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import datasets
import pandas as pd
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {makeup-detection-dataset},
author = {TrainingDataPro},
year = {2023}
}
"""
_DESCRIPTION = """\
The dataset consists of photos featuring the same individuals captured in two
distinct scenarios - *with and without makeup*. The dataset contains a diverse
range of individuals with various *ages, ethnicities and genders*. The images
themselves would be of high quality, ensuring clarity and detail for each
subject.
In photos with makeup, it is applied **to only specific parts** of the face,
such as *eyes, lips, or skin*.
In photos without makeup, individuals have a bare face with no visible
cosmetics or beauty enhancements. These images would provide a clear contrast
to the makeup images, allowing for significant visual analysis.
"""
_NAME = 'makeup-detection-dataset'
_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
_LICENSE = ""
_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
class MakeupDetectionDataset(datasets.GeneratorBasedBuilder):
"""Small sample of image-text pairs"""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
'no_makeup': datasets.Image(),
'with_makeup': datasets.Image(),
'part': datasets.Value('string'),
'gender': datasets.Value('string'),
'age': datasets.Value('int8'),
'country': datasets.Value('string')
}),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
no_makeup = dl_manager.download(f"{_DATA}no_makeup.tar.gz")
with_makeup = dl_manager.download(f"{_DATA}with_makeup.tar.gz")
annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
no_makeup = dl_manager.iter_archive(no_makeup)
with_makeup = dl_manager.iter_archive(with_makeup)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN,
gen_kwargs={
"no_makeup": no_makeup,
'with_makeup': with_makeup,
'annotations': annotations
}),
]
def _generate_examples(self, no_makeup, with_makeup, annotations):
annotations_df = pd.read_csv(annotations, sep=';')
for idx, ((image_path, image),
(mask_path, mask)) in enumerate(zip(no_makeup, with_makeup)):
yield idx, {
"no_makeup": {
"path": image_path,
"bytes": image.read()
},
"with_makeup": {
"path": mask_path,
"bytes": mask.read()
},
'part':
annotations_df.loc[annotations_df['no_makeup'].str.lower() ==
image_path.lower()]['part'].values[0],
'gender':
annotations_df.loc[annotations_df['no_makeup'].str.lower() ==
image_path.lower()]['gender'].values[0],
'age':
annotations_df.loc[annotations_df['no_makeup'].str.lower() ==
image_path.lower()]['age'].values[0],
'country':
annotations_df.loc[annotations_df['no_makeup'].str.lower() ==
image_path.lower()]['country'].values[0]
}
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