def post_processing(result, video_info, soft_nms_alpha, soft_nms_low_threshold,
soft_nms_high_threshold, post_process_top_k,
feature_extraction_interval):
"""Post process for temporal proposals generation.
Args:
result (np.ndarray): Proposals generated by network.
video_info (dict): Meta data of video. Required keys are
'duration_frame', 'duration_second'.
soft_nms_alpha (float): Alpha value of Gaussian decaying function.
soft_nms_low_threshold (float): Low threshold for soft nms.
soft_nms_high_threshold (float): High threshold for soft nms.
post_process_top_k (int): Top k values to be considered.
feature_extraction_interval (int): Interval used in feature extraction.
Returns:
list[dict]: The updated proposals, e.g.
[{'score': 0.9, 'segment': [0, 1]},
{'score': 0.8, 'segment': [0, 2]},
...].
"""
if len(result) > 1:
result = soft_nms(result, soft_nms_alpha, soft_nms_low_threshold,
soft_nms_high_threshold, post_process_top_k)
result = result[result[:, -1].argsort()[::-1]]
video_duration = float(
video_info['duration_frame'] // feature_extraction_interval *
feature_extraction_interval
) / video_info['duration_frame'] * video_info['duration_second']
proposal_list = []
for j in range(min(post_process_top_k, len(result))):
proposal = {}
proposal['score'] = float(result[j, -1])
proposal['segment'] = [
max(0, result[j, 0]) * video_duration,
min(1, result[j, 1]) * video_duration
]
proposal_list.append(proposal)
return proposal_list
def test_soft_nms():
proposals = np.array([[0., 1., 1., 1., 0.5, 0.5],
[0., 0.4, 1., 1., 0.4, 0.4],
[0., 0.95, 1., 1., 0.6, 0.6]])
proposal_list = soft_nms(proposals, 0.75, 0.65, 0.9, 1)
assert_array_equal(proposal_list, [[0., 0.95, 0.6], [0., 0.4, 0.4]])