Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
100K<n<1M
ArXiv:
License:
File size: 4,535 Bytes
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import os
import datasets
from datasets.tasks import ImageClassification
from requests import get
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',
'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."""
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),
}
),
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}")
for img in dl_manager.download_and_extract(batch):
if img in train_val_list:
train_files.append(img)
else:
test_files.append(img)
print(train_files)
print(test_files)
return [
datatsets.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):
pass
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