## Asciify

Most images nowadays are represented using pixels. They are square, often relatively small and numerous, come in $(2^8)^3$ different colors and thereby do a good job being the fundamental building block of images. But one can imagine more coarse-grained and differently shaped pixels.
An interesting fact is, that in most monotype fonts two characters placed right next to each other (for example ‘’) occupy roughly a square area. So simple ASCII characters can indeed be used to approximately describe any ordinary image.
Asciify does exactly this; it takes in an image and some optional parameters and maps the pixels’ intensity onto a character set. Both the large and small default character sets are taken from a post by Paul Bourke.

In conjunction with asciify.py, I wrote index.py, which asciifies a bunch of images and results in their html form; it also creates an index. All images asciified for this post can be viewed through this index.

Converting an image to its asciified form works best when there is a lot of contrast in the image. Because of this, some pre-processing of the image may be required for best results (all images shown where only cropped or rotated). The built-in color functionality also only knows of $8$ colors, so bright and different colors look the best, as they interestingly differentiate from one another. The asciified image’s size also plays a role, the larger it is, the better the characters blend into one and appear to be one image.

Asciify is operated on a command prompt; python asciify.py img.png. To parse arguments, the built-in python module argparse is used. The images are opened and read using the Python Imaging Library module PIL, which needs to be installed for this program to work.
Optional arguments include --size N, where the maximum size can be specified, --invert and --smallcharset, which can sometimes increase the asciified image’s visual appeal and --html, which will output an html file to be viewed in a browser. To see the program’s full potential, simply run python asciify.py --help.
Source code for both asciify.py and index.py can be downloaded, the first is also listed below.

The two examples above use the color mode, though certain images also work in default black and white mode, such as this spider I photographed.

Then again, the colored text also has its charm, especially when the source image has bright colors and a lot of contrast.

# Python 2.7 Code
# Jonathan Frech 3rd, 4th of March 2017
#      rewritten 12th of April 2017
#         edited 13th of April, 13th, 14th, 15th of July 2017

## Bifurcation Diagram

Generating the famous fractal, which can be used to model populations with various cycles, generate pseudo-random numbers and determine one of nature’s fundamental constants, the Feigenbaum constant $\delta$.
The fractal nature comes from iteratively applying a simple function, $f(x) = \lambda \cdot x \cdot (1-x)$ with $0 \leq \lambda \leq 4$, and looking at its poles.
The resulting image looks mundane at first, when looking at $0 \leq \lambda \leq 3$, though the last quarter section is where the interesting things are happening (hence the image below only shows the diagram for $2 \leq \lambda \leq 4$).
From $\lambda = 3$ on, the diagram bifurcates, always doubling its number of poles, until it enters the beautiful realm of chaos and fractals.

For more on bifurcation, fractals and $\delta$, I refer to this Wikipedia entry and WolframMathworld.

# Python 2.7.7 Code
# Jonathan Frech, 24th of March 2017

## Maze Solving

Mazes have been a subject of human interest for thousands of years. The Greeks used them to trap a bull-man hybrid, the French built them to show how they could impose order on nature, and even nowadays people enjoy wandering around corn mazes.
The algorithmic art of using computers to solve mazes — and even to find the shortest path through a maze –, however, has only emerged in the last couple of decades.

I was inspired by a recent Computerphile video in which Michael Pound talks about implementing different path finding algorithms for use in maze solving. And as he used Python — one of my favourite languages out there –, I thought I could give it a try and came up with this maze solver.

The mazes given to the solver (through a .png file) have to have a specific form. The maze needs to have a border all around (painted black) with two holes at the top and bottom, marking the maze’s start and exit (all path pixels are white).
Then the solver — using PIL — reads in the maze file, determines start and exit and starts at the maze’s start, labelling each maze path according to its shortest distance to the start. After it has found the exit, it stops looking at the maze and traces its origins back from the exit, marking the path it goes along as the maze’s optimal solution (highlighted in red).
The different hues of blue indicate the tile’s distance to the start, the white tiles are tiles the solver did not even look at.
The different shadings also reveal information about the maze. Mazes with only one solution tend to have sharp changes as there are parts of the maze separated by only one wall, yet separated by a huge walk distance through the maze. The one maze with multiple solutions (see upper right image below) — in contrast — has only gradual changes in hue.

To solve a 4 megapixel maze, the solver takes around 3 seconds, for a 16 megapixel maze around 14 seconds and for a 225 megapixel maze around 7 minutes and 22 seconds.
Performance was measured on an Intel Core i7 (4.00 GHz).

All mazes shown were downloaded from Michael Pound’s mazesolving GitHub repository, which were mostly generated using Daedalus.

The solver’s source code is listed below, though you can also download the .py file.

# Python 2.7.7 Code
# Jonathan Frech, 25th of Feburary 2017
#          edited 26th of February 2017
#          edited 27th of February 2017
#          edited 22nd of March    2017
#          edited 29th of March    2017