title: visualize boxes in tensorflow object detection
body: <pre><code>for image_path in TEST_IMAGE_PATHS:
    image = Image.open(image_path)
    # the array based representation of the image will be used later in order to prepare the
    # result image with boxes and labels on it.
    image_np = load_image_into_numpy_array(image)
    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
    image_np_expanded = np.expand_dims(image_np, axis=0)
    # Actual detection.
    output_dict = run_inference_for_single_image(image_np, detection_graph)
    # Visualization of the results of a detection.
    vis_util.visualize_boxes_and_labels_on_image_array(
        image_np,
        output_dict['detection_boxes'],
        output_dict['detection_classes'],
        output_dict['detection_scores'],
        category_index,
        instance_masks=output_dict.get('detection_masks'),
        use_normalized_coordinates=True,
        line_thickness=8)
    plt.figure(figsize=IMAGE_SIZE)
    plt.imshow(image_np)
  </code></pre>
  
  <p>In one of the codes for tensorflow object detection API, there is the vis_util.visualize_boxes_and_labels_on_image_array method which draws bounding boxes on the image and returns the new image_np which is fed into plt.imshow(image_np) to display. However, <strong>vis_util.visualize_boxes_and_labels_on_image_array method is not assigned to image_np variable</strong> as shown in the code. How does plt.imshow(image_np) get the latest(The image which has the drawn bounding box) image?</p>