def prepare_diff_folder(fusion_param):
diff_predict_path = fusion_param['renew_pid_path'].replace(ctrl_msg['data_folder_path'],
ctrl_msg['data_folder_path'] + '_diff')
diff_folder_path = folder(diff_predict_path)
safe_mkdir(diff_folder_path)
# although copy all info including pid info, but not use in later training
shutil.copy(fusion_param['renew_pid_path'], diff_predict_path)
def write_unequal_rand_st_model(fusion_param):
# fusion_param = get_fusion_param()
rand_answer_path = fusion_param['answer_path'].replace(ctrl_msg['data_folder_path'],
ctrl_msg['data_folder_path'] + '_uerand')
rand_folder_path = folder(rand_answer_path)
safe_mkdir(rand_folder_path)
# although copy all info including pid info, but not use in later training
shutil.copy(fusion_param['answer_path'], rand_answer_path)
rand_path = rand_folder_path + '/renew_pid.log'
safe_remove(rand_path)
origin_tracks = get_tracks(fusion_param)
pid_cnt = len(origin_tracks)
origin_pids = map(lambda x: x + 1, range(pid_cnt))
persons_rand_predict_idx_s = [random.sample(origin_pids, pid_cnt) for _ in range(pid_cnt)]
viz_pid_path = fusion_param['renew_pid_path']
viz_score_path = fusion_param['renew_ac_path']
viz_pids = read_lines(viz_pid_path)
viz_pids = [per_viz_pids.split() for per_viz_pids in viz_pids]
viz_scores = read_lines(viz_score_path)
viz_scores = [per_viz_scores.split() for per_viz_scores in viz_scores]
viz_same_pids = [
[
int(viz_pid) for viz_pid, viz_score in zip(per_viz_pids, per_viz_scores) if float(viz_score) > 0.7
] for per_viz_scores, per_viz_pids in zip(viz_scores, viz_pids)
]
persons_unequal_rand_predict_idx_s = list()
for i in range(pid_cnt):
diff_persons = list(set(persons_rand_predict_idx_s[i]) ^ set(viz_same_pids[i]))
diff_cnt = len(diff_persons)
persons_unequal_rand_predict_idx_s.append(
random.sample(diff_persons, diff_cnt)
)
write_content = ''
for rand_predict_idx_s in persons_unequal_rand_predict_idx_s:
for rand_predict_idx in rand_predict_idx_s:
write_content += str(rand_predict_idx) + ' '
write_content += '\n'
write(rand_path, write_content)
def write_rand_pid(fusion_param):
# 对每张左图, 随机生成250个右图的pid，相当于一个随机的renew_pid.log，rand_path也会每次都删除，所以不存在缓存
# todo 不一定需要生成250个右图
# fusion_param = get_fusion_param()
rand_answer_path = fusion_param['answer_path'].replace(ctrl_msg['data_folder_path'], ctrl_msg['data_folder_path'] + '_rand')
rand_folder_path = folder(rand_answer_path)
safe_mkdir(rand_folder_path)
# although copy all info including pid info, but not use in later training
shutil.copy(fusion_param['answer_path'], rand_answer_path)
rand_path = rand_folder_path + '/renew_pid.log'
safe_remove(rand_path)
origin_tracks = get_tracks(fusion_param)
pid_cnt = len(origin_tracks)
origin_pids = map(lambda x: x+1, range(pid_cnt))
persons_rand_predict_idx_s = [random.sample(origin_pids, pid_cnt) for _ in range(pid_cnt)]
write_content = ''
for rand_predict_idx_s in persons_rand_predict_idx_s:
for rand_predict_idx in rand_predict_idx_s:
write_content += str(rand_predict_idx) + ' '
write_content += '\n'
write(rand_path, write_content)
def percent_shot_eval(target_path, top_cnt, test_mode=False):
global gallery_cnt
gallery_cnt = 0
answer_path = folder(target_path) + '/test_tracks.txt'
predict_path = target_path
answer_lines = read_lines(answer_path)
real_pids = [int(answer.split('_')[0]) for answer in answer_lines]
def is_shot(line):
global line_idx
global shot_line_cnt
global shot_cnt
global predict_cnt
global predict_line_cnt
global gallery_cnt
predict_idx_es = line.split()
has_shot = False
if len(predict_idx_es) > top_cnt:
predict_cnt += top_cnt
else:
predict_cnt += len(predict_idx_es)
if len(predict_idx_es) > 0:
predict_line_cnt += 1
# line_idx > 774 means label img,
# gallery_idxs[(line_idx - 775)/2] means iseven in gallery,
# if iseven is equal, means gallery img
if test_mode and line_idx > 774 and (line_idx - 774) % 2 == 1:
gallery_cnt += 1
line_idx += 1
return
for i, predict_idx in enumerate(predict_idx_es):
if i >= top_cnt:
break
# print(line_idx)
if real_pids[int(predict_idx) - 1] == real_pids[line_idx]:
if not has_shot:
shot_line_cnt += 1
has_shot = True
shot_cnt += 1
line_idx += 1
read_lines_and(predict_path, is_shot)
global line_idx
global shot_line_cnt
global shot_cnt
global predict_cnt
global predict_line_cnt
# print('all predict shot(ac1): %f' % (float(shot_cnt) / predict_cnt))
if test_mode:
valid_line_cnt = 125
else:
valid_line_cnt = 250
shot_rate = shot_line_cnt / float(valid_line_cnt)
print('top%d shot: %f' % (top_cnt, shot_rate))
print('gallery cnt: %d' % gallery_cnt)
line_idx = 0
shot_cnt = 0
shot_line_cnt = 0
predict_cnt = 0
predict_line_cnt = 0
return shot_rate