90 lines
3.4 KiB
Markdown
90 lines
3.4 KiB
Markdown
# Labelme2YOLO
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[](https://pypi.org/project/labelme2yolo)
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[](https://pepy.tech/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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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.
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## New Features
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* export data as yolo polygon annotation (for YOLOv5 & YOLOV8 segmentation)
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* Now you can choose the output format of the label text. The two available alternatives are `polygon` and bounding box (`bbox`).
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## Installation
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```shell
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pip install labelme2yolo
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```
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## Arguments
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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 0.1 means 10% 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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**--label\_list (Optional)** The pre-assigned category labels.
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## How to Use
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### 1. Converting JSON files and splitting training, validation datasets
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You may need to place all LabelMe JSON files under **labelme\_json\_dir** and then run the following command:
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```shell
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labelme2yolo --json_dir /path/to/labelme_json_dir/
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```
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This tool will generate dataset labels and images with YOLO format in different folders, such as
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```plaintext
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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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### 2. Converting JSON files and splitting training, validation, and test datasets with --val\_size and --test\_size
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You may need to place all LabelMe JSON files under **labelme\_json\_dir** and then run the following command:
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```shell
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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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This tool will generate dataset labels and images with YOLO format in different folders, such as
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```plaintext
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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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## 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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**Forked from [rooneysh/Labelme2YOLO](https://github.com/rooneysh/Labelme2YOLO)**
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`labelme2yolo` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
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