def test_merge_with_package(min_labels_robot, tmpdir):
# Add a suggestion and save with images.
labels = min_labels_robot
labels.suggestions.append(
sleap.io.dataset.SuggestionFrame(video=labels.video, frame_idx=1))
pkg_path = os.path.join(tmpdir, "test.pkg.slp")
assert len(labels.predicted_instances) == 0
labels.save(pkg_path, with_images=True, embed_suggested=True)
# Load package.
labels_pkg = sleap.load_file(pkg_path)
assert isinstance(labels_pkg.video.backend, sleap.io.video.HDF5Video)
assert labels_pkg.video.backend.has_embedded_images
assert isinstance(labels_pkg.video.backend._source_video.backend,
sleap.io.video.MediaVideo)
assert len(labels_pkg.predicted_instances) == 0
# Add prediction.
inst = labels_pkg.user_instances[0]
inst_pr = sleap.PredictedInstance.from_pointsarray(
inst.numpy(), skeleton=labels_pkg.skeleton)
labels_pkg.append(
sleap.LabeledFrame(
video=labels_pkg.suggestions[0].video,
frame_idx=labels_pkg.suggestions[0].frame_idx,
instances=[inst_pr],
))
# Save labels without image data.
preds_path = pkg_path + ".predictions.slp"
labels_pkg.save(preds_path)
# Load predicted labels created from package.
labels_pr = sleap.load_file(preds_path)
assert len(labels_pr.predicted_instances) == 1
# Merge with base labels.
base_video_path = labels.video.backend.filename
merged, extra_base, extra_new = sleap.Labels.complex_merge_between(
labels, labels_pr)
assert len(labels.videos) == 1
assert labels.video.backend.filename == base_video_path
assert len(labels.predicted_instances) == 1
assert len(extra_base) == 0
assert len(extra_new) == 0
assert labels.predicted_instances[0].frame.frame_idx == 1
# Merge predictions to package instead.
labels_pkg = sleap.load_file(pkg_path)
labels_pr = sleap.load_file(preds_path)
assert len(labels_pkg.predicted_instances) == 0
base_video_path = labels_pkg.video.backend.filename
merged, extra_base, extra_new = sleap.Labels.complex_merge_between(
labels_pkg, labels_pr)
assert len(labels_pkg.videos) == 1
assert labels_pkg.video.backend.filename == base_video_path
assert len(labels_pkg.predicted_instances) == 1
assert len(extra_base) == 0
assert len(extra_new) == 0
assert labels_pkg.predicted_instances[0].frame.frame_idx == 1
def load_slp(self):
self.slp = sleap.load_file(self.fname_slp)