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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 | |