Changing the contrast and brightness of an image! — OpenCV 2.4.13.4 documentation

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# Changing the contrast and brightness of an image!

## Goal

In this tutorial you will learn how to:

- Access pixel values
- Initialize a matrix with zeros
- Learn what saturate_cast does and why it is useful
- Get some cool info about pixel transformations

## Theory

Note

The explanation below belongs to the book Computer Vision: Algorithms and Applications by Richard Szeliski

### Image Processing

- A general image processing operator is a function that takes one or more input images and produces an output image.
- Image transforms can be seen as:
  - Point operators (pixel transforms)
  - Neighborhood (area-based) operators

#### Pixel Transforms

- In this kind of image processing transform, each output pixel’s value depends on only the corresponding input pixel value (plus, potentially, some globally collected information or parameters).
- Examples of such operators include brightness and contrast adjustments as well as color correction and transformations.

##### Brightness and contrast adjustments

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

  g(x) = \alpha f(x) + \beta

- The parameters \alpha > 0 and \beta 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) = \alpha \cdot f(i,j) + \beta

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

## Code

- The following code performs the operation g(i,j) = \alpha \cdot f(i,j) + \beta :

    #include <cv.h>
    #include <highgui.h>
    #include <iostream>

    using namespace cv;

    double alpha; /**< Simple contrast control */
    int beta;  /**< Simple brightness control */

    int main( int argc, char** argv )
    {
     /// Read image given by user
     Mat image = imread( argv[1] );
     Mat new_image = Mat::zeros( image.size(), image.type() );

     /// Initialize values
     std::cout<<" Basic Linear Transforms "<<std::endl;
     std::cout<<"-------------------------"<<std::endl;
     std::cout<<"* Enter the alpha value [1.0-3.0]: ";std::cin>>alpha;
     std::cout<<"* Enter the beta value [0-100]: "; std::cin>>beta;

     /// Do the operation new_image(i,j) = alpha*image(i,j) + beta
     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 );
                 }
        }
        }

     /// Create Windows
     namedWindow("Original Image", 1);
     namedWindow("New Image", 1);

     /// Show stuff
     imshow("Original Image", image);
     imshow("New Image", new_image);

     /// Wait until user press some key
     waitKey();
     return 0;
    }

## Explanation

1. We begin by creating parameters to save \alpha and \beta to be entered by the user:

       double alpha;
       int beta;

2. We load an image using imread and save it in a Mat object:

       Mat image = imread( argv[1] );

3. Now, since we will make some transformations to this image, we need a new Mat object to store it. Also, we want this to have the following features:

   - Initial pixel values equal to zero
   - Same size and type as the original image

       Mat new_image = Mat::zeros( image.size(), image.type() );

   We observe that Mat::zeros returns a Matlab-style zero initializer based on image.size() and image.type()

4. Now, to perform the operation g(i,j) = \alpha \cdot f(i,j) + \beta 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 < 3; c++ )
                    { new_image.at<Vec3b>(y,x)[c] =
                                saturate_cast<uchar>( alpha*( image.at<Vec3b>(y,x)[c] ) + beta ); }
          }
          }

   Notice the following:

   - 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 R, G or B (0, 1 or 2).
   - Since the operation \alpha \cdot p(i,j) + \beta can give values out of range or not integers (if \alpha is float), we use saturate_cast to make sure the values are valid.

5. Finally, we create windows and show the images, the usual way.

       namedWindow("Original Image", 1);
       namedWindow("New Image", 1);

       imshow("Original Image", image);
       imshow("New Image", new_image);

       waitKey(0);

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 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.

## Result

- Running our code and using \alpha = 2.2 and \beta = 50

      $ ./BasicLinearTransforms lena.jpg
      Basic Linear Transforms
      -------------------------
      * Enter the alpha value [1.0-3.0]: 2.2
      * Enter the beta value [0-100]: 50

- We get this:

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