# YOLO Prediction to Labelme and Anylabeling Json
<p align="center">
<img alt="yolosegment2labelme" style="width: 128px; max-width: 100%; height: auto;" src="images/labelme_test/logo.png"/>
<h1 align="center">🌟 yolosegment2labelme 🌟</h1>
<p align="center">Convert your yolo model prediction results to json to view and edit in Labelme and Anylabeling. <b>YOLO Result to Json with single line cmd</b>!</p>
<p align="center"><b>yolosegment2labelme = Easy Coversion + Predicted to Json + Auto-labeling</b></p>
</p>
[](https://pypi.org/project/yolosegment2labelme/)
[](https://github.com/Abonia1/yolosegment2labelme/blob/main/LICENSE)
[](https://github.com/abonia1/yolosegment2labelme/issues)
[](https://abonia1.github.io/)
[](https://www.linkedin.com/in/aboniasojasingarayar/)
> ⭐ Follow [AboniaSojasingarayar](https://www.linkedin.com/in/aboniasojasingarayar) for project updates.
**yolosegment2labelme** is a Python package that allows you to convert YOLO segmentation prediction results to LabelMe JSON format. This tool facilitates the annotation process by generating JSON files that are compatible with LabelMe and other labeling annotation tools.
## Features
- Convert YOLO segmentation prediction results to LabelMe JSON format.
- Compatible with various YOLO models.
- Easy-to-use command-line interface.
- Supports batch processing of images.
- Customizable confidence threshold for predictions.
- Highly customizable and extensible for specific use cases.
## Installation
You can install **yolosegment2labelme** via pip:
```bash
pip install yolosegment2labelme
```
## Usage
After installation, you can use the `yolosegment2labelme` command-line interface to convert YOLO segmentation prediction results to LabelMe JSON format. Here's a basic example:
```bash
yolosegment2labelme --images /path/to/images
```
or with custom yolo segmentation model
```bash
yolosegment2labelme --model yolov8n-seg.pt --images /path/to/images --conf 0.3
```
This command will process the images located in the specified directory (`/path/to/images`), using the YOLO model weights file `yolov8n-seg.pt` and here you can add path/to/your/customYOLOModel, and generate LabelMe JSON files with a confidence threshold of your choice and here it is of 0.3.
## Sample Images
The table below displays sample images along with their corresponding annotations generated using yolosegment2labelme:
| Sample Image 1 | Sample Image 2 |
|-----------------------------------------------------|-----------------------------------------------------|
|  |  |
| Sample Annotation for Image 1 | Sample Annotation for Image 2 |
| Sample Image 3 | Sample Image 4 |
|-----------------------------------------------------|-----------------------------------------------------|
|  |  |
| Sample Annotation for Image 3 | Sample Annotation for Image 4 |
## Documentation
The documentation for **yolosegment2labelme** can be found on GitHub: [yolosegment2labelme Documentation](https://github.com/Abonia1/yolosegment2labelme)
## Contributing
Contributions are welcome! If you'd like to contribute to **yolosegment2labelme**, please check out the [Contribution Guidelines](CONTRIBUTING.md).
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Authors
- Abonia Sojasingarayar - [GitHub](https://github.com/Abonia1)
## Support
If you encounter any issues or have questions about **yolosegment2labelme**, please feel free to open an issue on GitHub: [yolosegment2labelme Issues](https://github.com/Abonia1/yolosegment2labelme/issues)
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"description": "# YOLO Prediction to Labelme and Anylabeling Json\n\n<p align=\"center\">\n <img alt=\"yolosegment2labelme\" style=\"width: 128px; max-width: 100%; height: auto;\" src=\"images/labelme_test/logo.png\"/>\n <h1 align=\"center\">\ud83c\udf1f yolosegment2labelme \ud83c\udf1f</h1>\n <p align=\"center\">Convert your yolo model prediction results to json to view and edit in Labelme and Anylabeling. <b>YOLO Result to Json with single line cmd</b>!</p>\n <p align=\"center\"><b>yolosegment2labelme = Easy Coversion + Predicted to Json + Auto-labeling</b></p>\n</p>\n\n\n\n[](https://pypi.org/project/yolosegment2labelme/)\n[](https://github.com/Abonia1/yolosegment2labelme/blob/main/LICENSE)\n[](https://github.com/abonia1/yolosegment2labelme/issues)\n[](https://abonia1.github.io/)\n[](https://www.linkedin.com/in/aboniasojasingarayar/)\n\n> \u2b50 Follow [AboniaSojasingarayar](https://www.linkedin.com/in/aboniasojasingarayar) for project updates.\n\n**yolosegment2labelme** is a Python package that allows you to convert YOLO segmentation prediction results to LabelMe JSON format. This tool facilitates the annotation process by generating JSON files that are compatible with LabelMe and other labeling annotation tools.\n\n## Features\n\n- Convert YOLO segmentation prediction results to LabelMe JSON format.\n- Compatible with various YOLO models.\n- Easy-to-use command-line interface.\n- Supports batch processing of images.\n- Customizable confidence threshold for predictions.\n- Highly customizable and extensible for specific use cases.\n\n## Installation\n\nYou can install **yolosegment2labelme** via pip:\n\n```bash\npip install yolosegment2labelme\n```\n\n## Usage\n\nAfter installation, you can use the `yolosegment2labelme` command-line interface to convert YOLO segmentation prediction results to LabelMe JSON format. Here's a basic example:\n\n```bash\nyolosegment2labelme --images /path/to/images\n```\n\nor with custom yolo segmentation model\n\n```bash\nyolosegment2labelme --model yolov8n-seg.pt --images /path/to/images --conf 0.3\n```\n\nThis command will process the images located in the specified directory (`/path/to/images`), using the YOLO model weights file `yolov8n-seg.pt` and here you can add path/to/your/customYOLOModel, and generate LabelMe JSON files with a confidence threshold of your choice and here it is of 0.3.\n\n\n## Sample Images\nThe table below displays sample images along with their corresponding annotations generated using yolosegment2labelme:\n\n\n| Sample Image 1 | Sample Image 2 |\n|-----------------------------------------------------|-----------------------------------------------------|\n|  |  |\n| Sample Annotation for Image 1 | Sample Annotation for Image 2 |\n\n| Sample Image 3 | Sample Image 4 |\n|-----------------------------------------------------|-----------------------------------------------------|\n|  |  |\n| Sample Annotation for Image 3 | Sample Annotation for Image 4 |\n\n\n## Documentation\n\nThe documentation for **yolosegment2labelme** can be found on GitHub: [yolosegment2labelme Documentation](https://github.com/Abonia1/yolosegment2labelme)\n\n## Contributing\n\nContributions are welcome! If you'd like to contribute to **yolosegment2labelme**, please check out the [Contribution Guidelines](CONTRIBUTING.md).\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Authors\n\n- Abonia Sojasingarayar - [GitHub](https://github.com/Abonia1)\n\n## Support\n\nIf you encounter any issues or have questions about **yolosegment2labelme**, please feel free to open an issue on GitHub: [yolosegment2labelme Issues](https://github.com/Abonia1/yolosegment2labelme/issues)\n",
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