Face Mask Detection

This project aims to detect whether a person is wearing a face mask or not using a deep learning model. The project includes training a model, detecting masks in images, and detecting masks in real-time video streams.

Project Structure

.DS_Store
.idea/
	.gitignore
	Face-Mask-Detection-master.iml
	inspectionProfiles/
		profiles_settings.xml
	misc.xml
	modules.xml
dataset/
	with_mask/
	without_mask/
detect_mask_video.py
face_detector/
	deploy.prototxt
	res10_300x300_ssd_iter_140000.caffemodel
FrontEnd.py
mask_detector.model
requirements.txt
train_mask_detector.py

Requirements

Install the required packages using the following command:

pip install -r requirements.txt

Training the Model

To train the face mask detection model, run the

train_mask_detector.py

script:

python train_mask_detector.py

This script will:

1. Load and preprocess the dataset.
2. Train a MobileNetV2 model on the dataset.
3. Save the trained model as

mask_detector.model

.

Detecting Masks in Video Streams

To detect masks in real-time video streams, run the

detect_mask_video.py

script:

python detect_mask_video.py

This script will:

1. Load the face detector model from

deploy.prototxt

and

res10_300x300_ssd_iter_140000.caffemodel

. 2. Load the face mask detection model from

mask_detector.model

. 3. Start the video stream and detect masks in real-time.

Frontend

To run a simple frontend that captures video from the webcam, run the

FrontEnd.py

script:

python FrontEnd.py

Dataset

The dataset should be organized as follows:

dataset/
	with_mask/
	without_mask/

Each folder should contain images of people with and without masks, respectively.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgements

• The face detection model is based on the Single Shot MultiBox Detector (SSD) framework with a ResNet-10 architecture.
• The face mask detection model is based on the MobileNetV2 architecture.