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- # copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
- #
- # 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.
- import numpy as np
- from ...modules.formula_recognition.model_list import MODELS
- from ..components import *
- from ..results import FormulaRecResult
- from .base import BasicPredictor
- class LaTeXOCRPredictor(BasicPredictor):
- entities = MODELS
- def _build_components(self):
- self._add_component(
- [
- ReadImage(format="RGB"),
- LaTeXOCRReisizeNormImg(),
- ]
- )
- predictor = ImagePredictor(
- model_dir=self.model_dir,
- model_prefix=self.MODEL_FILE_PREFIX,
- option=self.pp_option,
- )
- self._add_component(predictor)
- op = self.build_postprocess(**self.config["PostProcess"])
- self._add_component(op)
- def build_postprocess(self, **kwargs):
- if kwargs.get("name") == "LaTeXOCRDecode":
- return LaTeXOCRDecode(
- character_list=kwargs.get("character_dict"),
- )
- else:
- raise Exception()
- def _pack_res(self, single):
- keys = ["input_path", "rec_text"]
- return FormulaRecResult({key: single[key] for key in keys})
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