Face Mask Detection

A Django-powered image processing application to detect whether a person is wearing a mask.

Live Link : https://face-mask-detection-12.onrender.com

Overview

This project leverages the power of Django and computer vision techniques to accurately identify individuals wearing face masks in images. It's a practical solution for various applications, such as security systems, public health monitoring, and more.

Features

• Real-time Image Processing: Quickly analyze images and return accurate results.
• Robust Model: A well-trained model capable of handling diverse face orientations and lighting conditions.
• User-Friendly Interface: A simple and intuitive interface for easy interaction.
• Scalability: Designed to handle large-scale image processing tasks.

Getting Started

1. Clone the Repository:

   git clone [https://github.com/your-Myash21/face-mask-detection.git](https://github.com/your-Myash21/face-mask-detection.git)

2. Set Up Virtual Environment:

   python -m venv venv
   source venv/bin/activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Run the Django Server:

   python manage.py runserver

How it Works

1. Image Upload: Users can upload an image to the web application.
2. Image Preprocessing: The uploaded image is preprocessed to enhance features and normalize the input.
3. Model Inference: The preprocessed image is fed into a trained deep learning model, which predicts whether a face mask is present.
4. Result Display: The application displays the prediction result, along with the processed image and confidence score.

Future Improvements

• Real-time Video Analysis: Extend the application to process video streams in real-time.
• Mobile Application: Develop a mobile app for on-the-go mask detection.
• Edge Deployment: Optimize the model for deployment on edge devices.

Contributing

We welcome contributions to improve this project. Feel free to fork the repository, make changes, and submit a pull request.