How to write a simple image processing program in C++?

In this article, we will learn how to write a simple image processing program in C. We'll cover all the basics from reading an image to applying filters and saving the image.

Before you start writing image processing programs, you need to install the OpenCV library. OpenCV is a popular computer vision library with rich features to help you create high-quality image processing applications.

Step 1: Load Image

To load an image, you need to declare an OpenCV object called Mat. Below is the code to load an image from a file:

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

using namespace cv;

int main(int argc, char** argv){
    Mat image;
    image = imread(argv[1], IMREAD_COLOR);

    if(! image.data ) {
        std::cout <<  "Could not open or find the image" << std::endl;
        return -1;
    }

    namedWindow("Display window", WINDOW_AUTOSIZE);
    imshow("Display window", image);

    waitKey(0);
    return 0;
}

The above code will be used to load an image via OpenCV. The program will first read the filename entered from the command line. If the file is not found, the user is prompted that the file cannot be opened or found.

If the image loads successfully, create a window to display it. Use the imshow function to display the image and use waitKey to wait for user action, such as pressing any key on the keyboard to close the window.

Step 2: Apply Filters

Now that we have the image loaded, we can start applying some filters. The OpenCV library provides many built-in functions to help us apply various filters, such as adding blur effects, edge detection or other operations common in image processing.

The following code will add a Gaussian blur filter to the image:

Mat blurred_image;
GaussianBlur(image, blurred_image, Size(7,7), 0);

namedWindow("Blurred Image", WINDOW_AUTOSIZE);
imshow("Blurred Image", blurred_image);

waitKey(0);

First, we declare a Mat object to store the blurred image. Next, we apply a Gaussian blur using the GaussianBlur function. In the function, the first parameter is the image to be blurred, the second parameter is the Mat object that will store the result, the third parameter is the size of the blur kernel, and the fourth parameter is the standard deviation. You can choose to set it to 0.

Finally, we display the blurred image in a new window.

Step 3: Save the Image

When you have finished processing the image, you may want to save the results to a file. This can be done using the imwrite function. Here is the code example:

imwrite("blur.jpg", blurred_image);

This will save the filtered blur image as blur.jpg.

Full Code Example

#include 
#include 

using namespace cv;

int main(int argc, char** argv){
    Mat image;
    image = imread(argv[1], IMREAD_COLOR);

    if(! image.data ) {
        std::cout <<  "Could not open or find the image" << std::endl;
        return -1;
    }

    namedWindow("Display window", WINDOW_AUTOSIZE);
    imshow("Display window", image);

    Mat blurred_image;
    GaussianBlur(image, blurred_image, Size(7,7), 0);

    namedWindow("Blurred Image", WINDOW_AUTOSIZE);
    imshow("Blurred Image", blurred_image);

    imwrite("blur.jpg", blurred_image);

    waitKey(0);
    return 0;
}

In this article, we learned how to write a simple image processing program using C and the OpenCV library. You can extend it to include more filters such as edge detection, sharpening, etc.

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