Visual Volume Control with Hand Tracking 🎯

This project demonstrates how to use computer vision to create a gesture-based volume controller. It uses OpenCV and MediaPipe to track hand gestures and adjust the system volume dynamically.

Features ✨

• Hand Tracking Module: Detect and track hand landmarks using MediaPipe.
• Coordinate Testing: Retrieve and print landmark coordinates in real-time.
• Finger Counter: Detect the number of fingers shown using custom logic.
• Volume Controller: Adjust system volume based on the distance between two fingers.

Technologies Used 🛠️

• Python 3.x
• OpenCV
• MediaPipe
• Pycaw (for audio control)

Project Structure 📁

visual-volume-control/ │ ├── HandTrackingModule.py # Hand tracking logic encapsulated in a module ├── FingerCounter.py # Detects number of fingers shown ├── VolumeControl.py # Gesture-controlled volume adjustment ├── FingerImages/ # Folder containing images for finger counting ├── README.md # Project documentation (this file)file)

How to Run the Project 🚀

1. Clone the Repository:

   git clone https://github.com/yaarya/visual-volume-control.git
   cd visual-volume-control
   

2. Install Required Libraries:

   pip install opencv-python-headless mediapipe comtypes numpy pycaw
   

3. Run the Volume Controller:

   python VolumeControl.py
   

Usage 💡

• The program opens the webcam feed.
• Show two fingers (thumb + index) to adjust volume.
• Move fingers closer to reduce volume and spread them apart to increase it.
• Real-time FPS is displayed on the video feed.

Possible Improvements 🔧

• Add gesture-based media controls (e.g., play/pause).
• Implement a GUI for smoother interaction.
• Integrate with smart home systems.