def voxelize()

in datasets/transforms/voxelizer.py [0:0]

• 21 lines of code
• 11 McCabe index (conditional complexity)

  def voxelize(self, coords, feats, labels, center=None):
    assert coords.shape[1] == 3 and coords.shape[0] == feats.shape[0] and coords.shape[0]
    if self.clip_bound is not None:
      trans_aug_ratio = np.zeros(3)
      if self.use_augmentation and self.translation_augmentation_ratio_bound is not None:
        for axis_ind, trans_ratio_bound in enumerate(self.translation_augmentation_ratio_bound):
          trans_aug_ratio[axis_ind] = np.random.uniform(*trans_ratio_bound)

      clip_inds = self.clip(coords, center, trans_aug_ratio)
      if clip_inds is not None:
        coords, feats = coords[clip_inds], feats[clip_inds]
        if labels is not None:
          labels = labels[clip_inds]

    # Get rotation and scale
    M_v, M_r = self.get_transformation_matrix()
    # Apply transformations
    rigid_transformation = M_v
    if self.use_augmentation:
      rigid_transformation = M_r @ rigid_transformation

    homo_coords = np.hstack((coords, np.ones((coords.shape[0], 1), dtype=coords.dtype)))
    coords_aug = np.floor(homo_coords @ rigid_transformation.T[:, :3])

    # key = self.hash(coords_aug)  # floor happens by astype(np.uint64)
    coords_aug, feats, labels = ME.utils.sparse_quantize(
        coords_aug, feats, labels=labels, ignore_label=self.ignore_label)

    return coords_aug, feats, labels, rigid_transformation.flatten()