Learn how to create a real-time face mask detector using Tensorflow, Keras, and OpenCV with your webcam or mobile camera

Introduction

Face Mask Detection Platform uses Artificial Network to recognize if a user is not wearing a mask. This is a deep learning project.I used Convolutional Neural Networks to classify Mask image.You will learn how to train a COVID-19 face mask detector with OpenCV, Keras/TensorFlow, and Deep Learning.

An example of Apps output.

Why mask_CNN.ipynb

In this project, Convolutional Neural Network(CNN) used as an image classifier. We used a pre-trained CNN model named ResNet50 to implement a transfer learning approach. We have two types of data face with mask and face with no mask. These two types of data are classified by the CNN model.

Why App.py

We implement an open-cv based face detection model. By using OpenCV haarcascade_frontalface_default.xml file we extract face ROI from an image. Then the face ROI image fits it to the CNN model. Furthermore, like a binary classifier, the CNN model classifies whether the face ROI image is face with a mask or not.

How to use my code

With my code, you can:

• Open cnn.ipynb => Train CNN model from scratch and save weight as h5 file

• Run App.py

• **for more to know click the image below(youtube video) **

Dependencies:

• python 3
• Tensorflow
• Keras
• opencv (cv2)
• Numpy
• Pillow

Datasets:

I used 4 different datases: VOC2007, VOC2012, COCO2014 and COCO2017. Statistics of datasets I used for experiments is shown below

Dataset	Classes	#Train images/objects	#Validation images/objects
Own	2	1200+	1200+