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An image segmentation GUI for generating ML ready mask tensors and annotations.

Project description

LazyLabel Logo LazyLabel Cursive

LazyLabel is an intuitive, AI-assisted image segmentation tool built with a modern, modular architecture. It uses Meta's Segment Anything Model (SAM) for quick, precise mask generation, alongside advanced polygon editing for fine-tuned control. Features comprehensive model management, customizable hotkeys, and outputs in clean, one-hot encoded .npz format for easy machine learning integration.

Inspired by LabelMe and Segment-Anything-UI.

LazyLabel Screenshot


✨ Core Features

AI-Powered Segmentation

  • Generate masks with simple left-click (positive) and right-click (negative) interactions
  • Multiple SAM model support with easy switching
  • Custom model loading from any directory

Advanced Editing Tools

  • Vector Polygon Tool: Full control to draw, edit, and reshape polygons
  • Vertex Editing: Drag vertices or move entire shapes with precision
  • Selection & Merging: Select, merge, and re-order segments intuitively

Professional Workflow

  • Customizable Hotkeys: Personalize keyboard shortcuts for all functions
  • Advanced Class Management: Assign multiple segments to single class IDs
  • Smart I/O: Load existing .npz masks; save as clean, one-hot encoded outputs
  • Interactive UI: Color-coded segments, sortable lists, and hover highlighting

Modern Architecture

  • Modular Design: Clean, maintainable codebase with separated concerns
  • Model Management: Dedicated model storage and switching system
  • Persistent Settings: User preferences saved between sessions

🚀 Getting Started

Prerequisites

Python 3.10+

Installation

For Users via PyPI

  1. Install LazyLabel directly:
    pip install lazylabel-gui
    
  2. Run the application:
    lazylabel-gui
    

For Developers (from Source)

  1. Clone the repository:
    git clone https://github.com/dnzckn/LazyLabel.git
    cd LazyLabel
    
  2. Install in editable mode:
    pip install -e .
    
  3. Run the application:
    lazylabel-gui
    

Model Management

  • Default Storage: Models are stored in src/lazylabel/models/ directory
  • Custom Models: Click "Browse Models" to select custom model folders
  • Model Switching: Use the dropdown to switch between available models
  • Auto-Detection: Application automatically detects all .pth files in selected directories

Note: On the first run, the application will automatically download the SAM model checkpoint (~2.5 GB) from Meta's repository to the models directory. This is a one-time download.


⌨️ Controls & Keybinds

💡 Tip: All hotkeys are fully customizable! Click the "Hotkeys" button in the control panel to personalize your shortcuts.

Modes

Key Action
1 Enter Point Mode (for AI segmentation).
2 Enter Polygon Drawing Mode.
E Toggle Selection Mode to select existing segments.
R Enter Edit Mode for selected polygons (drag shape or vertices).
Q Toggle Pan Mode (click and drag the image).

Actions

Key(s) Action
L-Click Add positive point (Point Mode) or polygon vertex.
R-Click Add negative point (Point Mode).
Ctrl + Z Undo last point.
Spacebar Finalize and save current AI segment.
Enter Save final mask for the current image to a .npz file.
M Merge selected segments into a single class.
V / Delete / Backspace Delete selected segments.
C Clear temporary points/vertices.
W/A/S/D Pan image.
Scroll Wheel Zoom-in or -out.

📦 Output Format

LazyLabel saves your work as a compressed NumPy array (.npz) with the same name as your image file.

The file contains a single data key, 'mask', holding a one-hot encoded tensor with the shape (H, W, C):

  • H: Image height.
  • W: Image width.
  • C: Total unique classes.

Each channel is a binary mask for a class, combining all assigned segments into a clean, ML-ready output.


🏗️ Architecture

LazyLabel features a modern, modular architecture designed for maintainability and extensibility:

  • Modular Design: Clean separation between UI, business logic, and configuration
  • Signal-Based Communication: Loose coupling between components using PyQt signals
  • Persistent Configuration: User settings and preferences saved between sessions
  • Extensible Model System: Easy integration of new SAM models and types

For detailed technical documentation, see ARCHITECTURE.md.


⌨️ Hotkey Customization

LazyLabel includes a comprehensive hotkey management system:

  • Full Customization: Personalize keyboard shortcuts for all 27+ functions
  • Category Organization: Hotkeys organized by function (Modes, Actions, Navigation, etc.)
  • Primary & Secondary Keys: Set multiple shortcuts for the same action
  • Persistent Settings: Custom hotkeys saved between sessions
  • Conflict Prevention: System prevents duplicate key assignments

For complete hotkey documentation, see HOTKEY_FEATURE.md.


☕ Support LazyLabel

If you found LazyLabel helpful, consider supporting the project!

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