Real-time Face Blurring using OpenCV

This Python script utilizes OpenCV to perform real-time face detection and blurring in video streams from a webcam. It detects faces in the video frames using a pre-trained Haar Cascade classifier and applies Gaussian blur to the detected faces while keeping the rest of the frame unchanged.

Features

• Real-time Face Detection: Detects faces in the video stream from the webcam in real-time.
• Face Blurring: Applies Gaussian blur to the detected faces to anonymize them while preserving the rest of the frame.
• Video Capture: Utilizes OpenCV's VideoCapture module to access the video stream from the default camera.
• User Interaction: The script exits when the Enter key is pressed, allowing for user-controlled termination.

Installation

1. Install OpenCV: Install OpenCV library using pip if not already installed.

   pip install opencv-python

2. Download Haar Cascade Classifier: Ensure that you have the Haar Cascade XML file (haarcascade_frontalface_default.xml) for face detection. You can find it in the data/haarcascades directory of your OpenCV installation.

3. Run the Script: Execute the Python script provided (face_blurring.py or any preferred filename) using a Python interpreter.

   python face_blurring.py

4. Terminate the Script: Press the Enter key to exit the script and close the video stream window.

Dependencies

• OpenCV: Open Source Computer Vision Library for image and video processing.

Notes

• You may need to adjust the parameters of the detectMultiScale function (e.g., scaleFactor, minNeighbors, minSize) based on the environmental conditions for optimal face detection performance.
• Ensure that your webcam is correctly configured and accessible by OpenCV.

License

This project is licensed under the MIT License. See the LICENSE file for details.