def fusion_dir_prepare(source, target):
fusion_data_path = '/home/cwh/coding/TrackViz/data/'
fusion_train_dir = fusion_data_path + '/' + source + '_' + target + '-train'
fusion_test_dir = fusion_data_path + '/' + source + '_' + target + '-test'
safe_mkdir(fusion_train_dir)
safe_mkdir(fusion_test_dir)
return fusion_train_dir, fusion_test_dir
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 init_strict_img_st_fusion():
# 全局调度入口，会同时做训练集和测试集上的融合与评分
fusion_param = get_fusion_param()
safe_mkdir('data/' + ctrl_msg['data_folder_path'])
get_predict_delta_tracks(fusion_param)
# # only get rand model for train dataset
prepare_rand_folder(fusion_param)
prepare_diff_folder(fusion_param)
ctrl_msg['data_folder_path'] = ctrl_msg['data_folder_path'] + '_rand'
fusion_param = get_fusion_param()
# 生成随机时空点的时空模型
get_predict_delta_tracks(fusion_param, random=True)
ctrl_msg['data_folder_path'] = ctrl_msg['data_folder_path'].replace(
'rand', 'diff')
fusion_param = get_fusion_param()
get_predict_delta_tracks(fusion_param, diff_person=True)
# 改回非随机的train目录
ctrl_msg['data_folder_path'] = ctrl_msg['data_folder_path'][:-5]
# has prepared more accurate ep, en
print('fusion on training dataset')
iter_strict_img_st_fusion(on_test=False)
# 改成测试目录
print('fusion on test dataset')
ctrl_msg['data_folder_path'] = ctrl_msg['data_folder_path'][:-4] + 'est'
safe_mkdir('data/' + ctrl_msg['data_folder_path'])
iter_strict_img_st_fusion(on_test=True)
def split_duke(img_list_path, data_type):
tracks, camera_cnt = load_train_img_infos(img_list_path)
mid = calculate_mid_frame(tracks)
head_tracks = filter(lambda x: x[2] < mid, tracks)
tail_tracks = filter(lambda x: x[2] > mid, tracks)
track_name = 'dukehead'
duke_tracks_realloc(head_tracks, track_name, data_type)
track_name = 'duketail'
safe_mkdir('../data/%s' % track_name)
duke_tracks_realloc(tail_tracks, track_name, data_type)
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 duke_tracks_realloc(tracks, track_name, data_type):
target_list_path = '../data/%s/%s.list' % (track_name, data_type)
target_track_info_dir_path = '/home/cwh/coding/TrackViz/data/%s' % track_name
safe_mkdir(target_track_info_dir_path)
target_track_dir_path = '/home/cwh/coding/%s' % track_name
safe_mkdir(target_track_dir_path)
target_track_type_dir_path = '/home/cwh/coding/%s/%s' % (track_name,
data_type)
safe_mkdir(target_track_type_dir_path)
for track in tracks:
source_img_name = '%04d_c%d_f%07d.jpg' % (int(
track[0]), track[1], track[2])
target_img_name = '%05d%07d%03d.jpg' % (int(
track[0]), track[2], track[1])
shutil.copyfile(
'/home/cwh/coding/DukeMTMC-reID/%s/%s' %
(data_type, source_img_name),
'%s/%s' % (target_track_type_dir_path, target_img_name))
safe_remove(target_list_path)
def duke_tracks_realloc(tracks, track_name, data_type):
target_list_path = '../data/%s/%s.list' % (track_name, data_type)
target_track_info_dir_path = '/home/cwh/coding/TrackViz/data/%s' % track_name
safe_mkdir(target_track_info_dir_path)
target_track_dir_path = '/home/cwh/coding/%s' % track_name
safe_mkdir(target_track_dir_path)
target_track_type_dir_path = '/home/cwh/coding/%s/%s' % (track_name,
data_type)
safe_mkdir(target_track_type_dir_path)
names = list()
for track in tracks:
img_name = '%04d_c%d_f%07d.jpg' % (int(track[0]), track[1], track[2])
shutil.copyfile(
'/home/cwh/coding/DukeMTMC-reID/%s/%s' % (data_type, img_name),
'%s/%s' % (target_track_type_dir_path, img_name))
names.append(img_name)
names.append('\n')
list_stmt = ''.join(names)
safe_remove(target_list_path)
write(target_list_path, list_stmt)
def get_fusion_param():
origin_dict = {
'gt_fusion':
False,
'renew_pid_path':
'data/' + ctrl_msg['data_folder_path'] + '/renew_pid.log',
'renew_ac_path':
'data/' + ctrl_msg['data_folder_path'] + '/renew_ac.log',
'predict_pid_path':
'data/' + ctrl_msg['data_folder_path'] + '/predict_pid.log',
'answer_path':
'data/' + ctrl_msg['data_folder_path'] + '/test_tracks.txt',
'probe_path':
'',
'train_path':
'',
'gallery_path':
'',
'distribution_pickle_path':
'data/' + ctrl_msg['data_folder_path'] + '/sorted_deltas.pickle',
'src_distribution_pickle_path':
'data/' + ctrl_msg['data_folder_path'].replace('test', 'train') +
'/sorted_deltas.pickle',
'persons_deltas_path':
'data/' + ctrl_msg['data_folder_path'] +
'/persons_deltas_score.pickle',
'persons_ap_path':
'data/' + ctrl_msg['data_folder_path'] + '/persons_ap_scores.pickle',
'predict_person_path':
'data/' + ctrl_msg['data_folder_path'] + '/predict_persons.pickle',
'mid_score_path':
'data/' + ctrl_msg['data_folder_path'] + '/cross_mid_score.log',
'eval_fusion_path':
'data/' + ctrl_msg['data_folder_path'] + '/cross_filter_pid.log',
'fusion_normal_score_path':
'data/' + ctrl_msg['data_folder_path'] + '/cross_filter_score.log',
'ep':
ctrl_msg['ep'],
'en':
ctrl_msg['en'],
'window_interval':
ctrl_msg['window_interval'],
'filter_interval':
ctrl_msg['filter_interval']
}
safe_mkdir('data/' + ctrl_msg['data_folder_path'])
if '_grid' in ctrl_msg['data_folder_path'] and '_grid_' not in ctrl_msg[
'data_folder_path']:
origin_dict['probe_path'] = 'data/grid/grid-cv' + str(
ctrl_msg['cv_num']) + '-probe.txt'
origin_dict['train_path'] = 'data/grid/grid-cv' + str(
ctrl_msg['cv_num']) + '-train.txt'
origin_dict['gallery_path'] = 'data/grid/grid-cv' + str(
ctrl_msg['cv_num']) + '-gallery.txt'
elif '_market' in ctrl_msg[
'data_folder_path'] and '_market_' not in ctrl_msg[
'data_folder_path']:
origin_dict['probe_path'] = 'data/market/probe.txt'
origin_dict['train_path'] = 'data/market/train.txt'
origin_dict['gallery_path'] = 'data/market/gallery.txt'
elif '_dukehead' in ctrl_msg['data_folder_path']:
origin_dict['probe_path'] = 'data/dukehead/probe.list'
origin_dict['train_path'] = 'data/dukehead/train.list'
origin_dict['gallery_path'] = 'data/dukehead/test.list'
elif '_duketail' in ctrl_msg['data_folder_path']:
origin_dict['probe_path'] = 'data/duketail/probe.list'
origin_dict['train_path'] = 'data/duketail/train.list'
origin_dict['gallery_path'] = 'data/duketail/test.list'
elif '_duketqtail' in ctrl_msg['data_folder_path']:
origin_dict['probe_path'] = 'data/duketqtail/probe.list'
origin_dict['train_path'] = 'data/duketqtail/train.list'
origin_dict['gallery_path'] = 'data/duketqtail/test.list'
elif '_dukequerytail' in ctrl_msg['data_folder_path']:
origin_dict['probe_path'] = 'data/dukequerytail/probe.list'
origin_dict['train_path'] = 'data/dukequerytail/train.list'
origin_dict['gallery_path'] = 'data/dukequerytail/test.list'
elif '_duke' in ctrl_msg['data_folder_path']:
origin_dict['probe_path'] = 'data/duke/probe.list'
origin_dict['train_path'] = 'data/duke/train.list'
origin_dict['gallery_path'] = 'data/duke/test.list'
if 'train' in ctrl_msg['data_folder_path']:
origin_dict['answer_path'] = origin_dict['train_path']
else:
origin_dict['answer_path'] = origin_dict['probe_path']
if 'r-' in origin_dict['src_distribution_pickle_path']:
# use track info before increment
origin_dict['rand_distribution_pickle_path'] = origin_dict[
'src_distribution_pickle_path'].replace('r-train', 'train_rand')
else:
# use track info after increment
origin_dict['rand_distribution_pickle_path'] = origin_dict[
'src_distribution_pickle_path'].replace('train', 'train_rand')
origin_dict['rand_distribution_pickle_path'] = origin_dict[
'src_distribution_pickle_path'].replace('train', 'train_rand')
for (k, v) in update_msg.items():
origin_dict[k] = v
return origin_dict
