README.md

metadata

title: imageup
emoji: 🎨
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
pinned: false

🎨 ImageUP - AI Image Colorization

📖 Project Overview

ImageUP is a full-stack web application that leverages Deep Learning to automatically colorize black-and-white images.

The project integrates a Flask backend with OpenCV's DNN module to run Caffe models for image processing. The frontend is designed with HTML5 and Tailwind CSS, providing a modern, drag-and-drop interface. The entire application is containerized using Docker for consistent deployment.

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📂 Repository Structure

File / Directory	Description
app.py	Main Application: Flask server handling routes, image processing, and inference logic.
download_models.py	Setup Script: Python script to automatically download the required Caffe model files.
Dockerfile	Container Config: Instructions to build the Docker image with Python 3.11 and system dependencies.
requirements.txt	Dependencies: List of Python libraries (flask, opencv-python, numpy, gunicorn).
templates/index.html	Frontend: User interface built with HTML and styled using Tailwind CSS (via CDN).
models/	Model Storage: Directory where colorization_release_v2.caffemodel and related files are stored.
static/	Assets: Contains uploads/ for input images and results/ for processed outputs.

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🛠️ Tech Stack & Dependencies

The project is built using the following technologies:

Category	Technology	Usage
Language	Python 3.11	Core programming language.
Web Framework	Flask	Handles HTTP requests and routing.
Computer Vision	OpenCV (cv2)	Image manipulation and Deep Learning inference.
Data Processing	NumPy	Matrix operations for image tensors.
Frontend	HTML5, JavaScript	Structure and client-side logic.
Styling	Tailwind CSS	Utility-first CSS framework for the UI.
Infrastructure	Docker	Containerization and deployment.
Server	Gunicorn	WSGI HTTP Server for production.

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✨ Key Features

• AI-Powered Colorization: Utilizes the Zhang et al. (2016) Caffe model for realistic color prediction.
• Drag & Drop Interface: User-friendly upload zone with immediate visual feedback.
• Real-time Processing: Fast inference pipeline using OpenCV's optimized DNN backend.
• Comparison View: Side-by-side display of the original grayscale and the new colorized image.
• Automatic Model Fetching: download_models.py handles the retrieval of large model weights.

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🚀 Usage Instructions

Prerequisites

• Docker (Recommended)
• OR Python 3.x with pip installed.

Option 1: Run with Docker (Easiest)

1. Build the Image

   docker build -t imageup-app .
   

2. Run the Container

   docker run -p 5000:5000 imageup-app
   

   Access the application at http://localhost:5000.

Option 2: Run Locally (Python)

1. Install Dependencies

   pip install -r requirements.txt
   

2. Download Models The model files are too large for git. Run this script to fetch them:

   python download_models.py
   

3. Start the Server

   python app.py
   

   Open your browser and navigate to http://localhost:5000.

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🧠 Model Details

The application uses the Colorful Image Colorization algorithm.

• Input: Lightness channel (L) from the LAB color space.
• Output: Predicted 'a' and 'b' color channels.
• Process: The system combines the original L channel with the predicted ab channels to reconstruct the final color image.

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👤 Author

Rishabh

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• LinkedIn