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
#include
#include
#include
#include
#include
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(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(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!" < 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(i);
Vec3b* ycrcb = (Vec3b*)ycrcb_image.ptr(i);
for(int j = 0; j < input_image.cols; j++)
{
if(skinCrCbHist.at(ycrcb[j][1], ycrcb[j][2]) > 0)
{
// input_image.at(i,j)[2]=255;
p[j] = 255;
}
}
}
// imwrite("test.jpg",input_image);
morphologyEx(output_mask,output_mask,MORPH_CLOSE,element);
vector< vector > contours;
vector< vector > 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<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<(0)<(0)<(0);
cout<<"Mb: "<(0);//求出第一部分cb的均值和均方差
cout<<"Db: "<(0);
cout<<"Mr: "<(0);;//求出第一部分cr的均值和均方差
cout<<"Dr: "<(i,j)[2]=255
if (((ybr[2].at(i,j)-b[0])<(1.5*Db[0]))&&((ybr[1].at(i,j)-c[0])<(1.5*Dr[0])))
{
double d1=frame.at(i,j)[0];
Bbaidian.at(0,n1)=d1;
double d2=frame.at(i,j)[1];
Gbaidian.at(0,n1)=d2;
double d3=frame.at(i,j)[2];
Rbaidian.at(0,n1)=d3;
n1++;
}
}
}
// cout<<"n1: "<(i,j)[0];
int tg=Ggain1*frame.at(i,j)[1];
int tr=Rgain1*frame.at(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(i,j)[0]=tb;
frame.at(i,j)[1]=tg;
frame.at(i,j)[2]=tr;
}
}
// cout<(i);
uchar* p2 = (uchar*)input_image.ptr(i);
uchar* p3 = (uchar*)frame.ptr(i);
for(int j = 0; j < 3*input_image.cols; )
{
if(p[j] != 0)
{
// cout<<"p: "<(i,j)[2]=255;
p2[j]=0;
p2[j] = p3[j++];
// cout<<"B: "<