point1 = (d_array[mask][0], Z_array[mask][0]) point2 = (d_array[mask][-1], Z_array[mask][-1]) # 在图上标记出两个点 plt.plot(point1[0], point1[1], 'o', color='red') plt.annotate('({}, {})'.format(point1[0], point1[1]), (point1[0], point1[1]), textcoords='offset points', xytext=(-20,-10), ha='center') plt.plot(point2[0], point2[1], 'o', color='red') plt.annotate('({}, {})'.format(point2[0], point2[1]), (point2[0], point2[1]), textcoords='offset points', xytext=(20,10), ha='center') # 显示图像 plt.xlabel('d') plt.ylabel('Z') plt.show()标小数点后两位
时间: 2024-01-20 22:02:47 浏览: 11
如果您想将坐标值标记为小数点后两位,可以使用以下代码:
```python
point1 = (d_array[mask][0], Z_array[mask][0])
point2 = (d_array[mask][-1], Z_array[mask][-1])
# 在图上标记出两个点
plt.plot(point1[0], point1[1], 'o', color='red')
plt.annotate('({:.2f}, {:.2f})'.format(point1[0], point1[1]), (point1[0], point1[1]), textcoords='offset points', xytext=(-20,-10), ha='center')
plt.plot(point2[0], point2[1], 'o', color='red')
plt.annotate('({:.2f}, {:.2f})'.format(point2[0], point2[1]), (point2[0], point2[1]), textcoords='offset points', xytext=(20,10), ha='center')
# 显示图像
plt.xlabel('d')
plt.ylabel('Z')
plt.show()
```
这将在图形上标记出小数点后两位的坐标值。
相关问题
import matplotlib.pyplot as plt import numpy as np import math def calculate_pressure(K, H, S, V1, T2, V2, theta, d): if H >= S: P = (K * H) / (S * (d/V1 + T2) * V2 * math.tan(theta)) else: P = (K * (S - (S - H) / 2)) / (S * (d/V1 + T2) * V2 * math.tan(theta)) return P K = 1.5 H = 7.5 S = 10 V1 = 300 T2 = 5 V2 = 10 theta = np.radians(3) d_array = np.linspace(0, 1000, 10000) P_array = np.array([calculate_pressure(K, H, S, V1, T2, V2, theta, d) for d in d_array]) Z_array = (d_array / V1 + T2) * V2 + d_array mask = P_array >= 0.8 plt.plot(d_array[mask], Z_array[mask]) plt.xlabel('d') plt.ylabel('Z') plt.show()在图上标出线段两端点的坐标值
根据提供的代码,您可以使用以下代码标记出线段两端点的坐标值:
```python
# 找到线段两端点的坐标值
point1 = (d_array[mask][0], Z_array[mask][0])
point2 = (d_array[mask][-1], Z_array[mask][-1])
# 在图上标记出两个点
plt.plot(point1[0], point1[1], 'o', color='red')
plt.annotate('({}, {})'.format(point1[0], point1[1]), (point1[0], point1[1]), textcoords='offset points', xytext=(-20,-10), ha='center')
plt.plot(point2[0], point2[1], 'o', color='red')
plt.annotate('({}, {})'.format(point2[0], point2[1]), (point2[0], point2[1]), textcoords='offset points', xytext=(20,10), ha='center')
# 显示图像
plt.xlabel('d')
plt.ylabel('Z')
plt.show()
```
这将在图形上标记出线段两端点的坐标值。请注意,您可能需要根据实际情况调整标记点的位置和文本偏移量。
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])