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https://github.com/kevinsbarnard/coco-lib
COCO dataset library.
https://github.com/kevinsbarnard/coco-lib
Last synced: 26 days ago
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COCO dataset library.
- Host: GitHub
- URL: https://github.com/kevinsbarnard/coco-lib
- Owner: kevinsbarnard
- License: mit
- Created: 2021-11-17T19:48:44.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-12-09T17:09:24.000Z (27 days ago)
- Last Synced: 2024-12-09T18:22:55.094Z (27 days ago)
- Language: Python
- Size: 8.79 KB
- Stars: 3
- Watchers: 2
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# coco-lib
COCO dataset library. Provides serializable native Python bindings for several COCO dataset formats.
Supported bindings and their corresponding modules:
- Object Detection: `objectdetection`
- Keypoint Detection: `keypointdetection`
- Panoptic Segmentation: `panopticsegmentation`
- Image Captioning: `imagecaptioning`
## Installation
`coco-lib` is available on PyPI:
``` bash
pip install coco-lib
```
## Usage
### Creating a dataset (Object Detection)
```python
>>> from coco_lib.common import Info, Image, License
>>> from coco_lib.objectdetection import ObjectDetectionAnnotation, \
... ObjectDetectionCategory, \
... ObjectDetectionDataset
>>> from datetime import datetime
>>> info = Info( # Describe the dataset
... year=datetime.now().year,
... version='1.0',
... description='This is a test dataset',
... contributor='Test',
... url='https://test',
... date_created=datetime.now()
... )
>>> mit_license = License( # Set the license
... id=0,
... name='MIT',
... url='https://opensource.org/licenses/MIT'
... )
>>> images = [ # Describe the images
... Image(
... id=0,
... width=640, height=480,
... file_name='test.jpg',
... license=mit_license.id,
... flickr_url='',
... coco_url='',
... date_captured=datetime.now()
... ),
... ...
... ]
>>> categories = [ # Describe the categories
... ObjectDetectionCategory(
... id=0,
... name='pedestrian',
... supercategory=''
... ),
... ...
... ]
>>> annotations = [ # Describe the annotations
... ObjectDetectionAnnotation(
... id=0,
... image_id=0,
... category_id=0,
... segmentation=[],
... area=800.0,
... bbox=[300.0, 100.0, 20.0, 40.0],
... is_crowd=0
... ),
... ...
... ]
>>> dataset = ObjectDetectionDataset( # Create the dataset
... info=info,
... images=images,
... licenses=[mit_license],
... categories=categories,
... annotations=annotations
... )
>>> dataset.save('test_dataset.json', indent=2) # Save the dataset
```
### Loading a dataset
```python
>>> from coco_lib.objectdetection import ObjectDetectionDataset
>>> dataset = ObjectDetectionDataset.load('test_dataset.json') # Load the dataset
```