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import datasets |
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_CITATION = """\ |
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@InProceedings{huggingface:dataset, |
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title = {Shrek Detection Dataset}, |
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author={Aurelio AI}, |
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year={2024} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Demo dataset for testing Shrek detection capabilities in images. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/aurelio-ai/shrek-detection" |
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_LICENSE = "" |
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_REPO = "https://huggingface.co/datasets/aurelio-ai/shrek-detection" |
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class ImageSet(datasets.GeneratorBasedBuilder): |
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"""Small sample of image-text pairs""" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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'text': datasets.Value("string"), |
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'image': datasets.Image(), |
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'is_shrek': datasets.Value("bool") |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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images_archive = dl_manager.download(f"{_REPO}/resolve/main/images.tgz") |
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image_iters = dl_manager.iter_archive(images_archive) |
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test_images_archive = dl_manager.download(f"{_REPO}/resolve/main/test_images.tgz") |
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test_image_iters = dl_manager.iter_archive(test_images_archive) |
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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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"images": image_iters |
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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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"images": test_image_iters |
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} |
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), |
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] |
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def _generate_examples(self, images): |
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""" This function returns the examples in the raw (text) form.""" |
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for idx, (filepath, image) in enumerate(images): |
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filename = filepath.split('/')[-1][:-4] |
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cls = filename[0] if filename[0] in ["0", "1"] else None |
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if cls: |
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filename = filename[1:] |
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description = filename.replace('_', ' ').replace('-', ' ') |
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yield idx, { |
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"image": {"path": filepath, "bytes": image.read()}, |
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"text": description, |
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"is_shrek": True if cls == "1" else False |
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} |
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