Skip to content

linsaneinthemembrane/mariopartyds

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

43 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Mario Party DS Automation Projects

This repository contains a collection of Python-based automation projects for various minigames in Mario Party DS. These projects demonstrate the application of computer vision techniques, input simulation, and game-specific algorithms to automate gameplay.

Projects

1. Domino Effect Bot

Automatically plays the Domino Effect minigame by detecting and responding to on-screen button prompts.

Key Techniques:

  • Real-time window capture
  • RGB-based button prompt detection
  • Automated key pressing
  • Stack-based sequence tracking

2. Rail Riders

Automates the Rail Riders minigame by simulating rapid touch screen swiping.

Key Techniques:

  • Mouse movement simulation
  • Keyboard input detection
  • Window management
  • Timing optimization

3. Star Catchers

Uses computer vision to detect and automatically click on stars in the Star Catchers minigame.

Key Techniques:

  • Binary masking for white pixel detection
  • Connected components analysis
  • Real-time star shape identification
  • Automated mouse control

Common Technologies

Across these projects, the following technologies and libraries are utilized:

  • OpenCV: For image processing and computer vision tasks
  • NumPy: For efficient array operations
  • PyAutoGUI: For mouse and keyboard input simulation
  • Keyboard: For key press detection and simulation
  • Win32GUI: For window management and focus
  • MSS: For screen capture

Getting Started

Each project has its own specific setup instructions, but generally, you'll need:

  1. Python 3.x installed
  2. A Nintendo DS emulator (e.g., melonDS)
  3. Mario Party DS ROM (not provided)

To install the required libraries:

pip install opencv-python numpy keyboard mss pywin32 pyautogui

Usage

  1. Clone this repository
  2. Navigate to the specific project directory
  3. Install the required dependencies
  4. Run the Python script while the emulator is open and focused on the appropriate minigame

Development Process

The development of these projects involved:

  1. Analyzing game mechanics and visual cues
  2. Implementing initial detection and automation strategies
  3. Optimizing performance through various techniques (e.g., region of interest reduction, algorithm refinement)
  4. Overcoming challenges specific to each minigame (e.g., timing issues, false positives)

License

All projects in this repository are released under the MIT License.

About

Automating mario party ds minigames

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages