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Fix "index out of range" error when using "Track normal" algorithm. #5283

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merged 1 commit into from
Dec 2, 2025
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Fix "index out of range" error when using "Track normal" algorithm. #5283

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28 changes: 18 additions & 10 deletions utils/curve/algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -172,23 +172,31 @@ def evaluate_array(self, ts):
ts = np.array(ts)
tknots, quats = self.tknots, self.quats
base_indexes = tknots.searchsorted(ts, side='left')-1
t1s, t2s = tknots[base_indexes], tknots[base_indexes+1]
dts = (ts - t1s) / (t2s - t1s)
shifted_indexes = base_indexes + 1
good = shifted_indexes < len(tknots)
shifted_indexes[np.logical_not(good)] = len(tknots) - 1
t1s, t2s = tknots[base_indexes], tknots[shifted_indexes]
dts = np.empty_like(ts)
dts[good] = (ts[good] - t1s[good]) / (t2s[good] - t1s[good])
dts[np.logical_not(good)] = 0
#dts = np.clip(dts, 0.0, 1.0) # Just in case...
matrix_out = []
# TODO: ideally this should be vectorized with numpy;
# but that would require implementation of quaternion
# interpolation in numpy.
for dt, base_index in zip(dts, base_indexes):
q1, q2 = quats[base_index], quats[base_index+1]
# spherical linear interpolation.
# TODO: implement `squad`.
if dt < 0:
q = q1
elif dt > 1.0:
q = q2
if base_index+1 < len(quats):
q1, q2 = quats[base_index], quats[base_index+1]
# spherical linear interpolation.
# TODO: implement `squad`.
if dt < 0:
q = q1
elif dt > 1.0:
q = q2
else:
q = q1.slerp(q2, dt)
else:
q = q1.slerp(q2, dt)
q = quats[base_index]
matrix = np.array(q.to_matrix())
matrix_out.append(matrix)
return np.array(matrix_out)
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