灰度差分统计法计算图像的对比度

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#include<opencv2/opencv.hpp>
#include<opencv2/core/core.hpp>
#include<iostream>
#include<math.h>
using namespace std;
using namespace cv;
/*绘制直方图函数*/
void show_his(Mat &Image,Mat &HI)
{
	int channels = 0;    //计算图像的通道
	MatND disHist;        //配置输出的结果
	int histSize[] = { 256 };//将数值分组，每个灰度范围一组
	float midRanges[] = { 0, 256 };  //确定每个维度的取值范围
	const float *ranges[] = { midRanges };
	//调用calcHist函数，将直方图的信息存储到disHist
	calcHist(&Image, 1, &channels, Mat(), disHist, 1, histSize, ranges, true, false);
	Mat drawImage = Mat::zeros(Size(256, 256), CV_8SC3);//黑底的图像
	//计算直方图后像素的最大个数
	double g_dHistMaxValue;
	minMaxLoc(disHist, 0, &g_dHistMaxValue, 0, 0);
	for (int i = 0; i < 256; i++)  //遍历直方图数据
	{
		int value = cvRound(disHist.at<float>(i) * 256 * 0.9 / g_dHistMaxValue);

		line(drawImage, Point(i, drawImage.rows - 1), Point(i, drawImage.rows - 1 - value), Scalar(255, 255, 255));
		//参数   要绘制的线段的图像  线段的起点      线段的终点                               线段的颜色
	}
	 HI = drawImage.clone();
}
int main()
{
	Mat img = imread("lena.bmp");
	imshow("Lena", img);
	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);
	}
	double CON = 0;
	double ASM = 0;
	double ENT = 0;
	double MEAN = 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
	}
	Mat HI;
	show_his(mi,HI);
	imshow("差值i的直方图", HI);
	cout << "灰度差分统计法求得的对比度为:" << CON << endl;
	cout << "灰度差分统计法求得的角度方向二阶矩为" << ASM << endl;
	cout << "灰度差分统计法求得的熵" << ENT << endl;
	cout << "灰度差分统计法求得的平均值" << MEAN << endl;
	waitKey(0);
	return 0;
}