Face Mask Detection

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Face Mask Detection system built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect face masks in static images and static videos. Also it is as well as in real-time video streams.

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👉 Which Tech & framework used ?

• OpenCV
• OpenH264
• Face detector Model
• Keras
• TensorFlow
• MobileNetV2
• Streamlit

🔥 What is Streamlit?

Streamlit is an open-source Python library that makes it easy to build beautiful custom web-apps for machine learning and data science. To use it, just pip install streamlit , then import it, write a couple lines of code, and run your script with streamlit run [filename]

🌈 Introduction

In the present scenario due to Covid-19, there is no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. Also, the absence of large datasets of ‘with_mask’ images has made this task more cumbersome and challenging.

⚡️ Project Demo

• Static Image

• Static Video

• Realtime - Webcam

📁 Dataset

The dataset used can be downloaded here - Click to Download

This dataset consists of 3835 images belonging to two classes:

• with_mask: 1916 images
• without_mask: 1919 images

The images used were real images of faces wearing masks. The images were collected from the following sources:

• Bing Search API (See Python script)
• Kaggle datasets
• RMFD dataset (See here)

📌 Prerequisites

All the dependencies and required libraries are included in the file requirements.txt See here

Also, If you want to upload to the web, you need to download OpenH264. Link ✅ (https://https://github.com/cisco/openh264)

After download openh264 dll, move it into python library.

🚀 How to Install

1. Clone the repo

$ git clone https://github.com/Hott-J/Face-Mask-Detection.git

2. Change your directory to the cloned repo and create a Python virtual environment named 'test'

$ mkvirtualenv test

3. Install the libraries required

$ pip3 install -r requirements.txt / pip install -r requirements.txt

💥 How to Run

1. Go into the cloned project directory folder and type the following command:

$ python3 train_mask_detector.py --dataset dataset

2. To detect face masks in a static image, type the following command:

$ python3 detect_mask_image.py --image images/pic1.jpeg

3. To detect face masks in a static video streams, type the following command:

$ python3 detect_mask_video.py 

🍭 Results

This Model gave 93% accuracy for Face Mask Detection after training via tensorflow-gpu==2.0.0

We got the following accuracy/loss training curve plot

🐶 How to Run in Streamlit Webapp

1. Go into the cloned project directory folder and type the following command:

$ streamlit run app.py 

☘️ Finish!

Feel free to mail me for any query! Thank you ❤️