pixelate turns your pictures into pixel art ! Well, sometimes.
It is a simple script based on PIL.
Algorithms will be added in the future. More precisely, I'd like to try to learn a mapping between input and pixelated space with a UNet-like encoder/decoder ConvNet.
It requires recent versions of both numpy and PIL.
pip install numpy
pip install Pillow
It was tested using Pillow 4.0.0 and numpy 1.12.1.
python main.py imgs/test.png pixelated/test.png -n 10 -p 10 -s 1.25 -c 1.2
n is the amount of colors wanted for the output. Small numbers typically give better results.
p is the superpixel size. Rule of thumb : the larger the image, the larger the superpixels.
s is the saturation factor. Saturation helps create similar color zones.
c is the contrast factor. It is often useful to increase contrast to get better results.
If the second argument refers to a folder, by default the name used for saving the processed file will be the same as the original file. An artifact is added if name refers to an existing file.