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import os |
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import glob |
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import random |
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import datasets |
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from datasets.tasks import ImageClassification |
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_HOMEPAGE = "https://github.com/your-github/renovation" |
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_CITATION = """\ |
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@ONLINE {renovationdata, |
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author="Your Name", |
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title="Renovation dataset", |
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month="January", |
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year="2023", |
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url="https://github.com/your-github/renovation" |
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} |
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""" |
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_DESCRIPTION = """\ |
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Renovations is a dataset of images of houses taken in the field using smartphone |
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cameras. It consists of 3 classes: cheap, average, and expensive renovations. |
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Data was collected by the your research lab. |
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""" |
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_URLS = { |
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"cheap": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/cheap.zip", |
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"average": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/average.zip", |
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"expensive": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/expensive.zip", |
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} |
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_NAMES = ["cheap", "average", "expensive"] |
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class Renovations(datasets.GeneratorBasedBuilder): |
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"""Renovations house images dataset.""" |
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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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"image_file_path": datasets.Value("string"), |
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"image": datasets.Image(), |
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"labels": datasets.features.ClassLabel(names=_NAMES), |
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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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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(_URLS) |
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files = glob.glob(data_files["cheap"] + '/*.jpeg', recursive=True) + \ |
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glob.glob(data_files["average"] + '/*.jpeg', recursive=True) + \ |
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glob.glob(data_files["expensive"] + '/*.jpeg', recursive=True) |
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random.shuffle(files) |
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num_files = len(files) |
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train_files = files[:int(num_files*0.7)] |
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val_files = files[int(num_files*0.7):int(num_files*0.85)] |
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test_files = files[int(num_files*0.85):] |
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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": train_files, |
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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": val_files, |
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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": test_files, |
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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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print(f"Processing {len(files)} files:") |
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for i, path in enumerate(files): |
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print(f"Processing file {i}: {path}") |
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label = os.path.basename(os.path.dirname(path)).lower() |
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print(f"Label: {label}") |
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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": label, |
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} |
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