import os import datasets from datasets.tasks import ImageClassification from requests import get from pandas import read_csv logger = datasets.logging.get_logger(__name__) _HOMEPAGE = "https://nihcc.app.box.com/v/ChestXray-NIHCC" _CITATION = """\ @ONLINE {beansdata, author="Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald Summer", title="ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases", month="January", year="2017", url="https://nihcc.app.box.com/v/ChestXray-NIHCC" } """ _DESCRIPTION = """\ The NIH Chest X-ray dataset consists of 100,000 de-identified images of chest x-rays. The images are in PNG format. The data is provided by the NIH Clinical Center and is available through the NIH download site: https://nihcc.app.box.com/v/ChestXray-NIHCC """ _IMAGE_URLS2 = [ 'https://nihcc.box.com/shared/static/vfk49d74nhbxq3nqjg0900w5nvkorp5c.gz', 'https://nihcc.box.com/shared/static/i28rlmbvmfjbl8p2n3ril0pptcmcu9d1.gz', 'https://nihcc.box.com/shared/static/f1t00wrtdk94satdfb9olcolqx20z2jp.gz', 'https://nihcc.box.com/shared/static/0aowwzs5lhjrceb3qp67ahp0rd1l1etg.gz', 'https://nihcc.box.com/shared/static/v5e3goj22zr6h8tzualxfsqlqaygfbsn.gz', 'https://nihcc.box.com/shared/static/asi7ikud9jwnkrnkj99jnpfkjdes7l6l.gz', 'https://nihcc.box.com/shared/static/jn1b4mw4n6lnh74ovmcjb8y48h8xj07n.gz', 'https://nihcc.box.com/shared/static/tvpxmn7qyrgl0w8wfh9kqfjskv6nmm1j.gz', 'https://nihcc.box.com/shared/static/upyy3ml7qdumlgk2rfcvlb9k6gvqq2pj.gz', 'https://nihcc.box.com/shared/static/l6nilvfa9cg3s28tqv1qc1olm3gnz54p.gz', 'https://nihcc.box.com/shared/static/hhq8fkdgvcari67vfhs7ppg2w6ni4jze.gz', 'https://nihcc.box.com/shared/static/ioqwiy20ihqwyr8pf4c24eazhh281pbu.gz' ] _IMAGE_URLS = [ 'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/dummy/0.0.0/images_001.tar.gz', 'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/dummy/0.0.0/images_002.tar.gz' ] _URLS = { 'train_val_list': 'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/raw/main/dummy/0.0.0/train_val_list.txt', 'test_list': 'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/raw/main/dummy/0.0.0/test_list.txt', 'labels': 'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/raw/main/dummy/0.0.0/Data_Entry_2017_v2020.csv', 'image_urls': _IMAGE_URLS } _LABEL2IDX = {'No Finding': 0, 'Atelactasis': 1, 'Cardiomegaly': 2, 'Effusion': 3, 'Infiltration': 4, 'Mass': 5, 'Nodule': 6, 'Pneumonia': 7, 'Pneumothorax': 8, 'Consolidation': 9, 'Edema': 10, 'Emphysema': 11, 'Fibrosis': 12, 'Pleural_Thickening': 13, 'Hernia': 14} _NAMES = list(_LABEL2IDX.keys()) class XChest(datasets.GeneratorBasedBuilder): """NIH Image Chest X-ray dataset.""" VERSION = datasets.Version("0.0.0") def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "image_file_path": datasets.Value("string"), "image": datasets.Image(), "labels": datasets.features.ClassLabel(names=_NAMES), #"multi-labels": datasets.features.Sequence( # datasets.features.ClassLabel(num_classes=len(_NAMES), # names=_NAMES) # ) } ), supervised_keys=("image", "labels"), homepage=_HOMEPAGE, citation=_CITATION, task_templates=[ImageClassification(image_column="image", label_column="labels")], ) def _split_generators(self, dl_manager): # Get the image names that belong to the train-val dataset logger.info("Downloading the train_val_list image names") train_val_list = get(_URLS['train_val_list']).iter_lines() train_val_list = set([x.decode('UTF8') for x in train_val_list]) logger.info(f"Check train_val_list: {train_val_list}") # Create list for store the name of the images for each dataset train_files = [] test_files = [] # Download batches data_files = dl_manager.download_and_extract(_URLS['image_urls']) # Iterate trought image folder and check if they belong to # the trainset or testset for batch in data_files: logger.info(f"Batch for data_files: {batch}") path_files = dl_manager.iter_files(batch) for img in path_files: if img.split('/')[-1] in train_val_list: train_files.append(img) else: test_files.append(img) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ 'files': iter(train_files) } ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ 'files': iter(test_files) } ) ] def _generate_examples(self, files): for i, path in enumerate(files): file_name = os.path.basename(path) if file_name.endswith(".png"): yield i, { "image_file_path": path, "image": path, #"labels": ["Mass", "Hernia"] #"labels": [5, 14] "labels": "Mass" #"multi-labels": list(map(int, [_LABEL2IDX[x] for x in ["Mass", "Hernia"])) }