How to Build a Face Mask Detection System: A Practical Guide for Beginners

Introduction:

Face mask detection has become an essential tool in ensuring public safety during the COVID-19 pandemic. In this post, I’ll show you how to build a simple face mask detection system using Python, OpenCV, and a pre-trained deep learning model. This project is based on my publication, "Face Mask Detection Application and Dataset," which you can find here.

1. Prerequisites

Before we begin, make sure you have the following installed:

• Python 3.x
• OpenCV
• TensorFlow or PyTorch

You’ll also need a dataset of images with and without face masks. You can use the dataset from my publication or create your own.

2. Loading the Dataset

Here’s how to load and preprocess the dataset:

import cv2
import os

def load_images_from_folder(folder):
    images = []
    for filename in os.listdir(folder):
        img = cv2.imread(os.path.join(folder, filename))
        if img is not None:
            images.append(img)
    return images

mask_images = load_images_from_folder('data/mask')
no_mask_images = load_images_from_folder('data/no_mask')

3. Training the Model

Use a pre-trained model like MobileNetV2 for transfer learning. Fine-tune the model on your dataset to classify images as “mask” or “no mask.”

4. Real-Time Detection

Integrate the model with OpenCV to perform real-time face mask detection using your webcam:

import cv2

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    # Add face detection and mask classification logic here
    cv2.imshow('Face Mask Detection', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Conclusion:

Building a face mask detection system is a great way to learn about computer vision and deep learning. If you’d like to see the full code or need help with implementation, feel free to reach out or check out my GitHub!