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import os |
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import datasets |
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from datasets.tasks import ImageClassification |
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_HOMEPAGE = "https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0/dataset/3" |
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_LICENSE = "CC BY 4.0" |
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_CITATION = """\ |
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@misc{ proj-2-qmdk0_dataset, |
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title = { proj 2 Dataset }, |
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type = { Open Source Dataset }, |
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author = { Yolo }, |
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howpublished = { \\url{ https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0 } }, |
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url = { https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0 }, |
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journal = { Roboflow Universe }, |
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publisher = { Roboflow }, |
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year = { 2023 }, |
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month = { oct }, |
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note = { visited on 2023-10-18 }, |
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} |
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""" |
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_CATEGORIES = ['Thermostat', 'Housing', 'Insert'] |
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class THERMOCLASSIFICATIONConfig(datasets.BuilderConfig): |
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"""Builder Config for thermo-classification""" |
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def __init__(self, data_urls, **kwargs): |
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""" |
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BuilderConfig for thermo-classification. |
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Args: |
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data_urls: `dict`, name to url to download the zip file from. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(THERMOCLASSIFICATIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs) |
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self.data_urls = data_urls |
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class THERMOCLASSIFICATION(datasets.GeneratorBasedBuilder): |
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"""thermo-classification image classification dataset""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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THERMOCLASSIFICATIONConfig( |
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name="full", |
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description="Full version of thermo-classification dataset.", |
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data_urls={ |
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"train": "https://huggingface.co/datasets/sargishunanyan/thermo-classification/resolve/main/data/train.zip", |
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"validation": "https://huggingface.co/datasets/sargishunanyan/thermo-classification/resolve/main/data/valid.zip", |
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"test": "https://huggingface.co/datasets/sargishunanyan/thermo-classification/resolve/main/data/test.zip", |
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} |
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, |
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), |
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THERMOCLASSIFICATIONConfig( |
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name="mini", |
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description="Mini version of thermo-classification dataset.", |
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data_urls={ |
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"train": "https://huggingface.co/datasets/sargishunanyan/thermo-classification/resolve/main/data/valid-mini.zip", |
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"validation": "https://huggingface.co/datasets/sargishunanyan/thermo-classification/resolve/main/data/valid-mini.zip", |
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"test": "https://huggingface.co/datasets/sargishunanyan/thermo-classification/resolve/main/data/valid-mini.zip", |
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}, |
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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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features=datasets.Features( |
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{ |
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"image_file_path": datasets.Value("string"), |
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"image": datasets.Image(), |
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"labels": datasets.features.ClassLabel(names=_CATEGORIES), |
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} |
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), |
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supervised_keys=("image", "labels"), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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license=_LICENSE, |
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task_templates=[ImageClassification(image_column="image", label_column="labels")], |
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) |
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def _split_generators(self, dl_manager): |
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data_files = dl_manager.download_and_extract(self.config.data_urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"files": dl_manager.iter_files([data_files["train"]]), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"files": dl_manager.iter_files([data_files["validation"]]), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"files": dl_manager.iter_files([data_files["test"]]), |
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}, |
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), |
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] |
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def _generate_examples(self, files): |
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for i, path in enumerate(files): |
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file_name = os.path.basename(path) |
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if file_name.endswith((".jpg", ".png", ".jpeg", ".bmp", ".tif", ".tiff")): |
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yield i, { |
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"image_file_path": path, |
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"image": path, |
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"labels": os.path.basename(os.path.dirname(path)), |
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} |
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