【图像算法】彩色图像分割专题八：基于MeanShift的彩色分割
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【图像算法】彩色图像分割专题八：基于MeanShift的彩色分割
SkySeraph July 14th 2011 HQU
Email：[email protected] QQ：452728574
Latest Modified Date：July 14th 2011 HQU
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》原理
不啰嗦了，看这：http://people.csail.mit.edu/sparis/#cvpr07
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》源码
核心代码(参考网络)
//============================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);
//-------------------------------------------计算搜索窗口半径 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，当前随机点与空间中其它点距离的平方。
cvCmp(cn3,distance,result,CV_CMP_LE); //距离小于搜索半径则result相应位为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
{
//计算出在搜索窗口内的特征点，并且生成对应的模板，即对应的点置一的矩阵表示对应的点在搜索框内
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它点距离的平方。
cvCmp(cn3,distance,result,CV_CMP_LE); //距离小于搜索半径则result相应位为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); //相应位set 1
cvscalar1 = cvSum(result); //reslut 作为 模板 ,返回搜索窗口内的特征数
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]; //因为小于等于的情况成立时，result对应位置是0XFF，不成立时对应位置为0
pNmin = (int)cvscalar1.val[0]; //此特征搜索窗口内，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]; //此特征搜索窗口内，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，为最近的特征中心
{
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 与特征比较
cvSetReal2D(feature[min_dis_index].result ,j ,i ,1);
}
}//完成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);
}
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》效果
运行时间16.5s
原图：
分割图：
被改写了的原图：
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Author: SKySeraph
Email/GTalk: [email protected] QQ：452728574
From： http://www.cnblogs.com/skyseraph/
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