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import datasets |
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import pandas as pd |
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_CITATION = """\ |
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@InProceedings{huggingface:dataset, |
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title = {helmet_detection}, |
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author = {TrainingDataPro}, |
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year = {2023} |
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} |
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""" |
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_DESCRIPTION = """\ |
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An example of a dataset that we've collected for a photo edit App. |
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The dataset includes 20 selfies of people (man and women) |
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in segmentation masks and their visualisations. |
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""" |
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_NAME = 'helmet_detection' |
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_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" |
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_LICENSE = "" |
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_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" |
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class FaceSegmentation(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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'id': datasets.Value('int32'), |
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'image': datasets.Image(), |
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'mask': datasets.Image(), |
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'bboxes': datasets.Value('string') |
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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 = dl_manager.download(f"{_DATA}images.tar.gz") |
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masks = dl_manager.download(f"{_DATA}masks.tar.gz") |
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annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") |
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images = dl_manager.iter_archive(images) |
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masks = dl_manager.iter_archive(masks) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"images": images, |
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'masks': masks, |
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'annotations': annotations |
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}), |
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] |
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def _generate_examples(self, images, masks, annotations): |
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annotations_df = pd.read_csv(annotations) |
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for idx, ((image_path, image), |
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(mask_path, mask)) in enumerate(zip(images, masks)): |
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yield idx, { |
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'id': annotations_df['image_id'].iloc[idx], |
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"image": { |
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"path": image_path, |
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"bytes": image.read() |
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}, |
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"mask": { |
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"path": mask_path, |
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"bytes": mask.read() |
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}, |
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'bboxes': annotations_df['bboxes'].iloc[idx] |
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} |
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