对opencv MeanShift 融合矩形框的改进

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
		}