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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .._utils.cli import (
add_simple_inference_args,
get_subcommand_args,
perform_simple_inference,
str2bool,
)
from .base import PaddleXPipelineWrapper, PipelineCLISubcommandExecutor
from .utils import create_config_from_structure
class TableRecognitionPipelineV2(PaddleXPipelineWrapper):
def __init__(
self,
layout_detection_model_name=None,
layout_detection_model_dir=None,
table_classification_model_name=None,
table_classification_model_dir=None,
wired_table_structure_recognition_model_name=None,
wired_table_structure_recognition_model_dir=None,
wireless_table_structure_recognition_model_name=None,
wireless_table_structure_recognition_model_dir=None,
wired_table_cells_detection_model_name=None,
wired_table_cells_detection_model_dir=None,
wireless_table_cells_detection_model_name=None,
wireless_table_cells_detection_model_dir=None,
doc_orientation_classify_model_name=None,
doc_orientation_classify_model_dir=None,
doc_unwarping_model_name=None,
doc_unwarping_model_dir=None,
text_detection_model_name=None,
text_detection_model_dir=None,
text_det_limit_side_len=None,
text_det_limit_type=None,
text_det_thresh=None,
text_det_box_thresh=None,
text_det_unclip_ratio=None,
text_recognition_model_name=None,
text_recognition_model_dir=None,
text_recognition_batch_size=None,
text_rec_score_thresh=None,
use_doc_orientation_classify=None,
use_doc_unwarping=None,
use_layout_detection=None,
use_ocr_model=None,
**kwargs,
):
params = locals().copy()
params.pop("self")
params.pop("kwargs")
self._params = params
super().__init__(**kwargs)
@property
def _paddlex_pipeline_name(self):
return "table_recognition_v2"
def predict_iter(
self,
input,
use_doc_orientation_classify=None,
use_doc_unwarping=None,
use_layout_detection=None,
use_ocr_model=None,
overall_ocr_res=None,
layout_det_res=None,
text_det_limit_side_len=None,
text_det_limit_type=None,
text_det_thresh=None,
text_det_box_thresh=None,
text_det_unclip_ratio=None,
text_rec_score_thresh=None,
use_e2e_wired_table_rec_model=False,
use_e2e_wireless_table_rec_model=False,
use_wired_table_cells_trans_to_html=False,
use_wireless_table_cells_trans_to_html=False,
use_table_orientation_classify=True,
use_ocr_results_with_table_cells=True,
**kwargs,
):
return self.paddlex_pipeline.predict(
input,
use_doc_orientation_classify=use_doc_orientation_classify,
use_doc_unwarping=use_doc_unwarping,
use_layout_detection=use_layout_detection,
use_ocr_model=use_ocr_model,
overall_ocr_res=overall_ocr_res,
layout_det_res=layout_det_res,
text_det_limit_side_len=text_det_limit_side_len,
text_det_limit_type=text_det_limit_type,
text_det_thresh=text_det_thresh,
text_det_box_thresh=text_det_box_thresh,
text_det_unclip_ratio=text_det_unclip_ratio,
text_rec_score_thresh=text_rec_score_thresh,
use_e2e_wired_table_rec_model=use_e2e_wired_table_rec_model,
use_e2e_wireless_table_rec_model=use_e2e_wireless_table_rec_model,
use_wired_table_cells_trans_to_html=use_wired_table_cells_trans_to_html,
use_wireless_table_cells_trans_to_html=use_wireless_table_cells_trans_to_html,
use_table_orientation_classify=use_table_orientation_classify,
use_ocr_results_with_table_cells=use_ocr_results_with_table_cells,
**kwargs,
)
def predict(
self,
input,
use_doc_orientation_classify=None,
use_doc_unwarping=None,
use_layout_detection=None,
use_ocr_model=None,
overall_ocr_res=None,
layout_det_res=None,
text_det_limit_side_len=None,
text_det_limit_type=None,
text_det_thresh=None,
text_det_box_thresh=None,
text_det_unclip_ratio=None,
text_rec_score_thresh=None,
use_e2e_wired_table_rec_model=False,
use_e2e_wireless_table_rec_model=False,
use_wired_table_cells_trans_to_html=False,
use_wireless_table_cells_trans_to_html=False,
use_table_orientation_classify=True,
use_ocr_results_with_table_cells=True,
**kwargs,
):
return list(
self.predict_iter(
input,
use_doc_orientation_classify=use_doc_orientation_classify,
use_doc_unwarping=use_doc_unwarping,
use_layout_detection=use_layout_detection,
use_ocr_model=use_ocr_model,
overall_ocr_res=overall_ocr_res,
layout_det_res=layout_det_res,
text_det_limit_side_len=text_det_limit_side_len,
text_det_limit_type=text_det_limit_type,
text_det_thresh=text_det_thresh,
text_det_box_thresh=text_det_box_thresh,
text_det_unclip_ratio=text_det_unclip_ratio,
text_rec_score_thresh=text_rec_score_thresh,
use_e2e_wired_table_rec_model=use_e2e_wired_table_rec_model,
use_e2e_wireless_table_rec_model=use_e2e_wireless_table_rec_model,
use_wired_table_cells_trans_to_html=use_wired_table_cells_trans_to_html,
use_wireless_table_cells_trans_to_html=use_wireless_table_cells_trans_to_html,
use_table_orientation_classify=use_table_orientation_classify,
use_ocr_results_with_table_cells=use_ocr_results_with_table_cells,
**kwargs,
)
)
@classmethod
def get_cli_subcommand_executor(cls):
return TableRecognitionPipelineV2CLISubcommandExecutor()
def _get_paddlex_config_overrides(self):
STRUCTURE = {
"SubPipelines.DocPreprocessor.use_doc_orientation_classify": self._params[
"use_doc_orientation_classify"
],
"SubPipelines.DocPreprocessor.use_doc_unwarping": self._params[
"use_doc_unwarping"
],
"use_layout_detection": self._params["use_layout_detection"],
"use_ocr_model": self._params["use_ocr_model"],
"SubModules.LayoutDetection.model_name": self._params[
"layout_detection_model_name"
],
"SubModules.LayoutDetection.model_dir": self._params[
"layout_detection_model_dir"
],
"SubModules.TableClassification.model_name": self._params[
"table_classification_model_name"
],
"SubModules.TableClassification.model_dir": self._params[
"table_classification_model_dir"
],
"SubModules.WiredTableStructureRecognition.model_name": self._params[
"wired_table_structure_recognition_model_name"
],
"SubModules.WiredTableStructureRecognition.model_dir": self._params[
"wired_table_structure_recognition_model_dir"
],
"SubModules.WirelessTableStructureRecognition.model_name": self._params[
"wireless_table_structure_recognition_model_name"
],
"SubModules.WirelessTableStructureRecognition.model_dir": self._params[
"wireless_table_structure_recognition_model_dir"
],
"SubModules.WiredTableCellsDetection.model_name": self._params[
"wired_table_cells_detection_model_name"
],
"SubModules.WiredTableCellsDetection.model_dir": self._params[
"wired_table_cells_detection_model_dir"
],
"SubModules.WirelessTableCellsDetection.model_name": self._params[
"wireless_table_cells_detection_model_name"
],
"SubModules.WirelessTableCellsDetection.model_dir": self._params[
"wireless_table_cells_detection_model_dir"
],
"SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_name": self._params[
"doc_orientation_classify_model_name"
],
"SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_dir": self._params[
"doc_orientation_classify_model_dir"
],
"SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_name": self._params[
"doc_unwarping_model_name"
],
"SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_dir": self._params[
"doc_unwarping_model_dir"
],
"SubPipelines.GeneralOCR.SubModules.TextDetection.model_name": self._params[
"text_detection_model_name"
],
"SubPipelines.GeneralOCR.SubModules.TextDetection.model_dir": self._params[
"text_detection_model_dir"
],
"SubPipelines.GeneralOCR.SubModules.TextDetection.limit_side_len": self._params[
"text_det_limit_side_len"
],
"SubPipelines.GeneralOCR.SubModules.TextDetection.limit_type": self._params[
"text_det_limit_type"
],
"SubPipelines.GeneralOCR.SubModules.TextDetection.thresh": self._params[
"text_det_thresh"
],
"SubPipelines.GeneralOCR.SubModules.TextDetection.box_thresh": self._params[
"text_det_box_thresh"
],
"SubPipelines.GeneralOCR.SubModules.TextDetection.unclip_ratio": self._params[
"text_det_unclip_ratio"
],
"SubPipelines.GeneralOCR.SubModules.TextRecognition.model_name": self._params[
"text_recognition_model_name"
],
"SubPipelines.GeneralOCR.SubModules.TextRecognition.model_dir": self._params[
"text_recognition_model_dir"
],
"SubPipelines.GeneralOCR.SubModules.TextRecognition.batch_size": self._params[
"text_recognition_batch_size"
],
"SubPipelines.GeneralOCR.SubModules.TextRecognition.score_thresh": self._params[
"text_rec_score_thresh"
],
}
return create_config_from_structure(STRUCTURE)
class TableRecognitionPipelineV2CLISubcommandExecutor(PipelineCLISubcommandExecutor):
@property
def subparser_name(self):
return "table_recognition_v2"
def _update_subparser(self, subparser):
add_simple_inference_args(subparser)
subparser.add_argument(
"--layout_detection_model_name",
type=str,
help="Name of the layout detection model.",
)
subparser.add_argument(
"--layout_detection_model_dir",
type=str,
help="Path to the layout detection model directory.",
)
subparser.add_argument(
"--table_classification_model_name",
type=str,
help="Name of the table classification model.",
)
subparser.add_argument(
"--table_classification_model_dir",
type=str,
help="Path to the table classification model directory.",
)
subparser.add_argument(
"--wired_table_structure_recognition_model_name",
type=str,
help="Name of the wired table structure recognition model.",
)
subparser.add_argument(
"--wired_table_structure_recognition_model_dir",
type=str,
help="Path to the wired table structure recognition model directory.",
)
subparser.add_argument(
"--wireless_table_structure_recognition_model_name",
type=str,
help="Name of the wireless table structure recognition model.",
)
subparser.add_argument(
"--wireless_table_structure_recognition_model_dir",
type=str,
help="Path to the wired table structure recognition model directory.",
)
subparser.add_argument(
"--wired_table_cells_detection_model_name",
type=str,
help="Name of the wired table cells detection model.",
)
subparser.add_argument(
"--wired_table_cells_detection_model_dir",
type=str,
help="Path to the wired table cells detection model directory.",
)
subparser.add_argument(
"--wireless_table_cells_detection_model_name",
type=str,
help="Name of the wireless table cells detection model.",
)
subparser.add_argument(
"--wireless_table_cells_detection_model_dir",
type=str,
help="Path to the wireless table cells detection model directory.",
)
subparser.add_argument(
"--doc_orientation_classify_model_name",
type=str,
help="Name of the document image orientation classification model.",
)
subparser.add_argument(
"--doc_orientation_classify_model_dir",
type=str,
help="Path to the document image orientation classification model directory.",
)
subparser.add_argument(
"--doc_unwarping_model_name",
type=str,
help="Name of the text image unwarping model.",
)
subparser.add_argument(
"--doc_unwarping_model_dir",
type=str,
help="Path to the image unwarping model directory.",
)
subparser.add_argument(
"--text_detection_model_name",
type=str,
help="Name of the text detection model.",
)
subparser.add_argument(
"--text_detection_model_dir",
type=str,
help="Path to the text detection model directory.",
)
subparser.add_argument(
"--text_det_limit_side_len",
type=int,
help="This sets a limit on the side length of the input image for the text detection model.",
)
subparser.add_argument(
"--text_det_limit_type",
type=str,
help="This determines how the side length limit is applied to the input image before feeding it into the text deteciton model.",
)
subparser.add_argument(
"--text_det_thresh",
type=float,
help="Detection pixel threshold for the text detection model. Pixels with scores greater than this threshold in the output probability map are considered text pixels.",
)
subparser.add_argument(
"--text_det_box_thresh",
type=float,
help="Detection box threshold for the text detection model. A detection result is considered a text region if the average score of all pixels within the border of the result is greater than this threshold.",
)
subparser.add_argument(
"--text_det_unclip_ratio",
type=float,
help="Text detection expansion coefficient, which expands the text region using this method. The larger the value, the larger the expansion area.",
)
subparser.add_argument(
"--text_recognition_model_name",
type=str,
help="Name of the text recognition model.",
)
subparser.add_argument(
"--text_recognition_model_dir",
type=str,
help="Path to the text recognition model directory.",
)
subparser.add_argument(
"--text_recognition_batch_size",
type=int,
help="Batch size for the text recognition model.",
)
subparser.add_argument(
"--text_rec_score_thresh",
type=float,
help="Text recognition threshold used in general OCR. Text results with scores greater than this threshold are retained.",
)
subparser.add_argument(
"--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_layout_detection",
type=str2bool,
help="Whether to use layout detection.",
)
subparser.add_argument(
"--use_ocr_model",
type=str2bool,
help="Whether to use OCR models.",
)
def execute_with_args(self, args):
params = get_subcommand_args(args)
perform_simple_inference(TableRecognitionPipelineV2, params)