Datasets:
Tags:
License:
| # 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=5ich3fqgpnbmkdho2eoe7fe4uwrplcfi&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"] | |
| } |