Skip to content

Latest commit

 

History

History
37 lines (22 loc) · 1.62 KB

README.md

File metadata and controls

37 lines (22 loc) · 1.62 KB
Raw

Image processing API build with FastAPI

An simple, filesystem based image processing API build on top of the FastAPI framework. The purpose of this project is to display an API building expertise while researching the capabilities of a very promising library.

Goals

I wanted to create an API capable of handling images via POST and GET requests.

  • POST "/images/" is capable of receiving image files and returning its a unique id.

  • GET "/images/:id?w=xx&h=xx" is capable of returning images stored in the filesystem. It takes 3 arguments where

    • id is the reference id of the image previously stored via POST requests.
    • w if specified is the width of an image to be returned.
    • h if specified is the height of an image to be returned.

    Additionally if only one of the optional (w or h) parameters is missing then the other will need to be calculated per ratio of the one provided, following this formula.

$$ h = (w/image.width) * image.height $$

System Environment

  • Python >= 3.9 (3.9.6 recommended).
  • FastAPI requires you to have a Rust bundler/compiler when installing a venv environment.

Setup

  1. Create a virtual environment by running python -m venv venv in your root directory.
  2. Install required packages by running venv/bin/pip install -r requirements.txt.

Running the server locally

In a venv shell run uvicorn server:app --reload. Given no errors, the server should be available per default configuration on http://127.0.0.1:8000. Go to http://127.0.0.1:8000/docs to see the implemented endpoints.