| import datasets |
| import pandas as pd |
|
|
| _CITATION = """\ |
| @InProceedings{huggingface:dataset, |
| title = {2d-masks-presentation-attack-detection}, |
| author = {TrainingDataPro}, |
| year = {2023} |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| The dataset consists of videos of individuals wearing printed 2D masks or |
| printed 2D masks with cut-out eyes and directly looking at the camera. |
| Videos are filmed in different lightning conditions and in different places |
| (indoors, outdoors). Each video in the dataset has an approximate duration of 2 |
| seconds. |
| """ |
| _NAME = '2d-masks-presentation-attack-detection' |
|
|
| _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" |
|
|
| _LICENSE = "" |
|
|
| _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" |
|
|
|
|
| class MasksPresentationAttackDetection(datasets.GeneratorBasedBuilder): |
| """Small sample of image-text pairs""" |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features({ |
| 'user': datasets.Value('string'), |
| 'real_1': datasets.Value('string'), |
| 'real_2': datasets.Value('string'), |
| 'real_3': datasets.Value('string'), |
| 'real_4': datasets.Value('string'), |
| 'mask_1': datasets.Value('string'), |
| 'mask_2': datasets.Value('string'), |
| 'mask_3': datasets.Value('string'), |
| 'mask_4': datasets.Value('string'), |
| 'cut_1': datasets.Value('string'), |
| 'cut_2': datasets.Value('string'), |
| 'cut_3': datasets.Value('string'), |
| 'cut_4': datasets.Value('string') |
| }), |
| supervised_keys=None, |
| homepage=_HOMEPAGE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| files = dl_manager.download(f"{_DATA}files.tar.gz") |
| annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") |
| files = dl_manager.iter_archive(files) |
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "files": files, |
| 'annotations': annotations |
| }), |
| ] |
|
|
| def _generate_examples(self, files, annotations): |
| annotations_df = pd.read_csv(annotations, sep=';') |
|
|
| for idx, (file_path, file) in enumerate(files): |
| if 'real_1' in file_path.lower(): |
| user = file_path.split('/')[-2] |
| yield idx, { |
| 'user': |
| user, |
| 'real_1': |
| annotations_df.loc[annotations_df['user'] == user] |
| ['real_1'].values[0], |
| 'real_2': |
| annotations_df.loc[annotations_df['user'] == user] |
| ['real_2'].values[0], |
| 'real_3': |
| annotations_df.loc[annotations_df['user'] == user] |
| ['real_3'].values[0], |
| 'real_4': |
| annotations_df.loc[annotations_df['user'] == user] |
| ['real_4'].values[0], |
| 'mask_1': |
| annotations_df.loc[annotations_df['user'] == user] |
| ['mask_1'].values[0], |
| 'mask_2': |
| annotations_df.loc[annotations_df['user'] == user] |
| ['mask_2'].values[0], |
| 'mask_3': |
| annotations_df.loc[annotations_df['user'] == user] |
| ['mask_3'].values[0], |
| 'mask_4': |
| annotations_df.loc[annotations_df['user'] == user] |
| ['mask_4'].values[0], |
| 'cut_1': |
| annotations_df.loc[annotations_df['user'] == user] |
| ['cut_1'].values[0], |
| 'cut_2': |
| annotations_df.loc[annotations_df['user'] == user] |
| ['cut_2'].values[0], |
| 'cut_3': |
| annotations_df.loc[annotations_df['user'] == user] |
| ['cut_3'].values[0], |
| 'cut_4': |
| annotations_df.loc[annotations_df['user'] == user] |
| ['cut_4'].values[0], |
| } |
|
|
