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 st_fusion(source, target):
ctrl_msg['data_folder_path'] = source + '_' + target + '-train'
init_strict_img_st_fusion()
ctrl_msg['data_folder_path'] = source + '_' + target + '-train'
fusion_data_path = '/home/ls/dataset/TFusion/TrackViz/'
fusion_param = get_fusion_param()
fusion_train_rank_pids_path = fusion_data_path + fusion_param[
'eval_fusion_path']
fusion_train_rank_scores_path = fusion_data_path + fusion_param[
'fusion_normal_score_path']
ctrl_msg['data_folder_path'] = source + '_' + target + '-test'
fusion_param = get_fusion_param()
fusion_test_rank_pids_path = fusion_data_path + fusion_param[
'eval_fusion_path']
fusion_test_rank_scores_path = fusion_data_path + fusion_param[
'fusion_normal_score_path']
return fusion_train_rank_pids_path, fusion_train_rank_scores_path, fusion_test_rank_pids_path, fusion_test_rank_scores_path
def save_market_test_truth():
ctrl_msg['data_folder_path'] = 'market_market-test'
fusion_param = get_fusion_param()
answer_path = fusion_param['answer_path']
answer_lines = read_lines(answer_path)
query_tracks = list()
for answer in answer_lines:
info = answer.split('_')
if 'bmp' in info[2]:
info[2] = info[2].split('.')[0]
if len(info) > 4 and 'jpe' in info[6]:
query_tracks.append([info[0], int(info[1][0]), int(info[2])])
else:
query_tracks.append(
[info[0],
int(info[1][1]),
int(info[2]),
int(info[1][3])])
gallery_path = fusion_param['gallery_path']
gallery_lines = read_lines(gallery_path)
gallery_tracks = list()
for gallery in gallery_lines:
info = gallery.split('_')
if 'bmp' in info[2]:
info[2] = info[2].split('.')[0]
if len(info) > 4 and 'jpe' in info[6]:
gallery_tracks.append([info[0], int(info[1][0]), int(info[2])])
else:
gallery_tracks.append(
[info[0],
int(info[1][1]),
int(info[2]),
int(info[1][3])])
gallery_tracks.extend(query_tracks)
print(len(gallery_tracks))
deltas = [[list() for j in range(6)] for i in range(6)]
for i, market_probe_track in enumerate(gallery_tracks):
if gallery_tracks[i][0] == 0 or gallery_tracks[i][0] == -1:
continue
for j in range(len(gallery_tracks)):
if gallery_tracks[i][0] == gallery_tracks[j][0] \
and i != j \
and gallery_tracks[i][3] == gallery_tracks[j][3] \
and gallery_tracks[i][1] != gallery_tracks[j][1]:
if gallery_tracks[i][1] == 4 and gallery_tracks[j][1] - 1 == 5:
if j >= 19732:
print gallery_tracks[i][2] - gallery_tracks[j][2]
deltas[gallery_tracks[i][1] -
1][gallery_tracks[j][1] -
1].append(gallery_tracks[i][2] -
gallery_tracks[j][2])
for camera_delta in deltas:
for delta_s in camera_delta:
delta_s.sort()
pickle_save('true_market_pg.pck', deltas)
def iter_strict_img_st_fusion(on_test=False):
"""
call after img classifier update, train with new vision score and ep en
:param on_test:
:return:
"""
fusion_param = get_fusion_param()
# ep, en = get_shot_rate()
if on_test:
test_fusion(fusion_param)
else:
train_fusion(fusion_param)
def save_grid_train_truth():
ctrl_msg['data_folder_path'] = 'market_grid-cv0-train'
fusion_param = get_fusion_param()
market_train_tracks = train_tracks(fusion_param)
deltas = [[list() for j in range(6)] for i in range(6)]
for i, market_train_track in enumerate(market_train_tracks):
for j in range(0, len(market_train_tracks)):
if market_train_tracks[i][0] == market_train_tracks[j][0] \
and i != j \
and market_train_tracks[i][1] != market_train_tracks[j][1]:
deltas[market_train_tracks[i][1] -
1][market_train_tracks[j][1] -
1].append(market_train_tracks[i][2] -
market_train_tracks[j][2])
for camera_delta in deltas:
for delta_s in camera_delta:
delta_s.sort()
pickle_save('true_grid-cv0_train.pck', deltas)
print 'save sorted fusion scores'
# for i, person_ap_pids in enumerate(persons_ap_pids):
# for j in range(len(person_ap_pids)):
# write(log_path, '%d ' % person_ap_pids[person_score_idx_s[i][j]])
# write(map_score_path, '%.3f ' % persons_cross_scores[i][person_score_idx_s[i][j]])
# write(log_path, '\n')
# write(map_score_path, '\n')
return person_score_idx_s
if __name__ == '__main__':
ctrl_msg['data_folder_path'] = 'cuhk_duke-r-test'
ctrl_msg['ep'] = 0.0
ctrl_msg['en'] = 0.0
# fusion_param = get_fusion_param()
# fusion_st_img_ranker(fusion_param, fusion_param['pos_shot_rate'], fusion_param['neg_shot_rate'])
# eval_on_train_test(fusion_param, test_mode=True)
fusion_param = get_fusion_param()
fusion_st_gallery_ranker(fusion_param)
os.environ.setdefault('LD_LIBRARY_PATH', '/usr/local/cuda/lib64')
# os.system('/home/cwh/anaconda2/bin/python /home/cwh/coding/rank-reid/rank_reid.py 2 '
# + 'market /home/cwh/coding/TrackViz/' + fusion_param['eval_fusion_path'])
os.system(
'/home/cwh/anaconda2/bin/python /home/cwh/coding/rank-reid/rank_reid.py 2 '
+ 'duke /home/cwh/coding/TrackViz/' + fusion_param['eval_fusion_path'])
# fusion_st_img_ranker(fusion_param)
# delta_range, over_probs = fusion_curve(fusion_param)
# viz_fusion_curve(delta_range, [over_probs])
# pt = fusion_heat(fusion_param)
# viz_heat_map(pt)