You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

722 lines
29 KiB

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