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import os |
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import datasets |
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from datasets.tasks import ImageClassification |
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from .classes import FRUITS30_CLASSES |
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class fruits30(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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assert len(FRUITS30_CLASSES) == 30 |
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return datasets.DatasetInfo( |
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description="The fruits-30 is an image dataset for fruit image classification task." |
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" It contains high-quality images of 30 types of fruit with annotation using segmentation.", |
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features=datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"label": datasets.ClassLabel(names=list(FRUITS30_CLASSES.values())), |
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} |
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), |
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homepage="https://github.com/VinayHajare/Fruit-Image-Dataset", |
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task_templates=[ImageClassification(image_column="image", label_column="label")], |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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data_dir = dl_manager.download("./FruitImageDataset") |
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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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"data_dir": data_dir, |
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"split": "train", |
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}, |
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) |
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] |
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def _generate_examples(self, data_dir, split): |
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"""Yields examples.""" |
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idx = 0 |
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for root, dirs, files in os.walk(data_dir): |
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for file in files: |
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if file.endswith(".jpg"): |
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_, synset_id = os.path.splitext(file)[0].rsplit("_", 1) |
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label = FRUITS30_CLASSES[synset_id] |
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image_path = os.path.join(root, file) |
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with open(image_path, "rb") as image_file: |
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ex = {"image": {"path": image_path, "bytes": image_file.read()}, "label": label} |
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yield idx, ex |
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idx += 1 |
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