| import datasets | |
| import pandas as pd | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {monitors-replay-attacks-dataset}, | |
| author = {TrainingDataPro}, | |
| year = {2023} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The dataset consists of videos of replay attacks played on different models of | |
| computers. The dataset solves tasks in the field of anti-spoofing and it is | |
| useful for buisness and safety systems. | |
| The dataset includes: **replay attacks** - videos of real people played | |
| on a computer and filmed on the phone. | |
| """ | |
| _NAME = 'monitors-replay-attacks-dataset' | |
| _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 MonitorsReplayAttacksDataset(datasets.GeneratorBasedBuilder): | |
| def _info(self): | |
| return datasets.DatasetInfo(description=_DESCRIPTION, | |
| features=datasets.Features({ | |
| 'file': datasets.Value('string'), | |
| 'phone': datasets.Value('string'), | |
| 'computer': datasets.Value('string'), | |
| 'gender': datasets.Value('string'), | |
| 'age': datasets.Value('int16'), | |
| 'country': datasets.Value('string'), | |
| }), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| license=_LICENSE) | |
| def _split_generators(self, dl_manager): | |
| attacks = dl_manager.download(f"{_DATA}attacks.tar.gz") | |
| annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") | |
| attacks = dl_manager.iter_archive(attacks) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "attacks": attacks, | |
| 'annotations': annotations | |
| }), | |
| ] | |
| def _generate_examples(self, attacks, annotations): | |
| annotations_df = pd.read_csv(annotations, sep=';') | |
| for idx, (video_path, video) in enumerate(attacks): | |
| yield idx, { | |
| 'file': | |
| video_path, | |
| 'phone': | |
| annotations_df.loc[annotations_df['file'].str.lower() == | |
| video_path.lower()]['phone'].values[0], | |
| 'computer': | |
| annotations_df.loc[annotations_df['file'].str.lower() == | |
| video_path.lower()] | |
| ['computer'].values[0], | |
| 'gender': | |
| annotations_df.loc[annotations_df['file'].str.lower() == | |
| video_path.lower()]['gender'].values[0], | |
| 'age': | |
| annotations_df.loc[annotations_df['file'].str.lower() == | |
| video_path.lower()]['age'].values[0], | |
| 'country': | |
| annotations_df.loc[annotations_df['file'].str.lower() == | |
| video_path.lower()]['country'].values[0] | |
| } | |