text: import os
  from collections import OrderedDict
  
  import json_tricks as json
  import numpy as np
  
  from mmpose.core.evaluation.top_down_eval import keypoint_nme
  from mmpose.datasets.builder import DATASETS
  from .face_base_dataset import FaceBaseDataset
  
  
  @DATASETS.register_module()
  class FaceCOFWDataset(FaceBaseDataset):
      """Face COFW dataset for top-down face keypoint localization.
  
      `Robust face landmark estimation under occlusion. (ICCV) 2013`.
  
      The dataset loads raw images and apply specified transforms
      to return a dict containing the image tensors and other information.
  
      The landmark annotations follow the 29 points mark-up. The definition
      can be found in `http://www.vision.caltech.edu/xpburgos/ICCV13/`.
  
      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.
          test_mode (bool): Store True when building test or
              validation dataset. Default: False.
      """
  
      def __init__(self,
                   ann_file,
                   img_prefix,
                   data_cfg,
                   pipeline,
                   test_mode=False):
  
          super().__init__(
              ann_file, img_prefix, data_cfg, pipeline, test_mode=test_mode)
  
          self.ann_info['use_different_joint_weights'] = False
          assert self.ann_info['num_joints'] == 29
          self.ann_info['joint_weights'] = \
              np.ones((self.ann_info['num_joints'], 1), dtype=np.float32)
  
          self.ann_info['flip_pairs'] = [[0, 1], [4, 6], [2, 3], [5, 7], [8, 9],
                                         [10, 11], [12, 14], [16, 17], [13, 15],
                                         [18, 19], [22, 23]]
  
          self.dataset_name = 'cofw'
          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:
              num_joints = self.ann_info['num_joints']
  
              ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=False)
              objs = self.coco.loadAnns(ann_ids)
  
              for obj in objs:
                  if max(obj['keypoints']) == 0:
                      continue
                  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['keypoints']).reshape(-1, 3)
                  joints_3d[:, :2] = keypoints[:, :2]
                  joints_3d_visible[:, :2] = np.minimum(1, keypoints[:, 2:3])
  
                  if 'center' in obj and 'scale' in obj:
                      center = np.array(obj['center'])
                      scale = np.array([obj['scale'], obj['scale']]) * 1.25
                  else:
                      center, scale = self._xywh2cs(*obj['bbox'][:4], 1.25)
  
                  image_file = os.path.join(self.img_prefix,
                                            self.id2name[img_id])
                  gt_db.append({
                      'image_file': image_file,
                      'center': center,
                      'scale': scale,
                      'rotation': 0,
                      'joints_3d': joints_3d,
                      'joints_3d_visible': joints_3d_visible,
                      'dataset': self.dataset_name,
                      'bbox': obj['bbox'],
                      '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):
          """Get normalize factor for evaluation.
  
          Args:
              gts (np.ndarray[N, K, 2]): Groundtruth keypoint location.
  
          Return:
              np.ndarray[N, 2]: normalized factor
          """
  
          interocular = np.linalg.norm(
              gts[:, 8, :] - gts[:, 9, :], axis=1, keepdims=True)
          return np.tile(interocular, [1, 2])
  
      def _report_metric(self, res_file, metrics):
          """Keypoint evaluation.
  
          Args:
              res_file (str): Json file stored prediction results.
              metrics (str | list[str]): Metric to be performed.
                  Options: 'NME'.
  
          Returns:
              dict: Evaluation results for evaluation metric.
          """
          info_str = []
  
          with open(res_file, 'r') as fin:
              preds = json.load(fin)
          assert len(preds) == len(self.db)
  
          outputs = []
          gts = []
          masks = []
  
          for pred, item in zip(preds, self.db):
              outputs.append(np.array(pred['keypoints'])[:, :-1])
              gts.append(np.array(item['joints_3d'])[:, :-1])
              masks.append((np.array(item['joints_3d_visible'])[:, 0]) > 0)
  
          outputs = np.array(outputs)
          gts = np.array(gts)
          masks = np.array(masks)
  
          if 'NME' in metrics:
              normalize_factor = self._get_normalize_factor(gts)
              info_str.append(
                  ('NME', keypoint_nme(outputs, gts, masks, normalize_factor)))
  
          return info_str
  
      def evaluate(self, outputs, res_folder, metric='NME', **kwargs):
          """Evaluate freihand 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:
              outputs (list(preds, boxes, image_path, output_heatmap))
                  :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, ['3', '0', '0', 'W', '/',
                      'i', 'b', 'u', 'g', '/', 'i', 'm', 'a', 'g', 'e', '_', '0',
                      '1', '8', '.', 'j', 'p', 'g']
                  :output_heatmap (np.ndarray[N, K, H, W]): model outpus.
              res_folder (str): Path of directory to save the results.
              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')
  
          res_file = os.path.join(res_folder, 'result_keypoints.json')
  
          kpts = []
          for output in outputs:
              preds = output['preds']
              boxes = output['boxes']
              image_paths = output['image_paths']
              bbox_ids = output['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)
  
          return name_value
  