text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import pycocotools.coco as coco
  from pycocotools.cocoeval import COCOeval
  import numpy as np
  import json
  import os
  
  import torch.utils.data as data
  
  
  class DanceTrackCOCOHP(data.Dataset):
      num_classes = 1
      num_joints = 17
      default_resolution = [512, 512]
      mean = np.array([0.40789654, 0.44719302, 0.47026115],
                      dtype=np.float32).reshape(1, 1, 3)
      std = np.array([0.28863828, 0.27408164, 0.27809835],
                     dtype=np.float32).reshape(1, 1, 3)
      flip_idx = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10],
                  [11, 12], [13, 14], [15, 16]]
  
      def __init__(self, opt, split):
          super(DanceTrackCOCOHP, self).__init__()
          self.edges = [[0, 1], [0, 2], [1, 3], [2, 4],
                        [4, 6], [3, 5], [5, 6],
                        [5, 7], [7, 9], [6, 8], [8, 10],
                        [6, 12], [5, 11], [11, 12],
                        [12, 14], [14, 16], [11, 13], [13, 15]]
  
          self.acc_idxs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
          self.data_dir = os.path.join(opt.data_dir, 'dancetrack_coco_hp')
          self.img_dir = os.path.join(self.data_dir)
          if split == 'train':
              self.annot_path = os.path.join(self.data_dir, 'annotations', 'train.json')
          else:
              assert 0, 'only train is supported!'
          self.max_objs = 32
          self._data_rng = np.random.RandomState(123)
          self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
                                   dtype=np.float32)
          self._eig_vec = np.array([
              [-0.58752847, -0.69563484, 0.41340352],
              [-0.5832747, 0.00994535, -0.81221408],
              [-0.56089297, 0.71832671, 0.41158938]
          ], dtype=np.float32)
          self.split = split
          self.opt = opt
          
          print('==> initializing dancetrack_coco_hp {} data.'.format(split))
          self.coco = coco.COCO(self.annot_path)
          image_ids = self.coco.getImgIds()
  
          if split == 'train':
              self.images = []
              for img_id in image_ids:
                  idxs = self.coco.getAnnIds(imgIds=[img_id])
                  if len(idxs) > 0:
                      self.images.append(img_id)
          else:
              self.images = image_ids
          self.num_samples = len(self.images)
          print('Loaded {} {} samples'.format(split, self.num_samples))
  
      def _to_float(self, x):
          return float("{:.2f}".format(x))
  
      def convert_eval_format(self, all_bboxes):
          # import pdb; pdb.set_trace()
          detections = []
          for image_id in all_bboxes:
              for cls_ind in all_bboxes[image_id]:
                  category_id = 1
                  for dets in all_bboxes[image_id][cls_ind]:
                      bbox = dets[:4]
                      bbox[2] -= bbox[0]
                      bbox[3] -= bbox[1]
                      score = dets[4]
                      bbox_out = list(map(self._to_float, bbox))
                      keypoints = np.concatenate([
                          np.array(dets[5:39], dtype=np.float32).reshape(-1, 2),
                          np.ones((17, 1), dtype=np.float32)], axis=1).reshape(51).tolist()
                      keypoints = list(map(self._to_float, keypoints))
  
                      detection = {
                          "image_id": int(image_id),
                          "category_id": int(category_id),
                          "bbox": bbox_out,
                          "score": float("{:.2f}".format(score)),
                          "keypoints": keypoints
                      }
                      detections.append(detection)
          return detections
  
      def __len__(self):
          return self.num_samples
  
      def save_results(self, results, save_dir):
          json.dump(self.convert_eval_format(results),
                    open('{}/results.json'.format(save_dir), 'w'))
  
      def run_eval(self, results, save_dir):
          # result_json = os.path.join(opt.save_dir, "results.json")
          # detections  = convert_eval_format(all_boxes)
          # json.dump(detections, open(result_json, "w"))
          self.save_results(results, save_dir)
          coco_dets = self.coco.loadRes('{}/results.json'.format(save_dir))
          coco_eval = COCOeval(self.coco, coco_dets, "keypoints")
          coco_eval.evaluate()
          coco_eval.accumulate()
          coco_eval.summarize()
          coco_eval = COCOeval(self.coco, coco_dets, "bbox")
          coco_eval.evaluate()
          coco_eval.accumulate()
          coco_eval.summarize()