:param frame: an (w,h,channels) numpy array (image)
        :return: DataBatch of (1,channels,data_shape,data_shape)

def create_batch(self, frame):
        """
        :param frame: an (w,h,channels) numpy array (image)
        :return: DataBatch of (1,channels,data_shape,data_shape)
        """
        frame_resize = mx.nd.array(cv2.resize(frame, (self.data_shape[0], self.data_shape[1])))
        #frame_resize = mx.img.imresize(frame, self.data_shape[0], self.data_shape[1], cv2.INTER_LINEAR)
        # Change dimensions from (w,h,channels) to (channels, w, h)
        frame_t = mx.nd.transpose(frame_resize, axes=(2,0,1))
        frame_norm = frame_t - self.mean_pixels_nd
        # Add dimension for batch, results in (1,channels,w,h)
        batch_frame = [mx.nd.expand_dims(frame_norm, axis=0)]
        batch_shape = [DataDesc('data', batch_frame[0].shape)]
        batch = DataBatch(data=batch_frame, provide_data=batch_shape)
        return batch