:py:mod:`anomalib.post_processing.post_process`
===============================================

.. py:module:: anomalib.post_processing.post_process

.. autoapi-nested-parse::

   Post Process This module contains utils function to apply post-processing to the output predictions.



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


Functions
~~~~~~~~~

.. autoapisummary::

   anomalib.post_processing.post_process.add_label
   anomalib.post_processing.post_process.add_normal_label
   anomalib.post_processing.post_process.add_anomalous_label
   anomalib.post_processing.post_process.anomaly_map_to_color_map
   anomalib.post_processing.post_process.superimpose_anomaly_map
   anomalib.post_processing.post_process.compute_mask



.. py:function:: add_label(image: numpy.ndarray, label_name: str, color: Tuple[int, int, int], confidence: Optional[float] = None, font_scale: float = 0.005, thickness_scale=0.001)

   Adds a label to an image.

   :param image: Input image.
   :type image: np.ndarray
   :param label_name: Name of the label that will be displayed on the image.
   :type label_name: str
   :param color: RGB values for background color of label.
   :type color: Tuple[int, int, int]
   :param confidence: confidence score of the label.
   :type confidence: Optional[float]
   :param font_scale: scale of the font size relative to image size. Increase for bigger font.
   :type font_scale: float
   :param thickness_scale: scale of the font thickness. Increase for thicker font.
   :type thickness_scale: float

   :returns: Image with label.
   :rtype: np.ndarray


.. py:function:: add_normal_label(image: numpy.ndarray, confidence: Optional[float] = None)

   Adds the normal label to the image.


.. py:function:: add_anomalous_label(image: numpy.ndarray, confidence: Optional[float] = None)

   Adds the anomalous label to the image.


.. py:function:: anomaly_map_to_color_map(anomaly_map: numpy.ndarray, normalize: bool = True) -> numpy.ndarray

   Compute anomaly color heatmap.

   :param anomaly_map: Final anomaly map computed by the distance metric.
   :type anomaly_map: np.ndarray
   :param normalize: Bool to normalize the anomaly map prior to applying
                     the color map. Defaults to True.
   :type normalize: bool, optional

   :returns: [description]
   :rtype: np.ndarray


.. py:function:: superimpose_anomaly_map(anomaly_map: numpy.ndarray, image: numpy.ndarray, alpha: float = 0.4, gamma: int = 0, normalize: bool = False) -> numpy.ndarray

   Superimpose anomaly map on top of in the input image.

   :param anomaly_map: Anomaly map
   :type anomaly_map: np.ndarray
   :param image: Input image
   :type image: np.ndarray
   :param alpha: Weight to overlay anomaly map
                 on the input image. Defaults to 0.4.
   :type alpha: float, optional
   :param gamma: Value to add to the blended image
                 to smooth the processing. Defaults to 0. Overall,
                 the formula to compute the blended image is
                 I' = (alpha*I1 + (1-alpha)*I2) + gamma
   :type gamma: int, optional
   :param normalize: whether or not the anomaly maps should
                     be normalized to image min-max

   :returns: Image with anomaly map superimposed on top of it.
   :rtype: np.ndarray


.. py:function:: compute_mask(anomaly_map: numpy.ndarray, threshold: float, kernel_size: int = 4) -> numpy.ndarray

   Compute anomaly mask via thresholding the predicted anomaly map.

   :param anomaly_map: Anomaly map predicted via the model
   :type anomaly_map: np.ndarray
   :param threshold: Value to threshold anomaly scores into 0-1 range.
   :type threshold: float
   :param kernel_size: Value to apply morphological operations to the predicted mask. Defaults to 4.
   :type kernel_size: int

   :returns: Predicted anomaly mask