keiki's Introduction

Keiki

A bullet hell game platform for research purpose (especially danmaku generation) written in Python.

In this platform, you can design "danmakus" by implementing Danmaku class provided in the platform. The platform guarantees subclasses of this class can be encoded into a parametric sequence for training Generative Adversarial Nets (GANs). We will update the documentations for the danmaku designing APIs at https://github.com/PneuC/Keiki/wiki/API-Docs soon.

Please use this bibtex if this repository is used in your work:

@inproceedings{wang2021keiki,
  title={Keiki: Towards Realistic Danmaku Generation via Sequential {GANs}},
  author={Wang, Ziqi and Liu, Jialin and Yannakakis, Georgios N.},
  booktitle={2021 IEEE Conference on Games (CoG)},
  pages={accepted},
  year={2021},
  organization={IEEE}
}

Environments that have been tested:

Python 3.7.6

Pygame 2.0.1

Pytorch 1.5.0

Numpy 1.18.1

Seaborn 0.11.1

Matlotlib 3.3.1

How to use:

Encoding Danmaku Class into Parametric Sequence:

Run run_make.py to encode all the subclasses of Danmaku found in data/code folder

Run Game Demo:

The recomanded way is putting encoded danmakus (in npy format) into danmakus folder, then run run_game.py to start. The platform will load all the npy files in this folder as the danmakus of the boss. You can also import danmaku classes at logic/boss.py and then add the imported classes into Boss.spells attributes.

How to play:

Use direction keys to move.

Hold Z to keep shooting

Hold Shift to keep slow mode. In slow mode the your moving speed will decrease to enhance operating accuracy.

You can also press S to skip the current danmaku.

Training GANs:

We provide 3 implemented GANs in generator folder. For each GANs you can run train.py at the corresonding folder to train it. You can execute python train.py -h or python train.py --help to check the parameters for the training.

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