:py:mod:`anomalib.utils.callbacks.visualizer.visualizer_base`
=============================================================

.. py:module:: anomalib.utils.callbacks.visualizer.visualizer_base

.. autoapi-nested-parse::

   Base Visualizer Callback.



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   anomalib.utils.callbacks.visualizer.visualizer_base.BaseVisualizerCallback




.. py:class:: BaseVisualizerCallback(task: str, mode: str, image_save_path: str, inputs_are_normalized: bool = True, show_images: bool = False, log_images: bool = True, save_images: bool = True)

   Bases: :py:obj:`pytorch_lightning.Callback`

   Callback that visualizes the results of a model.

   To save the images to the filesystem, add the 'local' keyword to the `project.log_images_to` parameter in the
   config.yaml file.

   .. py:method:: _add_to_logger(image: numpy.ndarray, module: anomalib.models.components.AnomalyModule, trainer: pytorch_lightning.Trainer, filename: Union[pathlib.Path, str])

      Log image from a visualizer to each of the available loggers in the project.

      :param image: Image that should be added to the loggers.
      :type image: np.ndarray
      :param module: Anomaly module.
      :type module: AnomalyModule
      :param trainer: Pytorch Lightning trainer which holds reference to `logger`
      :type trainer: Trainer
      :param filename: Path of the input image. This name is used as name for the generated image.
      :type filename: Path


   .. py:method:: on_test_end(trainer: pytorch_lightning.Trainer, pl_module: anomalib.models.components.AnomalyModule) -> None

      Sync logs.

      Currently only ``AnomalibWandbLogger.save`` is called from this method.
      This is because logging as a single batch ensures that all images appear as part of the same step.

      :param trainer: Pytorch Lightning trainer
      :type trainer: pl.Trainer
      :param pl_module: Anomaly module (unused)
      :type pl_module: AnomalyModule