code: def visualize_results(
      img_path, bboxes, labels, scores, cfg, store_to_path=None
  ):
      """
      Renders the detection results (bboxes and labels) onto the image.
      :param img_path: the path to the image
      :param bboxes: the predicted bounding boxes
      :param labels: the single class label per bounding box
      :param scores: the probability for the assigned class label per bounding box
      :param cfg: the configuration
      :param store_to_path: optional: a path where to store the rendered image.
                            If set to 'None' the image will be displayed on screen.
      :return:
      """
  
      from matplotlib.pyplot import imsave, imshow, show
      from utils.plot_helpers import visualize_detections
  
      img = visualize_detections(
          img_path,
          bboxes,
          labels,
          scores,
          cfg.IMAGE_WIDTH,
          cfg.IMAGE_HEIGHT,
          classes=cfg["DATA"].CLASSES,
          draw_negative_rois=cfg.DRAW_NEGATIVE_ROIS,
      )
  
      if store_to_path is not None:
          imsave(store_to_path, img)
      else:
          imshow(img)
          show()
  
file_path: CNTK/Examples/Image/Detection/utils/od_utils.py
imports: import easydict
  from utils.nms_wrapper import apply_nms_to_single_image_results
label_desc: Use image node_id 5 for calling a global function with example usage: visualize_results(img_path, bboxes, labels, scores, cfg, store_to_path) without return types