Covert art variational autoencoder for generating new cover art
get the code
$ git clone https://github.com/csteinmetz1/cavae
create a virtual env
$ virtualenv cavae_env
$ source cavae_env/bin/activate
install dependancies
$ pip install -r requirements.txt
I am using a very small subset of the albums covers in this dataset, which contains over 1 million album covers.
The dataset is split up into smaller .tar files by filename. I am using album_covers_s.tar, but feel free to use any or all of the archives.
The dataset is quite messy and includes a lot of non-album cover images (different file formats, dead link images, non-square images, etc.), so to clean up the dataset for this project I created a script, clean.py, that iterates over every (.jpg) image in the user specified directory and does the following.
- Check the dimensions - if not 1:1 aspect ratio discard the image
- Resize the image - there is small ( 28, 28 ) and large ( 128, 128 )
- Save the new images - user specificed output directory
Note: It took a significant amount of time to process all 100,000 images in album_covers_s.tar, about 1 hour.
Here are some of the actual size ( 128, 128 ) covers after preprocessing.