This repository contains the code written for my master's thesis "Symmetry Recognition in Wallpaper Patterns Using Deep Learning"
at the Technical University of Munich.
The two main models for the classification of wallpaper patterns by lattice type and by wallpaper group are defined in
the modules ornaments.modeling.models and main.py. The latter is trained on artificial patterns generated with
the script pattern_generator.py. The code for fine-tuning this model can be found in fine_tune_group_classifier.py.
The main dataset used to train the model was downloaded from the website
Ornament World Exhibition with the script ornaments.data_loading.download_data.py.
Additional preprocessing code can be found in the same package.
The weights for the pre-trained models can be found in the models/ directory. To reproduce the results in the thesis the
OWE dataset must be downloaded and preprocessed and then the scripts inference.py and lattice_inference.py can be executed.
The code for the extraction of a lattice basis described in Section 4.4 of the thesis can be found in the script run_lattice_extraction.py.
The code in this repository uses Tensorflow 2 as deep learning framework. All dependencies can be installed using Anaconda:
conda env create -f environment.yml
Many of the plots in the thesis were generated with Sage and its LaTeX integration SageTeX. Installation instructions can
be found here. The code in the file plots.sage must be imported
from the LaTeX files to compile the thesis.