def get_oracle_summary_self(user_summary):
"""
create a single ground-truth set of keyframes from multiple user-annotated ones for each video.
Args:
user_summary: [n_user,n_frame]
"""
n_user, n_frame = user_summary.shape # [n_user,n_frame] e.g.[15,4494] for video
oracle_summary = np.zeros(n_frame) # single ground-truth
priority_idx = np.argsort(
-(user_summary.sum(axis=0))
) # [n_frame], the most select frame -> the least select frame(because of "-" sign)
best_fscore = 0
for idx in priority_idx:
oracle_summary[idx] = 1 # assume fake sum
n_user_fscore = np.zeros(n_user) # record of every user to fake sum
for usr_i in range(n_user):
y_pred = user_summary[usr_i]
y_true = oracle_summary
fscore = eval_tools.eval_metrics(y_pred,
y_true)[2] # only get fscore
n_user_fscore[usr_i] = fscore
cur_fscore = n_user_fscore.mean() # avg. of each sum to fake sum
if cur_fscore > best_fscore:
best_fscore = cur_fscore
else:
oracle_summary[idx] = 0 # assume sum is not real
#print("pcshih", best_fscore)
return oracle_summary
320) # [1,2,320] -> [2,320]
# we only want key frame prob. -> [1]
pred_score = torch.softmax(pred_score, dim=0)[1] # [320]
#print("epoch:{:0>3d} video_index:{:0>2d} key_frame_score(first20):{}".format(epoch, index, pred_score[:20]))
video_name = "video_{}".format(index)
video_info = data_file[video_name]
# select key shots by video_info and pred_score
# pred_summary: [N] binary keyshot
N, pred_score_upsample, _, pred_summary = eval_tools.select_keyshots(
video_info, pred_score)
true_summary_arr = video_info['user_summary'][(
)] # shape (n_users,N), summary from some users, each row is a binary vector
eval_res = [
eval_tools.eval_metrics(pred_summary, true_summary)
for true_summary in true_summary_arr
] # shape [n_user,3],3 for[precision, recall, fscore]
#eval_res = np.mean(eval_res, axis=0).tolist() # for tvsum
eval_res = np.max(eval_res, axis=0).tolist() # for summe
eval_res_avg.append(
eval_res
) # [[precision1, recall1, fscore1], [precision2, recall2, fscore2]......]
# plot split 0 and first test histogram only save epoch4 and epoch last
# if (i==0 and j==1 and (epoch==4 or epoch==EPOCHS-1)):
# fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(100, 60))
# ax[0].bar(list(range(320)), pred_score.tolist(), alpha=1.0, width=0.6)
# ax[0].set_xlabel('frame_index', fontsize=50)
# ax[0].set_ylabel('Probability', fontsize=50)
# ax[0].tick_params(axis='both', which='major', labelsize=50)