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import os
import json
import shutil
import datasets
import tifffile
import numpy as np
import pandas as pd
S2_MEAN = [0.12375696117681, 0.10927746363683, 0.10108552032678, 0.11423986161140, 0.15926566920230, 0.18147236008771,
0.17457403122913, 0.19501607349635, 0.15428468872076, 0.10905050699570]
S2_STD = [0.03958795, 0.04777826, 0.06636616, 0.06358874, 0.07744387, 0.09101635, 0.09218466, 0.10164581, 0.09991773, 0.08780632]
class So2SatDataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
DATA_URL = "https://huggingface.co/datasets/GFM-Bench/So2Sat/resolve/main/So2Sat.zip"
metadata = {
"s2c": {
"bands": ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12'],
"channel_wv": [492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 832.8, 864.7, 1613.7, 2202.4],
"mean": S2_MEAN,
"std": S2_STD,
},
"s1": {
"bands": None,
"channel_wv": None,
"mean": None,
"std": None
}
}
SIZE = HEIGHT = WIDTH = 32
NUM_CLASSES = 17
spatial_resolution = 10
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def _info(self):
metadata = self.metadata
metadata['size'] = self.SIZE
metadata['num_classes'] = self.NUM_CLASSES
metadata['spatial_resolution'] = self.spatial_resolution
return datasets.DatasetInfo(
description=json.dumps(metadata),
features=datasets.Features({
"optical": datasets.Array3D(shape=(10, self.HEIGHT, self.WIDTH), dtype="float32"),
"label": datasets.Value("int32"),
"optical_channel_wv": datasets.Sequence(datasets.Value("float32")),
"spatial_resolution": datasets.Value("float32"),
}),
)
def _split_generators(self, dl_manager):
if isinstance(self.DATA_URL, list):
downloaded_files = dl_manager.download(self.DATA_URL)
combined_file = os.path.join(dl_manager.download_config.cache_dir, "combined.tar.gz")
with open(combined_file, 'wb') as outfile:
for part_file in downloaded_files:
with open(part_file, 'rb') as infile:
shutil.copyfileobj(infile, outfile)
data_dir = dl_manager.extract(combined_file)
os.remove(combined_file)
else:
data_dir = dl_manager.download_and_extract(self.DATA_URL)
return [
datasets.SplitGenerator(
name="train",
gen_kwargs={
"split": 'train',
"data_dir": data_dir,
},
),
datasets.SplitGenerator(
name="val",
gen_kwargs={
"split": 'val',
"data_dir": data_dir,
},
),
datasets.SplitGenerator(
name="test",
gen_kwargs={
"split": 'test',
"data_dir": data_dir,
},
)
]
def _generate_examples(self, split, data_dir):
optical_channel_wv = np.array(self.metadata["s2c"]["channel_wv"])
spatial_resolution = self.spatial_resolution
data_dir = os.path.join(data_dir, "So2Sat")
metadata = pd.read_csv(os.path.join(data_dir, "metadata.csv"))
metadata = metadata[metadata["split"] == split].reset_index(drop=True)
for index, row in metadata.iterrows():
optical_path = os.path.join(data_dir, row.optical_path)
optical = self._read_image(optical_path).astype(np.float32) # CxHxW
label = int(row.label)
sample = {
"optical": optical,
"label": label,
"optical_channel_wv": optical_channel_wv,
"spatial_resolution": spatial_resolution,
}
yield f"{index}", sample
def _read_image(self, image_path):
"""Read tiff image from image_path
Args:
image_path:
Image path to read from
Return:
image:
C, H, W numpy array image
"""
image = tifffile.imread(image_path)
if len(image.shape) == 3:
image = np.transpose(image, (2, 0, 1))
return image |