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from typing import Optional, Union, Tuple |
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from pathlib import Path |
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import logging |
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from cnstd.yolo_detector import YoloDetector |
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from .consts import AVAILABLE_MODELS |
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from .utils import data_dir, prepare_model_files |
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logger = logging.getLogger(__name__) |
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BACKEND_TO_EXTENSION_MAPPING = { |
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'pytorch': 'pt', |
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'onnx': 'onnx', |
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'coreml': 'mlpackage', |
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'torchscript': 'torchscript', |
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} |
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class MathFormulaDetector(YoloDetector): |
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def __init__( |
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self, |
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*, |
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model_name: str = 'mfd-1.5', |
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model_backend: str = 'onnx', |
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device: Optional[str] = None, |
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model_path: Optional[Union[str, Path]] = None, |
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root: Union[str, Path] = data_dir(), |
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static_resized_shape: Optional[Union[int, Tuple[int, int]]] = None, |
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**kwargs, |
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): |
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""" |
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Math Formula Detector based on YOLO. |
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Args: |
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model_name (str): model name, default is 'mfd-1.5'. |
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model_backend (str): model backend, default is 'onnx'. |
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device (optional str): device to use, default is None. |
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model_path (optional str): model path, default is None. |
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root (optional str): root directory to save model files, default is data_dir(). |
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static_resized_shape (optional int or tuple): static resized shape, default is None. |
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When it is not None, the input image will be resized to this shape before detection, |
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ignoring the input parameter `resized_shape` if .detect() is called. |
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Some format of models may require a fixed input size, such as CoreML. |
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**kwargs (): other parameters. |
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""" |
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if model_path is None: |
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model_info = AVAILABLE_MODELS.get_info(model_name, model_backend) |
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model_path = prepare_model_files(root, model_info) |
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extension = BACKEND_TO_EXTENSION_MAPPING.get(model_backend, model_backend) |
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cand_paths = find_files(model_path, f'.{extension}') |
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if not cand_paths: |
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raise FileNotFoundError(f'can not find available file in {model_path}') |
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model_path = cand_paths[0] |
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logger.info(f'Use model path for MFD: {model_path}') |
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super().__init__( |
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model_path=model_path, |
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device=device, |
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static_resized_shape=static_resized_shape, |
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**kwargs, |
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) |
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def find_files(directory, extension): |
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dir_path = Path(directory) |
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pattern = f"*mfd*{extension}" |
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outs = [] |
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for file_path in dir_path.rglob(pattern): |
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if not file_path.name.startswith('.') or file_path.suffix == extension: |
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outs.append(file_path) |
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return outs |
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