mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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ae21c3d3a9
If there is no model specified select the best available model for the specified language Fix #15642
615 lines
22 KiB
Python
615 lines
22 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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# TODO: Should we use a third-party CLI library to auto-generate command-line
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# arguments from the pipeline class, to reduce boilerplate and improve
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# maintainability?
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import sys
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import warnings
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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 .._utils.deprecation import (
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DeprecatedOptionAction,
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deprecated,
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warn_deprecated_param,
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)
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from .._utils.logging import logger
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from .base import PaddleXPipelineWrapper, PipelineCLISubcommandExecutor
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from .utils import create_config_from_structure
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_DEPRECATED_PARAM_NAME_MAPPING = {
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"det_model_dir": "text_detection_model_dir",
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"det_limit_side_len": "text_det_limit_side_len",
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"det_limit_type": "text_det_limit_type",
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"det_db_thresh": "text_det_thresh",
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"det_db_box_thresh": "text_det_box_thresh",
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"det_db_unclip_ratio": "text_det_unclip_ratio",
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"rec_model_dir": "text_recognition_model_dir",
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"rec_batch_num": "text_recognition_batch_size",
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"use_angle_cls": "use_textline_orientation",
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"cls_model_dir": "textline_orientation_model_dir",
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"cls_batch_num": "textline_orientation_batch_size",
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}
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_SUPPORTED_OCR_VERSIONS = ["PP-OCRv3", "PP-OCRv4", "PP-OCRv5"]
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# Be comptable with PaddleOCR 2.x interfaces
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class PaddleOCR(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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text_detection_model_name=None,
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text_detection_model_dir=None,
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textline_orientation_model_name=None,
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textline_orientation_model_dir=None,
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textline_orientation_batch_size=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_textline_orientation=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_det_input_shape=None,
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text_rec_score_thresh=None,
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text_rec_input_shape=None,
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lang=None,
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ocr_version=None,
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**kwargs,
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):
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if ocr_version is not None and ocr_version not in _SUPPORTED_OCR_VERSIONS:
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raise ValueError(
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f"Invalid OCR version: {ocr_version}. Supported values are {_SUPPORTED_OCR_VERSIONS}."
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)
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if all(
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map(
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lambda p: p is None,
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(
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text_detection_model_name,
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text_detection_model_dir,
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text_recognition_model_name,
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text_recognition_model_dir,
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),
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)
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):
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if lang is not None or ocr_version is not None:
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det_model_name, rec_model_name = self._get_ocr_model_names(
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lang, ocr_version
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)
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if det_model_name is None or rec_model_name is None:
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raise ValueError(
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f"No models are available for the language {repr(lang)} and OCR version {repr(ocr_version)}."
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)
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text_detection_model_name = det_model_name
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text_recognition_model_name = rec_model_name
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else:
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if lang is not None or ocr_version is not None:
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warnings.warn(
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"`lang` and `ocr_version` will be ignored when model names or model directories are not `None`.",
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stacklevel=2,
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)
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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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"text_detection_model_name": text_detection_model_name,
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"text_detection_model_dir": text_detection_model_dir,
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"textline_orientation_model_name": textline_orientation_model_name,
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"textline_orientation_model_dir": textline_orientation_model_dir,
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"textline_orientation_batch_size": textline_orientation_batch_size,
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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_textline_orientation": use_textline_orientation,
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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_det_input_shape": text_det_input_shape,
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"text_rec_score_thresh": text_rec_score_thresh,
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"text_rec_input_shape": text_rec_input_shape,
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}
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base_params = {}
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for name, val in kwargs.items():
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if name in _DEPRECATED_PARAM_NAME_MAPPING:
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new_name = _DEPRECATED_PARAM_NAME_MAPPING[name]
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warn_deprecated_param(name, new_name)
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assert (
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new_name in params
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), f"{repr(new_name)} is not a valid parameter name."
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if params[new_name] is not None:
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raise ValueError(
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f"`{name}` and `{new_name}` are mutually exclusive."
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)
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params[new_name] = val
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else:
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base_params[name] = val
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self._params = params
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super().__init__(**base_params)
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@property
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def _paddlex_pipeline_name(self):
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return "OCR"
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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_doc_orientation_classify=None,
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use_doc_unwarping=None,
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use_textline_orientation=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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):
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return 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_textline_orientation=use_textline_orientation,
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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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)
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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_textline_orientation=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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):
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return list(
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self.predict_iter(
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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_textline_orientation=use_textline_orientation,
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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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)
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)
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@deprecated("Please use `predict` instead.")
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def ocr(self, img, **kwargs):
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return self.predict(img, **kwargs)
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@classmethod
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def get_cli_subcommand_executor(cls):
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return PaddleOCRCLISubcommandExecutor()
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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.TextDetection.model_name": self._params[
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"text_detection_model_name"
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],
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"SubModules.TextDetection.model_dir": self._params[
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"text_detection_model_dir"
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],
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"SubModules.TextLineOrientation.model_name": self._params[
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"textline_orientation_model_name"
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],
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"SubModules.TextLineOrientation.model_dir": self._params[
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"textline_orientation_model_dir"
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],
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"SubModules.TextLineOrientation.batch_size": self._params[
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"textline_orientation_batch_size"
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],
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"SubModules.TextRecognition.model_name": self._params[
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"text_recognition_model_name"
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],
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"SubModules.TextRecognition.model_dir": self._params[
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"text_recognition_model_dir"
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],
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"SubModules.TextRecognition.batch_size": self._params[
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"text_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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"use_textline_orientation": self._params["use_textline_orientation"],
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"SubModules.TextDetection.limit_side_len": self._params[
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"text_det_limit_side_len"
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],
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"SubModules.TextDetection.limit_type": self._params["text_det_limit_type"],
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"SubModules.TextDetection.thresh": self._params["text_det_thresh"],
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"SubModules.TextDetection.box_thresh": self._params["text_det_box_thresh"],
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"SubModules.TextDetection.unclip_ratio": self._params[
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"text_det_unclip_ratio"
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],
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"SubModules.TextDetection.input_shape": self._params[
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"text_det_input_shape"
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],
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"SubModules.TextRecognition.score_thresh": self._params[
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"text_rec_score_thresh"
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],
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"SubModules.TextRecognition.input_shape": self._params[
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"text_rec_input_shape"
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],
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}
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return create_config_from_structure(STRUCTURE)
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def _get_ocr_model_names(self, lang, ppocr_version):
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LATIN_LANGS = [
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"af",
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"az",
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"bs",
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"cs",
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"cy",
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"da",
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"de",
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"es",
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"et",
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"fr",
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"ga",
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"hr",
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"hu",
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"id",
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"is",
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"it",
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"ku",
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"la",
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"lt",
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"lv",
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"mi",
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"ms",
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"mt",
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"nl",
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"no",
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"oc",
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"pi",
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"pl",
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"pt",
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"ro",
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"rs_latin",
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"sk",
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"sl",
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"sq",
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"sv",
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"sw",
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"tl",
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"tr",
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"uz",
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"vi",
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"french",
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"german",
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]
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ARABIC_LANGS = ["ar", "fa", "ug", "ur"]
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CYRILLIC_LANGS = [
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"ru",
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"rs_cyrillic",
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"be",
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"bg",
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"uk",
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"mn",
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"abq",
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"ady",
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"kbd",
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"ava",
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"dar",
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"inh",
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"che",
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"lbe",
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"lez",
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"tab",
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]
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DEVANAGARI_LANGS = [
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"hi",
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"mr",
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"ne",
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"bh",
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"mai",
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"ang",
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"bho",
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"mah",
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"sck",
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"new",
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"gom",
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"sa",
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"bgc",
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]
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SPECIFIC_LANGS = [
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"ch",
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"en",
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"korean",
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"japan",
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"chinese_cht",
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"te",
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"ka",
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"ta",
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]
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if lang is None:
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lang = "ch"
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if ppocr_version is None:
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if lang in ("ch", "chinese_cht", "en", "japan"):
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ppocr_version = "PP-OCRv5"
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elif lang in (
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LATIN_LANGS
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+ ARABIC_LANGS
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+ CYRILLIC_LANGS
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+ DEVANAGARI_LANGS
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+ SPECIFIC_LANGS
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):
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ppocr_version = "PP-OCRv3"
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else:
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# Unknown language specified
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return None, None
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if ppocr_version == "PP-OCRv5":
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if lang in ("ch", "chinese_cht", "en", "japan"):
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return "PP-OCRv5_server_det", "PP-OCRv5_server_rec"
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else:
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return None, None
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elif ppocr_version == "PP-OCRv4":
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if lang == "ch":
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return "PP-OCRv4_mobile_det", "PP-OCRv4_mobile_rec"
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elif lang == "en":
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return "PP-OCRv4_mobile_det", "en_PP-OCRv4_mobile_rec"
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else:
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return None, None
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else:
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# PP-OCRv3
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rec_lang = None
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if lang in LATIN_LANGS:
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rec_lang = "latin"
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elif lang in ARABIC_LANGS:
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rec_lang = "arabic"
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elif lang in CYRILLIC_LANGS:
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rec_lang = "cyrillic"
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elif lang in DEVANAGARI_LANGS:
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rec_lang = "devanagari"
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else:
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if lang in SPECIFIC_LANGS:
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rec_lang = lang
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rec_model_name = None
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if rec_lang == "ch":
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rec_model_name = "PP-OCRv3_mobile_rec"
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elif rec_lang is not None:
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rec_model_name = f"{rec_lang}_PP-OCRv3_mobile_rec"
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return "PP-OCRv3_mobile_det", rec_model_name
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class PaddleOCRCLISubcommandExecutor(PipelineCLISubcommandExecutor):
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@property
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def subparser_name(self):
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return "ocr"
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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 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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"--textline_orientation_model_name",
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type=str,
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help="Name of the text line orientation classification model.",
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)
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subparser.add_argument(
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"--textline_orientation_model_dir",
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type=str,
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help="Path to the text line orientation classification model directory.",
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)
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subparser.add_argument(
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"--textline_orientation_batch_size",
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type=int,
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help="Batch size for the text line orientation classification model.",
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)
|
|
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(
|
|
"--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(
|
|
"--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_det_input_shape",
|
|
nargs=3,
|
|
type=int,
|
|
metavar=("C", "H", "W"),
|
|
help="Input shape of the text detection model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--text_rec_score_thresh",
|
|
type=float,
|
|
help="Text recognition threshold. Text results with scores greater than this threshold are retained.",
|
|
)
|
|
subparser.add_argument(
|
|
"--text_rec_input_shape",
|
|
nargs=3,
|
|
type=int,
|
|
metavar=("C", "H", "W"),
|
|
help="Input shape of the text recognition model.",
|
|
)
|
|
subparser.add_argument(
|
|
"--lang", type=str, help="Language in the input image for OCR processing."
|
|
)
|
|
subparser.add_argument(
|
|
"--ocr_version",
|
|
type=str,
|
|
choices=_SUPPORTED_OCR_VERSIONS,
|
|
help="PP-OCR version to use.",
|
|
)
|
|
|
|
deprecated_arg_types = {
|
|
"det_model_dir": str,
|
|
"det_limit_side_len": int,
|
|
"det_limit_type": str,
|
|
"det_db_thresh": float,
|
|
"det_db_box_thresh": float,
|
|
"det_db_unclip_ratio": float,
|
|
"rec_model_dir": str,
|
|
"rec_batch_num": int,
|
|
"use_angle_cls": str2bool,
|
|
"cls_model_dir": str,
|
|
"cls_batch_num": int,
|
|
}
|
|
|
|
for name, new_name in _DEPRECATED_PARAM_NAME_MAPPING.items():
|
|
assert name in deprecated_arg_types, name
|
|
subparser.add_argument(
|
|
"--" + name,
|
|
action=DeprecatedOptionAction,
|
|
type=str,
|
|
help=f"[Deprecated] Please use `--{new_name}` instead.",
|
|
)
|
|
|
|
def execute_with_args(self, args):
|
|
params = get_subcommand_args(args)
|
|
for name, new_name in _DEPRECATED_PARAM_NAME_MAPPING.items():
|
|
assert name in params
|
|
val = params[name]
|
|
new_val = params[new_name]
|
|
if val is not None and new_val is not None:
|
|
logger.error(
|
|
"`--%s` and `--%s` are mutually exclusive.", name, new_name
|
|
)
|
|
sys.exit(2)
|
|
if val is None:
|
|
params.pop(name)
|
|
|
|
perform_simple_inference(PaddleOCR, params)
|