【圖像算法】彩色圖像分割專題八：基於MeanShift的彩色分割

原創 ctgushiwei 2020-02-20 19:28

》原理以前的博客中已經有對meanshift原理的解釋，這裏就不囉嗦了，國外的資料看這：http://people.csail.mit.edu/sparis/#cvpr07

》源碼

核心代碼(參考網絡)

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//============================Meanshift==============================//
void MyClustering::MeanShiftImg(IplImage * src , IplImage * dst , float r , int Nmin ,int Ncon )
{
    int i , j , p ,k=0,run_meanshift_slec_number=0;
    int pNmin;                              //mean shift產生的特徵的搜索框內的特徵數
    IplImage * temp , * gray;                       //轉換到Luv空間的圖像
    CvMat * distance , * result , *mask;                //
    CvMat * temp_mat ,*temp_mat_sub ,*temp_mat_sub2 ,* final_class_mat;         //Luv空間的圖像到矩陣，圖像矩陣與隨機選擇點之差，
    CvMat * cn ,* cn1 , * cn2 , * cn3;
    double /*covar_img[3] ,*/ avg_img[3];       //圖像的協方差主對角線上的元素和，各個通道的均值
    double r1;          //搜索半徑
    int temp_number;
    meanshiftpoint meanpoint[25];       //存儲隨機產生的25點
    CvScalar    cvscalar1,cvscalar2;
    int order[25];
    Feature feature[100];           //特徵
    double  shiftor;
    CvMemStorage * storage=NULL;
    CvSeq * seq=0 , * temp_seq=0 , *prev_seq;
//---------------------------------------------RGB to Luv空間,初始化----------------------------------------------
    temp            =   cvCreateImage(cvSize(src->width,src->height),IPL_DEPTH_8U, src->nChannels);
    gray            =   cvCreateImage(cvSize(src->width,src->height),IPL_DEPTH_8U, 1);
    temp_mat        =   cvCreateMat(src->height,src->width,CV_8UC3);
    final_class_mat =   cvCreateMat(src->height,src->width,CV_8UC3);
    mask            =   cvCloneMat(temp_mat);
    temp_mat_sub    =   cvCreateMat(src->height,src->width,CV_32FC3);
    temp_mat_sub2   =   cvCreateMat(src->height,src->width,CV_32FC3);
    cvZero(temp);
    cvCvtColor(src,temp,CV_RGB2Luv);                    //RGB to Luv空間
    distance        =   cvCreateMat(src->height,src->width,CV_32FC1);
    result          =   cvCreateMat(src->height,src->width,CV_8UC1);
    cvConvert(temp,temp_mat);                           //IplImage to Mat
    cn  =   cvCreateMat(src->height,src->width,CV_32FC1);
    cn1 =   cvCloneMat(cn);
    cn2 =   cvCloneMat(cn);
    cn3 =   cvCloneMat(cn);
    storage = cvCreateMemStorage(0);
//-------------------------------------------計算搜索窗口半徑 r --------------------------------------------
    if(r!=NULL)
        r1=r;
    else
    {
        cvscalar1   =   cvSum(temp_mat);
        avg_img[0]  =   cvscalar1.val[0]/(src->width * src->height);
        avg_img[1]  =   cvscalar1.val[1]/(src->width * src->height);
        avg_img[2]  =   cvscalar1.val[2]/(src->width * src->height);
        cvscalar1   =   cvScalar(avg_img[0],avg_img[1],avg_img[2],NULL);
        cvScale(temp_mat,temp_mat_sub,1.0,0.0);
        cvSubS(temp_mat_sub , cvscalar1 , temp_mat_sub ,NULL);
        cvMul(temp_mat_sub , temp_mat_sub , temp_mat_sub2);
        cvscalar1   =   cvSum(temp_mat_sub2);
        r1          =   0.4*cvSqrt( (cvscalar1.val[0] + cvscalar1.val[1] + cvscalar1.val[2])/(src->width * src->height));;
    }
    //初始化隨機數生成種子
    srand((unsigned)time(NULL));
     
//--------------------循環，使用meanshift進行特徵空間分析，終止條件是Nmin--------------------------------------
    do
    {
//--------------------------------------------初始化搜索窗口位置-------------------------------------------
        run_meanshift_slec_number++;
        cvSet(distance,cvScalar(r1*r1,NULL,NULL,NULL),NULL);
        for( i = 0 ; i < 25 ; i++)
        {
            meanpoint[i].pt.x = rand()%src->width;
            meanpoint[i].pt.y = rand()%src->height;
        }
        cvScale(temp_mat,temp_mat_sub,1.0,0.0);
        for( i = 0 ; i < 25 ; i++)
        {
            /*cvSubS(temp_mat_sub ,cvScalar(cvGetReal3D(temp_mat,meanpoint[i].pt.x,meanpoint[i].pt.y,0),
                cvGetReal3D(temp_mat,meanpoint[i].pt.x,meanpoint[i].pt.y,1),
                cvGetReal3D(temp_mat,meanpoint[i].pt.x,meanpoint[i].pt.y,2),
                NULL),temp_mat_sub,NULL);*/
            cvSplit(temp_mat_sub,cn,cn1,cn2,NULL);
            cvSubS(temp_mat_sub,cvScalar(cvmGet(cn,meanpoint[i].pt.y,meanpoint[i].pt.x),
                cvmGet(cn1,meanpoint[i].pt.y,meanpoint[i].pt.x),
                cvmGet(cn2,meanpoint[i].pt.y,meanpoint[i].pt.x),NULL),temp_mat_sub,NULL);
            cvMul(temp_mat_sub,temp_mat_sub,temp_mat_sub2,1);
            cvSplit(temp_mat_sub2,cn,cn1,cn2,NULL);
            cvAdd(cn,cn1,cn3,NULL);
            cvAdd(cn2,cn3,cn3,NULL);            //cn3中存放着，當前隨機點與空間中其它點距離的平方。
            cvCmp(cn3,distance,result,CV_CMP_LE);       //距離小於搜索半徑則result相應位爲1
            cvAndS(result,cvScalar(1,NULL,NULL,NULL),result,NULL);
            cvscalar1   =   cvSum(result);
            meanpoint[i].con_f_number = (int)cvscalar1.val[0];
        }
        for(i = 0 ; i < 25 ; i++)
        {
            order[i]=i;
        }
        for(i = 0 ; i < 25 ; i++)
            for(j = 0 ; j < 25-i-1; j++)
            {
                if(meanpoint[order[j]].con_f_number < meanpoint[order[j+1]].con_f_number)
                {
                    temp_number=order[j];
                    order[j]=order[j+1];
                    order[j+1]=temp_number;
                }
            }
//--------------------------------------------meanshift算法------------------------------------------------  
        double  temp_mean[3];
 
        for( i = 0 ; i < 25 ; i++)
        {
            cvScale(temp_mat,temp_mat_sub,1.0,0.0);
            cvSplit(temp_mat_sub,cn,cn1,cn2,NULL);
            temp_mean[0]    =   cvmGet(cn  , meanpoint[order[i]].pt.y , meanpoint[order[i]].pt.x);
            temp_mean[1]    =   cvmGet(cn1 , meanpoint[order[j]].pt.y , meanpoint[order[i]].pt.x);
            temp_mean[2]    =   cvmGet(cn2 , meanpoint[order[j]].pt.y , meanpoint[order[i]].pt.x);
 
            //meanshift過程
            do
            {
                //計算出在搜索窗口內的特徵點，並且生成對應的模板，即對應的點置一的矩陣表示對應的點在搜索框內
                cvScale(temp_mat,temp_mat_sub,1.0,0.0);
                cvSubS(temp_mat_sub,cvScalar(temp_mean[0],temp_mean[1],temp_mean[2],NULL),temp_mat_sub,NULL);
                cvMul(temp_mat_sub,temp_mat_sub,temp_mat_sub2,1);
                cvSplit(temp_mat_sub2 , cn , cn1 , cn2 , NULL );
                cvAdd(cn,cn1,cn3,NULL);
                cvAdd(cn2,cn3,cn3,NULL);            //cn3中存放着，當前隨機點與空間中其它點距離的平方。
                cvCmp(cn3,distance,result,CV_CMP_LE);       //距離小於搜索半徑則result相應位爲0XFF
                 
                 
                //計算shiftor
                cvCopy(temp_mat , final_class_mat ,NULL);               //
                cvMerge(result , result ,result ,NULL,mask);
                cvAnd(final_class_mat , mask ,final_class_mat ,NULL);   //與mask（3通道,0XFF）做與操作，把搜索半徑外的點置零
                cvScale(final_class_mat,temp_mat_sub,1.0,0.0);          //搜索半徑內的點從8U轉換成32F
 
                cvAndS(result,cvScalar(1,NULL,NULL,NULL),result,NULL);      //相應位set 1
                cvscalar1   =   cvSum(result);              //reslut 作爲 模板 ,返回搜索窗口內的特徵數
 
                cvSubS(temp_mat_sub,cvScalar(temp_mean[0],temp_mean[1],temp_mean[2],NULL),temp_mat_sub,result);
                cvscalar2   =   cvSum(temp_mat_sub);
                cvscalar2.val[0] = cvscalar2.val[0]/cvscalar1.val[0] ;
                cvscalar2.val[1] = cvscalar2.val[1]/cvscalar1.val[0] ;
                cvscalar2.val[2] = cvscalar2.val[2]/cvscalar1.val[0] ;
                shiftor     =   cvSqrt(pow(cvscalar2.val[0], 2) + pow(cvscalar2.val[1], 2) +    pow(cvscalar2.val[2], 2));
                temp_mean[0]=temp_mean[0]+cvscalar2.val[0];
                temp_mean[1]=temp_mean[1]+cvscalar2.val[1];
                temp_mean[2]=temp_mean[2]+cvscalar2.val[2];
                /*cvCopy(temp_mat , final_class_mat ,NULL); //
                cvMerge(result , result ,result ,NULL,mask);
                cvAnd(final_class_mat , mask ,final_class_mat ,NULL);   //與result做與操作，把搜索半徑外的點置零
                cvScale(final_class_mat,temp_mat_sub,1.0,0.0);          //搜索半徑內的點從8U轉換成32F
                cvSplit(temp_mat_sub,cn,cn1,cn2,NULL);
                cvSubS(cn , cvScalar(temp_mean[0],NULL,NULL,NULL),cn,result);
                cvSubS(cn1, cvScalar(temp_mean[1],NULL,NULL,NULL),cn1,result);
                cvSubS(cn2, cvScalar(temp_mean[2],NULL,NULL,NULL),cn2,result);
                cvMerge(cn,cn1,cn2,NULL,temp_mat_sub);
                cvscalar2   =   cvSum(temp_mat_sub);
                shiftor     =   cvSqrt(pow(cvscalar2.val[0] , 2) + pow(cvscalar2.val[1] , 2) +  pow(cvscalar2.val[2] , 2));
                temp_mean[0]=temp_mean[0]+cvscalar2.val[0];
                temp_mean[1]=temp_mean[1]+cvscalar2.val[1];
                temp_mean[2]=temp_mean[2]+cvscalar2.val[2];*/
            }
            while(shiftor>0.1);  //meanshift算法過程
//--------------------------------------------去除不重要特徵-----------------------------------------------
            if(k==0)
            {
                feature[k].pt.x = temp_mean[0];
                feature[k].pt.y = temp_mean[1];
                feature[k].pt.z = temp_mean[2];
                feature[k].number= (int)cvscalar1.val[0];   //因爲小於等於的情況成立時，result對應位置是0XFF，不成立時對應位置爲0
                pNmin   = (int)cvscalar1.val[0];                //此特徵搜索窗口內，特徵空間的向量個數
                feature[k].result=cvCreateMat(src->height,src->width,CV_8UC1);
                cvAndS(result,cvScalar(1,NULL,NULL,NULL),result,NULL);
                cvCopy(result,feature[k].result,NULL);
                k++;
            }
            else
            {
                int flag = 0;
                for(j = 0 ; j < k ; j++)
                {
                    if(pow(temp_mean[0]-feature[j].pt.x , 2) + pow(temp_mean[1]-feature[j].pt.y ,2) + pow(temp_mean[2]-feature[j].pt.z, 2)
                        < r1*r1)
                    {
                        flag = 1;
                        break;
                    }
                }
                if(flag==0)
                {
                    feature[k].pt.x = temp_mean[0];
                    feature[k].pt.y = temp_mean[1];
                    feature[k].pt.z = temp_mean[2];
                    feature[k].number=(int)cvscalar1.val[0];
                    pNmin   = (int)cvscalar1.val[0];                //此特徵搜索窗口內，特徵空間的向量個數
                    feature[k].result=cvCreateMat(src->height,src->width,CV_8UC1);
                    cvCopy(result,feature[k].result,NULL);
                    k++;
                    //if(pNmin < Nmin )
                    //  break;
                }
            }//去除不重要特徵
            //if(pNmin < Nmin)
            //  break;
        }   //
 
    }while(pNmin > Nmin || run_meanshift_slec_number>60 );
 
    //------------------------------------------------後處理---------------------------------------------------------
    cvSetZero(result);
    for( i = 0 ; i < k ; i ++)
    {
        cvOr(result,feature[i].result,result,NULL);
    }
 
    cvScale(temp_mat,temp_mat_sub,1.0,0.0);
    cvSplit(temp_mat_sub,cn,cn1,cn2,NULL);
 
    for(i = 0 ; i < src->width ; i++)
        for( j = 0 ; j < src->height ; j++)
        {
            if(cvGetReal2D(result,j,i)==0)      //未分類的像素點，進行分類，爲最近的特徵中心
            {
                double unclass_dis , min_dis;
                int min_dis_index;
                for( p = 0 ; p < k ; p++ )
                {
                    unclass_dis = pow(feature[p].pt.x - cvmGet(cn,j,i),2)   //(temp_mat,i,j,0) ,2)
                        + pow(feature[p].pt.y - cvmGet(cn1,j,i),2) //(temp_mat,i,j,1) ,2)
                        + pow(feature[p].pt.z - cvmGet(cn2,j,i),2);//(temp_mat,i,j,2) ,2);
                    if(p==0)
                    {
                        min_dis = unclass_dis;
                        min_dis_index = p;
                    }
                    else
                    {
                        if(unclass_dis < min_dis)
                        {
                            min_dis = unclass_dis;
                            min_dis_index = p;
                        }
                    }
                }// end for 與特徵比較
                cvSetReal2D(feature[min_dis_index].result ,j  ,i ,1);
            }
        }//完成未分類的像素點的分類
    cvSetZero(final_class_mat);
    for( i = 0 ; i < k ; i++)
    {
        cvSet(temp_mat, cvScalar(rand()%255,rand()%255,rand()%255,rand()%255), feature[i].result);
        cvCopy(temp_mat,final_class_mat,feature[i].result);
    }
    cvConvert(final_class_mat,dst);
    //刪除小於Ncon大小的區域
    for( i = 0 ; i < k ; i++)
    {
        cvClearMemStorage(storage);
        if(seq) cvClearSeq(seq);
        cvConvert( feature[i].result , gray);
        cvFindContours( gray , storage , & seq ,sizeof(CvContour) , CV_RETR_LIST);
        for(temp_seq = seq ; temp_seq ; temp_seq = temp_seq->h_next)
        {
            CvContour * cnt = (CvContour*)seq;
            if(cnt->rect.width * cnt->rect.height < Ncon)
            {
                prev_seq = temp_seq->h_prev;
                if(prev_seq)
                {
                    prev_seq->h_next = temp_seq->h_next;
                    if(temp_seq->h_next) temp_seq->h_next->h_prev = prev_seq ;
                }
                else
                {
                    seq = temp_seq->h_next ;
                    if(temp_seq->h_next ) temp_seq->h_next->h_prev = NULL ;
                }
            }
        }//
        cvDrawContours(src, seq , CV_RGB(0,0,255) ,CV_RGB(0,0,255),1);
    }
 
    //----------------釋放空間-------------------------------------------------------  
    cvReleaseImage(& temp);
    cvReleaseImage(& gray);
    cvReleaseMat(&distance);
    cvReleaseMat(&result);
    cvReleaseMat(&temp_mat);
    cvReleaseMat(&temp_mat_sub);
    cvReleaseMat(&temp_mat_sub2);
    cvReleaseMat(&final_class_mat);
    cvReleaseMat(&cn);
    cvReleaseMat(&cn1);
    cvReleaseMat(&cn2);
    cvReleaseMat(&cn3);
}

》效果

運行時間16.5s

原圖：

分割圖：

被改寫了的原圖：

    From：         http://www.cnblogs.com/skyseraph/

新浪微博：http://weibo.com/u/1645794700/home?wvr=5&c=spr_web_360_hao360_weibo_t001

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