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)), }