def main(args):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if os.path.isfile(args.config):
config.merge_from_file(args.config)
config.freeze()
model = build_tracker(config.MODEL)
if os.path.isfile(args.model):
model.load_state_dict(torch.load(args.model, map_location='cpu'))
model.cuda().eval()
h, w = [int(s) for s in args.insize.split('x')]
tracker = JDETracker()
if os.path.isfile(args.img_path):
dataloader = dataset.VideoLoader(args.img_path, (h,w,3))
else:
dataloader = dataset.ImagesLoader(args.img_path, (h,w,3), formats=['*.jpg', '*.png'])
strs = re.split(r'[\\, /]', args.img_path)
imgpath = os.path.join(args.workspace, 'result', strs[-3], 'img')
mkdir(imgpath)
traj_path = os.path.join(args.workspace, 'result', '{}.txt'.format(strs[-3]))
os.system('rm -f {}'.format(os.path.join(imgpath, '*')))
for i, (path, im, lb_im) in enumerate(dataloader):
input = torch.from_numpy(lb_im).unsqueeze(0).to(device)
with torch.no_grad():
outputs = model(input)
print('{} {} {} {}'.format(path, im.shape, lb_im.shape, outputs.size()), end=' ')
outputs = nonmax_suppression(outputs, args.score_thresh, args.iou_thresh)[0]
if outputs is None:
print('no object detected!')
segments = re.split(r'[\\, /]', path)
cv2.imwrite(os.path.join(imgpath, segments[-1]), im)
continue
print('{}'.format(outputs.size()), end=' ')
outputs[:, :4] = ltrb_net2img(outputs[:, :4], (h,w), im.shape[:2])
if not args.only_detect:
trajectories = tracker.update(outputs.numpy())
print('{}'.format(len(trajectories)))
result = overlap_trajectory(trajectories, im)
save_trajectories(traj_path, trajectories, i + 1)
else:
print('')
result = overlap(outputs, im)
segments = re.split(r'[\\, /]', path)
cv2.imwrite(os.path.join(imgpath, segments[-1]), result)
os.system('ffmpeg -f image2 -i {} {}.mp4 -y'.format(os.path.join(imgpath, '%06d.jpg'),
os.path.join(args.workspace, 'result', strs[-3])))
def main():
args = parse_args()
if os.path.isfile(args.config):
config.merge_from_file(args.config)
config.merge_from_list(args.opts)
config.freeze()
print(config)
model = build_tracker(config.MODEL)
classes = (nn.Conv2d, nn.BatchNorm2d, nn.Linear, nn.ReLU, nn.MaxPool2d)
with open(args.save_name, 'w') as fd:
for name, module in model.named_modules():
if isinstance(module, classes):
fd.write('{}\n'.format(name))
def main():
args = parse_args()
if os.path.isfile(args.config):
config.merge_from_file(args.config)
config.merge_from_list(args.opts)
config.freeze()
print(config)
model = build_tracker(config.MODEL)
if os.path.isfile(args.weight):
model.load_state_dict(torch.load(args.weight, map_location='cpu'))
model.cuda().eval()
print(model)
input = torch.rand(64, 3, 320, 576)
with torch.no_grad():
output = model(input.cuda())
print('output size: {}'.format(output.size()))
def main(args):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# if args.backbone == 'darknet':
# model = darknet.DarkNet(np.random.randint(0, 100, (12, 2))).to(device)
# elif args.backbone == 'shufflenetv2':
# model = shufflenetv2.ShuffleNetV2(np.random.randint(0, 100, (12, 2)),
# model_size=args.thin).to(device)
# else:
# print('unknown backbone architecture!')
# sys.exit(0)
#
# # load state dict except the classifier layer
# model_dict = model.state_dict()
# trained_model_dict = torch.load(os.path.join(args.model), map_location='cpu')
# trained_model_dict = {k : v for (k, v) in trained_model_dict.items() if k in model_dict}
# trained_model_dict = collections.OrderedDict(trained_model_dict)
# model_dict.update(trained_model_dict)
# model.load_state_dict(model_dict)
#
# model.eval()
if os.path.isfile(args.config):
config.merge_from_file(args.config)
config.freeze()
model = build_tracker(config.MODEL)
if os.path.isfile(args.model):
model.load_state_dict(torch.load(args.model, map_location='cpu'))
model.cuda().eval()
# if '320x576' in args.insize:
# anchors = ((6,16), (8,23), (11,32), (16,45),
# (21,64), (30,90), (43,128), (60,180),
# (85,255), (120,360), (170,420), (340,320))
# elif '480x864' in args.insize:
# anchors = ((6,19), (9,27), (13,38), (18,54),
# (25,76), (36,107), (51,152), (71,215),
# (102,305), (143,429), (203,508), (407,508))
# elif '608x1088' in args.insize:
# anchors = ((8,24), (11,34), (16,48), (23,68),
# (32,96), (45,135), (64,192), (90,271),
# (128,384), (180,540), (256,640), (512,640))
h, w = [int(s) for s in args.insize.split('x')]
# decoder = jde.JDEcoder((h, w), embd_dim=args.embedding)
tracker = JDETracker()
if os.path.isfile(args.img_path):
dataloader = dataset.VideoLoader(args.img_path, (h, w, 3))
else:
dataloader = dataset.ImagesLoader(args.img_path, (h, w, 3),
formats=['*.jpg', '*.png'])
strs = re.split(r'[\\, /]', args.img_path)
imgpath = os.path.join(args.workspace, 'result', strs[-3], 'img')
mkdir(imgpath)
traj_path = os.path.join(args.workspace, 'result',
'{}.txt'.format(strs[-3]))
os.system('rm -f {}'.format(os.path.join(imgpath, '*')))
for i, (path, im, lb_im) in enumerate(dataloader):
input = torch.from_numpy(lb_im).unsqueeze(0).to(device)
with torch.no_grad():
outputs = model(input)
# outputs = decoder(outputs)
print('{} {} {} {}'.format(path, im.shape, lb_im.shape,
outputs.size()),
end=' ')
outputs = nonmax_suppression(outputs, args.score_thresh,
args.iou_thresh)[0]
if outputs is None:
print('no object detected!')
segments = re.split(r'[\\, /]', path)
cv2.imwrite(os.path.join(imgpath, segments[-1]), im)
continue
print('{}'.format(outputs.size()), end=' ')
outputs[:, :4] = ltrb_net2img(outputs[:, :4], (h, w), im.shape[:2])
if not args.only_detect:
trajectories = tracker.update(outputs.numpy())
print('{}'.format(len(trajectories)))
result = overlap_trajectory(trajectories, im)
save_trajectories(traj_path, trajectories, i + 1)
else:
print('')
result = overlap(outputs, im)
segments = re.split(r'[\\, /]', path)
cv2.imwrite(os.path.join(imgpath, segments[-1]), result)
os.system('ffmpeg -f image2 -i {} {}.mp4 -y'.format(
os.path.join(imgpath, '%06d.jpg'),
os.path.join(args.workspace, 'result', strs[-3])))
def main():
args = parse_args()
if osp.isfile(args.config):
config.merge_from_file(args.config)
config.freeze()
calibs = [parse_calibration_data(f) for f in args.calibration]
print('calibration:\n{}'.format(calibs))
# Create input and output queue for each tracker.
ncamera = len(args.inputs)
images = [] # image queues
tracklets = [] # tracklet queues
for i in range(ncamera):
images.append(Queue(maxsize=0))
tracklets.append(Queue(maxsize=0))
trajectories = [] # global trajectory list
# Feature extractor.
extractor = FeatureExtractor(model_name='osnet_x1_0',
model_path=args.reid,
device='cuda')
# Create working threads.
tid = 0
threads = []
exit = Value('i', 0) # shared thread exit switch
for i in range(ncamera):
# Datastore thread.
tid += 1
threads.append(Datastore(tid, args.inputs[i], images[i]))
# MTSCT thread.
tid += 1
model = build_tracker(config.MODEL)
model.load_state_dict(torch.load(args.tracker, map_location='cpu'))
locator = ImageToWorldTsai(calibs[i])
threads.append(
MTSCT(tid, images[i], tracklets[i], exit, model, locator))
# MTMCT thread.
tid += 1
threads.append(MTMCT(tid, tracklets, trajectories, exit, extractor))
# Start all threads.
for thread in threads:
thread.start()
# Waiting for Datastore finish.
ndead = 0
while ndead != ncamera:
ndead = sum([int(not t.is_alive()) for t in threads[:-1][0::2]])
time.sleep(1)
print('Datastore done.')
# Waiting for MTSCT finish.
nempty = 0
while nempty != ncamera:
nempty = sum([int(q.empty()) for q in images])
time.sleep(1)
print('MTSCT done.')
# Waiting for MTMCT finish.
nempty = 0
while nempty != ncamera:
nempty = sum([int(q.empty()) for q in tracklets])
time.sleep(1)
print('MTMCT done.')
exit.value = 1
for thread in threads:
thread.join()
print('All works done.')