opencv神经网络识别美女

Posted 2020-07-07 BHY_

最近比较闲，想做一个判断是否是美女的算法
从网上搜集了一些图片，首先要提取这些图片中的人脸并保存作为训练集，可以参考文章：
http://blog.csdn.net/qq_15947787/article/details/51393030

下面是完整的代码

//opencv2.4.9 + vs2012 + win7 x64
#include <opencv2/opencv.hpp>
#include <iostream>
#include <stdio.h>
#include <windows.h>

using namespace std;
using namespace cv;

/** Function Headers */
void detectAndCut( Mat img ,string dir ,string filename );
void AllImagePro( string src, string dst, const int number );
char* WcharToChar(const wchar_t* wp);
wchar_t* CharToWchar(const char* c);
wchar_t* StringToWchar(const string& s);
string getstring ( const int n );

CascadeClassifier face_cascade;
String face_cascade_name = "haarcascade_frontalface_alt.xml";

//主函数
int main()
{
    const int sample_mun_perclass = 12;//训练每类图片数量
    const int class_mun = 2;//训练类数 一类是美女，一类是丑女 ^-^

    const int image_cols = 30;
    const int image_rows = 30;

    string  fileReadName,fileReadPath;

    float trainingData[class_mun*sample_mun_perclass][image_rows*image_cols] = {{0}};//每一行一个训练样本
    float labels[class_mun*sample_mun_perclass][class_mun]={{0}};//训练样本标签

    if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading\\n"); return -1; };
    AllImagePro( "0", "0cut" ,sample_mun_perclass);
    AllImagePro( "1", "1cut" ,sample_mun_perclass);
    cout<<"cut……OK！"<<endl;

     for(int i=0;i<class_mun;++i)//不同类
    {
        //读取每个类文件夹下所有图像
        int j = 0;//每一类读取图像个数计数
        fileReadPath = getstring(i) + "cut/" + "*.*";
        cout<<"文件夹"<<i<<endl;
        HANDLE hFile;
        LPCTSTR lpFileName = StringToWchar(fileReadPath);//指定搜索目录和文件类型，如搜索d盘的音频文件可以是"D:\\\\*.mp3"
        WIN32_FIND_DATA pNextInfo;  //搜索得到的文件信息将储存在pNextInfo中;
        hFile = FindFirstFile(lpFileName,&pNextInfo);//请注意是 &pNextInfo , 不是 pNextInfo;
        if(hFile == INVALID_HANDLE_VALUE)
        {
            exit(-1);//搜索失败
        }
        //do-while循环读取
        do
        {
            if(pNextInfo.cFileName[0] == '.')//过滤.和..
                continue;
            //wcout<<pNextInfo.cFileName<<endl;
            j++;
            printf("%s\\n",WcharToChar(pNextInfo.cFileName));
            //对读入的图片进行处理
            Mat srcImage = imread( getstring(i) + "/" + WcharToChar(pNextInfo.cFileName),CV_LOAD_IMAGE_GRAYSCALE);
            Mat trainImage;

            resize(srcImage,trainImage,Size(image_cols,image_rows),(0,0),(0,0),CV_INTER_AREA);//使用象素关系重采样。当图像缩小时候，该方法可以避免波纹出现
           // threshold(trainImage,trainImage,0,255,CV_THRESH_BINARY|CV_THRESH_OTSU);
            Canny(trainImage ,trainImage ,150,100,3,false);
            for(int k = 0; k<image_rows*image_cols; ++k)
            {
                trainingData[i*sample_mun_perclass+(j-1)][k] = (float)trainImage.data[k];
                //trainingData[i*sample_mun_perclass+(j-1)][k] = (float)trainImage.at<unsigned char>((int)k/8,(int)k%8);//(float)train_image.data[k];
                //cout<<trainingData[i*sample_mun_perclass+(j-1)][k] <<" "<< (float)trainImage.at<unsigned char>(k/8,k%8)<<endl;
            }

        } while (FindNextFile(hFile,&pNextInfo) );

    }

    // Set up training data Mat
    Mat trainingDataMat(class_mun*sample_mun_perclass, image_rows*image_cols, CV_32FC1, trainingData);
    //cout<<"trainingDataMat:"<<endl;
    //cout<<trainingDataMat<<endl;
    cout<<"trainingDataMat——OK！"<<endl;

    // Set up label data  
    for(int i=0;i<=class_mun-1;++i)
    {
        for(int j=0;j<=sample_mun_perclass-1;++j)
        {
            for(int k = 0;k<class_mun;++k)
            {
                if(k==i)
                    labels[i*sample_mun_perclass + j][k] = 1;
                else labels[i*sample_mun_perclass + j][k] = 0;
            }
        }
    }

    // Set up label data 
    Mat labelsMat(class_mun*sample_mun_perclass, class_mun, CV_32FC1,labels);
    cout<<"labelsMat:"<<endl;
    cout<<labelsMat<<endl;
    cout<<"labelsMat——OK！"<<endl;

    //训练代码

    cout<<"training start...."<<endl;
    CvANN_MLP bp;

    // Set up BPNetwork's parameters
    CvANN_MLP_TrainParams params;
    params.train_method=CvANN_MLP_TrainParams::BACKPROP;
    params.bp_dw_scale=0.001;
    params.bp_moment_scale=0.1;
    params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS,10000,0.0001);  //设置结束条件
    //params.train_method=CvANN_MLP_TrainParams::RPROP;
    //params.rp_dw0 = 0.1;
    //params.rp_dw_plus = 1.2;
    //params.rp_dw_minus = 0.5;
    //params.rp_dw_min = FLT_EPSILON;
    //params.rp_dw_max = 50.;

    //Setup the BPNetwork
    Mat layerSizes=(Mat_<int>(1,4) << image_rows*image_cols,int(image_rows*image_cols/2),int(image_rows*image_cols/2),class_mun);
    bp.create(layerSizes,CvANN_MLP::SIGMOID_SYM,1.0,1.0);//CvANN_MLP::SIGMOID_SYM
                                               //CvANN_MLP::GAUSSIAN
                                               //CvANN_MLP::IDENTITY
    cout<<"training...."<<endl;
    bp.train(trainingDataMat, labelsMat, Mat(),Mat(), params);

    bp.save("bpcharModel.xml"); //save classifier
    cout<<"training finish...bpModel1.xml saved "<<endl;

    //测试神经网络
    cout<<"测试："<<endl;

    Mat test_image = imread("25.jpg",CV_LOAD_IMAGE_GRAYSCALE);
    Mat test_temp;
    resize(test_image,test_temp,Size(image_cols,image_rows),(0,0),(0,0),CV_INTER_AREA);//使用象素关系重采样。当图像缩小时候，该方法可以避免波纹出现
    //threshold(test_temp,test_temp,0,255,CV_THRESH_BINARY|CV_THRESH_OTSU);
    Canny(test_temp ,test_temp ,150,100,3,false);
    Mat_<float>sampleMat(1,image_rows*image_cols); 
    for(int i = 0; i<image_rows*image_cols; ++i)  
    {  
        sampleMat.at<float>(0,i) = (float)test_temp.data[i];
       // sampleMat.at<float>(0,i) = (float)test_temp.at<uchar>(i/8,i%8);  //(float)resizeImage.data[k]
    }  

    Mat responseMat;  
    bp.predict(sampleMat,responseMat); 

    float* p=responseMat.ptr<float>(0);  
    float max= -1,min =0;  
    int index = 0;  
    for(int k=0;k<class_mun;++k)  
    {  
        cout<<(float)(*(p+k))<<" ";  
        if(k==class_mun-1)  
            cout<<endl;  
        if((float)(*(p+k))>max)  
        {  
            min = max;  
            max = (float)(*(p+k));  
            index = k;  
        }  
        else  
        {  
            if(min < (float)(*(p+k)))  
                min = (float)(*(p+k));  
        }  
    }  
    //对应上美、丑
    string judge = "";
    if (index==0)
        judge = "美美的！";
    if (index==1)
        judge = "略丑啊！";
    cout<<"识别结果:"<<judge<<endl<<"识别置信度:"<<(((max-min)*100) > 100 ? 100:((max-min)*100))<<endl;

    /*Point maxLoc;
    double maxVal = 0;
    minMaxLoc(responseMat,NULL,&maxVal,NULL,&maxLoc);
    cout<<"识别结果："<<maxLoc.x<<"  置信度:"<<maxVal*100<<"%"<<endl;*/
    imshow("test_image",test_image);  
    imshow("test_temp",test_temp);  
    waitKey(0);

    return 0;
}



//读取目录src下min（number，所有图像）图像提取人脸并保存到srccut目录，
//参数：原图片目录src       剪切图片保存目录dst     读取最大数量number    
void AllImagePro( string src, string dst, static int number )
{
    int count=0;
    string src1 = src;  
    string src1cut = dst;
    HANDLE hFile;
    LPCTSTR lpFileName = StringToWchar(src1+"/"+"*.*"); //指定搜索目录和文件类型，如搜索d盘的音频文件可以是"D:\\\\*.mp3"
    WIN32_FIND_DATA pNextInfo;  //搜索得到的文件信息将储存在pNextInfo中;
    hFile = FindFirstFile(lpFileName,&pNextInfo);//请注意是 &pNextInfo , 不是 pNextInfo;
    if(hFile == INVALID_HANDLE_VALUE)
    {
        //搜索失败
        exit(-1);
    }
    cout<<"文件夹"<<src<<"找到的图片："<<endl;
    do
    {
        if(pNextInfo.cFileName[0] == '.')//过滤.和..
            continue;
        count++;
        printf("%s\\n",WcharToChar(pNextInfo.cFileName));
        Mat img = imread( src1 + "/" + WcharToChar(pNextInfo.cFileName) , 1 );
        detectAndCut( img ,src1cut ,WcharToChar(pNextInfo.cFileName) );
    }while (FindNextFile(hFile,&pNextInfo) && count<number);//如果设置读入的图片数量，则以设置的为准，如果图片不够，则读取文件夹下所有图片
}

//人脸检测
//参数：待检测图像img       保存路径dir     保存文件名name
void detectAndCut( Mat img ,string dir, string filename)
{
   std::vector<Rect> faces;
   Mat img_gray;

   cvtColor( img, img_gray, COLOR_BGR2GRAY );
   equalizeHist( img_gray, img_gray );
   //-- Detect faces
   face_cascade.detectMultiScale( img_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );

   for( size_t i = 0; i < faces.size(); i++ )
    {
      Point rec( faces[i].x, faces[i].y );
      Point rec2( faces[i].x + faces[i].width, faces[i].y + faces[i].height );
      Mat roi_img = img( Range(faces[i].y,faces[i].y + faces[i].height), Range(faces[i].x,faces[i].x + faces[i].width) );
      imwrite( dir+"/"+filename, roi_img );   
   }
}


char* WcharToChar(const wchar_t* wp)  
{  
    char *m_char;
    int len= WideCharToMultiByte(CP_ACP,0,wp,wcslen(wp),NULL,0,NULL,NULL);  
    m_char=new char[len+1];  
    WideCharToMultiByte(CP_ACP,0,wp,wcslen(wp),m_char,len,NULL,NULL);  
    m_char[len]='\\0';  
    return m_char;  
}  

wchar_t* CharToWchar(const char* c)  
{   
    wchar_t *m_wchar;
    int len = MultiByteToWideChar(CP_ACP,0,c,strlen(c),NULL,0);  
    m_wchar=new wchar_t[len+1];  
    MultiByteToWideChar(CP_ACP,0,c,strlen(c),m_wchar,len);  
    m_wchar[len]='\\0';  
    return m_wchar;  
}  

wchar_t* StringToWchar(const string& s)  
{  
    const char* p=s.c_str();  
    return CharToWchar(p);  
}  

string getstring ( const int n )
{
    std::stringstream newstr;
    newstr<<n;
    return newstr.str();
}

美女原图：

NOT美女原图：

进行人脸检测截取后：
美女训练集：

NOT美女训练集：

照片都是百度随便搜的……

测试结果：

识别正确率80%左右
主要原因：
1、面部提取的不是特别准确，感觉略大
2、训练集太少，因为只是为了玩玩，只有16张图做训练集，所以会一定程度上影响测试的准确性
3、图像中脸部有的倾斜太严重，妹子们拍照总喜欢歪着脸，……会影响结果
4、所有人脸测试时均标准化为30*30，略小
5、采用canny算子得到的边缘进行训练，忽略了气色等因素，脸白脸黑其实边缘都差不多