def test_frame_merge_between_predicted_and_user(skeleton, centered_pair_vid):
user_inst = Instance(skeleton=skeleton, points={skeleton.nodes[0]: Point(1, 2)},)
user_labels = Labels(
[LabeledFrame(video=centered_pair_vid, frame_idx=0, instances=[user_inst],)]
)
pred_inst = PredictedInstance(
skeleton=skeleton,
points={skeleton.nodes[0]: PredictedPoint(1, 2, score=1.0)},
score=1.0,
)
pred_labels = Labels(
[LabeledFrame(video=centered_pair_vid, frame_idx=0, instances=[pred_inst],)]
)
# Merge predictions into current labels dataset
_, _, new_conflicts = Labels.complex_merge_between(
user_labels,
new_labels=pred_labels,
unify=False, # since we used match_to when loading predictions file
)
# new predictions should replace old ones
Labels.finish_complex_merge(user_labels, new_conflicts)
# We should be able to cleanly merge the user and the predicted instance,
# and we want to retain both even though they perfectly match.
assert user_inst in user_labels[0].instances
assert pred_inst in user_labels[0].instances
assert len(user_labels[0].instances) == 2
def merge_results(self):
"""Merges result frames into labels dataset."""
# Remove any frames without instances
new_lfs = list(filter(lambda lf: len(lf.instances), self.results))
# Merge predictions into current labels dataset
_, _, new_conflicts = Labels.complex_merge_between(
self.labels,
new_labels=Labels(new_lfs),
unify=False, # since we used match_to when loading predictions file
)
# new predictions should replace old ones
Labels.finish_complex_merge(self.labels, new_conflicts)
