def __init__(self,
config,
model=None,
display=True,
device='cpu',
name='TADT',
model_from_mat=True):
"""
args:
target_location - [x1,y1,w,h], 1-index for OTB benchmark
TODO: model_from_mat
"""
super(Tadt_Tracker, self).__init__()
#---------------trackers parameters initialization--------------------------
self.name = name
self.config = config
self.display = display
self.device = device
self.rescale = 1
self.results = []
self.model_from_mat = model_from_mat
#-------------model initialization--------------------
if model is None:
self.model = build_vgg16(self.config).to(self.device)
else:
self.model = model.to(self.device)
self.model.train()
self.siamese_model = SiameseNet().to(self.device)
self.toc = 0
return img_list, gt_bboxes
if __name__ == "__main__":
from defaults import _C as cfg
import time
import torch
assert (
False
), 'please download "imagenet-vgg-verydeep-16.mat" from "http://www.vlfeat.org/matconvnet/models/imagenet-vgg-verydeep-16.mat" and set its path in defaults.py'
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
root_path = join(realpath(dirname(__file__)), 'sequences/')
img_list, gt_bboxes = load_sequece(root_path)
#------------------demo------------------------------------------------------------------
model = build_vgg16(cfg)
tracker = Tadt_Tracker(cfg, model=model, device=device, display=True)
tracker.initialize_tadt(img_list[0], gt_bboxes[0])
#if want to visualize the selected feature, uncomment these lines
#tracker.visualize_feature(
# features = tracker.features,
# stage = 'conv4_3',
# srch_window_size = (180,180),
# subwindow = tracker.subwindow,
# feature_weights = tracker.feature_weights,
# balance_weights = tracker.balance_weights
# )
for i in range(1, len(img_list)):
tracker.tracking(img_list[i], i)
print('fps: ', tracker.cal_fps(i))