本文實例為大家分享了Python實點雲分割k-means（sklearn），供大家參考，具體內容如下
植物葉片分割
import numpy as 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] #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.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()
[limiyoyo ] python實點雲分割k-means(sklearn)詳解已經有267次圍觀