recognition model.",
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)
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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(
|
|
"--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(
|
|
"--seal_text_detection_model_name",
|
|
type=str,
|
|
help="Name of the seal text detection model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--seal_text_detection_model_dir",
|
|
type=str,
|
|
help="Path to the seal text detection model directory.",
|
|
)
|
|
subparser.add_argument(
|
|
"--seal_det_limit_side_len",
|
|
type=int,
|
|
help="This sets a limit on the side length of the input image for the seal text detection model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--seal_det_limit_type",
|
|
type=str,
|
|
help="This determines how the side length limit is applied to the input image before feeding it into the seal text deteciton model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--seal_det_thresh",
|
|
type=float,
|
|
help="Detection pixel threshold for the seal text detection model. Pixels with scores greater than this threshold in the output probability map are considered text pixels.",
|
|
)
|
|
subparser.add_argument(
|
|
"--seal_det_box_thresh",
|
|
type=float,
|
|
help="Detection box threshold for the seal 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(
|
|
"--seal_det_unclip_ratio",
|
|
type=float,
|
|
help="Seal text detection expansion coefficient, which expands the text region using this method. The larger the value, the larger the expansion area.",
|
|
)
|
|
subparser.add_argument(
|
|
"--seal_text_recognition_model_name",
|
|
type=str,
|
|
help="Name of the seal text recognition model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--seal_text_recognition_model_dir",
|
|
type=str,
|
|
help="Path to the seal text recognition model directory.",
|
|
)
|
|
subparser.add_argument(
|
|
"--seal_text_recognition_batch_size",
|
|
type=int,
|
|
help="Batch size for the seal text recognition model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--seal_rec_score_thresh",
|
|
type=float,
|
|
help="Seal text recognition threshold. Text results with scores greater than this threshold are retained.",
|
|
)
|
|
|
|
subparser.add_argument(
|
|
"--formula_recognition_model_name",
|
|
type=str,
|
|
help="Name of the formula recognition model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--formula_recognition_model_dir",
|
|
type=str,
|
|
help="Path to the formula recognition model directory.",
|
|
)
|
|
subparser.add_argument(
|
|
"--formula_recognition_batch_size",
|
|
type=int,
|
|
help="Batch size for the formula recognition model.",
|
|
)
|
|
|
|
subparser.add_argument(
|
|
"--use_doc_orientation_classify",
|
|
type=str2bool,
|
|
help="Whether to use the document image orientation classification model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--use_doc_unwarping",
|
|
type=str2bool,
|
|
help="Whether to use the text image unwarping model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--use_general_ocr",
|
|
type=str2bool,
|
|
help="Whether to use general OCR.",
|
|
)
|
|
subparser.add_argument(
|
|
"--use_seal_recognition",
|
|
type=str2bool,
|
|
help="Whether to use seal recognition.",
|
|
)
|
|
subparser.add_argument(
|
|
"--use_table_recognition",
|
|
type=str2bool,
|
|
help="Whether to use table recognition.",
|
|
)
|
|
subparser.add_argument(
|
|
"--use_formula_recognition",
|
|
type=str2bool,
|
|
help="Whether to use formula recognition.",
|
|
)
|
|
|
|
def execute_with_args(self, args):
|
|
params = get_subcommand_args(args)
|
|
perform_simple_inference(PPStructureV3, params)
|