直方图均衡

Content

  • 计算灰度图像的归一化直方图

    具体内容:利用OpenCV对图像像素进行操作，计算归一化直方图。并在窗口中以图形的方式显示出来。

  • 灰度图像直方图均衡处理

    具体内容:通过计算归一化直方图，设计算法实现直方图均衡化处理。

  • 彩色图像直方图均衡处理

    具体内容:在灰度图像直方图均衡处理的基础上实现彩色直方图均衡处理。

Code

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#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <vector>
using namespace cv;
using namespace std;

// 计算归一化直方图
vector<float> computeNormalizedHistogram(const Mat& image)
{
    vector<float> histogram(256, 0);
	for (int r = 0; r < image.rows; r++)
	{
		for (int c = 0; c < image.cols; c++)
		{
			histogram[image.at<uchar>(r, c)]++;
		}
	}
	// 归一化直方图
	for (int i = 0; i < 256; i++)
	{
		histogram[i] /= (image.rows * image.cols);
	}
	return histogram;
}

// 显示直方图
void showHistogram(const vector<float>& histogram, string name)
{
	//CV_8UC3 8位无符号整型，3通道 
	//Scalar(0, 0, 0) 初始化黑色
	Mat histImage(256, 256, CV_8UC3, Scalar(0, 0, 0));
	// 每一列宽度
	int histWidth = histImage.cols / 256;
	// 画出直方图
	for (int i = 0; i < 256; i++) {
		int histHeight = static_cast<int>(histogram[i] * histImage.rows*10);
		rectangle(histImage, Point(i * histWidth, histImage.rows), Point((i + 1) * histWidth, histImage.rows - histHeight), Scalar(0, 0, 255), -1);
	}

	// 显示直方图
	namedWindow(name, WINDOW_AUTOSIZE);
	imshow(name, histImage);
	imwrite(name + ".jpg", histImage);
}

// 计算累计直方图
vector<float> computecumulativeHistogram(const vector<float>& histogram)
{
	vector<float> equalizedHistogram(256, 0);
	// 计算累计直方图
	vector<float> cumulativeHistogram(256, 0);
	cumulativeHistogram[0] = histogram[0];
	for (int i = 1; i < 256; i++)
	{
		cumulativeHistogram[i] = cumulativeHistogram[i - 1] + histogram[i];
	}
	return cumulativeHistogram;
}

// 灰度图像直方图均衡化处理
Mat equalizeHistogram(const Mat& image)
{
	vector<float> hist = computeNormalizedHistogram(image);
	vector<float> cumulativeHist = computecumulativeHistogram(hist);
	Mat equalizedImage = image.clone();
	for (int y = 0; y < image.rows; y++) {
		for (int x = 0; x < image.cols; x++) {
			int intensity = image.at<uchar>(y, x);
			equalizedImage.at<uchar>(y, x) = cvRound(255 * cumulativeHist[intensity]);
		}
	}
	return equalizedImage;
}

// 彩色图像直方图均衡化处理
Mat equalizedColorHistogram(const Mat& image)
{
	Mat bgrChannels[3];
	Mat equalizedBGRChannels[3];
	split(image, bgrChannels);
	for (int i = 0; i < 3; i++)
	{
		equalizedBGRChannels[i] = equalizeHistogram(bgrChannels[i]);
	}
	Mat equalizedColorImage;
	merge(equalizedBGRChannels, 3, equalizedColorImage);
	return equalizedColorImage;
}

int main()
{
	Mat myImage = imread("wallhaven.jpg");
	imshow("MyImage", myImage);
	Mat grayImage;
	cvtColor(myImage, grayImage, COLOR_BGR2GRAY); // 转换为灰度图像
	imshow("GrayImage", grayImage);
	imwrite("GrayImage.jpg", grayImage);

	// 归一化直方图 0-1
	vector<float> hist = computeNormalizedHistogram(grayImage);
	showHistogram(hist,"Hist");

	// 灰度图像直方图均衡化处理
	Mat equalizedImage = equalizeHistogram(grayImage);
	imshow("EqualizedImage", equalizedImage);
	imwrite("EqualizedImage.jpg", equalizedImage);
	// 显示直方图
	vector<float> equalizedHist = computeNormalizedHistogram(equalizedImage);
	showHistogram(equalizedHist, "equalizedHist");

	// opencv直方图均衡化 用与对比正确性
	//Mat equalizedImageCV;
	//equalizeHist(grayImage, equalizedImageCV);
	//imshow("OpenCV's Equalized Image", equalizedImage);

	// 彩色图像直方图均衡化处理
	Mat equalizedColorImage = equalizedColorHistogram(myImage);
	imshow("EqualizedColorImage", equalizedColorImage);
	imwrite("EqualizedColorImage.jpg", equalizedColorImage);

	//opencv彩色图像直方图均衡化处理 用于对比正确性
	//Mat bgrChannelsCV[3];
	//split(myImage, bgrChannelsCV);
	//for (int i = 0; i < 3; i++)
	//{
	//	equalizeHist(bgrChannelsCV[i], bgrChannelsCV[i]);
	//}
	//Mat equalizedColorImageCV;
	//merge(bgrChannelsCV, 3, equalizedColorImageCV);
	//imshow("OpenCV's Equalized Color Image", equalizedColorImageCV);

	waitKey(0);
	return 0;
}

Output

灰图 彩图
GrayImage wallhaven
灰度图像直方图均衡处理 彩色图像直方图均衡处理
EqualizedImage EqualizedColorImage
直方图 均衡化直方图
Hist equalizedHist