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from pathlib import Path |
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import datasets |
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import numpy as np |
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from PIL import Image |
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project_name = 'xiazeyu/WildfireSimMaps' |
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map_names = sorted([x.name for x in Path('dataset').iterdir() if x.is_dir()]) |
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_CITATION = """\ |
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""" |
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_DESCRIPTION = 'A real-world dataset for wildfire simulation.' |
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_HOMEPAGE = 'https://huggingface.co./datasets/xiazeyu/WildfireSimMaps' |
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_LICENSE = 'CC BY-NC 4.0' |
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def load_map(map_name): |
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map_root = Path('dataset') / map_name |
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return {'canopy': np.array(Image.open(map_root / 'canopy.tif')), |
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'density': np.array(Image.open(map_root / 'density.tif')), |
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'slope': np.array(Image.open(map_root / 'slope.tif')), } |
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data = {'name': [], 'canopy': [], 'density': [], "slope": [], 'shape': [], } |
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for name in map_names: |
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map_data = load_map(name) |
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data['name'].append(name) |
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data['canopy'].append(map_data['canopy'].flatten()) |
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data['density'].append(map_data['density'].flatten()) |
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data['slope'].append(map_data['slope'].flatten()) |
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data['shape'].append(map_data['canopy'].shape) |
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features = datasets.Features({'name': datasets.Value('string'), 'canopy': datasets.Sequence(datasets.Value('int8')), |
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'density': datasets.Sequence(datasets.Value('float32')), |
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'slope': datasets.Sequence(datasets.Value('int8')), |
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'shape': datasets.Sequence(datasets.Value('int16'), length=2), }) |
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data_info = datasets.DatasetInfo(description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, |
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citation=_CITATION, ) |
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ds = datasets.Dataset.from_dict(data, features=features, info=data_info, ) |
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ds.VERSION = datasets.Version("1.0.0") |
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ds.push_to_hub(project_name) |
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