import utility.read as read
时间: 2024-05-26 18:14:27 浏览: 12
This code imports a module called "read" from a package or module called "utility". The function or variable being imported from the "read" module is not specified.
相关问题
pcd.points = o3d.utility.Vector3dVector(point_cloud) TypeError: __init__(): incompatible constructor arguments. The following argument types are supported: 1. open3d.cpu.pybind.utility.Vector3dVector() 2. open3d.cpu.pybind.utility.Vector3dVector(arg0: numpy.ndarray[float64]) 3. open3d.cpu.pybind.utility.Vector3dVector(arg0: open3d.cpu.pybind.utility.Vector3dVector) 4. open3d.cpu.pybind.utility.Vector3dVector(arg0: iterable) Invoked with: PointCloud with 55424544 points.
这个错误是因为你传递给 `Vector3dVector` 的参数类型不正确。根据错误信息,你的 `point_cloud` 是一个包含 55424544 个点的 `PointCloud` 对象,但是 `Vector3dVector` 的构造函数只支持以下四种参数类型:
1. `open3d.cpu.pybind.utility.Vector3dVector()`
2. `open3d.cpu.pybind.utility.Vector3dVector(arg0: numpy.ndarray[float64])`
3. `open3d.cpu.pybind.utility.Vector3dVector(arg0: open3d.cpu.pybind.utility.Vector3dVector)`
4. `open3d.cpu.pybind.utility.Vector3dVector(arg0: iterable)`
因此,你需要将 `PointCloud` 对象转换为一个 `numpy` 数组或者一个可迭代对象,然后再将其传递给 `Vector3dVector` 的构造函数。具体的代码实现可以参考以下示例:
```python
import open3d as o3d
import numpy as np
# 读取点云数据
pcd = o3d.io.read_point_cloud("point_cloud.pcd")
# 将点云数据转换为 numpy 数组
point_cloud = np.asarray(pcd.points)
# 将 numpy 数组转换为 Vector3dVector 对象
points = o3d.utility.Vector3dVector(point_cloud)
```
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]) 加上预处理
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)
# 预处理:去除离群点
mean = np.mean(points, axis=0)
std = np.std(points, axis=0)
inlier_mask = np.all(np.abs(points - mean) < 2 * std, axis=1)
points = points[inlier_mask]
# 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])
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