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"""
Non-Maximum Suppression for video proposals.
"""
def compute_temporal_iou(pred, gt):
""" deprecated due to performance concerns
compute intersection-over-union along temporal axis
Args:
pred: [st (float), ed (float)]
gt: [st (float), ed (float)]
Returns:
iou (float):
Ref: https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/eval.py
"""
intersection = max(0, min(pred[1], gt[1]) - max(pred[0], gt[0]))
union = max(pred[1], gt[1]) - min(pred[0], gt[0]) # not the correct union though
if union == 0:
return 0
else:
return 1.0 * intersection / union
def temporal_nms(predictions, nms_thd, max_after_nms=100):
"""
Args:
predictions: list(sublist), each sublist is [st (float), ed(float), score (float)],
note larger scores are better and are preserved. For metrics that are better when smaller,
please convert to its negative, e.g., convert distance to negative distance.
nms_thd: float in [0, 1]
max_after_nms:
Returns:
predictions_after_nms: list(sublist), each sublist is [st (float), ed(float), score (float)]
References:
https://github.com/wzmsltw/BSN-boundary-sensitive-network/blob/7b101fc5978802aa3c95ba5779eb54151c6173c6/Post_processing.py#L42
"""
if len(predictions) == 1: # only has one prediction, no need for nms
return predictions
predictions = sorted(predictions, key=lambda x: x[2], reverse=True) # descending order
tstart = [e[0] for e in predictions]
tend = [e[1] for e in predictions]
tscore = [e[2] for e in predictions]
rstart = []
rend = []
rscore = []
while len(tstart) > 1 and len(rscore) < max_after_nms: # max 100 after nms
idx = 1
while idx < len(tstart): # compare with every prediction in the list.
if compute_temporal_iou([tstart[0], tend[0]], [tstart[idx], tend[idx]]) > nms_thd:
# rm highly overlapped lower score entries.
tstart.pop(idx)
tend.pop(idx)
tscore.pop(idx)
# print("--------------------------------")
# print(compute_temporal_iou([tstart[0], tend[0]], [tstart[idx], tend[idx]]))
# print([tstart[0], tend[0]], [tstart[idx], tend[idx]])
# print(tstart.pop(idx), tend.pop(idx), tscore.pop(idx))
else:
# move to next
idx += 1
rstart.append(tstart.pop(0))
rend.append(tend.pop(0))
rscore.append(tscore.pop(0))
if len(rscore) < max_after_nms and len(tstart) >= 1: # add the last, possibly empty.
rstart.append(tstart.pop(0))
rend.append(tend.pop(0))
rscore.append(tscore.pop(0))
predictions_after_nms = [[st, ed, s] for s, st, ed in zip(rscore, rstart, rend)]
return predictions_after_nms
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