采用线性回归方法降低双目测距到平面的误差（python+sklearn）

remove_idx1 = np.where(Z <= 0)
remove_idx2 = np.where(Z > 500)#将Z轴坐标限定在0-500，以减少非标定板区域的坐标影响

from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

#删除掉无效及多余点后，得到points_3d
X = points_3d[:, 0]
Y = points_3d[:, 1]
Z = points_3d[:, 2]
XY = points_3d[:, :2]

#利用线性回归计算新的Z轴坐标
reg = LinearRegression()
reg.fit(XY, Z)
Z_predict = reg.predict(XY)

fig=plt.figure()
ax = plt.axes(projection=‘3d‘)
ax.scatter3D(X, Y, Z, c=‘gray‘, s=1)#显示原始点信息
ax.scatter3D(X, Y, Z_predict, c=‘red‘)#显示修正点信息
ax.set_xlabel(r‘$x_1$‘,fontsize = 20, color = ‘blue‘)
ax.set_ylabel(r‘$x_2$‘,fontsize = 20, color = ‘blue‘)
ax.set_zlabel(r‘$x_3$‘,fontsize = 20, color = ‘blue‘)