优化 import numpy as np import open3d as o3d from sklearn.cluster import DBSCAN # 读取点云数据 pcd = o3d.io.read_point_cloud("laser.pcd") points = np.asarray(pcd.points) # DBSCAN聚类 dbscan = DBSCAN(eps=0.2, min_samples=10) dbscan.fit(points) labels = dbscan.labels_ # 获取可行驶区域点云数据 drivable_mask = labels != -1 drivable_points = points[drivable_mask] # 获取路沿点云数据 curb_mask = np.logical_and(labels != -1, points[:, 1] < 0) curb_points = points[curb_mask] # 获取车道线点云数据 line_mask = np.logical_and(labels != -1, points[:, 1] >= 0) line_points = points[line_mask] # 可视化结果 drivable_pcd = o3d.geometry.PointCloud() drivable_pcd.points = o3d.utility.Vector3dVector(drivable_points) o3d.visualization.draw_geometries([drivable_pcd]) curb_pcd = o3d.geometry.PointCloud() curb_pcd.points = o3d.utility.Vector3dVector(curb_points) o3d.visualization.draw_geometries([curb_pcd]) line_pcd = o3d.geometry.PointCloud() line_pcd.points = o3d.utility.Vector3dVector(line_points) o3d.visualization.draw_geometries([line_pcd])
时间: 2023-06-14 21:08:03 浏览: 113
这段代码看起来已经很不错了,不过还有一些可以优化的地方:
1. 可以将 `np.asarray(pcd.points)` 改为 `pcd.points`,因为 `read_point_cloud` 函数返回的就是一个 `PointCloud` 对象,不需要再转换一次。
2. 可以使用 `o3d.visualization.draw_geometries([drivable_pcd, curb_pcd, line_pcd])` 来同时可视化三个点云,这样可以方便地对比它们之间的关系。
3. 如果需要更好的性能,可以考虑使用 `open3d.geometry.VoxelGrid` 对点云进行下采样,这样可以减少点云数量并提高聚类速度。
下面是优化后的代码:
```python
import open3d as o3d
from sklearn.cluster import DBSCAN
# 读取点云数据
pcd = o3d.io.read_point_cloud("laser.pcd")
# DBSCAN聚类
dbscan = DBSCAN(eps=0.2, min_samples=10)
labels = dbscan.fit_predict(pcd.points)
# 获取可行驶区域点云数据
drivable_mask = labels != -1
drivable_pcd = pcd.select_by_index(np.where(drivable_mask)[0])
# 获取路沿点云数据
curb_mask = np.logical_and(labels != -1, pcd.points[:, 1] < 0)
curb_pcd = pcd.select_by_index(np.where(curb_mask)[0])
# 获取车道线点云数据
line_mask = np.logical_and(labels != -1, pcd.points[:, 1] >= 0)
line_pcd = pcd.select_by_index(np.where(line_mask)[0])
# 可视化结果
o3d.visualization.draw_geometries([drivable_pcd, curb_pcd, line_pcd])
```
希望能对你有所帮助!