📘 Image Processing API (CAB432 Assignment 1)

A REST API built with Node.js + Express + TypeScript, containerised with Docker, and deployable on AWS EC2 via ECR.
This app demonstrates CPU-intensive image processing using Sharp — resizing, blurring, and sharpening images up to 8K resolution — to sustain >80% CPU load.

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✨ Features

• REST API with endpoints for auth, jobs, and images.
• JWT Authentication (two hardcoded users: alice + bob).
• CPU-intensive image pipeline using Sharp (resize + blur + sharpen).
• Two data types:
  • Unstructured → images (.png)
  • Structured → job metadata (UUIDs in memory)
• Containerised with Docker and ready for AWS deployment.
• Includes a load test script to drive CPU >80%.

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🚀 Getting Started (Local Dev)

1. Install dependencies

npm install

2. Generate seed image

npx ts-node scripts/generate_seed.ts

3. Run the API (dev mode)

export JWT_SECRET=changeme
npm run dev

API will be available at: http://localhost:3000

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🔑 Authentication

Login with hardcoded credentials:

curl -X POST http://localhost:3000/v1/auth/login  -H "Content-Type: application/json"  -d '{"username":"alice","password":"password1"}'

Response:

{ "token": "<JWT_TOKEN>" }

Use this token in all Authorization headers:

Authorization: Bearer <JWT_TOKEN>

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🖼️ Example Job

Submit a CPU-heavy image job:

curl -X POST http://localhost:3000/v1/jobs  -H "Authorization: Bearer <JWT_TOKEN>"  -H "Content-Type: application/json"  -d '{
  "sourceId":"seed",
  "ops":[
    {"op":"resize","width":7680,"height":4320},
    {"op":"blur","sigma":10},
    {"op":"sharpen","sigma":2},
    {"op":"resize","width":7680,"height":4320}
  ]
}'

Response:

{
  "id": "ea93a2fb-1984-42d9-a924-ff35f2955284",
  "output": {
    "imageId": "ea93a2fb-1984-42d9-a924-ff35f2955284",
    "url": "/v1/images/ea93a2fb-1984-42d9-a924-ff35f2955284"
  }
}

View the result:

http://localhost:3000/v1/images/ea93a2fb-1984-42d9-a924-ff35f2955284

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📈 Load Testing

Hammer the API to prove >80% CPU:

export JWT=<YOUR_TOKEN>
npm run load:test

Optional parameters:

C=12 D=300 npm run load:test   # 12 workers for 5 minutes

Watch CPU usage with Activity Monitor (Mac) or top.

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🐳 Docker

Build

docker buildx build --platform linux/amd64 -t imgproc-api:0.1.0 .

Run

docker run --rm --platform linux/amd64 -p 3000:3000  -e JWT_SECRET=$(openssl rand -hex 16)  imgproc-api:0.1.0

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☁️ AWS Deployment (Summary)

1. Build & tag the image.

2. Push to AWS ECR:

   aws ecr create-repository --repository-name imgproc-api
   aws ecr get-login-password --region ap-southeast-2    | docker login --username AWS --password-stdin <ACCOUNT_ID>.dkr.ecr.ap-southeast-2.amazonaws.com
   
   docker tag imgproc-api:0.1.0 <ACCOUNT_ID>.dkr.ecr.ap-southeast-2.amazonaws.com/imgproc-api:0.1.0
   docker push <ACCOUNT_ID>.dkr.ecr.ap-southeast-2.amazonaws.com/imgproc-api:0.1.0

3. On EC2 (Ubuntu 24.04), install Docker, then:

   sudo docker pull <ACCOUNT_ID>.dkr.ecr.ap-southeast-2.amazonaws.com/imgproc-api:0.1.0
   sudo docker run -d -p 80:3000 -e JWT_SECRET=$(openssl rand -hex 16) imgproc-api:0.1.0

4. Test via EC2 public DNS:

   http://<ec2-public-dns>/v1/auth/login
   

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📂 Project Structure

src/
 ├── routes/          # Express routes (auth, jobs, images)
 ├── middleware/      # JWT auth, error handler
 ├── services/        # Sharp image pipeline
 ├── app.ts           # App setup
 └── server.ts        # Entrypoint

scripts/
 ├── generate_seed.ts # Writes seed.png to originals/
 └── load_test.ts     # Load generator

storage/
 ├── originals/       # Input images (seed.png)
 └── outputs/         # Job results

Dockerfile
.dockerignore
README.md

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👥 Users

• alice / password1
• bob / password2

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📜 License

For educational use (CAB432 assignment).