50 lines
1.4 KiB
Python
50 lines
1.4 KiB
Python
import open3d as o3d
|
|
import numpy as np
|
|
|
|
|
|
def dbscan_open3d(pcd, eps=0.1, min_points=10):
|
|
"""
|
|
使用 Open3D 进行 DBSCAN 聚类
|
|
Args:
|
|
pcd: Open3D 点云对象
|
|
eps: 邻域半径
|
|
min_points: 最小邻域点数
|
|
Returns:
|
|
labels: 每个点的簇标签（-1 表示噪声）
|
|
clusters: 各簇的点索引列表
|
|
"""
|
|
# DBSCAN 聚类
|
|
labels = np.array(pcd.cluster_dbscan(eps=eps, min_points=min_points))
|
|
|
|
# 提取各簇索引
|
|
unique_labels = np.unique(labels)
|
|
clusters = []
|
|
for label in unique_labels:
|
|
if label == -1:
|
|
continue
|
|
clusters.append(np.where(labels == label)[0])
|
|
|
|
return labels, clusters
|
|
|
|
|
|
# 使用示例
|
|
pcd = o3d.io.read_point_cloud("E:\point_distance\data\part_car_head.ply")
|
|
|
|
# 预处理
|
|
pcd = pcd.voxel_down_sample(voxel_size=0.05)
|
|
pcd, _ = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=2.0)
|
|
|
|
# DBSCAN 聚类
|
|
labels, clusters = dbscan_open3d(pcd, eps=0.1, min_points=50)
|
|
|
|
print(f"找到 {len(clusters)} 个聚类")
|
|
print(f"噪声点: {np.sum(labels == -1)}")
|
|
|
|
# 为每个聚类着色
|
|
colors = np.random.rand(len(clusters), 3)
|
|
colored_points = np.zeros((len(pcd.points), 3))
|
|
for i, cluster in enumerate(clusters):
|
|
colored_points[cluster] = colors[i]
|
|
pcd.colors = o3d.utility.Vector3dVector(colored_points)
|
|
|
|
o3d.visualization.draw_geometries([pcd]) |