import datasets import pandas as pd _CITATION = """\ @InProceedings{huggingface:dataset, title = {FaceSegmentation}, author = {TrainingDataPro}, year = {2023} } """ _DESCRIPTION = """\ An example of a dataset that we've collected for a photo edit App. The dataset includes 20 selfies of people (man and women) in segmentation masks and their visualisations. """ _NAME = 'FaceSegmentation' _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" _LICENSE = "" _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" class FaceSegmentation(datasets.GeneratorBasedBuilder): """Small sample of image-text pairs""" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features({ 'image': datasets.Image(), 'mask': datasets.Image(), 'id': datasets.Value('string'), 'gender': datasets.Value('string'), 'age': datasets.Value('int8') }), supervised_keys=None, homepage=_HOMEPAGE, citation=_CITATION, ) def _split_generators(self, dl_manager): images = dl_manager.download(f"{_DATA}images.tar.gz") masks = dl_manager.download(f"{_DATA}masks.tar.gz") annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") images = dl_manager.iter_archive(images) masks = dl_manager.iter_archive(masks) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={ "images": images, 'masks': masks, 'annotations': annotations }), ] def _generate_examples(self, images, masks, annotations): annotations_df = pd.read_csv(annotations, sep=';') for idx, ((image_path, image), (mask_path, mask)) in enumerate(zip(images, masks)): yield idx, { "image": { "path": image_path, "bytes": image.read() }, "mask": { "path": mask_path, "bytes": mask.read() }, 'id': annotations_df['id'].iloc[idx], 'gender': annotations_df['gender'].iloc[idx], 'age': annotations_df['age'].iloc[idx] }