with h5py.File(PATH_TVSum, 'r') as hdf:
for video_name in keys:
video_index = video_name[6:]
user_summary = np.array(
hdf.get('video_' + video_index + '/user_summary'))
sb = np.array(hdf.get('video_' + video_index + '/change_points'))
n_frames = np.array(hdf.get('video_' + video_index + '/n_frames'))
positions = np.array(hdf.get('video_' + video_index + '/picks'))
all_user_summary.append(user_summary)
all_shot_bound.append(sb)
all_nframes.append(n_frames)
all_positions.append(positions)
all_summaries = generate_summary(all_shot_bound, all_scores, all_nframes,
all_positions)
all_f_scores = []
# compare the resulting summary with the ground truth one, for each video
for video_index in range(len(all_summaries)):
summary = all_summaries[video_index]
user_summary = all_user_summary[video_index]
f_score = evaluate_summary(summary, user_summary, eval_method)
all_f_scores.append(f_score)
f_score_epochs.append(np.mean(all_f_scores))
print("f_score: ", np.mean(all_f_scores))
with open(path + '/f_scores.txt', 'w') as outfile:
json.dump(f_score_epochs, outfile)
n_frames = np.array(hdf.get('video_' + video_index + '/n_frames'))
positions = np.array(hdf.get('video_' + video_index + '/picks'))
all_gt_scores.append(gt_score)
all_shot_bound.append(sb)
all_nframes.append(n_frames)
all_positions.append(positions)
# generate summary based on the auto-generated scores
all_summaries = generate_summary(all_shot_bound, all_scores, all_nframes,
all_positions)
# generate summary based on the ground-truth scores
all_gt_summary = generate_summary(all_shot_bound, all_gt_scores,
all_nframes, all_positions)
all_f_scores = []
# Compare the resulting summary with the ground truth one, for each video
for video_index in range(len(all_summaries)):
summary = all_summaries[video_index]
gt_summary = all_gt_summary[video_index]
f_score = evaluate_summary(summary, np.expand_dims(gt_summary, axis=0),
eval_method)
all_f_scores.append(f_score)
f_score_epochs.append(np.mean(all_f_scores))
print("f_score: ", np.mean(all_f_scores))
with open(path + '/f_scores.txt', 'w') as outfile:
json.dump(f_score_epochs, outfile)