LabelMe to Yolo

Convert LabelMe format into Ultralytics Yolo format for instance segmentation.

Installation

You can install labelme-to-yolo from Pypi. It's going to install the library itself and its prerequisites as well.

pip install labelme2yolo

You can install labelme2yolo from its source code.

git clone https://github.com/Tlaloc-Es/labelme-to-yolo.git
cd labelme2yolo
pip install -e .

Usage

First of all, make your dataset with LabelMe, after that call to the following command

labelme2yolo --source-path /labelme/dataset --output-path /another/path

The arguments are:

• --source-path: That indicates the path where are the json output of LabelMe and their images, both will have been in the same folder
• --output-path: The path where you will save the converted files and a copy of the images following the yolov7 folder estructure

Expected output

If you execute the following command:

labelme2yolo --source-path /labelme/dataset --output-path /another/datasets

You will get something like this

datasets
├── images
│   ├── train
│   │   ├── img_1.jpg
│   │   ├── img_2.jpg
│   │   ├── img_3.jpg
│   │   ├── img_4.jpg
│   │   └── img_5.jpg
│   └── val
│       ├── img_6.jpg
│       └── img_7.jpg
├── labels
│   ├── train
│   │   ├── img_1.txt
│   │   ├── img_2.txt
│   │   ├── img_3.txt
│   │   ├── img_4.txt
│   │   └── img_5.txt
│   └── val
│       ├── img_6.txt
│       └── img_7.txt
├── labels.txt
├── test.txt
├── train.txt
└── project.yml

Donation

If you want to contribute you can make a donation at https://www.buymeacoffee.com/tlaloc, thanks in advance