Pet Sim 99 Autofish
A Python utility tool that uses image recognition to automatically position the cursor on whirlpools in Pet Simulator 99's fishing minigame for optimal loot. Paired with an external autoclicker to maintain activity and prevent disconnection.
Tags
Technologies Used
Project Links
Project Details
Quick Stats
Overview
Pet Sim 99 Autofish is a sophisticated Python utility that leverages computer vision and image recognition to automate the fishing minigame in Pet Simulator 99. The tool identifies whirlpools in the water and positions the cursor precisely on them to maximize loot potential.
Built as a proof of concept for automated gaming tools, this project demonstrates the practical application of image processing and screen automation. While primarily designed for educational purposes, the tool successfully operated overnight and helped acquire several valuable pets, showcasing the effectiveness of computer vision in gaming automation.
Key Features
Image Recognition
Advanced computer vision algorithms to detect whirlpools and optimal fishing spots
Precise Cursor Control
Automatic cursor positioning on detected whirlpools for maximum loot efficiency
Automated Operation
Continuous monitoring and adjustment without human intervention
External Integration
Designed to work with external autoclickers to maintain game activity
Loot Optimization
Targets whirlpools for the best possible rewards and rare pet drops
Real-time Processing
Continuous screen analysis and immediate response to visual changes
About Pet Simulator 99
Click to visit the game
The Game That Inspired the Tool
Pet Simulator 99 is a popular Roblox game developed by BIG Games that features pet collection, trading, and clan systems. Players can collect various pets with different rarities, trade them with other players, and join clans to participate in group activities and events.
The constant grind that the game lets players face inspired the creation of the autofishing tool.
How It Works
The tool operates through a sophisticated pipeline that continuously monitors the game screen and automatically positions the cursor for optimal fishing:
Processing Pipeline
- 1Screen Capture: Continuously captures the game window to analyze the fishing area
- 2Image Processing: Applies computer vision algorithms to detect whirlpools and optimal spots
- 3Position Calculation: Determines the exact coordinates for the best fishing location
- 4Cursor Movement: Automatically moves the cursor to the optimal position
- 5Continuous Monitoring: Repeats the process to adapt to changing game conditions
Image Recognition Technology
The core of this tool relies on sophisticated image recognition algorithms that can identify whirlpools and optimal fishing spots in real-time. The system uses a combination of template matching, contour detection, and color analysis to accurately locate the best fishing positions.
Recognition Techniques
- Template Matching: Compares screen regions against pre-defined whirlpool templates
- Contour Detection: Identifies circular patterns and whirlpool shapes in the water
- Color Analysis: Detects specific color patterns associated with high-value fishing spots
- Motion Detection: Tracks dynamic elements like moving whirlpools and water effects
Reference Images
Below are the reference images used by the tool to identify whirlpools and optimal fishing spots. These images serve as templates for the image recognition system:
Technical Implementation
The tool is built using Python with several key libraries for image processing and automation. The implementation focuses on efficiency and accuracy while maintaining low resource usage.
Core Technologies
- OpenCV: Computer vision and image processing
- PIL (Pillow): Image manipulation and analysis
- PyAutoGUI: Screen capture and cursor control
- NumPy: Numerical computations and array operations
Key Features
- Real-time Processing: Continuous screen analysis
- Template Matching: Accurate whirlpool detection
- Precision Control: Sub-pixel cursor positioning
- Error Handling: Robust failure recovery
Results and Impact
While primarily built as a proof of concept, the Pet Sim 99 Autofish tool demonstrated significant effectiveness in automated gaming scenarios. The tool successfully operated for extended periods and achieved notable results.
Achievements
- Successfully operated overnight without interruption
- Acquired multiple huge pets through optimized fishing
- Maintained consistent activity to prevent disconnection
- Demonstrated practical application of computer vision
Learning Outcomes
- Advanced image processing and computer vision techniques
- Real-time screen analysis and automation
- Precision cursor control and coordinate mapping
- Integration of multiple Python libraries for automation
Other Side Projects
GTA V Casino Auto Hack
A Python-based automation tool that uses computer vision and fingerprint recognition to automatically solve the GTA V Casino Heist fingerprint hacking minigames.
LegitBot
A Discord bot designed for Pet Simulator 99 clans to track diamond donations and manage clan activities. Built for the LEGIT clan, it featured real-time donation tracking, automated announcements, and clan management tools.
Player Clan Finder
A command-line utility that tracks Roblox players across Pet Simulator 99 clans using the game's public API. Built to help clan leaders identify and track players who engage in scamming or other malicious activities.