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| """Beans leaf dataset with images of diseased and health leaves.""" |
|
|
| from pathlib import Path |
|
|
| import datasets |
| from datasets.tasks import ImageClassification |
|
|
|
|
| _CITATION = """\ |
| @ONLINE {beansdata, |
| author="Makerere AI Lab", |
| title="Bean disease dataset", |
| month="January", |
| year="2020", |
| url="https://github.com/AI-Lab-Makerere/ibean/" |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| Beans is a dataset of images of beans taken in the field using smartphone |
| cameras. It consists of 3 classes: 2 disease classes and the healthy class. |
| Diseases depicted include Angular Leaf Spot and Bean Rust. Data was annotated |
| by experts from the National Crops Resources Research Institute (NaCRRI) in |
| Uganda and collected by the Makerere AI research lab. |
| """ |
|
|
| _URLS = { |
| 'train': "https://storage.googleapis.com/ibeans/train.zip", |
| 'validation': "https://storage.googleapis.com/ibeans/validation.zip", |
| 'test': "https://storage.googleapis.com/ibeans/test.zip" |
| } |
|
|
| _NAMES = ["angular_leaf_spot", "bean_rust", "healthy"] |
|
|
| class Beans(datasets.GeneratorBasedBuilder): |
| """Beans plant leaf images dataset.""" |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features({ |
| "file": |
| datasets.Value("string"), |
| "labels": |
| datasets.features.ClassLabel(names=sorted(tuple(_NAMES))), |
| }), |
| supervised_keys=("file", "labels"), |
| homepage="https://github.com/AI-Lab-Makerere/ibean/", |
| citation=_CITATION, |
| task_templates=[ImageClassification(image_file_path_column="file", label_column="labels", labels=sorted(tuple(_NAMES)))] |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| data_files = dl_manager.download_and_extract(_URLS) |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "archive": data_files['train'], |
| }), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "archive": data_files['validation'], |
| }), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "archive": data_files['test'], |
| }), |
| ] |
|
|
| def _generate_examples(self, archive): |
| labels = self.info.features['labels'] |
| for i, path in enumerate(Path(archive).glob('**/*')): |
| if path.suffix == '.jpg': |
| yield i, dict(file=path.as_posix(), labels=labels.encode_example(path.parent.name.lower())) |
