Datasets:

1aurent
/

ADE20K

	from pathlib import Path

	import datasets
	import json
	from datetime import datetime

	_VERSION = "0.1.0"

	_CITATION = """
	@inproceedings{8100027,
	title = {Scene Parsing through ADE20K Dataset},
	author = {Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
	year = 2017,
	booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	volume = {},
	number = {},
	pages = {5122--5130},
	doi = {10.1109/CVPR.2017.544},
	keywords = {Image segmentation;Semantics;Sun;Labeling;Visualization;Neural networks;Computer vision}
	}
	@misc{zhou2018semantic,
	title = {Semantic Understanding of Scenes through the ADE20K Dataset},
	author = {Bolei Zhou and Hang Zhao and Xavier Puig and Tete Xiao and Sanja Fidler and Adela Barriuso and Antonio Torralba},
	year = 2018,
	eprint = {1608.05442},
	archiveprefix = {arXiv},
	primaryclass = {cs.CV}
	}
	"""

	_DESCRIPTION = """
	ADE20K is composed of more than 27K images from the SUN and Places databases.
	Images are fully annotated with objects, spanning over 3K object categories.
	Many of the images also contain object parts, and parts of parts.
	We also provide the original annotated polygons, as well as object instances for amodal segmentation.
	Images are also anonymized, blurring faces and license plates.
	"""

	_HOMEPAGE = "https://groups.csail.mit.edu/vision/datasets/ADE20K/"

	_LICENSE = "Creative Commons BSD-3 License Agreement"

	_FEATURES = datasets.Features(
	{
	"image": datasets.Image(mode="RGB"),
	"segmentations": datasets.Sequence(datasets.Image(mode="RGB")),
	"instances": datasets.Sequence(datasets.Image(mode="L")),
	"filename": datasets.Value("string"),
	"folder": datasets.Value("string"),
	"source": datasets.Features(
	{
	"folder": datasets.Value("string"),
	"filename": datasets.Value("string"),
	"origin": datasets.Value("string"),
	}
	),
	"scene": datasets.Sequence(datasets.Value("string")),
	"objects": [
	{
	"id": datasets.Value("uint16"),
	"name": datasets.Value("string"),
	"name_ndx": datasets.Value("uint16"),
	"hypernym": datasets.Sequence(datasets.Value("string")),
	"raw_name": datasets.Value("string"),
	"attributes": datasets.Value("string"),
	"depth_ordering_rank": datasets.Value("uint16"),
	"occluded": datasets.Value("bool"),
	"crop": datasets.Value(dtype="bool"),
	"parts": {
	"is_part_of": datasets.Value("uint16"),
	"part_level": datasets.Value("uint8"),
	"has_parts": datasets.Sequence(datasets.Value("uint16")),
	},
	"polygon": {
	"x": datasets.Sequence(datasets.Value("uint16")),
	"y": datasets.Sequence(datasets.Value("uint16")),
	"click_date": datasets.Sequence(datasets.Value("timestamp[us]")),
	},
	"saved_date": datasets.Value("timestamp[us]"),
	}
	],
	}
	)


	class ADE20K(datasets.GeneratorBasedBuilder):
	DEFAULT_WRITER_BATCH_SIZE = 1000

	def _info(self):
	return datasets.DatasetInfo(
	features=_FEATURES,
	supervised_keys=None,
	description=_DESCRIPTION,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	version=_VERSION,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager: datasets.DownloadManager):
	archive_training = Path("ADE20K_2021_17_01/images/ADE/training")
	archive_validation = Path("ADE20K_2021_17_01/images/ADE/validation")

	jsons_training = sorted(list(archive_training.rglob("*.json")))
	jsons_validation = sorted(list(archive_validation.rglob("*.json")))

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"jsons": jsons_training},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"jsons": jsons_validation},
	),
	]

	def parse_date(self, date: str) -> datetime:
	if date == []:
	return None

	try:
	timestamp = datetime.strptime(date, "%d-%m-%y %H:%M:%S:%f")
	return timestamp
	except:
	pass

	try:
	timestamp = datetime.strptime(date, "%d-%b-%Y %H:%M:%S:%f")
	return timestamp
	except:
	pass

	try:
	timestamp = datetime.strptime(date, "%d-%m-%y %H:%M:%S")
	return timestamp
	except:
	pass

	try:
	timestamp = datetime.strptime(date, "%d-%b-%Y %H:%M:%S")
	return timestamp
	except:
	pass

	raise ValueError(f"Could not parse date: {date}")

	def parse_imsize(self, imsize: list[int]) -> list[int]:
	if len(imsize) == 2:
	return imsize + [3]
	return imsize

	def parse_json(self, json_path: Path):
	with json_path.open("r", encoding="ISO-8859-1") as f:
	data = json.load(f)
	annotation = data["annotation"]
	objects = annotation["object"]

	segmentations = list(
	json_path.parent.glob(
	f"{annotation['filename'].removesuffix(".jpg")}_parts*"
	)
	)
	segmentations = [str(part) for part in segmentations]
	main_mask = json_path.parent / annotation["filename"]
	main_mask = str(main_mask.with_suffix("")) + "_seg.png"
	segmentations.insert(0, main_mask)

	instances = [
	json_path.parent / object["instance_mask"] for object in objects
	]
	instances = [str(instance) for instance in instances]

	return {
	"image": str(json_path.parent / annotation["filename"]),
	"segmentations": segmentations,
	"instances": instances,
	"filename": annotation["filename"],
	"folder": annotation["folder"],
	"source": {
	"folder": annotation["source"]["folder"],
	"filename": annotation["source"]["filename"],
	"origin": annotation["source"]["origin"],
	},
	"scene": annotation["scene"],
	"objects": [
	{
	"id": object["id"],
	"name": object["name"],
	"name_ndx": object["name_ndx"],
	"hypernym": object["hypernym"],
	"raw_name": object["raw_name"],
	"attributes": ""
	if object["attributes"] == []
	else object["attributes"],
	"depth_ordering_rank": object["depth_ordering_rank"],
	"occluded": object["occluded"] == "yes",
	"crop": object["crop"] == "1",
	"parts": {
	"part_level": object["parts"]["part_level"],
	"is_part_of": None
	if object["parts"]["ispartof"] == []
	else object["parts"]["ispartof"],
	"has_parts": [object["parts"]["hasparts"]]
	if isinstance(object["parts"]["hasparts"], int)
	else object["parts"]["hasparts"],
	},
	"polygon": {
	"x": list(
	map(lambda x: int(max(0, x)), object["polygon"]["x"])
	),
	"y": list(
	map(lambda y: int(max(0, y)), object["polygon"]["y"])
	),
	"click_date": []
	if "click_date" not in object["polygon"]
	else list(
	map(self.parse_date, object["polygon"]["click_date"])
	),
	},
	"saved_date": self.parse_date(object["saved_date"]),
	}
	for object in objects
	],
	}

	def _generate_examples(self, jsons: list[Path]):
	for i, json_path in enumerate(jsons):
	yield i, self.parse_json(json_path)