parvvaresh / face-mask

The Face Mask Detection script is a real-time application that utilizes deep learning techniques to identify whether a person in a webcam feed is wearing a face mask or not.

Face Mask Detection using TensorFlow and OpenCV

This is a Python script for real-time face mask detection using a pre-trained MobileNetV2 model with TensorFlow and OpenCV. It can process live video from a webcam and classify faces as wearing a mask or not wearing a mask.

Requirements

Before running the code, make sure you have the following dependencies installed:

• TensorFlow
• OpenCV
• imutils
• NumPy

You can install these dependencies using pip:

pip install tensorflow opencv-python imutils numpy

Usage

1. Clone the repository or download the code files to your local machine.

2. Download the pre-trained face detection model and save it in the face_detector directory. You can get the model files from the OpenCV GitHub repository:

   • deploy.prototxt
   • res10_300x300_ssd_iter_140000.caffemodel

3. Download the pre-trained face mask detection model (mask_detector.model) and place it in the same directory as the script.

4. Run the script:

python face_mask_detection.py

5. A window will open, showing the webcam feed with face mask predictions. Press 'q' to quit the application.

How It Works

• The script uses the MobileNetV2 model for face mask detection. It first detects faces in each frame using the pre-trained face detection model.

• If a face is detected, it extracts the face region, pre-processes it, and passes it through the face mask detection model to classify whether the person is wearing a mask or not.

• The result is displayed on the video feed, with bounding boxes and labels.

License

This project is licensed under the MIT License.