mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-07-30 12:22:10 +00:00
3d03ca5500
* Init new paddleocr * Remove unused dependency * Fix typos * Fix * Add doc understanding modules * Fix package finding * Normalize name * Fix setting bugs * Fix setting bug * Support single model inference * Add PP-ChatOCRv4-doc * Add pp_chatocrv4_doc tests * Enable MKL-DNN when available * add seal_text_detection modules * add layout_detection and table_cells_detection modules * add testing scripts * Fix desc * add text_image_unwarping and table_structure_recognition modules * add formula_recognition and doc_vlm modules * update formula_recognition default_model_name * add MKLDNN_BLOCKLIST * update MKLDNN log * add seal rec pipeline * fix sth * fix sth * add doc preprocessor pipeline * fix sth * add doc understanding * add table_rec_v2, ppstructurev3, formula_rec pipelines * move test files * forward kwargs to pipeline.predict * clean test files * Add missing kwargs * Fix typo * Fix typo * rerun CI * update mkldnn BLOCKLIST * update * update warning message * fix cli args * update PIPELINE_MKLDNN_BLOCKLIST * update of workflow * skip resource_intensive tests * update config * skip ppdocbee test_predict_params --------- Co-authored-by: zhangyue66 <zhangyue66@baidu.com> Co-authored-by: zhangzelun <zhangzelun@baidu.com>
384 lines
15 KiB
Python
384 lines
15 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 TableRecognitionPipelineV2(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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table_classification_model_name=None,
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table_classification_model_dir=None,
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wired_table_structure_recognition_model_name=None,
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wired_table_structure_recognition_model_dir=None,
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wireless_table_structure_recognition_model_name=None,
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wireless_table_structure_recognition_model_dir=None,
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wired_table_cells_detection_model_name=None,
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wired_table_cells_detection_model_dir=None,
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wireless_table_cells_detection_model_name=None,
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wireless_table_cells_detection_model_dir=None,
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doc_orientation_classify_model_name=None,
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doc_orientation_classify_model_dir=None,
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doc_unwarping_model_name=None,
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doc_unwarping_model_dir=None,
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text_detection_model_name=None,
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text_detection_model_dir=None,
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text_det_limit_side_len=None,
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text_det_limit_type=None,
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text_det_thresh=None,
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text_det_box_thresh=None,
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text_det_unclip_ratio=None,
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text_recognition_model_name=None,
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text_recognition_model_dir=None,
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text_recognition_batch_size=None,
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text_rec_score_thresh=None,
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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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use_ocr_model=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 "table_recognition_v2"
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def predict(
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self,
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input,
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use_doc_orientation_classify=None,
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use_doc_unwarping=None,
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use_layout_detection=None,
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use_ocr_model=None,
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overall_ocr_res=None,
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layout_det_res=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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use_table_cells_ocr_results=None,
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use_e2e_wired_table_rec_model=None,
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use_e2e_wireless_table_rec_model=None,
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**kwargs,
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):
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result = []
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for res in self.paddlex_pipeline.predict(
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input,
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use_doc_orientation_classify=use_doc_orientation_classify,
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use_doc_unwarping=use_doc_unwarping,
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use_layout_detection=use_layout_detection,
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use_ocr_model=use_ocr_model,
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overall_ocr_res=overall_ocr_res,
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layout_det_res=layout_det_res,
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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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use_table_cells_ocr_results=use_table_cells_ocr_results,
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use_e2e_wired_table_rec_model=use_e2e_wired_table_rec_model,
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use_e2e_wireless_table_rec_model=use_e2e_wireless_table_rec_model,
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**kwargs,
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):
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result.append(res)
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return result
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@classmethod
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def get_cli_subcommand_executor(cls):
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return TableRecognitionPipelineV2CLISubcommandExecutor()
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def _get_paddlex_config_overrides(self):
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STRUCTURE = {
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"SubPipelines.DocPreprocessor.use_doc_orientation_classify": self._params[
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"use_doc_orientation_classify"
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],
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"SubPipelines.DocPreprocessor.use_doc_unwarping": self._params[
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"use_doc_unwarping"
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],
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"use_layout_detection": self._params["use_layout_detection"],
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"use_ocr_model": self._params["use_ocr_model"],
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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.TableClassification.model_name": self._params[
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"table_classification_model_name"
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],
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"SubModules.TableClassification.model_dir": self._params[
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"table_classification_model_dir"
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],
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"SubModules.WiredTableStructureRecognition.model_name": self._params[
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"wired_table_structure_recognition_model_name"
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],
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"SubModules.WiredTableStructureRecognition.model_dir": self._params[
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"wired_table_structure_recognition_model_dir"
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],
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"SubModules.WirelessTableStructureRecognition.model_name": self._params[
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"wireless_table_structure_recognition_model_name"
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],
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"SubModules.WirelessTableStructureRecognition.model_dir": self._params[
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"wireless_table_structure_recognition_model_dir"
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],
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"SubModules.WiredTableCellsDetection.model_name": self._params[
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"wired_table_cells_detection_model_name"
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],
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"SubModules.WiredTableCellsDetection.model_dir": self._params[
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"wired_table_cells_detection_model_dir"
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],
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"SubModules.WirelessTableCellsDetection.model_name": self._params[
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"wireless_table_cells_detection_model_name"
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],
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"SubModules.WirelessTableCellsDetection.model_dir": self._params[
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"wireless_table_cells_detection_model_dir"
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],
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"SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_name": self._params[
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"doc_orientation_classify_model_name"
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],
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"SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_dir": self._params[
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"doc_orientation_classify_model_dir"
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],
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"SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_name": self._params[
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"doc_unwarping_model_name"
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],
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"SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_dir": self._params[
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"doc_unwarping_model_dir"
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],
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"SubPipelines.GeneralOCR.SubModules.TextDetection.model_name": self._params[
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"text_detection_model_name"
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],
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"SubPipelines.GeneralOCR.SubModules.TextDetection.model_dir": self._params[
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"text_detection_model_dir"
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],
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"SubPipelines.GeneralOCR.SubModules.TextDetection.limit_side_len": self._params[
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"text_det_limit_side_len"
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],
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"SubPipelines.GeneralOCR.SubModules.TextDetection.limit_type": self._params[
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"text_det_limit_type"
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],
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"SubPipelines.GeneralOCR.SubModules.TextDetection.thresh": self._params[
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"text_det_thresh"
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],
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"SubPipelines.GeneralOCR.SubModules.TextDetection.box_thresh": self._params[
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"text_det_box_thresh"
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],
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"SubPipelines.GeneralOCR.SubModules.TextDetection.unclip_ratio": self._params[
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"text_det_unclip_ratio"
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],
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"SubPipelines.GeneralOCR.SubModules.TextRecognition.model_name": self._params[
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"text_recognition_model_name"
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],
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"SubPipelines.GeneralOCR.SubModules.TextRecognition.model_dir": self._params[
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"text_recognition_model_dir"
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],
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"SubPipelines.GeneralOCR.SubModules.TextRecognition.batch_size": self._params[
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"text_recognition_batch_size"
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],
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"SubPipelines.GeneralOCR.SubModules.TextRecognition.score_thresh": self._params[
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"text_rec_score_thresh"
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],
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}
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return create_config_from_structure(STRUCTURE)
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class TableRecognitionPipelineV2CLISubcommandExecutor(PipelineCLISubcommandExecutor):
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@property
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def subparser_name(self):
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return "table_recognition_v2"
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def _update_subparser(self, subparser):
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add_simple_inference_args(subparser)
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subparser.add_argument(
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"--layout_detection_model_name",
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type=str,
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help="Name of the layout detection model.",
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)
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subparser.add_argument(
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"--layout_detection_model_dir",
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type=str,
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help="Path to the layout detection model directory.",
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)
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subparser.add_argument(
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"--table_classification_model_name",
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type=str,
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help="Name of the table classification model.",
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)
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subparser.add_argument(
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"--table_classification_model_dir",
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type=str,
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help="Path to the table classification model directory.",
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)
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subparser.add_argument(
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"--wired_table_structure_recognition_model_name",
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type=str,
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help="Name of the wired table structure recognition model.",
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)
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subparser.add_argument(
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"--wired_table_structure_recognition_model_dir",
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type=str,
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help="Path to the wired table structure recognition model directory.",
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)
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subparser.add_argument(
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"--wireless_table_structure_recognition_model_name",
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type=str,
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help="Name of the wireless table structure recognition model.",
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)
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subparser.add_argument(
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"--wireless_table_structure_recognition_model_dir",
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type=str,
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help="Path to the wired table structure recognition model directory.",
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)
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subparser.add_argument(
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"--wired_table_cells_detection_model_name",
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type=str,
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help="Name of the wired table cells detection model.",
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)
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subparser.add_argument(
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"--wired_table_cells_detection_model_dir",
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type=str,
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help="Path to the wired table cells detection model directory.",
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)
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subparser.add_argument(
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"--wireless_table_cells_detection_model_name",
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type=str,
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help="Name of the wireless table cells detection model.",
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)
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subparser.add_argument(
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"--wireless_table_cells_detection_model_dir",
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type=str,
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help="Path to the wireless table cells detection model directory.",
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)
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subparser.add_argument(
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"--doc_orientation_classify_model_name",
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type=str,
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help="Name of the document image orientation classification model.",
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)
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subparser.add_argument(
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"--doc_orientation_classify_model_dir",
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type=str,
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help="Path to the document image orientation classification model directory.",
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)
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subparser.add_argument(
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"--doc_unwarping_model_name",
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type=str,
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help="Name of the text image unwarping model.",
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)
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subparser.add_argument(
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"--doc_unwarping_model_dir",
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type=str,
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help="Path to the image unwarping model directory.",
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)
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subparser.add_argument(
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"--text_detection_model_name",
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type=str,
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help="Name of the text detection model.",
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)
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subparser.add_argument(
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"--text_detection_model_dir",
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type=str,
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help="Path to the text detection model directory.",
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)
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subparser.add_argument(
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"--text_det_limit_side_len",
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type=int,
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help="This sets a limit on the side length of the input image for the text detection model.",
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)
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subparser.add_argument(
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"--text_det_limit_type",
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type=str,
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help="This determines how the side length limit is applied to the input image before feeding it into the text deteciton model.",
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)
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subparser.add_argument(
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"--text_det_thresh",
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type=float,
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help="Detection pixel threshold for the text detection model. Pixels with scores greater than this threshold in the output probability map are considered text pixels.",
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)
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subparser.add_argument(
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"--text_det_box_thresh",
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type=float,
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help="Detection box threshold for the text detection model. A detection result is considered a text region if the average score of all pixels within the border of the result is greater than this threshold.",
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)
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subparser.add_argument(
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"--text_det_unclip_ratio",
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type=float,
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help="Text detection expansion coefficient, which expands the text region using this method. The larger the value, the larger the expansion area.",
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)
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subparser.add_argument(
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"--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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"--text_rec_score_thresh",
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type=float,
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help="Text recognition threshold used in general OCR. Text results with scores greater than this threshold are retained.",
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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 text 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 layout detection.",
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)
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subparser.add_argument(
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"--use_ocr_model",
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type=str2bool,
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help="Whether to use OCR models.",
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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(TableRecognitionPipelineV2, params)
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