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[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/labelme2yolo.svg)](https://pypi.org/project/labelme2yolo) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/labelme2yolo.svg)](https://pypi.org/project/labelme2yolo)
[![Codacy Badge](https://app.codacy.com/project/badge/Grade/12122fe86f8643c4aa5667c20d528f61)](https://www.codacy.com/gh/GreatV/labelme2yolo/dashboard?utm_source=github.com\&utm_medium=referral\&utm_content=GreatV/labelme2yolo\&utm_campaign=Badge_Grade) [![Codacy Badge](https://app.codacy.com/project/badge/Grade/12122fe86f8643c4aa5667c20d528f61)](https://www.codacy.com/gh/GreatV/labelme2yolo/dashboard?utm_source=github.com\&utm_medium=referral\&utm_content=GreatV/labelme2yolo\&utm_campaign=Badge_Grade)
Help converting LabelMe Annotation Tool JSON format to YOLO text file format. Labelme2YOLO is a powerful tool for converting LabelMe's JSON format to [YOLOv5](https://github.com/ultralytics/yolov5) dataset format. This tool can also be used for YOLOv5/YOLOv8 segmentation datasets, if you have already made your segmentation dataset with LabelMe, it is easy to use this tool to help convert to YOLO format dataset.
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 ## New Features
* export data as yolo polygon annotation (for YOLOv5 v7.0 segmentation) * export data as yolo polygon annotation (for YOLOv5 v7.0 segmentation)
* Now you can choose the output format of the label text. The two available alternatives are `polygon` and bounding box (`bbox`). * Now you can choose the output format of the label text. The two available alternatives are `polygon` and bounding box (`bbox`).
## Installation ## Installation
```console ```shell
pip install labelme2yolo pip install labelme2yolo
``` ```
## Parameters Explain ## Arguments
**--json\_dir** LabelMe JSON files folder path. **--json\_dir** LabelMe JSON files folder path.
@ -37,17 +36,17 @@ pip install labelme2yolo
## How to Use ## How to Use
### 1. Convert JSON files, split training, validation and test dataset by --val\_size and --test\_size ### 1. Converting JSON files and splitting training, validation, and test datasets with --val\_size and --test\_size
Put all LabelMe JSON files under **labelme\_json\_dir**, and run this python command. You may need to place all LabelMe JSON files under **labelme\_json\_dir** and then run the following command:
```bash ```shell
labelme2yolo --json_dir /path/to/labelme_json_dir/ --val_size 0.15 --test_size 0.15 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, This tool will generate dataset labels and images with YOLO format in different folders, such as
```bash ```plaintext
/path/to/labelme_json_dir/YOLODataset/labels/train/ /path/to/labelme_json_dir/YOLODataset/labels/train/
/path/to/labelme_json_dir/YOLODataset/labels/test/ /path/to/labelme_json_dir/YOLODataset/labels/test/
/path/to/labelme_json_dir/YOLODataset/labels/val/ /path/to/labelme_json_dir/YOLODataset/labels/val/
@ -58,26 +57,24 @@ Script would generate YOLO format dataset labels and images under different fold
/path/to/labelme_json_dir/YOLODataset/dataset.yaml /path/to/labelme_json_dir/YOLODataset/dataset.yaml
``` ```
### 2. Convert JSON files, split training and validation dataset by folder ### 2. Converting JSON files and splitting training and validation datasets by folders
If you already split train dataset and validation dataset for LabelMe by yourself, please put these folder under labelme\_json\_dir, for example, If you have split the LabelMe training dataset and validation dataset on your own, please put these folders under **labelme\_json\_dir** as shown below:
```bash ```plaintext
/path/to/labelme_json_dir/train/ /path/to/labelme_json_dir/train/
/path/to/labelme_json_dir/val/ /path/to/labelme_json_dir/val/
``` ```
Put all LabelMe JSON files under **labelme\_json\_dir**. This tool will read the training and validation datasets by folder. You may run the following command to do this:
Script would read train and validation dataset by folder.
Run this python command.
```bash ```shell
labelme2yolo --json_dir /path/to/labelme_json_dir/ labelme2yolo --json_dir /path/to/labelme_json_dir/
``` ```
Script would generate YOLO format dataset labels and images under different folders, for example, This tool will generate dataset labels and images with YOLO format in different folders, such as
```bash ```plaintext
/path/to/labelme_json_dir/YOLODataset/labels/train/ /path/to/labelme_json_dir/YOLODataset/labels/train/
/path/to/labelme_json_dir/YOLODataset/labels/val/ /path/to/labelme_json_dir/YOLODataset/labels/val/
/path/to/labelme_json_dir/YOLODataset/images/train/ /path/to/labelme_json_dir/YOLODataset/images/train/
@ -86,21 +83,6 @@ Script would generate YOLO format dataset labels and images under different fold
/path/to/labelme_json_dir/YOLODataset/dataset.yaml /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.
```bash
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,
```bash
/path/to/labelme_json_dir/2.text
/path/to/labelme_json_dir/2.png
```
## How to build package/wheel ## How to build package/wheel
1. [install hatch](https://hatch.pypa.io/latest/install/) 1. [install hatch](https://hatch.pypa.io/latest/install/)