opencv 人脸识别 （二）训练和识别

上一篇中我们对训练数据做了一些预处理，检测出人脸并保存在piccolorx文件夹下（x=1,2,3,...类别号），本文做训练和识别。为了识别，首先将人脸训练数据 转为灰度、对齐、归一化，再放入分类器（EigenFaceRecognizer）,最后用训练出的model进行predict。

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环境：vs2010+opencv 2.4.6.0

特征：eigenface

Input：一个人脸数据库，15个人，每人20个样本（左右）。

Output：人脸检测，并识别出每张检测到的人脸。

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1. 为训练数据预处理（ 转为灰度、对齐、归一化 ）

• 转为灰度和对齐是后面做训练时EigenFaceRecognizer的要求；
• 归一化是防止光照带来的影响

在上一篇的 2.2 Prehelper.cpp文件中加入函数

void resizeandtogray(char* dir,int k,  vector<Mat> &images, vector<int> &labels,
vector<Mat> &testimages, vector<int> &testlabels);

void resizeandtogray(char* dir,int K, vector<Mat> &images, vector<int> &labels,
    vector<Mat> &testimages, vector<int> &testlabels)
{
    IplImage* standard = cvLoadImage("D:\privacy\picture\photo\2.jpg",CV_LOAD_IMAGE_GRAYSCALE);
    string cur_dir;
    char id[5];
    int i,j;
    for(int i=1; i<=K; i++)
    {
        cur_dir = dir;
        cur_dir.append("gray\");
        _itoa(i,id,10);
        cur_dir.append(id);
        const char* dd = cur_dir.c_str();
        CStatDir statdir;
        if (!statdir.SetInitDir(dd))
        {
            puts("Dir not exist");
            return;
        }
        cout<<"Processing samples in Class "<<i<<endl;
        vector<char*>file_vec = statdir.BeginBrowseFilenames("*.*");
        for (j=0;j<file_vec.size();j++)
        {
            IplImage* cur_img = cvLoadImage(file_vec[j],CV_LOAD_IMAGE_GRAYSCALE);
            cvResize(cur_img,standard,CV_INTER_AREA);
            Mat cur_mat = cvarrToMat(standard,true),des_mat;
            cv::normalize(cur_mat,des_mat,0, 255, NORM_MINMAX, CV_8UC1);
            cvSaveImage(file_vec[j],cvCloneImage(&(IplImage) des_mat));
            if(j!=file_vec.size())
            {
                    images.push_back(des_mat);
                    labels.push_back(i);
            }
            else
            {
                testimages.push_back(des_mat);
                testlabels.push_back(i);
            }
        }
        cout<<file_vec.size()<<" images."<<endl;
    }
}

并在main中调用：

int main( )
{
    CvCapture* capture = 0;
    Mat frame, frameCopy, image;
    string inputName;
    int mode;

    char dir[256] = "D:\Courses\CV\Face_recognition\pic\";
    //preprocess_trainingdata(dir,K); //face_detection and extract to file
    vector<Mat> images,testimages;
    vector<int> labels,testlabels;
    resizeandtogray(dir,K,images,labels,testimages,testlabels); //togray, normalize and resize

    system("pause");
    return 0;
}

2. 训练

有了vector<Mat> images,testimages; vector<int> labels,testlabels; 可以开始训练了，我们采用EigenFaceRecognizer建模。

在Prehelper.cpp中加入函数

Ptr<FaceRecognizer> Recognition(vector<Mat> images, vector<int> labels,vector<Mat> testimages, vector<int> testlabels)；

Ptr<FaceRecognizer> Recognition(vector<Mat> images, vector<int> labels,
    vector<Mat> testimages, vector<int> testlabels)
{
    Ptr<FaceRecognizer> model = createEigenFaceRecognizer(10);//10 Principal components
    cout<<"train"<<endl;
    model->train(images,labels);
    int i,acc=0,predict_l;
    for (i=0;i<testimages.size();i++)
    {
        predict_l = model->predict(testimages[i]);
        if(predict_l != testlabels[i])
        {
            cout<<"An error in recognition: sample "<<i+1<<", predict "<<
                predict_l<<", groundtruth "<<testlabels[i]<<endl;
            imshow("error 1",testimages[i]);
            waitKey();
        }
        else
            acc++;
    }
    cout<<"Recognition Rate: "<<acc*1.0/testimages.size()<<endl;
    return model;
}

Recognization()输出分错的样本和正确率，最后返回建模结果Ptr<FaceRecognizer> model

主函数改为：

int main( )
{
    CvCapture* capture = 0;
    Mat frame, frameCopy, image;
    string inputName;
    int mode;

    char dir[256] = "D:\Courses\CV\Face_recognition\pic\";
    //preprocess_trainingdata(dir,K); //face_detection and extract to file
    vector<Mat> images,testimages;
    vector<int> labels,testlabels;
    //togray, normalize and resize; load to images,labels,testimages,testlabels
    resizeandtogray(dir,K,images,labels,testimages,testlabels);
    //recognition
    Ptr<FaceRecognizer> model = Recognition(images,labels,testimages,testlabels);
    char* dirmodel = new char [256];
    strcpy(dirmodel,dir); strcat(dirmodel,"model.out");
    FILE* f = fopen(dirmodel,"w");
    fwrite(model,sizeof(model),1,f);
    system("pause");
    return 0;
}

最终结果：一个错分样本，正确率93.3%

文章所用代码打包链接：http://download.csdn.net/detail/abcjennifer/7047853

from: http://blog.csdn.net/abcjennifer/article/details/20446077