kidney-ct-abnormality.py

import datasets
import numpy as np
import pandas as pd
from pathlib import Path
import os
import json

_CITATION = """\
Alves, N., & Boulogne, L. (2023). Kidney CT Abnormality [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8043408
"""

_DESCRIPTION = """\
This dataset in total is comprised of 986 .mha (medical high-resolution image) files. Each of these files contains multiple layers of CT scans of the kidney. 
The dataset has been speparated into train and test set by initial processing.

"""

_HOMEPAGE = "https://zenodo.org/records/8043408"

_LICENSE = "CC BY-NC-SA 4.0"

_URL = "https://huggingface.co/datasets/Euniceyeee/kidney-ct-abnormality"

# _URLS = {
# 'train': "https://huggingface.co/datasets/Euniceyeee/kidney-ct-abnormality/resolve/main/train.zip",
# 'test': "https://huggingface.co/datasets/Euniceyeee/kidney-ct-abnormality/resolve/main/test.zip"
# }
_URLS = {
 'kidney_CT': 'https://huggingface.co/datasets/Euniceyeee/kidney-ct-abnormality/resolve/main/kidney_ct/kidney_CT.zip'
}

_METADATA_URL = {
 "metadata": "https://huggingface.co/datasets/Euniceyeee/kidney-ct-abnormality/resolve/main/dataset_m.json"
}

LABELS = [
 'Normal',
 'Abnormal'
]


class KidneyCTAbnormality(datasets.GeneratorBasedBuilder):
 """Collection of brain xray images for fine-grain classification."""
 VERSION = datasets.Version("1.0.0")

 def _info(self):
 return datasets.DatasetInfo(
 description=_DESCRIPTION,
 features=datasets.Features(
 {
 "image": datasets.Image(),
 "label": datasets.ClassLabel(num_classes=2, names=LABELS),
 }
 ),
 supervised_keys=("image", "label"),
 homepage=_HOMEPAGE,
 citation=_CITATION,
 license=_LICENSE
 )

 def _split_generators(self, dl_manager):
 metadata_url = dl_manager.download(_METADATA_URL)
 files_metadata = {}
 with open(metadata_url["metadata"], encoding="utf-8") as f:
 for lines in f.read().splitlines():
 file_json_metdata = json.loads(lines)
 files_metadata.setdefault(file_json_metdata["split"], []).append(file_json_metdata)
 
 # data_dir = dl_manager.download(_URLS)
 downloaded_files = dl_manager.download_and_extract(_URLS)

 return [
 datasets.SplitGenerator(
 name=datasets.Split.TRAIN,
 gen_kwargs={
 "filepath": downloaded_files['kidney_CT'],
 "metadata": files_metadata["train"]
 },
 ),
 datasets.SplitGenerator(
 name=datasets.Split.TEST,
 gen_kwargs={
 "filepath": downloaded_files['kidney_CT'],
 "metadata": files_metadata["test"]
 },
 ),
 ]

 def _generate_examples(self, filepath, metadata):
 """Generate images and labels for splits."""
 print(filepath)
 for i, meta in enumerate(metadata):
 img_path = os.path.join(
 filepath,
 'kidney_CT',
 meta['split'],
 meta["image"]
 )
 yield i, {
 "image": img_path,
 "label": meta["abnormality"],
 }