使用k均值进行图像分割

时间:2017-01-14 07:42:05

标签: python-2.7 image-processing machine-learning computer-vision k-means

我试图将k-means算法用于图像分割任务。问题是我的程序没有分割图像。 你能帮我在我的代码中找到错误吗?

事实上,我已将群集数量固定为32。 我使用了以下数据结构:

  • 3个数组bleu,vert,rouge用于存储每个像素的RGB值

  • 3个数组cluster_bleu,cluster_rouge,cluster_vert用于存储每个群集的RGB值

  • groupe [i,0] = k将每个像素i映射到簇k

    import cv2
    
    import numpy
    
    import random
    def main():
        MAX_LARGEUR = 400
        MAX_HAUTEUR = 400
    
        K = 32 #Le fameux parametre K de l'algorithme
        imagecolor = cv2.imread('perr.jpg')
    
    
        if imagecolor.shape[0] > MAX_LARGEUR or imagecolor.shape[1] > MAX_HAUTEUR:
            factor1 = float(MAX_LARGEUR) / imagecolor.shape[0]
            factor2 = float(MAX_HAUTEUR) / imagecolor.shape[1]
            factor = min(factor1, factor2)
            imagecolor = cv2.resize(imagecolor, None, fx=factor, fy=factor, interpolation=cv2.INTER_AREA)
    
    
        nb_pixels = imagecolor.shape[0] * imagecolor.shape[1]
    
        bleu = imagecolor[:, :, 0].reshape(nb_pixels, 1)
        vert = imagecolor[:, :, 1].reshape(nb_pixels, 1)
        rouge = imagecolor[:, :, 2].reshape(nb_pixels, 1)
    
    
        cluster_bleu = numpy.zeros(K)
        cluster_vert = numpy.zeros(K)
        cluster_rouge = numpy.zeros(K)
    
        groupe = numpy.zeros((nb_pixels, 1)) 
    
        for i in range(0,K):
            groupe[i,0]=i
    
        for i in range(K,nb_pixels):
            groupe[i,0]=random.randint(0, K-1)
    
    
        condition =False
    
    
    
    
        def etape1(indices,i):
        s=indices.size
        rouge_s=0
        vert_s=0
        bleu_s=0
        #calcul de barycentre des points
        if s==0:
            cluster_rouge[i]=0  
            cluster_vert[i]=0
            cluster_bleu[i]=0
    
    
        if s >=1:
            for j in range(0,s):
                rouge_s=rouge_s+rouge[indices[j]]
                vert_s=vert_s+vert[indices[j]]
                bleu_s=bleu_s+bleu[indices[j]]
    
            #mise  jour des clusters 
    
            cluster_rouge[i]=rouge_s/s  
            cluster_vert[i]=vert_s/s
            cluster_bleu[i]=bleu_s/s        
    
    
        iteration=0
        oldGroupe = numpy.copy(groupe)
        while(condition==False) :
    
    
        for i in range(0,K):
    
            indices=numpy.where(groupe==i)[0]
            etape1(indices,i)
    
    
    
        for i in range(0,nb_pixels):
            minimum=10000;
            dist=0;
            index=-1;
            for j in range(0,K):
                 dist=(cluster_rouge[j]-rouge[i])**2+(cluster_vert[j]-vert[i])**2+(cluster_bleu[j]-bleu[i])**2;
                 if(dist<=minimum):
                    minimum=dist;
                    index=j;
    
    
    
            groupe[i,0]=index;
    
    
    
        condition=numpy.all(groupe==oldGroupe)
    
        oldGroupe = numpy.copy(groupe)  
    
    
        groupe=numpy.reshape(groupe, (imagecolor.shape[0], imagecolor.shape[1]))
    
    
    
    
        for i in range(0, imagecolor.shape[0]):
            for j in range(0, imagecolor.shape[1]):
                imagecolor[i,j,0] = (cluster_bleu[groupe[i,j]])
                imagecolor[i,j,1] = (cluster_vert[groupe[i,j]])
                imagecolor[i,j,2] = (cluster_rouge[groupe[i,j]])
    
        cv2.namedWindow("sortie")
        cv2.imshow("sortie", imagecolor)
        key = cv2.waitKey(0)
    if __name__ == "__main__":
        main()
    

1 个答案:

答案 0 :(得分:0)

问题是赋值$$('video', 'audio').forEach((element) => element.muted = true); ,它不复制数组,但创建具有不同名称(oldGroupe=groupe;)的引用,指向与oldGroupe相同的数据。因此,当您更改groupe时,您也会更改groupeoldGroupe始终为True。

您想要的是使用conditiongroupe中创建数据副本。