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vsum_tools.py
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''''
Courtesy of KaiyangZhou
https://github.com/KaiyangZhou/pytorch-vsumm-reinforce
@article{zhou2017reinforcevsumm,
title={Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward},
author={Zhou, Kaiyang and Qiao, Yu and Xiang, Tao},
journal={arXiv:1801.00054},
year={2017}
}
Modifications by Jiri Fajtl
- knapsack replaced with knapsack_ortools
- added evaluate_user_summaries() for user summaries ground truth evaluation
'''
import numpy as np
from knapsack import knapsack_dp
from knapsack import knapsack_ortools
import math
def generate_summary(ypred, cps, n_frames, nfps, positions, proportion=0.15, method='knapsack'):
"""Generate keyshot-based video summary i.e. a binary vector.
Args:
---------------------------------------------
- ypred: predicted importance scores.
- cps: change points, 2D matrix, each row contains a segment.
- n_frames: original number of frames.
- nfps: number of frames per segment.
- positions: positions of subsampled frames in the original video.
- proportion: length of video summary (compared to original video length).
- method: defines how shots are selected, ['knapsack', 'rank'].
"""
n_segs = cps.shape[0]
frame_scores = np.zeros((n_frames), dtype=np.float32)
if positions.dtype != int:
positions = positions.astype(np.int32)
if positions[-1] != n_frames:
positions = np.concatenate([positions, [n_frames]])
for i in range(len(positions) - 1):
pos_left, pos_right = positions[i], positions[i+1]
if i == len(ypred):
frame_scores[pos_left:pos_right] = 0
else:
frame_scores[pos_left:pos_right] = ypred[i]
seg_score = []
for seg_idx in range(n_segs):
start, end = int(cps[seg_idx,0]), int(cps[seg_idx,1]+1)
scores = frame_scores[start:end]
seg_score.append(float(scores.mean()))
limits = int(math.floor(n_frames * proportion))
if method == 'knapsack':
# picks = knapsack_dp(seg_score, nfps, n_segs, limits)
picks = knapsack_ortools(seg_score, nfps, n_segs, limits)
elif method == 'rank':
order = np.argsort(seg_score)[::-1].tolist()
picks = []
total_len = 0
for i in order:
if total_len + nfps[i] < limits:
picks.append(i)
total_len += nfps[i]
else:
raise KeyError("Unknown method {}".format(method))
summary = np.zeros((1), dtype=np.float32) # this element should be deleted
for seg_idx in range(n_segs):
nf = nfps[seg_idx]
if seg_idx in picks:
tmp = np.ones((nf), dtype=np.float32)
else:
tmp = np.zeros((nf), dtype=np.float32)
summary = np.concatenate((summary, tmp))
summary = np.delete(summary, 0) # delete the first element
return summary
def evaluate_summary(machine_summary, user_summary, eval_metric='avg'):
"""Compare machine summary with user summary (keyshot-based).
Args:
--------------------------------
machine_summary and user_summary should be binary vectors of ndarray type.
eval_metric = {'avg', 'max'}
'avg' averages results of comparing multiple human summaries.
'max' takes the maximum (best) out of multiple comparisons.
"""
machine_summary = machine_summary.astype(np.float32)
user_summary = user_summary.astype(np.float32)
n_users,n_frames = user_summary.shape
# binarization
machine_summary[machine_summary > 0] = 1
user_summary[user_summary > 0] = 1
if len(machine_summary) > n_frames:
machine_summary = machine_summary[:n_frames]
elif len(machine_summary) < n_frames:
zero_padding = np.zeros((n_frames - len(machine_summary)))
machine_summary = np.concatenate([machine_summary, zero_padding])
f_scores = []
prec_arr = []
rec_arr = []
for user_idx in range(n_users):
gt_summary = user_summary[user_idx,:]
overlap_duration = (machine_summary * gt_summary).sum()
precision = overlap_duration / (machine_summary.sum() + 1e-8)
recall = overlap_duration / (gt_summary.sum() + 1e-8)
if precision == 0 and recall == 0:
f_score = 0.
else:
f_score = (2 * precision * recall) / (precision + recall)
f_scores.append(f_score)
prec_arr.append(precision)
rec_arr.append(recall)
if eval_metric == 'avg':
final_f_score = np.mean(f_scores)
final_prec = np.mean(prec_arr)
final_rec = np.mean(rec_arr)
elif eval_metric == 'max':
final_f_score = np.max(f_scores)
max_idx = np.argmax(f_scores)
final_prec = prec_arr[max_idx]
final_rec = rec_arr[max_idx]
return final_f_score, final_prec, final_rec,machine_summary,user_summary
def evaluate_user_summaries(user_summary, eval_metric='avg'):
"""Compare machine summary with user summary (keyshot-based).
Args:
--------------------------------
machine_summary and user_summary should be binary vectors of ndarray type.
eval_metric = {'avg', 'max'}
'avg' averages results of comparing multiple human summaries.
'max' takes the maximum (best) out of multiple comparisons.
"""
user_summary = user_summary.astype(np.float32)
n_users, n_frames = user_summary.shape
# binarization
user_summary[user_summary > 0] = 1
f_scores = []
prec_arr = []
rec_arr = []
for user_idx in range(n_users):
gt_summary = user_summary[user_idx, :]
for other_user_idx in range(user_idx+1, n_users):
other_gt_summary = user_summary[other_user_idx, :]
overlap_duration = (other_gt_summary * gt_summary).sum()
precision = overlap_duration / (other_gt_summary.sum() + 1e-8)
recall = overlap_duration / (gt_summary.sum() + 1e-8)
if precision == 0 and recall == 0:
f_score = 0.
else:
f_score = (2 * precision * recall) / (precision + recall)
f_scores.append(f_score)
prec_arr.append(precision)
rec_arr.append(recall)
if eval_metric == 'avg':
final_f_score = np.mean(f_scores)
final_prec = np.mean(prec_arr)
final_rec = np.mean(rec_arr)
elif eval_metric == 'max':
final_f_score = np.max(f_scores)
max_idx = np.argmax(f_scores)
final_prec = prec_arr[max_idx]
final_rec = rec_arr[max_idx]
return final_f_score, final_prec, final_rec