NIH-Chest-X-ray-dataset.py

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/data/train_val_list.txt',
	'test_list': 'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/raw/main/data/test_list.txt',
	'labels': 'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/raw/main/data/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),
			"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):
		# 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.split('/')[-1]
		    image_labels = label_csv[label_csv['Image Index'] == image_id]['Finding Labels'].values[0].split('|')
		    if file_name.endswith(".png"):
		        yield i, {
			    "image_file_path": path,
			    "image": path,
			    "labels": image_labels,
			}