def run_on_image(self, original_image):
        """

        Args:
            original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).
                This is the format used by OpenCV.

        Returns:
            predictions (np.ndarray): normalized feature of the model.
        """
        # the model expects RGB inputs
        original_image = original_image[:, :, ::-1]
        # Apply pre-processing to image.
        image = cv2.resize(original_image, tuple(self.cfg.INPUT.SIZE_TEST[::-1]), interpolation=cv2.INTER_CUBIC)
        # Make shape with a new batch dimension which is adapted for
        # network input
        image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))[None]
        predictions = self.predictor(image)
        return predictions


        Args:
            original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).
                This is the format used by OpenCV.

        Returns:
            predictions (np.ndarray): normalized feature of the model.
        
demo/predictor.py