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1033 lines
41 KiB
Python
1033 lines
41 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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import warnings
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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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from ._patch_layout_parsing import apply_patches as _apply_layout_parsing_patches
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_apply_layout_parsing_patches()
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_SUPPORTED_OCR_VERSIONS = ["PP-OCRv3", "PP-OCRv4", "PP-OCRv5"]
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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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chart_recognition_model_name=None,
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chart_recognition_model_dir=None,
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chart_recognition_batch_size=None,
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region_detection_model_name=None,
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region_detection_model_dir=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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table_orientation_classify_model_name=None,
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table_orientation_classify_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_textline_orientation=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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use_chart_recognition=None,
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use_region_detection=None,
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format_block_content=None,
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markdown_ignore_labels=None,
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lang=None,
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ocr_version=None,
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**kwargs,
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):
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if ocr_version is not None and ocr_version not in _SUPPORTED_OCR_VERSIONS:
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raise ValueError(
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f"Invalid OCR version: {ocr_version}. Supported values are {_SUPPORTED_OCR_VERSIONS}."
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)
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if all(
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map(
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lambda p: p is None,
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(
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text_detection_model_name,
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text_detection_model_dir,
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text_recognition_model_name,
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text_recognition_model_dir,
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),
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)
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):
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if lang is not None or ocr_version is not None:
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det_model_name, rec_model_name = self._get_ocr_model_names(
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lang, ocr_version
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)
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if det_model_name is None or rec_model_name is None:
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raise ValueError(
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f"No models are available for the language {repr(lang)} and OCR version {repr(ocr_version)}."
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)
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text_detection_model_name = det_model_name
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text_recognition_model_name = rec_model_name
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else:
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if lang is not None or ocr_version is not None:
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warnings.warn(
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"`lang` and `ocr_version` will be ignored when model names or model directories are not `None`.",
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stacklevel=2,
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)
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params = locals().copy()
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params["text_detection_model_name"] = text_detection_model_name
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params["text_recognition_model_name"] = text_recognition_model_name
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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_iter(
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self,
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input,
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*,
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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_seal_recognition=None,
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use_table_recognition=None,
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use_formula_recognition=None,
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use_chart_recognition=None,
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use_region_detection=None,
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format_block_content=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_wired_table_cells_trans_to_html=False,
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use_wireless_table_cells_trans_to_html=False,
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use_table_orientation_classify=True,
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use_ocr_results_with_table_cells=True,
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use_e2e_wired_table_rec_model=False,
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use_e2e_wireless_table_rec_model=True,
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markdown_ignore_labels=None,
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**kwargs,
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):
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return 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_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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use_chart_recognition=use_chart_recognition,
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use_region_detection=use_region_detection,
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format_block_content=format_block_content,
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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_wired_table_cells_trans_to_html=use_wired_table_cells_trans_to_html,
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use_wireless_table_cells_trans_to_html=use_wireless_table_cells_trans_to_html,
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use_table_orientation_classify=use_table_orientation_classify,
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use_ocr_results_with_table_cells=use_ocr_results_with_table_cells,
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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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markdown_ignore_labels=markdown_ignore_labels,
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**kwargs,
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)
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def predict(
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self,
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input,
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*,
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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_seal_recognition=None,
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use_table_recognition=None,
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use_formula_recognition=None,
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use_chart_recognition=None,
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use_region_detection=None,
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format_block_content=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_wired_table_cells_trans_to_html=False,
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use_wireless_table_cells_trans_to_html=False,
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use_table_orientation_classify=True,
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use_ocr_results_with_table_cells=True,
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use_e2e_wired_table_rec_model=False,
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use_e2e_wireless_table_rec_model=True,
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markdown_ignore_labels=None,
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**kwargs,
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):
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return list(
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self.predict_iter(
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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_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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use_chart_recognition=use_chart_recognition,
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use_region_detection=use_region_detection,
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format_block_content=format_block_content,
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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_wired_table_cells_trans_to_html=use_wired_table_cells_trans_to_html,
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use_wireless_table_cells_trans_to_html=use_wireless_table_cells_trans_to_html,
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use_table_orientation_classify=use_table_orientation_classify,
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use_ocr_results_with_table_cells=use_ocr_results_with_table_cells,
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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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markdown_ignore_labels=markdown_ignore_labels,
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**kwargs,
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)
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)
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def concatenate_markdown_pages(self, markdown_list):
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return self.paddlex_pipeline.concatenate_markdown_pages(markdown_list)
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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_doc_preprocessor": self._params["use_doc_orientation_classify"]
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or self._params["use_doc_unwarping"],
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"SubPipelines.GeneralOCR.use_textline_orientation": self._params[
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"use_textline_orientation"
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],
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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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"use_chart_recognition": self._params["use_chart_recognition"],
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"use_region_detection": self._params["use_region_detection"],
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"format_block_content": self._params["format_block_content"],
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"markdown_ignore_labels": self._params["markdown_ignore_labels"],
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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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"SubModules.ChartRecognition.model_name": self._params[
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"chart_recognition_model_name"
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],
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"SubModules.ChartRecognition.model_dir": self._params[
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"chart_recognition_model_dir"
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],
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"SubModules.ChartRecognition.batch_size": self._params[
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"chart_recognition_batch_size"
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],
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"SubModules.RegionDetection.model_name": self._params[
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"region_detection_model_name"
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],
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"SubModules.RegionDetection.model_dir": self._params[
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"region_detection_model_dir"
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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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],
|
|
"SubPipelines.TableRecognition.SubModules.TableClassification.model_name": self._params[
|
|
"table_classification_model_name"
|
|
],
|
|
"SubPipelines.TableRecognition.SubModules.TableClassification.model_dir": self._params[
|
|
"table_classification_model_dir"
|
|
],
|
|
"SubPipelines.TableRecognition.SubModules.WiredTableStructureRecognition.model_name": self._params[
|
|
"wired_table_structure_recognition_model_name"
|
|
],
|
|
"SubPipelines.TableRecognition.SubModules.WiredTableStructureRecognition.model_dir": self._params[
|
|
"wired_table_structure_recognition_model_dir"
|
|
],
|
|
"SubPipelines.TableRecognition.SubModules.WirelessTableStructureRecognition.model_name": self._params[
|
|
"wireless_table_structure_recognition_model_name"
|
|
],
|
|
"SubPipelines.TableRecognition.SubModules.WirelessTableStructureRecognition.model_dir": self._params[
|
|
"wireless_table_structure_recognition_model_dir"
|
|
],
|
|
"SubPipelines.TableRecognition.SubModules.WiredTableCellsDetection.model_name": self._params[
|
|
"wired_table_cells_detection_model_name"
|
|
],
|
|
"SubPipelines.TableRecognition.SubModules.WiredTableCellsDetection.model_dir": self._params[
|
|
"wired_table_cells_detection_model_dir"
|
|
],
|
|
"SubPipelines.TableRecognition.SubModules.WirelessTableCellsDetection.model_name": self._params[
|
|
"wireless_table_cells_detection_model_name"
|
|
],
|
|
"SubPipelines.TableRecognition.SubModules.WirelessTableCellsDetection.model_dir": self._params[
|
|
"wireless_table_cells_detection_model_dir"
|
|
],
|
|
"SubPipelines.TableRecognition.SubModules.TableOrientationClassify.model_name": self._params[
|
|
"table_orientation_classify_model_name"
|
|
],
|
|
"SubPipelines.TableRecognition.SubModules.TableOrientationClassify.model_dir": self._params[
|
|
"table_orientation_classify_model_dir"
|
|
],
|
|
"SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextDetection.model_name": self._params[
|
|
"text_detection_model_name"
|
|
],
|
|
"SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextDetection.model_dir": self._params[
|
|
"text_detection_model_dir"
|
|
],
|
|
"SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextDetection.limit_side_len": self._params[
|
|
"text_det_limit_side_len"
|
|
],
|
|
"SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextDetection.limit_type": self._params[
|
|
"text_det_limit_type"
|
|
],
|
|
"SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextDetection.thresh": self._params[
|
|
"text_det_thresh"
|
|
],
|
|
"SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextDetection.box_thresh": self._params[
|
|
"text_det_box_thresh"
|
|
],
|
|
"SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextDetection.unclip_ratio": self._params[
|
|
"text_det_unclip_ratio"
|
|
],
|
|
"SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextLineOrientation.model_name": self._params[
|
|
"textline_orientation_model_name"
|
|
],
|
|
"SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextLineOrientation.model_dir": self._params[
|
|
"textline_orientation_model_dir"
|
|
],
|
|
"SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextLineOrientation.batch_size": self._params[
|
|
"textline_orientation_batch_size"
|
|
],
|
|
"SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextRecognition.model_name": self._params[
|
|
"text_recognition_model_name"
|
|
],
|
|
"SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextRecognition.model_dir": self._params[
|
|
"text_recognition_model_dir"
|
|
],
|
|
"SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextRecognition.batch_size": self._params[
|
|
"text_recognition_batch_size"
|
|
],
|
|
"SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextRecognition.score_thresh": self._params[
|
|
"text_rec_score_thresh"
|
|
],
|
|
"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.model_name": self._params[
|
|
"seal_text_detection_model_name"
|
|
],
|
|
"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.model_dir": self._params[
|
|
"seal_text_detection_model_dir"
|
|
],
|
|
"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.limit_side_len": self._params[
|
|
"text_det_limit_side_len"
|
|
],
|
|
"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.limit_type": self._params[
|
|
"seal_det_limit_type"
|
|
],
|
|
"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.thresh": self._params[
|
|
"seal_det_thresh"
|
|
],
|
|
"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.box_thresh": self._params[
|
|
"seal_det_box_thresh"
|
|
],
|
|
"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.unclip_ratio": self._params[
|
|
"seal_det_unclip_ratio"
|
|
],
|
|
"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextRecognition.model_name": self._params[
|
|
"seal_text_recognition_model_name"
|
|
],
|
|
"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextRecognition.model_dir": self._params[
|
|
"seal_text_recognition_model_dir"
|
|
],
|
|
"SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextRecognition.batch_size": self._params[
|
|
"seal_text_recognition_batch_size"
|
|
],
|
|
"SubPipelines.FormulaRecognition.SubModules.FormulaRecognition.model_name": self._params[
|
|
"formula_recognition_model_name"
|
|
],
|
|
"SubPipelines.FormulaRecognition.SubModules.FormulaRecognition.model_dir": self._params[
|
|
"formula_recognition_model_dir"
|
|
],
|
|
"SubPipelines.FormulaRecognition.SubModules.FormulaRecognition.batch_size": self._params[
|
|
"formula_recognition_batch_size"
|
|
],
|
|
}
|
|
return create_config_from_structure(STRUCTURE)
|
|
|
|
def _get_ocr_model_names(self, lang, ppocr_version):
|
|
LATIN_LANGS = [
|
|
"af",
|
|
"az",
|
|
"bs",
|
|
"cs",
|
|
"cy",
|
|
"da",
|
|
"de",
|
|
"es",
|
|
"et",
|
|
"fr",
|
|
"ga",
|
|
"hr",
|
|
"hu",
|
|
"id",
|
|
"is",
|
|
"it",
|
|
"ku",
|
|
"la",
|
|
"lt",
|
|
"lv",
|
|
"mi",
|
|
"ms",
|
|
"mt",
|
|
"nl",
|
|
"no",
|
|
"oc",
|
|
"pi",
|
|
"pl",
|
|
"pt",
|
|
"ro",
|
|
"rs_latin",
|
|
"sk",
|
|
"sl",
|
|
"sq",
|
|
"sv",
|
|
"sw",
|
|
"tl",
|
|
"tr",
|
|
"uz",
|
|
"vi",
|
|
"french",
|
|
"german",
|
|
]
|
|
ARABIC_LANGS = ["ar", "fa", "ug", "ur"]
|
|
ESLAV_LANGS = ["ru", "be", "uk"]
|
|
CYRILLIC_LANGS = [
|
|
"ru",
|
|
"rs_cyrillic",
|
|
"be",
|
|
"bg",
|
|
"uk",
|
|
"mn",
|
|
"abq",
|
|
"ady",
|
|
"kbd",
|
|
"ava",
|
|
"dar",
|
|
"inh",
|
|
"che",
|
|
"lbe",
|
|
"lez",
|
|
"tab",
|
|
]
|
|
DEVANAGARI_LANGS = [
|
|
"hi",
|
|
"mr",
|
|
"ne",
|
|
"bh",
|
|
"mai",
|
|
"ang",
|
|
"bho",
|
|
"mah",
|
|
"sck",
|
|
"new",
|
|
"gom",
|
|
"sa",
|
|
"bgc",
|
|
]
|
|
SPECIFIC_LANGS = [
|
|
"ch",
|
|
"en",
|
|
"korean",
|
|
"japan",
|
|
"chinese_cht",
|
|
"te",
|
|
"ka",
|
|
"ta",
|
|
]
|
|
|
|
if lang is None:
|
|
lang = "ch"
|
|
|
|
if ppocr_version is None:
|
|
if (
|
|
lang
|
|
in ["ch", "chinese_cht", "en", "japan", "korean", "th", "el"]
|
|
+ LATIN_LANGS
|
|
+ ESLAV_LANGS
|
|
):
|
|
ppocr_version = "PP-OCRv5"
|
|
elif lang in (
|
|
LATIN_LANGS
|
|
+ ARABIC_LANGS
|
|
+ CYRILLIC_LANGS
|
|
+ DEVANAGARI_LANGS
|
|
+ SPECIFIC_LANGS
|
|
):
|
|
ppocr_version = "PP-OCRv3"
|
|
else:
|
|
# Unknown language specified
|
|
return None, None
|
|
|
|
if ppocr_version == "PP-OCRv5":
|
|
rec_lang, rec_model_name = None, None
|
|
if lang in ("ch", "chinese_cht", "en", "japan"):
|
|
rec_model_name = "PP-OCRv5_server_rec"
|
|
elif lang in LATIN_LANGS:
|
|
rec_lang = "latin"
|
|
elif lang in ESLAV_LANGS:
|
|
rec_lang = "eslav"
|
|
elif lang == "korean":
|
|
rec_lang = "korean"
|
|
elif lang == "th":
|
|
rec_lang = "th"
|
|
elif lang == "el":
|
|
rec_lang = "el"
|
|
|
|
if rec_lang is not None:
|
|
rec_model_name = f"{rec_lang}_PP-OCRv5_mobile_rec"
|
|
return "PP-OCRv5_server_det", rec_model_name
|
|
|
|
elif ppocr_version == "PP-OCRv4":
|
|
if lang == "ch":
|
|
return "PP-OCRv4_mobile_det", "PP-OCRv4_mobile_rec"
|
|
elif lang == "en":
|
|
return "PP-OCRv4_mobile_det", "en_PP-OCRv4_mobile_rec"
|
|
else:
|
|
return None, None
|
|
else:
|
|
# PP-OCRv3
|
|
rec_lang = None
|
|
if lang in LATIN_LANGS:
|
|
rec_lang = "latin"
|
|
elif lang in ARABIC_LANGS:
|
|
rec_lang = "arabic"
|
|
elif lang in CYRILLIC_LANGS:
|
|
rec_lang = "cyrillic"
|
|
elif lang in DEVANAGARI_LANGS:
|
|
rec_lang = "devanagari"
|
|
else:
|
|
if lang in SPECIFIC_LANGS:
|
|
rec_lang = lang
|
|
|
|
rec_model_name = None
|
|
if rec_lang == "ch":
|
|
rec_model_name = "PP-OCRv3_mobile_rec"
|
|
elif rec_lang is not None:
|
|
rec_model_name = f"{rec_lang}_PP-OCRv3_mobile_rec"
|
|
return "PP-OCRv3_mobile_det", rec_model_name
|
|
|
|
|
|
class PPStructureV3CLISubcommandExecutor(PipelineCLISubcommandExecutor):
|
|
@property
|
|
def subparser_name(self):
|
|
return "pp_structurev3"
|
|
|
|
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(
|
|
"--layout_threshold",
|
|
type=float,
|
|
help="Score threshold for the layout detection model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--layout_nms",
|
|
type=str2bool,
|
|
help="Whether to use NMS in layout detection.",
|
|
)
|
|
subparser.add_argument(
|
|
"--layout_unclip_ratio",
|
|
type=float,
|
|
help="Expansion coefficient for layout detection.",
|
|
)
|
|
subparser.add_argument(
|
|
"--layout_merge_bboxes_mode",
|
|
type=str,
|
|
help="Overlapping box filtering method.",
|
|
)
|
|
|
|
subparser.add_argument(
|
|
"--chart_recognition_model_name",
|
|
type=str,
|
|
help="Name of the chart recognition model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--chart_recognition_model_dir",
|
|
type=str,
|
|
help="Path to the chart recognition model directory.",
|
|
)
|
|
subparser.add_argument(
|
|
"--chart_recognition_batch_size",
|
|
type=int,
|
|
help="Batch size for the chart recognition model.",
|
|
)
|
|
|
|
subparser.add_argument(
|
|
"--region_detection_model_name",
|
|
type=str,
|
|
help="Name of the region detection model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--region_detection_model_dir",
|
|
type=str,
|
|
help="Path to the region 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(
|
|
"--textline_orientation_model_name",
|
|
type=str,
|
|
help="Name of the text line orientation classification model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--textline_orientation_model_dir",
|
|
type=str,
|
|
help="Path to the text line orientation classification directory.",
|
|
)
|
|
subparser.add_argument(
|
|
"--textline_orientation_batch_size",
|
|
type=int,
|
|
help="Batch size for the text line orientation classification model.",
|
|
)
|
|
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(
|
|
"--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 document image orientation classification.",
|
|
)
|
|
subparser.add_argument(
|
|
"--use_doc_unwarping",
|
|
type=str2bool,
|
|
help="Whether to use text image unwarping.",
|
|
)
|
|
subparser.add_argument(
|
|
"--use_textline_orientation",
|
|
type=str2bool,
|
|
help="Whether to use text line orientation classification.",
|
|
)
|
|
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.",
|
|
)
|
|
subparser.add_argument(
|
|
"--use_chart_recognition",
|
|
type=str2bool,
|
|
help="Whether to use chart recognition.",
|
|
)
|
|
subparser.add_argument(
|
|
"--use_region_detection",
|
|
type=str2bool,
|
|
help="Whether to use region detection.",
|
|
)
|
|
|
|
subparser.add_argument(
|
|
"--format_block_content",
|
|
type=str2bool,
|
|
help="Whether to format block content to Markdown.",
|
|
)
|
|
subparser.add_argument(
|
|
"--markdown_ignore_labels",
|
|
type=str,
|
|
nargs="+",
|
|
help="List of layout labels to ignore in Markdown output.",
|
|
)
|
|
|
|
def execute_with_args(self, args):
|
|
params = get_subcommand_args(args)
|
|
perform_simple_inference(
|
|
PPStructureV3,
|
|
params,
|
|
)
|