Metadata-Version: 2.3
Name: labelme2yolo
Version: 0.2.2
Classifier: Programming Language :: Rust
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
License-File: LICENSE
Summary: This script converts the JSON format output by LabelMe to the text format required by YOLO serirs.
Keywords: 
Author-email: "GreatV(Wang Xin)" <xinwang614@gmail.com>
License: MIT
Requires-Python: >=3.8
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: Documentation, https://github.com/greatv/labelme2yolo#readme
Project-URL: Issues, https://github.com/greatv/labelme2yolo/issues
Project-URL: Source, https://github.com/greatv/labelme2yolo

# Labelme2YOLO

[![PyPI - Version](https://img.shields.io/pypi/v/labelme2yolo.svg)](https://pypi.org/project/labelme2yolo)
![PyPI - Downloads](https://img.shields.io/pypi/dm/labelme2yolo?style=flat)
[![PYPI - Downloads](https://static.pepy.tech/badge/labelme2yolo)](https://pepy.tech/project/labelme2yolo)

Labelme2YOLO efficiently converts LabelMe's JSON format to the YOLOv5 dataset format. It also supports YOLOv5/YOLOv8 segmentation datasets, making it simple to convert existing LabelMe segmentation datasets to YOLO format.

## New Features

* export data as yolo polygon annotation (for YOLOv5 & YOLOV8 segmentation)
* Now you can choose the output format of the label text. The two available alternatives are `polygon` and bounding box(`bbox`).

## Performance

Labelme2YOLO is implemented in Rust, which makes it significantly faster than equivalent Python implementations. In fact, it can be up to 100 times faster, allowing you to process large datasets more efficiently.

## Installation

```shell
pip install labelme2yolo
```

## Arguments

**--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 0.1 means 10% 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. Converting JSON files and splitting training, validation datasets

You may need to place all LabelMe JSON files under **labelme\_json\_dir** and then run the following command:

```shell
labelme2yolo --json_dir /path/to/labelme_json_dir/
```

This tool will generate dataset labels and images with YOLO format in different folders, such as

```plaintext
/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
```

### 2. Converting JSON files and splitting training, validation, and test datasets with --val\_size and --test\_size

You may need to place all LabelMe JSON files under **labelme\_json\_dir** and then run the following command:

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

This tool will generate dataset labels and images with YOLO format in different folders, such as

```plaintext
/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
```

## How to build package/wheel

```shell
pip install maturin
maturin develop
```

## License

`labelme2yolo` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.

