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Upload dataset.py

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dataset.py ADDED
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+ import datasets
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+ import pandas as pd
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+
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+ _CITATION = """\
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+ @InProceedings{huggingface:dataset,
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+ title = {RSNA-ATD2023},
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+ author = {Yeow Zi Qin},
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+ year = {2023}
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ The dataset is the processed version of Kaggle Competition: RSNA 2023 Abdominal Trauma Detection.
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+ It comprises of segmentation of 205 series of CT scans with 5 classes (liver, spleen, right_kidney,
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+ left_kidney, bowel).
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+ """
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+
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+ _NAME = "RSNA-ATD2023"
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+
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+ _HOMEPAGE = f"https://huggingface.co/datasets/ziq/{_NAME}"
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+
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+ _LICENSE = "MIT"
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+
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+ _DATA = f"https://huggingface.co/datasets/ziq/{_NAME}/resolve/main/data/"
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+
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+
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+ class PeopleTrackingDataset(datasets.GeneratorBasedBuilder):
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+ """Small sample of image-text pairs"""
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features(
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+ {
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+ "patient_id": datasets.Value("int32"),
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+ "series_id": datasets.Value("int32"),
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+ "frame_id": datasets.Value("int32"),
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+ "image": datasets.Image(),
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+ "mask": datasets.Image(),
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+ }
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+ ),
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+ supervised_keys=None,
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+ homepage=_HOMEPAGE,
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ train_images = dl_manager.download(f"{_DATA}train.tar.gz")
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+ train_masks = dl_manager.download(f"{_DATA}seg.tar.gz")
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+
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+ metadata = dl_manager.download(f"{_DATA}train.csv")
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+ train_images = dl_manager.iter_archive(train_images)
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+ train_masks = dl_manager.iter_archive(train_masks)
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+
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ gen_kwargs={
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+ "images": train_images,
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+ "masks": train_masks,
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+ "metadata": metadata,
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+ },
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+ ),
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+ ]
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+
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+ def _generate_examples(self, images, masks, metadata):
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+ df = pd.read_csv(metadata)
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+
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+ for idx, ((image_path, image), (mask_path, mask)) in enumerate(
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+ zip(images, masks)
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+ ):
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+ row = df.loc[df["path"] == image_path.lower()]
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+
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+ yield idx, {
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+ "patient_id": row["patient_id"].values[0],
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+ "series_id": row["series_id"].values[0],
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+ "frame_id": row["frame_id"].values[0],
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+ "image": {"path": image_path, "bytes": image.read()},
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+ "mask": {"path": mask_path, "bytes": mask.read()},
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+ }