This project is an attempt to develop an AI capable of playing Mini Metro, a minimalist strategy game where players design a metro system to efficiently transport passengers. The primary challenge is the lack of an official API, requiring alternative methods to extract game data.
- Extract real-time game data (stations, lines, trains, resources, score)
- Analyze optimal metro layouts and improve network efficiency
- Develop an AI that can suggest or automate metro system management
- Test different input methods, including image recognition and manual input
Since direct data extraction is not available, two methods were considered:
- Image Recognition (Current Method) ✅
- Used MSS and Tesseract to detect stations, lines, and score.
- Currently poor accuracy due to overlapping elements and false detections (e.g Areas for Improvement).
- Manual Data Input (Failed) ❌
- The user manually inputs game state updates using an overlay.
- Some gameplay elements of the game are too complicated for me to implement (extending a line from the center is one example, there are others)
- Image recognition failed to provide reliable data.
- Manual input is time-consuming and inefficient for fast-paced gameplay.
- No direct access to game mechanics, requiring creative workarounds.
- Explore alternative computer vision techniques for better recognition.
- Investigate potential ways to hook into game memory.
- Maybe improve the manual input system for better usability ?
- Implement basic AI logic to suggest metro expansions based on available data.
Any contributions, suggestions, or feedback are welcome ! Feel free to fork the repo, submit issues, or open a pull request. You can contact me on Reddit "Damsday - u/Primary_Cheesecake63" --> https://www.reddit.com/user/Primary_Cheesecake63/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button
This project is open-source