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

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

.. autoapi-nested-parse::

   Image Visualizer Callback.



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

Classes
~~~~~~~

.. autoapisummary::

   anomalib.utils.callbacks.visualizer.visualizer_image.ImageVisualizerCallback




.. py:class:: ImageVisualizerCallback(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:`anomalib.utils.callbacks.visualizer.visualizer_base.BaseVisualizerCallback`

   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:: on_predict_batch_end(_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(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