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