k-means--python版本

本文出自：http://blog.csdn.net/xizhibei

自从上次介绍过c++版本的k-meansK-means之C++及OpenCV实现，感觉有些不足的地方，加上近些时间在学习python（好吧，是觉得Python比Perl好点），而且刚好有对应的OpenCVPython库，于是就写了个Python版本的

from cv import *
import numpy as np

class Cluster:
    center = []
    pre_center = []
    pts = []
    color = ()

def is_good_result(cluster):
    for c in cluster:
        if np.linalg.norm(c.center - c.pre_center) > 1.0:
            return False
    return True

def update_center(cluster):
    for c in cluster:
        c.pre_center = c.center
        #print len(c.pts)
        c.center = np.sum(c.pts,0) / len(c.pts)
        s = 0
        for p in c.pts:
            s = s + (p[0] - c.center[0])**2 + (p[1] - c.center[1])**2
        print s#输出方差，可以看到运行的时候不断减小
        c.pts = []
    return cluster

def get_rand_pts(K,img_size,num):#这里根据K直接随机生成相应的椭圆区域，不再是矩形区域了
    #pts = np.random.rand(num,2) * img_size
    center = np.random.rand(K,2) * (np.array(img_size) - np.array([300,300])) + np.array([150,150])
    r = np.random.rand(K,2) * (200,200) + (100,100)

    pts = []
    for i in xrange(num):
        tmp = np.random.rand(2) * np.pi
        tmp[0] = np.cos(tmp[0])
        tmp[1] = np.sin(tmp[1])
        pts.append(center[i % K] + r[i % K] * np.random.rand(2) * tmp)
    return pts

def show_outcome(img,cluster):
##    K = len(cluster)#这里注释掉的内容是因为太耗时间，不知道怎么回事，现在还解决不了
##    for y in xrange(img.height):
##        for x in xrange(img.width):
##            min_k = 0
##            min_val = 100000
##            for k in xrange(K):
##                p = (x,y)
##                #val = np.sqrt((p[0] - cluster[k].center[0])**2 + (p[1] - cluster[k].center[1])**2)
##                val = np.linalg.norm(p - cluster[k].center)
##                if val < min_val:
##                    min_k = k
##                    min_val = val
##            img[y,x] = cluster[min_k].color
    for c in cluster:
        #print c.pts
        Circle(img,(int(c.center[0]),int(c.center[1])),10,c.color,CV_FILLED)
        for x, y in np.int32(c.pts):
            Circle(img,(x,y),3,c.color,CV_FILLED)
            #Line(img,(x + 5,y),(x - 5,y),c.color,2)
            #Line(img,(x,y + 5),(x,y - 5),c.color,2)
    NamedWindow("Image")
    ShowImage("Image",img)
    WaitKey(0)
    DestroyWindow("Image")

def main():
    K = 4
    PTS_NUM = 600
    img = CreateImage((1200,800),IPL_DEPTH_8U,3)
    pts = get_rand_pts(K,(img.width,img.height),PTS_NUM)
    
    
    cluster = [Cluster() for i in xrange(K)]
    init_k = np.arange(0,PTS_NUM - 1)
    np.random.shuffle(init_k)
    init_k = init_k[:K]
    
    for i in xrange(K):
        cluster[i].pre_center = [0,0]
        cluster[i].center = pts[init_k[i]]
        cluster[i].color = map(int,np.random.randint(0,1024,3) * 4 % 255)
        cluster[i].pts = []
        #print cluster[i].center
    times = 0

    while(True):
        for p in pts:
            min_k = 0
            min_val = 100000
            for j in xrange(K):
                val = np.linalg.norm(p - cluster[j].center)
                if val < min_val:
                    min_k = j
                    min_val = val
            cluster[min_k].pts.append(p)
        times = times + 1
        if is_good_result(cluster):
            break
        print "Times: %d"%(times)
        update_center(cluster)
     
    show_outcome(img,cluster)

if __name__ == "__main__":
    main()

这次的效果就很不错了：

关于kmeans不多说，上次已经介绍过了，值得提一下的是numpy这个库太强大了！！！