跟我一起学opencv 第五课之调整图像亮度和对比度

一.调整图像亮度与对比度

1.图像变换

---像素变换-点操作

---邻域操作-区域操作

调整图像亮度和对比度属于像素变换-点操作

公式为：g(i,j) = αf(i,j) + β 其中α>0 ,β是增益变量

处理图像经常会对图像色彩进行增强，这就是改变图像的亮度β和对比度α，

我们看看实例代码：

 1 #include<opencv2\opencv.hpp>
 2 #include<iostream>
 3 
 4 using namespace std;
 5 using namespace cv;
 6 /*图像操作*/
 7 int main(int argc, char **argv)
 8 {
 9     Mat src1 = imread("E:\\vsprom\\learn05\\v15.jpg");
10     
11     if (src1.empty())
12     {
13         cout << "can not load imagefile1...." << endl;
14         return -1;
15     }
16     namedWindow("in1 image win", CV_WINDOW_AUTOSIZE);
17     imshow("in1 image win", src1);
18 
19     int height = src1.rows;
20     int width = src1.cols;
21 
22     Mat dst = Mat::zeros(src1.size(), src1.type());//创建一副与src1同样的图像，并将像素值全部给0
23     float alpha = 1.2;
24     float beta = 30;
25     for (int row = 0; row < height; row++)
26     {
27         for (int col = 0; col < width; col++)
28         {
29             if (src1.channels() == 3)//三通道图像
30             {
31                 float b = src1.at<Vec3b>(row, col)[0];//通道1
32                 float g = src1.at<Vec3b>(row, col)[1];//通道2
33                 float r = src1.at<Vec3b>(row, col)[2];//通道3
34 
35                 dst.at<Vec3b>(row, col)[0] = saturate_cast<uchar>(b*alpha + beta);//使用公式
36                 dst.at<Vec3b>(row, col)[1] = saturate_cast<uchar>(g*alpha + beta);
37                 dst.at<Vec3b>(row, col)[2] = saturate_cast<uchar>(r*alpha + beta);
38 
39             }
40             else if (src1.channels() == 1)//单通道图像
41             {
42                 float v = src1.at<uchar>(row, col);
43                 dst.at<uchar>(row, col) = saturate_cast<uchar>(v*alpha + beta);
44             }
45         }
46     }
47     namedWindow("dst image win", CV_WINDOW_AUTOSIZE);
48     imshow("dst image win", dst);
49 
50 
51     waitKey(0);
52     return 0;
53 }

效果如下此时α=1.2，β=30

效果如下α=1.2，β=100时，此时更亮

效果如下α=5，β=30时,对比更明显

转换图像格式：

src2.convertTo(src1, CV_32F);

代码为：

#include<opencv2\opencv.hpp>
#include<iostream>

using namespace std;
using namespace cv;
/*图像操作*/
int main(int argc, char **argv)
{
    Mat src2 = imread("E:\\vsprom\\learn05\\v15.jpg");
    
    if (src2.empty())
    {
        cout << "can not load imagefile1...." << endl;
        return -1;
    }
    namedWindow("in1 image win", CV_WINDOW_AUTOSIZE);
    imshow("in1 image win", src2);

    Mat src1;
    src2.convertTo(src1, CV_32F);

    int height = src1.rows;
    int width = src1.cols;

    Mat dst = Mat::zeros(src2.size(), src2.type());//创建一副与src1同样的图像，并将像素值全部给0
    float alpha = 1.2;
    float beta = 30;
    for (int row = 0; row < height; row++)
    {
        for (int col = 0; col < width; col++)
        {
            if (src1.channels() == 3)//三通道图像
            {
                float b = src1.at<Vec3f>(row, col)[0];//通道1
                float g = src1.at<Vec3f>(row, col)[1];//通道2
                float r = src1.at<Vec3f>(row, col)[2];//通道3
                //修改像素值
                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 (src1.channels() == 1)//单通道图像
            {
                float v = src1.at<uchar>(row, col);
                dst.at<uchar>(row, col) = saturate_cast<uchar>(v*alpha + beta);
            }
        }
    }
    namedWindow("dst image win", CV_WINDOW_AUTOSIZE);
    imshow("dst image win", dst);


    waitKey(0);
    return 0;
}

效果图：