code: def draw_outputs(img, outputs, class_names):
      boxes, objectness, classes, nums = outputs
      boxes, objectness, classes, nums = (
          boxes[0],
          objectness[0],
          classes[0],
          nums[0],
      )
      wh = np.flip(img.shape[0:2])
      for i in range(nums):
          x1y1 = tuple((np.array(boxes[i][0:2]) * wh).astype(np.int32))
          x2y2 = tuple((np.array(boxes[i][2:4]) * wh).astype(np.int32))
          img = cv2.rectangle(img, x1y1, x2y2, (255, 0, 0), 2)
          img = cv2.putText(
              img,
              "{} {:.4f}".format(
                  class_names[int(classes[i])], objectness[i]
              ),
              x1y1,
              cv2.FONT_HERSHEY_COMPLEX_SMALL,
              1,
              (0, 0, 255),
              2,
          )
      return img
  
file_path: analytics-zoo/pyzoo/zoo/examples/orca/learn/tf2/yolov3/predict.py
imports: import time
  import cv2
  import numpy
  import tensorflow
  from yoloV3 import YoloV3, transform_images
  import argparse
label_desc: Use image node_id 1 for calling a global function with example usage: draw_outputs(img, outputs, class_names) and returns: img