矩形聚类

问题描述

我正在尝试对不知道簇数的矩形进行聚类。 下面是通过程序生成的图像以及矩形的坐标。

下面是Python中用于重现输出的代码：

from PIL import Image,ImageDraw

width = 1024
height = 768
im = Image.new('RGBA',(width,height),(255,255,255))
draw = ImageDraw.Draw(im)

coords = [[392,441,62],[889,106,958,146],[144,215,184,299],[224,44,288,[219,243,277],[760,450,847,547],[390,311,399,351],[439,232,520,274],[72,438,129,506],[64,177,151,207],[758,160,828,228],[310,543,314,587],[300,310,271],[787,850,332],[63,198,86,276],[372,432,409,448],[235,121,302,217],[140,614,216,643],[160,203,168,244],[364,645,394,708],[294,219,299,294],[101,119,251],[776,65,792,151],[421,608,447,659],[127,183,189,196],[350,114,439,140],[134,600,207,686],[55,126,148,224],[159,130,239,219],[705,743,164],[824,601,893,620],[916,541,966,557],[82,251,178,324],[216,317,235,349],[806,443,898,483],[297,618,320,[887,213,929,256],[17,117,109,182],[755,233,818,287],[165,392,463],[202,256,248,308],[61,499,103,595],[163,182,200,268],[167,143,257,243],[3,468,99,549],[126,51,112],[24,63,113,67],[830,555,924,585],[881,297,918,356],[917,226,998,295],[59,59,156],[56,16,82,113]]

for coord in coords:
    x1,y1,x2,y2 = coord
    draw.rectangle((x1,y2),fill=(192,192,192),outline=(122,122,122))
    draw = ImageDraw.Draw(im)

im.save('./1.png',)

是否可以自动检测群集数？

如果是这样，您能否提供将这些数据聚类的最小工作示例？

解决方法

您是否正在寻找类似的东西

from PIL import Image,ImageDraw
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans

width = 1024
height = 768
im = Image.new('RGBA',(width,height),(255,255,255))
draw = ImageDraw.Draw(im)

im2 = Image.new('RGBA',255))
draw2 = ImageDraw.Draw(im2)

im3 = Image.new('RGBA',255))
draw3 = ImageDraw.Draw(im3)

im4 = Image.new('RGBA',255))
draw4 = ImageDraw.Draw(im4)


coords = [[392,441,62],[889,106,958,146],[144,215,184,299],[224,44,288,[219,243,277],[760,450,847,547],[390,311,399,351],[439,232,520,274],[72,438,129,506],[64,177,151,207],[758,160,828,228],[310,543,314,587],[300,310,271],[787,850,332],[63,198,86,276],[372,432,409,448],[235,121,302,217],[140,614,216,643],[160,203,168,244],[364,645,394,708],[294,219,299,294],[101,119,251],[776,65,792,151],[421,608,447,659],[127,183,189,196],[350,114,439,140],[134,600,207,686],[55,126,148,224],[159,130,239,219],[705,743,164],[824,601,893,620],[916,541,966,557],[82,251,178,324],[216,317,235,349],[806,443,898,483],[297,618,320,[887,213,929,256],[17,117,109,182],[755,233,818,287],[165,392,463],[202,256,248,308],[61,499,103,595],[163,182,200,268],[167,143,257,243],[3,468,99,549],[126,51,112],[24,63,113,67],[830,555,924,585],[881,297,918,356],[917,226,998,295],[59,59,156],[56,16,82,113]]

centers = []

for coord in coords:
    x1,y1,x2,y2 = coord
    draw.rectangle((x1,y2),fill=(192,192,192),outline=(122,122,122))
    draw = ImageDraw.Draw(im)
    c = [(x1+x2)/2,(y1+y2)/2]
    centers.append(c)
    draw2.point((c[0],c[1]),fill = 0)
    draw2.ellipse((c[0]-2,c[1]-2,c[0]+2,c[1]+2),outline=(255,0))

im.save('./1.png',)
im2.save('./2.png',)

x = np.array(centers)

kmeans = KMeans(n_clusters=3)
kmeans.fit(x)

print(kmeans.cluster_centers_)

print(kmeans.labels_)

for c,label,cord in zip(centers,kmeans.labels_,coords):
    draw3.point((c[0],fill = 0)
    if label==0:
        draw3.ellipse((c[0]-5,c[1]-5,c[0]+5,c[1]+5),0))
        draw4.rectangle((cord[0],cord[1],cord[2],cord[3]),fill=(255,0),122))
    elif label==1:
        draw3.ellipse((c[0]-5,outline=(0,fill=(0,122))
    else:
        draw3.ellipse((c[0]-5,255))
        draw4.rectangle((cord[0],255),122))

im3.save("./3.png")
im4.save("./4.png")

我找到了矩形的中心点并将中心用作矩形位置，然后使用kmeans进行分组，甚至可以使用矩形着色

在kmeans中，您可以设置要创建的集群的数量

它也包含用于中心点表示图像的代码，并且未附加

[更新]添加了剪影得分作为衡量簇数的指标

我添加了Silhouette Score计算，它将在多个聚类数上运行，并给出介于0和1之间的值，以描述聚类的区别程度

import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

coords = [[392,113]]
centers = []

for coord in coords:
    x1,y2 = coord
    c = [(x1+x2)/2,(y1+y2)/2]
    centers.append(c)

# Vector with center coordinates
x = np.array(centers)

bestScore = -1
idx=-1

for i in range (2,10):
    kmeans = KMeans(n_clusters=i,random_state=10)
    kmeans.fit(x)
    silhouette_avg = silhouette_score(x,kmeans.labels_)
    print("For n_clusters =",i,"The average silhouette_score is :",silhouette_avg)
    if silhouette_avg > bestScore:
        bestScore=silhouette_avg
        idx=i

print("Best score :",bestScore," Number Of Clusters:",idx)

输出：

For n_clusters = 2 The average silhouette_score is : 0.5998398985890973
For n_clusters = 3 The average silhouette_score is : 0.5537982490021037
For n_clusters = 4 The average silhouette_score is : 0.533919415288406
For n_clusters = 5 The average silhouette_score is : 0.460641507207192
For n_clusters = 6 The average silhouette_score is : 0.4685516097102802
For n_clusters = 7 The average silhouette_score is : 0.42313268667935106
For n_clusters = 8 The average silhouette_score is : 0.43283812579237735
For n_clusters = 9 The average silhouette_score is : 0.41602365463779123
Best score : 0.5998398985890973  Number Of Clusters: 2