https://github.com/wkentaro/labelme
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
https://github.com/wkentaro/labelme
annotations classification computer-vision deep-learning image-annotation instance-segmentation python semantic-segmentation video-annotation
Last synced: about 1 month ago
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Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
- Host: GitHub
- URL: https://github.com/wkentaro/labelme
- Owner: wkentaro
- License: other
- Created: 2016-05-09T12:30:26.000Z (almost 9 years ago)
- Default Branch: main
- Last Pushed: 2024-11-19T15:01:51.000Z (5 months ago)
- Last Synced: 2024-11-23T18:39:54.520Z (5 months ago)
- Topics: annotations, classification, computer-vision, deep-learning, image-annotation, instance-segmentation, python, semantic-segmentation, video-annotation
- Language: Python
- Homepage: https://labelme.io
- Size: 44.5 MB
- Stars: 13,582
- Watchers: 147
- Forks: 3,414
- Open Issues: 148
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-cv - labelme - level flag annotation) (Annotation Tools / Tags: Object Classification `[ObjCls]`, Object Detection `[ObjDet]`, Object Segmentation `[ObjSeg]`, General Library `[GenLib]`, Text Reading / Object Character Recognition `[OCR]`, Action Recognition `[ActRec]`, Object Tracking `[ObjTrk]`, Data Augmentation `[DatAug]`, Simultaneous Localization and Mapping `[SLAM]`, Outlier/Anomaly/Novelty Detection `[NvlDet]`, Content-based Image Retrieval `[CBIR]`, Image Enhancement `[ImgEnh]`, Aesthetic Assessment `[AesAss]`, Explainable Artificial Intelligence `[XAI]`, Text-to-Image Generation `[TexImg]`, Pose Estimation `[PosEst]`, Video Matting `[VidMat]`, Eye Tracking `[EyeTrk]`)
- awesome-object-detection-datasets - labelme - level flag annotation). (Summary)
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README
labelme
Image Polygonal Annotation with Python
## Description
Labelme is a graphical image annotation tool inspired by .
It is written in Python and uses Qt for its graphical interface.
VOC dataset example of instance segmentation.
Other examples (semantic segmentation, bbox detection, and classification).
Various primitives (polygon, rectangle, circle, line, and point).
## Features
- [x] Image annotation for polygon, rectangle, circle, line and point. ([tutorial](examples/tutorial))
- [x] Image flag annotation for classification and cleaning. ([#166](https://github.com/wkentaro/labelme/pull/166))
- [x] Video annotation. ([video annotation](examples/video_annotation))
- [x] GUI customization (predefined labels / flags, auto-saving, label validation, etc). ([#144](https://github.com/wkentaro/labelme/pull/144))
- [x] Exporting VOC-format dataset for semantic/instance segmentation. ([semantic segmentation](examples/semantic_segmentation), [instance segmentation](examples/instance_segmentation))
- [x] Exporting COCO-format dataset for instance segmentation. ([instance segmentation](examples/instance_segmentation))
## Installation
There are 2 options to install labelme:
### Option 1: Using pip
For more detail, check ["Install Labelme using Pip"](https://www.labelme.io/docs/install-labelme-pip).
```bash
pip install labelme
```
### 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"](https://www.labelme.io/docs/install-labelme-app).
It's a one-time payment for lifetime access, and it helps us to maintain this project.
## Usage
Run `labelme --help` for detail.
The annotations are saved as a [JSON](http://www.json.org/) file.
```bash
labelme # just open gui
# tutorial (single image example)
cd examples/tutorial
labelme apc2016_obj3.jpg # specify image file
labelme apc2016_obj3.jpg -O apc2016_obj3.json # close window after the save
labelme apc2016_obj3.jpg --nodata # not include image data but relative image path in JSON file
labelme apc2016_obj3.jpg \
--labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball # specify label list
# semantic segmentation example
cd examples/semantic_segmentation
labelme data_annotated/ # Open directory to annotate all images in it
labelme data_annotated/ --labels labels.txt # specify label list with a file
```
### Command Line Arguments
- `--output` 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 in `~/.labelmerc`. You can edit this file and the changes will be applied the next time that you launch labelme. If you would prefer to use a config file from another location, you can specify this file with the `--config` flag.
- Without the `--nosortlabels` 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](examples/classification)
- Labels are assigned to a single polygon. [Example](examples/bbox_detection)
### FAQ
- **How to convert JSON file to numpy array?** See [examples/tutorial](examples/tutorial#convert-to-dataset).
- **How to load label PNG file?** See [examples/tutorial](examples/tutorial#how-to-load-label-png-file).
- **How to get annotations for semantic segmentation?** See [examples/semantic_segmentation](examples/semantic_segmentation).
- **How to get annotations for instance segmentation?** See [examples/instance_segmentation](examples/instance_segmentation).
## Examples
* [Image Classification](examples/classification)
* [Bounding Box Detection](examples/bbox_detection)
* [Semantic Segmentation](examples/semantic_segmentation)
* [Instance Segmentation](examples/instance_segmentation)
* [Video Annotation](examples/video_annotation)
## How to build standalone executable
```bash
LABELME_PATH=./labelme
OSAM_PATH=$(python -c 'import os, osam; print(os.path.dirname(osam.__file__))')
pyinstaller labelme/labelme/__main__.py \
--name=Labelme \
--windowed \
--noconfirm \
--specpath=build \
--add-data=$(OSAM_PATH)/_models/yoloworld/clip/bpe_simple_vocab_16e6.txt.gz:osam/_models/yoloworld/clip \
--add-data=$(LABELME_PATH)/config/default_config.yaml:labelme/config \
--add-data=$(LABELME_PATH)/icons/*:labelme/icons \
--add-data=$(LABELME_PATH)/translate/*:translate \
--icon=$(LABELME_PATH)/icons/icon.png \
--onedir
```
## Acknowledgement
This repo is the fork of [mpitid/pylabelme](https://github.com/mpitid/pylabelme).