def test_metrics_perfect_prediction(): bb = BBox(0, 5, 0, 5).normalize(10, 10) annotations = [ Annotation.ground_truth("a", bb), Annotation.prediction("a", bb, 0.9), ] metrics = get_metrics([annotations]) assert metrics.mAP == 1.0
def test_metrics_missing_gt(): bb = BBox(0, 5, 0, 5).normalize(10, 10) annotations = [ Annotation.prediction("a", bb, 0.9), Annotation.prediction("b", bb, 0.9), Annotation.prediction("b", bb, 0.8), ] metrics = get_metrics([annotations]) assert metrics.per_class["a"].total_FP == 1 assert metrics.per_class["b"].total_FP == 2 assert metrics.mAP == 0.0
def test_per_class_map(): annotations = [ Annotation.ground_truth("a", BBox(0, 5, 0, 5)), Annotation.prediction("a", BBox(0, 5, 0, 5), 0.9), Annotation.ground_truth("b", BBox(0, 5, 0, 5)), Annotation.prediction("b", BBox(5, 6, 5, 6), 0.9), ] metrics = get_metrics([annotations], iou_threshold=0.9) assert metrics.per_class["a"].AP == 1 assert metrics.per_class["b"].AP == 0 assert metrics.mAP == 0.5
def test_metrics_two_predictions_one_gt_2(): width, height = 10, 10 bbox = BBox(0, 2, 0, 2).normalize(width, height) annotations = [ Annotation.ground_truth("a", bbox), Annotation.prediction("a", bbox, 0.9), Annotation.prediction("a", bbox.move(0.5, 0.5), 0.5), ] metrics = get_metrics([annotations]) assert metrics.per_class["a"].total_FP == 1 assert metrics.per_class["a"].total_TP == 1 assert metrics.mAP == 1
def test_metrics_multiple_images_perfect_prediction(): width, height = 10, 10 bbox = BBox(0, 5, 0, 5).normalize(width, height) image_a = [ Annotation.ground_truth("a", bbox), Annotation.prediction("a", bbox, 0.9), ] image_b = [ Annotation.ground_truth("a", bbox), Annotation.prediction("a", bbox, 0.9), ] metrics = get_metrics([image_a, image_b]) assert metrics.mAP == 1.0
def test_iou_threshold(): bbox = BBox(0, 5, 0, 5) annotations = [ Annotation.ground_truth("a", bbox), Annotation.prediction("a", bbox.move(2.5, 0), 0.9), ] metrics = get_metrics([annotations], iou_threshold=0.9) assert metrics.per_class["a"].total_FP == 1 assert metrics.per_class["a"].total_TP == 0 assert metrics.mAP == 0 metrics = get_metrics([annotations], iou_threshold=0.2) assert metrics.per_class["a"].total_FP == 0 assert metrics.per_class["a"].total_TP == 1 assert metrics.mAP == 1
def test_metrics_missing_prediction(): bb = BBox(0, 5, 0, 5).normalize(10, 10) annotations = [Annotation.ground_truth("a", bb)] metrics = get_metrics([annotations]) assert metrics.mAP == 0.0