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Confusion Matrix

class ConfusionMatrix

Calculate and visualize confusion matrix of Object Detection model.

from_detections

def from_detections(cls, true_batches, detection_batches, num_classes, conf_threshold, iou_threshold)
Calculate confusion matrix based on ground-true and detected objects across all images in concerned dataset.

Args

  • true_batches: List[np.ndarray] representing ground-truth objects across all images in concerned dataset. Each element of true_batches list describe single image and has shape = (N, 5) where N is number of ground-truth objects. Each row is expected to be in (x_min, y_min, x_max, y_max, class).

  • detection_batches: List[np.ndarray] representing detected objects across all images in concerned dataset. Each element of detection_batches list describe single image and has shape = (M, 6) where M is number of detected objects. Each row is expected to be in (x_min, y_min, x_max, y_max, class, conf).

  • num_classes: int number of classes detected by model.

  • conf_threshold: float detection confidence threshold between 0 and 1. Detections with lower confidence will be excluded.

  • iou_threshold: float detection iou threshold between 0 and 1. Detections with lower iou will be classified as FP.

Returns
  • confusion_matrix: ConfusionMatrix object raw confusion matrix 2d np.ndarray.
Example usage
>>> import numpy as np

>>> from onemetric.cv.object_detection import ConfusionMatrix

>>> true_batches = [
...     np.array([
...         [0.0, 0.0, 3.0, 3.0, 1],
...         [2.0, 2.0, 5.0, 5.0, 1],
...         [6.0, 1.0, 8.0, 3.0, 2],
...     ]),
...     np.array([
...         [1.0, 1.0, 2.0, 2.0, 2],
...     ]),
... ]

>>> detection_batches = [
...     np.array([
...         [0.0, 0.0, 3.0, 3.0, 1, 0.9],
...         [0.1, 0.1, 3.0, 3.0, 0, 0.9],
...         [6.0, 1.0, 8.0, 3.0, 1, 0.8],
...         [1.0, 6.0, 2.0, 7.0, 1, 0.8],
...     ]),
...     np.array([
...         [1.0, 1.0, 2.0, 2.0, 2, 0.8],
...     ]),
... ]

>>> confusion_matrix = ConfusionMatrix.from_detections(
...     true_batches=true_batches,
...     detection_batches=detection_batches,
...     num_classes=3
... )

>>> confusion_matrix.matrix
... array([
...     [0., 0., 0., 0.],
...     [0., 1., 0., 1.],
...     [0., 1., 1., 0.],
...     [1., 1., 0., 0.]
... ])

plot

def plot(target_path, title, class_names, normalize)
Create confusion matrix plot and save it at selected location.

Args

  • target_path: str selected target location of confusion matrix plot.

  • title: Optional[str] title displayed at the top of the confusion matrix plot. Default None.

  • class_names: Optional[List[str]] list of class names detected my model. If non given class indexes will be used. Default None.

  • normalize: bool if set to False chart will display absolute number of detections falling into given category. Otherwise percentage of detections will be displayed.