#include"contrast_enhance.h"

Mat CalcGrayHist(const Mat& image) {
	//存储256个灰度级的像素
	Mat	hist_gram = Mat::zeros(Size(256, 1), CV_32SC1);
	//获取图像的行高和列宽
	int rows = image.rows;
	int cols = image.cols;
	//计算每个灰度级的个数
	for (int r = 0; r < rows; r++)
	{
		for (int c = 0; c < cols; c++)
		{
			int index = int(image.at<uchar>(r, c));

			hist_gram.at<int>(0, index) += 1;
		}
	}

	return hist_gram;
}

Mat LinearTransform(const Mat& image, int rtype, double a, double b) {
	Mat Out;
	image.convertTo(Out, rtype, a, b);
	return Out;
}

Mat HistgramNormalization(Mat gray_image)
{	
	//获取输入矩阵的最大值和最小值
	double Imax, Imin;
	minMaxLoc(gray_image, &Imin, &Imax, NULL, NULL);

	// 定义输出矩阵的最大值和最小值
	double Omin = 0, Omax = 255;
	// 确定映射关系函数中的系数，其实就是确定线性变换中的系数a和b
	double a = (Omax - Omin) / (Imax - Imin);
	double b = Omin - a * Imin;
	Mat Out;//输出矩阵
	convertScaleAbs(gray_image, Out, a, b);//进行线性变换
	return Out;
}

Mat GammaTransform(Mat gray_image, double gamma)
{
	//1.归一化
	Mat fI;
	gray_image.convertTo(fI, CV_64F, 1 / 255, 0);
	//2.伽马变换，其实就是幂运算，gamma的区间为（0，1）
	Mat Out;
	pow(fI, gamma, Out);
	//3.再次进行线性变换，转换为0，255之间的像素值
	Out.convertTo(Out, CV_8U, 255, 0);
	return Out;
}

Mat equalHist(Mat image)
{
	//检查图像数据类型是否符合要求，如不符合，则抛出异常
	// CV_Assert的官方简要说明如下
	// Checks a condition at runtime and throws exception if it fails
	CV_Assert(image.type() == CV_8UC1);
	// 灰度图像的宽和高
	int rows = image.rows;
	int cols = image.cols;
	//第一步：计算图像的灰度直方图
	Mat gray_hist = CalcGrayHist(image);
	//第二步：计算累加灰度直方图
	Mat zero_comu_moment = Mat::zeros(Size(256, 1), CV_32SC1); //生成一行256列的零矩阵

	for (int p = 0; p < 256; p++)
	{
		if (p==0)
		{
			zero_comu_moment.at<int>(0, p) = gray_hist.at<int>(0, 0);
		}
		else
		{
			zero_comu_moment.at<int>(0, p) = zero_comu_moment.at<int>(0, p - 1) + gray_hist.at<int>(0, p);
		}
	}

	//第三步：根据累加直方图得到输入灰度级和输出灰度级直方图的映射关系
	Mat output_q = Mat::zeros(Size(256, 1), CV_8UC1);

	float cofficient = 256.0 / (rows * cols);

	for (int p = 0; p < 256; p++)
	{
		float q = cofficient * zero_comu_moment.at<int>(0, p) - 1;
		if (q>=0)
		{
			output_q.at<uchar>(0, p) = uchar(floor(q));
		}
		else
		{
			output_q.at<uchar>(0, p) = 0;
		}
	}
	//第四步：得到直方图均衡化后的图像
	Mat equal_hist_image = Mat::zeros(image.size(), CV_8UC1);

	for (int r = 0; r < rows; r++)
	{
		for (int c = 0; c < cols; c++)
		{
			int	p = image.at<uchar>(r, c);
			//根据原像素值p获得新的像素值q重新赋值给对应位置
			equal_hist_image.at<uchar>(r, c) = output_q.at<uchar>(0, p);
		}
	}
	return equal_hist_image;
}