Method update

tutorials/fairmot/tracker.py:232–412

(self, im_blob, img0)

        return results

    def update(self, im_blob, img0):
        self.frame_id += 1
        activated_starcks = []
        refind_stracks = []
        lost_stracks = []
        removed_stracks = []

        width = img0.shape[1]
        height = img0.shape[0]
        inp_height = im_blob.shape[2]
        inp_width = im_blob.shape[3]
        c = np.array([width / 2., height / 2.], dtype=np.float32)
        s = max(float(inp_width) / float(inp_height) * height, width) * 1.0
        meta = {'c': c, 's': s,
                'out_height': inp_height // self.opt.down_ratio,
                'out_width': inp_width // self.opt.down_ratio}

        ''' Step 1: Network forward, get detections & embeddings'''
        with torch.no_grad():
            output = self.model(im_blob)[-1]
            hm = output['hm'].sigmoid_()
            wh = output['wh']
            id_feature = output['id']
            id_feature = F.normalize(id_feature, dim=1)

            reg = output['reg'] if self.opt.reg_offset else None
            dets, inds = mot_decode(hm, wh, reg=reg, ltrb=self.opt.ltrb, K=self.opt.K)
            id_feature = _tranpose_and_gather_feat(id_feature, inds)
            id_feature = id_feature.squeeze(0)
            id_feature = id_feature.cpu().numpy()

        dets = self.post_process(dets, meta)
        dets = self.merge_outputs([dets])[1]

        remain_inds = dets[:, 4] > self.opt.conf_thres
        inds_low = dets[:, 4] > 0.2
        #inds_low = dets[:, 4] > self.opt.conf_thres
        inds_high = dets[:, 4] < self.opt.conf_thres
        inds_second = np.logical_and(inds_low, inds_high)
        dets_second = dets[inds_second]
        id_feature_second = id_feature[inds_second]
        dets = dets[remain_inds]
        id_feature = id_feature[remain_inds]

        # vis
        ''&#x27;
        for i in range(0, dets.shape[0]):
            bbox = dets[i][0:4]
            cv2.rectangle(img0, (bbox[0], bbox[1]),
                          (bbox[2], bbox[3]),
                          (0, 255, 0), 2)
        cv2.imshow('dets', img0)
        cv2.waitKey(0)
        id0 = id0-1
        ''&#x27;

        if len(dets) > 0:
            '''Detections'''