1. 绘制直方图
api不在做详细介绍,具体看以下代码例子
#include <iostream>
#include<opencv.hpp>
#include<opencv2\highgui\highgui.hpp>
using namespace std;
using namespace cv;
int main()
{
Mat src = imread("src.jpg");
if (src.empty())
{
cout << "could not open file!";
cout << endl;
return -1;
}
imshow("src", src);
//分离
vector<Mat>mv;
split(src, mv);
//1. 计算直方图
int histSize = 256;
Mat b_hist, g_hist, r_hist;
float range[] = { 0,255 };
const float* histRanges = { range };
calcHist(&mv[0], 1, 0, Mat(), b_hist, 1, &histSize, &histRanges, true, false);
calcHist(&mv[1], 1, 0, Mat(), g_hist, 1, &histSize, &histRanges, true, false);
calcHist(&mv[2], 1, 0, Mat(), r_hist, 1, &histSize, &histRanges, true, false);
Mat result = Mat::zeros(Size(600, 400), CV_8UC3);
int margin = 50;
int nm = result.rows - 2 * margin;
normalize(b_hist, b_hist, 0, nm, NORM_MINMAX, -1, Mat());
normalize(g_hist, g_hist, 0, nm, NORM_MINMAX, -1, Mat());
normalize(r_hist, r_hist, 0, nm, NORM_MINMAX, -1, Mat());
float step = 500.0 / 256.0;
for (int i = 0; i < 255; i++)
{
line(result, Point(step * i, 50 + (nm - b_hist.at<float>(i, 0))), Point(step * (i + 1), 50 + (nm - b_hist.at<float>(i + 1, 0))), Scalar(255, 0, 0), 2, 8, 0);
line(result, Point(step * i, 50 + (nm - g_hist.at<float>(i, 0))), Point(step * (i + 1), 50 + (nm - g_hist.at<float>(i + 1, 0))), Scalar(0, 255, 0), 2, 8, 0);
line(result, Point(step * i, 50 + (nm - r_hist.at<float>(i, 0))), Point(step * (i + 1), 50 + (nm - r_hist.at<float>(i + 1, 0))), Scalar(0, 0, 255), 2, 8, 0);
}
imshow("hist-result", result);
waitKey(0);
destroyAllWindows();
return 0;
}
2. 直方图均衡化
作用:它通过重新分布图像的像素值,使得图像的直方图在整个灰度范围内均匀分布。
好处:增加对比度,增加细节,在处理灰色图像的时候经常会用到这个方法来做图像增强
api很简单,下面是个例子。
#include <iostream>
#include<opencv.hpp>
#include<opencv2\highgui\highgui.hpp>
using namespace std;
using namespace cv;
int main()
{
Mat src = imread("src.jpg");
if (src.empty())
{
cout << "could not open file!";
cout << endl;
return -1;
}
imshow("src", src);
Mat gray, dst;
//转成灰度图
cvtColor(src, gray, COLOR_BGR2GRAY);
imshow("gray", gray);
//均衡化
equalizeHist(gray, dst);
imshow("dst", dst);
waitKey(0);
destroyAllWindows();
return 0;
}
结果:可以明显看到图片的细节变多了,图像对比度增加了。