selfie-and-video-on-back-camera.py

import datasets
import pandas as pd

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {selfie-and-video-on-back-camera},
author = {TrainingDataPro},
year = {2023}
}
"""

_DESCRIPTION = """\
The dataset consists of selfies and video of real people made on a back camera
of the smartphone. The dataset solves tasks in the field of anti-spoofing and
it is useful for buisness and safety systems.
"""
_NAME = 'selfie-and-video-on-back-camera'

_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"

_LICENSE = ""

_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"


class SelfieAndVideoOnBackCamera(datasets.GeneratorBasedBuilder):
    """Small sample of image-text pairs"""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                'photo': datasets.Image(),
                'video': datasets.Value('string'),
                'phone': 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):
        images = dl_manager.download(f"{_DATA}photo.tar.gz")
        videos = dl_manager.download(f"{_DATA}video.tar.gz")
        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
        images = dl_manager.iter_archive(images)
        videos = dl_manager.iter_archive(videos)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                    gen_kwargs={
                                        "images": images,
                                        'videos': videos,
                                        'annotations': annotations
                                    }),
        ]

    def _generate_examples(self, images, videos, annotations):
        annotations_df = pd.read_csv(annotations, sep=';')

        for idx, ((image_path, image),
                  (video_path, video)) in enumerate(zip(images, videos)):
            yield idx, {
                "photo": {
                    "path": image_path,
                    "bytes": image.read()
                },
                "video":
                    video_path,
                'phone':
                    annotations_df.loc[annotations_df['photo'].str.startswith(
                        str(idx))]['phone'].values[0],
                'gender':
                    annotations_df.loc[annotations_df['photo'].str.startswith(
                        str(idx))]['gender'].values[0],
                'age':
                    annotations_df.loc[annotations_df['photo'].str.startswith(
                        str(idx))]['age'].values[0],
                'country':
                    annotations_df.loc[annotations_df['photo'].str.startswith(
                        str(idx))]['country'].values[0],
            }