Opencv+C++之人脸识别二

这两天课比较多，上次的两步法人脸识别代码一直没有补充完整，今天将整个实验代码show一下，同时将该方法的主要思想介绍下：

上一节我们已经将图片进行降维处理，这样做的目的就是要在保持对象间差异的同时降低处理数据量。除了PCA外，LDA也是一种比较简单实用的降维方法，大家可以对比两种降维方法；基于PCA降维后的数据，我们接着要做的是用训练数据将测试数据表示出来

接着通过以下的误差判别式来找到M近邻（误差值越小说明该训练样本跟测试样本的相似度越大）

       

以上就完成了两步法中的第一步，第二步中用M近邻样本将测试样本再次标出（实际上这里的本质还是稀疏表示的方法，但是改进之处是单纯的稀疏法中稀疏项不确定，两步法中通过第一步的误差筛选确定了贡献度较大的训练样本）

在M近邻中包含多个类的训练样本，我们要将每个类的训练样本累加起来，分别同测试样本做误差对比，将测试样本判定给误差最下的类

       

OK，主要思想介绍了，下面就看代码实现

/************************************************************************/

/* ZhaoChaofeng

*/ 2013.4.16

/************************************************************************/



#include <opencv2/core/core.hpp>

#include <opencv2/highgui/highgui.hpp>



#include <fstream>

#include <sstream>

#include <iostream>

#include <string>



using namespace cv;

using namespace std;



const double u=0.01f;

const double v=0.01f;//the global parameter

const int MNeighbor=40;//the M nearest neighbors

// Number of components to keep for the PCA

const int num_components = 100;

//the M neighbor mats

vector<Mat> MneighborMat;

//the class index of M neighbor mats

vector<int> MneighborIndex;

//the number of object which used to training

const int Training_ObjectNum=40;

//the number of image that each object used

const int Training_ImageNum=7;

//the number of object used to testing

const int Test_ObjectNum=40;

//the image number

const int Test_ImageNum=3;



// Normalizes a given image into a value range between 0 and 255.

Mat norm_0_255(const Mat& src) {

    // Create and return normalized image:

    Mat dst;

    switch(src.channels()) {

    case 1:

        cv::normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1);

        break;

    case 3:

        cv::normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC3);

        break;

    default:

        src.copyTo(dst);

        break;

    }

    return dst;

}



// Converts the images given in src into a row matrix.

Mat asRowMatrix(const vector<Mat>& src, int rtype, double alpha = 1, double beta = 0) {

    // Number of samples:

    size_t n = src.size();

    // Return empty matrix if no matrices given:

    if(n == 0)

        return Mat();

    // dimensionality of (reshaped) samples

    size_t d = src[0].total();

    // Create resulting data matrix:

    Mat data(n, d, rtype);

    // Now copy data:

    for(int i = 0; i < n; i++) {

        //

        if(src[i].empty()) {

            string error_message = format("Image number %d was empty, please check your input data.", i);

            CV_Error(CV_StsBadArg, error_message);

        }

        // Make sure data can be reshaped, throw a meaningful exception if not!

        if(src[i].total() != d) {

            string error_message = format("Wrong number of elements in matrix #%d! Expected %d was %d.", i, d, src[i].total());

            CV_Error(CV_StsBadArg, error_message);

        }

        // Get a hold of the current row:

        Mat xi = data.row(i);

        // Make reshape happy by cloning for non-continuous matrices:

        if(src[i].isContinuous()) {

            src[i].reshape(1, 1).convertTo(xi, rtype, alpha, beta);

        } else {

            src[i].clone().reshape(1, 1).convertTo(xi, rtype, alpha, beta);

        }

    }

    return data;

}



//convert int to string

string Int_String(int index)

{

    stringstream ss;

    ss<<index;

    return ss.str();

}



////show the element of mat(used to test code)

//void showMat(Mat RainMat)

//{

//    for (int i=0;i<RainMat.rows;i++)

//    {

//        for (int j=0;j<RainMat.cols;j++)

//        {

//            cout<<RainMat.at<float>(i,j)<<"  ";

//        }

//        cout<<endl;

//    }

//}

//

////show the element of vector

//void showVector(vector<int> index)

//{

//    for (int i=0;i<index.size();i++)

//    {

//        cout<<index[i]<<endl;

//    }

//}

//

//void showMatVector(vector<Mat> neighbor)

//{

//    for (int e=0;e<neighbor.size();e++)

//    {

//        showMat(neighbor[e]);

//    }

//}





//Training function





void Trainging()

{

    // Holds some training images:

    vector<Mat> db;



    // This is the path to where I stored the images, yours is different!

    for (int i=1;i<=Training_ObjectNum;i++)

    {

        for (int j=1;j<=Training_ImageNum;j++)

        {

            string filename="s"+Int_String(i)+"/"+Int_String(j)+".pgm";

            db.push_back(imread(filename,IMREAD_GRAYSCALE));

        }

    }



    // Build a matrix with the observations in row:

    Mat data = asRowMatrix(db, CV_32FC1);



    // Perform a PCA:

    PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW, num_components);



    // And copy the PCA results:

    Mat mean = pca.mean.clone();

    Mat eigenvalues = pca.eigenvalues.clone();

    Mat eigenvectors = pca.eigenvectors.clone();



    // The mean face:

    //imshow("avg", norm_0_255(mean.reshape(1, db[0].rows)));



    // The first three eigenfaces:

    //imshow("pc1", norm_0_255(pca.eigenvectors.row(0)).reshape(1, db[0].rows));

    //imshow("pc2", norm_0_255(pca.eigenvectors.row(1)).reshape(1, db[0].rows));

    //imshow("pc3", norm_0_255(pca.eigenvectors.row(2)).reshape(1, db[0].rows));



    ////get and save the training image information which decreased on dimensionality

    Mat mat_trans_eigen;

    Mat temp_data=data.clone();

    Mat temp_eigenvector=pca.eigenvectors.clone();

    gemm(temp_data,temp_eigenvector,1,NULL,0,mat_trans_eigen,CV_GEMM_B_T);



    //save the eigenvectors

    FileStorage fs(".\\eigenvector.xml", FileStorage::WRITE);

    fs<<"eigenvector"<<eigenvectors;

    fs<<"TrainingSamples"<<mat_trans_eigen;

    fs.release();

}



//Line combination of test sample used by training samples 

//parameter:y stand for the test sample column vector;

//x stand for the training samples matrix

Mat LineCombination(Mat y,Mat x)

{

    //the number of training samples

    size_t col=x.cols;

    //the result mat 

    Mat result=cvCreateMat(col,1,CV_32FC1);

    //the transposition of x and also work as a temp matrix

    Mat trans_x_mat=cvCreateMat(col,col,CV_32FC1);

    //construct the identity matrix

    Mat I=Mat::ones(col,col,CV_32FC1);



    //solve the Y=XA

    //result=x.inv(DECOMP_SVD);

        //result*=y;

    Mat temp=(x.t()*x+u*I);

    

    Mat temp_one=temp.inv(DECOMP_SVD);

    Mat temp_two=x.t()*y;

    result=temp_one*temp_two;



    return result;

}



//Error test

//parameter:y stand for the test sample column vector;

//x stand for the training samples matrix

//coeff stand for the coefficient of training samples 

void  ErrorTest(Mat y,Mat x,Mat coeff)

{

    //the array store the coefficient

    map<double,int> Efficient;



    //compute the error

    for (int i=0;i<x.cols;i++)

    {

        Mat temp=x.col(i);

        double coefficient=coeff.at<float>(i,0);

        temp=coefficient*temp;

        double e=norm((y-temp),NORM_L2);

        Efficient[e]=i;//insert a new element

    }



    //select the minimum w col as the w nearest neighbors

    map<double,int>::const_iterator map_it=Efficient.begin();

    int num=0;

    //the map could sorted by the key one

    while (map_it!=Efficient.end() && num<MNeighbor)

    {

        MneighborMat.push_back(x.col(map_it->second));

        MneighborIndex.push_back(map_it->second);

        ++map_it;

        ++num;

    }



    //return MneighborMat;

}



//error test of two step

//parameter:MneighborMat store the class information of M nearest neighbor samples

int ErrorTest_Two(Mat y,Mat x,Mat coeff)

{

    int result;

    bool flag=true;

    double minimumerror;

    //

    map<int,vector<Mat>> ErrorResult;



    //count the class of M neighbor

    for (int i=0;i<x.cols;i++)

    {

        //compare

        //Mat temp=x.col(i)==MneighborMat[i];

          //showMat(temp);

        //if (temp.at<float>(0,0)==255)

        //{

            int classinf=MneighborIndex[i];

            double coefficient=coeff.at<float>(i,0);

            Mat temp=x.col(i);

            temp=coefficient*temp;

            ErrorResult[classinf/Training_ImageNum].push_back(temp);

        //}

        

    }



    //

    map<int,vector<Mat>>::const_iterator map_it=ErrorResult.begin();

    while(map_it!=ErrorResult.end())

    {

        vector<Mat> temp_mat=map_it->second;

        int num=temp_mat.size();

        Mat temp_one;

        temp_one=Mat::zeros(temp_mat[0].rows,temp_mat[0].cols,CV_32FC1);

        while (num>0)

        {

            temp_one+=temp_mat[num-1];

            num--;

        }

        double e=norm((y-temp_one),NORM_L2);

        if (flag)

        {

            minimumerror=e;

            result=map_it->first+1;

            flag=false;

        }

        if (e<minimumerror)

        {

            minimumerror=e;

            result=map_it->first+1;

        }

        ++map_it;

    }

    return result;

}



//testing function

//parameter:y stand for the test sample column vector;

//x stand for the training samples matrix

int testing(Mat x,Mat y)

{

    // the class that test sample belongs to

    int classNum;



    //the first step: get the M nearest neighbors

    Mat coffecient=LineCombination(y.t(),x.t());



    //cout<<"the first step coffecient"<<endl;

    //showMat(coffecient);



    //map<Mat,int> MneighborMat=ErrorTest(y,x,coffecient);

    ErrorTest(y.t(),x.t(),coffecient);



    //cout<<"the M neighbor index"<<endl;

    //showVector(MneighborIndex);

    //cout<<"the M neighbor mats"<<endl;

    //showMatVector(MneighborMat);



    //the second step:

    //construct the W nearest neighbors mat

    int row=x.cols;//should be careful 

    Mat temp(row,MNeighbor,CV_32FC1);

    for (int i=0;i<MneighborMat.size();i++)

    {

        Mat temp_x=temp.col(i);

        if (MneighborMat[i].isContinuous())

        {

            MneighborMat[i].convertTo(temp_x,CV_32FC1,1,0);

        }

        else

        {

            MneighborMat[i].clone().convertTo(temp_x,CV_32FC1,1,0);

        }

    }



    //cout<<"the second step mat"<<endl;

    //showMat(temp);



    Mat coffecient_two=LineCombination(y.t(),temp);



    //cout<<"the second step coffecient"<<endl;

    //showMat(coffecient_two);



    classNum=ErrorTest_Two(y.t(),temp,coffecient_two);

    return classNum;

}



int main(int argc, const char *argv[]) {

    //the number which test true

    int TrueNum=0;

    //the Total sample which be tested

    int TotalNum=Test_ObjectNum*Test_ImageNum;



    //if there is the eigenvector.xml, it means we have got the training data and go to the testing stage directly;

    FileStorage fs(".\\eigenvector.xml", FileStorage::READ);

    if (fs.isOpened())

    {

        //if the eigenvector.xml file exist,read the mat data

        Mat mat_eigenvector;

        fs["eigenvector"] >> mat_eigenvector;    

        Mat mat_Training;

        fs["TrainingSamples"]>>mat_Training;



        for (int i=1;i<=Test_ObjectNum;i++)

        {

            int ClassTestNum=0;

            for (int j=Training_ImageNum+1;j<=Training_ImageNum+Test_ImageNum;j++)

            {

                string filename="s"+Int_String(i)+"/"+Int_String(j)+".pgm";

                Mat TestSample=imread(filename,IMREAD_GRAYSCALE);

                Mat TestSample_Row;

                TestSample.reshape(1,1).convertTo(TestSample_Row,CV_32FC1,1,0);//convert to row mat

                Mat De_deminsion_test;

                gemm(TestSample_Row,mat_eigenvector,1,NULL,0,De_deminsion_test,CV_GEMM_B_T);// get the test sample which decrease the dimensionality



                //cout<<"the test sample"<<endl;

                //showMat(De_deminsion_test.t());

                //cout<<"the training samples"<<endl;

                //showMat(mat_Training);



                int result=testing(mat_Training,De_deminsion_test);

                //cout<<"the result is"<<result<<endl;

                if (result==i)

                {

                    TrueNum++;

                    ClassTestNum++;

                }

                MneighborIndex.clear();

                MneighborMat.clear();//及时清除空间

            }

            cout<<"第"<<Int_String(i)<<"类测试正确的图片数:  "<<Int_String(ClassTestNum)<<endl;

        }

        fs.release();

    }

    else

    {

        Trainging();

    }

    // Show the images:

    waitKey(0);



    // Success!

    return 0;

}

在以上的实现中，有些opencv的实现需要特别注意一下：

（1）坑爹的Mat类型，它虽然可以方便的让我们实现图像数据的矩阵化，并给出了一系列的操作方法，但是，在调试中，它却不能像一般变量一样，让我们直观的看到；我用一个比较笨的方法：自己写一个方法，在调试中调用，呈现关键矩阵的数据

（2）另外一个就是将训练数据做一个保存，用到了opencv中的FileStorage类；有关对中间数据的存储通常会用到.xml或者.yml文件，以下对其做个简单介绍

  新版本的OpenCV的C++接口中，imwrite()和imread()只能保存整数数据，且需要以图像格式。当需要保存浮点数据或者XML/YML文件时，之前的C语言接口cvSave()函数已经在C++接口中被删除，代替它的是    FileStorage类。这个类非常的方便，封装了很多数据结构的细节，编程的时候可以根据统一的接口对数据结构进行保存。

    1. FileStorage类写XML/YML文件

         •   新建一个FileStorage对象，以FileStorage::WRITE的方式打开一个文件。

         •   使用 << 操作对该文件进行操作。

         •   释放该对象，对文件进行关闭。

        例子如下：

FileStorage fs("test.yml", FileStorage::WRITE);

    fs << "frameCount" << 5;

    time_t rawtime; time(&rawtime);

    fs << "calibrationDate" << asctime(localtime(&rawtime));

    Mat cameraMatrix = (Mat_<double>(3,3) << 1000, 0, 320, 0, 1000, 240, 0, 0, 1); //又一种Mat初始化方式

    Mat distCoeffs = (Mat_<double>(5,1) << 0.1, 0.01, -0.001, 0, 0);

    fs << "cameraMatrix" << cameraMatrix << "distCoeffs" << distCoeffs;

    

    //features为一个大小为3的向量,其中每个元素由随机数x,y和大小为8的uchar数组组成

    fs << "features" << "[";

    for( int i = 0; i < 3; i++ )

    {

        int x = rand() % 640;

        int y = rand() % 480;

        uchar lbp = rand() % 256;

        fs << "{:" << "x" << x << "y" << y << "lbp" << "[:";

        for( int j = 0; j < 8; j++ )

            fs << ((lbp >> j) & 1);

        fs << "]" << "}";

    }

    fs << "]";

    fs.release();

2. FileStorage类读XML/YML文件

       FileStorage对存储内容在内存中是以层次的节点组成的，每个节点类型为FileNode，FileNode可以使单个的数值、数组或者一系列FileNode的集合。FileNode又可以看做是一个容器，使用iterator接口可以对该节点内更小单位的内容进行访问，例如访问到上面存储的文件中"features"的内容。步骤与写文件类似：

         •   新建FileStorage对象，以FileStorage::READ 方式打开一个已经存在的文件

         •   使用FileStorage::operator []()函数对文件进行读取，或者使用FileNode和FileNodeIterator

         •   使用FileStorage::release()对文件进行关闭

         例子如下：

FileStorage fs("test.yml", FileStorage::READ);



    //方式一: []操作符

    int frameCount = (int)fs["frameCount"];

    

    //方式二: FileNode::operator >>()

    string date;

    fs["calibrationDate"] >> date;

    

    Mat cameraMatrix2, distCoeffs2;

    fs["cameraMatrix"] >> cameraMatrix2;

    fs["distCoeffs"] >> distCoeffs2;

    

    //注意FileNodeIterator的使用,似乎只能用一维数组去读取里面所有的数据

    FileNode features = fs["features"];

    FileNodeIterator it = features.begin(), it_end = features.end();

    int idx = 0;

    std::vector<uchar> lbpval;

    for( ; it != it_end; ++it, idx++ )

    {

        cout << "feature #" << idx << ": ";

        cout << "x=" << (int)(*it)["x"] << ", y=" << (int)(*it)["y"] << ", lbp: (";

        (*it)["lbp"] >> lbpval;  //直接读出一维向量



        for( int i = 0; i < (int)lbpval.size(); i++ )

            cout << " " << (int)lbpval[i];

        cout << ")" << endl;

    }

    fs.release();

另外，注意在新建FileStorage对象之后，并以READ或WRITE模式打开文件之后，可以用FileStorage::isOpened()查看文件状态，判断是否成功打开了文件。

有关FileStorage类的相关内容引用自：http://www.cnblogs.com/summerRQ/articles/2524560.html

我使用的是opencv2.4.0版本实现的方法，opencv有个欠缺的地方就是版本间的兼容性，虽然做了些工作，但是使用起来还是有些不流畅。不过，值得称赞的是其将OOP的思想应用到库的开发中，很多核心对象和相关操作被封装起来，方便使用。