Goal

In this tutorial you will learn how to:

Access pixel values
Initialize a matrix with zeros
Learn what cv::saturate_cast does and why it is useful
Get some cool info about pixel transformations
Improve the brightness of an image on a practical example

Brightness and contrast adjustments

Two commonly used point processes are multiplication and addition with a constant:

g(x)=αf(x)+β
The parameters α>0 and β are often called the gain and bias parameters; sometimes these parameters are said to control contrast and brightness respectively.
You can think of f(x) as the source image pixels and g(x) as the output image pixels. Then, more conveniently we can write the expression as:

g(i,j)=α⋅f(i,j)+β

where i and j indicates that the pixel is located in the i-th row and j-th column.

Note

Instead of using the for loops to access each pixel, we could have simply used this command:

image.convertTo(new_image, -1, alpha, beta);

where cv::Mat::convertTo would effectively perform *new_image = a*image + beta*. However, we wanted to show you how to access each pixel. In any case, both methods give the same result but convertTo is more optimized and works a lot faster.

to perform the operation g(i,j)=α⋅f(i,j)+β we will access to each pixel in image. Since we are operating with BGR images, we will have three values per pixel (B, G and R), so we will also access them separately. Here is the piece of code:

for( int y = 0; y < image.rows; y++ ) {for( int x = 0; x < image.cols; x++ ) {for( int c = 0; c < image.channels(); c++ ) {new_image.at<Vec3b>(y,x)[c] =saturate_cast<uchar>( alpha*image.at<Vec3b>(y,x)[c] + beta );}}}

Notice the following (C++ code only):

To access each pixel in the images we are using this syntax: image.at<Vec3b>(y,x)[c] where y is the row, x is the column and c is B, G or R (0, 1 or 2).
Since the operation α⋅p(i,j)+β can give values out of range or not integers (if α is float), we use cv::saturate_cast to make sure the values are valid.

C++

#include <iostream>
#include <opencv.hpp>using namespace cv;// 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;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_);hconcat(img, res, img_corrected);imshow("Brightness and contrast adjustments", 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);Mat res = img.clone();LUT(img, lookUpTable, res);//! [changing-contrast-brightness-gamma-correction]hconcat(img, res, img_gamma_corrected);imshow("Gamma correction", img_gamma_corrected);}void alpha_beta_value(const Mat &image, const double alpha_, const int beta_){//Mat new_image = Mat(img_original.rows, img_original.cols, img_original.type());Mat new_image = Mat(img_original.size(), img_original.type());for (int y = 0; y < image.rows; y++) {for (int x = 0; x < image.cols; x++) {for (int c = 0; c < image.channels(); c++) {new_image.at<Vec3b>(y, x)[c] =saturate_cast<uchar>(alpha_*image.at<Vec3b>(y, x)[c] + beta_);}}}hconcat(image, new_image, img_corrected);imshow("Brightness and contrast adjustments", img_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);alpha_beta_value(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);alpha_beta_value(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)
{img_original = imread("Grass.jpg", IMREAD_COLOR);//Mat image_grass = imread("Grass.jpg", IMREAD_GRAYSCALE);if (img_original.empty()){printf("No data\n");return -1;}//pyrDown(img_original, img_original, Size(img_original.cols / 3, img_original.rows / 3));pyrDown(img_original, img_original);resize(img_original, img_original, Size(img_original.cols / 1.5, img_original.rows / 1.5), 0, 0, 0);img_corrected = Mat(img_original.rows, img_original.cols * 2, img_original.type());img_gamma_corrected = Mat(img_original.rows, img_original.cols * 2, img_original.type());hconcat(img_original, img_original, img_corrected);hconcat(img_original, img_original, img_gamma_corrected);// 类似于hconcat()水平连接两张图， 也会有垂直连接的函数//Mat img_vtest = Mat(img_original.rows * 2, img_original.cols, img_original.type());//vconcat(img_original, img_original, img_vtest);//imshow("image1", img_corrected);//imshow("image2", img_vtest);//waitKey();namedWindow("Brightness and contrast adjustments");namedWindow("Gamma correction");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);on_linear_transform_alpha_trackbar(0, 0);on_gamma_correction_trackbar(0, 0);waitKey();imwrite("linear_transform_correction.png", img_corrected);imwrite("gamma_correction.png", img_gamma_corrected);return 0;
}

Python

https://github.com/opencv/opencv/blob/master/samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/BasicLinearTransforms.py

from __future__ import print_function
from builtins import input
import cv2 as cv
import numpy as np
import argparse# Read image given by user
## [basic-linear-transform-load]
parser = argparse.ArgumentParser(description='Code for Changing the contrast and brightness of an image! tutorial.')
parser.add_argument('--input', help='Path to input image.', default='lena.jpg')
args = parser.parse_args()image = cv.imread(cv.samples.findFile(args.input))
if image is None:print('Could not open or find the image: ', args.input)exit(0)
## [basic-linear-transform-load]## [basic-linear-transform-output]
new_image = np.zeros(image.shape, image.dtype)
## [basic-linear-transform-output]## [basic-linear-transform-parameters]
alpha = 1.0 # Simple contrast control
beta = 0    # Simple brightness control# Initialize values
print(' Basic Linear Transforms ')
print('-------------------------')
try:alpha = float(input('* Enter the alpha value [1.0-3.0]: '))beta = int(input('* Enter the beta value [0-100]: '))
except ValueError:print('Error, not a number')
## [basic-linear-transform-parameters]# Do the operation new_image(i,j) = alpha*image(i,j) + beta
# Instead of these 'for' loops we could have used simply:
# new_image = cv.convertScaleAbs(image, alpha=alpha, beta=beta)
# but we wanted to show you how to access the pixels :)
## [basic-linear-transform-operation]
for y in range(image.shape[0]):for x in range(image.shape[1]):for c in range(image.shape[2]):new_image[y,x,c] = np.clip(alpha*image[y,x,c] + beta, 0, 255)
## [basic-linear-transform-operation]## [basic-linear-transform-display]
# Show stuff
cv.imshow('Original Image', image)
cv.imshow('New Image', new_image)# Wait until user press some key
cv.waitKey()

Practical example

https://docs.opencv.org/4.3.0/d3/dc1/tutorial_basic_linear_transform.html

Additional resources

Gamma correction in graphics rendering

https://learnopengl.com/Advanced-Lighting/Gamma-Correction

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