Metadata-Version: 2.1
Name: labelme2coco
Version: 0.2.0
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: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: sahi (>=0.8.19)
Requires-Dist: jsonschema (>=2.6.0)

[![Downloads](https://pepy.tech/badge/labelme2coco)](https://pepy.tech/project/labelme2coco)
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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 -U labelme2coco
```

### Basic Usage

```python
labelme2coco path/to/labelme/dir
```

```python
labelme2coco path/to/labelme/dir --train_split_rate 0.85
```

### Advanced Usage

```python
# import package
import labelme2coco

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

# set export dir
export_dir = "tests/data/"

# set train split rate
train_split_rate = 0.85

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

```python
# import functions
from labelme2coco import get_coco_from_labelme_folder, save_json

# set labelme training data directory
labelme_train_folder = "tests/data/labelme_annot"

# set labelme validation data directory
labelme_val_folder = "tests/data/labelme_annot"

# set path for coco json to be saved
export_dir = "tests/data/"

# create train coco object
train_coco = get_coco_from_labelme_folder(labelme_train_folder)

# export train coco json
save_json(train_coco.json, export_dir+"train.json")

# create val coco object
val_coco = get_coco_from_labelme_folder(labelme_val_folder, coco_category_list=train_coco.json_categories)

# export val coco json
save_json(val_coco.json, export_dir+"val.json")
```

