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import os |
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import random |
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import datasets |
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from datasets.tasks import ImageClassification |
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_HOMEPAGE = f"https://www.modelscope.cn/datasets/Genius-Society/{os.path.basename(__file__)[:-3]}" |
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_URL = f"{_HOMEPAGE}/resolve/master/images.zip" |
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_NAMES = ["Centromere", "Golgi", "Homogeneous", "NuMem", "Nucleolar", "Speckled"] |
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class HEp2(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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return datasets.DatasetInfo( |
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features=datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"label": datasets.features.ClassLabel(names=_NAMES), |
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} |
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), |
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supervised_keys=("image", "label"), |
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homepage=_HOMEPAGE, |
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license="mit", |
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version="0.0.1", |
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task_templates=[ |
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ImageClassification( |
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task="image-classification", |
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image_column="image", |
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label_column="label", |
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) |
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], |
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) |
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def _ground_truth(self, id): |
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if id < 2495: |
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return "Homogeneous" |
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elif id < 5326: |
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return "Speckled" |
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elif id < 7924: |
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return "Nucleolar" |
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elif id < 10665: |
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return "Centromere" |
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elif id < 12873: |
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return "NuMem" |
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else: |
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return "Golgi" |
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def _split_generators(self, dl_manager): |
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data_files = dl_manager.download_and_extract(_URL) |
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files = dl_manager.iter_files([data_files]) |
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dataset = [] |
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for path in files: |
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file_name = os.path.basename(path) |
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if file_name.endswith(".png"): |
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dataset.append( |
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{ |
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"image": path, |
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"label": self._ground_truth(int(file_name.split(".")[0])), |
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} |
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) |
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random.shuffle(dataset) |
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data_count = len(dataset) |
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p80 = int(data_count * 0.8) |
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p90 = int(data_count * 0.9) |
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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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"files": dataset[:p80], |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"files": dataset[p80:p90], |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"files": dataset[p90:], |
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}, |
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), |
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] |
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def _generate_examples(self, files): |
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for i, path in enumerate(files): |
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yield i, path |
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