text: #from https://github.com/facebookresearch/detectron2/blob/main/datasets/prepare_panoptic_fpn.py
  #https://github.com/facebookresearch/detectron2/tree/main/datasets
  import functools
  import json
  import multiprocessing as mp
  import numpy as np
  import os
  import time
  from fvcore.common.download import download
  from panopticapi.utils import rgb2id
  from PIL import Image
  
  from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
  
  
  def _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map):
      panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32)
      panoptic = rgb2id(panoptic)
      output = np.zeros_like(panoptic, dtype=np.uint8) + 255
      for seg in segments:
          cat_id = seg["category_id"]
          new_cat_id = id_map[cat_id]
          output[panoptic == seg["id"]] = new_cat_id
      Image.fromarray(output).save(output_semantic)
  
  
  def separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories):
      """
      Create semantic segmentation annotations from panoptic segmentation
      annotations, to be used by PanopticFPN.
      It maps all thing categories to class 0, and maps all unlabeled pixels to class 255.
      It maps all stuff categories to contiguous ids starting from 1.
      Args:
          panoptic_json (str): path to the panoptic json file, in COCO's format.
          panoptic_root (str): a directory with panoptic annotation files, in COCO's format.
          sem_seg_root (str): a directory to output semantic annotation files
          categories (list[dict]): category metadata. Each dict needs to have:
              "id": corresponds to the "category_id" in the json annotations
              "isthing": 0 or 1
      """
      os.makedirs(sem_seg_root, exist_ok=True)
  
      stuff_ids = [k["id"] for k in categories if k["isthing"] == 0]
      thing_ids = [k["id"] for k in categories if k["isthing"] == 1]
      id_map = {}  # map from category id to id in the output semantic annotation
      assert len(stuff_ids) <= 254
      for i, stuff_id in enumerate(stuff_ids):
          id_map[stuff_id] = i + 1
      for thing_id in thing_ids:
          id_map[thing_id] = 0
      id_map[0] = 255
  
      with open(panoptic_json) as f:
          obj = json.load(f)
  
      pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4))
  
      def iter_annotations():
          for anno in obj["annotations"]:
              file_name = anno["file_name"]
              segments = anno["segments_info"]
              input = os.path.join(panoptic_root, file_name)
              output = os.path.join(sem_seg_root, file_name)
              yield input, output, segments
  
      print("Start writing to {} ...".format(sem_seg_root))
      start = time.time()
      pool.starmap(
          functools.partial(_process_panoptic_to_semantic, id_map=id_map),
          iter_annotations(),
          chunksize=100,
      )
      print("Finished. time: {:.2f}s".format(time.time() - start))
  
  
  if __name__ == "__main__":
      #created image folders of panoptic_stuff_train2017 and panoptic_stuff_val2017 in the coco folder
      dataset_dir = os.path.join("/DATA5T2/Datasets/COCO2017/", "coco")
      for s in ["val2017", "train2017"]:
          separate_coco_semantic_from_panoptic(
              os.path.join(dataset_dir, "annotations/panoptic_{}.json".format(s)),
              os.path.join(dataset_dir, "annotations/panoptic_{}".format(s)),
              os.path.join(dataset_dir, "panoptic_stuff_{}".format(s)),
              COCO_CATEGORIES,
          )
  
      # Prepare val2017_100 for quick testing:
  
      # dest_dir = os.path.join(dataset_dir, "panoptic_val2017_100/")
      # URL_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/"
      # download(URL_PREFIX + "annotations/coco/panoptic_val2017_100.json", dest_dir)
      # with open(os.path.join(dest_dir, "panoptic_val2017_100.json")) as f:
      #     obj = json.load(f)
  
      # def link_val100(dir_full, dir_100):
      #     print("Creating " + dir_100 + " ...")
      #     os.makedirs(dir_100, exist_ok=True)
      #     for img in obj["images"]:
      #         basename = os.path.splitext(img["file_name"])[0]
      #         src = os.path.join(dir_full, basename + ".png")
      #         dst = os.path.join(dir_100, basename + ".png")
      #         src = os.path.relpath(src, start=dir_100)
      #         os.symlink(src, dst)
  
      # link_val100(
      #     os.path.join(dataset_dir, "panoptic_val2017"),
      #     os.path.join(dataset_dir, "panoptic_val2017_100"),
      # )
  
      # link_val100(
      #     os.path.join(dataset_dir, "panoptic_stuff_val2017"),
      #     os.path.join(dataset_dir, "panoptic_stuff_val2017_100"),
      # )