def eval_seq(opt,
dataloader,
data_type,
result_filename,
save_dir=None,
show_image=True,
frame_rate=30):
if save_dir:
mkdir_if_missing(save_dir)
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
results = []
frame_id = 0
tid_max, tid_temp = 1, 1
for path, img, img0 in dataloader:
if frame_id < 300:
frame_id += 1
continue
elif frame_id > 302:
break
else:
print(frame_id)
filename = path.split('/')[-1]
if '0000001' in path:
tid_max = tid_temp + 1
print(path, tid_max)
tracker = JDETracker(opt, frame_rate=frame_rate)
if frame_id % 100 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1. / max(1e-5, timer.average_time)))
# if frame_id >20:
# break
# run tracking
timer.tic()
blob = torch.from_numpy(img).cuda().unsqueeze(0)
online_targets = tracker.update_sep3(
blob,
img0,
conf_thres=[0.4, 0.3, 0.4, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5])
# print(online_targets)
online_tlwhs = []
online_ids = []
online_cids = [] #class id
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id + tid_max
tcid = t.class_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > opt.min_box_area: # and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
online_cids.append(tcid)
tid_temp = max(tid, tid_temp)
timer.toc()
# save results
results.append((filename, online_tlwhs, online_ids, online_cids))
if show_image or save_dir is not None:
online_im = vis.plot_tracking(img0,
online_tlwhs,
online_ids,
frame_id=frame_id,
fps=1. / timer.average_time)
if show_image:
cv2.imshow('online_im', online_im)
if save_dir is not None:
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)),
online_im)
frame_id += 1
# save results
write_results(result_filename, results, data_type)
return frame_id, timer.average_time, timer.calls
def eval_seq(opt,
dataloader,
data_type,
result_filename,
save_dir=None,
show_image=True,
frame_rate=30):
if save_dir:
mkdir_if_missing(save_dir)
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
results = []
len_all = len(dataloader)
start_frame = int(len_all / 2)
frame_id = int(len_all / 2)
for i, (path, img, img0) in enumerate(dataloader):
if i < start_frame:
continue
if frame_id % 20 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1. / max(1e-5, timer.average_time)))
img_pil = Image.open(path).convert('RGB')
normalize = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
img_norm = transforms.Compose(
[T.RandomResize([800], max_size=1333), normalize])
img_norm = img_norm(img_pil)
if frame_id % 20 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1. / max(1e-5, timer.average_time)))
# run tracking
timer.tic()
blob = torch.from_numpy(img).cuda().unsqueeze(0)
online_targets = tracker.update(img_norm.cuda().unsqueeze(0), img0)
online_tlwhs = []
online_ids = []
#online_scores = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
#online_scores.append(t.score)
timer.toc()
# save results
results.append((frame_id + 1, online_tlwhs, online_ids))
#results.append((frame_id + 1, online_tlwhs, online_ids, online_scores))
if show_image or save_dir is not None:
online_im = vis.plot_tracking(img0,
online_tlwhs,
online_ids,
frame_id=frame_id,
fps=1. / timer.average_time)
if show_image:
cv2.imshow('online_im', online_im)
if save_dir is not None:
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)),
online_im)
frame_id += 1
# save results
write_results(result_filename, results, data_type)
#write_results_score(result_filename, results, data_type)
return frame_id, timer.average_time, timer.calls
def eval_seq(opt,
dataloader,
data_type,
result_filename,
save_dir='.',
show_image=True,
frame_rate=25):
tracker = JDETracker(opt, frame_rate=frame_rate)
p = path_root_index[5]
if save_dir:
save_dir = osp.join(save_dir, p)
mkdir_if_missing(save_dir)
image_path = getimage_path(path_root + p)
timer = Timer()
results = []
frame_id = -1
result_array_list = []
result = []
for path in image_path:
# img=cv2.imread(path)
img0 = cv2.imread(path) # BGR
# assert img0 is not None, 'Failed to load ' + img_path
img_height = img0.shape[0]
img_width = img0.shape[1]
# print(img_height,img_width)
# print(img0.shape)
# Padded resize
img, _, _, _ = letterbox(img0, height=608, width=1088)
# Normalize RGB
img = img[:, :, ::-1].transpose(2, 0, 1)
img = np.ascontiguousarray(img, dtype=np.float32)
img /= 255.0
frame_id += 1
# if frame_id % 20 == 0:
# logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time)))
# if frame_id==2:
# break
# run tracking
timer.tic()
blob = torch.from_numpy(img).cuda().unsqueeze(0)
online_targets = tracker.update(blob, img0)
online_tlwhs = []
online_ids = []
online_cref = []
# result_array_list=[]
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
confidence = t.score
vertical = tlwh[2] / tlwh[3] > 1.6
if confidence < 0.3:
if tlwh[2] * tlwh[3] > 2700 and not vertical and tlwh[
2] * tlwh[3] < 100000:
res = [frame_id, tid]
res += list(tlwh)
res += [1, 0]
online_tlwhs.append(tlwh)
result_array_list.append(res)
online_cref.append(confidence)
# print(confidence)
# result_array_list.append(tlwh)
online_ids.append(tid)
elif confidence >= 0.3:
if tlwh[2] * tlwh[3] > 1000 and not vertical and tlwh[
2] * tlwh[3] < 100000:
# print(tlwh[2] * tlwh[3])
res = [frame_id, tid]
res += list(tlwh)
res += [1, 0]
online_tlwhs.append(tlwh)
result_array_list.append(res)
online_cref.append(confidence)
# print(confidence)
# result_array_list.append(tlwh)
online_ids.append(tid)
# if frame_id==2:
# break
timer.toc()
# save results
print(frame_id)
# if result_array_list:
online_tlwhs = np.array(online_tlwhs)
online_ids = np.array(online_ids)
online_cref = np.array(online_cref)
# print(online_tlwhs)
# print(online_tlwhs.shape)
# print(online_ids.shape)
# pick=non_max_suppression(online_tlwhs,0.7,online_cref)
# online_tlwhsnms=online_tlwhs[pick]
# online_idsnms=online_ids[pick]
# online_crefnms=online_cref[pick]
# result_array_list2=np.array(result_array_list).copy()[pick]
# result+=list(result_array_list2)
# print(result)
# print(frame_id,online_idsnms)
# result.append(online_tlwhsnms)
if show_image or save_dir is not None:
online_im = vis.plot_tracking(img0,
online_tlwhs,
online_ids,
scores=online_cref,
frame_id=frame_id,
fps=1. / timer.average_time)
if show_image:
cv2.imshow('online_im', online_im)
cv2.waitKey(1)
if save_dir is not None:
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)),
online_im)
res_array = np.array(result_array_list)
# print(res_array)
# print(res_array.shape)
tmp_data1 = np.where(res_array[:, [2]] < 0,
res_array[:, [2]] + res_array[:, [4]], res_array[:,
[4]])
res_array[:, [4]] = tmp_data1
tmp_data = np.where(res_array[:, [3]] < 0,
res_array[:, [3]] + res_array[:, [5]], res_array[:,
[5]])
res_array[:, [5]] = tmp_data
res_array[:, [2, 3]] = np.maximum(res_array[:, [2, 3]], 0)
# print(res_array)
res_array = np.round(res_array, 0)
# res_array=cutmorecord(res_array,img_width,img_height)
# print(res_array)
np.savetxt("{}.txt".format(p),
res_array,
fmt='%d,%d,%d,%d,%d,%d,%d,%d',
delimiter=',')
# save results
# write_results(result_filename, results, data_type)
return frame_id, timer.average_time, timer.calls
def eval_seq(opt,
dataloader,
data_type,
result_filename=None,
save_dir=None,
show_image=False,
frame_rate=10,
use_cuda=True):
if save_dir:
mkdir_if_missing(save_dir)
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
person_mot = PersonMOT()
results = []
frame_id = 0
rflag = False
# for path, img, img0 in dataloader:
for i, (path, img, img0) in enumerate(dataloader):
if i % 12 != 0:
continue
if frame_id % 20 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1. / max(1e-5, timer.average_time)))
# run tracking
timer.tic()
if use_cuda:
blob = torch.from_numpy(img).cuda().unsqueeze(0)
else:
blob = torch.from_numpy(img).unsqueeze(0)
online_targets = tracker.update(blob, img0)
online_tlwhs = []
online_ids = []
#online_scores = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
# online_scores.append(t.score)
timer.toc()
# 发送atc帧信息
rflag = atcs.send_info(opt.device_id, opt.url, online_ids,
online_tlwhs, opt.input_stream)
if rflag and i % opt.check_setp == 0:
person_mot.handle_crime(opt.device_id, opt.url, opt.trigger,
online_ids, online_tlwhs, img0)
# 是否渲染图像
if show_image or save_dir:
online_im = vis.plot_tracking(img0,
online_tlwhs,
online_ids,
frame_id=frame_id,
fps=1. / timer.average_time)
results.append((frame_id + 1, online_tlwhs, online_ids))
# 得分
# results.append((frame_id + 1, online_tlwhs, online_ids, online_scores))
# 展示图像
if show_image:
cv2.imshow('online_im', online_im)
cv2.waitKey(1)
# 存储图像
if save_dir:
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)),
online_im)
frame_id += 1
# save results
if opt.save:
write_results(result_filename, results, data_type)
#write_results_score(result_filename, results, data_type)
return frame_id, timer.average_time, timer.calls
class VideoTracker(object):
def __init__(self, args, video_path):
self.args = args
self.video_path = video_path
self.logger = get_logger("root")
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.rtsp != "":
print("Using rtsp " + str(args.rtsp))
self.vdo = cv2.VideoCapture(args.rtsp)
elif args.cam != -1:
print("Using webcam " + str(args.cam))
self.vdo = cv2.VideoCapture(args.cam)
else:
self.vdo = cv2.VideoCapture()
frame_rate = 30
cfg_dict = parse_model_cfg(args.cfg)
self.width, self.height = int(cfg_dict[0]['width']), int(
cfg_dict[0]['height'])
args.img_size = [int(cfg_dict[0]['width']), int(cfg_dict[0]['height'])]
self.tracker = JDETracker(args, frame_rate=frame_rate)
self.known_faces = {}
self.faceUtils = FaceUtils()
self.camUtils = CamUtils(args)
self.tmp_imgs = []
self.tmp_moves = []
self.result_imgs = queue.Queue()
self.is_running = True
print("Loading Control Done.")
def __enter__(self):
if self.args.rtsp != "":
ret, frame = self.vdo.read()
assert ret, "Error: RTSP error"
self.im_width = frame.shape[0]
self.im_height = frame.shape[1]
elif self.args.cam != -1:
ret, frame = self.vdo.read()
assert ret, "Error: Camera error"
self.im_width = frame.shape[0]
self.im_height = frame.shape[1]
else:
assert os.path.isfile(self.video_path), "Path error"
self.vdo.open(self.video_path)
self.im_width = int(self.vdo.get(cv2.CAP_PROP_FRAME_WIDTH))
self.im_height = int(self.vdo.get(cv2.CAP_PROP_FRAME_HEIGHT))
assert self.vdo.isOpened()
if self.args.save_path:
os.makedirs(self.args.save_path, exist_ok=True)
# path of saved video and results
self.save_video_path = os.path.join(self.args.save_path,
"results.avi")
self.save_results_path = os.path.join(self.args.save_path,
"results.txt")
# create video writer
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
fps = int(self.vdo.get(cv2.CAP_PROP_FPS))
self.writer = cv2.VideoWriter(self.save_video_path, fourcc, fps,
(self.im_width, self.im_height))
# logging
self.logger.info("Save results to {}".format(self.args.save_path))
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
if exc_type:
print(exc_type, exc_value, exc_traceback)
def read_thread(self):
while True:
time_start = time.time()
# _, ori_im = self.vdo.retrieve()
ret, ori_im = self.vdo.read()
if not ret:
if self.args.rtsp != "":
print("Using rtsp " + str(self.args.rtsp))
self.vdo = cv2.VideoCapture(self.args.rtsp)
elif self.args.cam != -1:
print("Using webcam " + str(self.args.cam))
self.vdo = cv2.VideoCapture(self.args.cam)
else:
self.vdo = cv2.VideoCapture()
continue
# ori_im = cv2.resize(ori_im, (800, 600))
self.tmp_imgs.append(copy.deepcopy(ori_im))
# print("read time: {}".format(time.time() - time_start))
def jpeg_thread(self):
mjpeg_server = MjpegServer(port=8080)
print('MJPEG server started... 8080')
while self.is_running:
ori_im = self.result_imgs.get()
mjpeg_server.send_img(ori_im)
mjpeg_server.shutdown()
def move_thread(self):
while True:
if len(self.tmp_moves) == 0:
continue
center, xywh = copy.deepcopy(self.tmp_moves[-1])
self.tmp_moves = []
self.camUtils.move_cam(center, xywh)
def start_thread(self):
thread_read = threading.Thread(target=self.read_thread, args=())
thread_read.daemon = True
thread_read.start()
thread_jpeg = threading.Thread(target=self.jpeg_thread, args=())
thread_jpeg.daemon = True
thread_jpeg.start()
thread_move = threading.Thread(target=self.move_thread, args=())
thread_move.daemon = True
thread_move.start()
def run(self):
results = []
idx_frame = 0
idx_frame1 = 0
face_names = {}
self.start_thread()
timer = Timer()
while True:
# _, ori_im = self.vdo.retrieve()
if self.args.rtsp != "" or self.args.cam != -1:
if len(self.tmp_imgs) == 0:
continue
ori_im = copy.deepcopy(self.tmp_imgs[-1])
self.tmp_imgs = []
else:
ret, ori_im = self.vdo.read()
idx_frame += 1
if idx_frame % self.args.frame_interval:
continue
idx_frame1 += 1
start = time.time()
im = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB)
center = (int(ori_im.shape[1] / 2), int(ori_im.shape[0] / 2))
# cv2.circle(ori_im, center, 2, (255,0,0), 0)
img, _, _, _ = letterbox(ori_im,
height=self.height,
width=self.width)
# Normalize RGB
img = img[:, :, ::-1].transpose(2, 0, 1)
img = np.ascontiguousarray(img, dtype=np.float32)
img /= 255.0
# run tracking
timer.tic()
time_start = time.time()
blob = torch.from_numpy(img).cuda().unsqueeze(0)
print("input time: {}".format(time.time() - time_start))
time_start = time.time()
online_targets = self.tracker.update(blob, ori_im)
print("tracker time: {}".format(time.time() - time_start))
online_tlwhs = []
online_ids = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
timer.toc()
for tlwh, tid in zip(online_tlwhs, online_ids):
face_name = face_names.get(tid)
if face_name is None:
bb_xyxy = tlwh[0], tlwh[
1], tlwh[0] + tlwh[2], tlwh[1] + tlwh[3]
face_name = self.faceUtils.detect_face(im, bb_xyxy)
face_names[tid] = face_name
print("{} {}".format(tid, face_names.get(tid)))
if (idx_frame1 % self.args.move_skip
) == 0 and face_names.get(tid) == self.args.obj:
print("move")
# self.tmp_moves.append((center, xywh))
self.camUtils.move_cam(center, tlwh)
# save results
results.append((idx_frame - 1, online_tlwhs, online_ids))
ori_im = vis.plot_tracking(ori_im,
online_tlwhs,
online_ids,
frame_id=idx_frame,
fps=1. / timer.average_time)
end = time.time()
self.result_imgs.put_nowait(ori_im)
# logging
self.logger.info("time: {:.03f}s, fps: {:.03f}, detection numbers: {}, tracking numbers: {}" \
.format(end - start, 1 / (end - start), len(online_tlwhs), len(online_ids)))
#print(ROS_Img)
#self.get_logger().info('I heard: "%s"' % msg.data)
demo(opt, msg, self.frame_id)
self.frame_id = self.frame_id + 1
#rospy.Timer(rospy.Duration(10000), stop_callback)
def get_ROS_Img(self):
return self.ROS_Img
if __name__ == '__main__':
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
print("============================HEREEEE1=============================")
opt = opts().init()
print("============================HEREEEE2=============================")
tracker = JDETracker(opt, 30)
#ROS STUFF
rclpy.init()
minimal_subscriber = MinimalSubscriber()
print(
"============================HEREEEE2.25=============================")
try:
rclpy.spin(minimal_subscriber)
# for i in range(150):
# rclpy.spin_once(minimal_subscriber)
#preprocessing(init)
except Exception as e:
print("SUB NO WORK", e)
ROS_Img = minimal_subscriber.get_ROS_Img()
# Destroy the node explicitly
def eval_seq(opt,
dataloader,
data_type,
result_filename,
save_dir=None,
show_image=True,
frame_rate=30):
'''
Processes the video sequence given and provides the output of tracking result (write the results in video file)
It uses JDE model for getting information about the online targets present.
Parameters
----------
opt : Namespace
Contains information passed as commandline arguments.
dataloader : LoadVideo
Instance of LoadVideo class used for fetching the image sequence and associated data.
data_type : String
Type of dataset corresponding(similar) to the given video.
result_filename : String
The name(path) of the file for storing results.
save_dir : String
Path to the folder for storing the frames containing bounding box information (Result frames).
show_image : bool
Option for shhowing individial frames during run-time.
frame_rate : int
Frame-rate of the given video.
Returns
-------
(Returns are not significant here)
frame_id : int
Sequence number of the last sequence
'''
'''
width = dataloader.vw
height = dataloader.vh
'''
width = 640
height = 480
'''
process = (
ffmpeg
#new added re
#new added preset ultrafast (try different mode if not ok)
.input('pipe:', format = 'rawvideo', pix_fmt = 'rgb24', s = '{}x{}'.format(width, height), re = None)
#new added
#.setpts('1.7*PTS')
.output('../try.m3u8', format = 'hls', pix_fmt = 'yuv420p', vcodec = 'libx264', preset = "ultrafast", hls_time = 10, hls_list_size = 2, start_number = 0, hls_flags = 'delete_segments+append_list', hls_segment_filename = '../try_%05d.ts')
.overwrite_output()
.run_async(pipe_stdin = True)
)
'''
track_id = 0
if save_dir:
mkdir_if_missing(save_dir)
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
results = []
frame_id = 0
cv2.namedWindow('online_im')
cv2.setMouseCallback('online_im', mouse_click)
#ffmpeg process
for path, img, img0 in dataloader:
if frame_id % 20 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1. / max(1e-5, timer.average_time)))
# run tracking
timer.tic()
blob = torch.from_numpy(img).cuda().unsqueeze(0)
online_targets = tracker.update(blob, img0)
online_tlwhs = []
online_ids = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
timer.toc()
# save results
results.append((frame_id + 1, online_tlwhs, online_ids))
if show_image or save_dir is not None:
online_im, track_id = vis.plot_tracking(img0,
online_tlwhs,
online_ids,
frame_id=frame_id,
fps=1. /
timer.average_time,
single=single,
mouse_x=mouse_x,
mouse_y=mouse_y,
track_id=track_id)
if show_image:
pass
#cv2.imshow('online_im', online_im)
#cv2.waitKey(1)
#plt.imshow(online_im)
#plt.show()
#online_im_rgb = cv2.cvtColor(online_im, cv2.COLOR_BGR2RGB)
#write_frame(process, online_im_rgb)
stream(online_im)
if save_dir is not None:
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)),
online_im)
frame_id += 1
# save results
write_results(result_filename, results, data_type)
# close process
#close_process(process)
terminate_stream()
return frame_id, timer.average_time, timer.calls
def run(opt):
"""
:param opt:
:return:
"""
# Set dataset and device
dataset = LoadImages(opt.source, img_size=opt.img_size)
device = torch_utils.select_device(device='cpu' if ONNX_EXPORT else opt.device)
opt.device = device
# Set result output
frame_dir = opt.save_dir + '/frame'
if not os.path.isdir(frame_dir):
os.makedirs(frame_dir)
else:
shutil.rmtree(frame_dir)
os.makedirs(frame_dir)
# class name to class id and class id to class name
names = load_classes(opt.names)
id2cls = defaultdict(str)
cls2id = defaultdict(int)
for cls_id, cls_name in enumerate(names):
id2cls[cls_id] = cls_name
cls2id[cls_name] = cls_id
# Set tracker
tracker = JDETracker(opt) # Joint detectionand embedding
for fr_id, (path, img, img0, vid_cap) in enumerate(dataset):
img = torch.from_numpy(img).to(opt.device)
img = img.float() # uint8 to fp32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# update tracking result of this frame
online_targets_dict = tracker.update_tracking(img, img0)
# print(online_targets_dict)
# aggregate frame's results
online_tlwhs_dict = defaultdict(list)
online_ids_dict = defaultdict(list)
for cls_id in range(opt.num_classes):
# process each object class
online_targets = online_targets_dict[cls_id]
for track in online_targets:
tlwh = track.tlwh
t_id = track.track_id
# vertical = tlwh[2] / tlwh[3] > 1.6 # box宽高比判断:w/h不能超过1.6?
# if tlwh[2] * tlwh[3] > opt.min_box_area: # and not vertical:
online_tlwhs_dict[cls_id].append(tlwh)
online_ids_dict[cls_id].append(t_id)
if opt.show_image:
if tracker.frame_id > 0:
online_im = vis.plot_tracks(image=img0,
tlwhs_dict=online_tlwhs_dict,
obj_ids_dict=online_ids_dict,
num_classes=opt.num_classes,
frame_id=fr_id,
id2cls=id2cls)
if opt.save_dir is not None:
save_path = os.path.join(frame_dir, '{:05d}.jpg'.format(fr_id))
cv2.imwrite(save_path, online_im)
# output tracking result as video
src_name = os.path.split(opt.source)[-1]
name, suffix = src_name.split('.')
result_video_path = opt.save_dir + '/' + name + '_track' + '.' + suffix
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -b 5000k -c:v mpeg4 {}' \
.format(frame_dir, result_video_path)
os.system(cmd_str)
def eval_seq(opt,
dataloader,
data_type,
result_filename,
save_dir=None,
show_image=True,
frame_rate=30,
use_cuda=True):
if save_dir:
mkdir_if_missing(save_dir)
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
results = []
frame_id = 0
#for path, img, img0 in dataloader:
for i, (path, img, img0) in enumerate(dataloader):
#if i % 8 != 0:
#continue
if frame_id % 20 == 0 and i > 1:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1. / max(1e-5, timer.average_time)))
# run tracking
if i > 1:
timer.tic()
if use_cuda:
blob = torch.from_numpy(img).cuda().unsqueeze(0)
else:
blob = torch.from_numpy(img).unsqueeze(0)
online_targets = tracker.update(blob, img0)
# print(f"Blob shape: {blob.shape}, Image shape: {img0.shape}: Online targets: {online_targets}")
online_tlwhs = []
online_ids = []
#online_scores = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
#online_scores.append(t.score)
if i > 1:
timer.toc()
# save results
results.append((frame_id + 1, online_tlwhs, online_ids))
#results.append((frame_id + 1, online_tlwhs, online_ids, online_scores))
if show_image or save_dir is not None and i > 1:
online_im = vis.plot_tracking(img0,
online_tlwhs,
online_ids,
frame_id=frame_id,
fps=1. / timer.average_time)
if show_image and i > 1:
cv2.imshow('online_im', online_im)
if save_dir is not None and i > 1:
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)),
online_im)
frame_id += 1
# save results
write_results(result_filename, results, data_type)
#write_results_score(result_filename, results, data_type)
return frame_id, timer.average_time, timer.calls
def eval_seq(opt,
dataloader,
data_type,
result_filename,
save_dir=None,
bbox_dir=None,
show_image=True,
frame_rate=30):
if save_dir:
mkdir_if_missing(save_dir)
if bbox_dir:
mkdir_if_missing(bbox_dir)
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
results = []
frame_id = 0
for path, img0, detection in dataloader:
if frame_id % 1 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1. / max(1e-5, timer.average_time)))
# run tracking
timer.tic()
online_targets, detection_boxes = tracker.update(detection, img0)
online_tlwhs = []
online_ids = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
#bbox detection plot
box_tlbrs = []
box_scores = []
img_bbox = img0.copy()
for box in detection_boxes:
tlbr = box.tlbr
tlwh = box.tlwh
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > opt.min_box_area:
box_tlbrs.append(tlbr)
box_scores.append(box.score)
timer.toc()
# save results
results.append(
([frame_id + 1] * len(online_tlwhs), online_tlwhs, online_ids))
if show_image or save_dir is not None:
online_im = vis.plot_tracking(img0,
online_tlwhs,
online_ids,
frame_id=frame_id,
fps=1. / timer.average_time)
bbox_im = vis.plot_detections(img_bbox,
box_tlbrs,
scores=box_scores)
if show_image:
cv2.imshow('online_im', online_im)
cv2.imshow('bbox_im', bbox_im)
if save_dir is not None:
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)),
online_im)
cv2.imwrite(os.path.join(bbox_dir, '{:05d}.jpg'.format(frame_id)),
bbox_im)
frame_id += 1
# save results
track_pools = []
id_pools = []
write_results(result_filename, results, data_type)
return frame_id, timer.average_time, timer.calls
def __init__(self, opt, frame_rate=30):
JDETracker.__init__(self, opt, frame_rate=frame_rate)
self.substractor = SubstracktorDetector()
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression')
parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes')
parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer')
parser.add_argument('--input-video', type=str, help='path to the input video')
parser.add_argument('--output-format', type=str, default='text', choices=['video', 'text'], help='Expected output format. Video or text.')
parser.add_argument('--output-root', type=str, default='results', help='expected output root path')
parser.add_argument("--count", action="store_true", default=False, help='Enable the counting output')
opt = parser.parse_args()
cfg_dict = parse_model_cfg(opt.cfg)
opt.img_size = [int(cfg_dict[0]['width']), int(cfg_dict[0]['height'])]
tracker = JDETracker(opt)
tracker.model.eval()
device = 'cuda'
# ------------------------ export -----------------------------
output_onnx = 'trmot.onnx'
print("==> Exporting model to ONNX format at '{}'".format(output_onnx))
input_names = ['images']
output_names = ['scores']
#inputs = torch.randn(1, 3, detector.net.width, detector.net.height).to(device)
inputs = torch.randn(1, 3, 480, 864).to(device)
torch_out = torch.onnx._export(tracker.model, inputs, output_onnx, export_params=True, verbose=False, opset_version=11, enable_onnx_checker=False,
input_names=input_names, output_names=output_names)
def eval_seq(opt,
dataloader,
data_type,
result_filename,
save_dir=None,
show_image=True,
frame_rate=30):
'''
Processes the video sequence given and provides the output of tracking result (write the results in video file)
It uses JDE model for getting information about the online targets present.
Parameters
----------
opt : Namespace
Contains information passed as commandline arguments.
dataloader : LoadVideo
Instance of LoadVideo class used for fetching the image sequence and associated data.
data_type : String
Type of dataset corresponding(similar) to the given video.
result_filename : String
The name(path) of the file for storing results.
save_dir : String
Path to the folder for storing the frames containing bounding box information (Result frames).
show_image : bool
Option for shhowing individial frames during run-time.
frame_rate : int
Frame-rate of the given video.
Returns
-------
(Returns are not significant here)
frame_id : int
Sequence number of the last sequence
'''
width, height = dataloader.w, dataloader.h
if save_dir:
mkdir_if_missing(save_dir)
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
results = []
frame_id = 0
# for selected object tracking
global click_pos
global is_selected
selected_id = None
# set video output writer
counter = 0
encode = 0x00000021
output_video = cv2.VideoWriter(
os.path.join(save_dir, f'result_{counter}.mp4'), encode, 5,
(width, height), True)
# start tracking
for path, img, img0 in dataloader:
if frame_id % 100 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1. / max(1e-5, timer.average_time)))
output_video.release()
# Call MP4Box to divide new mp4 file
output_video = cv2.VideoWriter(
os.path.join(save_dir, f'result_{counter}.mp4'), encode, 5,
(width, height), True)
counter += 1
# run tracking
timer.tic()
blob = torch.from_numpy(img).cuda().unsqueeze(0)
online_targets = tracker.update(blob, img0)
online_tlwhs = []
online_ids = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
timer.toc()
# save results
results.append((frame_id + 1, online_tlwhs, online_ids))
if show_image or save_dir is not None:
# get visualization result and some control flags for selected object tracking
online_im, click_pos, selected_id, is_selected = vis.plot_tracking(
img0,
online_tlwhs,
online_ids,
frame_id=frame_id,
fps=1. / timer.average_time,
selected_id=selected_id,
click_pos=click_pos,
is_selected=is_selected)
if show_image:
# bind mouse event linstener
cv2.setMouseCallback("online_im", on_click)
cv2.imshow('online_im', online_im)
if save_dir is not None:
cv2.imwrite(
os.path.join(save_dir, 'frame', '{:05d}.jpg'.format(frame_id)),
online_im)
output_video.write(online_im)
frame_id += 1
output_video.release()
# save results
write_results(result_filename, results, data_type)
return frame_id, timer.average_time, timer.calls
cfg_dict = parse_model_cfg(path=command_args.cfg)
command_args.img_size = [int(cfg_dict[0]["width"]), int(cfg_dict[0]["height"])]
timer = Timer()
logger.info('Starting tracking...')
dataloader = datasets.LoadVideo(command_args.input_video, command_args.img_size)
frame_rate = dataloader.frame_rate
orig_width = dataloader.vw
orig_height = dataloader.vh
width = dataloader.w
height = dataloader.h
video_writer = cv2.VideoWriter(os.path.join(command_args.output_dir, os.path.splitext(os.path.basename(command_args.input_video))[0] + ".mp4"),
cv2.VideoWriter_fourcc(*"mp4v"), float(frame_rate), (orig_width, orig_height))
tracker = JDETracker(opt=command_args, frame_rate=frame_rate)
video_info = {}
frame_id = 0
for path, img, img0, orig_img in dataloader:
frame_info = {}
if frame_id % 20 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time)))
# run tracking
timer.tic()
blob = torch.from_numpy(img).cuda().unsqueeze(0)
online_targets = tracker.update(blob, img0)
online_tlwhs = []
online_ids = []
for t in online_targets:
def eval_seq(opt,
dataloader,
data_type,
result_filename,
save_dir=None,
show_image=True,
frame_rate=30):
if save_dir:
mkdir_if_missing(save_dir)
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
results = []
frame_id = 0
'''
img: Normalized RGB image
img0: BGR image
'''
for path, img, img0 in dataloader:
if frame_id % 20 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1. / max(1e-5, timer.average_time)))
# run tracking
timer.tic()
blob = torch.from_numpy(img).cuda().unsqueeze(0)
# print("\n==> blob.size", blob.size()) 1, 3, 608, 1088
'''
tracker update
'''
online_targets = tracker.update(blob, img0)
online_tlwhs = []
online_ids = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
'''
print("==> [track.eval_seq] tracker's output-> online_targets:", online_targets)
try:
print("==> [track.eval_seq] len(online_tlwhs):", len(online_tlwhs))
print("==> [track.eval_seq] online_tlwhs[0]:", online_tlwhs[0])
print("==> [track.eval_seq] online_ids[0]:", online_ids[0])
except:
pass
partial output:
==> [multi-tracker.update] len(output_stracks): 5
==> [track.eval_seq] tracker's output-> online_targets: [OT_1_(1-13), OT_2_(1-13), OT_3_(1-13), OT_20_(10-13), OT_7_(2-13)]
==> [track.eval_seq] len(online_tlwhs): 5
==> [track.eval_seq] online_tlwhs[0]: [ 802.38 163.64 24.074 57.376]
==> [track.eval_seq] online_ids[0]: 1
==> [multi-tracker.update] len(output_stracks): 7
==> [track.eval_seq] tracker's output-> online_targets: [OT_1_(1-14), OT_2_(1-14), OT_3_(1-14), OT_20_(10-14), OT_7_(2-14), OT_23_(13-14), OT_13_(4-14)]
==> [track.eval_seq] len(online_tlwhs): 7
==> [track.eval_seq] online_tlwhs[0]: [ 809.96 163.69 25.305 60.319]
==> [track.eval_seq] online_ids[0]: 1
==> [multi-tracker.update] len(output_stracks): 7
==> [track.eval_seq] tracker's output-> online_targets: [OT_1_(1-15), OT_2_(1-15), OT_3_(1-15), OT_20_(10-15), OT_7_(2-15), OT_23_(13-15), OT_19_(10-15)]
==> [track.eval_seq] len(online_tlwhs): 7
==> [track.eval_seq] online_tlwhs[0]: [ 818.46 164.4 26.832 63.971]
==> [track.eval_seq] online_ids[0]: 1
'''
timer.toc()
# save results
results.append((frame_id + 1, online_tlwhs, online_ids))
if show_image or save_dir is not None:
online_im = vis.plot_tracking(img0,
online_tlwhs,
online_ids,
frame_id=frame_id,
fps=1. / timer.average_time)
if show_image:
cv2.imshow('online_im', online_im)
if save_dir is not None:
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)),
online_im)
frame_id += 1
# save results
write_results(result_filename, results, data_type)
return frame_id, timer.average_time, timer.calls
def eval_seq(opt,
dataloader,
data_type,
result_filename,
save_dir=None,
show_image=True,
frame_rate=30,
use_cuda=True,
gt_path=None):
gt_results_dict = dict()
if gt_path:
if os.path.isfile(gt_path):
with open(gt_path, 'r') as f:
for line in f.readlines():
linelist = line.split(',')
if len(linelist) < 7:
continue
fid = int(linelist[0])
if fid < 1:
continue
gt_results_dict.setdefault(fid, list())
box_size = float(linelist[4]) * float(linelist[5])
score = 1
# if box_size > 7000:
# if box_size <= 7000 or box_size >= 15000:
# if box_size < 15000:
# continue
tlwh = tuple(map(float, linelist[2:6]))
target_id = (linelist[1])
gt_results_dict[fid].append((tlwh, target_id, score))
if save_dir:
mkdir_if_missing(save_dir)
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
results = []
frame_id = 0
#for path, img, img0 in dataloader:
for i, (path, img, img0) in enumerate(dataloader):
#if i % 8 != 0:
#continue
if frame_id % 20 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1. / max(1e-5, timer.average_time)))
# run tracking
timer.tic()
if use_cuda:
blob = torch.from_numpy(img).cuda().unsqueeze(0)
else:
blob = torch.from_numpy(img).unsqueeze(0)
online_targets = tracker.update(blob, img0)
online_tlwhs = []
online_ids = []
#online_scores = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
#online_scores.append(t.score)
timer.toc()
# save results
results.append((frame_id + 1, online_tlwhs, online_ids))
#results.append((frame_id + 1, online_tlwhs, online_ids, online_scores))
if show_image or save_dir is not None:
online_im = vis.plot_tracking(img0,
online_tlwhs,
online_ids,
frame_id=frame_id,
fps=1. / timer.average_time,
gt_box=gt_results_dict[frame_id + 1])
if show_image:
cv2.imshow('online_im', online_im)
if save_dir is not None:
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)),
online_im)
frame_id += 1
# save results
write_results(result_filename, results, data_type)
#write_results_score(result_filename, results, data_type)
return frame_id, timer.average_time, timer.calls
def eval_seq(opt,
dataloader,
data_type,
result_filename,
save_dir=None,
show_image=True,
frame_rate=30):
'''
Processes the video sequence given and provides the output of tracking result (write the results in video file)
It uses JDE model for getting information about the online targets present.
Parameters
----------
opt : Namespace
Contains information passed as commandline arguments.
dataloader : LoadVideo
Instance of LoadVideo class used for fetching the image sequence and associated data.
data_type : String
Type of dataset corresponding(similar) to the given video.
result_filename : String
The name(path) of the file for storing results.
save_dir : String
Path to the folder for storing the frames containing bounding box information (Result frames).
show_image : bool
Option for shhowing individial frames during run-time.
frame_rate : int
Frame-rate of the given video.
Returns
-------
(Returns are not significant here)
frame_id : int
Sequence number of the last sequence
'''
if save_dir:
mkdir_if_missing(save_dir)
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
results = []
frame_id = 0
for path, img, img0 in dataloader:
if frame_id % 20 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1. / max(1e-5, timer.average_time)))
# run tracking
timer.tic()
blob = torch.from_numpy(img).cuda().unsqueeze(0)
online_targets = tracker.update(blob, img0)
online_tlwhs = []
online_ids = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
timer.toc()
# save results
results.append((frame_id + 1, online_tlwhs, online_ids))
if show_image or save_dir is not None:
online_im = vis.plot_tracking(img0,
online_tlwhs,
online_ids,
frame_id=frame_id,
fps=1. / timer.average_time)
if show_image:
cv2.imshow('online_im', online_im)
if save_dir is not None:
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)),
online_im)
frame_id += 1
# save results
write_results(result_filename, results, data_type)
return frame_id, timer.average_time, timer.calls
def eval_seq(opt,
dataloader,
data_type,
result_filename,
countFileName,
save_dir=None,
show_image=True,
frame_rate=30):
device = torch.device("cpu" if opt.cpu else "cuda")
if save_dir:
mkdir_if_missing(save_dir)
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
results = []
frame_id = 0
ids = set()
if opt.le:
skip_frames = 30
for path, img, img0 in tqdm(dataloader):
if opt.le and frame_id % skip_frames != 0:
frame_id += 1
continue
if frame_id % opt.skip != 0:
frame_id += 1
continue
#logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1./max(1e-5, timer.average_time)))
# run trackering
timer.tic()
start = time.time()
blob = torch.from_numpy(img).to(device).unsqueeze(0)
online_targets = tracker.update(blob, img0)
online_tlwhs = []
online_ids = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
ids.add(tid)
end = time.time()
timer.toc()
# save results
results.append((frame_id + 1, online_tlwhs, online_ids))
if show_image or save_dir is not None:
online_im = vis.plot_tracking(img0,
online_tlwhs,
online_ids,
frame_id=frame_id,
fps=1. / timer.average_time)
if show_image:
cv2.imshow('online_im', online_im)
if save_dir is not None:
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)),
online_im)
ratio_x = float(dataloader.width) / img0.shape[1]
ratio_y = float(dataloader.height) / img0.shape[0]
if opt.AVA:
ratio_y = 1080. / img0.shape[0]
countFile = open(countFileName, 'a')
txt = '{:.6f}'.format(end - start)
for id, box in zip(online_ids, online_tlwhs):
txt += ',{:.1f},{:.1f},{:.1f},{:.1f},-2,-2,-2,-2,{:d},-2,-2'.format(
box[0] * ratio_x, box[1] * ratio_y,
(box[0] + box[2]) * ratio_x, (box[1] + box[3]) * ratio_y,
id)
txt += '\n'
countFile.write(txt)
countFile.close()
if opt.video_path == 0:
for id, box in zip(online_ids, online_tlwhs):
cv2.rectangle(img0,
(int(box[0] * ratio_x), int(box[1] * ratio_y)),
(int((box[0] + box[2]) * ratio_x),
int((box[1] + box[3]) * ratio_y)), (0, 255, 0),
2)
cv2.imshow('Live', img0)
cv2.waitKey(1)
frame_id += 1
# save results
write_results(result_filename, results, data_type)
return frame_id, timer.average_time, timer.calls
def eval_seq_realtime(opt, save_dir=None, show_image=True, frame_rate=30):
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
frame_id = 0
cv2.namedWindow('online_im', cv2.WINDOW_FREERATIO)
cv2.setWindowProperty('online_im', cv2.WND_PROP_AUTOSIZE,
cv2.WND_PROP_AUTOSIZE)
cap = cv2.VideoCapture(0)
ret, im = cap.read()
height = im.shape[0]
width = im.shape[1]
while True:
ret, img0 = cap.read()
# Padded resize
img, _, _, _ = letterbox(img0, height=height, width=width)
# Normalize RGB
img = img[:, :, ::-1].transpose(2, 0, 1)
img = np.ascontiguousarray(img, dtype=np.float32)
img /= 255.0
if frame_id % 20 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1. / max(1e-5, timer.average_time)))
# run tracking
timer.tic()
blob = torch.from_numpy(img).cuda().unsqueeze(0)
online_targets = tracker.update(blob, img0)
online_tlwhs = []
online_ids = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
timer.toc()
online_im = vis.plot_tracking(img0,
online_tlwhs,
online_ids,
frame_id=frame_id,
fps=1. / timer.average_time)
text_scale = max(1, online_im.shape[1] / 1600.)
text_thickness = 1 if text_scale > 1.1 else 1
line_thickness = max(1, int(online_im.shape[1] / 500.))
cv2.putText(online_im,
'Press ESC to STOP', (300, int(15 * text_scale)),
cv2.FONT_HERSHEY_PLAIN,
text_scale, (0, 0, 255),
thickness=2)
cv2.imshow('online_im', online_im)
key = cv2.waitKey(1)
if key == 27:
break
frame_id += 1
cv2.destroyAllWindows()
def eval_seq(opt, dataloader, data_type, result_filename, gt_filename, save_dir=None, show_image=True, frame_rate=30):
if save_dir:
mkdir_if_missing(save_dir)
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
results = []
gts = []
frame_id = 0
tid_max, tid_temp = 1, 1
for path, img, img0 in dataloader:
if frame_id % 100 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time)))
# if frame_id>400:
# break
if '0000001' in path:
tid_max = tid_temp
tracker = JDETracker(opt, frame_rate=frame_rate)
# run tracking
timer.tic()
blob = torch.from_numpy(img).cuda().unsqueeze(0)
# online_targets = tracker.update(blob, img0)
online_targets = tracker.update_sep3(blob, img0, conf_thres=[0.4, 0.3, 0.4, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5]) ## use class-separated tracker
# print(online_targets)
online_tlwhs = []
online_ids = []
online_cids = [] #class id
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id + tid_max
tcid = t.class_id
# vertical = tlwh[2] / tlwh[3] > 1.6
# if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
if tlwh[2] * tlwh[3] > opt.min_box_area:
online_tlwhs.append(tlwh)
online_ids.append(tid)
online_cids.append(tcid)
tid_temp = max(tid, tid_temp)
timer.toc()
gt_tlwhs = []
gt_ids = []
gt_cids = []
gt_path = path.replace('images', 'labels_with_ids').replace('jpg', 'txt')
gt_targets = read_gt_txts(gt_path)
for (tlwh, tid, tcid) in gt_targets:
gt_tlwhs.append(tlwh)
gt_ids.append(tid)
gt_cids.append(tcid)
# save results
results.append((frame_id + 1, online_tlwhs, online_ids, online_cids))
# save gts
gts.append((frame_id + 1, gt_tlwhs, gt_ids, gt_cids))
if show_image or save_dir is not None:
online_im = vis.plot_tracking(img0, online_tlwhs, online_ids, frame_id=frame_id,
fps=1. / timer.average_time)
if show_image:
cv2.imshow('online_im', online_im)
if save_dir is not None:
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), online_im)
frame_id += 1
# save results
write_results(result_filename, results, data_type)
# save gts
write_results(gt_filename, gts, data_type)
return frame_id, timer.average_time, timer.calls
