def test_pointcloud()

in downstream/semseg/lib/datasets/scannet.py [0:0]
33 lines of code
13 McCabe index (conditional complexity)

  def test_pointcloud(self, pred_dir):
    print('Running full pointcloud evaluation.')
    eval_path = os.path.join(pred_dir, 'fulleval')
    os.makedirs(eval_path, exist_ok=True)
    # Join room by their area and room id.
    # Test independently for each room.
    sys.setrecursionlimit(100000)  # Increase recursion limit for k-d tree.
    hist = np.zeros((self.NUM_LABELS, self.NUM_LABELS))
    for i, data_path in enumerate(self.data_paths):
      room_id = self.get_output_id(i)
      pred = np.load(os.path.join(pred_dir, 'pred_%04d_%02d.npy' % (i, 0)))

      # save voxelized pointcloud predictions
      save_point_cloud(
          np.hstack((pred[:, :3], np.array([SCANNET_COLOR_MAP[i] for i in pred[:, -1]]))),
          f'{eval_path}/{room_id}_voxel.ply',
          verbose=False)

      fullply_f = self.data_root / data_path
      query_pointcloud = read_plyfile(fullply_f)
      query_xyz = query_pointcloud[:, :3]
      query_label = query_pointcloud[:, -1]
      # Run test for each room.
      pred_tree = spatial.KDTree(pred[:, :3], leafsize=500)
      _, result = pred_tree.query(query_xyz)
      ptc_pred = pred[result, 3].astype(int)
      # Save prediciton in txt format for submission.
      np.savetxt(f'{eval_path}/{room_id}.txt', ptc_pred, fmt='%i')
      # Save prediciton in colored pointcloud for visualization.
      save_point_cloud(
          np.hstack((query_xyz, np.array([SCANNET_COLOR_MAP[i] for i in ptc_pred]))),
          f'{eval_path}/{room_id}.ply',
          verbose=False)
      # Evaluate IoU.
      if self.IGNORE_LABELS is not None:
        ptc_pred = np.array([self.label_map[x] for x in ptc_pred], dtype=np.int)
        query_label = np.array([self.label_map[x] for x in query_label], dtype=np.int)
      hist += fast_hist(ptc_pred, query_label, self.NUM_LABELS)
    ious = per_class_iu(hist) * 100
    print('mIoU: ' + str(np.nanmean(ious)) + '\n'
          'Class names: ' + ', '.join(CLASS_LABELS) + '\n'
          'IoU: ' + ', '.join(np.round(ious, 2).astype(str)))