:py:mod:`anomalib.utils.callbacks.visualizer_callback`
======================================================

.. py:module:: anomalib.utils.callbacks.visualizer_callback

.. autoapi-nested-parse::

   Visualizer Callback.



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

Classes
~~~~~~~

.. autoapisummary::

   anomalib.utils.callbacks.visualizer_callback.VisualizerCallback




.. py:class:: VisualizerCallback(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 inference results of a model.

   The callback generates a figure showing the original image, the ground truth segmentation mask,
   the predicted error heat map, and the predicted segmentation mask.

   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(self, image: numpy.ndarray, module: anomalib.models.components.AnomalyModule, trainer: pytorch_lightning.Trainer, filename: pathlib.Path)

      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_predict_batch_end(self, _trainer: pytorch_lightning.Trainer, _pl_module: anomalib.models.components.AnomalyModule, outputs: Optional[pytorch_lightning.utilities.types.STEP_OUTPUT], _batch: Any, _batch_idx: int, _dataloader_idx: int) -> None

      Show images at the end of every batch.

      :param _trainer: Pytorch lightning trainer object (unused).
      :type _trainer: Trainer
      :param _pl_module: Lightning modules derived from BaseAnomalyLightning object as
      :type _pl_module: LightningModule
      :param currently only they support logging images.:
      :param outputs: Outputs of the current test step.
      :type outputs: Dict[str, Any]
      :param _batch: Input batch of the current test step (unused).
      :type _batch: Any
      :param _batch_idx: Index of the current test batch (unused).
      :type _batch_idx: int
      :param _dataloader_idx: Index of the dataloader that yielded the current batch (unused).
      :type _dataloader_idx: int


   .. py:method:: on_test_batch_end(self, trainer: pytorch_lightning.Trainer, pl_module: anomalib.models.components.AnomalyModule, outputs: Optional[pytorch_lightning.utilities.types.STEP_OUTPUT], _batch: Any, _batch_idx: int, _dataloader_idx: int) -> None

      Log images at the end of every batch.

      :param trainer: Pytorch lightning trainer object (unused).
      :type trainer: Trainer
      :param pl_module: Lightning modules derived from BaseAnomalyLightning object as
      :type pl_module: LightningModule
      :param currently only they support logging images.:
      :param outputs: Outputs of the current test step.
      :type outputs: Dict[str, Any]
      :param _batch: Input batch of the current test step (unused).
      :type _batch: Any
      :param _batch_idx: Index of the current test batch (unused).
      :type _batch_idx: int
      :param _dataloader_idx: Index of the dataloader that yielded the current batch (unused).
      :type _dataloader_idx: int


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

      Sync logs.

      Currently only ``AnomalibWandbLogger`` 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 (unused)
      :type _trainer: pl.Trainer
      :param pl_module: Anomaly module
      :type pl_module: AnomalyModule