理论公式
![](https://img.haomeiwen.com/i11388737/6d1202a596e00029.png)
f(i,j)是输入图像的像素点,g(i,j)是输出图像的像素点,
亮度和对比度属于像素变换
#include "pch.h"
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
//图像亮度,对比度
int main(int argc, char** argv) {
Mat src, dst;
src = imread("D:/test.jpg");
if (!src.data) {
printf("could not load image...\n");
return -1;
}
char input_win[] = "原图";
//cvtColor(src, src, CV_BGR2GRAY);//灰度图
namedWindow(input_win, CV_WINDOW_AUTOSIZE);
imshow(input_win, src);
// contrast and brigthtness changes | 对比度和亮度变化
int height = src.rows;
int width = src.cols;
dst = Mat::zeros(src.size(), src.type());
float alpha = 2.1;//对比度
float beta = 30;//增量-亮度
src.convertTo(src, CV_32F);//将CV_8UC1/3转成Vec3f 精度更高点
for (int row = 0; row < height; row++) {
for (int col = 0; col < width; col++) {
if (src.channels() == 3) {
float b = src.at<Vec3f>(row, col)[0];// blue
float g = src.at<Vec3f>(row, col)[1]; // green
float r = src.at<Vec3f>(row, col)[2]; // red
// output
dst.at<Vec3b>(row, col)[0] = saturate_cast<uchar>(b*alpha + beta);
dst.at<Vec3b>(row, col)[1] = saturate_cast<uchar>(g*alpha + beta);
dst.at<Vec3b>(row, col)[2] = saturate_cast<uchar>(r*alpha + beta);
}
else if (src.channels() == 1) {
float v = src.at<uchar>(row, col);
dst.at<uchar>(row, col) = saturate_cast<uchar>(v*alpha + beta);
}
}
}
char output_title[] = "test6";
namedWindow(output_title, CV_WINDOW_AUTOSIZE);
imshow(output_title, dst);
waitKey(0);
return 0;
}
![](https://img.haomeiwen.com/i11388737/9496a3bff776b349.png)
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