def make_representation(bounding_boxes,
is_ground_truth=False,
score=None,
meta=None):
"""
Args:
bounding_boxes: string or list of strings `score label x0 y0 x1 y1; label score x0 y0 x1 y1; ...`.
is_ground_truth: True if bbs are annotation boxes.
score: value in [0, 1], if not None, all prediction boxes are considered with the given score.
meta: metadata for representation
"""
if not isinstance(bounding_boxes, list):
bounding_boxes = [bounding_boxes]
result = []
for idx, box in enumerate(bounding_boxes):
if box == "":
arr = np.array([]).reshape((0, 5))
else:
arr = np.array(
[np.fromstring(row, sep=' ') for row in box.split(';')])
if is_ground_truth or score:
assert arr.shape[1] == 5
elif not is_ground_truth and not score:
assert arr.shape[1] == 6
if not is_ground_truth and score:
score_ = score
if np.isscalar(score_) or len(score_) == 1:
score_ = np.full(arr.shape[0], score_)
arr = np.c_[score_, arr]
if is_ground_truth:
detection = DetectionAnnotation(str(idx), arr[:, 0], arr[:, 1],
arr[:, 2], arr[:, 3], arr[:, 4])
else:
detection = DetectionPrediction(str(idx), arr[:, 1], arr[:, 0],
arr[:, 2], arr[:, 3], arr[:, 4],
arr[:, 5])
if meta:
detection.metadata = meta[idx]
result.append(detection)
return result
def test_accuracy_with_wrong_annotation_type_raise_config_error_exception(
self):
annotations = [DetectionAnnotation('identifier', 3)]
predictions = [
ClassificationPrediction('identifier', [1.0, 1.0, 1.0, 4.0])
]
dispatcher = MetricsExecutor([{'type': 'accuracy', 'top_k': 1}], None)
with pytest.raises(ConfigError):
dispatcher.update_metrics_on_batch(range(len(annotations)),
annotations, predictions)
def test_accuracy_with_unsupported_annotation_type_as_annotation_source_for_container_raises_config_error(
self):
annotations = [
ContainerAnnotation(
{'annotation': DetectionAnnotation('identifier', 3)})
]
predictions = [
ClassificationPrediction('identifier', [1.0, 1.0, 1.0, 4.0])
]
dispatcher = MetricsExecutor([{
'type': 'accuracy',
'top_k': 1,
'annotation_source': 'annotation'
}], None)
with pytest.raises(ConfigError):
dispatcher.update_metrics_on_batch(range(len(annotations)),
annotations, predictions)