text: # Copyright (c) 2018-present, Facebook, Inc.
  # All rights reserved.
  #
  # This source code is licensed under the license found in the
  # LICENSE file in the root directory of this source tree.
  #
  
  import numpy as np
  import copy
  from common.skeleton import Skeleton
  from common.mocap_dataset import MocapDataset
  from common.camera import normalize_screen_coordinates, image_coordinates
  
  humaneva_skeleton = Skeleton(parents=[-1, 0, 1, 2, 3, 1, 5, 6, 0, 8, 9, 0, 11, 12, 1],
                               joints_left=[2, 3, 4, 8, 9, 10],
                               joints_right=[5, 6, 7, 11, 12, 13])
  
  humaneva_cameras_intrinsic_params = [
      {
          'id': 'C1',
          'res_w': 640,
          'res_h': 480,
          'azimuth': 0,  # Only used for visualization
      },
      {
          'id': 'C2',
          'res_w': 640,
          'res_h': 480,
          'azimuth': -90,  # Only used for visualization
      },
      {
          'id': 'C3',
          'res_w': 640,
          'res_h': 480,
          'azimuth': 90,  # Only used for visualization
      },
  ]
  
  humaneva_cameras_extrinsic_params = {
      'S1': [
          {
              'orientation': [0.424207, -0.4983646, -0.5802981, 0.4847012],
              'translation': [4062.227, 663.2477, 1528.397],
          },
          {
              'orientation': [0.6503354, -0.7481602, -0.0919284, 0.0941766],
              'translation': [844.8131, -3805.2092, 1504.9929],
          },
          {
              'orientation': [0.0664734, -0.0690535, 0.7416416, -0.6639132],
              'translation': [-797.67377, 3916.3174, 1433.6602],
          },
      ],
      'S2': [
          {
              'orientation': [0.4214752, -0.4961493, -0.5838273, 0.4851187],
              'translation': [4112.9121, 626.4929, 1545.2988],
          },
          {
              'orientation': [0.6501393, -0.7476588, -0.0954617, 0.0959808],
              'translation': [923.5740, -3877.9243, 1504.5518],
          },
          {
              'orientation': [0.0699353, -0.0712403, 0.7421637, -0.662742],
              'translation': [-781.4915, 3838.8853, 1444.9929],
          },
      ],
      'S3': [
          {
              'orientation': [0.424207, -0.4983646, -0.5802981, 0.4847012],
              'translation': [4062.2271, 663.2477, 1528.3970],
          },
          {
              'orientation': [0.6503354, -0.7481602, -0.0919284, 0.0941766],
              'translation': [844.8131, -3805.2092, 1504.9929],
          },
          {
              'orientation': [0.0664734, -0.0690535, 0.7416416, -0.6639132],
              'translation': [-797.6738, 3916.3174, 1433.6602],
          },
      ],
      'S4': [
          {},
          {},
          {},
      ],
  
  }
  
  
  class HumanEvaDataset(MocapDataset):
      def __init__(self, path):
          super().__init__(fps=60, skeleton=humaneva_skeleton)
  
          self._cameras = copy.deepcopy(humaneva_cameras_extrinsic_params)
          for cameras in self._cameras.values():
              for i, cam in enumerate(cameras):
                  cam.update(humaneva_cameras_intrinsic_params[i])
                  for k, v in cam.items():
                      if k not in ['id', 'res_w', 'res_h']:
                          cam[k] = np.array(v, dtype='float32')
                  if 'translation' in cam:
                      cam['translation'] = cam['translation'] / 1000  # mm to meters
  
          for subject in list(self._cameras.keys()):
              data = self._cameras[subject]
              del self._cameras[subject]
              for prefix in ['Train/', 'Validate/', 'Unlabeled/Train/', 'Unlabeled/Validate/', 'Unlabeled/']:
                  self._cameras[prefix + subject] = data
  
          # Load serialized dataset
          data = np.load(path, allow_pickle=True)['positions_3d'].item()
  
          self._data = {}
          for subject, actions in data.items():
              self._data[subject] = {}
              for action_name, positions in actions.items():
                  self._data[subject][action_name] = {
                      'positions': positions,
                      'cameras': self._cameras[subject],
                  }
  