Automated Image Processing Application with Algorithm Optimization

Aplikasi Pemrosesan Gambar Otomatis dengan Optimasi Algoritma

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📖 Introduction

This project aims to develop a web-based image processing application that is fast, efficient, and reliable.
The application is designed to:

• Enhance image quality through various visual filters.
• Optimize image file sizes without sacrificing quality.
• Provide users with flexibility to resize images as needed.

With the rapid growth of digital technology, the need for quick and efficient image management tools is increasing, catering to personal, social media, and professional requirements.

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🚀 Key Features

📷 Image Filters

• Blur: Adds a soft blur effect to focus on the main subject or smooth the background.
• Grayscale: Converts color images to black-and-white with tonal gradations.
• Sharpen: Enhances image sharpness and detail.
• Sepia: Creates a vintage or nostalgic effect with a warm brown tint.
• Edge Detection: Highlights object boundaries for artistic or analytical purposes.
• Selective Filter: Applies effects to specific parts of an image without affecting the whole.

🗜️ Image Compression

• Lossless Compression: Reduces file size without any quality loss.
• Compression by Percentage: Allows users to choose compression levels (e.g., 20%, 50%, 90%).

📏 Image Resizing

• Resize by Percentage: Adjusts image dimensions relative to the original size (e.g., 50%).
• Resize by Pixel: Sets image dimensions to specific width × height.
• Algorithm-Based Resizing: Uses optimization algorithms like Divide and Conquer for efficiency.

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🔧 Technologies Used

• Frontend: HTML, CSS, JavaScript for interactive user interfaces.
• Backend: Python with Flask for image processing logic.
• Libraries: Pillow, OpenCV, Numpy for image manipulation and analysis.

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📝 Objectives

• Provide a practical and efficient solution for image editing.
• Simplify compression, resizing, and filter application for users.
• Enhance computational efficiency using algorithms like Greedy, Divide and Conquer, and Dynamic Programming.

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📂 Project Structure

• app.py: Backend for image processing.
• static/: Static files such as CSS and JavaScript.
• templates/: HTML files for the user interface.
• README.md: Project documentation.

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🌟 How to Run the Project

1. Clone the repository:

   git clone https://github.com/username/automated-image-processing.git
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the application::

   python app.py