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1.4 KiB
Filter Tiny Segments
author: Chen Zhang
Description
This operator can filter tiny detected segments with format of list of [start_second_1, start_second_2, end_second_1, end_second_2]
Code Example
import towhee
towhee.dc['pred']([[[0, 0, 100, 100], [0, 0, 10, 10], [0, 0, 60, 10]]]) \
.video_copy_detection.filter_tiny_segments['pred', 'filtered_pred'](filter_s_thresh=20) \
.show()
Factory Constructor
Create the operator via the following factory method
filter_tiny_segments(filter_s_thresh, segment_len_rate)
Parameters:
filter_s_thresh: float
Use a thresh to filter detected box which is smaller than it.
segment_len_rate: float
Filter expect longer then segment_len_rate * video length. Only useful for filter expect near video length segments.
Interface
A Temporal Network operator takes two numpy.ndarray(shape(N,D) N: number of features. D: dimension of features) and get the duplicated ranges and scores.
Parameters:
pred_value: List
List of predicted segment second infos of a video pair
sim_hw: Tuple
Similarity matrix height and weight of a video pair. If sample rate is 1s, sim_hw is also the lengths of these videos.
Returns:
res_pred_list: List
List of filtered predicted segment second infos
1.4 KiB
Filter Tiny Segments
author: Chen Zhang
Description
This operator can filter tiny detected segments with format of list of [start_second_1, start_second_2, end_second_1, end_second_2]
Code Example
import towhee
towhee.dc['pred']([[[0, 0, 100, 100], [0, 0, 10, 10], [0, 0, 60, 10]]]) \
.video_copy_detection.filter_tiny_segments['pred', 'filtered_pred'](filter_s_thresh=20) \
.show()
Factory Constructor
Create the operator via the following factory method
filter_tiny_segments(filter_s_thresh, segment_len_rate)
Parameters:
filter_s_thresh: float
Use a thresh to filter detected box which is smaller than it.
segment_len_rate: float
Filter expect longer then segment_len_rate * video length. Only useful for filter expect near video length segments.
Interface
A Temporal Network operator takes two numpy.ndarray(shape(N,D) N: number of features. D: dimension of features) and get the duplicated ranges and scores.
Parameters:
pred_value: List
List of predicted segment second infos of a video pair
sim_hw: Tuple
Similarity matrix height and weight of a video pair. If sample rate is 1s, sim_hw is also the lengths of these videos.
Returns:
res_pred_list: List
List of filtered predicted segment second infos