コード例 #1
0
    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
コード例 #2
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))