The Core Functionality6(Adding (Changing the contrast and brightness of an image! ))

Goal

Initialize a matrix with zeros（零初始化矩阵））
Access pixel values (获取像素)
Learn what cv::saturate_cast does and why it is useful（使用saturate_cast避免超出范围）
Get some cool info about pixel transformations(像素变换？)
Improve the brightness of an image on a practical example（提高图片的亮度）

Theory

来源于《计算机视觉：算法以及应用》

Image Processing

通常是一个或者几个图像产生几个图像
图像变换：
- 点运算
- 邻域运算

Brightness and contrast adjustments

f(x)：source image pixel，g(x):destination image pixel: 公式如下：很容易理解，不过多解释

g (i, j) = α \cdot f (i, j) + β

Code

#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
// we're NOT "using namespace std;" here, to avoid collisions between the beta variable and std::beta in c++17
using std::cin;
using std::cout;
using std::endl;
using namespace cv;
int main( int argc, char** argv )
{
double alpha = 1.0; /*< Simple contrast control */ //gain
int beta = 0; /*< Simple brightness control */ //bias
String imageName("lena.jpg"); // by default
if (argc > 1)
{
imageName = argv[1];
}
Mat image = imread( imageName );
Mat new_image = Mat::zeros( image.size(), image.type() ); //零初始化
cout << " Basic Linear Transforms " << endl;
cout << "-------------------------" << endl;
cout << "* Enter the alpha value [1.0-3.0]: ";
//cin >> alpha;
alpha = 2.2;
cout << "* Enter the beta value [0-100]: ";
//cin >> beta;
beta = 50;
//遍历所有的像素
for( int y = 0; y < image.rows; y++ ) {
for( int x = 0; x < image.cols; x++ ) {
for( int c = 0; c < 3; c++ ) {
new_image.at<Vec3b>(y,x)[c] =
saturate_cast<uchar>( alpha*( image.at<Vec3b>(y,x)[c] ) + beta ); //避免超过范围
}
}
}
namedWindow("Original Image", WINDOW_AUTOSIZE);
namedWindow("New Image", WINDOW_AUTOSIZE);
imshow("Original Image", image);
imshow("New Image", new_image);
waitKey();
return 0;

}

代码没有什么好解释的，挺简单的！

CMakeLists.txt文件

    cmake_minimum_required(VERSION 2.8)
    set(CMAKE_CXX_FLAGS "-std=c++11")
    set(VMAKE_BUILD_TYPE "Debug")
    project( DisplayImage )
    find_package( OpenCV REQUIRED )
    include_directories( ${OpenCV_INCLUDE_DIRS} )
    add_executable( DisplayImage main.cpp )
    target_link_libraries( DisplayImage ${OpenCV_LIBS} )

install(TARGETS DisplayImage RUNTIME DESTINATION bin)

测试结果如下：

Brightness and contrast adjustments

bias可以提高图片的亮度，但同时由于对比度的降低，图片会略模糊(veil)，这时候使用gain>=1解决(deminue)这个问题,也会损失一部分原图像的信息。

Gamma correction

gamma correction是一个非线性变换，会让黑色区域更亮，而较亮区域相对变黑。公式如下和图如下：

O = (I 255) γ \times 255

When γ<1, the original dark regions will be brighter and the histogram will be shifted to the right whereas it will be the opposite with γ>1.

Code

#include <iostream>
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
// we're NOT "using namespace std;" here, to avoid collisions between the beta variable and std::beta in c++17
using namespace cv;

namespace
{
/** Global Variables */
int alpha = 100;
int beta = 100;
int gamma_cor = 100;
Mat img_original, img_corrected, img_gamma_corrected;

void basicLinearTransform(const Mat &img, const double alpha_, const int beta_)
{
Mat res;
img.convertTo(res, -1, alpha_, beta_); //res = alpha*img+bata,其中-1表示尺寸相同
hconcat(img, res, img_corrected);
}

void gammaCorrection(const Mat &img, const double gamma_)
{
CV_Assert(gamma_ >= 0);
//![changing-contrast-brightness-gamma-correction]
Mat lookUpTable(1, 256, CV_8U);
uchar* p = lookUpTable.ptr();
for( int i = 0; i < 256; ++i)
p[i] = saturate_cast<uchar>(pow(i / 255.0, gamma_) * 255.0); //gamma transform table

Mat res = img.clone();
LUT(img, lookUpTable, res);
//![changing-contrast-brightness-gamma-correction]
hconcat(img, res, img_gamma_corrected);
}

void on_linear_transform_alpha_trackbar(int, void *)
{
double alpha_value = alpha / 100.0;
int beta_value = beta - 100;
basicLinearTransform(img_original, alpha_value, beta_value);
}

void on_linear_transform_beta_trackbar(int, void *)
{
double alpha_value = alpha / 100.0;
int beta_value = beta - 100;
basicLinearTransform(img_original, alpha_value, beta_value);
}

void on_gamma_correction_trackbar(int, void *)
{
double gamma_value = gamma_cor / 100.0;
gammaCorrection(img_original, gamma_value);
}
}

int main( int argc, char** argv )
{

String imageName("lena.jpg"); // by default，读取图片
if (argc > 1)
{
imageName = argv[1];
}

img_original = imread( imageName );
img_corrected = Mat(img_original.rows, img_original.cols*2, img_original.type()); //两个矩阵，需要容纳两个图片
img_gamma_corrected = Mat(img_original.rows, img_original.cols*2, img_original.type());

hconcat(img_original, img_original, img_corrected); //第一和第二个生成第三个
hconcat(img_original, img_original, img_gamma_corrected); //地一个和第二个生成第三个

namedWindow("Brightness and contrast adjustments", WINDOW_AUTOSIZE);
namedWindow("Gamma correction", WINDOW_AUTOSIZE);

createTrackbar("Alpha gain (contrast)", "Brightness and contrast adjustments", &alpha, 500, on_linear_transform_alpha_trackbar);
createTrackbar("Beta bias (brightness)", "Brightness and contrast adjustments", &beta, 200, on_linear_transform_beta_trackbar);
createTrackbar("Gamma correction", "Gamma correction", &gamma_cor, 200, on_gamma_correction_trackbar);

while (true)
{
imshow("Brightness and contrast adjustments", img_corrected);
imshow("Gamma correction", img_gamma_corrected);

int c = waitKey(30); //ESC:27
if (c == 27)
break;
}

imwrite("linear_transform_correction.png", img_corrected);
imwrite("gamma_correction.png", img_gamma_corrected);

return 0;
}

CMakeLists.txt文件：

target_link_libraries( DisplayImage ${OpenCV_LIBS} )

install(TARGETS DisplayImage RUNTIME DESTINATION bin)

运行结果如下，且表面：当alpha>=1和bias>=0时，图片亮度增加；当gamma<1时，图片亮度增加！