code: def visualize_panoptic(
      panoptic,
      save_path,
      label_divisor,
      colormap,
      image=None,
      weight=0.5,
      ignore_index=255,
  ):
      """
      Save panoptic segmentation results.
  
      Args:
          panoptic(np.ndarray): The panoptic segmentation results, shape is (h, w).
          save_path(str): The save path.
          label_divisor(int): Used to convert panoptic id = semantic id * label_divisor + instance_id.
          colormap(np.ndarray): A color map for visualization.
          image(np.ndarray, optional): Origin image to prediction, merge panoptic with
              image if provided. Default: None.
          weight(float, optional): The image weight when merge panoptic with image. Default: 0.5.
          ignore_index(int, optional): Specifies a target value that is ignored. Default: 255.
      """
      colored_panoptic = np.zeros(
          (panoptic.shape[0], panoptic.shape[1], 3), dtype=np.uint8
      )
      taken_colors = set((0, 0, 0))
  
      def _random_color(base, max_dist=30):
          color = base + np.random.randint(
              low=-max_dist, high=max_dist + 1, size=3
          )
          return tuple(np.maximum(0, np.minimum(255, color)))
  
      for lab in np.unique(panoptic):
          mask = panoptic == lab
  
          ignore_mask = panoptic == ignore_index
          ins_mask = panoptic > label_divisor
          if lab > label_divisor:
              base_color = colormap[lab // label_divisor]
          elif lab != ignore_index:
              base_color = colormap[lab]
          else:
              continue
          if tuple(base_color) not in taken_colors:
              taken_colors.add(tuple(base_color))
              color = base_color
          else:
              while True:
                  color = _random_color(base_color)
                  if color not in taken_colors:
                      taken_colors.add(color)
                      break
          colored_panoptic[mask] = color
  
      if image is not None:
          colored_panoptic = cv2.addWeighted(
              image, weight, colored_panoptic, 1 - weight, 0
          )
      cv2.imwrite(save_path, colored_panoptic)
  
file_path: PaddleSeg/contrib/PanopticDeepLab/utils/visualize.py
imports: import os
  import cv2
  import numpy
  from PIL import Image
label_desc: Use image node_id 5 for calling a global function with example usage: visualize_panoptic(panoptic, save_path, label_divisor, colormap, image, weight, ignore_index) and returns: tuple