python實點云分割k-means(sklearn)詳解

本文實例為大家分享了Python實點云分割k-means（sklearn），供大家參考，具體內容如下

植物葉片分割

import numpy as npimport matplotlib.pyplot as pltimport pandas as pdfrom sklearn.cluster import KMeansfrom sklearn.preprocessing import StandardScalerfrom mpl_toolkits.mplot3d import Axes3Ddata = pd.read_csv('jiaaobo1.txt',sep = ' ')data1 = data.iloc[:,0:3]#標準化transfer = StandardScaler()data_new = transfer.fit_transform(data1)data_new#預估計流程estimator = KMeans(n_clusters = 10)estimator.fit(data_new)y_pred = estimator.predict(data_new)#也可以不預測#cluster = KMeans(n_clusters = 9).fit(data_new)#y_pred = cluster.labels_s#質心 #centroid = cluster.cluster_centers_#centroid.shapefig = 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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