# Copyright 2022 Cristóbal Alcázar # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """NIH Chest X-ray Dataset""" import os import datasets from datasets.tasks import ImageClassification from requests import get from pandas import read_csv logger = datasets.logging.get_logger(__name__) _CITATION = """\ @inproceedings{Wang_2017, doi = {10.1109/cvpr.2017.369}, url = {https://doi.org/10.1109%2Fcvpr.2017.369}, year = 2017, month = {jul}, publisher = {{IEEE} }, author = {Xiaosong Wang and Yifan Peng and Le Lu and Zhiyong Lu and Mohammadhadi Bagheri and Ronald M. Summers}, title = {{ChestX}-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases}, booktitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})} } """ _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 """ _HOMEPAGE = "https://nihcc.app.box.com/v/chestxray-nihcc" _REPO = "https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/data" _IMAGE_URLS = [ f"{_REPO}/images/images_001.zip", f"{_REPO}/images/images_002.zip", f"{_REPO}/images/images_003.zip", f"{_REPO}/images/images_004.zip", f"{_REPO}/images/images_005.zip", f"{_REPO}/images/images_006.zip", f"{_REPO}/images/images_007.zip", f"{_REPO}/images/images_008.zip", f"{_REPO}/images/images_009.zip", f"{_REPO}/images/images_010.zip", f"{_REPO}/images/images_011.zip", f"{_REPO}/images/images_012.zip" #'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": f"{_REPO}/train_val_list.txt", "test_list": f"{_REPO}/test_list.txt", "labels": f"{_REPO}/Data_Entry_2017_v2020.csv", "BBox": f"{_REPO}/BBox_List_2017.csv", "image_urls": _IMAGE_URLS } _LABEL2IDX = {"No Finding": 0, "Atelectasis": 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 ChestXray14Config(datasets.BuilderConfig): """NIH Image Chest X-ray14 configuration.""" def __init__(self, name, **kwargs): super(ChestXray14Config, self).__init__( version=datasets.Version("1.0.0"), name=name, description="NIH ChestX-ray14", **kwargs, ) class ChestXray14(datasets.GeneratorBasedBuilder): """NIH Image Chest X-ray14 dataset.""" BUILDER_CONFIGS = [ ChestXray14Config("image-classification"), ChestXray14Config("object-detection"), ] def _info(self): if self.config.name == "image-classification": features = datasets.Features( { "image": datasets.Image(), "labels": datasets.features.Sequence( datasets.features.ClassLabel( num_classes=len(_NAMES), names=_NAMES ) ), } ) keys = ("image", "labels") if self.config.name == "object-detection": features = datasets.Features( { "image_id": datasets.Value("string"), "patient_id": datasets.Value("int32"), "image": datasets.Image(), "width": datasets.Value("int32"), "height": datasets.Value("int32"), } ) object_dict = { "image_id": datasets.Value("string"), "area": datasets.Value("int64"), "bbox": datasets.Sequence(datasets.Value("float32"), length=4), } features["objects"] = [object_dict] keys = ("image", "objects") return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=keys, homepage=_HOMEPAGE, citation=_CITATION, ) 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 os.path.basename(img) in train_val_list: train_files.append(img) else: test_files.append(img) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "files": train_files } ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "files": test_files } ) ] def _generate_examples(self, files): if self.config.name == "image-classification": # Read csv with image labels label_csv = read_csv(_URLS["labels"]) for i, path in enumerate(files): file_name = os.path.basename(path) # Get image id to filter the respective row of the csv image_id = file_name image_labels = label_csv[label_csv["Image Index"] == image_id]["Finding Labels"].values[0].split("|") if file_name.endswith(".png"): yield i, { "image": path, "labels": image_labels, }