初识Opencv4.X----图像直方图均衡

//图像直方图均衡
#include <stdio.h>
#include <iostream>
#include <string>
#include <opencv2\opencv.hpp>
using namespace std;
using namespace cv;
void drawHist(Mat &hist, int hist_w, int hist_h, int type, string name);
int main()
{
    
    
	/*
	直方图均衡可以把原本灰度集中的情况变为分散，这样就可以避免图像过暗或者过亮，使得图像的轮廓更加清晰
	*/
	Mat img = imread("gearwheel.jpg", IMREAD_GRAYSCALE);
	//统计0~255各个灰度值的像素个数
	const int channels[1] = {
    
     0 };
	Mat hist;
	const int histSize[1] = {
    
     256 };
	float a[2] = {
    
     0,255 };
	const float * ranges[1] = {
    
     a };
	calcHist(&img, 1, channels, Mat(), hist, 1, histSize, ranges);//得到的hist是一个单通道，width=1，height=256的Mat变量
	//归一化的直方图比较
	drawHist(hist, 512, 400, NORM_INF, "hist_nor");
	Mat img_equ;
	equalizeHist(img, img_equ);
	calcHist(&img_equ, 1, channels, Mat(), hist, 1, histSize, ranges);//得到的hist是一个单通道，width=1，height=256的Mat变量
	drawHist(hist, 512, 400, NORM_INF, "hist_equ_nor");
	//均衡后的灰度图对比
	imshow("img", img);
	imshow("img_equ", img_equ);
	waitKey(0);
	return 0;
}
//画直方图函数，传入参数：hist,图像宽，图像高，归一化方式，窗口名称
void drawHist(Mat &hist, int hist_w, int hist_h,int type, string name)//这个函数适合用NORM_INF方法
{
    
    
	int width = 2;
	Mat histImage = Mat::zeros(hist_h, hist_w, CV_8UC3);
	normalize(hist, hist, 1, 0, type, -1, Mat());
	for (int i = 1; i <= hist.rows; i++)
	{
    
    
		rectangle(histImage, Point(width*(i - 1), hist_h - 1),
			Point(width*i - 1, hist_h - cvRound(hist_h*hist.at<float>(i - 1)) - 1),
			Scalar(255, 255, 255), -1);
	}
	imshow(name, histImage);
}