Image Processing Service

A service for processing images. This service allows users to upload images and apply various transformations such as resizing, cropping, rotating, and applying filters like grayscale and sepia. The transformations are processed asynchronously using Celery and RabbitMQ.

Project Description

For a complete project overview, visit the Roadmap.

---

Features

• Image Upload: Upload images for processing.
• Image Transformations: Apply transformations such as resize, crop, rotate, grayscale, and sepia.
• Asynchronous Processing: Image transformations are processed asynchronously using Celery and RabbitMQ.

---

Getting Started

These instructions will help you set up the project locally for development and testing.

Prerequisites

• Python 3.12+
• uv: Environment management
• RabbitMQ: For Celery to work, RabbitMQ must be running.
• Database: SQLite (by default, but can be configured for other databases).

Clone the Repository

git clone [email protected]:devid8642/image-processing-service.git
cd image-processing-service

Setting Up the Environment

uv sync

Set up the environment variables

cp env.example .env

Database Migrations

alembic upgrade head

Running RabbitMQ

docker run -d -p 5672:5672 -p 15672:15672 --name rabbitmq rabbitmq:management

Running the Project

task run

In another terminal:

task celery

Running Tests

task test

Project Structure

• image_processing_service/main.py: The entry point for the FastAPI application.
• image_processing_service/models.py: SQLAlchemy models for the application (e.g., Image).
• image_processing_service/services: Business logic.
• image_processing_service/tasks.py: Celery tasks.
• image_processing_service/celery.py: Configuration for Celery and RabbitMQ.
• image_processing_service/settings.py: Configuration for the application (e.g., database URL, secret key).
• image_processing_service/schemas/: Pydantic schemas for validating data.
• uploads/: Directory for storing uploaded images.