Virtual_Screen

Virtual Screen enables users to write on their screen using hand gestures tracked by a webcam. With real-time hand detection and motion tracking, users can draw, annotate, or interact with the screen without physical input devices, offering a seamless and intuitive virtual writing experience.

Features

• Real-Time Hand Tracking: Detects the user's hand and tracks the index finger.
• Smooth Drawing: Implements position smoothing for stable and seamless drawing.
• Boundary Constraint: Ensures drawing is allowed only within a predefined rectangle.
• Customizable Canvas: Clear the canvas using a key press (c).
• Live Feedback: Displays the tracked finger position and boundary area.
• Color Palette: Choose different colors for drawing.
• Brush Size Palette: Adjust brush size for precise or bold strokes.
• Palm Erase: Use your palm gesture to erase parts of the canvas.

---

Technologies Used

• Python 3.7+
• OpenCV: For video capture and frame processing.
• MediaPipe: For hand and finger tracking.
• NumPy: For efficient array operations.

---

Setup Instructions

1. Clone the Repository

   git clone https://github.com/rishraks/Virtual_Screen.git
   cd virtual-drawing-canvas

2. Install Dependencies Use pip to install the required libraries

pip install opencv-python mediapipe numpy

3. Run the Application Execute the Python script

python Virtual_Screen.py

How to Use

1. Start Drawing:

• Position your index finger within the boundary rectangle displayed on the screen.
• Move your finger to draw lines on the virtual canvas.

2. Select Color:

Move your hand over the color palette area on the screen and tap with your index finger to select a color.

3. Adjust Brush Size:

Interact with the brush size palette to switch between various brush thicknesses.

4. Clear Canvas:

• Press the c key to clear the canvas.

5. Exit:

• Press the q key to quit the application.

Future Enhancements

• Save the canvas as an image file.
• Implement multi-finger gesture recognition for advanced controls.
• Add undo/redo functionality for enhanced editing.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• MediaPipe for robust hand tracking.
• OpenCV for frame processing.
• Community tutorials and resources on computer vision.