FaceSpotter - Real-Time Face Detection

Real-Time Face Detection using OpenCV and dlib

Welcome to FaceSpotter, a Streamlit web application for real-time face detection using OpenCV and dlib. This application allows users to start and stop face detection on live video streams from their camera.

What This Project Does

FaceSpotter utilizes computer vision techniques to detect human faces in real-time video streams. It draws bounding boxes around detected faces and labels them sequentially. Users can control the face detection with a simple start/stop toggle button.

Demonstration

Installation and Usage Instructions

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/FaceSpotter-Real-Time-Face-Detection.git
   cd FaceSpotter-Real-Time-Face-Detection

2. Install dependencies:

   pip install -r requirements.txt

Usage

Run the Streamlit app:

streamlit run app.py

Follow the on-screen instructions to start and stop face detection.

File Structure

• app.py: Main Streamlit application script.
• main.py: Contains the face detection logic using OpenCV and dlib.
• requirements.txt: List of Python dependencies required for the project.

Contributing

Contributions are welcome! If you'd like to contribute to FaceSpotter, please follow these steps:

1. Fork the repository and create your branch (git checkout -b feature/AmazingFeature).
2. Commit your changes (git commit -am 'Add some AmazingFeature').
3. Push to the branch (git push origin feature/AmazingFeature).
4. Open a pull request.

Please ensure your code follows the existing style and includes necessary tests.

Thank you for using FaceSpotter! Detect faces in real-time effortlessly.