Image generation technology in C++

With the increasing popularity of computer vision and image processing in various fields, the demand for image generation technology is also getting higher and higher. As a programming language widely used in the fields of image processing and computer vision, C is also widely used in image generation. This article will introduce image generation technologies commonly used in C, including fractal generation, gradient color generation, texture image generation, etc. implemented through code.

1. Fractal generation

Fractal refers to a geometric figure with self-similarity. Its implementation includes recursion and iteration. In C, we can use recursion to achieve fractal generation. Common fractal graphics include Koch snowflakes, triangle fractals, fractal trees, etc.

Take the Koch snowflake as an example. The generation process is to divide the edge into three equal parts, remove the middle part and replace it with two edges. This cycle is recursively generated until the predetermined number of layers is reached. The following is a simple code implementation:

void DrawKoch( int n, POINT a, POINT b ) {
    if (n == 0) {
        line(a.x, a.y, b.x, b.y);
    }
    else
    {
        POINT offset = { (b.x - a.x) / 3, (b.y - a.y) / 3 };
        POINT p1 = { a.x + offset.x, a.y + offset.y };
        POINT p2 = { b.x - offset.x, b.y - offset.y };
        POINT p3 = { (int)(0.5*(a.x + b.x) - sqrt(3) / 6 * (b.y - a.y)), 
                     (int)(0.5*(a.y + b.y) + sqrt(3) / 6 * (b.x - a.x)) };
        DrawKoch(n - 1, a, p1);
        DrawKoch(n - 1, p1, p3);
        DrawKoch(n - 1, p3, p2);
        DrawKoch(n - 1, p2, b);
    }
}

2. Gradient color generation

Gradient color refers to the process of gradient from one color to another. In C, we can generate natural gradient colors by calculating the RGB value of each pixel. Common gradient colors include radial gradients and linear gradients.

Taking linear gradient as an example, we can calculate the RGB value of the color of each pixel according to its position in the image, and fill it in the corresponding pixel position. The following is a simple code implementation:

for (int i = 0; i < height; i++) {
    for (int j = 0; j < width; j++) {
        float ratio_x = (float)j / width;
        float ratio_y = (float)i / height;
        int r = ratio_x * start_r + (1 - ratio_x) * end_r;
        int g = ratio_x * start_g + (1 - ratio_x) * end_g;
        int b = ratio_x * start_b + (1 - ratio_x) * end_b;
        int c = RGB(r, g, b);
        image.setPixel(j, i, c);
    }
}

3. Texture image generation

Texture image refers to pasting various patterns, colors, etc. on the image to obtain good visual effects. In C, we can use different functions to generate various texture images. Common texture images include noise images, wood grain images, paper images, etc.

Take noise images as an example. Noise images refer to images with random properties. They are widely used in text, web page backgrounds, games and other fields. We can use Perlin noise or a simple noise function to generate the RGB values ​​of each pixel of the image, thereby obtaining a noise image with random properties. The following is a simple code implementation:

for (int i = 0; i < height; i++) {
    for (int j = 0; j < width; j++) {
        float ratio_x = (float)j / width;
        float ratio_y = (float)i / height;
        int r = (int)((1.0 - ratio_x) * (1.0 - ratio_y) * 255.0);
        int g = (int)(ratio_x * (1.0 - ratio_y) * 255.0);
        int b = (int)(ratio_x * ratio_y * 255.0);
        int c = RGB(r, g, b);
        image.setPixel(j, i, c);
    }
}

To sum up, the image generation technology in C mainly includes fractal generation, gradient color generation, texture image generation, etc. The application of these technologies can greatly broaden the application scope of image processing and computer vision, and also puts forward higher requirements for developers.

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