Create rsna-atd.py

rsna-atd.py

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+import datasets
+import pandas as pd
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {rsna-atd},
+author = {Yeow Zi Qin},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset is the processed version of Kaggle Competition: RSNA 2023 Abdominal Trauma Detection.
+It comprises of segmentation of 205 series of CT scans with 5 classes (liver, spleen, right_kidney, 
+left_kidney, bowel).
+"""
+
+_NAME = "rsna-atd"
+
+_HOMEPAGE = f"https://huggingface.co/datasets/ziq/{_NAME}"
+
+_LICENSE = "MIT"
+
+_DATA = f"https://huggingface.co/datasets/ziq/{_NAME}/resolve/main/data/"
+
+
+class RSNAATD(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image_path": datasets.Value("string"),
+                    "mask_path": datasets.Value("string")
+                    # "patient_id": datasets.Value("int64"),
+                    # "series_id": datasets.Value("int64"),
+                    # "aortic_hu": datasets.Value("float64"),
+                    # "incomplete_organ": datasets.Value("int64"),
+                    # "bowel_healthy": datasets.Value("int64"),
+                    # "bowel_injury": datasets.Value("int64"),
+                    # "extravasation_healthy": datasets.Value("int64"),
+                    # "extravasation_injury": datasets.Value("int64"),
+                    # "kidney_healthy": datasets.Value("int64"),
+                    # "kidney_low": datasets.Value("int64"),
+                    # "kidney_high": datasets.Value("int64"),
+                    # "liver_healthy": datasets.Value("int64"),
+                    # "liver_low": datasets.Value("int64"),
+                    # "liver_high": datasets.Value("int64"),
+                    # "spleen_healthy": datasets.Value("int64"),
+                    # "spleen_low": datasets.Value("int64"),
+                    # "spleen_high": datasets.Value("int64"),
+                    # "any_injury": datasets.Value("int64"),
+                    # "image": datasets.Array3D(shape=(None, 512, 512)),
+                    # "mask": datasets.Array3D(shape=(None, 512, 512)),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_images = dl_manager.download(f"{_DATA}images.tar.gz")
+        train_masks = dl_manager.download(f"{_DATA}masks.tar.gz")
+
+        metadata = dl_manager.download(f"{_DATA}metadata.csv")
+        train_images = dl_manager.iter_archive(train_images)
+        train_masks = dl_manager.iter_archive(train_masks)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": train_images,
+                    "masks": train_masks,
+                    "metadata": metadata,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, images, masks, metadata):
+        df = pd.read_csv(metadata)
+
+        for idx, ((image_path), (mask_path)) in enumerate(zip(images, masks)):
+            row = df.loc[df["path"] == image_path.lower().replace("images/", "")]
+
+            yield idx, {
+                "image_path": image_path,
+                "mask_path": mask_path
+                # "patient_id": row["patient_id"].values[0],
+                # "series_id": row["series_id"].values[0],
+                # "frame_id": row["frame_id"].values[0],
+                # "image": {"path": image_path, "bytes": image.read()},
+                # "mask": {"path": mask_path, "bytes": mask.read()},
+            }