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
coco-lib is available on PyPI:
pip install coco-lib>>> 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>>> from coco_lib.objectdetection import ObjectDetectionDataset
>>> dataset = ObjectDetectionDataset.load('test_dataset.json') # Load the dataset