图像处理学习笔记之MATLAB中imhist、imadjust、stretchlim函数实现

vector<int> imhist(Mat &srcImage, unsigned int n = 256)
{
	CV_Assert(srcImage.channels() == 1);
	vector<int> hist(n, 0);
	double a = n / 256.0;
	int index = 0;
	int rows = srcImage.rows;
	int cols = srcImage.cols;
	for (int i = 0; i < rows;i++)
	{
		uchar* pdata = srcImage.ptr<uchar>(i);
		for (int j = 0; j < cols;j++)
		{
			index = a*pdata[j];
			++hist[index];
		}
	}
	return hist;
}

void stretchlim(Mat& src, Mat& lowHigh,double tol_low = 0.01, double tol_high = 0.99)
{
	CV_Assert(tol_low <= tol_high);

	int channelNum = src.channels();
	lowHigh.create(channelNum, 2,CV_64F);
	int nbins;
	if (src.depth() == CV_8U)
	{
		nbins = 256;
	}
	else
	{
		nbins = 65536;
	}		
	//通道分离
	vector<Mat> channels;
	split(src, channels);


	for (int i = 0; i < channelNum; i++)
	{
		//获取灰度统计信息
		double low, high;
		auto hist = imhist(channels[i]);
		auto toltalSize = std::accumulate(hist.begin(), hist.end(), 0);
		//得到 >tol_low的分布概率的灰度等级
		for (int j = 0; j < hist.size(); ++j)
		{
			auto sum = std::accumulate(hist.begin(), hist.begin() + j, 0.0);
			if ((sum / toltalSize) > tol_low)  // > tol_low
			{
				low = j / (double)nbins;
				break;
			}
		}
		//得到 >tol_high的分布概率的灰度等级
		for (int k = 0; k < hist.size(); ++k)
		{
			auto sum = std::accumulate(hist.begin(), hist.begin() + k, 0.0);
			if ((sum / toltalSize) >= tol_high) // > tol_high
			{
				high = k / double(nbins);
				break;
			}
		}
		if (low==high)
		{
			lowHigh.ptr<double>(i)[0] = 0;
			lowHigh.ptr<double>(i)[1] = 1;
		}
		else
		{
			lowHigh.ptr<double>(i)[0] = low;
			lowHigh.ptr<double>(i)[1] = high;
		}
	}
}

void imadjust(Mat& src, Mat& dst,Mat& lowHighIn, Mat&lowHighOut, double gamma=1)
{
	CV_Assert(src.data != NULL);

	int chl = src.channels();
	int rowNum = src.rows;
	int colNum = src.cols;

	//通道分离
	vector<Mat> channels;
	split(src, channels);
	
	
	//设置默认值
	if (lowHighIn.data==NULL)
	{
		lowHighIn=Mat::zeros(chl,2,CV_64F);
		for (int i = 0; i < chl; i++)
		{
			lowHighIn.at<double>(i, 1) = 1;
		}
	}
	
	if (lowHighOut.data==NULL)
	{
		lowHighOut = Mat::zeros(chl, 2, CV_64F);
		for (int i = 0; i < chl; i++)
		{
			lowHighOut.at<double>(i, 1) = 1;
		}
	}
	for (int m = 0; m < chl;m++)
	{
		//gamma校正查表
		vector<double> lookuptable(256, 0);
		vector<uchar> img(256,0);
		for (int i = 0; i < 256; i++)
		{
			lookuptable[i] = i / 255.0;
			if (lookuptable[i]<=lowHighIn.at<double>(m,0))
			{
				lookuptable[i] = lowHighIn.at<double>(m, 0);
			}
			if (lookuptable[i] >= lowHighIn.at<double>(m, 1))
			{
				lookuptable[i] = lowHighIn.at<double>(m, 1);
			}
			lookuptable[i] = (lookuptable[i] - lowHighIn.at<double>(m, 0)) / (lowHighIn.at<double>(m, 1) - lowHighIn.at<double>(m, 0));
			lookuptable[i] = pow(lookuptable[i], gamma);
			lookuptable[i] = lookuptable[i] * (lowHighOut.at<double>(m, 1) - lowHighOut.at<double>(m,0))+lowHighOut.at<double>(m,0);
			img[i] = lookuptable[i] * 255;
		}
		for (int j = 0; j < rowNum;j++)
		{
			for (int k = 0; k < colNum; k++)
			{
				channels[m].at<uchar>(j, k) = img[channels[m].at<uchar>(j, k)];
			}
		}
	}
	merge(channels, dst);
}

int main()
{
    Mat srcImage = imread("高圆圆.jpg");
	Mat grayImage, dstImage, lowHigh;
	cvtColor(srcImage, grayImage, CV_RGB2GRAY);
	Mat lh1,lh2;
	stretchlim(grayImage, lh1);
	imadjust(grayImage,dstImage, lh1, lh2,1);
    imshow("处理后的图像", dstImage);
    waitKey(0);
    return 0;
}