open3d 点云聚类dbscan

关键代码：

labels = np.array(pcd.cluster_dbscan(eps=0.02, min_points=10, print_progress=True))

point_cloud_dbscan_clustering.py 

import open3d as o3d
import numpy as np
import matplotlib.pyplot as plt

if __name__ == "__main__":
    # 1. read pcd
    sample_ply_data = o3d.data.PLYPointCloud()
    pcd = o3d.io.read_point_cloud(sample_ply_data.path)
    # Flip it, otherwise the pointcloud will be upside down.
    """
    [
    [1, 0, 0, 0],
    [0, -1, 0, 0],
    [0, 0, -1, 0],
    [0, 0, 0, 1]
    ]
    """
    pcd.transform([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])

    # 2. cluster_dbscan. 聚类
    """
    eps. Density parameter that is used to find neighbouring points.
    min_points. Minimum number of points to form a cluster.
    print_progress (default False). If 'True' the progress is visualized in the console.
    return: label. 每个点都有类别值
    """
    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))

    # 3. view:
    max_label = labels.max()  # 最大的类别值
    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  # 类别为0的，颜色设置为黑色
    pcd.colors = o3d.utility.Vector3dVector(colors[:, :3])  # ndarray to vector3d
    o3d.visualization.draw([pcd])