机器学习之利用k-means算法对点云数据进行目标分割，提取其中的建筑物、房屋等 - 好文

原始点云数据在CloudCompare的显示如下：

点云原始数据以及提取出的房屋道路等数据下载地址：https://download.csdn.net/download/qq_39343904/10863193
<https://download.csdn.net/download/qq_39343904/10863193>

代码下载地址：https://download.csdn.net/download/qq_39343904/10863209
<https://download.csdn.net/download/qq_39343904/10863209>

接着利用k-means算法提取出其中的建筑物、房屋等，我这里的代码是根据k-means算法的原理编写的代码，这样有助于大家对k-means算法的运行原理有一个深层次的了解，当然也可以直接调用sklearn里的算法，但是那样的话对于将来发展是不利的，毕竟知道算法的原理并根据原理编写代码学到的知识还是更多一些的。

代码如下：
import numpy as np def kmean(x,k,maxtimes): m,n = np.shape(x) dataset =
np.zeros([m,n+1]) dataset[:,:-1] = x #middle =
dataset[np.random.randint(m,size=k),:] middle = dataset[0:4,:] middle[:,-1] =
range(1,k+1) times = 0 oldmiddle = None while not
shouldstop(oldmiddle,middle,times,maxtimes): print('times:',times)
print('dataset:',dataset) print('middle:',middle) oldmiddle = np.copy(middle)
times = times + 1 update(dataset,middle) middle = getmiddles(dataset,k) return
dataset def shouldstop(oldmiddle,middle,times,maxtimes): if times > maxtimes:
return True return np.array_equal(oldmiddle,middle) def update(dataset,middle):
m,n =dataset.shape for i in range(0,m): dataset[i,-1] =
getLabelFromCloestCentroid(dataset[i,:-1],middle) def
getLabelFromCloestCentroid(datasetRow,middle): label = middle[0,-1] minDist =
np.linalg.norm(datasetRow - middle[0,:-1]) for i in range(1,middle.shape[0]):
dist = np.linalg.norm(datasetRow - middle[i,:-1]) if dist < minDist: minDist =
dist label = middle[i,-1] print('minDist',minDist) print('label',label) return
label def getmiddles(datatset,k): result = np.zeros((k,datatset.shape[1])) for
i in range(1,k+1): oneCluster = datatset[datatset[:,-1]==i,:-1] result[i-1,:-1]
= np.mean(oneCluster,axis=0) result[i-1,-1] = i return result import numpy as
np import csv file = open(r'全部点云数据.csv','r') reader = csv.reader(file) reader =
list(reader) m,n = np.shape(reader) for i in range(0,m): for j in range(0,4):
reader[i][j] = float(reader[i][j]) m,n = np.shape(reader) list1 =
np.zeros([m,2]) for i in range(0,m): for j in range(2,4): list1[i][j-2] =
reader[i][j] # x = np.vstack((a,b,c,d)) result = kmean(list1,4,10)
print('result:',result[0]) print(reader[0]) reader0 = np.zeros([m,5]) for i in
range(0,m): for j in range(0,4): reader0[i][j] = reader[i][j] for i in
range(0,m): reader0[i][-1] = int(result[i][-1]) print(reader0)
w1=open("1.txt","w") w2=open("2.txt","w") w3=open("3.txt","w")
w4=open("4.txt","w") for i in range(m): if(reader0[i][-1]==1): w1.write("%s %s
%s %s\n"%(reader0[i][0],reader0[i][1],reader0[i][2],reader0[i][3]))
if(reader0[i][-1]==2): w2.write("%s %s %s %s\n" % (reader0[i][0],
reader0[i][1], reader0[i][2],reader0[i][3])) if (reader0[i][-1] ==3):
w3.write("%s %s %s %s\n" % (reader0[i][0], reader0[i][1],
reader0[i][2],reader0[i][3])) if (reader0[i][-1] == 4): w4.write("%s %s %s
%s\n" % (reader0[i][0], reader0[i][1], reader0[i][2],reader0[i][3]))
以上代码将k值设置为4，也就是将点云聚成4类。输出为4个txt数据，加载进软件,效果图如下，分别是提取的道路，建筑物：

还是利用上述代码，将道路数据放进代码，将K设置为2，可以将道路数据聚类为两类，提取出道路的主干和边界：

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