Upload kidney-ct-abnormality.py

kidney-ct-abnormality.py

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+import datasets
+import numpy as np
+import pandas as pd
+from pathlib import Path
+import os
+
+_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"
+}
+
+_METADATA_URL = {
+    "meta": "https://huggingface.co/datasets/Euniceyeee/kidney-ct-abnormality/resolve/main/dataset.csv"
+}
+
+LABELS = [
+    'Normal',
+    'Abnormal'
+]
+
+
+class KidneyCTAbnormality(datasets.GeneratorBasedBuilder):
+    """Collection of brain xray images for fine-grain classification."""
+
+    VERSION = datasets.Version("1.0")
+
+    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,
+            citation=_CITATION
+        )
+
+    def _split_generators(self, dl_manager):
+        metadata_url = dl_manager.download(_METADATA_URL)
+        df_metadata = pd.read_csv(metadata_url)
+        files_metadata = {}
+        for _, row in df_metadata.iterrows():
+            split_value = row['split']
+            files_metadata.setdefault(split_value, [])
+            files_metadata[split_value].append(row.to_dict())
+        
+        img_url = self._URLS
+        data_dir = dl_manager.download_and_extract(img_url)
+        print("Test"+data_dir)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir['train'],
+                    "meta": files_metadata["train"]
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir['train'],
+                    "meta": files_metadata["test"]
+                },
+            ),
+        ]
+
+    def _generate_examples(self, download_path, metadata):
+        """Generate images and labels for splits."""
+        for i, meta in enumerate(metadata):
+            img_path = os.path.join(
+                download_path,
+                meta["split"],
+                meta["abnormality"],
+                meta["file_name"],
+            )
+            yield i, {
+                "image": img_path,
+                "label": meta["abnormality"],
+            }