Image Processing API

This is a Go backend service that provides an API for image processing tasks, including converting images to JPEG format, resizing images, and compressing images.

Features

• Convert PNG images to JPEG format
• Resize images to specified dimensions
• Compress images with adjustable quality

Prerequisites

• Go (version 1.16 or later)
• Echo web framework
• Imaging library

Installation

1. Clone the repository:

git clone https://github.com/ilhamnyto/ubersnap-image-processing.git

2. Navigate to the project directory:

cd ubersnap-image-processing

3. Install the required dependencies:

go mod tidy

Usage

1. Start the server:

go run main.go

The server will start running on http://localhost:8000.

2. API Endpoints:

• GET / - Healthcheck endpoint
• POST /convert - Convert a PNG image to JPEG format
• POST /resize - Resize an image to specified dimensions
• POST /compress - Compress an image with adjustable quality

Convert Image

POST /convert

Request Body:

• file (multipart/form-data): The PNG image file to be converted.

Example:

curl -X POST "http://localhost:8000/convert" -F "file=@logo.png" > output.jpeg

Resize Image

Request Body:

• file (multipart/form-data): The image file to be resized.
• width (query parameter): The desired width for the resized image.
• height (query parameter): The desired height for the resized image.

Example:

curl -X POST "http://localhost:8000/resize?width=50&height=50" -F "file=@logo.png" > output.png

Compress Image

POST /compress

Request Body:

• file (multipart/form-data): The image file to be compressed.
• quality (query parameter): The desired quality for the compressed image (0-100).

Example:

curl -X POST "http://localhost:8000/compress?quality=80" -F "file=@logo.png" > output.png

Implementation Details

The API is implemented using the Echo web framework and the Imaging library for image processing tasks. The server spawns a pool of worker goroutines to handle concurrent image processing requests. The processed images are returned in the response body as JPEG data.

The code utilizes a semaphore-based approach to limit the number of concurrent workers and prevent resource exhaustion. Tasks are enqueued in a buffered channel and picked up by available workers.

Testing

To run the test suite, navigate to the project directory and execute the following command:

go test ./test

The test suite includes test cases for various scenarios, such as invalid input parameters and successful image processing operations.