content: # ------------------------------------------------------------------------
  # UP-DETR
  # Copyright (c) Tencent, Inc. and its affiliates. All Rights Reserved.
  # ------------------------------------------------------------------------
  # Modified from DETR (https://github.com/facebookresearch/detr)
  # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
  # ------------------------------------------------------------------------
  import json
  from pathlib import Path
  
  import numpy as np
  import torch
  from PIL import Image
  
  from panopticapi.utils import rgb2id
  from util.box_ops import masks_to_boxes
  
  from .coco import make_coco_transforms
  
  
  class CocoPanoptic:
      def __init__(self, img_folder, ann_folder, ann_file, transforms=None, return_masks=True):
          with open(ann_file, 'r') as f:
              self.coco = json.load(f)
  
          # sort 'images' field so that they are aligned with 'annotations'
          # i.e., in alphabetical order
          self.coco['images'] = sorted(self.coco['images'], key=lambda x: x['id'])
          # sanity check
          if "annotations" in self.coco:
              for img, ann in zip(self.coco['images'], self.coco['annotations']):
                  assert img['file_name'][:-4] == ann['file_name'][:-4]
  
          self.img_folder = img_folder
          self.ann_folder = ann_folder
          self.ann_file = ann_file
          self.transforms = transforms
          self.return_masks = return_masks
  
      def __getitem__(self, idx):
          ann_info = self.coco['annotations'][idx] if "annotations" in self.coco else self.coco['images'][idx]
          img_path = Path(self.img_folder) / ann_info['file_name'].replace('.png', '.jpg')
          ann_path = Path(self.ann_folder) / ann_info['file_name']
  
          img = Image.open(img_path).convert('RGB')
          w, h = img.size
          if "segments_info" in ann_info:
              masks = np.asarray(Image.open(ann_path), dtype=np.uint32)
              masks = rgb2id(masks)
  
              ids = np.array([ann['id'] for ann in ann_info['segments_info']])
              masks = masks == ids[:, None, None]
  
              masks = torch.as_tensor(masks, dtype=torch.uint8)
              labels = torch.tensor([ann['category_id'] for ann in ann_info['segments_info']], dtype=torch.int64)
  
          target = {}
          target['image_id'] = torch.tensor([ann_info['image_id'] if "image_id" in ann_info else ann_info["id"]])
          if self.return_masks:
              target['masks'] = masks
          target['labels'] = labels
  
          target["boxes"] = masks_to_boxes(masks)
  
          target['size'] = torch.as_tensor([int(h), int(w)])
          target['orig_size'] = torch.as_tensor([int(h), int(w)])
          if "segments_info" in ann_info:
              for name in ['iscrowd', 'area']:
                  target[name] = torch.tensor([ann[name] for ann in ann_info['segments_info']])
  
          if self.transforms is not None:
              img, target = self.transforms(img, target)
  
          return img, target
  
      def __len__(self):
          return len(self.coco['images'])
  
      def get_height_and_width(self, idx):
          img_info = self.coco['images'][idx]
          height = img_info['height']
          width = img_info['width']
          return height, width
  
  
  def build(image_set, args):
      img_folder_root = Path(args.coco_path)
      ann_folder_root = Path(args.coco_panoptic_path)
      assert img_folder_root.exists(), f'provided COCO path {img_folder_root} does not exist'
      assert ann_folder_root.exists(), f'provided COCO path {ann_folder_root} does not exist'
      mode = 'panoptic'
      PATHS = {
          "train": ("train2017", Path("annotations") / f'{mode}_train2017.json'),
          "val": ("val2017", Path("annotations") / f'{mode}_val2017.json'),
      }
  
      img_folder, ann_file = PATHS[image_set]
      img_folder_path = img_folder_root / img_folder
      ann_folder = ann_folder_root / f'{mode}_{img_folder}'
      ann_file = ann_folder_root / ann_file
  
      dataset = CocoPanoptic(img_folder_path, ann_folder, ann_file,
                             transforms=make_coco_transforms(image_set), return_masks=args.masks)
  
      return dataset
  