text: # ----------------------------------------------------------------------------
  # -                        Open3D: www.open3d.org                            -
  # ----------------------------------------------------------------------------
  # The MIT License (MIT)
  #
  # Copyright (c) 2018-2021 www.open3d.org
  #
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  #
  # The above copyright notice and this permission notice shall be included in
  # all copies or substantial portions of the Software.
  #
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
  # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
  # IN THE SOFTWARE.
  # ----------------------------------------------------------------------------
  
  import open3d as o3d
  import numpy as np
  import matplotlib.pyplot as plt
  
  if __name__ == "__main__":
      sample_ply_data = o3d.data.SamplePointCloudPLY()
      pcd = o3d.io.read_point_cloud(sample_ply_data.path)
      # Flip it, otherwise the pointcloud will be upside down.
      pcd.transform([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])
  
      with o3d.utility.VerbosityContextManager(
              o3d.utility.VerbosityLevel.Debug) as cm:
          labels = np.array(
              pcd.cluster_dbscan(eps=0.02, min_points=10, print_progress=True))
  
      max_label = labels.max()
      print(f"point cloud has {max_label + 1} clusters")
      colors = plt.get_cmap("tab20")(labels / (max_label if max_label > 0 else 1))
      colors[labels < 0] = 0
      pcd.colors = o3d.utility.Vector3dVector(colors[:, :3])
      o3d.visualization.draw([pcd])
  