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

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

代码下载地址：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，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[0:3,:]
    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(list1,3,10)
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：