99 lines
3.7 KiB
Markdown
99 lines
3.7 KiB
Markdown
**Forked from [rooneysh/Labelme2YOLO](https://github.com/rooneysh/Labelme2YOLO)**
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# Labelme2YOLO
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[](https://pypi.org/project/labelme2yolo)
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[](https://pypi.org/project/labelme2yolo)
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[](https://www.codacy.com/gh/GreatV/labelme2yolo/dashboard?utm_source=github.com&utm_medium=referral&utm_content=GreatV/labelme2yolo&utm_campaign=Badge_Grade)
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Help converting LabelMe Annotation Tool JSON format to YOLO text file format.
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If you've already marked your segmentation dataset by LabelMe, it's easy to use this tool to help converting to YOLO format dataset.
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---------
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## New
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- export data as yolo polygon annotation (for YOLOv5 v7.0 segmentation)
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- Now you can choose the output format of the label text. The available options are `plygon` and `bbox`.
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## Installation
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```console
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pip install labelme2yolo
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```
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## Parameters Explain
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**--json_dir** LabelMe JSON files folder path.
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**--val_size (Optional)** Validation dataset size, for example 0.2 means 20% for validation.
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**--test_size (Optional)** Test dataset size, for example 0.2 means 20% for Test.
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**--json_name (Optional)** Convert single LabelMe JSON file.
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**--output_format (Optional)** The output format of label.
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## How to Use
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### 1. Convert JSON files, split training, validation and test dataset by --val_size and --test_size
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Put all LabelMe JSON files under **labelme_json_dir**, and run this python command.
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```bash
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labelme2yolo --json_dir /path/to/labelme_json_dir/ --val_size 0.15 --test_size 0.15
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```
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Script would generate YOLO format dataset labels and images under different folders, for example,
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```bash
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/path/to/labelme_json_dir/YOLODataset/labels/train/
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/path/to/labelme_json_dir/YOLODataset/labels/test/
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/path/to/labelme_json_dir/YOLODataset/labels/val/
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/path/to/labelme_json_dir/YOLODataset/images/train/
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/path/to/labelme_json_dir/YOLODataset/images/test/
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/path/to/labelme_json_dir/YOLODataset/images/val/
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/path/to/labelme_json_dir/YOLODataset/dataset.yaml
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```
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### 2. Convert JSON files, split training and validation dataset by folder
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If you already split train dataset and validation dataset for LabelMe by yourself, please put these folder under labelme_json_dir, for example,
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```bash
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/path/to/labelme_json_dir/train/
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/path/to/labelme_json_dir/val/
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```
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Put all LabelMe JSON files under **labelme_json_dir**.
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Script would read train and validation dataset by folder.
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Run this python command.
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```bash
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labelme2yolo --json_dir /path/to/labelme_json_dir/
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```
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Script would generate YOLO format dataset labels and images under different folders, for example,
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```bash
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/path/to/labelme_json_dir/YOLODataset/labels/train/
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/path/to/labelme_json_dir/YOLODataset/labels/val/
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/path/to/labelme_json_dir/YOLODataset/images/train/
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/path/to/labelme_json_dir/YOLODataset/images/val/
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/path/to/labelme_json_dir/YOLODataset/dataset.yaml
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```
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### 3. Convert single JSON file
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Put LabelMe JSON file under **labelme_json_dir**. , and run this python command.
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```bash
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labelme2yolo --json_dir /path/to/labelme_json_dir/ --json_name 2.json
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```
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Script would generate YOLO format text label and image under **labelme_json_dir**, for example,
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```bash
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/path/to/labelme_json_dir/2.text
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/path/to/labelme_json_dir/2.png
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```
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## How to build package/wheel
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1. [install hatch](https://hatch.pypa.io/latest/install/)
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2. Run the following command:
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```shell
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hatch build
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```
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## License
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`labelme2yolo` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
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