def save_trajectory(self):
for i, trajectory in enumerate(self.trajectories):
dir = osp.join(self.traj_dir, '%06d' % trajectory.id)
mkdirs(dir)
for j, tracklet in enumerate(trajectory.data):
fname = osp.join(dir, '%06d.%d.jpg' % (j, tracklet.chan))
cv2.imwrite(fname, tracklet.im)
def __init__(self, tid, tracklets, trajectories, exit, model=None):
super(MTMCT, self).__init__(name=tid)
self.tid = tid
self.tracklets = tracklets
self.trajectories = trajectories
self.exit = exit
self.model = model # ReID model
self.gplane = np.zeros((2048, 2048, 3), dtype=np.uint8)
self.counter = 0
self.gplane_dir = osp.join('tasks', 'gplane')
mkdirs(self.gplane_dir)
self.local2global = defaultdict(int) # `channel-local_id`
self.undetermined = defaultdict(
lambda: defaultdict(list)) # `channel`->`local_id`
self.least = 5 # at least `least` tracklets for matching
self.atmost = 15 # at most `atmost` tracklets for matching
self.ncamera = 0
self.max_dist = 25
self.traj_dir = osp.join('tasks', 'trajectories')
mkdirs(self.traj_dir)
def main():
# Parse configurations.
args = parse_args()
if os.path.isfile(args.config):
config.merge_from_file(args.config)
config.merge_from_list(args.opts)
torch.backends.cudnn.benchmark = True
mkdirs(config.SYSTEM.TASK_DIR)
# Build dataset
dataset = build_dataset(config.DATASET)
# Build model.
num_ide = int(dataset.max_id + 1)
config.MODEL.ARGS.HEAD.ARGS[1]['num_ide'] = num_ide
model = build_tracker(config.MODEL)
# Train tracker now.
train_tracker(model, dataset, config)
def __init__(self,
tid,
images,
tracklets,
exit,
model,
locator=None,
insize=(320, 576)):
super(MTSCT, self).__init__(name=tid)
self.tid = tid
self.images = images
self.tracklets = tracklets
self.exit = exit
self.model = model.cuda() # Tracker model
self.model.eval()
self.locator = locator # ground plane location
self.insize = insize
self.score_thresh = 0.5
self.iou_thresh = 0.4
self.tracker = trackernew.JDETracker()
self.save_dir = osp.join('tasks', 'mtsct_{}'.format(self.tid))
mkdirs(self.save_dir)
parser.add_argument('--root', '-r', type=str,
help='dataset data root directory.')
parser.add_argument('--data', '-d', type=str,
help='dataset configuration filepath.')
parser.add_argument('-k', type=int, default=1000,
help='The maximum number of generated samples.')
parser.add_argument('--save-path', '-sp', type=str,
default='./samples',
help='Samples saving path.')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
all_samples = []
with open(args.data, 'r') as fd1:
files = fd1.readlines()
files = [f.strip() for f in files]
files = list(filter(lambda f: len(f) > 0, files))
for file in files:
with open(file, 'r') as fd2:
samples = fd2.readlines()
samples = [s.strip() for s in samples]
samples = list(filter(lambda s: len(s) > 0, samples))
samples = [osp.join(args.root, s) for s in samples]
all_samples += samples
k = min(len(all_samples), args.k)
selections = random.sample(all_samples, k)
mkdirs(args.save_path)
for s in selections:
print('copy {} to {}'.format(s, args.save_path))
os.system('cp {} {}'.format(s, args.save_path))
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
vc = cv2.VideoCapture(args.video)
width = int(vc.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(vc.get(cv2.CAP_PROP_FRAME_HEIGHT))
frames = int(vc.get(cv2.CAP_PROP_FRAME_COUNT))
print('width {}, height {}, frames {}'.format(width, height, frames))
root, _ = os.path.splitext(args.video)
cache_path = os.path.join(root, 'frames')
if not os.path.exists(cache_path):
mkdirs(cache_path)
i = 0
while True:
retval, im = vc.read()
if not retval:
break
cv2.imwrite(os.path.join(cache_path, '{}.jpg'.format(i)), im)
i = i + 1
print('\rextract frame {}/{}'.format(i, frames),
end='',
flush=True)
print('')
mot_path = os.path.join(root, 'mot')
mkdirs(mot_path)
im_path = os.path.join(mot_path, 'images')
return args
if __name__ == '__main__':
args = parse_args()
vc = cv2.VideoCapture(args.video)
width = int(vc.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(vc.get(cv2.CAP_PROP_FRAME_HEIGHT))
frames = int(vc.get(cv2.CAP_PROP_FRAME_COUNT))
print('width {}, height {}, possible frames {}'.format(
width, height, frames))
root, _ = os.path.splitext(args.video)
cache_path = os.path.join(root, 'frames')
if not os.path.exists(cache_path):
mkdirs(cache_path)
i = 0
while True:
retval, im = vc.read()
if not retval:
break
cv2.imwrite(os.path.join(cache_path, '{}.jpg'.format(i)), im)
i = i + 1
print('\rextract frame {}/{}'.format(i, frames),
end='',
flush=True)
print('')
reid_path = os.path.join(root, 'reid')
mkdirs(reid_path)