Datasets:

trpakov
/

chest-xray-classification

	import os

	import datasets
	from datasets.tasks import ImageClassification


	_HOMEPAGE = "https://universe.roboflow.com/mohamed-traore-2ekkp/chest-x-rays-qjmia/dataset/3"
	_LICENSE = "CC BY 4.0"
	_CITATION = """\

	"""
	_CATEGORIES = ['PNEUMONIA', 'NORMAL']


	class CHESTXRAYCLASSIFICATIONConfig(datasets.BuilderConfig):
	"""Builder Config for chest-xray-classification"""

	def __init__(self, data_urls, **kwargs):
	"""
	BuilderConfig for chest-xray-classification.

	Args:
	data_urls: `dict`, name to url to download the zip file from.
	**kwargs: keyword arguments forwarded to super.
	"""
	super(CHESTXRAYCLASSIFICATIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
	self.data_urls = data_urls


	class CHESTXRAYCLASSIFICATION(datasets.GeneratorBasedBuilder):
	"""chest-xray-classification image classification dataset"""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	CHESTXRAYCLASSIFICATIONConfig(
	name="full",
	description="Full version of chest-xray-classification dataset.",
	data_urls={
	"train": "https://huggingface.co/datasets/trpakov/chest-xray-classification/resolve/main/data/train.zip",
	"validation": "https://huggingface.co/datasets/trpakov/chest-xray-classification/resolve/main/data/valid.zip",
	"test": "https://huggingface.co/datasets/trpakov/chest-xray-classification/resolve/main/data/test.zip",
	}
	,
	),
	CHESTXRAYCLASSIFICATIONConfig(
	name="mini",
	description="Mini version of chest-xray-classification dataset.",
	data_urls={
	"train": "https://huggingface.co/datasets/trpakov/chest-xray-classification/resolve/main/data/valid-mini.zip",
	"validation": "https://huggingface.co/datasets/trpakov/chest-xray-classification/resolve/main/data/valid-mini.zip",
	"test": "https://huggingface.co/datasets/trpakov/chest-xray-classification/resolve/main/data/valid-mini.zip",
	},
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	features=datasets.Features(
	{
	"image_file_path": datasets.Value("string"),
	"image": datasets.Image(),
	"labels": datasets.features.ClassLabel(names=_CATEGORIES),
	}
	),
	supervised_keys=("image", "labels"),
	homepage=_HOMEPAGE,
	citation=_CITATION,
	license=_LICENSE,
	task_templates=[ImageClassification(image_column="image", label_column="labels")],
	)

	def _split_generators(self, dl_manager):
	data_files = dl_manager.download_and_extract(self.config.data_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"files": dl_manager.iter_files([data_files["train"]]),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"files": dl_manager.iter_files([data_files["validation"]]),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"files": dl_manager.iter_files([data_files["test"]]),
	},
	),
	]

	def _generate_examples(self, files):
	for i, path in enumerate(files):
	file_name = os.path.basename(path)
	if file_name.endswith((".jpg", ".png", ".jpeg", ".bmp", ".tif", ".tiff")):
	yield i, {
	"image_file_path": path,
	"image": path,
	"labels": os.path.basename(os.path.dirname(path)),
	}