| 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] |
| } |
|
|
