OOP Image Processing Application

A desktop application built with Python, Tkinter, and OpenCV that demonstrates Object-Oriented Programming principles and provides comprehensive image processing capabilities.

📋 Project Overview

This application is developed as part of HIT137 Assignment 3, showcasing:

• Object-Oriented Programming principles (Encapsulation, Constructors, Methods, Class Interaction)
• GUI Development using Tkinter
• Image Processing using OpenCV

✨ Features

Image Processing Operations

• Grayscale Conversion - Convert images to black and white
• Blur Effect - Apply Gaussian blur with adjustable intensity
• Edge Detection - Canny edge detection algorithm
• Brightness Adjustment - Increase or decrease image brightness
• Contrast Adjustment - Modify image contrast levels
• Image Rotation - Rotate by 90°, 180°, or 270°
• Image Flip - Flip horizontally or vertically
• Resize/Scale - Adjust image dimensions

GUI Features

• Menu Bar with File (Open, Save, Save As, Exit) and Edit (Undo, Redo) options
• Image Display Area for real-time preview
• Control Panel with easy-to-use buttons and sliders
• Status Bar showing current image information
• File Dialog Support for JPG, PNG, and BMP formats
• Error Handling with message boxes

🛠️ Technologies Used

• Python 3
• Tkinter – GUI development
• OpenCV (cv2) – Image processing
• Pillow (PIL) – Image rendering in GUI
• NumPy – Image data handling

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🚀 Getting Started

Prerequisites

• Python 3.7 or higher
• pip (Python package installer)

Installation

1. Clone the repository

git clone https://github.com/mazharsnow/oop-image-editor.git
cd oop-image-editor

2. Create a virtual environment

Open terminal in VS Code and run:

Windows:

python -m venv venv
venv\Scripts\activate

macOS / Linux:

python3 -m venv venv
source venv/bin/activate

You should see (venv) in your terminal prompt.

3. Install required libraries

pip install opencv-python pillow numpy

4. Verify installation

python -c "import cv2, PIL, numpy; print('All libraries installed successfully!')"

🎮 Usage

1. Run the application

python main.py

2. Open an image

   • Click File → Open or use the Open button
   • Select an image file (JPG, PNG, or BMP)

3. Apply effects

   • Use the control panel buttons to apply various effects
   • Adjust sliders for intensity-based effects (blur, brightness, contrast)
   • Preview changes in real-time

4. Save your work

   • Click File → Save to overwrite the original
   • Click File → Save As to save with a new name

5. Undo/Redo

   • Use Edit → Undo or Edit → Redo to navigate through changes

📁 Project Structure

OOP_PROJECT/
│
├── assets/
│   ├── icons/              # UI icons
│   │   ├── exit.png
│   │   ├── open.png
│   │   ├── redo.png
│   │   ├── save_as.png
│   │   ├── save.png
│   │   └── undo.png
│   └── istockphoto-183412466-612x612.jpg
│
├── outputs/                # Saved processed images
│   └── edited_image.jpg
│
├── venv/                   # Virtual environment (not tracked)
│
├── gui.py                  # GUI class implementation
├── image_manager.py        # Image management class
├── image_processor.py      # Image processing class
├── main.py                 # Main application entry point
├── github_link.txt         # GitHub repository link
└── README.md              # This file

🏗️ Architecture

Object-Oriented Design

The application follows OOP principles with three main classes:

1. ImageProcessor (image_processor.py)

   • Handles all image processing operations
   • Encapsulates OpenCV functions
   • Methods for filters, effects, and transformations

2. ImageManager (image_manager.py)

   • Manages image state and history
   • Implements undo/redo functionality
   • Handles file I/O operations

3. GUI (gui.py)

   • Manages the Tkinter interface
   • Handles user interactions
   • Coordinates between ImageProcessor and ImageManager

Class Interaction

main.py
   └── GUI
       ├── ImageManager (manages image state)
       └── ImageProcessor (processes images)

🔮 Future Enhancements

• Additional filters (sepia, vintage, etc.)
• Batch processing capabilities
• Image filters preview before applying
• Keyboard shortcuts for common operations
• Custom filter creation

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Note: Make sure to keep your virtual environment activated when running the application. If you close the terminal, reactivate it using the appropriate command for your operating system.