| 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_youtube"), | |
| datasets.BuilderConfig(name="Estonia_platesmania"), | |
| datasets.BuilderConfig(name="Finland_platesmania"), | |
| datasets.BuilderConfig(name="Kazakhstan_platesmania"), | |
| datasets.BuilderConfig(name="Kazakhstan_youtube"), | |
| datasets.BuilderConfig(name="Lithuania_platesmania"), | |
| datasets.BuilderConfig(name="Serbia_platesmania"), | |
| datasets.BuilderConfig(name="Serbia_youtube"), | |
| datasets.BuilderConfig(name="UAE_platesmania"), | |
| datasets.BuilderConfig(name="UAE_youtube") | |
| ] | |
| DEFAULT_CONFIG_NAME = "Brazil" | |
| def _info(self): | |
| features = datasets.Features({ | |
| 'bbox_id': datasets.Value('uint32'), | |
| 'bbox': datasets.Value('string'), | |
| 'image': datasets.Image(), | |
| 'labeled_image': datasets.Image(), | |
| '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) | |
| annotations = dl_manager.download(f'{_DATA}{self.config.name}.csv') | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "data": data, | |
| 'annotations': annotations | |
| }), | |
| ] | |
| def _generate_examples(self, data, annotations): | |
| annotations_df = pd.read_csv(annotations, sep=',', index_col=0) | |
| images = {} | |
| for idx, (file_path, file) in enumerate(data): | |
| file_name = file_path.split('/')[-1] | |
| images[file_name] = (file_path, file.read()) | |
| annotations_df.drop( | |
| columns=['license_plate.region', 'license_plate.color'], | |
| inplace=True, | |
| errors='ignore') | |
| annotations_df.fillna(0, inplace=True) | |
| annotations_df.sort_values(by='file_name', inplace=True) | |
| for row in annotations_df.itertuples(index=True): | |
| image = images[row[1]] | |
| name, ext = row[1].split('.') | |
| labeled_image = images[f'{name}_labeled.{ext}'] | |
| yield idx, { | |
| 'bbox_id': row[0], | |
| 'bbox': row[2], | |
| "image": { | |
| "path": image[0], | |
| "bytes": image[1] | |
| }, | |
| "labeled_image": { | |
| "path": labeled_image[0], | |
| "bytes": labeled_image[1] | |
| }, | |
| 'license_plate.id': row[3], | |
| 'license_plate.visibility': row[4], | |
| 'license_plate.rows_count': row[5], | |
| 'license_plate.number': row[6], | |
| 'license_plate.serial': row[7], | |
| 'license_plate.country': row[8], | |
| 'license_plate.mask': row[9] | |
| } | |