MacBook-Attacks-Dataset.py

import datasets
import pandas as pd

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {MacBook-Attacks-Dataset},
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 = 'MacBook-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 MacBookAttacksDataset(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):
            file_name = '/'.join(video_path.split('/')[-2:])

            yield idx, {
                'file':
                    file_name,
                'phone':
                    annotations_df.loc[
                        annotations_df['file'].str.lower() == file_name]
                    ['phone'].values[0],
                'computer':
                    annotations_df.loc[
                        annotations_df['file'].str.lower() == file_name]
                    ['computer'].values[0],
                'gender':
                    annotations_df.loc[
                        annotations_df['file'].str.lower() == file_name]
                    ['gender'].values[0],
                'age':
                    annotations_df.loc[
                        annotations_df['file'].str.lower() == file_name]
                    ['age'].values[0],
                'country':
                    annotations_df.loc[
                        annotations_df['file'].str.lower() == file_name]
                    ['country'].values[0]
            }