anomalib.post_processing.post_process

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

Module Contents

Functions

add_label(image, label_name, color[, confidence, ...])

Adds a label to an image.

add_normal_label(image[, confidence])

Adds the normal label to the image.

add_anomalous_label(image[, confidence])

Adds the anomalous label to the image.

anomaly_map_to_color_map(→ numpy.ndarray)

Compute anomaly color heatmap.

superimpose_anomaly_map(→ numpy.ndarray)

Superimpose anomaly map on top of in the input image.

compute_mask(→ numpy.ndarray)

Compute anomaly mask via thresholding the predicted anomaly map.
anomalib.post_processing.post_process.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)[source]

Adds a label to an image.

Parameters

  • image (np.ndarray) – Input image.

  • label_name (str) – Name of the label that will be displayed on the image.

  • color (Tuple[int, int, int]) – RGB values for background color of label.

  • confidence (Optional[float]) – confidence score of the label.

  • font_scale (float) – scale of the font size relative to image size. Increase for bigger font.

  • thickness_scale (float) – scale of the font thickness. Increase for thicker font.

Returns

Image with label.

Return type

np.ndarray

anomalib.post_processing.post_process.add_normal_label(image: numpy.ndarray, confidence: Optional[float] = None)[source]

Adds the normal label to the image.

anomalib.post_processing.post_process.add_anomalous_label(image: numpy.ndarray, confidence: Optional[float] = None)[source]

Adds the anomalous label to the image.

anomalib.post_processing.post_process.anomaly_map_to_color_map(anomaly_map: numpy.ndarray, normalize: bool = True) → numpy.ndarray[source]

Compute anomaly color heatmap.

Parameters

  • anomaly_map (np.ndarray) – Final anomaly map computed by the distance metric.

  • normalize (bool, optional) – Bool to normalize the anomaly map prior to applying the color map. Defaults to True.

Returns

[description]

Return type

np.ndarray

anomalib.post_processing.post_process.superimpose_anomaly_map(anomaly_map: numpy.ndarray, image: numpy.ndarray, alpha: float = 0.4, gamma: int = 0, normalize: bool = False) → numpy.ndarray[source]

Superimpose anomaly map on top of in the input image.

Parameters

  • anomaly_map (np.ndarray) – Anomaly map

  • image (np.ndarray) – Input image

  • alpha (float, optional) – Weight to overlay anomaly map on the input image. Defaults to 0.4.

  • gamma (int, optional) – 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

  • normalize – whether or not the anomaly maps should be normalized to image min-max

Returns

Image with anomaly map superimposed on top of it.

Return type

np.ndarray

anomalib.post_processing.post_process.compute_mask(anomaly_map: numpy.ndarray, threshold: float, kernel_size: int = 4) → numpy.ndarray[source]

Compute anomaly mask via thresholding the predicted anomaly map.

Parameters

  • anomaly_map (np.ndarray) – Anomaly map predicted via the model

  • threshold (float) – Value to threshold anomaly scores into 0-1 range.

  • kernel_size (int) – Value to apply morphological operations to the predicted mask. Defaults to 4.

Returns

Predicted anomaly mask