How to Convert Images to ASCII Art Using C : Intensity-Based vs. Character Fitting?

Image to ASCII Art Conversion using C

This approach uses mono-spaced fonts and can handle grayscale images.

Pixel/Area Intensity Based Conversion (Shading)

• Divide the image into pixels or rectangular areas (dots).
• Compute the average gray scale intensity for each dot.
• Replace each dot with a character having the closest intensity.
• Use a character map to associate characters with intensities.
• The resulting ASCII art maintains the aspect ratio and intensity distribution of the original image.

Character Fitting (Hybrid Approach)

This method tries to match areas in the image with characters not only based on intensity but also on shape.

• Divide the image into rectangular areas with the same aspect ratio as the target font.
• Divide each character in the font into zones (left, right, up, down, center).
• Compute the intensity for each zone in the image and font.
• Find the character that best matches the intensity and shape of the image area.
• The result produces visually more pleasing ASCII art with larger fonts, preserving details.

Example Code for Character Fitting

<code class="c++">class intensity {
    char c;
    int il, ir, iu, id, ic;

    void compute(DWORD **p, int xs, int ys, int xx, int yy) {
        // Compute intensities for left, right, up, down, and center zones
        ...
    }
};

AnsiString bmp2txt_big(Graphics::TBitmap *bmp, TFont *font) {
    // Character sized areas
    ...
}</code>

Comparison

• Intensity based conversion: Easy to implement, fast, produces large output with pixel-to-character mapping. Suitable for larger images.
• Character fitting: Slowers, more complex, produces better results even with larger fonts. Preserves shape and details for faces and some complex images.

Tips

• Use fixed pitch fonts (e.g., System) with an OEM character set.
• Experiment with different character maps and zone layouts.
• Consider inverted intensity, where black pixels have maximum value, for better text preview on white backgrounds.

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