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.
Our face mask detector didn't use any morphed masked images dataset. The model is accurate, and since we used the CNN architecture, it’s also computationally efficient and thus making it easier to deploy the model to embedded systems (Raspberry Pi, Google Coral, etc.).
This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed.
The dataset used can be downloaded here - Click to Download
This dataset consists of 4000 images belonging to two classes:
- with_mask: 2000 images
- without_mask: 2000 images
The images used were real images of faces wearing masks. The images were collected from the following sources:
All the dependencies and required libraries are included in the file requirements.txt See here
- Clone the repo
$ git clone https://github.com/KingK619/Facemask-Detection.git
- Change your directory to the cloned repo
$ cd Face-Mask-Detection
- Create a Python virtual environment named 'test' and activate it
$ virtualenv test
$ source test/bin/activate
- Now, run the following command in your Terminal/Command Prompt to install the libraries required
$ pip3 install -r requirements.txt
- Open terminal. Go into the cloned project directory and type the following command:
$ python3 train_mask_detector.py --dataset dataset
- To detect face masks in an image type the following command:
$ python3 detect_mask_image.py --image images/pic1.jpeg
- To detect face masks in real-time video streams type the following command:
$ python3 detect_mask_video.py
Face Mask Detector webapp using Tensorflow & Streamlit command
$ streamlit run deploy.py
Feel free to file a new issue with a respective title and description on the the Facemask_Detection repository. If you already found a solution to your problem, We would love to review your pull request!