text: import os
  from collections import OrderedDict
  
  import json_tricks as json
  import numpy as np
  
  from mmpose.datasets.builder import DATASETS
  from .hand_base_dataset import HandBaseDataset
  
  
  @DATASETS.register_module()
  class InterHand2DDataset(HandBaseDataset):
      """InterHand2.6M 2D dataset for top-down hand pose estimation.
  
      `InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose
      Estimation from a Single RGB Image' Moon, Gyeongsik etal. ECCV'2020
      More details can be found in the `paper
      <https://arxiv.org/pdf/2008.09309.pdf>`__ .
  
      The dataset loads raw features and apply specified transforms
      to return a dict containing the image tensors and other information.
  
      InterHand2.6M keypoint indexes::
  
          0: 'thumb4',
          1: 'thumb3',
          2: 'thumb2',
          3: 'thumb1',
          4: 'forefinger4',
          5: 'forefinger3',
          6: 'forefinger2',
          7: 'forefinger1',
          8: 'middle_finger4',
          9: 'middle_finger3',
          10: 'middle_finger2',
          11: 'middle_finger1',
          12: 'ring_finger4',
          13: 'ring_finger3',
          14: 'ring_finger2',
          15: 'ring_finger1',
          16: 'pinky_finger4',
          17: 'pinky_finger3',
          18: 'pinky_finger2',
          19: 'pinky_finger1',
          20: 'wrist'
  
      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 (str): Store True when building test or
              validation dataset. Default: False.
      """
  
      def __init__(self,
                   ann_file,
                   camera_file,
                   joint_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 = 'interhand2d'
          self.camera_file = camera_file
          self.joint_file = joint_file
          self.db = self._get_db()
  
          print(f'=> num_images: {self.num_images}')
          print(f'=> load {len(self.db)} samples')
  
      @staticmethod
      def _cam2pixel(cam_coord, f, c):
          """Transform the joints from their camera coordinates to their pixel
          coordinates.
  
          Note:
              N: number of joints
  
          Args:
              cam_coord (ndarray[N, 3]): 3D joints coordinates
                  in the camera coordinate system
              f (ndarray[2]): focal length of x and y axis
              c (ndarray[2]): principal point of x and y axis
  
          Returns:
              img_coord (ndarray[N, 3]): the coordinates (x, y, 0)
                  in the image plane.
          """
          x = cam_coord[:, 0] / (cam_coord[:, 2] + 1e-8) * f[0] + c[0]
          y = cam_coord[:, 1] / (cam_coord[:, 2] + 1e-8) * f[1] + c[1]
          z = np.zeros_like(x)
          img_coord = np.concatenate((x[:, None], y[:, None], z[:, None]), 1)
          return img_coord
  
      @staticmethod
      def _world2cam(world_coord, R, T):
          """Transform the joints from their world coordinates to their camera
          coordinates.
  
          Note:
              N: number of joints
  
          Args:
              world_coord (ndarray[3, N]): 3D joints coordinates
                  in the world coordinate system
              R (ndarray[3, 3]): camera rotation matrix
              T (ndarray[3]): camera position (x, y, z)
  
          Returns:
              cam_coord (ndarray[3, N]): 3D joints coordinates
                  in the camera coordinate system
          """
          cam_coord = np.dot(R, world_coord - T)
          return cam_coord
  
      def _get_db(self):
          """Load dataset.
  
          Adapted from 'https://github.com/facebookresearch/InterHand2.6M/'
                          'blob/master/data/InterHand2.6M/dataset.py'
          Copyright (c) FaceBook Research, under CC-BY-NC 4.0 license.
          """
          with open(self.camera_file, 'r') as f:
              cameras = json.load(f)
          with open(self.joint_file, 'r') as f:
              joints = json.load(f)
          gt_db = []
          bbox_id = 0
          for img_id in self.img_ids:
              num_joints = self.ann_info['num_joints']
  
              ann_id = self.coco.getAnnIds(imgIds=img_id, iscrowd=False)
              ann = self.coco.loadAnns(ann_id)[0]
              img = self.coco.loadImgs(img_id)[0]
  
              capture_id = str(img['capture'])
              camera_name = img['camera']
              frame_idx = str(img['frame_idx'])
              image_file = os.path.join(self.img_prefix, self.id2name[img_id])
  
              camera_pos, camera_rot = np.array(
                  cameras[capture_id]['campos'][camera_name],
                  dtype=np.float32), np.array(
                      cameras[capture_id]['camrot'][camera_name],
                      dtype=np.float32)
              focal, principal_pt = np.array(
                  cameras[capture_id]['focal'][camera_name],
                  dtype=np.float32), np.array(
                      cameras[capture_id]['princpt'][camera_name],
                      dtype=np.float32)
              joint_world = np.array(
                  joints[capture_id][frame_idx]['world_coord'], dtype=np.float32)
              joint_cam = self._world2cam(
                  joint_world.transpose(1, 0), camera_rot,
                  camera_pos.reshape(3, 1)).transpose(1, 0)
              joint_img = self._cam2pixel(joint_cam, focal, principal_pt)[:, :2]
              joint_img = joint_img.reshape(2, -1, 2)
  
              joint_valid = np.array(
                  ann['joint_valid'], dtype=np.float32).reshape(2, -1)
              # if root is not valid -> root-relative 3D pose is also not valid.
              # Therefore, mark all joints as invalid
              for hand in range(2):
                  joint_valid[hand, :] *= joint_valid[hand][-1]
  
                  if np.sum(joint_valid[hand, :]) > 11:
                      joints_3d = np.zeros((num_joints, 3), dtype=np.float32)
                      joints_3d_visible = np.zeros((num_joints, 3),
                                                   dtype=np.float32)
                      joints_3d[:, :2] = joint_img[hand, :, :]
                      joints_3d_visible[:, :2] = np.minimum(
                          1, joint_valid[hand, :].reshape(-1, 1))
  
                      # use the tightest bbox enclosing all keypoints as bbox
                      bbox = [img['width'], img['height'], 0, 0]
                      for i in range(num_joints):
                          if joints_3d_visible[i][0]:
                              bbox[0] = min(bbox[0], joints_3d[i][0])
                              bbox[1] = min(bbox[1], joints_3d[i][1])
                              bbox[2] = max(bbox[2], joints_3d[i][0])
                              bbox[3] = max(bbox[3], joints_3d[i][1])
  
                      bbox[2] -= bbox[0]
                      bbox[3] -= bbox[1]
  
                      # use 1.5bbox as input
                      center, scale = self._xywh2cs(*bbox, 1.5)
  
                      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': 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 interhand2d 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, ['C', 'a', 'p', 't',
                      'u', 'r', 'e', '1', '2', '/', '0', '3', '9', '0', '_',
                      'd', 'h', '_', 't', 'o', 'u', 'c', 'h', 'R', 'O', 'M',
                      '/', 'c', 'a', 'm', '4', '1', '0', '2', '0', '9', '/',
                      'i', 'm', 'a', 'g', 'e', '6', '2', '4', '3', '4', '.',
                      '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: '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
  