在二维点云中寻找空洞

Question

我有一组二维点。它们是标准笛卡尔网格系统上的 X、Y 坐标。有谁知道一种方法来实现（优先在 Python 中）一种算法，该算法将隔离每个 "hole's area" 以便找到每个孔的最大直径。

下面是实际点集的示例：

更新：

我设法用固定数量的簇隔离了每个区域，但是如何根据 "hole's area" 的数量来定义簇的数量？

from sklearn.cluster import KMeans
import numpy as np
import  ipyvolume.pylab as p

dat     = xyz
xycoors = dat[:,0:2]


fit = KMeans(n_clusters=5).fit(xycoors)
clus_datas={i: xycoors[np.where(fit.labels_ == i)] for i in 
range(fit.n_clusters)}

clus_1=clus_datas[0]
clus_2=clus_datas[1]
clus_3=clus_datas[2]
clus_4=clus_datas[3]
clus_5=clus_datas[4]



min_bloc=np.array(nuage)
fig = p.figure(width=1000)
fig.xlabel='x'
fig.ylabel='z'
fig.zlabel='y'

p.scatter(clus_1[:,1], clus_1[:,1]*0, clus_1[:,0], color="black", size=.1)     
p.scatter(clus_2[:,1], clus_2[:,1]*0, clus_2[:,0], color="red",  size=.1) 
p.scatter(clus_3[:,1], clus_3[:,1]*0, clus_3[:,0], color="green",  size=.1) 
p.scatter(clus_4[:,1], clus_1[:,1]*0, clus_4[:,0], color="bleu",  size=.1)     
p.scatter(clus_5[:,1], clus_2[:,1]*0, clus_5[:,0], color="red", size=.1) 

p.squarelim()
p.show()

结果：

Answer 1

已解决 Density-based spatial clustering of applications with noise (DBSCAN) Identify each hole according to estimated number of clusters, the diameter can be calculated using the Convex hull

在二维点云中寻找空洞

Finding holes in 2d point cloud

python

geometry

2d

computational-geometry

point-clouds