mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-10-30 17:29:13 +00:00
b25dcaae0e
* Add serving and hpi docs * Optimize CLI logging info * Update interface * Add on-device deployment and onnx model conversion docs * Enhance CLI * _gen->_iter * Fix CLI help message * Update table_recognition_v2 and PP-StructureV3 interfaces * Update installation doc * Update interface * Update interface * Add logging doc * Update default values --------- Co-authored-by: cuicheng01 <45199522+cuicheng01@users.noreply.github.com>
284 lines
10 KiB
Python
284 lines
10 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 FormulaRecognitionPipeline(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_orientation_classify_batch_size=None,
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doc_unwarping_model_name=None,
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doc_unwarping_model_dir=None,
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doc_unwarping_batch_size=None,
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use_doc_orientation_classify=None,
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use_doc_unwarping=None,
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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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layout_detection_batch_size=None,
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use_layout_detection=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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**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 "formula_recognition"
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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_layout_detection=None,
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use_doc_orientation_classify=None,
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use_doc_unwarping=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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**kwargs,
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):
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return self.paddlex_pipeline.predict(
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input,
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use_layout_detection=use_layout_detection,
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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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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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**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_layout_detection=None,
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use_doc_orientation_classify=None,
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use_doc_unwarping=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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**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_layout_detection=use_layout_detection,
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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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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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**kwargs,
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)
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)
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@classmethod
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def get_cli_subcommand_executor(cls):
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return FormulaRecognitionPipelineCLISubcommandExecutor()
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def _get_paddlex_config_overrides(self):
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STRUCTURE = {
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"use_layout_detection": self._params["use_layout_detection"],
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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.LayoutDetection.batch_size": self._params[
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"layout_detection_batch_size"
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],
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"SubModules.FormulaRecognition.model_name": self._params[
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"formula_recognition_model_name"
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],
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"SubModules.FormulaRecognition.model_dir": self._params[
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"formula_recognition_model_dir"
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],
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"SubModules.FormulaRecognition.batch_size": self._params[
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"formula_recognition_batch_size"
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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.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.DocOrientationClassify.batch_size": self._params[
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"doc_orientation_classify_batch_size"
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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.DocPreprocessor.SubModules.DocUnwarping.batch_size": self._params[
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"doc_unwarping_batch_size"
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],
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}
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return create_config_from_structure(STRUCTURE)
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class FormulaRecognitionPipelineCLISubcommandExecutor(PipelineCLISubcommandExecutor):
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@property
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def subparser_name(self):
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return "formula_recognition_pipeline"
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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="Directory 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_batch_size",
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type=int,
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help="Batch size for document image orientation classification.",
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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 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="Directory of the document unwarping model.",
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)
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subparser.add_argument(
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"--doc_unwarping_batch_size",
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type=int,
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help="Batch size for document unwarping.",
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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="Use document image orientation classification.",
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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="Use document unwarping.",
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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="Directory of 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.",
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)
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subparser.add_argument(
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"--layout_nms",
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type=str2bool,
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help="Non-maximum suppression 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="Unclip ratio for layout detection.",
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)
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subparser.add_argument(
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"--layout_merge_bboxes_mode",
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type=str,
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help="Mode for merging bounding boxes in layout detection.",
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)
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subparser.add_argument(
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"--layout_detection_batch_size",
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type=int,
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help="Batch size for layout detection.",
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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="Use layout detection.",
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)
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subparser.add_argument(
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"--formula_recognition_model_name",
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type=str,
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help="Name of the formula recognition model.",
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)
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subparser.add_argument(
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"--formula_recognition_model_dir",
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type=str,
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help="Directory of the formula recognition model.",
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)
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subparser.add_argument(
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"--formula_recognition_batch_size",
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type=int,
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help="Batch size for formula recognition.",
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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(FormulaRecognitionPipeline, params)
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