irrorCloneFX
MirrorCloneFX is a real-time computer vision application that creates stylized visual clones of yourself using a webcam. It uses hand gesture recognition powered by MediaPipe and OpenCV to switch between dynamic visual effects instantly.
Features
Split-screen view Displays the original webcam feed alongside a stylized clone in real time.
Gesture-based controls Switch effects using simple hand gestures detected via MediaPipe hand tracking.
Multiple visual styles
Dots – Stippled dot rendering with variable sizes
Lines – Edge-based outline rendering with enhanced color effects
ASCII – Unicode-based text art representation
Particles – Physics-driven particles that follow movement dynamically
Hand Gesture Controls Gesture Effect Description Two fingers (peace sign) Dots Creates a stippled dot version using original colors One finger up Lines Shows edge outlines and contours with glow effects Thumb + pinky out ASCII Renders the image as ASCII art with color intensity mapping Open palm (all fingers) Particles Generates physics-based particles that follow movement Installation Prerequisites
Python 3.7 or higher
A working webcam
Good lighting for accurate hand detection
Setup
Clone the repository
git clone https://github.com/tubakhxn/MirrorCloneFX.git cd MirrorCloneFX
Install dependencies
pip install -r requirements.txt
Run the application
python main.py
Usage
Position yourself clearly in front of the webcam
Keep your hand visible in the camera frame
Perform hand gestures to switch effects
Press q to exit the application
The currently active mode is displayed in the top-left corner of the window.
Technical Overview Core Technologies
OpenCV – Video capture and image processing
MediaPipe – Real-time hand landmark detection
NumPy – Efficient numerical operations
Visual Effects
Dots – Intensity-based circular rendering
Lines – Canny edge detection with color enhancement
ASCII – Unicode block character mapping
Particles – Physics-based system with gravity and momentum
Performance
Runs at ~30 FPS on modern hardware
Tracks 21 hand landmarks in real time
Optimized for smooth transitions between effects
System Requirements Hardware
OS: Windows, macOS, or Linux
RAM: 4GB minimum (8GB recommended)
CPU: Multi-core processor recommended
Webcam: USB or built-in camera
Software Dependencies
opencv-python >= 4.5.0
mediapipe >= 0.8.9
numpy >= 1.21.0
Contributing
Contributions are welcome and appreciated.
How to Contribute
Report bugs via GitHub Issues
Suggest new effects or improvements
Submit pull requests for features or fixes
Improve documentation or testing coverage
Development Workflow
Fork the repository
Clone your fork
git clone https://github.com/YOUR-USERNAME/MirrorCloneFX.git
Create a feature branch
git checkout -b feature-name
Make and test your changes
Commit and push
Open a pull request
Code Guidelines
Follow PEP 8
Add docstrings and comments
Ensure compatibility with Python 3.7+
Adding New Visual Effects
Create a new method in the main class:
def create_custom_effect(self, frame): return processed_frame
Register the effect in the modes dictionary
Assign a gesture trigger
Test under different lighting conditions
Update documentation accordingly
Troubleshooting Common Issues
Webcam not detected
Ensure it’s not in use by another app
Check system camera permissions
Gesture recognition issues
Improve lighting
Keep hand fully visible
Hold gestures steady for 1–2 seconds
Performance drops
Close background applications
Improve lighting to reduce processing load
Dependency installation errors
Update pip
Install packages individually if needed
Platform Notes
Windows
May require Visual C++ redistributables
macOS
Camera permission may need manual approval
Linux
Additional OpenCV packages may be required
Customization
Each visual effect can be tweaked by adjusting parameters such as:
Density and size (Dots)
Edge thresholds and glow intensity (Lines)
Character sets and spacing (ASCII)
Physics parameters and particle count (Particles)
License
Released under the MIT License. See the LICENSE file for details.
Acknowledgments
MediaPipe team for real-time hand tracking
OpenCV community for computer vision tools
Python open-source ecosystem
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