lazylabel-gui 1.2.1

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 and computer vision research.

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🚀 Quick Start

Installation

pip install lazylabel-gui
lazylabel-gui

Optional: SAM-2 Support

For advanced SAM-2 models, install manually:

pip install git+https://github.com/facebookresearch/sam2.git

Note: SAM-2 is optional - LazyLabel works with SAM 1.0 models by default

Usage

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

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✨ Key Features

• 🧠 One-click AI segmentation with Meta's SAM and SAM2 models
• 🎨 Manual polygon drawing with full vertex control
• ⚡ Smart editing tools - merge segments, adjust class names, and class order
• 📊 ML-ready exports - One-hot encoded .npz format and .json for YOLO format
• 🔧 Image enhancement - brightness, contrast, gamma adjustment
• 🔍 Image viewer - zoom, pan, brightness, contrast, and gamma adjustment
• ✂️ Edge cropping - define custom crop areas to focus on specific regions
• 🔄 Undo/Redo - full history of all actions
• 💾 Auto-saving - Automatic saving of your labels when navigating between images
• 🎛️ Advanced filtering - FFT thresholding and color channel thresholding
• ⌨️ Customizable hotkeys for all functions

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⌨️ Essential Hotkeys

Action	Key	Description
AI Mode	1	Point-click segmentation
Draw Mode	2	Manual polygon drawing
Edit Mode	E	Select and modify shapes
Save Segment	Space	Confirm current mask
Merge	M	Combine selected segments
Pan	Q + drag	Navigate large images
Positive Point	Left Click	Add to segment
Negative Point	Right Click	Remove from segment

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

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📦 Output Format

Perfect for ML training - clean, structured 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]  # Background
class_1_mask = mask[:, :, 1]  # Object type 1
class_2_mask = mask[:, :, 2]  # Object type 2

Ideal for:

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

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🛠️ Development

Requirements: Python 3.10+ 2.5GB disk space for SAM model (auto-downloaded)

Installation from Source

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

Testing & Quality

# Run full test suite
python -m pytest --cov=lazylabel --cov-report=html

# Code formatting & linting
ruff check . && ruff format .

Architecture

• Modular design with clean component separation
• Signal-based communication between UI elements
• Extensible model system for new SAM variants
• Comprehensive test suite (150+ tests, 60%+ coverage)

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🤝 Contributing

LazyLabel welcomes contributions! Check out:

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

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🙏 Acknowledgments

• LabelMe
• Segment-Anything-UI

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☕ Support

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

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Made with ❤️ for the computer vision community