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》原理曾经的博客中已经有对meanshift原理的解释，这里就不啰嗦了。国外的资料看这：http://people.csail.mit.edu/sparis/#cvpr07
》源代码
核心代码(參考网络)
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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产生的特征的搜索框内的特征数 IplImage
* temp , * gray; //转换到Luv空间的图像 CvMat
* distance , * result , *mask; // CvMat
* temp_mat ,*temp_mat_sub ,*temp_mat_sub2 ,* final_class_mat; //Luv空间的图像到矩阵，图像矩阵与随机选择点之差。 CvMat
* cn ,* cn1 , * cn2 , * cn3; double /*covar_img[3]
,*/ avg_img[3]; //图像的协方差主对角线上的元素和，各个通道的均值 double r1; //搜索半径 int temp_number; meanshiftpoint
meanpoint[25]; //存储随机产生的25点 CvScalar
cvscalar1,cvscalar2; int order[25]; Feature
feature[100]; //特征 double shiftor; CvMemStorage
* storage=NULL; CvSeq
* seq=0 , * temp_seq=0 , *prev_seq;//---------------------------------------------RGB
to Luv空间,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空间 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);//-------------------------------------------计算搜索窗体半径
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));; } //初始化随机数生成种子 srand((unsigned)time(NULL)); //--------------------循环，使用meanshift进行特征空间分析。终止条件是Nmin-------------------------------------- do {//--------------------------------------------初始化搜索窗体位置------------------------------------------- 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，当前随机点与空间中其他点距离的平方。 cvCmp(cn3,distance,result,CV_CMP_LE); //距离小于搜索半径则result对应位为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过程 do { //计算出在搜索窗体内的特征点，而且生成相应的模板，即相应的点置一的矩阵表示相应的点在搜索框内 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。当前随机点与空间中其他点距离的平方。 cvCmp(cn3,distance,result,CV_CMP_LE); //距离小于搜索半径则result对应位为0XFF //计算shiftor cvCopy(temp_mat
, final_class_mat ,NULL); // cvMerge(result
, result ,result ,NULL,mask); cvAnd(final_class_mat
, mask ,final_class_mat ,NULL); //与mask（3通道,0XFF）做与操作，把搜索半径外的点置零 cvScale(final_class_mat,temp_mat_sub,1.0,0.0); //搜索半径内的点从8U转换成32F cvAndS(result,cvScalar(1,NULL,NULL,NULL),result,NULL); //对应位set
1 cvscalar1
= cvSum(result); //reslut
作为 模板 ,返回搜索窗体内的特征数 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); //与result做与操作，把搜索半径外的点置零 cvScale(final_class_mat,temp_mat_sub,1.0,0.0);
//搜索半径内的点从8U转换成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算法过程//--------------------------------------------去除不重要特征----------------------------------------------- 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]; //由于小于等于的情况成立时。result相应位置是0XFF，不成立时相应位置为0 pNmin
= (int)cvscalar1.val[0]; //此特征搜索窗体内，特征空间的向量个数 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]; //此特征搜索窗体内，特征空间的向量个数 feature[k].result=cvCreateMat(src->height,src->width,CV_8UC1); cvCopy(result,feature[k].result,NULL); k++; //if(pNmin
< Nmin ) //
break; } }//去除不重要特征 //if(pNmin
< Nmin) //
break; } // }while(pNmin
> Nmin || run_meanshift_slec_number>60 ); //------------------------------------------------后处理--------------------------------------------------------- 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) //未分类的像素点。进行分类。为近期的特征中心 { 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 与特征比較 cvSetReal2D(feature[min_dis_index].result
,j ,i ,1); } }//完毕未分类的像素点的分类 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); //删除小于Ncon大小的区域 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); } //----------------释放空间------------------------------------------------------- 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
执行时间16.5s
原图：
切割图：
被改写了的原图：
新浪微博：http://weibo.com/u/1645794700/home?
wvr=5&c=spr_web_360_hao360_weibo_t001
【图像算法】彩色图像切割专题八：基于MeanShift的彩色切割
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原文地址：http://www.cnblogs.com/gcczhongduan/p/5347731.html
