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stitch_tracklets crashes when not enough tracks are found #2273

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Merged
MMathisLab merged 1 commit into from
Jun 15, 2023
Merged

stitch_tracklets crashes when not enough tracks are found #2273

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76 changes: 42 additions & 34 deletions deeplabcut/refine_training_dataset/stitch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -723,40 +723,41 @@ def stitch(self, add_back_residuals=True):
for node in self.G
if node not in ("source", "sink")
)
if (
incomplete_tracks == 1
): # All remaining nodes must belong to the same track
# Verify whether there are overlapping tracklets
for t1, t2 in combinations(remaining_nodes, 2):
if t1 in t2:
# Pick the segment that minimizes "smoothness", computed here
# with the coefficient of variation of the differences.
if t1 in remaining_nodes:
remaining_nodes.remove(t1)
if t2 in remaining_nodes:
remaining_nodes.remove(t2)
track = sum(remaining_nodes)
hyp1 = track + t1
hyp2 = track + t2
dx1 = np.diff(hyp1.centroid, axis=0)
cv1 = dx1.std() / np.abs(dx1).mean()
dx2 = np.diff(hyp2.centroid, axis=0)
cv2 = dx2.std() / np.abs(dx2).mean()
if cv1 < cv2:
remaining_nodes.add(t1)
self.residuals.append(t2)
else:
remaining_nodes.add(t2)
self.residuals.append(t1)
paths.append(list(remaining_nodes))
elif incomplete_tracks > 1:
# Rebuild a full graph from the remaining nodes without
# temporal constraint on what tracklets can be stitched together.
self.build_graph(list(remaining_nodes), max_gap=np.inf)
self.G.nodes["source"]["demand"] = -incomplete_tracks
self.G.nodes["sink"]["demand"] = incomplete_tracks
_, self.flow = nx.capacity_scaling(self.G)
paths += self.reconstruct_paths()
if len(remaining_nodes) > 0:
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@jeylau jeylau Jun 13, 2023

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Great! By the way, I checked, and we could also have:

if not remaining_nodes:
    return

as the finally clause is executed before the function exits, no matter what.
But that's a detail 😃

if (
incomplete_tracks == 1
): # All remaining nodes must belong to the same track
# Verify whether there are overlapping tracklets
for t1, t2 in combinations(remaining_nodes, 2):
if t1 in t2:
# Pick the segment that minimizes "smoothness", computed here
# with the coefficient of variation of the differences.
if t1 in remaining_nodes:
remaining_nodes.remove(t1)
if t2 in remaining_nodes:
remaining_nodes.remove(t2)
track = sum(remaining_nodes)
hyp1 = track + t1
hyp2 = track + t2
dx1 = np.diff(hyp1.centroid, axis=0)
cv1 = dx1.std() / np.abs(dx1).mean()
dx2 = np.diff(hyp2.centroid, axis=0)
cv2 = dx2.std() / np.abs(dx2).mean()
if cv1 < cv2:
remaining_nodes.add(t1)
self.residuals.append(t2)
else:
remaining_nodes.add(t2)
self.residuals.append(t1)
paths.append(list(remaining_nodes))
elif incomplete_tracks > 1:
# Rebuild a full graph from the remaining nodes without
# temporal constraint on what tracklets can be stitched together.
self.build_graph(list(remaining_nodes), max_gap=np.inf)
self.G.nodes["source"]["demand"] = -incomplete_tracks
self.G.nodes["sink"]["demand"] = incomplete_tracks
_, self.flow = nx.capacity_scaling(self.G)
paths += self.reconstruct_paths()
self.paths = paths
if len(self.paths) != self.n_tracks:
warnings.warn(f"Only {len(self.paths)} tracks could be reconstructed.")
Expand Down Expand Up @@ -867,6 +868,13 @@ def concatenate_data(self):
temp = np.full((self.n_frames, flat_data.shape[1]), np.nan)
temp[track.inds - self._first_frame] = flat_data
data.append(temp)

# If there isn't a track for each animal, fill in the dataframe with NaNs
missing_tracks = self.n_tracks - len(self.tracks)
if missing_tracks > 0:
track_shape = self.tracks[0].flat_data.shape[1]
data += missing_tracks * [np.full((self.n_frames, track_shape), np.nan)]

return np.hstack(data)

def format_df(self, animal_names=None):
Expand Down