license_plates.py

import datasets
import pandas as pd

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {license_plates},
author = {TrainingDataPro},
year = {2023}
}
"""

_DESCRIPTION = """\
Over 1.2 million annotated license plates from vehicles around the world.
This dataset is tailored for License Plate Recognition tasks and includes
images from both YouTube and PlatesMania. 
Annotation details are provided in the About section below. 
"""
_NAME = 'license_plates'

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

_LICENSE = ""

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


class LicensePlates(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="Brazil"),
        datasets.BuilderConfig(name="Estonia"),
        datasets.BuilderConfig(name="Finland"),
        datasets.BuilderConfig(name="Kazakhstan"),
        datasets.BuilderConfig(name="Lithuania"),
        datasets.BuilderConfig(name="Serbia"),
        datasets.BuilderConfig(name="UAE"),
    ]

    DEFAULT_CONFIG_NAME = "Brazil"

    def _info(self):

        features = datasets.Features({
            'image': datasets.Image(),
            'labeled_image': datasets.Image(),
            'bbox': datasets.Value('string'),
            'license_plate.id': datasets.Value('string'),
            'license_plate.visibility': datasets.Value('string'),
            'license_plate.rows_count': datasets.Value('uint8'),
            'license_plate.number': datasets.Value('string'),
            'license_plate.serial': datasets.Value('string'),
            'license_plate.country': datasets.Value('string'),
            'license_plate.mask': datasets.Value('string')
        })

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data = dl_manager.download(f"{_DATA}{self.config.name}.tar.gz")
        data = dl_manager.iter_archive(data)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                    gen_kwargs={
                                        "data": data
                                    }),
        ]

    def _generate_examples(self, data):

        annotations = []
        images = []

        for idx, (file_path, file) in enumerate(data):
            if file_path.endswith('.csv'):
                annotations
            yield idx, {
                "image": {
                    "path": image_path,
                    "bytes": image.read()
                },
                "mask": {
                    "path": mask_path,
                    "bytes": mask.read()
                },
                'id': annotations_df['id'].iloc[idx],
                'gender': annotations_df['gender'].iloc[idx],
                'age': annotations_df['age'].iloc[idx]
            }