Udagram Image Filtering Microservice

Udagram is a simple cloud application developed alongside the Udacity Cloud Engineering Nanodegree. It allows users to register and log into a web client, post photos to the feed, and process photos using an image filtering microservice.

The project is split into three parts:

1. The Simple Frontend A basic Ionic client web application which consumes the RestAPI Backend.
2. The RestAPI Backend, a Node-Express server which can be deployed to a cloud service.
3. The Image Filtering Microservice, the final project for the course. It is a Node-Express application which runs a simple Python script to process images.

Tasks

Setup Python Environment

You'll need to set up and use a virtual environment for this project.

To create a virtual environment run the following from within the project directory:

1. Install virtualenv dependency: pip install virtualenv
2. Create a virtual environment: virtualenv venv
3. Activate the virtual environment: source venv/bin/activate (Note: You'll need to do this every time you open a new terminal)
4. Install dependencies: pip install -r requirements.txt

When you're done working and leave the virtual environment, run: deactivate

Setup Node Environment

You'll need to create a new node server. Open a new terminal within the project directory and run:

1. Initialize a new project: npm init
2. Install express: npm i express --save
3. Install typescript dependencies: npm i ts-node-dev tslint typescript @types/bluebird @types/express @types/node --save-dev
4. Look at the package.json file from the RestAPI repo and copy the scripts block into the auto-generated package.json in this project. This will allow you to use shorthand commands like npm run dev

Create a new server.ts file

Use our basic server as an example to set up this file. For this project, it's ok to keep all of your business logic in the one server.ts file, but you can try to use feature directories and app.use routing if you're up for it. Use the RestAPI structure to guide you.

Add an endpoint to handle POST /imagetoprocess requests

It should accept two POST parameter:

image_url: string - a public URL of a valid image file

upload_image_signedUrl: string (OPTIONAL) - a URL which will allow a PUT request with the processed image

It should respond with 422 unprocessable if either POST parameters are invalid.

It should require a token in the Auth Header or respond with 401 unauthorized.

It should be versioned.

The matching token should be saved as an environment variable

(TIP we broke this out into its own auth.router before, but you can access headers as part of the req.headers within your endpoint block)

It should respond with the image as the body if upload_image_signedUrl is included in the request.

It should respond with a success message if upload_image_signedUrl is NOT included in the request.

Refactor your RestApi server

Add a request to the image-filter server within the RestAPI POST feed endpoint

It should create new SignedURLs required for the imagetoprocess POST Request body.

It should include a POST request to the new server (TIP keep the server address and token as environment variables).

It should overwrite the image in the bucket with the filtered image (in other words, it will have the same filename in S3).

Deploying your system!

Follow the process described in the course to eb init a new application and eb create a new environment to deploy your image-filter service!

Stand Out

Postman Integration Tests

Try writing a postman collection to test your endpoint. Be sure to cover:

POST requests with and without tokens POST requests with valid and invalid parameters

Refactor Data Models

Try adding another column to your tables to save a separate key for your filtered image. Remember, you'll have to rename the file before adding it to S3!

(ADVANCED) Refactor Data Models

Try adding a second OpenCV filter script and add an additional parameter to select which filter to use as a POST parameter