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import datasets
import pandas as pd
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {selfie_and_video},
author = {TrainingDataPro},
year = {2023}
}
"""
_DESCRIPTION = """\
4000 people in this dataset. Each person took a selfie on a webcam,
took a selfie on a mobile phone. In addition, people recorded video from
the phone and from the webcam, on which they pronounced a given set of numbers.
Includes folders corresponding to people in the dataset. Each folder includes
8 files (4 images and 4 videos).
"""
_NAME = 'selfie_and_video'
_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
_LICENSE = ""
_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
class SelfieAndVideo(datasets.GeneratorBasedBuilder):
"""Small sample of image-text pairs"""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
'photo_1': datasets.Image(),
'photo_2': datasets.Image(),
'video_3': datasets.Value('string'),
'video_4': datasets.Value('string'),
'photo_5': datasets.Image(),
'photo_6': datasets.Image(),
'video_7': datasets.Value('string'),
'video_8': datasets.Value('string'),
'set_id': datasets.Value('string'),
'worker_id': datasets.Value('string'),
'age': datasets.Value('int8'),
'country': datasets.Value('string'),
'gender': datasets.Value('string')
}),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
images = dl_manager.download(f"{_DATA}data.tar.gz")
annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
images = dl_manager.iter_archive(images)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN,
gen_kwargs={
"images": images,
'annotations': annotations
}),
]
def _generate_examples(self, images, annotations):
annotations_df = pd.read_csv(annotations, sep=';')
images_data = pd.DataFrame(columns=['Link', 'Bytes'])
for idx, (image_path, image) in enumerate(images):
if image_path.lower().endswith('.jpg'):
images_data.loc[idx] = {
'Link': image_path,
'Bytes': image.read()
}
annotations_df = pd.merge(annotations_df,
images_data,
on=['Link'],
how='left')
for idx, worker_id in enumerate(pd.unique(annotations_df['WorkerId'])):
annotation = annotations_df.loc[annotations_df['WorkerId'] ==
worker_id]
annotation = annotation.sort_values(['Link'])
data = {
(f'photo_{row[7][37]}' if row[7].lower().endswith('.jpg') else f'video_{row[7][37]}'):
({
'path': row[7],
'bytes': row[8]
} if row[7].lower().endswith('.jpg') else row[7])
for row in annotation.itertuples()
}
age = annotation.loc[annotation['Link'].str.lower().str.endswith(
'1.jpg')]['Age'].values[0]
country = annotation.loc[annotation['Link'].str.lower().str.
endswith('1.jpg')]['Country'].values[0]
gender = annotation.loc[annotation['Link'].str.lower().str.
endswith('1.jpg')]['Gender'].values[0]
set_id = annotation.loc[annotation['Link'].str.lower().str.
endswith('1.jpg')]['SetId'].values[0]
data['worker_id'] = worker_id
data['age'] = age
data['country'] = country
data['gender'] = gender
data['set_id'] = set_id
yield idx, data
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