Metadata-Version: 2.1
Name: labelme2coco
Version: 0.1.2
Summary: Convert labelme annotations into coco format in one step
Home-page: https://github.com/fcakyon/labelme2coco
Author: Fatih Cagatay Akyon
Author-email: 
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.15.1)
Requires-Dist: pillow (>=4.3.0)
Requires-Dist: jsonschema (>=2.6.0)

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# labelme2coco Python Package for Linux/MacOS/Windows
Make your own dataset for object detection/instance segmentation using [labelme](https://github.com/wkentaro/labelme) and transform the format to coco json format 

## Convert LabelMe annotations to COCO format in one step
[labelme](https://github.com/wkentaro/labelme) is a widely used is a graphical image annotation tool that supports classification, segmentation, instance segmentation and object detection formats.
However, widely used frameworks/models such as Yolact/Solo, Detectron, MMDetection etc. requires COCO formatted annotations.

You can use this package to convert labelme annotations to COCO format.

## Getting started
### Installation
```
pip install labelme2coco
```

### Usage
```python
# import package
import labelme2coco

# set directory that contains labelme annotations and image files
labelme_folder = "tests/data/labelme_annot"

# set path for coco json to be saved
save_json_path = "tests/data/test_coco.json"

# convert labelme annotations to coco
labelme2coco.convert(labelme_folder, save_json_path)
```



