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import datasets
import numpy as np
import pandas as pd
from pathlib import Path
import os
import json
import SimpleITK as sitk
_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(
{
"images": datasets.Sequence(datasets.Image()),
# "img_f64_array": datasets.Array3D(dtype = 'float64'),
"img_path": datasets.Value(dtype = 'string'),
"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."""
h = 0
w = 0
for i, meta in enumerate(metadata):
img_path = os.path.join(
filepath,
'kidney_CT',
meta['split'],
meta["image"]
)
img = sitk.ReadImage(img_path)
img_array = sitk.GetArrayFromImage(img)
img_array_c = img_array.astype('uint8')
# if img_array_c.shape[0]>h:
# h = img_array_c.shape[0]
# print(h)
# if img_array_c.shape[1]>w:
# w = img_array_c.shape[1]
# print(w)
# img_array_cr = img_array_c.transpose(2, 0, 1)[..., np.newaxis]
yield i, {
"images": img_array_c,
# "img_f64_array": img_array,
"img_path": img_path,
"label": meta["abnormality"],
} |