zensvi.cv.ClassifierQuality
===========================

.. py:class:: zensvi.cv.ClassifierQuality(device=None, verbosity=1)

   Bases: :py:obj:`zensvi.cv.classification.base.BaseClassifier`


   A classifier for identifying quality. The model is from Hou et al (2024) (https://github.com/ualsg/global-streetscapes).

   :param device: The device that the model should be loaded onto. Options are "cpu", "cuda", or "mps".
                  If `None`, the model tries to use a GPU if available; otherwise, falls back to CPU.
   :type device: str, optional
   :param verbosity: Level of verbosity for progress bars. Defaults to 1.
                     0 = no progress bars, 1 = outer loops only, 2 = all loops.
   :type verbosity: int, optional


   .. py:method:: classify(dir_input: Union[str, pathlib.Path], dir_summary_output: Union[str, pathlib.Path], batch_size=1, save_format='json csv', verbosity: int = None) -> List[str]

      Classifies images based on quality.

      :param dir_input: Directory containing input images or path to a single image.
      :type dir_input: Union[str, Path]
      :param dir_summary_output: Directory to save summary output.
      :type dir_summary_output: Union[str, Path]
      :param batch_size: Batch size for inference. Defaults to 1.
      :type batch_size: int, optional
      :param save_format: Space-separated string of formats to save results.
                          Options are "json" and/or "csv". Defaults to "json csv".
      :type save_format: str, optional
      :param verbosity: Level of verbosity for progress bars.
                        If None, uses the instance's verbosity level.
                        0 = no progress bars, 1 = outer loops only, 2 = all loops.
      :type verbosity: int, optional

      :returns: List of classification results.
      :rtype: List[str]



   .. py:property:: verbosity

      Property for the verbosity level of progress bars.

      :returns: verbosity level (0=no progress, 1=outer loops only, 2=all loops)
      :rtype: int