Hand Gesture Based Presentation Control System

A Python-based desktop application that allows you to control presentations (like PowerPoint, Keynote, or Google Slides) using hand gestures. By leveraging computer vision and hand-tracking algorithms, this system maps specific hand movements to keyboard events, enabling a seamless, touch-free presentation experience.

• Real-Time Hand Tracking: Utilizes MediaPipe and OpenCV for highly accurate and fast hand landmark detection.

• Intuitive Gestures:

• Use your Right Hand to advance to the next slide (Right Arrow).

• Use your Left Hand to go back to the previous slide (Left Arrow).

• Modern User Interface: Built with CustomTkinter for a sleek, responsive, and user-friendly dark-mode GUI.

• Smooth Performance: Runs the computer vision backend on a separate thread to ensure the UI remains responsive.

• Visual Feedback: Displays real-time webcam feed with landmark connections, gesture states ("Locked" / "Not Locked"), and visual "CLICKED" confirmations.

└── Hand Gesture Based Presentation Control System/
    ├── Backend.py          # Core computer vision & gesture recognition logic
    ├── GUI.py              # CustomTkinter user interface and threading setup
    ├── diagram.mmd         # Mermaid architecture diagram
    ├── main.py             # Entry point for the application
    ├── requirements.txt    # Python dependencies
    └── res/                # Resource folder containing instruction images
        ├── img1.jpg
        └── img2.jpg

• Python 3.10 (Note: This project was specifically built and tested using Python 3.10)
• A working webcam

1. Clone the repository (or download the source code):

git clone https://github.com/SIDD-KIDD/Hand-Gesture-Based-Presentation-Control-Systen
cd "Hand Gesture Based Presentation Control System"

2. Create a Virtual Environment (Recommended):

python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

3. Install Dependencies:

pip install -r requirements.txt

1. Launch the application:

python main.py

2. Start Tracking: Click the "Start Tracking" button in the GUI. The application will request access to your webcam.
3. Perform Gestures:

• Step 1: Lock the Gesture Close your middle, ring, and pinky fingers. Leave your thumb and index finger open. The screen will display "Gesture Locked" in green.

• Step 2: Click/Trigger Pinch your thumb and index finger together (bringing the normalized distance below 0.2).

• Navigation:
• Doing this with your Right Hand simulates a Right Arrow key press (Next Slide).
• Doing this with your Left Hand simulates a Left Arrow key press (Previous Slide).

4. Stop Tracking: Click "Stop Tracking" to safely release the webcam and return to the instruction screen, or simply close the window.

All the below libs will be downloaded automatically.

• OpenCV (opencv-python): For capturing webcam frames and rendering visual feedback.
• MediaPipe (mediapipe): Google's framework used for detecting and tracking hand landmarks in real-time.
• PyAutoGUI (PyAutoGUI): For programmatically triggering keyboard events.
• CustomTkinter (customtkinter): For building the modern, dark-themed graphical user interface.