Datasets:

yonigozlan
/

coco_detection_dataset_script

	import json
	import os

	import datasets


	class COCOBuilderConfig(datasets.BuilderConfig):
	def __init__(self, name, splits, **kwargs):
	super().__init__(name, **kwargs)
	self.splits = splits


	# Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@article{DBLP:journals/corr/LinMBHPRDZ14,
	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}
	}
	"""

	# Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	COCO is a large-scale object detection, segmentation, and captioning dataset.
	"""

	# Add a link to an official homepage for the dataset here
	_HOMEPAGE = "http://cocodataset.org/#home"

	# Add the licence for the dataset here if you can find it
	_LICENSE = ""

	# Add link to the official dataset URLs here
	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)

	# This script is supposed to work with local (downloaded) COCO dataset.
	_URLs = {}


	# Name of the dataset usually match the script name with CamelCase instead of snake_case
	class COCODataset(datasets.GeneratorBasedBuilder):
	"""An example dataset script to work with the local (downloaded) COCO dataset"""

	VERSION = datasets.Version("0.0.0")

	BUILDER_CONFIG_CLASS = COCOBuilderConfig
	BUILDER_CONFIGS = [
	COCOBuilderConfig(name="2017", splits=["train", "val"]),
	]
	DEFAULT_CONFIG_NAME = "2017"

	def _info(self):
	# This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset

	feature_dict = {
	"id": datasets.Value("int64"),
	"objects": {
	"bbox_id": datasets.Sequence(datasets.Value("int64")),
	"category_id": datasets.Sequence(
	datasets.ClassLabel(
	names=[
	"N/A",
	"person",
	"bicycle",
	"car",
	"motorcycle",
	"airplane",
	"bus",
	"train",
	"truck",
	"boat",
	"traffic light",
	"fire hydrant",
	"street sign",
	"stop sign",
	"parking meter",
	"bench",
	"bird",
	"cat",
	"dog",
	"horse",
	"sheep",
	"cow",
	"elephant",
	"bear",
	"zebra",
	"giraffe",
	"hat",
	"backpack",
	"umbrella",
	"shoe",
	"eye glasses",
	"handbag",
	"tie",
	"suitcase",
	"frisbee",
	"skis",
	"snowboard",
	"sports ball",
	"kite",
	"baseball bat",
	"baseball glove",
	"skateboard",
	"surfboard",
	"tennis racket",
	"bottle",
	"plate",
	"wine glass",
	"cup",
	"fork",
	"knife",
	"spoon",
	"bowl",
	"banana",
	"apple",
	"sandwich",
	"orange",
	"broccoli",
	"carrot",
	"hot dog",
	"pizza",
	"donut",
	"cake",
	"chair",
	"couch",
	"potted plant",
	"bed",
	"mirror",
	"dining table",
	"window",
	"desk",
	"toilet",
	"door",
	"tv",
	"laptop",
	"mouse",
	"remote",
	"keyboard",
	"cell phone",
	"microwave",
	"oven",
	"toaster",
	"sink",
	"refrigerator",
	"blender",
	"book",
	"clock",
	"vase",
	"scissors",
	"teddy bear",
	"hair drier",
	"toothbrush",
	]
	)
	),
	"bbox": datasets.Sequence(
	datasets.Sequence(datasets.Value("float64"), length=4)
	),
	"iscrowd": datasets.Sequence(datasets.Value("int64")),
	"area": datasets.Sequence(datasets.Value("float64")),
	},
	"height": datasets.Value("int64"),
	"width": datasets.Value("int64"),
	"file_name": datasets.Value("string"),
	"coco_url": datasets.Value("string"),
	"image_path": datasets.Value("string"),
	}

	features = datasets.Features(feature_dict)

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	data_dir = self.config.data_dir
	if not data_dir:
	raise ValueError(
	"This script is supposed to work with local (downloaded) COCO dataset. The argument `data_dir` in `load_dataset()` is required."
	)

	_DL_URLS = {
	"train": os.path.join(data_dir, "train2017.zip"),
	"val": os.path.join(data_dir, "val2017.zip"),
	"annotations_trainval": os.path.join(
	data_dir, "annotations_trainval2017.zip"
	),
	}
	archive_path = dl_manager.download_and_extract(_DL_URLS)

	splits = []
	for split in self.config.splits:
	if split == "train":
	dataset = datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"json_path": os.path.join(
	archive_path["annotations_trainval"],
	"annotations",
	"instances_train2017.json",
	),
	"image_dir": os.path.join(archive_path["train"], "train2017"),
	"split": "train",
	},
	)
	elif split in ["val", "valid", "validation", "dev"]:
	dataset = datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"json_path": os.path.join(
	archive_path["annotations_trainval"],
	"annotations",
	"instances_val2017.json",
	),
	"image_dir": os.path.join(archive_path["val"], "val2017"),
	"split": "valid",
	},
	)
	else:
	continue

	splits.append(dataset)

	return splits

	def _generate_examples(
	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	self,
	json_path,
	image_dir,
	split,
	):
	"""Yields examples as (key, example) tuples."""
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is here for legacy reason (tfds) and is not important in itself.

	features = [
	"id",
	"objects",
	"height",
	"width",
	"file_name",
	"coco_url",
	"image_path",
	]
	object_features = [
	"bbox_id",
	"category_id",
	"bbox",
	"iscrowd",
	"area",
	]

	with open(json_path, "r", encoding="UTF-8") as fp:
	data = json.load(fp)

	images = data["images"]
	images_entry = {image["id"]: image for image in images}
	for image_id, image_entry in images_entry.items():
	image_entry["image_path"] = os.path.join(
	image_dir, image_entry["file_name"]
	)
	image_entry["objects"] = []

	objects = data["annotations"]
	for id_, object_entry in enumerate(objects):
	image_id = object_entry["image_id"]

	entry = {k: v for k, v in object_entry.items() if k in object_features}
	entry["bbox_id"] = object_entry["id"]
	if entry["iscrowd"]:
	continue
	images_entry[image_id]["objects"].append(entry)

	for id_, entry in images_entry.items():
	entry = {k: v for k, v in entry.items() if k in features}
	# collate objects
	objects = entry.pop("objects")
	if not objects:
	continue
	entry["objects"] = {
	object_feature: [obj[object_feature] for obj in objects]
	for object_feature in object_features
	}

	yield str(entry["id"]), entry