| import datasets | |
| import pandas as pd | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {2d-printed_masks_attacks}, | |
| author = {TrainingDataPro}, | |
| year = {2023} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The dataset consists of 40,000 videos and selfies with unique people. 15,000 | |
| attack replays from 4,000 unique devices. 10,000 attacks with A4 printouts and | |
| 10,000 attacks with cut-out printouts. | |
| """ | |
| _NAME = '2d-printed_masks_attacks' | |
| _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" | |
| _LICENSE = "cc-by-nc-nd-4.0" | |
| _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" | |
| class PrintedMasksAttacks(datasets.GeneratorBasedBuilder): | |
| def _info(self): | |
| return datasets.DatasetInfo(description=_DESCRIPTION, | |
| features=datasets.Features({ | |
| '2d_mask': datasets.Value('string'), | |
| 'live_selfie': datasets.Image(), | |
| 'live_video': datasets.Value('string'), | |
| 'phone_model': datasets.Value('string') | |
| }), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| license=_LICENSE) | |
| def _split_generators(self, dl_manager): | |
| masks = dl_manager.download(f"{_DATA}2d_masks.tar.gz") | |
| live_selfies = dl_manager.download(f"{_DATA}live_selfie.tar.gz") | |
| live_videos = dl_manager.download(f"{_DATA}live_video.tar.gz") | |
| annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") | |
| masks = dl_manager.iter_archive(masks) | |
| live_selfies = dl_manager.iter_archive(live_selfies) | |
| live_videos = dl_manager.iter_archive(live_videos) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| 'masks': masks, | |
| "live_selfies": live_selfies, | |
| 'live_videos': live_videos, | |
| 'annotations': annotations | |
| }), | |
| ] | |
| def _generate_examples(self, masks, live_selfies, live_videos, | |
| annotations): | |
| for idx, ((mask_path, mask), (live_selfie_path, live_selfie), | |
| (live_video_path, live_video)) in enumerate( | |
| zip(masks, live_selfies, live_videos)): | |
| annotations_df = pd.read_csv(annotations, sep=';') | |
| yield idx, { | |
| '2d_mask': | |
| mask_path, | |
| 'live_selfie': { | |
| 'path': live_selfie_path, | |
| 'bytes': live_selfie.read() | |
| }, | |
| 'live_video': | |
| live_video_path, | |
| 'phone_model': | |
| annotations_df.loc[ | |
| annotations_df['live_selfie'] == live_selfie_path] | |
| ['phone_model'].values[0] | |
| } | |