338 lines
13 KiB
Python
338 lines
13 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ..utils.cli import (
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add_simple_inference_args,
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get_subcommand_args,
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perform_simple_inference,
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str2bool,
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)
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from .base import PaddleXPipelineWrapper, PipelineCLISubcommandExecutor
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from .utils import create_config_from_structure
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class SealRecognition(PaddleXPipelineWrapper):
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def __init__(
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self,
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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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layout_detection_model_name=None,
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layout_detection_model_dir=None,
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seal_text_detection_model_name=None,
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seal_text_detection_model_dir=None,
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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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use_doc_orientation_classify=None,
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use_doc_unwarping=None,
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use_layout_detection=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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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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self._params = {
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"doc_orientation_classify_model_name": doc_orientation_classify_model_name,
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"doc_orientation_classify_model_dir": doc_orientation_classify_model_dir,
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"doc_unwarping_model_name": doc_unwarping_model_name,
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"doc_unwarping_model_dir": doc_unwarping_model_dir,
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"layout_detection_model_name": layout_detection_model_name,
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"layout_detection_model_dir": layout_detection_model_dir,
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"seal_text_detection_model_name": seal_text_detection_model_name,
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"seal_text_detection_model_dir": seal_text_detection_model_dir,
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"text_recognition_model_name": text_recognition_model_name,
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"text_recognition_model_dir": text_recognition_model_dir,
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"text_recognition_batch_size": text_recognition_batch_size,
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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_layout_detection": use_layout_detection,
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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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"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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}
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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 "seal_recognition"
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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_layout_detection=None,
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layout_det_res=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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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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result = []
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for res in self.paddlex_pipeline.predict(
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input,
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use_doc_orientation_classify=use_doc_orientation_classify,
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use_doc_unwarping=use_doc_unwarping,
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use_layout_detection=use_layout_detection,
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layout_det_res=layout_det_res,
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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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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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result.append(res)
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return result
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@classmethod
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def get_cli_subcommand_executor(cls):
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return SealRecognitionCLISubcommandExecutor()
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def _get_paddlex_config_overrides(self):
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STRUCTURE = {
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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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"SubModules.LayoutDetection.model_name": self._params[
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"layout_detection_model_name"
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],
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"SubModules.LayoutDetection.model_dir": self._params[
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"layout_detection_model_dir"
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],
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"SubModules.LayoutDetection.threshold": self._params["layout_threshold"],
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"SubModules.LayoutDetection.layout_nms": self._params["layout_nms"],
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"SubModules.LayoutDetection.layout_unclip_ratio": self._params[
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"layout_unclip_ratio"
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],
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"SubModules.LayoutDetection.layout_merge_bboxes_mode": self._params[
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"layout_merge_bboxes_mode"
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],
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"SubPipelines.DocPreprocessor.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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"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.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.SealOCR.SubModules.TextDetection.limit_side_len": self._params[
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"seal_det_limit_side_len"
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],
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"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.SealOCR.SubModules.TextDetection.thresh": self._params[
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"seal_det_thresh"
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],
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"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.SealOCR.SubModules.TextDetection.unclip_ratio": self._params[
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"seal_det_unclip_ratio"
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],
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"SubPipelines.SealOCR.SubModules.TextRecognition.model_name": self._params[
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"text_recognition_model_name"
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],
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"SubPipelines.SealOCR.SubModules.TextRecognition.model_dir": self._params[
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"text_recognition_model_dir"
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],
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"SubPipelines.SealOCR.SubModules.TextRecognition.batch_size": self._params[
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"text_recognition_batch_size"
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],
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"SubPipelines.SealOCR.SubModules.TextRecognition.score_thresh": self._params[
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"seal_rec_score_thresh"
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],
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"use_layout_detection": self._params["use_layout_detection"],
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}
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return create_config_from_structure(STRUCTURE)
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class SealRecognitionCLISubcommandExecutor(PipelineCLISubcommandExecutor):
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@property
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def subparser_name(self):
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return "seal_recognition"
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def _update_subparser(self, subparser):
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add_simple_inference_args(subparser)
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subparser.add_argument(
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"--doc_orientation_classify_model_name",
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type=str,
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help="Name of the document image orientation classification model.",
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)
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subparser.add_argument(
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"--doc_orientation_classify_model_dir",
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type=str,
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help="Path to the document image orientation classification model directory.",
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)
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subparser.add_argument(
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"--doc_unwarping_model_name",
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type=str,
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help="Name of the document image unwarping model.",
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)
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subparser.add_argument(
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"--doc_unwarping_model_dir",
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type=str,
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help="Path to the document image unwarping model directory.",
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)
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subparser.add_argument(
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"--layout_detection_model_name",
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type=str,
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help="Name of the layout detection model.",
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)
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subparser.add_argument(
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"--layout_detection_model_dir",
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type=str,
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help="Path to the layout detection model directory.",
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)
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subparser.add_argument(
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"--seal_text_detection_model_name",
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type=str,
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help="Name of the seal text detection model.",
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)
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subparser.add_argument(
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"--seal_text_detection_model_dir",
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type=str,
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help="Path to the seal text detection model directory.",
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)
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subparser.add_argument(
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"--text_recognition_model_name",
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type=str,
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help="Name of the text recognition model.",
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)
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subparser.add_argument(
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"--text_recognition_model_dir",
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type=str,
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help="Path to the text recognition model directory.",
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)
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subparser.add_argument(
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"--text_recognition_batch_size",
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type=int,
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help="Batch size for the text recognition model.",
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)
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subparser.add_argument(
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"--use_doc_orientation_classify",
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type=str2bool,
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help="Whether to use the document image orientation classification model.",
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)
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subparser.add_argument(
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"--use_doc_unwarping",
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type=str2bool,
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help="Whether to use the document image unwarping model.",
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)
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subparser.add_argument(
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"--use_layout_detection",
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type=str2bool,
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help="Whether to use the layout detection model.",
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)
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subparser.add_argument(
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"--layout_threshold",
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type=float,
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help="Threshold for layout detection model.",
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)
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subparser.add_argument(
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"--layout_nms",
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type=float,
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help="Non-Maximum Suppression threshold for layout detection.",
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)
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subparser.add_argument(
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"--layout_unclip_ratio",
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type=float,
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help="Layout detection expansion coefficient.",
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)
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subparser.add_argument(
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"--layout_merge_bboxes_mode",
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type=str,
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help="Mode for merging bounding boxes in layout detection.",
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)
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subparser.add_argument(
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"--seal_det_limit_side_len",
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type=int,
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help="This sets a limit on the side length of the input image for the seal text detection model.",
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)
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subparser.add_argument(
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"--seal_det_limit_type",
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type=str,
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help="This determines how the side length limit is applied to the input image before feeding it into the seal text detection model.",
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)
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subparser.add_argument(
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"--seal_det_thresh",
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type=float,
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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.",
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)
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subparser.add_argument(
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"--seal_det_box_thresh",
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type=float,
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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.",
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)
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subparser.add_argument(
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"--seal_det_unclip_ratio",
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type=float,
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help="Seal text detection expansion coefficient, which expands the text region using this method. The larger the value, the larger the expansion area.",
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)
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subparser.add_argument(
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"--seal_rec_score_thresh",
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type=float,
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help="Text recognition threshold. Text results with scores greater than this threshold are retained.",
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)
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def execute_with_args(self, args):
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params = get_subcommand_args(args)
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perform_simple_inference(SealRecognition, params)
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