用ORL人脸数据库和opencv的facererc_demo.cpp做人脸检测

用OpenCV的\opencv\soruce\samples\cpp\facerrc_demo.cpp文件可以做人脸特征检测的训练和测试。要使用这个程序主要做的工作就是提供一个图像集，ORL,然后再生成图像位置和标签的文件，用于faceerc_demo.cpp的训练和测试，生成的“face_at.txt”文件如下

//util.h#ifndef __UTIL_H__#define __UTIL_H__//获取目录先的文件void getFiles(std::string path, std::vector<std::string>& files);//分离字符串std::string splitString(const std::string& source, const std::string& toc1, const std::string& toc2);//将图像文件的位置和标签信息写到保存文件里void writeGraphInfo(const std::string& fileName, std::vector<std::string>& files, const std::string& toc1, const std::string& toc2);#endif

//util.cpp#include <Windows.h>#include <iostream>#include <cstdlib>#include <io.h>#include <stdlib.h>#include <stdio.h>#include <vector>using namespace std;void getFiles(string path, vector<string> & files){    //文件句柄    long   hFile   =   0;    //文件信息    struct _finddata_t fileinfo;    string p;    if((hFile = _findfirst(p.assign(path).append("\\*").c_str(),&fileinfo)) !=  -1)    {        do        {            //如果是目录,迭代之            //如果不是,加入列表            if((fileinfo.attrib &  _A_SUBDIR))            {                if(strcmp(fileinfo.name,".") != 0  &&  strcmp(fileinfo.name,"..") != 0)                    getFiles( p.assign(path).append("\\").append(fileinfo.name), files );            }            else            {                files.push_back(p.assign(path).append("\\").append(fileinfo.name));            }        }while(_findnext(hFile, &fileinfo)  == 0);        _findclose(hFile);    }}string splitString(const string& s, const string& toc1, const string& toc2){    string::size_type pos1, pos2;    string result;    pos1 = s.find(toc1);    pos2 = s.find(toc2);    result = s.substr(++pos1, pos2-pos1);    return result;}void writeGraphInfo(const string& fileName, vector<string>& files, const string& toc1, const string& toc2){    FILE* pFile;    string label;    pFile = fopen(fileName.c_str(), "w");    vector<string>::iterator iter = files.begin();    while(iter!=files.end())    {        label = splitString(*iter, toc1, toc2);        fprintf(pFile, "%s;%s\n", (*iter).c_str(), label.c_str());        iter++;    }    fclose(pFile);}

//prep.h#ifndef __PREP_H__#define __PREP_H__#include "opencv2/core/core.hpp"#include "opencv2/highgui/highgui.hpp"#include "opencv2/contrib/contrib.hpp"#include <iostream>#include <fstream>#include <sstream>#include <stdlib.h>#endif

#include "prep.h"#include "util.h"using namespace cv;using namespace std;static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {    std::ifstream file(filename.c_str(), ifstream::in);    if (!file) {        string error_message = "No valid input file was given, please check the given filename.";        CV_Error(CV_StsBadArg, error_message);    }    string line, path, classlabel;    while (getline(file, line)) {        stringstream liness(line);        getline(liness, path, separator);        getline(liness, classlabel);        if(!path.empty() && !classlabel.empty()) {            images.push_back(imread(path, 0));            labels.push_back(atoi(classlabel.c_str()));        }    }}int main(int argc, const char *argv[]) {    string grpPath;         //人脸图像路径    string fn_csv;          //保存人脸和相应标签的文件    vector<string> files;    vector<Mat> images;    vector<int> labels;    grpPath = "ORL";    fn_csv = "face_at.txt";    getFiles(grpPath, files);    writeGraphInfo(fn_csv, files, "s", "_");    // Read in the data. This can fail if no valid    // input filename is given.    try {        read_csv(fn_csv, images, labels);    } catch (cv::Exception& e) {        cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;        // nothing more we can do        exit(1);    }    // Quit if there are not enough images for this demo.    if(images.size() <= 1) {        string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";        CV_Error(CV_StsError, error_message);    }    // Get the height from the first image. We'll need this    // later in code to reshape the images to their original    // size:    int height = images[0].rows;    //作为我的唯一test图片    Mat testSample = images[images.size() - 1];    int testLabel = labels[labels.size() - 1];    images.pop_back();    labels.pop_back();    Ptr<FaceRecognizer> model = createEigenFaceRecognizer();    model->train(images, labels);    // The following line predicts the label of a given    // test image:    int predictedLabel = model->predict(testSample);    //    // To get the confidence of a prediction call the model with:    //    //    int predictedLabel = -1;    //    double confidence = 0.0;    //    model->predict(testSample, predictedLabel, confidence);    //    string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);    cout << result_message << endl;    waitKey(0);    system("pause");    return 0;}