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.
Repository of 3D Object Detection with Pointformer (CVPR2021)

3D Object Detection with Pointformer This repository contains the code for the paper 3D Object Detection with Pointformer (CVPR 2021) [arXiv]. This wo

Zhuofan Xia 117 Jan 06, 2023
An implementation of chunked, compressed, N-dimensional arrays for Python.

Zarr Latest Release Package Status License Build Status Coverage Downloads Gitter Citation What is it? Zarr is a Python package providing an implement

Zarr Developers 1.1k Dec 30, 2022
Vision-Language Pre-training for Image Captioning and Question Answering

VLP This repo hosts the source code for our AAAI2020 work Vision-Language Pre-training (VLP). We have released the pre-trained model on Conceptual Cap

Luowei Zhou 373 Jan 03, 2023
Ganilla - Official Pytorch implementation of GANILLA

GANILLA We provide PyTorch implementation for: GANILLA: Generative Adversarial Networks for Image to Illustration Translation. Paper Arxiv Updates (Fe

Samet Hi 462 Dec 05, 2022
This is the official PyTorch implementation of our paper: "Artistic Style Transfer with Internal-external Learning and Contrastive Learning".

Artistic Style Transfer with Internal-external Learning and Contrastive Learning This is the official PyTorch implementation of our paper: "Artistic S

51 Dec 20, 2022
Generic Foreground Segmentation in Images

Pixel Objectness The following repository contains pretrained model for pixel objectness. Please visit our project page for the paper and visual resul

Suyog Jain 157 Nov 21, 2022
Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent

Narya The Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent. This repository

Paul Garnier 121 Dec 30, 2022
code for paper"A High-precision Semantic Segmentation Method Combining Adversarial Learning and Attention Mechanism"

PyTorch implementation of UAGAN(U-net Attention Generative Adversarial Networks) This repository contains the source code for the paper "A High-precis

Tong 8 Apr 25, 2022
Pytorch implementation of SenFormer: Efficient Self-Ensemble Framework for Semantic Segmentation

SenFormer: Efficient Self-Ensemble Framework for Semantic Segmentation Efficient Self-Ensemble Framework for Semantic Segmentation by Walid Bousselham

61 Dec 26, 2022
Improving Object Detection by Estimating Bounding Box Quality Accurately

Improving Object Detection by Estimating Bounding Box Quality Accurately Abstrac

2 Apr 14, 2022
Automatic self-diagnosis program (python required)Automatic self-diagnosis program (python required)

auto-self-checker 자동으로 자가진단 해주는 프로그램(python 필요) 중요 이 프로그램이 실행될때에는 절대로 마우스포인터를 움직이거나 키보드를 건드리면 안된다(화면인식, 마우스포인터로 직접 클릭) 사용법 프로그램을 구동할 폴더 내의 cmd창에서 pip

1 Dec 30, 2021
PyAF is an Open Source Python library for Automatic Time Series Forecasting built on top of popular pydata modules.

PyAF (Python Automatic Forecasting) PyAF is an Open Source Python library for Automatic Forecasting built on top of popular data science python module

CARME Antoine 405 Jan 02, 2023
Implementation of "Efficient Regional Memory Network for Video Object Segmentation" (Xie et al., CVPR 2021).

RMNet This repository contains the source code for the paper Efficient Regional Memory Network for Video Object Segmentation. Cite this work @inprocee

Haozhe Xie 76 Dec 14, 2022
An Industrial Grade Federated Learning Framework

DOC | Quick Start | 中文 FATE (Federated AI Technology Enabler) is an open-source project initiated by Webank's AI Department to provide a secure comput

Federated AI Ecosystem 4.8k Jan 09, 2023
A toolkit for controlling Euro Truck Simulator 2 with python to develop self-driving algorithms.

europilot Overview Europilot is an open source project that leverages the popular Euro Truck Simulator(ETS2) to develop self-driving algorithms. A con

1.4k Jan 04, 2023
A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-t

Facebook Research 5.1k Jan 04, 2023
DeepLab2: A TensorFlow Library for Deep Labeling

DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a unified and state-of-the-art TensorFlow codebase for dense pixel labeling tasks.

Google Research 845 Jan 04, 2023
PyTorch implementation of a Real-ESRGAN model trained on custom dataset

Real-ESRGAN PyTorch implementation of a Real-ESRGAN model trained on custom dataset. This model shows better results on faces compared to the original

Sber AI 160 Jan 04, 2023
This code is the implementation of the paper "Coherence-Based Distributed Document Representation Learning for Scientific Documents".

Introduction This code is the implementation of the paper "Coherence-Based Distributed Document Representation Learning for Scientific Documents". If

tsc 0 Jan 11, 2022
App customer segmentation cohort rfm clustering

CUSTOMER SEGMENTATION COHORT RFM CLUSTERING TỔNG QUAN VỀ HỆ THỐNG DỮ LIỆU Nên chuyển qua theme màu dark thì sẽ nhìn đẹp hơn https://customer-segmentat

hieulmsc 3 Dec 18, 2021