wkentaro / labelme

labelme Image annotation with Python. --> Installation | Usage | Examples | labelme.io ↗ Community --> Youtube FAQ --> Description Labelme is a graphical image annotation tool inspired by . It is written in Python and uses Qt for its graphical interface. > Looking for a simple install without Python or Qt? Get the standalone app at **labelme.io**. VOC dataset example of instance segmentation. Other examples (semantic segmentation, bbox detection, and classification). Various primitives (polygon, rectangle, circle, line, and point). Multi-language support (English, 中文, 日本語, 한국어, Deutsch, Français, and more). Features • [x] Image annotation for polygon, rectangle, circle, line and point (tutorial) • [x] Image flag annotation for classification and cleaning (#166) • [x] Video annotation (video annotation) • [x] GUI customization (predefined labels / flags, auto-saving, label validation, etc) (#144) • [x] Exporting VOC-format dataset for semantic segmentation, instance segmentation • [x] Exporting COCO-format dataset for instance segmentation • [x] AI-assisted point-to-polygon/mask annotation by SAM, EfficientSAM models • [x] AI text-to-annotation by YOLO-world, SAM3 models **🌏 Available in 16 languages** - English · 日本語 · 한국어 · 简体中文 · 繁體中文 · Deutsch · Français · Español · Italiano · Português · Nederlands · Magyar · Tiếng Việt · Türkçe · Polski · فارسی ( ) Installation There are 3 options to install labelme: Option 1: Using pip For more detail, check "Install Labelme using Terminal" Option 2: Using standalone executable (Easiest) If you're willing to invest in the convenience of simple installation without any dependencies (Python, Qt), you can download the standalone executable from "Install Labelme as App". It's a one-time payment for lifetime access, and it helps us to maintain this project. Option 3: Using a package manager in each Linux distribution In some Linux distributions, you can install labelme via their package managers (e.g., apt, pacman). The following systems are currently available: Usage Run for detail. The annotations are saved as a JSON file. Command Line Arguments • specifies the location that annotations will be written to. If the location ends with .json, a single annotation will be written to this file. Only one image can be annotated if a location is specified with .json. If the location does not end with .json, the program will assume it is a directory. Annotations will be stored in this directory with a name that corresponds to the image that the annotation was made on. • The first time you run labelme, it will create a config file at . Add only the settings you want to override. For all available options and their defaults, see . If you would prefer to use a config file from another location, you can specify this file with the flag. • Without the flag, the program will list labels in alphabetical order. When the program is run with this flag, it will display labels in the order that they are provided. • Flags are assigned to an entire image. Example • Labels are assigned to a single polygon. Example FAQ • **How to convert JSON file to numpy array?** See examples/tutorial. • **How to load label PNG file?** See examples/tutorial. • **How to get annotations for semantic segmentation?** See examples/semantic_segmentation. • **How to get annotations for instance segmentation?** See examples/instance_segmentation. Examples • Image Classification • Bounding Box Detection • Semantic Segmentation • Instance Segmentation • Video Annotation How to build standalone executable Acknowledgement This repo is the fork of mpitid/pylabelme.