#include <vector>
#include <iostream>
#include <exception>
#include <opencv/cv.h>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/video.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/video/background_segm.hpp>
#include <sys/time.h>
#include "ImageDetectConfig.h"
#include "log_manager.h"


using namespace std;
using namespace cv;

// cast 计算出的偏差值，小于1.0表示比较正常，大于1.0表示存在亮度异常；
// 当cast异常时，da大于0表示过亮，da小于0表示过暗。
void detect_brightness(cv::Mat input_img, float& cast, float& da)
{   
    cv::Mat gray_img;  
    cv::cvtColor(input_img, gray_img, CV_BGR2GRAY);

    float a = 0, Ma = 0;   
    int hist[256] = { 0 };

    for (int i = 0; i < gray_img.rows; i++)   
    {      
        for (int j = 0; j < gray_img.cols; j++) {         
            a += float(gray_img.at<uchar>(i, j) - 128); // 在计算过程中，考虑128为亮度均值点    
            hist[gray_img.at<uchar>(i, j)]++;       
        }    
    }    

    da = a / float(gray_img.total());   
  
    for (int i = 0; i < 256; i++) {       
        Ma += abs(i - 128 - da) * hist[i]; 
    }

    Ma /= float(gray_img.total());   
    cast = abs(da) / abs(Ma);
}

void detect_saturation(cv::Mat input_img, float& satu)
{
    Mat dstImage1;
	cvtColor(input_img, dstImage1, COLOR_RGB2HSV);
	vector<Mat> channels;
	split(dstImage1, channels);//分离HSV
    Scalar mean;  //均值
    Scalar stddev;  //标准差
    meanStdDev( channels[1], mean, stddev );  //计算均值和标准差
    satu = mean.val[0];  
}

int main(int argc, char *argv[])
{
    Mat img = imread(argv[1]);
    float cast, da, satu;
    detect_brightness(img, cast, da);
    detect_saturation(img, satu);
    cout<<cast<<"||"<<da<<"||"<<satu;
    return 0;
}