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import os |
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import numpy as np |
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from PIL import Image |
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import csv |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """""" |
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_DESCRIPTION = """Persian Licensee plate dataset. Primarily taken from AmirKabir University Challenge. |
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Annotation are provided by the authors""" |
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_DOWNLOAD_URLS = { |
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"train": "https://huggingface.co./datasets/hezarai/persian-license-plate-v1/resolve/main/persian_license_plate_train.csv", |
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"val": "https://huggingface.co./datasets/hezarai/persian-license-plate-v1/resolve/main/persian_license_plate_val.csv", |
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"test": "https://huggingface.co./datasets/hezarai/persian-license-plate-v1/resolve/main/persian_license_plate_test.csv", |
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'train_dataset': "https://huggingface.co./datasets/hezarai/persian-license-plate-v1/resolve/main/persian_license_plate_train.zip", |
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'val_dataset': "https://huggingface.co./datasets/hezarai/persian-license-plate-v1/resolve/main/persian_license_plate_val.zip", |
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'test_dataset': "https://huggingface.co./datasets/hezarai/persian-license-plate-v1/resolve/main/persian_license_plate_test.zip", |
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} |
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class PersianLicensePlateConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(PersianLicensePlateConfig, self).__init__(**kwargs) |
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class PersianLicensePlate(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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PersianLicensePlateConfig( |
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name="PersianLicensePlate", |
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version=datasets.Version("1.0.0"), |
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description=_DESCRIPTION, |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"label": datasets.Value("string"), |
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"filename": datasets.Value("string"), |
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"image": datasets.Sequence(datasets.Value("int32")), |
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} |
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), |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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""" |
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Return SplitGenerators. |
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""" |
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train_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["train"]) |
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val_path = dl_manager.download_and_extract(_DOWNLOAD_URLS['val']) |
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test_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["test"]) |
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archive_path = dl_manager.download(_DOWNLOAD_URLS['train_dataset']) |
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train_extracted_path = dl_manager.extract(archive_path) if not dl_manager.is_streaming else None |
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archive_path = dl_manager.download(_DOWNLOAD_URLS['val_dataset']) |
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val_extracted_path = dl_manager.extract(archive_path) if not dl_manager.is_streaming else None |
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archive_path = dl_manager.download(_DOWNLOAD_URLS['test_dataset']) |
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test_extracted_path = dl_manager.extract(archive_path) if not dl_manager.is_streaming else None |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path, "dataset_dir": train_extracted_path} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"filepath": test_path, "dataset_dir": test_extracted_path} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path, "dataset_dir": val_extracted_path} |
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), |
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] |
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def _generate_examples(self, filepath, dataset_dir): |
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logger.info("⏳ Generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as csv_file: |
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csv_reader = csv.reader(csv_file, quotechar='"', skipinitialspace=True) |
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next(csv_reader, None) |
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for id_, row in enumerate(csv_reader): |
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label, filename = row |
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image_path = os.path.join(dataset_dir, filename) |
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yield id_, {"label": label, "image_path": image_path} |
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