def __init__(self,
min_cls_score=0.4,
min_ap_dist=0.64,
max_time_lost=30,
use_tracking=True,
use_refind=True,
models={},
configs={}):
self.models = models
self.configs = configs
self.min_cls_score = min_cls_score
self.min_ap_dist = min_ap_dist
self.max_time_lost = max_time_lost
self.kalman_filter = KalmanFilter()
self.tracked_stracks = [] # type: list[STrack]
self.lost_stracks = [] # type: list[STrack]
self.removed_stracks = [] # type: list[STrack]
self.use_refind = use_refind
self.use_tracking = use_tracking
self.classifier = PatchClassifier()
self.reid_model = load_reid_model()
self.frame_id = 0
def __init__(self, **kwargs):
self.min_cls_score = kwargs['tracker_min_cls_score']
self.min_ap_dist = kwargs['tracker_min_ap_dist']
self.max_time_lost = kwargs['tracker_max_time_lost']
self.kalman_filter = KalmanFilter()
self.tracked_stracks = [] # type: list[STrack]
self.lost_stracks = [] # type: list[STrack]
self.removed_stracks = [] # type: list[STrack]
self.use_refind = not kwargs['tracker_no_refind']
self.use_tracking = not kwargs['tracker_no_tracking']
self.classifier = PatchClassifier(
ckpt=kwargs['tracker_squeezenet_ckpt'])
self.reid_model = load_reid_model(
ckpt=kwargs['tracker_googlenet_ckpt'])
self.frame_id = 0
def __init__(self,
min_cls_score=0.4,
min_ap_dist=0.64,
max_time_lost=30,
use_tracking=True,
use_refind=True,
metric_net=False,
ide=False):
self.min_cls_score = min_cls_score
self.min_ap_dist = min_ap_dist
self.max_time_lost = max_time_lost
self.kalman_filter = KalmanFilter()
self.tracked_stracks = [] # type: list[STrack]
self.lost_stracks = [] # type: list[STrack]
self.removed_stracks = [] # type: list[STrack]
self.use_refind = use_refind
self.use_tracking = use_tracking
self.classifier = PatchClassifier()
self.reid_model = load_reid_model(ide=ide)
if ide:
self.min_ap_dist = 25
if metric_net:
self.metric_net = MetricNet(feature_dim=512 if not ide else 256,
num_class=2).cuda()
checkpoint = torch.load(
'/home/houyz/Code/DeepCC/src/hyper_score/logs/1fps_train_IDE_40/GT/metric_net_L2_1200.pth.tar'
)
model_dict = checkpoint['state_dict']
self.metric_net.load_state_dict(model_dict)
self.metric_net.eval()
self.min_ap_dist = 1
else:
self.metric_net = None
self.frame_id = 0
def __init__(self, cam, ide=False):
self.reid_model = load_reid_model(ide=ide)
self.frame_id = 0
self.cam = cam
self.lines = np.array([]).reshape(0, (512 if not ide else 256) + 3)