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722 lines
29 KiB
722 lines
29 KiB
# 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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get_subcommand_args,
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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 PPChatOCRv4Doc(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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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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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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table_structure_recognition_model_name=None,
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table_structure_recognition_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_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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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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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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retriever_config=None,
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mllm_chat_bot_config=None,
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chat_bot_config=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-ChatOCRv4-doc"
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def visual_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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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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**kwargs,
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):
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return self.paddlex_pipeline.visual_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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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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**kwargs,
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)
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def visual_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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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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**kwargs,
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):
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return list(
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self.visual_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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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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**kwargs,
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)
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)
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def build_vector(
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self,
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visual_info,
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*,
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min_characters=3500,
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block_size=300,
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flag_save_bytes_vector=False,
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retriever_config=None,
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):
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return self.paddlex_pipeline.build_vector(
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visual_info,
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min_characters=min_characters,
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block_size=block_size,
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flag_save_bytes_vector=flag_save_bytes_vector,
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retriever_config=retriever_config,
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)
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def mllm_pred(self, input, key_list, *, mllm_chat_bot_config=None):
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return self.paddlex_pipeline.mllm_pred(
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input,
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key_list,
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mllm_chat_bot_config=mllm_chat_bot_config,
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)
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def chat(
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self,
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key_list,
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visual_info,
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*,
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use_vector_retrieval=True,
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vector_info=None,
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min_characters=3500,
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text_task_description=None,
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text_output_format=None,
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text_rules_str=None,
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text_few_shot_demo_text_content=None,
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text_few_shot_demo_key_value_list=None,
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table_task_description=None,
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table_output_format=None,
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table_rules_str=None,
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table_few_shot_demo_text_content=None,
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table_few_shot_demo_key_value_list=None,
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mllm_predict_info=None,
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mllm_integration_strategy="integration",
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chat_bot_config=None,
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retriever_config=None,
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):
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return self.paddlex_pipeline.chat(
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key_list,
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visual_info,
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use_vector_retrieval=use_vector_retrieval,
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vector_info=vector_info,
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min_characters=min_characters,
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text_task_description=text_task_description,
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text_output_format=text_output_format,
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text_rules_str=text_rules_str,
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text_few_shot_demo_text_content=text_few_shot_demo_text_content,
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text_few_shot_demo_key_value_list=text_few_shot_demo_key_value_list,
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table_task_description=table_task_description,
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table_output_format=table_output_format,
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table_rules_str=table_rules_str,
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table_few_shot_demo_text_content=table_few_shot_demo_text_content,
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table_few_shot_demo_key_value_list=table_few_shot_demo_key_value_list,
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mllm_predict_info=mllm_predict_info,
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mllm_integration_strategy=mllm_integration_strategy,
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chat_bot_config=chat_bot_config,
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retriever_config=retriever_config,
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)
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@classmethod
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def get_cli_subcommand_executor(cls):
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return PPChatOCRv4DocCLISubcommandExecutor()
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def _get_paddlex_config_overrides(self):
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STRUCTURE = {
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"SubPipelines.LayoutParser.SubModules.LayoutDetection.model_name": self._params[
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"layout_detection_model_name"
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],
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"SubPipelines.LayoutParser.SubModules.LayoutDetection.model_dir": self._params[
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"layout_detection_model_dir"
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],
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"SubPipelines.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.SubPipelines.TableRecognition.SubModules.TableStructureRecognition.model_name": self._params[
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"table_structure_recognition_model_name"
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],
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"SubPipelines.LayoutParser.SubPipelines.TableRecognition.SubModules.TableStructureRecognition.model_dir": self._params[
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"table_structure_recognition_model_dir"
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],
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"SubPipelines.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.SubPipelines.DocPreprocessor.use_doc_unwarping": self._params[
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"use_doc_unwarping"
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],
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"SubPipelines.LayoutParser.SubPipelines.GeneralOCR.use_textline_orientation": self._params[
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"use_textline_orientation"
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],
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"SubPipelines.LayoutParser.use_seal_recognition": self._params[
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"use_seal_recognition"
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],
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"SubPipelines.LayoutParser.use_table_recognition": self._params[
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"use_table_recognition"
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],
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"SubPipelines.LayoutParser.SubModules.LayoutDetection.threshold": self._params[
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"layout_threshold"
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],
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"SubPipelines.LayoutParser.SubModules.LayoutDetection.nms": self._params[
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"layout_nms"
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],
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"SubPipelines.LayoutParser.SubModules.LayoutDetection.unclip_ratio": self._params[
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"layout_unclip_ratio"
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],
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"SubPipelines.LayoutParser.SubModules.LayoutDetection.merge_bboxes_mode": self._params[
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"layout_merge_bboxes_mode"
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],
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"SubPipelines.LayoutParser.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.LayoutParser.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.LayoutParser.SubPipelines.GeneralOCR.SubModules.TextDetection.thresh": self._params[
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"text_det_thresh"
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],
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"SubPipelines.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.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.LayoutParser.SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextRecognition.score_thresh": self._params[
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"seal_rec_score_thresh"
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],
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"SubModules.LLM_Retriever": self._params["retriever_config"],
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"SubModules.MLLM_Chat": self._params["mllm_chat_bot_config"],
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"SubModules.LLM_Chat": self._params["chat_bot_config"],
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}
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return create_config_from_structure(STRUCTURE)
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class PPChatOCRv4DocCLISubcommandExecutor(PipelineCLISubcommandExecutor):
|
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@property
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def subparser_name(self):
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return "pp_chatocrv4_doc"
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def _update_subparser(self, subparser):
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subparser.add_argument(
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"-i",
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"--input",
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type=str,
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required=True,
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help="Input path or URL.",
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)
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subparser.add_argument(
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"-k",
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"--keys",
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type=str,
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nargs="+",
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required=True,
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metavar="KEY",
|
|
help="Keys use for information extraction.",
|
|
)
|
|
subparser.add_argument(
|
|
"--save_path",
|
|
type=str,
|
|
default="output",
|
|
help="Path to the output directory.",
|
|
)
|
|
subparser.add_argument(
|
|
"--invoke_mllm",
|
|
type=str2bool,
|
|
default=False,
|
|
help="Whether to invoke the multimodal large language model.",
|
|
)
|
|
|
|
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(
|
|
"--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(
|
|
"--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 model 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(
|
|
"--table_structure_recognition_model_name",
|
|
type=str,
|
|
help="Name of the table structure recognition model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--table_structure_recognition_model_dir",
|
|
type=str,
|
|
help="Path to the table structure recognition 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_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(
|
|
"--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.",
|
|
)
|
|
# TODO: Support dict and list types
|
|
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(
|
|
"--text_det_limit_side_len",
|
|
type=int,
|
|
help="This sets a limit on the side length of the input image for the text detection model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--text_det_limit_type",
|
|
type=str,
|
|
help="This determines how the side length limit is applied to the input image before feeding it into the text deteciton model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--text_det_thresh",
|
|
type=float,
|
|
help="Detection pixel threshold for the text detection model. Pixels with scores greater than this threshold in the output probability map are considered text pixels.",
|
|
)
|
|
subparser.add_argument(
|
|
"--text_det_box_thresh",
|
|
type=float,
|
|
help="Detection box threshold for the text detection model. A detection result is considered a text region if the average score of all pixels within the border of the result is greater than this threshold.",
|
|
)
|
|
subparser.add_argument(
|
|
"--text_det_unclip_ratio",
|
|
type=float,
|
|
help="Text detection expansion coefficient, which expands the text region using this method. The larger the value, the larger the expansion area.",
|
|
)
|
|
subparser.add_argument(
|
|
"--text_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(
|
|
"--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_rec_score_thresh",
|
|
type=float,
|
|
help="Seal text recognition threshold. Text results with scores greater than this threshold are retained.",
|
|
)
|
|
|
|
# FIXME: Passing API key through CLI is not secure; consider using
|
|
# environment variables.
|
|
subparser.add_argument(
|
|
"--qianfan_api_key",
|
|
type=str,
|
|
help="Configuration for the embedding model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--pp_docbee_base_url",
|
|
type=str,
|
|
help="Configuration for the multimodal large language model.",
|
|
)
|
|
|
|
def execute_with_args(self, args):
|
|
params = get_subcommand_args(args)
|
|
input = params.pop("input")
|
|
keys = params.pop("keys")
|
|
save_path = params.pop("save_path")
|
|
invoke_mllm = params.pop("invoke_mllm")
|
|
qianfan_api_key = params.pop("qianfan_api_key")
|
|
if qianfan_api_key is not None:
|
|
params["retriever_config"] = {
|
|
"module_name": "retriever",
|
|
"model_name": "embedding-v1",
|
|
"base_url": "https://qianfan.baidubce.com/v2",
|
|
"api_type": "qianfan",
|
|
"api_key": qianfan_api_key,
|
|
}
|
|
params["chat_bot_config"] = {
|
|
"module_name": "chat_bot",
|
|
"model_name": "ernie-3.5-8k",
|
|
"base_url": "https://qianfan.baidubce.com/v2",
|
|
"api_type": "openai",
|
|
"api_key": qianfan_api_key,
|
|
}
|
|
pp_docbee_base_url = params.pop("pp_docbee_base_url")
|
|
if pp_docbee_base_url is not None:
|
|
params["mllm_chat_bot_config"] = {
|
|
"module_name": "chat_bot",
|
|
"model_name": "PP-DocBee",
|
|
# PaddleX requires endpoints such as ".../chat/completions",
|
|
# which, as the parameter name suggests, are not base URLs.
|
|
"base_url": pp_docbee_base_url,
|
|
"api_type": "openai",
|
|
"api_key": "fake_key",
|
|
}
|
|
|
|
chatocr = PPChatOCRv4Doc(**params)
|
|
|
|
result_visual = chatocr.visual_predict(input)
|
|
|
|
visual_info_list = []
|
|
for res in result_visual:
|
|
visual_info_list.append(res["visual_info"])
|
|
if save_path:
|
|
res["layout_parsing_result"].save_all(save_path)
|
|
|
|
vector_info = chatocr.build_vector(visual_info_list)
|
|
|
|
if invoke_mllm:
|
|
result_mllm = chatocr.mllm_pred(input, keys)
|
|
mllm_predict_info = result_mllm["mllm_res"]
|
|
else:
|
|
mllm_predict_info = None
|
|
|
|
result_chat = chatocr.chat(
|
|
keys,
|
|
visual_info_list,
|
|
vector_info=vector_info,
|
|
mllm_predict_info=mllm_predict_info,
|
|
)
|
|
|
|
# Print the result to stdout
|
|
for k, v in result_chat["chat_res"].items():
|
|
print(f"{k} {v}")
|