python实点云分割k-means(sklearn)详解
本文实例为大家分享了Python实点云分割k-means（sklearn），供大家参考，具体内容如下
植物叶片分割
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from mpl_toolkits.mplot3d import Axes3D
data = pd.read_csv("jiaaobo1.txt",sep = " ")
data1 = data.iloc[:,0:3]
#标准化
transfer = StandardScaler()
data_new = transfer.fit_transform(data1)
data_new
#预估计流程
estimator = KMeans(n_clusters = 10)
estimator.fit(data_new)
y_pred = estimator.predict(data_new)
#也可以不预测
#cluster = KMeans(n_clusters = 9).fit(data_new)
#y_pred = cluster.labels_s
#质心
#centroid = cluster.cluster_centers_
#centroid.shape
fig = plt.figure()
ax = Axes3D(fig)
for i in range(9):
ax.scatter3D(data_new[y_pred == i,0],data_new[y_pred == i,1],data_new[y_pred == i,2],marker = ".")
ax.view_init(elev = 60,azim = 30)
ax.set_zlabel('Z')
ax.set_ylabel('Y')
ax.set_xlabel('X')
plt.show()
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