ML之Hierarchical clustering：利用层次聚类算法来把100张图片自动分成红绿蓝三种色调

ML之Hierarchical clustering：利用层次聚类算法来把100张图片自动分成红绿蓝三种色调

目录

输出结果

实现代码

输出结果

实现代码

!/usr/bin/python
 coding:utf-8
from PIL import Image, ImageDraw   
from HierarchicalClustering import hcluster   
from HierarchicalClustering import getheight
from HierarchicalClustering import getdepth
import numpy as np
import os
 
def drawdendrogram(clust, imlist, jpeg= 'clusters.jpg'): 
    h = getheight(clust)*20      
    w = 1200
    depth = getdepth(clust)     
    scaling = float(w - 150)/depth 
    
    img = Image.new('RGB', (w, h), (255, 255, 255)) 
    draw = ImageDraw.Draw(img)                       
 
    draw.line((0, h/2, 10, h/2), fill=(255, 0, 0))   
    drawnode(draw, clust, 10, int(h/2), scaling, imlist, img)  
    img.save(jpeg)    
 
def drawnode(draw,clust,x,y,scaling,imlist,img):     if clust.id < 0: 
        h1 = getheight(clust.left)*20
        h2 = getheight(clust.right)*20
        top = y - (h1 + h2)/2
        bottom = y + (h1 + h2)/2
        ll = clust.distance * scaling
        draw.line((x, top + h1/2, x, bottom - h2/2), fill=(255, 0, 0))
 
        draw.line((x, top + h1/2, x + ll, top + h1/2), fill=(255, 0, 0))
 
        draw.line((x, bottom - h2/2, x + ll, bottom - h2/2), fill=(255, 0, 0))
 
        drawnode(draw, clust.left, x + ll, top + h1/2, scaling, imlist, img)
        drawnode(draw, clust.right, x + ll, bottom - h2/2, scaling, imlist, img)
    else: 
        nodeim = Image.open(imlist[clust.id])
        nodeim.thumbnail((20, 20))   
        ns = nodeim.size
        print (x,y - ns[1]//2)
        print (x + ns[0])
        print (img.paste(nodeim, (int(x), int(y - ns[1]//2), int(x + ns[0]),int(y + ns[1] - ns[1]//2))))
 
imlist=[]
folderpath = r'F:\File_Python\Crawler'        
for filename in os.listdir(folderpath): 
    if os.path.splitext(filename)[1]=='.jpg':
        imlist.append(os.path.join(folderpath,filename))
n=len(imlist)  
print(n)
 
features =np.zeros((n,3))  
for i in range(n):         
    im=np.array(Image.open(imlist[i])) 
    R = np.mean(im[:,:,0].flatten())    
    G = np.mean(im[:,:,1].flatten())
    B = np.mean(im[:,:,2].flatten())
    features[i]=np.array([R,G,B])
    
tree = hcluster(features)   
drawdendrogram(tree, imlist, jpeg=r'C:\Users\99386\Desktop\result.jpg')  

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