#include "stdafx.h"
#include <cv.h>
#include <cxcore.h>
#include <highgui.h>
#include <stdio.h>
#include <io.h>
#include <iostream>
#include <stdio.h>
#include "GObeautifyhelper.h" //算法库
#include "opencv2/contrib/contrib.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"                                                                      
#include <math.h>
#include <string>
#include <time.h>
using namespace cv;
using namespace std;

int main()
{
                Mat src = imread( "F:\\my\\head_src_2.jpg" );
                imshow( "原始图片" ,src);
                Mat dst=src.clone();
                 ////////////////////////////////////////////////////////////////////////// Face detection in color images
                 //////////////////////////////////////////////////////////////////////////根据高光区域直方图计算进行光线补偿
                 const float thresholdco = 0.05;
                 const int thresholdnum = 100;

                 int histogram[256] = {0};
                 for(int i=0;i<dst.rows;i++)
                {      
                                 for(int j=0;j<dst.cols;j++)
                                {  
                                                 int b = dst.at<Vec3b>(i,j)[0];
                                                 int g = dst.at<Vec3b>(i,j)[1];
                                                 int r = dst.at<Vec3b>(i,j)[2];
                                                 //计算灰度值
                                                 int gray = (r*299+g*587+b*114)/1000;
                                                histogram[gray]++;
                                }
                }

                 int calnum =0;
                 int total = dst.rows * dst.cols ;
                 int num;
                 //下面的循环得到满足系数thresholdco的临界灰度级
                 for(int i =0;i<256;i++)
                {
                                 if((float )calnum/total < thresholdco) //得到前5%的高亮像素。
                                {
                                                calnum+= histogram[255-i];//histogram保存的是某一灰度值的像素个数,calnum是边界灰度之上的像素数
                                                num = i;
                                }
                                 else
                                                 break;
                }
                 int averagegray = 0;
                calnum =0;
                 //得到满足条件的象素总的灰度值
                 for(int i = 255;i>=255-num;i--)
                {
                                averagegray += histogram[i]*i; //总的像素的个数*灰度值
                                calnum += histogram[i]; //总的像素数
                }
                averagegray /=calnum;
                 //得到光线补偿的系数
                 float co = 255.0/(float )averagegray;
 
                 for(int i=0;i<dst.rows;i++)
                {      
                                 for(int j=0;j<dst.cols;j++)
                                {  
                                    dst.at<Vec3b>(i,j)[0]= CLAMP0255(co*dst.at<Vec3b>(i,j)[0]+0.5);
                                                dst.at<Vec3b>(i,j)[1]=CLAMP0255(co*dst.at<Vec3b>(i,j)[1]+0.5);
                                                dst.at<Vec3b>(i,j)[2]=CLAMP0255(co*dst.at<Vec3b>(i,j)[2]+0.5);
                                                
                                }
                }
                imshow( "Face detection in color images" ,dst);


 

                cv::waitKey();
                 return 0;
}