#include <vector>
#include <iostream>
#include <exception>
#include <opencv2/opencv.hpp>
#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, COLOR_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];  
}

void detect_graydiff(cv::Mat img, float& CON, float& ASM, float& ENT, float& MEAN)
{
	Mat mi;
	int row = img.rows;
	int col = img.cols;
	//CON=i*i*P(I)
	//计算i
	mi.create(row, col, img.type());
	for (int i = 0; i < row-1; i++)
		for (int j = 0; j < col; j++)
		{   
			//cout << "BEF" << int(img.ptr<uchar>(i)[j]) << endl;
			mi.at<Vec3b>(i, j) = img.at<Vec3b>(i + 1, j) - img.at<Vec3b>(i, j);
			//cout << "IMF:" << int(mi.ptr<uchar>(i)[j]) << endl;
		}
	//计算p(I)  差值的概率
	int new_cols = mi.cols;
	int new_rows = mi.rows;
	int gray[256] = { 0 };
	double gray_prob[256] = { 0 };
	int num = 0;// 像素的总个数
	//统计直方图各个像素灰度值
	for (int i = 0; i < mi.rows; i++)
	{
		uchar *p = mi.ptr<uchar>(i);
		for (int j = 0; j < mi.cols; j++)
		{
			int value = p[j];
			gray[value]++;
			num++;
		}
	}
	//计算直方图概率分布
	for (int i = 0; i < 256; i++)
	{
		gray_prob[i] = ((double)gray[i] / num);
	}
	CON = 0.0;
	ASM = 0.0;
	ENT = 0.0;
	MEAN = 0.0;
	double v = 1.0/ 255.0;
	for (int i = 0; i <= 255; i++)
	{
		CON += i*i*gray_prob[i];//计算CON
		ASM += gray_prob[i] * gray_prob[i];//计算角度方向二阶矩
		if (gray_prob[i]!=0)
		ENT += (0-gray_prob[i] * log10(gray_prob[i]));//计算熵
		MEAN += v*i*gray_prob[i];//计算平均值
	}
	return; 
}

void detect_definition(cv::Mat input_img, float& sb, float& lap, float& dev)
{
    Mat imgGray, imgSobel, imgLap, imgDev, meanValueImage;
    cvtColor(input_img, imgGray, CV_BGR2GRAY);
    Sobel(imgGray, imgSobel, CV_16U, 1, 1);
    Laplacian(imgGray, imgLap, CV_16U);
    meanStdDev(imgGray,meanValueImage,imgDev);
    sb = mean(imgSobel)[0];
    lap = mean(imgLap)[0];
    dev = mean(imgDev)[0];
}


int main(int argc, char *argv[])
{
    Mat img = imread(argv[1]);
    float cast, da, satu, CON, ASM, ENT, MEAN, sb, lap, dev;
    Mat img_brightness = img.clone();
    detect_brightness(img_brightness, cast, da);
    Mat img_saturation = img.clone();
    detect_saturation(img_saturation, satu);
    Mat img_graydiff = img.clone();
    detect_graydiff(img_graydiff, CON, ASM, ENT, MEAN);
    Mat img_definition = img.clone();
    detect_definition(img_definition, sb, lap, dev);
    cout<<cast<<"||"<<da<<"||"<<satu<<"||"<<CON<<"||"<<ASM<<"||"<<ENT<<"||"<<MEAN<<"||"<<sb<<"||"<<lap<<"||"<<dev;
    return 0;
}