def _preprocess(self, im_crops):
        """
        TODO:
            1. to float with scale from 0 to 1
            2. resize to (64, 128) as Market1501 dataset did
            3. concatenate to a numpy array
            3. to torch Tensor
            4. normalize
        """
        def _resize(im, size):
            return cv2.resize(im.astype(np.float32)/255., size)

        im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(
            0) for im in im_crops], dim=0).float()
        return im_batch

        TODO:
            1. to float with scale from 0 to 1
            2. resize to (64, 128) as Market1501 dataset did
            3. concatenate to a numpy array
            3. to torch Tensor
            4. normalize
        
trackers/deepsort_tracker/reid_model.py