皮肤检测及对检测到皮肤单独校色 （基于自动白平衡）

原理见前几篇博客，修改修改可以做美颜的程序直接贴代码：

/*对照片中的皮肤单独进行较色，然后塞进原始图片作为输出*/
/*包含皮肤的检测、皮肤的校正两步*/
/*单张照片测试效果不错，但对于一百多张照片，结果还是有偏差，这种单独校正然后再用于贴图的方法 行不通*/
/*时间:2015.8.24*/
#include <opencv2/core/core.hpp>  
#include <opencv2/highgui/highgui.hpp>  
#include <opencv2/face.hpp>
#include <opencv2/imgproc/imgproc.hpp>  
#include <iostream>  
#include <vector>  
  
using namespace std;  
using namespace cv;  
double baidianave(Mat frame,int n)
{  
    int a[256];
    for (int i=0;i<256;i++)
    {
        a[i]=0;
    }
    double sum=0;
    double ave;
    for (int i=0;i<n;i++)
    {
        int d=frame.at<double>(0,i);
        a[d]++;
    }
    int n0=255;
    for (int k=255;k>0;k--)
    {
        sum+=a[k];
        if (sum>frame.rows*frame.cols/25)
        {
            break;
        }
        n0--;
    }
    sum=0;
    for (int i=n0;i<256;i++)
    {
        sum+=a[i]*i;
    }
    ave=sum/(frame.rows*frame.cols/25);
    return ave;
}
double baidianave(Mat frame)
{ 
    int a[256];
//cvZero(a);
    for (int i=0;i<256;i++)
    {
        a[i]=0;
    }
    double sum=0;
    double ave;
    for (int i=0;i<frame.rows;i++)
    {
        for (int j=0;j<frame.cols;j++)
        {
            int d=(int)frame.at<uchar>(i,j);
            a[d]++;
        }
    }

    int n0=255;
    for (int k=255;k>0;k--)
    {
        sum+=a[k];
        if (sum>frame.rows*frame.cols/25)
        {
            break;
        }
        n0--;
    }
    sum=0;
    for (int i=n0;i<256;i++)
    {
        sum+=a[i]*i;
    }
    ave=sum/(frame.rows*frame.cols/25);
    return ave;

}

Mat input_image;  
Mat output_mask;  
Mat output_image;  
Mat mask;  
  
int main(int argc,char *argv[])  
{  
    if (2 != argc) 
    {
        cout << "Please enter the image list!" <<endl;
        return -1;
    }
    vector<string>  file_names;
    FILE *file_list =  fopen(argv[1],"r");
    char buf[255];
    memset(&buf,0,sizeof(buf));

    while(fgets(buf,255,file_list))
    {
        if(buf[strlen(buf)-1] == '\n') 
            buf[strlen(buf)-1] = '\0';
        file_names.push_back(string(buf));

    }
    
    fclose(file_list);
    int count = file_names.size();
    
    Mat skinCrCbHist = Mat::zeros(Size(256, 256), CV_8UC1);  
    ellipse(skinCrCbHist, Point(113, 155.6), Size(25,12), -20, 0.0, 360.0, Scalar(255, 255, 255), -1);  
  
    Mat element = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1) );  
    for(int  i=0; i<count; i++)
    {  
        string img_nm = file_names[i];
        string img_mask = "mask" + img_nm;
        int pos = img_nm.rfind('.');
        string img_fmt = img_nm.substr(pos+1);
        if("jpg" != img_fmt)
        {
          cout << "Unknown format: " << img_fmt << endl;
          continue;
        }
        input_image=imread(img_nm,1);  
        if(input_image.empty())  
            return 0;  
  
        Mat ycrcb_image;  
        output_mask = Mat::zeros(input_image.size(), CV_8UC1);  
        cvtColor(input_image, ycrcb_image, CV_BGR2YCrCb); 
        CvScalar s;
        s.val[0]=0;
        s.val[1]=0;
        s.val[2]=255;
        for(int i = 0; i < input_image.rows; i++) 
        {  
            uchar* p = (uchar*)output_mask.ptr<uchar>(i);  
            Vec3b* ycrcb = (Vec3b*)ycrcb_image.ptr<Vec3b>(i);  
            for(int j = 0; j < input_image.cols; j++)  
            {  
                if(skinCrCbHist.at<uchar>(ycrcb[j][1], ycrcb[j][2]) > 0)  
                {
                    // input_image.at<Vec3b>(i,j)[2]=255;
                    p[j] = 255;  
                }
            }  
        }     
        // imwrite("test.jpg",input_image);
        morphologyEx(output_mask,output_mask,MORPH_CLOSE,element);   
        vector< vector<Point> > contours;   
        vector< vector<Point> > filterContours; 
        vector< Vec4i > hierarchy;   
        contours.clear();    
        hierarchy.clear();   
        filterContours.clear();  
  
        findContours(output_mask, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);    
        for (size_t i = 0; i < contours.size(); i++)   
        {  
             if (fabs(contourArea(Mat(contours[i]))) > 2000&&fabs(arcLength(Mat(contours[i]),true))>500) 
                 filterContours.push_back(contours[i]);  
        }  
        output_mask.setTo(0);  
        drawContours(output_mask, filterContours, -1, Scalar(255,0,0), CV_FILLED);     
        input_image.copyTo(output_image, output_mask);  
        Mat tempimage=Mat::zeros(input_image.size(), CV_8UC3); 
        threshold(output_mask,output_mask,20, 255, THRESH_BINARY);
        cvtColor(output_mask,output_mask,CV_GRAY2BGR);



        Mat frame=Mat::zeros(input_image.size(), CV_8UC3); 
        output_image.copyTo(frame);
        // imshow("frame",frame);
        // waitKey(0);
        //cout<<frame.rows<<"  "<<frame.cols<<endl;
        // cvShowImage("处理前图像",frame);
        int heightyiban=frame.rows;
        int widthyiban=frame.cols;
        double Ysum=0;//Y的总和
        double Cbsum[4]={0,0,0.0};//图像分成四部分，每部分Cb的总和
        double Crsum[4]={0,0,0,0};//图像分成四部分，每部分Cr的总和
        double Mb[4],Db[4];//图像分成四部分，每部分Cb的均值和均方差
        double Mr[4],Dr[3];//Cr的均值和均方差  
        Mat imageYCrCb =  Mat::zeros(frame.size(), CV_8UC3);
        Mat imageCb = Mat::zeros(frame.size(), CV_8UC1);
        Mat imageCr = Mat::zeros(frame.size(), CV_8UC1);
        Mat imageY = Mat::zeros(frame.size(), CV_8UC1);
        // imageYCrCb = cvCreateImage(cvGetSize(frame),8,3);   
        // imageCb = cvCreateImage(cvGetSize(frame),8,1);  
        // imageCr = cvCreateImage(cvGetSize(frame),8,1); 
        // imageY = cvCreateImage(cvGetSize(frame),8,1); 
        cvtColor(frame,imageYCrCb,CV_BGR2YCrCb); 

        std::vector<cv::Mat>ybr(imageYCrCb.channels());
        split(imageYCrCb,ybr);
        // namedWindow("test",0);
        // imshow("test",ybr[2]);
        // waitKey(0);
        //分成三个通道，，，
        // imageY,imageCr,imageCb

        Mat imageb=Mat::zeros(frame.size(), CV_8UC1);
        Mat imagec=Mat::zeros(frame.size(), CV_8UC1);
        ybr[1].copyTo(imageb);
        ybr[2].copyTo(imagec);
        Mat  savg,sfangcha;//全局scalar 变量用来放平均值和方差
        meanStdDev(ybr[2],savg,sfangcha);
        // cvAvgSdv(imageb,&savg,&sfangcha,NULL);
        // cout<<savg.at<double>(0)<<endl;
        // cout<<sfangcha.at<double>(0)<<endl;
        Mb[0]=savg.at<double>(0);
         cout<<"Mb:  "<<Mb[0]<<endl;
        Db[0]=sfangcha.at<double>(0);//求出第一部分cb的均值和均方差
         cout<<"Db:  "<<Db[0]<<endl;
        // cvAvgSdv(imagec,&savg,&sfangcha,NULL);
        meanStdDev(ybr[1],savg,sfangcha);
        Mr[0]=savg.at<double>(0);
        cout<<"Mr:  "<<Mr[0]<<endl;
        Dr[0]=sfangcha.at<double>(0);;//求出第一部分cr的均值和均方差
         cout<<"Dr:  "<<Dr[0]<<endl;
        double b[4],c[4];
        for (int i=0;i<1;i++)
        {
            if (Mb[i]<0)//计算mb+db*sign（mb）
            { 
                b[i]=Mb[i]+Db[i]*(-1);
            }
            else
                b[i]=Mb[i]+Db[i];
        }
        for (int i=0;i<4;i++)
        {
            if (Mr[i]<0)//计算1.5*mr+dr*sign（mb）；
            {
                c[i]=1.5*Mr[i]+Dr[i]*(-1);
            }
            else
                c[i]=1.5*Mr[i]+Dr[i];
        }


        double Ymax=baidianave(ybr[0]);
        //下面是对第一部分进行白点的选择
        Mat Bbaidian=Mat::zeros(1,6000000,CV_64FC1);
        Mat Gbaidian=Mat::zeros(1,6000000,CV_64FC1);
        Mat Rbaidian=Mat::zeros(1,6000000,CV_64FC1);

        //CvScalar s1;
        int n1=0;
        cout<<"b[0]:   "<<b[0]<<"  c[0]:  "<<c[0]<<endl;
        for (int i=0;i<heightyiban;i++)
        {
            for (int j=0;j<widthyiban;j++)
            {
                 // input_image.at<Vec3b>(i,j)[2]=255
                if (((ybr[2].at<uchar>(i,j)-b[0])<(1.5*Db[0]))&&((ybr[1].at<uchar>(i,j)-c[0])<(1.5*Dr[0])))
                {
            
                    double d1=frame.at<Vec3b>(i,j)[0];
                    Bbaidian.at<double>(0,n1)=d1;
                    
                    double d2=frame.at<Vec3b>(i,j)[1];
                    Gbaidian.at<double>(0,n1)=d2;
                
                    double d3=frame.at<Vec3b>(i,j)[2];
                    Rbaidian.at<double>(0,n1)=d3;
                    n1++;
                }
            }
        }

        // cout<<"n1:  "<<n1<<endl;
        double Bave1=baidianave(Bbaidian,n1);
        double Gave1=baidianave(Gbaidian,n1);
        double Rave1=baidianave(Rbaidian,n1);
        cout<<"Bave1:  "<<Bave1<<"    Gave1:  "<<Gave1<<"      Rave1:   "<<Rave1<<"  Ymax:  "<<Ymax<<endl;
        Ymax=Ymax;
        double Bgain1=Ymax/(Bave1);
        double Ggain1=Ymax/(Gave1);
        double Rgain1=Ymax/(Rave1);
    
        // CvScalar s1;
        // cout<<Bgain1<<"  "<<Ggain1<<"  "<<Rgain1<<endl; 
        // int count_out=0;
        for (int i=0;i<heightyiban;i++)
        {
            for (int j=0;j<widthyiban;j++)
            {
                int tb=Bgain1*frame.at<Vec3b>(i,j)[0];
                int tg=Ggain1*frame.at<Vec3b>(i,j)[1];
                int tr=Rgain1*frame.at<Vec3b>(i,j)[2];
                if (tb>255)
                {
                    tb=255;
                    // count_out++;
                }
                if (tg>255)
                {
                    tg=255;
                    // count_out++;
                }
                if (tr>255)
                {
                    tr=255;
                    // count_out++;
                }
                frame.at<Vec3b>(i,j)[0]=tb;
                frame.at<Vec3b>(i,j)[1]=tg;
                frame.at<Vec3b>(i,j)[2]=tr;
            }
        }
        // cout<<count_out<<endl;
         imwrite("frame.jpg",frame);        
        // cout<<"Finish!"<<endl;
        imwrite("img_nm1.jpg",input_image);     
        for(int i = 0; i < input_image.rows; i++) 
        {  
            uchar* p = (uchar*)output_mask.ptr<uchar>(i);  
            uchar* p2 = (uchar*)input_image.ptr<uchar>(i);  
            uchar* p3 = (uchar*)frame.ptr<uchar>(i);  
            for(int j = 0; j < 3*input_image.cols; )  
            {  
                if(p[j] != 0)  
                {
                    // cout<<"p:  "<<i<<"   "<<j<<"  "<<float(p[j])<<endl;
                    // input_image.at<Vec3b>(i,j)[2]=255;
                    p2[j]=0;
                    p2[j] = p3[j++];  
                    // cout<<"B:   "<<int (p2[j-1])<<"  ";
                    p2[j]=0;
                    p2[j] = p3[j++];  
                    // cout<<"G:  "<<int (p2[j-1])<<"  ";
                    p2[j]=0;
                    p2[j] = p3[j++];  
                    // cout<<"R:  "<<int (p2[j-1])<<endl;;
                     // cout<<"p2:  "<<i<<"   "<<j<<"  "<<float(p2[j])<<endl;
                }
                else
                    j++;
            }  
        }
        // Mat tempimage=Mat::zeros(input_image.size(), CV_8UC3); 
        // cvtColor(output_mask,tempimage,CV_GRAY2BGR);
        // imwrite("output_mask.jpg",output_mask); 
        // imwrite("tempimage.jpg",tempimage);     
        imwrite(img_nm,input_image);     
        imwrite(img_mask,output_image);
        // imwrite(img_mask,output_mask);
        // namedWindow("input image",0);
        // // namedWindow("output mask",0);
        // namedWindow("output image",0);
        // imshow("input image", input_image);  
        // imshow("output image", output_image);  
        cout<<"Finish!"<<endl;
        // output_image.setTo(0);  
        // waitKey(0);
        
    }  
    return 0;  
}