GUI for TOAD-GAN, a PCG-ML algorithm for Token-based Super Mario Bros. Levels.

Overview

If you are using this code in your own project, please cite our paper:

@inproceedings{awiszus2020toadgan,
  title={TOAD-GAN: Coherent Style Level Generation from a Single Example},
  author={Awiszus, Maren and Schubert, Frederik and Rosenhahn, Bodo},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment},
  year={2020}
}

TOAD-GUI

TOAD-GUI is a Framework with which Super Mario Bros. levels can be randomly generated, loaded, saved, edited and played using a graphical user interface. Generation is done with pre-trained TOAD-GAN (Token-based, One-shot, Arbitrary Dimension Generative Adversarial Network). For more information on TOAD-GAN, please refer to the paper (arxiv link) and the Github.

TOAD-GUI_linux_example

This project uses the Mario-AI-Framework by Ahmed Khalifa and includes graphics from the game Super Mario Bros. It is not affiliated with or endorsed by Nintendo. The project was built for research purposes only.

AIIDE 2020

Our paper "TOAD-GAN: Coherent Style Level Generation from a Single Example" was accepted for oral presentation at AIIDE 2020! You can find our video presentation on YouTube.

Our code for TOAD-GUI and TOAD-GAN has been accepted for the AIIDE 2020 Artifact Evaluation Track! It will be recognized in the AIIDE 2020 Program.

Getting Started

This section includes the necessary steps to get TOAD-GUI running on your system.

Python

You will need Python 3 and the packages specified in requirements.txt. We recommend setting up a virtual environment with pip and installing the packages there.

$ pip3 install -r requirements.txt -f "https://download.pytorch.org/whl/torch_stable.html"

Make sure you use the pip3 that belongs to your previously defined virtual environment.

The GUI is made with Tkinter, which from Python 3.7 onwards is installed by default. If you don't have it installed because of an older version, follow the instructions here.

Java

TOAD-GUI uses the Mario-AI-Framework to play the generated levels. For the Framework to run, Java 11 (or higher) needs to be installed.

Running TOAD-GUI

Once all prerequisites are installed, TOAD-GUI can be started by running main.py.

$ python main.py

Make sure you are using the python installation you installed the prerequisites into.

TOAD-GUI

When running TOAD-GUI you can:

  • toad folder Open a Folder containing a Generator (TOAD-GAN)
  • level folder Open a (previously saved) level .txt to view and/or play
  • gear toad Generate a level of the size defined in the entries below
  • save button Save the currently loaded level level to a .txt or .png image file
  • play button Play the currently loaded level

NOTE: When a generator is opened, it will not show any files in the dialog window. That is intended behavior for askdirectory() of tkinter. Just navigate to the correct path and click "Open" regardless.

When a level is loaded, right clicking a point in the preview will allow you to change the token at that specific spot. If you resample the level, any changes made will be lost.

The labels at the bottom will display the currently loaded path and information. This program was made mostly by one researcher and is not optimized. Impatiently clicking buttons might crash the program.

Edit Mode

In this mode, parts of a generated level can be resampled with TOAD-GAN. The red bounding box shows the area to be changed, while the yellow bounding box shows which blocks can still be affected by that change. The area of effect depends on the scale which is to be resampled and is a result of the Field of View produced by the convolutional layers. Changes in a lower scale will result in larger changes in the final level.

Use the control panel to set the bounding box. The representation inside the panel shows which pixels in the noise map will be changed.

TOAD-GUI_bbox

Resample the noise map in the chosen scale. The "Noise influence" is a learned parameter that indicates how big the effect of resampling in this scale will be.

TOAD-GUI_sc3

Scale 0 is the first scale and results in the most changes. Note that the tokens outside of the bounding box change. This is because of the field of view from the convolutional layers applied to the noise map.

TOAD-GUI_sc0

You can right click a token you want to change and replace it with another token present in the level. This should be done after resampling, as resampling will regenerate the level from the noise maps which will undo these edits.

TOAD-GUI_edit

TOAD-GAN

If you are interested in training your own Generator, refer to the TOAD-GAN Github and copy the folder of your trained generator into the generators/ folder. You should now be able to open it just like the provided generators.

The necessary files are:

generators.pth
noise_amplitudes.pth
noise_maps.pth
num_layer.pth
reals.pth
token_list.pth

Any other files can be deleted if you want to keep your folders tidy.

NOTE: When a generator is opened, it will not show these files in the dialog window. That is intended behavior for askdirectory() of tkinter. Just navigate to the correct path and click "Open" regardless.

Known Bugs

  • If the level play is quit using the window ('x' button in the corner), an error message regarding py4j will occur. In spite of that, the program should continue running normally.

  • If you have two monitors with different resolutions, the GUI and the Java window might not be displayed in the correct resolution. Try moving the windows to the monitor with the other resolution if you encounter this problem. You can also change the DPI awareness for the program in the beginning of GUI.py.

Built With

  • Tkinter - Python package for building GUIs
  • py4j - Python to Java interface
  • Pillow - Python Image Library for displaying images
  • Pytorch - Deep Learning Framework
  • Maven - Used for building the Mario-AI-Framework

Authors

  • Maren Awiszus - Institut für Informationsverarbeitung, Leibniz University Hanover
  • Frederik Schubert - Institut für Informationsverarbeitung, Leibniz University Hanover

Copyright

This program is not endorsed by Nintendo and is only intended for research purposes. Mario is a Nintendo character which the authors don’t own any rights to. Nintendo is also the sole owner of all the graphical assets in the game.

Owner
Maren A.
Maren A.
Sharing of contents on mitochondrial encounter networks

mito-network-sharing Sharing of contents on mitochondrial encounter networks Required: R with igraph, brainGraph, ggplot2, and XML libraries; igraph l

Stochastic Biology Group 0 Oct 01, 2021
Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image

Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image This repository is an implementation of the method described in the following pap

21 Dec 15, 2022
Source code for "MusCaps: Generating Captions for Music Audio" (IJCNN 2021)

MusCaps: Generating Captions for Music Audio Ilaria Manco1 2, Emmanouil Benetos1, Elio Quinton2, Gyorgy Fazekas1 1 Queen Mary University of London, 2

Ilaria Manco 57 Dec 07, 2022
Github Traffic Insights as Prometheus metrics.

github-traffic Github Traffic collects your repository's traffic data and exposes it as Prometheus metrics. Grafana dashboard that displays the metric

Grafana Labs 34 Oct 27, 2022
A python comtrade load library accelerated by go

Comtrade-GRPC Code for python used is mainly from dparrini/python-comtrade. Just patch the code in BinaryDatReader.parse for parsing a little more eff

Bo 1 Dec 27, 2021
Machine Learning Time-Series Platform

cesium: Open-Source Platform for Time Series Inference Summary cesium is an open source library that allows users to: extract features from raw time s

632 Dec 26, 2022
This is a five-step framework for the development of intrusion detection systems (IDS) using machine learning (ML) considering model realization, and performance evaluation.

AB-TRAP: building invisibility shields to protect network devices The AB-TRAP framework is applicable to the development of Network Intrusion Detectio

Lab-C2DC - Laboratory of Command and Control and Cyber-security 17 Jan 04, 2023
RL-driven agent playing tic-tac-toe on starknet against challengers.

tictactoe-on-starknet RL-driven agent playing tic-tac-toe on starknet against challengers. GUI reference: https://pythonguides.com/create-a-game-using

21 Jul 30, 2022
PyTorch inference for "Progressive Growing of GANs" with CelebA snapshot

Progressive Growing of GANs inference in PyTorch with CelebA training snapshot Description This is an inference sample written in PyTorch of the origi

320 Nov 21, 2022
Code examples and benchmarks from the paper "Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective"

Code For the Paper "Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective" Author: Robert Bamler Date: 22 D

4 Nov 02, 2022
Pretraining on Dynamic Graph Neural Networks

Pretraining on Dynamic Graph Neural Networks Our article is PT-DGNN and the code is modified based on GPT-GNN Requirements python 3.6 Ubuntu 18.04.5 L

7 Dec 17, 2022
OpenMMLab Pose Estimation Toolbox and Benchmark.

Introduction English | 简体中文 MMPose is an open-source toolbox for pose estimation based on PyTorch. It is a part of the OpenMMLab project. The master b

OpenMMLab 2.8k Dec 31, 2022
Paddle pit - Rethinking Spatial Dimensions of Vision Transformers

基于Paddle实现PiT ——Rethinking Spatial Dimensions of Vision Transformers,arxiv 官方原版代

Hongtao Wen 4 Jan 15, 2022
A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization

University1652-Baseline [Paper] [Slide] [Explore Drone-view Data] [Explore Satellite-view Data] [Explore Street-view Data] [Video Sample] [中文介绍] This

Zhedong Zheng 335 Jan 06, 2023
keyframes-CNN-RNN(action recognition)

keyframes-CNN-RNN(action recognition) Environment: python=3.7 pytorch=1.2 Datasets: Following the format of UCF101 action recognition. Run steps: Mo

4 Feb 09, 2022
This is a re-implementation of TransGAN: Two Pure Transformers Can Make One Strong GAN (CVPR 2021) in PyTorch.

TransGAN: Two Transformers Can Make One Strong GAN [YouTube Video] Paper Authors: Yifan Jiang, Shiyu Chang, Zhangyang Wang CVPR 2021 This is re-implem

Ahmet Sarigun 79 Jan 05, 2023
Implementation of Neural Style Transfer in Pytorch

PytorchNeuralStyleTransfer Code to run Neural Style Transfer from our paper Image Style Transfer Using Convolutional Neural Networks. Also includes co

Leon Gatys 396 Dec 01, 2022
AntroPy: entropy and complexity of (EEG) time-series in Python

AntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to e

Raphael Vallat 153 Dec 27, 2022
PyTorch implementation of paper: AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer, ICCV 2021.

AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer [Paper] [PyTorch Implementation] [Paddle Implementation] Overview This reposit

148 Dec 30, 2022
The official implementation for "FQ-ViT: Fully Quantized Vision Transformer without Retraining".

FQ-ViT [arXiv] This repo contains the official implementation of "FQ-ViT: Fully Quantized Vision Transformer without Retraining". Table of Contents In

132 Jan 08, 2023