/aws-eduscan

Primary LanguagePython

EduScan

A classroom assistant for seamless student identification and academic data retrieval.

Project Description

Eduscan is a python application that uses AWS Lambda and related services to process uploaded classroom videos, recognize students' faces and retrieve academic data.

Key Tasks:

  1. Video Upload: Users upload videos to an S3 input bucket.

  2. Video Processing: Lambda function processes videos, extracts frames, and recognizes faces.

  3. Face Recognition: Academic data is retrieved based on recognized faces from DynamoDB.

  4. Data Preloading: Student data is preloaded into DynamoDB.

  5. Custom Lambda Function: Utilize a custom container image with preinstalled tools for Lambda.

  6. Academic Data Storage: Store academic info in CSV format in an S3 output bucket.

  7. Testing: Sample videos and workload generator for testing.

Deliverables:

  • User-friendly UI for video uploads.
  • AWS Lambda for video processing.
  • DynamoDB preloading with academic data.
  • Properly formatted content in the output S3 bucket.
  • Sample videos for testing.
  • Workload generator for validation.

This project aims to enhance classroom management with efficient student recognition and data retrieval.

AWS Eduscan Architecture

Installation and Usage

Prerequisites

Before you begin, ensure you have met the following requirements:

  • Python 3.6 or higher
  • AWS account with access to S3, ECR, Lambda, DynamoDB
  • Streamlit
  • VS Code

Usage

  1. Clone the repository:

    git clone https://github.com/sreeharsha-rav/aws-eduscan.git

  2. Navigate to the project folder:

    cd aws-eduscan

  3. Go to the dynamoDB-setup folder and use the code to set up DynamoDB.

  4. Go to the aws-lambda-setup folder and use the code to set up AWS Lambda.

  5. Create a virtual environment (optional):

    python -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate

  6. Go to the python-frontend folder and follow the instructions to run the frontend.

Note

  • To run tests for AWS Lambda locally on terminal goto workload_test folder and follow instructions.

Credits

This project is part of CSE 546 - Cloud Computing course curriculum at ASU.