text: # Copyright (c) OpenMMLab. All rights reserved.
  import os.path as osp
  import tempfile
  import warnings
  from collections import OrderedDict
  
  import numpy as np
  from mmcv import Config, deprecated_api_warning
  
  from mmpose.datasets.builder import DATASETS
  from ..base import Kpt2dSviewRgbImgTopDownDataset
  
  
  @DATASETS.register_module()
  class FaceCocoWholeBodyDataset(Kpt2dSviewRgbImgTopDownDataset):
      """CocoWholeBodyDataset for face keypoint localization.
  
      `Whole-Body Human Pose Estimation in the Wild', ECCV'2020.
      More details can be found in the `paper
      <https://arxiv.org/abs/2007.11858>`__ .
  
      The dataset loads raw features and apply specified transforms
      to return a dict containing the image tensors and other information.
  
      The face landmark annotations follow the 68 points mark-up.
  
      Args:
          ann_file (str): Path to the annotation file.
          img_prefix (str): Path to a directory where images are held.
              Default: None.
          data_cfg (dict): config
          pipeline (list[dict | callable]): A sequence of data transforms.
          dataset_info (DatasetInfo): A class containing all dataset info.
          test_mode (bool): Store True when building test or
              validation dataset. Default: False.
      """
  
      def __init__(self,
                   ann_file,
                   img_prefix,
                   data_cfg,
                   pipeline,
                   dataset_info=None,
                   test_mode=False):
  
          if dataset_info is None:
              warnings.warn(
                  'dataset_info is missing. '
                  'Check https://github.com/open-mmlab/mmpose/pull/663 '
                  'for details.', DeprecationWarning)
              cfg = Config.fromfile('configs/_base_/datasets/'
                                    'coco_wholebody_face.py')
              dataset_info = cfg._cfg_dict['dataset_info']
  
          super().__init__(
              ann_file,
              img_prefix,
              data_cfg,
              pipeline,
              dataset_info=dataset_info,
              test_mode=test_mode)
  
          self.ann_info['use_different_joint_weights'] = False
          self.db = self._get_db()
  
          print(f'=> num_images: {self.num_images}')
          print(f'=> load {len(self.db)} samples')
  
      def _get_db(self):
          """Load dataset."""
          gt_db = []
          bbox_id = 0
          num_joints = self.ann_info['num_joints']
          for img_id in self.img_ids:
  
              ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=False)
              objs = self.coco.loadAnns(ann_ids)
  
              for obj in objs:
                  if obj['face_valid'] and max(obj['face_kpts']) > 0:
                      joints_3d = np.zeros((num_joints, 3), dtype=np.float32)
                      joints_3d_visible = np.zeros((num_joints, 3),
                                                   dtype=np.float32)
  
                      keypoints = np.array(obj['face_kpts']).reshape(-1, 3)
                      joints_3d[:, :2] = keypoints[:, :2]
                      joints_3d_visible[:, :2] = np.minimum(1, keypoints[:, 2:3])
  
                      image_file = osp.join(self.img_prefix,
                                            self.id2name[img_id])
                      gt_db.append({
                          'image_file': image_file,
                          'rotation': 0,
                          'joints_3d': joints_3d,
                          'joints_3d_visible': joints_3d_visible,
                          'dataset': self.dataset_name,
                          'bbox': obj['face_box'],
                          'bbox_score': 1,
                          'bbox_id': bbox_id
                      })
                      bbox_id = bbox_id + 1
          gt_db = sorted(gt_db, key=lambda x: x['bbox_id'])
  
          return gt_db
  
      def _get_normalize_factor(self, gts, *args, **kwargs):
          """Get inter-ocular distance as the normalize factor, measured as the
          Euclidean distance between the outer corners of the eyes.
  
          Args:
              gts (np.ndarray[N, K, 2]): Groundtruth keypoint location.
  
          Returns:
              np.ndarray[N, 2]: normalized factor
          """
  
          interocular = np.linalg.norm(
              gts[:, 36, :] - gts[:, 45, :], axis=1, keepdims=True)
          return np.tile(interocular, [1, 2])
  
      @deprecated_api_warning(name_dict=dict(outputs='results'))
      def evaluate(self, results, res_folder=None, metric='NME', **kwargs):
          """Evaluate COCO-WholeBody Face keypoint results. The pose prediction
          results will be saved in ``${res_folder}/result_keypoints.json``.
  
          Note:
              - batch_size: N
              - num_keypoints: K
              - heatmap height: H
              - heatmap width: W
  
          Args:
              results (list[dict]): Testing results containing the following
                  items:
  
                  - preds (np.ndarray[1,K,3]): The first two dimensions are \
                      coordinates, score is the third dimension of the array.
                  - boxes (np.ndarray[1,6]): [center[0], center[1], scale[0], \
                      scale[1],area, score]
                  - image_path (list[str]): For example, ['coco/train2017/\
                      000000000009.jpg']
                  - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
              res_folder (str, optional): The folder to save the testing
                  results. If not specified, a temp folder will be created.
                  Default: None.
              metric (str | list[str]): Metric to be performed.
                  Options: 'NME'.
  
          Returns:
              dict: Evaluation results for evaluation metric.
          """
          metrics = metric if isinstance(metric, list) else [metric]
          allowed_metrics = ['NME']
          for metric in metrics:
              if metric not in allowed_metrics:
                  raise KeyError(f'metric {metric} is not supported')
  
          if res_folder is not None:
              tmp_folder = None
              res_file = osp.join(res_folder, 'result_keypoints.json')
          else:
              tmp_folder = tempfile.TemporaryDirectory()
              res_file = osp.join(tmp_folder.name, 'result_keypoints.json')
  
          kpts = []
          for result in results:
              preds = result['preds']
              boxes = result['boxes']
              image_paths = result['image_paths']
              bbox_ids = result['bbox_ids']
  
              batch_size = len(image_paths)
              for i in range(batch_size):
                  image_id = self.name2id[image_paths[i][len(self.img_prefix):]]
  
                  kpts.append({
                      'keypoints': preds[i].tolist(),
                      'center': boxes[i][0:2].tolist(),
                      'scale': boxes[i][2:4].tolist(),
                      'area': float(boxes[i][4]),
                      'score': float(boxes[i][5]),
                      'image_id': image_id,
                      'bbox_id': bbox_ids[i]
                  })
          kpts = self._sort_and_unique_bboxes(kpts)
  
          self._write_keypoint_results(kpts, res_file)
          info_str = self._report_metric(res_file, metrics)
          name_value = OrderedDict(info_str)
  
          if tmp_folder is not None:
              tmp_folder.cleanup()
  
          return name_value
  