def test_metrics_perfect_prediction():
bb = BBox(0, 5, 0, 5).normalize(10, 10)
annotations = [
AnnotatedBBox.ground_truth("a", bb),
AnnotatedBBox.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 = [
AnnotatedBBox.prediction("a", bb, 0.9),
AnnotatedBBox.prediction("b", bb, 0.9),
AnnotatedBBox.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 = [
AnnotatedBBox.ground_truth("a", BBox(0, 5, 0, 5)),
AnnotatedBBox.prediction("a", BBox(0, 5, 0, 5), 0.9),
AnnotatedBBox.ground_truth("b", BBox(0, 5, 0, 5)),
AnnotatedBBox.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 = [
AnnotatedBBox.ground_truth("a", bbox),
AnnotatedBBox.prediction("a", bbox, 0.9),
AnnotatedBBox.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 = [
AnnotatedBBox.ground_truth("a", bbox),
AnnotatedBBox.prediction("a", bbox, 0.9),
]
image_b = [
AnnotatedBBox.ground_truth("a", bbox),
AnnotatedBBox.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 = [
AnnotatedBBox.ground_truth("a", bbox),
AnnotatedBBox.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_do_not_contain_numpy_type():
annotations = [
AnnotatedBBox.ground_truth("a", BBox(0, 5, 0, 5)),
AnnotatedBBox.prediction("a", BBox(0, 5, 0, 5), 0.9),
AnnotatedBBox.ground_truth("b", BBox(0, 5, 0, 5)),
AnnotatedBBox.prediction("b", BBox(5, 6, 5, 6), 0.9),
]
metrics = get_metrics([annotations], iou_threshold=0.9)
assert not isinstance(metrics.mAP, np.floating)
for value in metrics.per_class.values():
for item in value.precision:
assert not isinstance(item, np.floating)
for item in value.recall:
assert not isinstance(item, np.floating)
for item in value.interpolated_precision:
assert not isinstance(item, np.floating)
for item in value.interpolated_recall:
assert not isinstance(item, np.floating)
assert not isinstance(value.AP, np.floating)
assert not isinstance(value.total_GT, np.integer)
assert not isinstance(value.total_TP, np.integer)
assert not isinstance(value.total_FP, np.integer)
def test_metrics_missing_prediction():
bb = BBox(0, 5, 0, 5).normalize(10, 10)
annotations = [AnnotatedBBox.ground_truth("a", bb)]
metrics = get_metrics([annotations])
assert metrics.mAP == 0.0