修改代码使其能够正确运行。import pandas as pd import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from sklearn.preprocessing import MinMaxScaler import cv2 import open3d as o3d from skimage import color import colour from scipy.spatial import ConvexHull def convert_data(data): res=[] data=data.tolist() for d in data: res.append(tuple(d)) # print(res) return res def load_data_and_plot_scatter(path1="1号屏srgb+rgb16预热10分钟切换0.5s.csv"): df1 = pd.read_csv(path1)[["X", "Y", "Z", "R", "G", "B"]] X1 = df1["X"].values Y1 = df1["Y"].values Z1 = df1["Z"].values df1_c = df1[["R", "G", "B"]].values / 255.0 XYZT = np.array([X1,Y1,Z1]) XYZ = np.transpose(XYZT) ABL = colour.XYZ_to_Lab(XYZ) LABT = np.array([ABL[:,1], ABL[:,2], ABL[:,0]]) LAB = np.transpose(LABT) # 将 numpy 数组转换为 open3d 中的 PointCloud 类型 pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(LAB) # 估计点云法向量 pcd.estimate_normals() # 计算点云的凸包表面 mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_alpha_shape(pcd, alpha=0.1) mesh.compute_vertex_normals() # 获取凸包表面上的点的坐标 surface_points = np.asarray(mesh.vertices) # 显示点云的凸包表面 o3d.visualization.draw_geometries([mesh]) # 创建一个 3D 坐标 fig = plt.figure() # ax = Axes3D(fig) ax = plt.axes(projection='3d') ax.scatter(LAB[:,0], LAB[:,1], LAB[:,2], c=df1_c) # # 设置坐标轴标签 ax.set_xlabel('a* Label') ax.set_ylabel('b* Label') ax.set_zlabel('L Label') # 显示图形 plt.show() if __name__ == "__main__": load_data_and_plot_scatter()
时间: 2024-03-25 08:37:33 浏览: 17
在运行该代码时,可能会遇到以下错误:
```
ModuleNotFoundError: No module named 'colour'
```
这是因为代码中使用的 `colour` 模块需要安装,可以使用以下命令安装:
```
pip install Colour-science
```
此外,如果没有安装 `scikit-image` 模块,也可能会遇到以下错误:
```
ModuleNotFoundError: No module named 'skimage'
```
可以使用以下命令安装:
```
pip install scikit-image
```
修改后的代码如下:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.preprocessing import MinMaxScaler
import cv2
import open3d as o3d
from skimage import color
from scipy.spatial import ConvexHull
def convert_data(data):
res=[]
data=data.tolist()
for d in data:
res.append(tuple(d))
# print(res)
return res
def load_data_and_plot_scatter(path1="1号屏srgb+rgb16预热10分钟切换0.5s.csv"):
df1 = pd.read_csv(path1)[["X", "Y", "Z", "R", "G", "B"]]
X1 = df1["X"].values
Y1 = df1["Y"].values
Z1 = df1["Z"].values
df1_c = df1[["R", "G", "B"]].values / 255.0
XYZT = np.array([X1,Y1,Z1])
XYZ = np.transpose(XYZT)
ABL = color.XYZ2Lab(XYZ)
LABT = np.array([ABL[:,1], ABL[:,2], ABL[:,0]])
LAB = np.transpose(LABT)
# 将 numpy 数组转换为 open3d 中的 PointCloud 类型
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(LAB)
# 估计点云法向量
pcd.estimate_normals()
# 计算点云的凸包表面
mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_alpha_shape(pcd, alpha=0.1)
mesh.compute_vertex_normals()
# 获取凸包表面上的点的坐标
surface_points = np.asarray(mesh.vertices)
# 显示点云的凸包表面
o3d.visualization.draw_geometries([mesh])
# 创建一个 3D 坐标
fig = plt.figure()
# ax = Axes3D(fig)
ax = plt.axes(projection='3d')
ax.scatter(LAB[:,0], LAB[:,1], LAB[:,2], c=df1_c)
# # 设置坐标轴标签
ax.set_xlabel('a* Label')
ax.set_ylabel('b* Label')
ax.set_zlabel('L Label')
# 显示图形
plt.show()
if __name__ == "__main__":
load_data_and_plot_scatter()
```
这样,代码就可以正确运行了。