OPENCV 中的代码改进。当然要依据自己的实际情况来，OPENCV 中行人检測有两种矩形框的融合算法。这里仅仅对meanshift 方法做改进
假设有更好的方法。希望能够跟我讲下。
对于去除重合部分。我也写了改进，看懂了能够加到自己程序中。
为什么要做局部MeanShift?
图1.全局MeanShift
如图所看到的：两幅图像距离较近且有多个矩形框。全局MeanShift融合后可能会造成这样的结果
而假设用局部融合就能避免这样的情况。
/*----------------------------------分数判定：begin--------------------------------------------------*/
//////////////将感兴趣区域归一化后计算HOG特征--begin////////////////////
cvSetImageROI(imageOrigin,r);
IplImage *test=cvCreateImage(cvGetSize(imageOrigin),8,3);
cvCopyImage(imageOrigin,test);
cvResetImageROI(imageOrigin);
IplImage* testTempImg=cvCreateImage(cvSize(40,40),8,3);
cvZero(testTempImg);
cvResize(test,testTempImg);
hog->compute(testTempImg, descriptors,Size(1,1), Size(0,0)); //Hog特征计算
Mat another(descriptors);
descriptors.clear();
////////////将感兴趣区域归一化后计算HOG特征--end///////////////
double ret = 0;
ret = ssvm.get_calc_liner(vec_count,var_count,a,another,results,alpha,0)-rho; //计算SVM分数
////////////////////////////
cvReleaseImage(&test);
cvReleaseImage(&testTempImg);
////////////////////////////
if(ret <0)
{
continue; //去掉SVM 分值小于0的图像
}
rc_.push_back(rcc);
weights.push_back(ret);
double rate = max(rcc.width*1.0/40,rcc.height*1.0/40);
//选取长宽最大值，作为尺度，考虑还不够周到，这里改变。Meanshift 所有都要改
//这里还能够改进
foundScales.push_back(rate);
/*----------------------------------分数判定：end--------------------------------------------------*/
//////////上述应该是一个循环，增加多个点//////////////
/////////////////////////融合过程-begin////////////////////////////
//vector<Rect> rc_;//矩形框
// vector<double> weights;//权重，score
// vector<double> foundScales;//放缩尺度
groupRectangles_meanshift1(rc_, weights, foundScales, 0.3, Size(40,40)); //框出来的矩形框进行融合
//groupRectangles_meanshift1该函数在最后定义//
///////////////////////融合过程-end///////////////////////////
//////////////////重合去重第一步：计算融合后的分值//////////////////
for( i = 0; i < rc_.size(); i++ )
{
//增加矩形框
Rect r = rc_[i];
found_filtered.push_back(r);
}
vector<float> found_score(found_filtered.size()); //矩形框的分数
for( i = 0; i < found_filtered.size(); i++ )
{
Rect r = found_filtered[i];
//////////////////////////////////
cvSetImageROI(imageOrigin,r);
IplImage *test=cvCreateImage(cvGetSize(imageOrigin),8,3);
cvCopyImage(imageOrigin,test);
cvResetImageROI(imageOrigin);
IplImage* testTempImg=cvCreateImage(cvSize(40,40),8,3);
cvZero(testTempImg);
cvResize(test,testTempImg);
hog->compute(testTempImg, descriptors,Size(1,1), Size(0,0)); //Hog特征计算
Mat another(descriptors);
descriptors.clear();
double ret = 0;
ret= ssvm.get_calc_liner(vec_count,var_count,a,another,results,alpha,0)-rho;
cvReleaseImage(&test);
cvReleaseImage(&testTempImg);
found_score[i]=ret;
////////////////////////////
}
////////重合去重第二步：矩形框内的部分。取分值最大的/////
//////////////////////found_filtered为融合后的矩形框//////////////////////////
for( i = 0; i < found_filtered.size(); i++ )
{
//进行了重合去除，选取附近分值最大矩形框
Rect r = found_filtered[i];
for( j = 0; j < rc_.size(); j++ )
if( j != i && (r & found_filtered[j]).area() == r.area())
{//这里是将重叠的部分，分值小的矩形框的权重设为-1,为了取最大值
if(found_score[i]>found_score[j])
{
found_score[j]=-1;
break;
}
else
{
found_score[i]=-1;
break;
}
}
}
////////重合去重第三步：重叠部分。取分值最大的/////
//////////////////////////////////////////////////////////////////
for( i = 0; i < found_filtered.size(); i++ )
{
//进行了重合去除
Rect r = found_filtered[i];
for( j = 0; j < rc_.size(); j++ )
//判定重合是否大于相较面积的70%（这个比例有待測试）
//判定都为上一步过滤后的结果，可能存在部分相较。但不包括的情况
if( j != i && (r & found_filtered[j]).area() >= r.area()*0.7 &&found_score[j]!=-1&&found_score[i]!=-1)
{//这里是将重叠的部分，分值小的矩形框的权重设为-1
if(found_score[i]>found_score[j])
{
found_score[j]=-1;
break;
}
else
{
found_score[i]=-1;
break;
}
}
}
for(int i=0;i<found_filtered.size();i++)
{
if (found_score[i]==-1)//将分值等于-1的过滤掉
{
continue;
}
Rect r = found_filtered[i];
r.x -= cvRound(r.width*0.05);
r.width = cvRound(r.width*1.05);
r.y -= cvRound(r.height*0.05);
r.height = cvRound(r.height*1.05);
rectangle(img_dst, r.tl(), r.br(), cv::Scalar(0,255,0), 2); //在图像上画矩形框
}
/*-----------------------MeanShift做局部的（源程序是对全局）------------------------*/
//meanshift 融合
class MeanshiftGrouping
{
public:
// MeanshiftGrouping msGrouping(smothing, hits,rectList, hitWeights, 1e-5, 100);//得到
///////////////////////////////////////////////////////////////////////
// msGrouping.getModes(resultHits, resultWeights, 1);
//////////////////////////////////////////////////////////////////////
MeanshiftGrouping(const Point3d& densKer, const vector<Point3d>& posV,const vector<Rect>&list,
const vector<double>& wV, double eps, int maxIter = 20)
{
densityKernel = densKer;
weightsV = wV;
positionsV = posV;
positionsCount = (int)posV.size();
meanshiftV.resize(positionsCount);
distanceV.resize(positionsCount);
iterMax = maxIter;
modeEps = eps;
for (unsigned i = 0; i<positionsV.size(); i++)
{
meanshiftV[i] = getNewValue(positionsV[i],list[i],list);//positionV 仅仅有中点坐标没有长宽。
distanceV[i] = moveToMode(meanshiftV[i],list[i],list);//做最大为iterMax次循环//均值漂移后的值
meanshiftV[i] -= positionsV[i];//这一步后面没用到
}
}
void getModes(vector<Point3d>& modesV, vector<double>& resWeightsV, const double eps)
{
for (size_t i=0; i <distanceV.size(); i++)
{
bool is_found = false;
for(size_t j=0; j<modesV.size(); j++)
{
if ( getDistance(distanceV[i], modesV[j]) < eps)//欧式距离小于阈值
{
is_found=true;
break;
}
}
if (!is_found)
{
modesV.push_back(distanceV[i]);//增加距离较大的点，也就是说两个点距离较大。不是同一个矩形框
}
}
resWeightsV.resize(modesV.size());
for (size_t i=0; i<modesV.size(); i++)
{
resWeightsV[i] = getResultWeight(modesV[i]);//得到点的权值
}
}
protected:
vector<Point3d> positionsV;
vector<double> weightsV;
Point3d densityKernel;
int positionsCount;
vector<Point3d> meanshiftV;
vector<Point3d> distanceV;
int iterMax;
double modeEps;
Point3d getNewValue(const Point3d& inPt ,const Rect inR ,const vector<Rect>list) const
{//inPt 输入三维坐标 、inR 为输入的矩形 list为所有矩形
Point3d resPoint(.0);
Point3d ratPoint(.0);
int value=20;//仅仅考虑矩形框四个角差值小于20的点。这个能够自己设定
for (size_t i=0; i<positionsV.size(); i++)
{
Point3d aPt= positionsV[i];
// double rate = exp(aPt.z);
if(inR.x -list[i].x>value||inR.y -list[i].y>value||inR.width -list[i].width>value||inR.height -list[i].height>value)
continue;//局部推断，假设不是同一个矩形附近，将排除附近矩形的影响
Point3d bPt = inPt;
Point3d sPt = densityKernel;//核
////////////////////////////////////////
sPt.x *= exp(aPt.z);//Z为尺度
sPt.y *= exp(aPt.z);
aPt.x /= sPt.x;
aPt.y /= sPt.y;
aPt.z /= sPt.z;
bPt.x /= sPt.x;
bPt.y /= sPt.y;
bPt.z /= sPt.z;
///映射到相应尺度的图片的坐标/////////sPt为scale//反归一化
////////////////////////////////////////////
double w = (weightsV[i])*std::exp(-((aPt-bPt).dot(aPt-bPt))/2)/std::sqrt(sPt.dot(Point3d(1,1,1)));
//又一次计算的权重，原权重weightsV[i]为线性SVM的得分
resPoint += w*aPt;//依据中心点的距离加相应的权值,距离越近,权值越大
ratPoint.x += w/sPt.x;//这边除以权重值，使得放缩后的图像权重变小
ratPoint.y += w/sPt.y;
ratPoint.z += w/sPt.z;
}
resPoint.x /= ratPoint.x;
resPoint.y /= ratPoint.y;
resPoint.z /= ratPoint.z;
return resPoint;//返回被周围点影响后的值
}
double getResultWeight(const Point3d& inPt) const
{
double sumW=0;//终于返回的值
int num=0;
size_t aa=positionsV.size();//位置点的个数
int len = int(aa);//位置点的个数
for (size_t i=0; i<aa; i++)
{
Point3d aPt = positionsV[i];
Point3d sPt = densityKernel;
sPt.x *= exp(aPt.z);
sPt.y *= exp(aPt.z);
aPt -= inPt;//讲个坐标之差
aPt.x /= sPt.x;
aPt.y /= sPt.y;
aPt.z /= sPt.z;
/*-----------------这块还能够优化，策略的考虑，权重的选取--begin-----------------*/
if(aa>10)//考虑假设aa的数量过小时
{
double mm = aPt.dot(aPt);
if(aPt.dot(aPt)<=0.5)//两个点的欧式距离
{
if(weightsV[i]>0.6)//weightsV[i] 为svm的权值
{
sumW+=0.35;
}
else if(weightsV[i]>0.3)
{
sumW+=0.3;
}
continue;
}
//////////////////////////原始///////////
sumW+=(weightsV[i])*std::exp(-(aPt.dot(aPt))/2)/std::sqrt(sPt.dot(Point3d(1,1,1)));
/////////////////////////////////////////
}
else
{
//sumW+=weightsV[i];
sumW+=(weightsV[i])*std::exp(-(aPt.dot(aPt))/2)*2.8;
}
/*---------待优化部分-----------------*/
/*------这块还能够优化。策略的考虑，权重的选取--end------*/
return sumW;//计算最后的权值
}
Point3d moveToMode(Point3d aPt ,Rect inR, const vector<Rect>list) const
{//均值漂移后的位置
Point3d bPt;
for (int i = 0; i<iterMax; i++)
{
bPt = aPt;
aPt = getNewValue(bPt,inR,list);
if ( getDistance(aPt, bPt) <= modeEps )//小于阈值时返回，说明达到稳定状态
{
break;
}
}
return aPt;//返回稳定状态的值
}
double getDistance(Point3d p1, Point3d p2) const
{//计算欧式距离
Point3d ns = densityKernel;
ns.x *= exp(p2.z);
ns.y *= exp(p2.z);
p2 -= p1;
p2.x /= ns.x;
p2.y /= ns.y;
p2.z /= ns.z;
return p2.dot(p2);
}
};
void groupRectangles_meanshift2(vector<Rect>& rectList, double detectThreshold, vector<double>* foundWeights,
vector<double>& scales, Size winDetSize)
//被groupRectangles_meanshift1调用
{
int detectionCount = (int)rectList.size();//矩形的个数
vector<Point3d> hits(detectionCount), resultHits;
vector<double> hitWeights(detectionCount), resultWeights;
Point2d hitCenter;
for (int i=0; i < detectionCount; i++)
{
hitWeights[i] = (*foundWeights)[i];
hitCenter = (rectList[i].tl() + rectList[i].br())*(0.5); //center of rectangles
hits[i] = Point3d(hitCenter.x, hitCenter.y, std::log(scales[i]));//中心坐标x、y、log(scale)
}
if (foundWeights)
foundWeights->clear();
double logZ = std::log(1.3);
Point3d smothing(8, 16, logZ);
MeanshiftGrouping msGrouping(smothing, hits,rectList, hitWeights, 1e-5, 100);//得到
///////////////////////////////////////////////////////////////////////
msGrouping.getModes(resultHits, resultWeights, 1);
//////////////////////////////////////////////////////////////////////
rectList.clear();
for (unsigned i=0; i < resultHits.size(); ++i)
{
double scale = exp(resultHits[i].z);//还原尺度
hitCenter.x = resultHits[i].x;//中心坐标
hitCenter.y = resultHits[i].y;
Size s( int(winDetSize.width*scale), int(winDetSize.height*scale) );//还原窗的大小
Rect resultRect( int(hitCenter.x-s.width/2), int(hitCenter.y-s.height/2),
int(s.width), int(s.height) );//还原窗的位置
if (resultWeights[i] > detectThreshold)//detectThreshold 大于阈值的权重值输出
{//返回矩形框 和 权值
rectList.push_back(resultRect);
foundWeights->push_back(resultWeights[i]);
}
}
}
void groupRectangles_meanshift1(vector<Rect>& rectList, vector<double>& foundWeights,
vector<double>& foundScales, double detectThreshold, Size winDetSize)
{
groupRectangles_meanshift2(rectList, detectThreshold, &foundWeights, foundScales, winDetSize);
//rectList矩形列表, detectThreshold阈值, foundWeights得分, foundScales尺度, winDetSize窗体大小
}
