This Python script captures video from a webcam and performs real-time face detection using OpenCV’s built-in Haar cascades.
Key features include:

• Detection of multiple faces per frame.
• Smoothing of face positions using Exponential Moving Average (EMA) to reduce jitter.
• Dynamic scaling of label text based on the detected face size.
• Drawing bounding boxes around detected faces.
• Display of FPS (Frames Per Second) to monitor processing speed.

All detection and display parameters are fully configurable via constants at the top of the script.
This includes:

• Camera source index (CAMERA_SOURCE)
• Haar cascade parameters (FD_SCALE_FACTOR, FD_MIN_NEIGHBORS)
• Face box appearance (FACEBOX_COLOR, FACEBOX_THICKNESS)
• Label text settings (TEXT_FONT, TEXT_DEFAULT_SIZE, TEXT_COEFF, TEXT_MARGIN)
• Smoothing parameters (SMOOTH_ALPHA, SMOOTH_THRESHOLD)
• Matching distance for tracking faces (MATCH_MAX_DISTANCE)
• FPS display settings (FPS_COLOR, FPS_FONT_SCALE, FPS_POSITION, FPS_FONT)

This makes it easy to tweak the behavior and appearance of the face detection system without modifying the core logic.

1. Make sure you have Python 3.10 or higher installed.
2. Clone or download this repository.
3. Install required dependencies using the requirements.txt file:

   pip install -r requirements.txt

1. Connect a webcam or ensure your camera source is available.

2. Run the script:

   python main.py

3. A window will open showing the live video feed. Detected faces will have rectangles and labels.

4. Press q to exit the video window.