PronOS Digital Image Processing (DIP) Platform

An advanced, interactive educational platform for Digital Image Processing (DIP). This application provides a hands-on, visually rich environment for understanding complex computer vision algorithms, mathematical transformations, and electromagnetic spectrum fundamentals.

It features a custom-built "PronOS" clean light theme for maximum readability and a professional UX.

🚀 Features

1. Fundamentals Concept Modules

Organized into interactive Chapters:

• Chapter 1: Fundamentals of Image Formation:
  • 1.1 Visual Perception (Scotopic/Photopic adaptation, Mach bands)
  • 1.2 Electromagnetic Spectrum
  • 1.3 Image Acquisition topology
• Chapter 2: Image Digitization:
  • 2.1 Sampling & Quantization resolution testing
  • 2.2 Pixel Connectivity (4, 8, m-paths)
  • 2.3 Distance Measures
• Chapter 3: Image Representation & Math Tools:
  • 3.1 Mathematical Tools (Interactive 6-tab suite for Arithmetic, Statistical, Trigonometric, Transforms, Morphology, and Filtering)
  • 3.2 Connected Components
  • 3.3 Image Statistics
• Chapter 4: Advanced Vision Concepts:
  • 4.1 3D Eye Vision Game Model: A sophisticated physics engine demonstrating light rays tracking a visual object into the eye.
  • Anatomical Eye Exploration: Interactive, dynamically rendered 2D eye cross-section. Toggle labels, highlight physiological features, and physically mutate sliders (Pupil Dilation, Lens Thickness, Cornea Bulge) to simulate Myopia and Astigmatism directly onto an image feed!

2. Advanced Processing Track

• Chapter 5: Spatial Domain Processing:
  • 5.1 Spatial Filtering (Convolution, median, and Gaussian blur kernels)
• Chapter 6: Frequency Domain Processing:
  • 6.1 Frequency Domain (Ideal, Butterworth, and Gaussian filtering)
• Chapter 7: Morphological Image Processing:
  • 7.1 Morphology (Erosion, Dilation, Opening, Closing routines)

Global Features

• Native Streamlit Light & Dark Mode: The custom CSS seamlessly inherits Streamlit's native theme engine without aggressive overrides. Toggle between light and dark modes in the settings menu for a pixel-perfect, highly readable aesthetic in any environment.
• Unified Global Image Override: Upload a custom image directly from the unified sidebar to inject it simultaneously across a dozen different physics and mathematical simulations across the app's chapters.

💻 Tech Stack

• Frontend / Framework: Streamlit
• Image Processing: OpenCV (opencv-python-headless)
• Math & Matrices: NumPy, SciPy
• Plotting: Matplotlib

🛠️ Local Installation

1. Clone the repository:

   git clone https://github.com/YOUR_USERNAME/YOUR_REPOSITORY.git
   cd YOUR_REPOSITORY

2. Create a Virtual Environment (Recommended):

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # macOS/Linux
   source venv/bin/activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Run the Application:

   streamlit run app.py

🌐 Deployment (Render Free Tier via GitHub)

This repository is fully configured to deploy as a Docker Container on the Render Free Tier, driven automatically by GitHub pushes.

Why Render + Docker?

Streamlit apps running complex image processing libraries (like OpenCV) often crash on standard cloud hosting due to missing C++ system dependencies. We have included a Dockerfile that automatically handles these Linux dependencies (like libgl1-mesa-glx) so the app runs flawlessly. The included render.yaml acts as a one-click deployment blueprint.

Deployment Steps:

1. Push to GitHub: Ensure this codebase is pushed to a public or private GitHub repository.

   git remote add origin https://github.com/YOUR_USERNAME/YOUR_REPOSITORY.git
   git push -u origin main

2. Connect Render:
   • Sign up / Log in to Render Dashboard.
   • Click the New + button and select Blueprint.
3. Deploy:
   • Connect your GitHub account.
   • Select the repository where you pushed this code.
   • Render will automatically detect the render.yaml file.
   • Click Apply.

Render will now read the blueprint, build the Docker container using our Dockerfile, install OpenCV and Streamlit, and generate a live URL for your application!

Note: Because this uses the Render Free Tier, the application will "spin down" after 15 minutes of inactivity. When you visit it again, it may take 30-50 seconds to wake up.

📁 Repository Structure

├── app.py                     # Main Streamlit application and routing
├── requirements.txt           # Python dependencies
├── .gitignore                 # Standard Python/Streamlit ignore file
└── chapter2_experiments/      # Modular logic files for various chapters
    ├── connected_components.py
    ├── distance_measures.py
    ├── image_statistics.py
    ├── pixel_relationships.py
    └── sampling_quantization_extended.py