Superpixel Labeling Tool

This project provides an interactive GUI to accelerate image annotation using superpixel segmentation. It uses SLIC-based superpixels to divide images into coherent regions, enabling users to label entire segments instead of individual pixels - significantly reducing annotation time and effort.

---

Features

• PyQt6-based GUI for efficient, manual region labeling using superpixels.
• Batch-compatible pre-segmentation pipeline using SLIC.
• Optional overlay visualization to aid labeling accuracy.
• Jupyter and CLI support for preprocessing.
• Parallel processing support for faster segmentation.
• Progress logging in label_log.csv.
• Auto-save on close, Ctrl+S, and after each frame.
• Installable via pip or available as standalone binaries.

Note: This tool is designed for binary segmentation labeling. Multi-class annotation is supported through multiple runs, each targeting a different class.

---

Quick Start Options

Option 1: Use via PyPI (Recommended for Python Users)

pip install superpixel_labeling_tool

Then run:

• GUI:

  superpixel_labeling_tool

• CLI for preprocessing:

  run_superpixel_segmentation /path/to/dataset --pixels_per_superpixel 150

---

Option 2: Use Prebuilt Executables (No Python Required)

Download the latest release from the Releases page:

1. Download

   • Linux: superpixel_labeling_tool-linux
   • Windows: superpixel_labeling_tool.exe

2. Make executable (Linux)

   chmod +x superpixel_labeling_tool-linux

3. Run

   • Linux: ./superpixel_labeling_tool-linux
   • Windows: Double-click superpixel_labeling_tool.exe

On Windows, a security warning may appear. Click “Run anyway” to proceed.

4. Select Your Dataset Folder
   The folder must contain an input/ subdirectory with images.

---

GUI Controls

• Left-click: Select/deselect superpixel.
• Right-click drag: Brush select.
• D: Change brush mode.
• F: Fill enclosed holes.
• X: Toggle superpixel boundary visibility.
• C: Toggle segmentation mask visibility.
• R + Drag on image: Regenerate superpixels in selected region.
• <- / -> (Arrow keys): Navigate images.
• Space: Pause/unpause.

---

Dataset Structure & Workflow

Your dataset must follow this structure:

/path/to/dataset/
├── input/                  # Required: input images
├── superpixel_masks/       # Optional: generated via CLI or GUI
└── segmentation_masks/     # Created by GUI for labeled output

Step 1: Precompute Superpixels (Optional)

run_superpixel_segmentation /path/to/dataset --pixels_per_superpixel 150 --num_workers 4

If superpixel_masks/ is missing, you can generate them via R + drag in the GUI.

Step 2: Launch the GUI

superpixel_labeling_tool

---

From Source (for Developers)

Prerequisites:

• Python 3.12
• Git

Setup:

Clone the repo and run:

Linux/macOS

bash install_env.sh
source .venv/bin/activate

Windows (PowerShell)

.\setup.ps1
.\.venv\Scripts\Activate.ps1

If scripts are blocked, use:

Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

---

Licensing Notice

This project uses open-source packages, including libraries under the GPLv3 License. Redistribution of modified versions must comply with GPLv3 terms.