最近在做人头统计方面的研究，尝试了多种办法，其中包括Adaboost+Haar特征、HOG特征+线性SVM两大模型。SVM+HOG的模型之前多数被应用于行人检测，，我在做人头统计的过程中考虑到人头的边缘形状比较明显，图像梯度比较容易提取，所以将该方法搬到人头统计上来，效果还不错。不足之处是多尺度的HOG计算太慢了，难以达到实时性要求，所以我采用的多数是单尺度检测（64*64的固定窗口大小）。
我现在的工作只是做了个入门，本文意在抛砖引玉，希望感兴趣的小伙伴能够一起研究。
1、预处理
从视频中采集了1416个人头正样本，以及1957个负样本。正样本主要就是人头部（头发）的图像，负样本是不包括人头目标的图像。然后将它们统一归一化到64*64的大小（我这里为了简化训练过程，将人头图像的大小设置为和窗口大小一致）。
2、人头训练
开发环境是winxp+vs2008+opencv2.3.1。训练代码主要包括MySVM.h，global.h和global.cpp三个文件。其中MySVM.h是SVM类定义文件，global.h和global.cpp分别是全局函数声明和定义文件。
（1）MySVM.h如下：
#pragma once#ifndef _MYSVM_H_#define _MYSVM_H_#include <opencv2/opencv.hpp>#include <opencv2/core/core.hpp>#include <opencv2/ml/ml.hpp>#include <opencv2/highgui/highgui.hpp>using namespace cv;class MySVM: public CvSVM{public:int get_alpha_count(){return this->sv_total;}int get_sv_dim(){return this->var_all;}int get_sv_count(){return this->decision_func->sv_count;}double* get_alpha(){return this->decision_func->alpha;}float** get_sv(){return this->sv;}float get_rho(){return this->decision_func->rho;}};#endif（2）global.h如下：#include "MySVM.h"#include <fstream>#include <iostream>#include <ctime>using namespace std;//函数名：Train//函数功能：SVM训练每张图片的HOG特征//参数说明：//const char* positivePath：正样本路径//int pCount：正样本个数//const char* negativePath：负样本路径//int nCount：负样本个数//const char* classifierSavePath：分类器保存路径//const char* detectorSavePath：检测器保存路径//返回bool：训练是否成功（true：成功，false：失败）bool Train(const char* positivePath, int pCount, const char* negativePath, int nCount,const char* classifierSavePath, const char* detectorSavePath);//函数名：CalDimension//函数功能：计算每张图片的HOG特征维度//参数说明：//CvSize winSize：窗口大小//CvSize blockSize：块大小//CvSize blockStride：块位移大小//CvSize cellSize：胞元大小//int nbins：bin数//返回int：HOG特征维度//参考计算方式详细：//参考参数说明详细：int CalDimension(CvSize winSize, CvSize blockSize, CvSize blockStride,CvSize cellSize, int nbins);//函数名：DetectMulti//函数功能：用SVM+HOG分类器对图片做多尺度检测//参数说明：//const char* detectorSavePath：检测器保存路径//const char* testPath：测试视频路径//返回bool：检测是否成功（true：成功，false：失败）bool DetectMulti(const char* detectorSavePath, const char* testPath);//函数名：DetectSingle//函数功能：用SVM+HOG分类器对图片做单尺度检测//参数说明：//const char* detectorSavePath：检测器保存路径//const char* testPath：测试视频路径//返回bool：检测是否成功（true：成功，false：失败）bool DetectSingle(const char* classifierSavePath, const char* testPath);（3）global.cpp如下：#include "global.h"///////////////////////参数设置///////////////////////////CvSize winSize = cvSize(64, 64);//等于训练样本图像大小CvSize blockSize = cvSize(16, 16);//block sizeCvSize blockStride = cvSize(8, 8);//block strideCvSize winStride = cvSize(8, 8);//window strideCvSize cellSize = cvSize(8, 8);//cell sizeint nbins = 9;//一般取9个梯度方向////////////函数定义//////////////////int CalDimension(CvSize winSize, CvSize blockSize, CvSize blockStride,CvSize cellSize, int nbins){//一个窗口(winSize)内宽和高方向分别有多少个块(blockSize)//int hBlockNum = (winSize.height – 1) / cellSize.height;//int wBlockNum = (winSize.width – 1) / cellSize.width;int hBlockNum = (winSize.height – blockSize.height) / blockStride.height + 1;int wBlockNum = (winSize.width – blockSize.width) / blockStride.width + 1;//一个块(blockSize)里面有多少个单元(cellSize)int hCellNum = blockSize.height / cellSize.height;int wCellNum = blockSize.width / cellSize.width;//一个单元(cellSize)里面有多少HOG特征维度int hogNum = nbins;//计算一个窗口的HOG特征维度：block的个数 * block内部cell的个数 * 每个cell的HOG特征维度int totalHogNum = (hBlockNum * wBlockNum) * (hCellNum * wCellNum) * hogNum;return totalHogNum;}bool Train(const char* positivePath, int pCount, const char* negativePath, int nCount,const char* classifierSavePath, const char* detectorSavePath){cout<<"******************** Train ********************"<<endl;//首先计算图像的HOG特征维度int dim = CalDimension(winSize, blockSize, blockStride, cellSize, nbins);int totalCount = pCount + nCount;cout<<"1: Start trainning for SVM:"<<endl;cout<<"total samples: "<<totalCount<<endl;cout<<"positive samples: "<<pCount<<endl;cout<<"negative samples: "<<nCount<<endl;cout<<"feature dimension is: "<<dim<<endl<<endl;//训练正样本cout<<"2: Start to train positive samples:"<<endl;CvMat *sampleFeaturesMat = cvCreateMat(totalCount , dim, CV_32FC1);//64*128的训练样本，该矩阵将是totalSample*3780//64*64的训练样本，该矩阵将是totalSample*1764cvSetZero(sampleFeaturesMat); CvMat *sampleLabelMat = cvCreateMat(totalCount, 1, CV_32FC1);//样本标识 cvSetZero(sampleLabelMat);char positiveImgPath[256];for(int i=0; i<pCount; i++) {//载入图像sprintf(positiveImgPath, "%s%d.bmp", positivePath, i);string strPosPath(positiveImgPath);cv::Mat img = cv::imread(strPosPath);if( img.data == NULL ){cout<<"positive image sample load error: "<<i<<" "<<strPosPath<<endl;//return false;//system("pause");continue;}cv::HOGDescriptor hog(winSize, blockSize, blockStride, cellSize, nbins);vector<float> featureVec;hog.compute(img, featureVec, winStride); //计算HOG特征向量int featureVecSize = featureVec.size();//加上类标，转化为CvMatfor (int j=0; j<featureVecSize; j++){CV_MAT_ELEM( *sampleFeaturesMat, float, i, j ) = featureVec[j]; }sampleLabelMat->data.fl[i] = 1;}cout<<"End of training for positive samples."<<endl<<endl;//训练负样本cout<<"3: Start to train negative samples: "<<endl;char negativeImgPath[256];for (int i=0; i<nCount; i++){//载入图像sprintf(negativeImgPath, "%s%d.bmp", negativePath, i);string strNegPath(negativeImgPath);cv::Mat img = cv::imread(strNegPath);if(img.data == NULL){cout<<"negative image sample load error: "<<strNegPath<<endl;//return false;//system("pause");continue;}cv::HOGDescriptor hog(winSize, blockSize, blockStride, cellSize, nbins);vector<float> featureVec;hog.compute(img,featureVec, winStride);//计算HOG特征向量int featureVecSize = featureVec.size();for ( int j=0; j<featureVecSize; j ++){CV_MAT_ELEM( *sampleFeaturesMat, float, i + pCount, j ) = featureVec[ j ];}sampleLabelMat->data.fl[ i + pCount ] = -1;}cout<<"End of training for negative samples."<<endl<<endl;//SVM训练cout<<"4: Start to train SVM classifier: "<<endl;//设置SVM参数CvSVMParams params;int iteration = 1000;double penaltyFactor = 0.01;params.svm_type = CvSVM::C_SVC;params.kernel_type = CvSVM::LINEAR;params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, iteration, FLT_EPSILON);params.C = penaltyFactor;//printcout<<"svm_type: C_SVC\nkernel_type: LINEAR\ntermination type: CV_TERMCRIT_ITER"<<"\ntermination iteration: "<<iteration<<"\ntermination epsilon: "<<FLT_EPSILON<<"\npenalty factor: "<<penaltyFactor<<endl;MySVM svm;svm.train( sampleFeaturesMat, sampleLabelMat, NULL, NULL, params ); //用线性SVM分类器训练svm.save(classifierSavePath);//将SVM训练完的数据保存到指定的文件中cvReleaseMat(&sampleFeaturesMat);cvReleaseMat(&sampleLabelMat);int supportVectorSize = svm.get_support_vector_count();cout<<"\nsupport vector size of SVM："<<supportVectorSize<<endl;cout<<"End of training SVM classifier."<<endl<<endl;//保存用于检测的HOG特征cout<<"5. Save SVM detector file: "<<endl;CvMat *sv,*alp,*re;//所有样本特征向量 sv = cvCreateMat(supportVectorSize , dim, CV_32FC1);alp = cvCreateMat(1 , supportVectorSize, CV_32FC1);re = cvCreateMat(1 , dim, CV_32FC1);CvMat *res = cvCreateMat(1 , 1, CV_32FC1);cvSetZero(sv);cvSetZero(re);for(int i=0; i<supportVectorSize; i++){memcpy( (float*)(sv->data.fl+i*dim), svm.get_support_vector(i), dim*sizeof(float));}double* alphaArr = svm.get_alpha();int alphaCount = svm.get_alpha_count();for(int i=0; i<supportVectorSize; i++){alp->data.fl[i] = alphaArr[i];}cvMatMul(alp, sv, re);int posCount = 0;for (int i=0; i<dim; i++){re->data.fl[i] *= -1;}//保存为文本文件FILE* fp = fopen(detectorSavePath,"wb");if( NULL == fp ){return false;}for(int i=0; i<dim; i++){fprintf(fp,"%f \n",re->data.fl[i]);}float rho = svm.get_rho();fprintf(fp, "%f", rho);fclose(fp);cout<<"Save "<<detectorSavePath<<" OK!"<<endl;return true;}//使用detectMultiScale检测bool DetectMulti(const char* detectorSavePath, const char* testPath){cout<<"\n******************** Detection Multi********************"<<endl;CvCapture* cap = cvCreateFileCapture(testPath);if (!cap){cout<<"avi file load error…"<<endl;return false;}vector<float> x;ifstream fileIn(detectorSavePath, ios::in);float val = 0.0f;while(!fileIn.eof()){fileIn>>val;x.push_back(val);}fileIn.close();vector<cv::Rect> found;cv::HOGDescriptor hog(winSize, blockSize, blockStride, cellSize, nbins);hog.setSVMDetector(x);IplImage* img = NULL;cvNamedWindow("img", 0);cvNamedWindow("video", 0);int frameCount = 0;double timeSum = 0.0;while(img=cvQueryFrame(cap)){cvShowImage("video", img);frameCount++;double begin = clock();hog.detectMultiScale(img, found, 0, winStride, cv::Size(0,0), 1.05, 2);double end = clock();double diff = (end-begin)/CLOCKS_PER_SEC*1000;timeSum += diff;cout<< "Detection time is: "<<diff<<"ms"<<endl;if (found.size() > 0){for (int i=0; i<found.size(); i++){CvRect tempRect = cvRect(found[i].x, found[i].y, found[i].width, found[i].height);cvRectangle(img, cvPoint(tempRect.x,tempRect.y),cvPoint(tempRect.x+tempRect.width,tempRect.y+tempRect.height),CV_RGB(255,0,0), 2);}}cvShowImage("img", img);if (cvWaitKey(1) == 27){break;}}cvReleaseCapture(&cap);cout<< "Average detection time is: "<<timeSum / frameCount<<"ms"<<endl;return true;}//使用detect检测bool DetectSingle(const char* detectorSavePath, const char* testPath){cout<<"\n******************** Detection Single********************"<<endl;CvCapture* cap = cvCreateFileCapture(testPath);if (!cap){cout<<"avi file load error…"<<endl;return false;}vector<float> x;ifstream fileIn(detectorSavePath, ios::in);float val = 0.0f;while(!fileIn.eof()){fileIn>>val;x.push_back(val);}fileIn.close();vector<cv::Point> found;cv::HOGDescriptor hog(winSize, blockSize, blockStride, cellSize, nbins);hog.setSVMDetector(x);IplImage* img = NULL;cvNamedWindow("img", 0);cvNamedWindow("video", 0);int frameCount = 0;double timeSum = 0.0;while(img=cvQueryFrame(cap)){cvShowImage("video", img);frameCount++;double begin = clock();//检测：found为检测目标的左上角坐标点hog.detect(img, found, 0, winStride, cvSize(0,0));double end = clock();double diff = (end-begin)/CLOCKS_PER_SEC*1000;timeSum += diff;cout<< "Detection time is: "<<diff<<"ms"<<endl;if (found.size() > 0){for (int i=0; i<found.size(); i++){CvRect tempRect = cvRect(found[i].x, found[i].y, winSize.width, winSize.height);cvRectangle(img, cvPoint(tempRect.x,tempRect.y),cvPoint(tempRect.x+tempRect.width,tempRect.y+tempRect.height),CV_RGB(255,0,0), 2);}}cvShowImage("img", img);if (cvWaitKey(1) == 27){break;}}cvReleaseCapture(&cap);cout<< "Average detection time is: "<<timeSum / frameCount<<"ms"<<endl;return true;}3、人头检测酒般的思念，一饮就醉，醉时就用全部的热情读这忧伤的月色，
