compute_metrics

compute_metrics(gt_labels: ObjectDetectionLabels, pred_labels: ObjectDetectionLabels, num_classes: int, iou_thresh: float = 0.5) → tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]

Compute per-class true positives, false positives, and false negatives.

Does the following:

1. Spatially join ground truth (GT) boxes with predicted boxes.

2. Compute intersection-overo-union (IoU) for each matched box-pair.

3. Filter matches by iou_thresh.

4. For each GT box >1 matches, keep only the max-IoU one.

5. For each pred box >1 matches, keep only the max-IoU one.

6. For each class, c, compute:

   1. True positives (TP) := #matches where GT class ID == c and pred class ID == c

   2. False positives := #preds where (class ID == c) minus TP

   3. False negatives := #GT where (class ID == c) minus TP

Parameters:

• gt_labels (ObjectDetectionLabels) –

• pred_labels (ObjectDetectionLabels) –

• num_classes (int) –

• iou_thresh (float) –

Return type:

tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]