How do ASCII art image conversion algorithms work? [closed]
The big-picture-level concept is simple:
Each printable character can be assigned an approximate gray-scale value; the "at" sign
@obviously is visually darker than the "plus" sign+, for example. The effect will vary, depending on the font and spacing actually used.Based on the proportions of the chosen font, group the input image into rectangular pixel blocks with constant width and height (e.g. a rectangle 4 pixels wide and 5 pixels high). Each such block will become one character in the output. (Using the pixel blocks just mentioned, a 240w-x-320h image would become 64 lines of 60 characters.)
Compute the average gray-scale value of each pixel block.
For each pixel block, select a character whose gray-scale value (from step 1) is a good approximation of the pixel block average (from step 3).
That's the simplest form of the exercise. A more sophisticated version will also take the actual shapes of the characters into account when breaking ties among candidates for a pixel block. For example, a "slash" (/) would be a better choice than a "backward slash" (\) for a pixel block that appears to have a bottom-left-to-upper-right contrast feature.
aalib (last release in 2001) is an open source ASCII art library that's used in applications like mplayer. You may want to check out its source code to see how it does it. Other than that, this page describes in more detail about how such algorithms work.
Also you can take a look at libcaca (latest release 2014), which acording to their website has the following improvements over aalib:
- Unicode support
- 2048 available colours (some devices can onlyhandle 16)
- dithering of colour images
- advanced text canvas operations (blitting, rotations)