# 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 SealRecognition(PaddleXPipelineWrapper): def __init__( self, doc_orientation_classify_model_name=None, doc_orientation_classify_model_dir=None, doc_unwarping_model_name=None, doc_unwarping_model_dir=None, layout_detection_model_name=None, layout_detection_model_dir=None, seal_text_detection_model_name=None, seal_text_detection_model_dir=None, text_recognition_model_name=None, text_recognition_model_dir=None, text_recognition_batch_size=None, use_doc_orientation_classify=None, use_doc_unwarping=None, use_layout_detection=None, layout_threshold=None, layout_nms=None, layout_unclip_ratio=None, layout_merge_bboxes_mode=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, ): self._params = { "doc_orientation_classify_model_name": doc_orientation_classify_model_name, "doc_orientation_classify_model_dir": doc_orientation_classify_model_dir, "doc_unwarping_model_name": doc_unwarping_model_name, "doc_unwarping_model_dir": doc_unwarping_model_dir, "layout_detection_model_name": layout_detection_model_name, "layout_detection_model_dir": layout_detection_model_dir, "seal_text_detection_model_name": seal_text_detection_model_name, "seal_text_detection_model_dir": seal_text_detection_model_dir, "text_recognition_model_name": text_recognition_model_name, "text_recognition_model_dir": text_recognition_model_dir, "text_recognition_batch_size": text_recognition_batch_size, "use_doc_orientation_classify": use_doc_orientation_classify, "use_doc_unwarping": use_doc_unwarping, "use_layout_detection": use_layout_detection, "layout_threshold": layout_threshold, "layout_nms": layout_nms, "layout_unclip_ratio": layout_unclip_ratio, "layout_merge_bboxes_mode": layout_merge_bboxes_mode, "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, } super().__init__(**kwargs) @property def _paddlex_pipeline_name(self): return "seal_recognition" def predict_iter( self, input, *, use_doc_orientation_classify=None, use_doc_unwarping=None, use_layout_detection=None, layout_det_res=None, layout_threshold=None, layout_nms=None, layout_unclip_ratio=None, layout_merge_bboxes_mode=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.predict( input, use_doc_orientation_classify=use_doc_orientation_classify, use_doc_unwarping=use_doc_unwarping, use_layout_detection=use_layout_detection, layout_det_res=layout_det_res, layout_threshold=layout_threshold, layout_nms=layout_nms, layout_unclip_ratio=layout_unclip_ratio, layout_merge_bboxes_mode=layout_merge_bboxes_mode, 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 predict( self, input, *, use_doc_orientation_classify=None, use_doc_unwarping=None, use_layout_detection=None, layout_det_res=None, layout_threshold=None, layout_nms=None, layout_unclip_ratio=None, layout_merge_bboxes_mode=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.predict_iter( input, use_doc_orientation_classify=use_doc_orientation_classify, use_doc_unwarping=use_doc_unwarping, use_layout_detection=use_layout_detection, layout_det_res=layout_det_res, layout_threshold=layout_threshold, layout_nms=layout_nms, layout_unclip_ratio=layout_unclip_ratio, layout_merge_bboxes_mode=layout_merge_bboxes_mode, 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, ) ) @classmethod def get_cli_subcommand_executor(cls): return SealRecognitionCLISubcommandExecutor() def _get_paddlex_config_overrides(self): STRUCTURE = { "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" ], "SubModules.LayoutDetection.model_name": self._params[ "layout_detection_model_name" ], "SubModules.LayoutDetection.model_dir": self._params[ "layout_detection_model_dir" ], "SubModules.LayoutDetection.threshold": self._params["layout_threshold"], "SubModules.LayoutDetection.layout_nms": self._params["layout_nms"], "SubModules.LayoutDetection.layout_unclip_ratio": self._params[ "layout_unclip_ratio" ], "SubModules.LayoutDetection.layout_merge_bboxes_mode": self._params[ "layout_merge_bboxes_mode" ], "SubPipelines.DocPreprocessor.use_doc_orientation_classify": self._params[ "use_doc_orientation_classify" ], "SubPipelines.DocPreprocessor.use_doc_unwarping": self._params[ "use_doc_unwarping" ], "use_doc_preprocessor": self._params["use_doc_orientation_classify"] or self._params["use_doc_unwarping"], "SubPipelines.SealOCR.SubModules.TextDetection.model_name": self._params[ "seal_text_detection_model_name" ], "SubPipelines.SealOCR.SubModules.TextDetection.model_dir": self._params[ "seal_text_detection_model_dir" ], "SubPipelines.SealOCR.SubModules.TextDetection.limit_side_len": self._params[ "seal_det_limit_side_len" ], "SubPipelines.SealOCR.SubModules.TextDetection.limit_type": self._params[ "seal_det_limit_type" ], "SubPipelines.SealOCR.SubModules.TextDetection.thresh": self._params[ "seal_det_thresh" ], "SubPipelines.SealOCR.SubModules.TextDetection.box_thresh": self._params[ "seal_det_box_thresh" ], "SubPipelines.SealOCR.SubModules.TextDetection.unclip_ratio": self._params[ "seal_det_unclip_ratio" ], "SubPipelines.SealOCR.SubModules.TextRecognition.model_name": self._params[ "text_recognition_model_name" ], "SubPipelines.SealOCR.SubModules.TextRecognition.model_dir": self._params[ "text_recognition_model_dir" ], "SubPipelines.SealOCR.SubModules.TextRecognition.batch_size": self._params[ "text_recognition_batch_size" ], "SubPipelines.SealOCR.SubModules.TextRecognition.score_thresh": self._params[ "seal_rec_score_thresh" ], "use_layout_detection": self._params["use_layout_detection"], } return create_config_from_structure(STRUCTURE) class SealRecognitionCLISubcommandExecutor(PipelineCLISubcommandExecutor): @property def subparser_name(self): return "seal_recognition" def _update_subparser(self, subparser): add_simple_inference_args(subparser) 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 document image unwarping model.", ) subparser.add_argument( "--doc_unwarping_model_dir", type=str, help="Path to the document image unwarping model directory.", ) 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( "--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( "--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 document image unwarping.", ) subparser.add_argument( "--use_layout_detection", type=str2bool, help="Whether to use layout detection.", ) subparser.add_argument( "--layout_threshold", type=float, help="Threshold for layout detection model.", ) subparser.add_argument( "--layout_nms", type=str2bool, help="Non-Maximum Suppression threshold for layout detection.", ) subparser.add_argument( "--layout_unclip_ratio", type=float, help="Layout detection expansion coefficient.", ) subparser.add_argument( "--layout_merge_bboxes_mode", type=str, help="Mode for merging bounding boxes in layout detection.", ) 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 detection 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="Text recognition threshold. Text results with scores greater than this threshold are retained.", ) def execute_with_args(self, args): params = get_subcommand_args(args) perform_simple_inference(SealRecognition, params)