def test_labels_predicted_hdf5(multi_skel_vid_labels, tmpdir):
labels = multi_skel_vid_labels
filename = os.path.join(tmpdir, "test.h5")
# Lets promote some of these Instances to predicted instances
for label in labels:
for i, instance in enumerate(label.instances):
if i % 2 == 0:
label.instances[i] = PredictedInstance.from_instance(
instance, 0.3)
# Lets also add some from_predicted values
for label in labels:
label.instances[1].from_predicted = label.instances[0]
# Try adding a node to the skeleton
labels.skeletons[0].add_node("new node")
# Save and compare the results
Labels.save_hdf5(filename=filename, labels=labels)
loaded_labels = Labels.load_hdf5(filename=filename)
_check_labels_match(labels, loaded_labels)
# Try deleting nodes from the skeleton
node = labels.skeletons[0].nodes[-1]
labels.skeletons[0].delete_node(node)
node = labels.skeletons[0].nodes[-1]
labels.skeletons[0].delete_node(node)
# Save and compare the results
Labels.save_hdf5(filename=filename, labels=labels)
loaded_labels = Labels.load_hdf5(filename=filename)
_check_labels_match(labels, loaded_labels)
def test_hdf5_from_predicted(multi_skel_vid_labels, tmpdir):
labels = multi_skel_vid_labels
filename = os.path.join(tmpdir, "test.h5")
# Add some predicted instances to create from_predicted links
for frame_num, frame in enumerate(labels):
if frame_num % 20 == 0:
frame.instances[
0].from_predicted = PredictedInstance.from_instance(
frame.instances[0], float(frame_num))
frame.instances.append(frame.instances[0].from_predicted)
# Save and load, compare the results
Labels.save_hdf5(filename=filename, labels=labels)
loaded_labels = Labels.load_hdf5(filename=filename)
for frame_num, frame in enumerate(loaded_labels):
if frame_num % 20 == 0:
assert frame.instances[0].from_predicted.score == float(frame_num)
def predicted_instances(instances):
return [PredictedInstance.from_instance(i, 1.0) for i in instances]