labelme-to-yolo


Namelabelme-to-yolo JSON
Version 0.2.0 PyPI version JSON
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home_pagehttps://github.com/Tlaloc-Es/labelme-to-yolo
SummaryA tool to convert LabelMe dataset annotations into YOLO format for instance segmentation.
upload_time2024-12-29 19:23:47
maintainerNone
docs_urlNone
authorTlaloc-Es
requires_python<4.0,>=3.10
licenseMIT
keywords
VCS
bugtrack_url
requirements No requirements were recorded.
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coveralls test coverage No coveralls. 
            # LabelMe to Yolo

<div align="center">

[![Downloads](https://static.pepy.tech/personalized-badge/labelme-to-yolo?period=month&units=international_system&left_color=grey&right_color=blue&left_text=PyPi%20Downloads)](https://pepy.tech/project/labelme-to-yolo)
[![Stars](https://img.shields.io/github/stars/Tlaloc-Es/labelme-to-yolo?color=yellow&style=flat)](https://github.com/Tlaloc-Es/labelme-to-yolo/stargazers)

</div>

Convert [LabelMe](https://github.com/wkentaro/labelme) format into [YoloV7](https://github.com/WongKinYiu/yolov7) format for instance segmentation.

## Installation [![PyPI](https://img.shields.io/pypi/v/labelme2yolo.svg)](https://pypi.org/project/labelme2yolo/)

You can install `labelme2yolo` from [Pypi](https://pypi.org/project/labelme-to-yolo/). It's going to install the library itself and its prerequisites as well.

```bash
pip install labelme2yolo
```

You can install `labelme2yolo` from its source code.

```bash
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

```bash
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
```

## Donation

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

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    "description": "# LabelMe to Yolo\n\n<div align=\"center\">\n\n[![Downloads](https://static.pepy.tech/personalized-badge/labelme-to-yolo?period=month&units=international_system&left_color=grey&right_color=blue&left_text=PyPi%20Downloads)](https://pepy.tech/project/labelme-to-yolo)\n[![Stars](https://img.shields.io/github/stars/Tlaloc-Es/labelme-to-yolo?color=yellow&style=flat)](https://github.com/Tlaloc-Es/labelme-to-yolo/stargazers)\n\n</div>\n\nConvert [LabelMe](https://github.com/wkentaro/labelme) format into [YoloV7](https://github.com/WongKinYiu/yolov7) format for instance segmentation.\n\n## Installation [![PyPI](https://img.shields.io/pypi/v/labelme2yolo.svg)](https://pypi.org/project/labelme2yolo/)\n\nYou can install `labelme2yolo` from [Pypi](https://pypi.org/project/labelme-to-yolo/). It's going to install the library itself and its prerequisites as well.\n\n```bash\npip install labelme2yolo\n```\n\nYou can install `labelme2yolo` from its source code.\n\n```bash\ngit clone https://github.com/Tlaloc-Es/labelme-to-yolo.git\ncd labelme2yolo\npip install -e .\n```\n\n## Usage\n\nFirst of all, make your dataset with LabelMe, after that call to the following command\n\n`labelme2yolo --source-path /labelme/dataset --output-path /another/path`\n\nThe arguments are:\n\n- `--source-path`: That indicates the path where are the json output of LabelMe and their images, both will have been in the same folder\n- `--output-path`: The path where you will save the converted files and a copy of the images following the yolov7 folder estructure\n\n### Expected output\n\nIf you execute the following command:\n\n`labelme2yolo --source-path /labelme/dataset --output-path /another/datasets`\n\nYou will get something like this\n\n```bash\ndatasets\n\u251c\u2500\u2500 images\n\u2502   \u251c\u2500\u2500 train\n\u2502   \u2502   \u251c\u2500\u2500 img_1.jpg\n\u2502   \u2502   \u251c\u2500\u2500 img_2.jpg\n\u2502   \u2502   \u251c\u2500\u2500 img_3.jpg\n\u2502   \u2502   \u251c\u2500\u2500 img_4.jpg\n\u2502   \u2502   \u2514\u2500\u2500 img_5.jpg\n\u2502   \u2514\u2500\u2500 val\n\u2502       \u251c\u2500\u2500 img_6.jpg\n\u2502       \u2514\u2500\u2500 img_7.jpg\n\u251c\u2500\u2500 labels\n\u2502   \u251c\u2500\u2500 train\n\u2502   \u2502   \u251c\u2500\u2500 img_1.txt\n\u2502   \u2502   \u251c\u2500\u2500 img_2.txt\n\u2502   \u2502   \u251c\u2500\u2500 img_3.txt\n\u2502   \u2502   \u251c\u2500\u2500 img_4.txt\n\u2502   \u2502   \u2514\u2500\u2500 img_5.txt\n\u2502   \u2514\u2500\u2500 val\n\u2502       \u251c\u2500\u2500 img_6.txt\n\u2502       \u2514\u2500\u2500 img_7.txt\n\u251c\u2500\u2500 labels.txt\n\u251c\u2500\u2500 test.txt\n\u2514\u2500\u2500 train.txt\n```\n\n## Donation\n\nIf you want to contribute you can make a donation at https://www.buymeacoffee.com/tlaloc, thanks in advance\n",
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