Digital Image Processing Web App
Web Application
Year
2023
Context
Academic Project [Individual Project]
Role
Fullstack Developer
Python
Flask
HTML/CSS
BootstrapProject Overview
A full-stack application performing server-side image manipulation, applying OpenCV transformations like Canny edge detection and histogram equalization to user uploads.
The Problem
The primary challenge was translating theoretical digital image processing algorithms into a functional web environment. Executing heavy mathematical transformations—such as histogram calculations and edge detection—on a server requires efficient file handling and low-latency processing to ensure the user interface remains responsive after an image upload.
The Solution
I engineered a Flask-based backend that integrates OpenCV to perform real-time image manipulation. The application allows users to upload photos and select from a suite of operations, including Canny edge detection and histogram equalization. I also implemented a dynamic histogram generation feature and a unique 'Image Puzzle' mini-game that programmatically splits and randomizes image segments to test spatial reconstruction logic.
The Impact & Learning
This project served as a deep dive into the practical application of image processing theorems. I gained significant experience in Python-based backend performance, particularly in managing binary file streams and optimizing OpenCV buffers for web delivery. It taught me how to bridge the gap between scientific computing and interactive web design, creating a tool that is both educational and engaging.
