lazylabel-gui 1.1.7

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

AI-Assisted Image Segmentation Made Simple

LazyLabel combines Meta's Segment Anything Model (SAM) with intuitive editing tools for fast, precise image labeling. Perfect for machine learning datasets, computer vision research, and annotation workflows.

---

🚀 Quick Start

Installation

pip install lazylabel-gui
lazylabel-gui

Usage

1. Open Folder → Select your image directory
2. Click on image → AI generates instant masks
3. Fine-tune → Edit polygons, merge segments, adjust classes
4. Export → Clean .npz files ready for ML training

---

✨ Key Features

🧠 AI-Powered Segmentation

• One-click masking with Meta's SAM model
• Smart prompting via positive/negative points
• Fragment filtering to remove small artifacts
• Multiple model support (VIT-H, VIT-L, VIT-B)

🎨 Advanced Editing

• Polygon drawing with full vertex control
• Bounding box annotation mode
• Shape merging and class assignment
• Edit mode for precision adjustments

⚡ Productivity Tools

• Image adjustments (brightness, contrast, gamma)
• Customizable hotkeys for all functions
• Undo/redo with full history
• Auto-save and session persistence

📊 ML-Ready Outputs

• One-hot encoded .npz format
• Clean class separation with shape (H, W, Classes)
• Batch processing support
• Existing mask loading for iterative work

---

⌨️ Essential Controls

Mode	Key	Action
AI Segmentation	1	Point mode for SAM
	Left Click	Add positive point
	Right Click	Add negative point
	Space	Save segment
Manual Drawing	2	Polygon mode
	Left Click	Add vertex
	Enter	Close polygon
Editing	E	Selection mode
	R	Edit selected shapes
	M	Merge selected segments
Navigation	Q	Pan mode
	W/A/S/D	Pan image
	Scroll	Zoom in/out

💡 All hotkeys are customizable - Click "Hotkeys" button to personalize shortcuts

---

📦 Output Format

LazyLabel exports clean, ML-ready data:

import numpy as np

# Load your labeled data
data = np.load('your_image.npz')
mask = data['mask']  # Shape: (height, width, num_classes)

# Each channel is a binary mask for one class
class_0_mask = mask[:, :, 0]  # Binary mask for class 0
class_1_mask = mask[:, :, 1]  # Binary mask for class 1
# ... and so on

Perfect for:

• Semantic segmentation training
• Instance segmentation datasets
• Computer vision research
• Automated annotation pipelines

---

🛠️ Advanced Features

Image Enhancement

• Brightness/Contrast adjustment sliders
• Gamma correction for better visibility
• Live preview of adjustments
• SAM integration with adjusted images

Smart Filtering

• Fragment threshold removes small segments
• Size-based filtering (0-100% of largest segment)
• Quality control for clean annotations

Professional Workflow

• Class management with custom aliases
• Segment organization with sortable tables
• Batch export for large datasets
• Model switching without restart

---

🏗️ Development

Installation from Source

git clone https://github.com/dnzckn/LazyLabel.git
cd LazyLabel
pip install -e .
lazylabel-gui

Code Quality & Testing

# Linting & formatting
ruff check . && ruff format .

# Run tests with coverage
python -m pytest --cov=lazylabel --cov-report=html

# All tests pass with 60%+ coverage

Architecture

• Modular design with clean separation of concerns
• Signal-based communication between components
• Extensible model system for new SAM variants
• Comprehensive test suite with 95% speed optimization

---

📋 Requirements

• Python 3.10+
• OpenCV for image processing
• PyQt6 for GUI
• NumPy for data handling
• 2.5GB disk space for SAM model (auto-downloaded)

---

🤝 Contributing

LazyLabel welcomes contributions! Check out our:

• Architecture Guide for technical details
• Hotkey System for customization
• Issues page for feature requests and bug reports

---

🙏 Acknowledgments

LazyLabel was inspired by and builds upon the excellent work of:

• LabelMe - The pioneering open-source image annotation tool
• Segment-Anything-UI - Early SAM integration concepts

---

☕ Support

If LazyLabel saves you time on annotation tasks, consider supporting the project!

---

Made with ❤️ for the computer vision community