This project implements an image segmentation tool using the Max-Flow Min-Cut algorithm (Edmonds-Karp). It treats an image as a graph where pixels are nodes and edges represent similarity. By finding the minimum cut in the graph, it separates the foreground object from the background.

• Project Overview
• How It Works
• Project Structure
• Installation
• Usage
• Author

While modern image segmentation often relies on Deep Learning, this project explores a classical algorithmic approach. It demonstrates how graph theory can be applied to computer vision tasks, providing interpretability and mathematical robustness.

Key Features:

• Graph Representation: Converts pixels into a grid graph.
• Intensity-Based Weights: Edge weights are based on pixel intensity differences.
• Max-Flow Min-Cut: Uses the Edmonds-Karp algorithm to find the optimal separation.
• Visualizations: Displays the original image and the resulting segmentation mask.
• Rich Output: Uses the rich library for colorful console output and status updates.

1. Preprocessing: The image is loaded, resized for performance, and converted to grayscale.
2. Graph Construction:
   • Nodes: Each pixel is a node. Two special nodes, Source (S) and Sink (T), are added.
   • n-links (Neighbor Links): Adjacent pixels are connected. The weight is high if pixels are similar (strong bond) and low if they are different (weak bond).
   • t-links (Terminal Links):
     • Pixels at the image border are connected to the Sink (Background seeds).
     • Pixels at the very center are connected to the Source (Foreground seeds).
3. Max-Flow Calculation: The Edmonds-Karp algorithm finds the maximum flow from Source to Sink. The "bottlenecks" in this flow correspond to the edges that should be cut.
4. Segmentation: The set of nodes reachable from the Source in the residual graph constitutes the foreground object.

.
├── reports/                   # Project reports and presentations
│   ├── jules-review/
│   │   └── jules_review.ipynb
│   └── presentation/
│       └── everything_is_a_graph.pdf
├── src/                       # Source code and assets
│   ├── images/                # Directory containing input images and outputs
│   │   ├── graphics/          # Graphics and visualizations
│   │   ├── jules_tracker/     # Jules tracking related images
│   │   ├── output/            # Generated output images
│   │   └── dragonite_og.jpeg  # Default sample image
│   ├── style.css              # Styling for PDF viewer
├── index.html                 # PDF viewer interface
├── main.py                    # Main Python source code file
├── requirements.txt           # List of Python dependencies
├── .gitignore                 # Git ignore rules
├── LICENSE                    # Project license
└── README.md                  # Project documentation

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>

2. Install dependencies: Ensure you have Python installed. Then run:

   pip install -r requirements.txt

To run the segmentation on the default image:

python main.py

You can modify main.py to change parameters:

• Image Path: Change the file path in the ImageGraph instantiation:

  processor = ImageGraph('path/to/your/image.jpg', width=40)

• Resolution: Adjust the width parameter. Higher values give better detail but increase computation time significantly (O(V E^2)).
• Seeds: You can adjust the seed_radius or the logic for placing source/sink seeds in the build_t_links method to suit different images.

Joshua Piña
Computer Science Department, Georgia State University
Data Science Senior | Program Manager | U.S Army Veteran