coco_detection.py

import json
import datasets
from pathlib import Path

_HOMEPAGE = 'https://cocodataset.org/'
_LICENSE = 'Creative Commons Attribution 4.0 License'
_DESCRIPTION = 'COCO is a large-scale object detection, segmentation, and captioning dataset.'
_CITATION = '''\
@article{cocodataset,
  author    = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and Lubomir D. Bourdev and Ross B. Girshick and James Hays and Pietro Perona and Deva Ramanan and Piotr Doll{'{a} }r and C. Lawrence Zitnick},
  title     = {Microsoft {COCO:} Common Objects in Context},
  journal   = {CoRR},
  volume    = {abs/1405.0312},
  year      = {2014},
  url       = {http://arxiv.org/abs/1405.0312},
  archivePrefix = {arXiv},
  eprint    = {1405.0312},
  timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
'''
_NAMES = [
    'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
    'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
    'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
    'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
    'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
    'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
    'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
    'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
    'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
    'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
    'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]


class COCODetectionConfig(datasets.BuilderConfig):
    '''Builder Config for coco2017'''

    def __init__(
        self, description, homepage,
        annotation_urls, **kwargs
    ):
        super(COCODetectionConfig, self).__init__(
            version=datasets.Version('1.0.0', ''),
            **kwargs
        )
        self.description = description
        self.homepage = homepage
        url = 'http://images.cocodataset.org/zips/'
        self.train_image_url = url + 'train2017.zip'
        self.val_image_url = url + 'val2017.zip'
        self.train_annotation_urls = annotation_urls['train']
        self.val_annotation_urls = annotation_urls['validation']


class COCODetection(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        COCODetectionConfig(
            description=_DESCRIPTION,
            homepage=_HOMEPAGE,
            annotation_urls={
                'train': 'data/instance_train.zip',
                'validation': 'data/instance_validation.zip'
            },
        )
    ]

    def _info(self):
        features = datasets.Features({
            'image': datasets.Image(mode='RGB', decode=True, id=None),
            'bboxes': datasets.Sequence(
                feature=datasets.Sequence(
                    feature=datasets.Value(dtype='float32', id=None),
                    length=4, id=None
                ), length=-1, id=None
            ),
            'categories': datasets.Sequence(
                feature=datasets.ClassLabel(names=_NAMES),
                length=-1, id=None
            ),
            'inst.rles': datasets.Sequence(
                feature={
                    'size': datasets.Sequence(
                        feature=datasets.Value(dtype='int32', id=None),
                        length=2, id=None
                    ),
                    'counts': datasets.Value(dtype='string', id=None)
                },
                length=-1, id=None
            ),
        })
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION
        )

    def _split_generators(self, dl_manager):
        train_image_path = dl_manager.download_and_extract(
            self.config.train_image_url
        )
        val_image_path = dl_manager.download_and_extract(
            self.config.val_image_url
        )
        train_annotation_paths = dl_manager.download_and_extract(
            self.config.train_annotation_urls
        )
        val_annotation_paths = dl_manager.download_and_extract(
            self.config.val_annotation_urls
        )
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    'image_path': f'{train_image_path}/train2017',
                    'annotation_path': f'{train_annotation_paths}/instance_train.jsonl'
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    'image_path': f'{val_image_path}/val2017',
                    'annotation_path': f'{val_annotation_paths}/instance_validation.jsonl'
                }
            )
        ]

    def _generate_examples(self, image_path, annotation_path):
        idx = 0
        image_path = Path(image_path)
        with open(annotation_path, 'r', encoding='utf-8') as f:
            for line in f:
                obj = json.loads(line.strip())
                example = {
                    'image': str(image_path / obj['image']),
                    'bboxes': obj['bboxes'],
                    'categories': obj['categories'],
                    'inst.rles': obj['inst.rles']
                }
                yield idx, example
                idx += 1