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from xml.etree import ElementTree as ET
import datasets
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {silicone-masks-biometric-attacks},
author = {TrainingDataPro},
year = {2023}
}
"""
_DESCRIPTION = """\
The dataset consists of videos of individuals and attacks with printed 2D masks and
silicone masks . Videos are filmed in different lightning conditions (*in a dark room,
daylight, light room and nightlight*). Dataset includes videos of people with different
attributes (*glasses, mask, hat, hood, wigs and mustaches for men*).
"""
_NAME = "silicone-masks-biometric-attacks"
_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
_LICENSE = ""
_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
_LABELS = ["real", "silicone", "mask"]
class SiliconeMasksBiometricAttacks(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int32"),
"video_name": datasets.Value("string"),
"video_path": datasets.Value("string"),
"label": datasets.ClassLabel(
num_classes=len(_LABELS),
names=_LABELS,
),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
videos = dl_manager.download(f"{_DATA}videos.tar.gz")
videos = dl_manager.iter_archive(videos)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"videos": videos,
},
),
]
def _generate_examples(self, videos):
for idx, ((video_path, video)) in enumerate(videos):
for lbl in _LABELS:
if lbl in video_path:
label = lbl
yield idx, {
"id": idx,
"video_name": video_path.split("/")[-1],
"video_path": video_path,
"label": label,
}
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