Upload brain-tumor-collection.py

brain-tumor-collection.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Collection of brain xray images for fine-grain classification."""
+
+import datasets
+import numpy as np
+import pandas as pd
+from pathlib import Path
+import os
+
+_CITATION = """\
+@misc{kaggle-brain-tumor-classification,
+  title={Kaggle: Brain Tumor Classification (MRI)},
+  howpublished={\\url{https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri?resource=download}},
+  note = {Accessed: 2022-06-30},
+}
+"""
+
+_DESCRIPTION = """\
+This dataset is intended as a test case for classification tasks (4 different kinds of brain xrays). The dataset consists of almost 1400 JPEG images grouped into two splits - training and validation. Each split contains 4 categories labeled as n0~n3, each corresponding to a cancer result of the mrt.
+
+
+| Label | Xray Category         | Train Images | Validation Images |
+| ----- | --------------------- | ------------ | ----------------- |
+| n0    | glioma_tumor          | 826          | 100               |
+| n1    | meningioma_tumor      | 822          | 115               |
+| n2    | pituitary_tumor       | 827          | 74                |
+| n3    | no_tumor              | 395          | 105               |
+
+
+"""
+
+_HOMEPAGE = "https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri?resource=download"
+
+_LICENSE = "cc0-1.0"
+
+_URLS = {
+    "original": "https://ibm.ent.box.com/index.php?rm=box_download_shared_file&shared_name=nf6md3mxww5k9rw4ks1hwffuo5iez1pc&file_id=f_978363130854"
+}
+
+LABELS = [
+    "Glioma Tumor",
+    "Meningioma Tumor",
+    "Pituitary Tumor",
+    "No Tumor"
+]
+
+
+class BrainTumorCollectionGenerator(datasets.GeneratorBasedBuilder):
+    """Collection of brain xray images for fine-grain classification."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="original", version=VERSION, description="Original JPEG files: images are 400x300 px or larger; ~550 MB"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "original"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image": datasets.Image(),
+                "label": datasets.ClassLabel(names=LABELS)
+            }
+        )
+        supervised_keys = ("image", "label")
+        
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=supervised_keys,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        url = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(url)
+        print("Test"+data_dir)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "xrays", "training", "training"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "xrays", "validation", "validation"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        paths = list(Path(filepath).glob("**/*.jpg"))
+        data = []
+
+        for path in paths:
+            tumor_folder = str(path).split("/")[-2]
+            index = int(tumor_folder[1])
+            label = LABELS[index]
+            data.append({"file": str(path), "label": label})
+
+        df = pd.DataFrame(data)
+        print(df)
+        df.sort_values("file", inplace=True)
+
+        for idx_, row in df.iterrows():
+            yield idx_, {
+                "image": row["file"],
+                "label": row["label"]
+            }