def test_tracklet_interpolate(real_tracklets):
data = np.stack(list(real_tracklets[0].values()))[:10]
inds = np.arange(len(data))
gap = 2
inds[len(inds) // 2:] += gap
tracklet = Tracklet(data, inds)
assert len(tracklet) == len(data)
new_tracklet = tracklet.interpolate(max_gap=1)
assert len(new_tracklet) == len(data)
new_tracklet = tracklet.interpolate(max_gap=gap)
assert len(new_tracklet) == len(data) + gap
missing_inds = list(set(range(inds.max())).difference(inds))
assert np.all(new_tracklet.data[missing_inds, :, 2] == 0.5)
def fake_stitcher():
inds = np.arange(TRACKLET_LEN)
data = np.random.rand(inds.size, N_DETS, 3)
track = Tracklet(data, inds)
idx = np.linspace(0, inds.size, N_TRACKLETS + 1, dtype=int)
tracklets = TrackletStitcher.split_tracklet(track, idx[1:-1])
return TrackletStitcher(tracklets, n_tracks=2)
def fake_tracklet():
inds = np.arange(TRACKLET_START, TRACKLET_START + TRACKLET_LEN)
data = np.empty((TRACKLET_LEN, N_DETS, 4))
data[..., :2] = np.arange(N_DETS).reshape(-1, 1) * [1, 1]
data[..., 2] = 1
data[..., 3] = TRACKLET_ID
return Tracklet(data, inds)
def test_tracklet_affinities(fake_tracklet):
other_tracklet = Tracklet(fake_tracklet.data, fake_tracklet.inds + TRACKLET_LEN)
_ = fake_tracklet.dynamic_similarity_with(other_tracklet)
_ = fake_tracklet.dynamic_dissimilarity_with(other_tracklet)
_ = fake_tracklet.shape_dissimilarity_with(other_tracklet)
_ = fake_tracklet.box_overlap_with(other_tracklet)
_ = fake_tracklet.motion_affinity_with(other_tracklet)
_ = fake_tracklet.distance_to(other_tracklet)
def _reconstruct_tracks_as_tracklets(df):
"""
Parameters:
-----------
df: DataFrame
loaded from an .h5 tracks file (obtained from `stitch_tracklets()`)
"""
from deeplabcut.refine_training_dataset.stitch import Tracklet
tracklets = []
for _, group in df.groupby("individuals", axis=1):
temp = group.dropna(how="all")
inds = temp.index.to_numpy()
track = Tracklet(temp.to_numpy().reshape((len(temp), -1, 3)), inds)
track = track.interpolate(max_gap=len(group))
tracklets.append(track)
return tracklets
def test_tracklet(tracklet):
assert len(tracklet) == TRACKLET_LEN
assert tracklet.likelihood == 1
assert tracklet.identity == TRACKLET_ID
assert tracklet.start == TRACKLET_START
assert tracklet.end == TRACKLET_START + TRACKLET_LEN - 1
np.testing.assert_equal(
tracklet.centroid,
np.full((TRACKLET_LEN, 2),
np.arange(tracklet.data.shape[1]).mean()))
tracklet2 = Tracklet(tracklet.data, tracklet.inds + TRACKLET_LEN)
assert tracklet not in tracklet2
tracklet_new = tracklet + tracklet2
tracklet_new -= tracklet
np.testing.assert_equal(tracklet_new.data, tracklet2.data)
np.testing.assert_equal(tracklet_new.inds, tracklet2.inds)
tracklet2 = tracklet + tracklet
assert tracklet2.contains_duplicates()
def test_tracklet(fake_tracklet):
assert len(fake_tracklet) == TRACKLET_LEN
assert fake_tracklet.likelihood == 1
assert fake_tracklet.identity == TRACKLET_ID
assert fake_tracklet.start == TRACKLET_START
assert fake_tracklet.end == TRACKLET_START + TRACKLET_LEN - 1
np.testing.assert_equal(
fake_tracklet.centroid, np.full((TRACKLET_LEN, 2), np.arange(N_DETS).mean())
)
fake_tracklet2 = Tracklet(fake_tracklet.data, fake_tracklet.inds + TRACKLET_LEN)
assert fake_tracklet not in fake_tracklet2
tracklet = fake_tracklet + fake_tracklet2
tracklet -= fake_tracklet
np.testing.assert_equal(tracklet.data, fake_tracklet2.data)
np.testing.assert_equal(tracklet.inds, fake_tracklet2.inds)
tracklet2 = fake_tracklet + fake_tracklet
assert tracklet2.contains_duplicates()
def test_tracklet_monotonic_indices(fake_tracklet):
tracklet_inv = Tracklet(fake_tracklet.data[::-1], fake_tracklet.inds[::-1])
np.testing.assert_equal(fake_tracklet.inds, tracklet_inv.inds)
np.testing.assert_equal(fake_tracklet.xy, tracklet_inv.xy)
def test_tracklet_wrong_inputs(fake_tracklet):
with pytest.raises(ValueError):
_ = Tracklet(fake_tracklet.data[..., :2], fake_tracklet.inds)
with pytest.raises(ValueError):
_ = Tracklet(fake_tracklet.data[:TRACKLET_LEN - 2], fake_tracklet.inds)
def make_fake_tracklets():
tracklet = fake_tracklet()
tracklet_single = Tracklet(tracklet.data[:, :1], tracklet.inds)
return tracklet, tracklet_single
