max_pid_id = max(max_pid_id, track[video_name][frame_name]['num_boxes'])
if use_orb:
cor_file = os.path.join(image_dir, video_name, "".join([frame_id, '_', next_frame_id, '_orb.txt']))
else:
cor_file = os.path.join(image_dir, video_name, "".join([frame_id, '_', next_frame_id, '.txt']))
# regenerate the missed pair-matching txt
if not os.path.exists(cor_file) or os.stat(cor_file).st_size<200:
dm = "/home/yuliang/code/PoseTrack-CVPR2017/external/deepmatching/deepmatching"
img1_path = os.path.join(image_dir,video_name,frame_name)
img2_path = os.path.join(image_dir,video_name,next_frame_name)
if use_orb:
orb_matching(img1_path,img2_path, os.path.join(image_dir, video_name), frame_id, next_frame_id)
else:
cmd = "%s %s %s -nt 20 -downscale 2 -out %s"%(dm,img1_path,img2_path,cor_file)
os.system(cmd)
all_cors = np.loadtxt(cor_file)
# if there is no people in this frame, then copy the info from former frame
if track[video_name][next_frame_name]['num_boxes'] == 0:
track[video_name][next_frame_name] = copy.deepcopy(track[video_name][frame_name])
continue
cur_all_pids, cur_all_pids_fff = stack_all_pids(track[video_name], frame_list[:-1], idx, max_pid_id, link_len)
match_indexes, match_scores = best_matching_hungarian(
all_cors, cur_all_pids, cur_all_pids_fff, track[video_name][next_frame_name], weights, weights_fff, num, mag)
for pid1, pid2 in match_indexes:
# init tracking info of the first frame in one video
if idx == 0:
for pid in range(1, track[frame_name]['num_boxes'] + 1):
track[frame_name][pid]['new_pid'] = pid
track[frame_name][pid]['match_score'] = 0
max_pid_id = max(max_pid_id, track[frame_name]['num_boxes'])
cor_file = os.path.join(
image_dir, "".join([frame_id, '_', next_frame_id, '_orb.txt']))
# regenerate the missed pair-matching txt
if not os.path.exists(cor_file) or os.stat(cor_file).st_size < 200:
img1_path = os.path.join(image_dir, frame_name)
img2_path = os.path.join(image_dir, next_frame_name)
orb_matching(img1_path, img2_path, image_dir, frame_id,
next_frame_id)
all_cors = np.loadtxt(cor_file)
# if there is no people in this frame, then copy the info from former frame
if track[next_frame_name]['num_boxes'] == 0:
track[next_frame_name] = copy.deepcopy(track[frame_name])
continue
cur_all_pids, cur_all_pids_fff = stack_all_pids(
track, frame_list[:-1], idx, max_pid_id, link_len)
match_indexes, match_scores = best_matching_hungarian(
all_cors, cur_all_pids, cur_all_pids_fff, track[next_frame_name],
weights, weights_fff, num, mag)
for pid1, pid2 in match_indexes:
if match_scores[pid1][pid2] > match_thres:
max_pid_id = max(max_pid_id, track[frame_name]['num_boxes'])
temp_folder = os.path.join('E:/3DMPB/temp',
image_dir.split('\\')[-2],
image_dir.split('\\')[-1])
if not os.path.exists(temp_folder):
os.makedirs(temp_folder)
cor_file = os.path.join(
temp_folder,
"".join([frame_id, '_', next_frame_id, '_orb.txt']))
# regenerate the missed pair-matching txt
if not os.path.exists(
cor_file) or os.stat(cor_file).st_size < 200:
img1_path = os.path.join(image_dir, name)
img2_path = os.path.join(image_dir, next_name)
orb_matching(img1_path, img2_path, temp_folder, frame_id,
next_frame_id)
all_cors = np.loadtxt(cor_file)
# if there is no people in this frame, then copy the info from former frame
if track[next_frame_name]['num_boxes'] == 0:
track[next_frame_name] = copy.deepcopy(track[frame_name])
continue
cur_all_pids, cur_all_pids_fff = stack_all_pids(
track, frame_list[:-1], idx, max_pid_id, link_len)
match_indexes, match_scores = best_matching_hungarian(
all_cors, cur_all_pids, cur_all_pids_fff,
track[next_frame_name], weights, weights_fff, num, mag)
for pid1, pid2 in match_indexes:
if match_scores[pid1][pid2] > match_thres: