Welcome to HandPython! An interactive real-time hand gesture recognition system that detects hand positions and converts them into visual text overlays and audio speech responses. Built using OpenCV, MediaPipe, and text-to-speech engines to create a seamless gesture-to-speech bridge.
- 🖐️ Real-Time Landmark Tracking: Precise detection of 21 hand landmarks using Google's MediaPipe framework.
- 💬 Dynamic Gesture Mapping: Recognizes specific gestures (e.g., open palm for "Halo", closed fist for custom responses) and maps them to distinct greetings.
- 🔊 Audio & Visual Feedback: Triggers real-time text overlays on screen along with synchronized voice output.
- ⚡ Low-Latency Frame Processing: Optimized loop ensuring quick gesture recognition and prompt audio triggers.
- 🤖 AI-Assisted Implementation: Built with AI support to refine gesture detection thresholds and audio execution logic.
- Language: Python 3.x
- Computer Vision: OpenCV (
cv2) - Gesture Tracking: MediaPipe Hands
- Text-to-Speech:
pyttsx3/gTTS - Mathematical Operations: NumPy
Follow these steps to set up and run the project locally on your machine.
Make sure you have installed:
- Python >= 3.8
pip(Python Package Installer)- A working Webcam and Speakers
-
Clone the repository:
git clone https://github.com/Madtch/handpython.git cd handpython -
Set up a Virtual Environment (Optional but recommended):
python -m venv venv # On Windows: venv\Scripts\activate # On macOS/Linux: source venv/bin/activate
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Install Dependencies:
pip install opencv-python mediapipe pyttsx3 numpy
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Run the Application:
python main.py
-
Interact:
- Show an open palm (5 fingers) to see and hear "Halo".
- Show different hand gestures (fist, index finger, etc.) to trigger other custom text and audio responses!
Zamzami Ahmad
- GitHub: @Madtch
Developed with modern Computer Vision standards to showcase real-time gesture recognition and Human-Computer Interaction (HCI).