code: # Copyright (c) OpenMMLab. All rights reserved.
  import os
  from collections import OrderedDict
  
  import numpy as np
  
  from mmpose.datasets.builder import DATASETS
  from .hand_base_dataset import HandBaseDataset
  
  
  @DATASETS.register_module()
  class FreiHandDataset(HandBaseDataset):
      """FreiHand dataset for top-down hand pose estimation.
  
      `FreiHAND: A Dataset for Markerless Capture of Hand Pose
      and Shape from Single RGB Images' ICCV'2019
      More details can be found in the `paper
      <https://arxiv.org/pdf/1909.04349.pdf>`__ .
  
      The dataset loads raw features and apply specified transforms
      to return a dict containing the image tensors and other information.
  
      FreiHand keypoint indexes::
  
          0: 'wrist',
          1: 'thumb1',
          2: 'thumb2',
          3: 'thumb3',
          4: 'thumb4',
          5: 'forefinger1',
          6: 'forefinger2',
          7: 'forefinger3',
          8: 'forefinger4',
          9: 'middle_finger1',
          10: 'middle_finger2',
          11: 'middle_finger3',
          12: 'middle_finger4',
          13: 'ring_finger1',
          14: 'ring_finger2',
          15: 'ring_finger3',
          16: 'ring_finger4',
          17: 'pinky_finger1',
          18: 'pinky_finger2',
          19: 'pinky_finger3',
          20: 'pinky_finger4'
  
      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'] == 21
          self.ann_info['joint_weights'] = \
              np.ones((self.ann_info['num_joints'], 1), dtype=np.float32)
  
          self.dataset_name = 'freihand'
          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 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])
  
                  # the ori image is 224x224
                  center, scale = self._xywh2cs(0, 0, 224, 224, 0.8)
  
                  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 evaluate(self, outputs, res_folder, metric='PCK', **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[N,K,3]): The first two dimensions are
                      coordinates, score is the third dimension of the array.
                  :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
                      , scale[1],area, score]
                  :image_paths (list[str]): For example, ['training/rgb/
                      00031426.jpg']
                  :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: 'PCK', 'AUC', 'EPE'.
  
          Returns:
              dict: Evaluation results for evaluation metric.
          """
          metrics = metric if isinstance(metric, list) else [metric]
          allowed_metrics = ['PCK', 'AUC', 'EPE']
          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
  
apis: numpy.array numpy.ones numpy.zeros numpy.minimum