This project is a face detection and queue management system developed using the AWS Rekognition API. It allows users to detect faces in uploaded images and manage their place in a queue for ordering bread. The project is implemented using Django framework and utilizes various AWS services.
- Face detection using AWS Rekognition API
- Queue management for bread ordering
- Integration with AWS S3 for image storage
- User-friendly web interface using Django framework
Before running the project, make sure you have the following prerequisites:
- Python 3.x installed on your machine
- AWS account with access to the Rekognition service
- Django framework installed
- dotenv library installed
- Clone the repository: pip install -r requirements.txt
The Face Detection and Queue Management System is a web application that leverages the power of the AWS Rekognition API to detect faces in images and provide queue management functionality for bread ordering. The system allows users to upload images, performs face detection using the AWS Rekognition service, and manages the queue of orders.
The application utilizes the Django framework for its backend implementation and integrates with various AWS services, including AWS Rekognition for face detection, AWS S3 for image storage, and AWS credentials for authentication.
By using this system, users can easily detect faces in images, check if a face matches an existing order, place new orders, and view their position in the queue along with the estimated waiting time. The system provides an intuitive and user-friendly interface for a seamless user experience.
The key features of the Face Detection and Queue Management System include:
- Face detection and matching using the AWS Rekognition API.
- Queue management functionality for bread ordering.
- Integration with AWS S3 for efficient storage of images.
- Real-time estimation of waiting time for each order.
- User-friendly web interface powered by Django.
With this system, businesses can streamline their bread ordering process, enhance customer experience, and efficiently manage the queue of orders.