lazylabel-gui 1.6.1

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

LazyLabel

AI-Assisted Image Segmentation for Machine Learning Dataset Preparation

LazyLabel combines Meta's Segment Anything Model (SAM) with comprehensive manual annotation tools to accelerate the creation of pixel-perfect segmentation masks for computer vision applications.

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Quick Start

pip install lazylabel-gui
lazylabel-gui

From source:

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

Requirements: Python 3.10+, 8GB RAM, ~2.5GB disk space (for model weights)

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Core Features

Annotation Tools

• AI (SAM): Single-click segmentation with point-based refinement (SAM 1.0 & 2.1, GPU/CPU)
• Polygon: Vertex-level drawing and editing for precise boundaries
• Box: Bounding box annotations for object detection
• Subtract: Remove regions from existing masks

Annotation Modes

• Single View: Fine-tune individual masks with maximum precision
• Multi View: Annotate up to 4 images simultaneously—ideal for objects in similar positions with slight variations
• Sequence: Propagate a refined mask across thousands of frames using SAM 2's video predictor

Image Processing

• FFT filtering: Remove noise and enhance edges
• Channel thresholding: Isolate objects by color
• Border cropping: Zero out pixels outside defined regions in saved outputs
• View adjustments: Brightness, contrast, gamma correction, color saturation

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Export Formats

NPZ Format (Semantic Segmentation)

One-hot encoded masks optimized for deep learning:

import numpy as np

data = np.load('image.npz')
mask = data['mask']  # Shape: (height, width, num_classes)

# Each channel represents one class
sky = mask[:, :, 0]
boats = mask[:, :, 1]
cats = mask[:, :, 2]
dogs = mask[:, :, 3]

YOLO Format (Object Detection)

Normalized polygon coordinates for YOLO training:

0 0.234 0.456 0.289 0.478 0.301 0.523 ...
1 0.567 0.123 0.598 0.145 0.612 0.189 ...

Class Aliases (JSON)

Maintains consistent class naming across datasets:

{
  "0": "background",
  "1": "person",
  "2": "vehicle"
}

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SAM 2.1 Setup

SAM 1.0 models are downloaded automatically on first use. For SAM 2.1 (improved accuracy, required for Sequence mode):

1. Install SAM 2: pip install git+https://github.com/facebookresearch/sam2.git
2. Download a model (e.g., sam2.1_hiera_large.pt) from the SAM 2 repository
3. Place in LazyLabel's models folder:
   • Via pip: ~/.local/share/lazylabel/models/
   • From source: src/lazylabel/models/
4. Select the model from the dropdown in settings

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Building Windows Executable

Create a standalone Windows executable with bundled models for offline use:

Requirements:

• Windows (native, not WSL)
• Python 3.10+
• PyInstaller: pip install pyinstaller

Build steps:

git clone https://github.com/dnzckn/LazyLabel.git
cd LazyLabel
python build_system/windows/build_windows.py

The executable will be created in dist/LazyLabel/. The entire folder (~7-8GB) can be moved anywhere and runs offline.

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Documentation

• Usage Manual - Comprehensive feature guide
• Architecture Guide - Technical implementation details
• Changelog - Version history and release notes
• GitHub Issues - Report bugs or request features

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