labelme2yolo 0.1.1

This script converts the JSON format output by LabelMe to the text format required by YOLO serirs.

Labelme2YOLO

Forked from rooneysh/Labelme2YOLO

Help converting LabelMe Annotation Tool JSON format to YOLO text file format. If you've already marked your segmentation dataset by LabelMe, it's easy to use this tool to help converting to YOLO format dataset.

New

• export data as yolo polygon annotation (for YOLOv5 v7.0 segmentation)
• Now you can choose the output format of the label text. The available options are plygon and bbox.

Installation

pip install labelme2yolo

Parameters Explain

--json_dir LabelMe JSON files folder path.

--val_size (Optional) Validation dataset size, for example 0.2 means 20% for validation.

--test_size (Optional) Test dataset size, for example 0.2 means 20% for Test.

--json_name (Optional) Convert single LabelMe JSON file.

--output_format (Optional) The output format of label.

--label_list (Optional) The pre-assigned category labels.

How to Use

1. Convert JSON files, split training, validation and test dataset by --val_size and --test_size

Put all LabelMe JSON files under labelme_json_dir, and run this python command.

labelme2yolo --json_dir /path/to/labelme_json_dir/ --val_size 0.15 --test_size 0.15

Script would generate YOLO format dataset labels and images under different folders, for example,

/path/to/labelme_json_dir/YOLODataset/labels/train/
/path/to/labelme_json_dir/YOLODataset/labels/test/
/path/to/labelme_json_dir/YOLODataset/labels/val/
/path/to/labelme_json_dir/YOLODataset/images/train/
/path/to/labelme_json_dir/YOLODataset/images/test/
/path/to/labelme_json_dir/YOLODataset/images/val/

/path/to/labelme_json_dir/YOLODataset/dataset.yaml

2. Convert JSON files, split training and validation dataset by folder

If you already split train dataset and validation dataset for LabelMe by yourself, please put these folder under labelme_json_dir, for example,

/path/to/labelme_json_dir/train/
/path/to/labelme_json_dir/val/

Put all LabelMe JSON files under labelme_json_dir. Script would read train and validation dataset by folder. Run this python command.

labelme2yolo --json_dir /path/to/labelme_json_dir/

Script would generate YOLO format dataset labels and images under different folders, for example,

/path/to/labelme_json_dir/YOLODataset/labels/train/
/path/to/labelme_json_dir/YOLODataset/labels/val/
/path/to/labelme_json_dir/YOLODataset/images/train/
/path/to/labelme_json_dir/YOLODataset/images/val/

/path/to/labelme_json_dir/YOLODataset/dataset.yaml

3. Convert single JSON file

Put LabelMe JSON file under labelme_json_dir. , and run this python command.

labelme2yolo --json_dir /path/to/labelme_json_dir/ --json_name 2.json

Script would generate YOLO format text label and image under labelme_json_dir, for example,

/path/to/labelme_json_dir/2.text
/path/to/labelme_json_dir/2.png

How to build package/wheel

1. install hatch
2. Run the following command:

hatch build

License

labelme2yolo is distributed under the terms of the MIT license.