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import os
import json
import shutil
import string
import tifffile
import datasets
import numpy as np
import pandas as pd
from tqdm import tqdm
class_sets = {
19: [
'Urban fabric',
'Industrial or commercial units',
'Arable land',
'Permanent crops',
'Pastures',
'Complex cultivation patterns',
'Land principally occupied by agriculture, with significant areas of'
' natural vegetation',
'Agro-forestry areas',
'Broad-leaved forest',
'Coniferous forest',
'Mixed forest',
'Natural grassland and sparsely vegetated areas',
'Moors, heathland and sclerophyllous vegetation',
'Transitional woodland, shrub',
'Beaches, dunes, sands',
'Inland wetlands',
'Coastal wetlands',
'Inland waters',
'Marine waters',
],
43: [
'Continuous urban fabric',
'Discontinuous urban fabric',
'Industrial or commercial units',
'Road and rail networks and associated land',
'Port areas',
'Airports',
'Mineral extraction sites',
'Dump sites',
'Construction sites',
'Green urban areas',
'Sport and leisure facilities',
'Non-irrigated arable land',
'Permanently irrigated land',
'Rice fields',
'Vineyards',
'Fruit trees and berry plantations',
'Olive groves',
'Pastures',
'Annual crops associated with permanent crops',
'Complex cultivation patterns',
'Land principally occupied by agriculture, with significant areas of'
' natural vegetation',
'Agro-forestry areas',
'Broad-leaved forest',
'Coniferous forest',
'Mixed forest',
'Natural grassland',
'Moors and heathland',
'Sclerophyllous vegetation',
'Transitional woodland/shrub',
'Beaches, dunes, sands',
'Bare rock',
'Sparsely vegetated areas',
'Burnt areas',
'Inland marshes',
'Peatbogs',
'Salt marshes',
'Salines',
'Intertidal flats',
'Water courses',
'Water bodies',
'Coastal lagoons',
'Estuaries',
'Sea and ocean',
],
}
label_converter = {
0: 0,
1: 0,
2: 1,
11: 2,
12: 2,
13: 2,
14: 3,
15: 3,
16: 3,
18: 3,
17: 4,
19: 5,
20: 6,
21: 7,
22: 8,
23: 9,
24: 10,
25: 11,
31: 11,
26: 12,
27: 12,
28: 13,
29: 14,
33: 15,
34: 15,
35: 16,
36: 16,
38: 17,
39: 17,
40: 18,
41: 18,
42: 18,
}
S2_MEAN = [752.40087073, 884.29673756, 1144.16202635, 1297.47289228, 1624.90992062, 2194.6423161, 2422.21248945, 2517.76053101, 2581.64687018, 2645.51888987, 2368.51236873, 1805.06846033]
S2_STD = [1108.02887453, 1155.15170768, 1183.6292542, 1368.11351514, 1370.265037, 1355.55390699, 1416.51487101, 1474.78900051, 1439.3086061, 1582.28010962, 1455.52084939, 1343.48379601]
S1_MEAN = [-12.54847273, -20.19237134]
S1_STD = [5.25697717, 5.91150917]
parts = [f"a{letter}" for letter in string.ascii_lowercase]
parts.extend([f"b{letter}" for letter in string.ascii_lowercase[:8]])
class BigEarthNetDataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
DATA_URL = [
f"https://huggingface.co/datasets/GFM-Bench/BigEarthNet/resolve/main/data/bigearthnet_part_{part}"
for part in parts
]
metadata = {
"s2c": {
"bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B11", "B12"],
"channel_wv": [442.7, 492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 832.8, 864.7, 945.1, 1613.7, 2202.4],
"mean": S2_MEAN,
"std": S2_STD
},
"s1": {
"bands": ["VV", "VH"],
"channel_wv": [5500, 5700],
"mean": S1_MEAN,
"std": S1_STD
}
}
SIZE = HEIGHT = WIDTH = 120
NUM_CLASSES = 19
spatial_resolution = 10
def __init__(self, *args, **kwargs):
self.class2idx = {c: i for i, c in enumerate(class_sets[43])}
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=(12, self.HEIGHT, self.WIDTH), dtype="float32"),
"radar": datasets.Array3D(shape=(2, self.HEIGHT, self.WIDTH), dtype="float32"),
"optical_channel_wv": datasets.Sequence(datasets.Value("float32")),
"radar_channel_wv": datasets.Sequence(datasets.Value("float32")),
"label": datasets.Sequence(datasets.Value("float32"), length=self.NUM_CLASSES),
"spatial_resolution": datasets.Value("int32"),
}),
)
def _split_generators(self, dl_manager):
print(dl_manager.download_config.cache_dir)
# Ensure cache directory is set
if dl_manager.download_config.cache_dir is None:
return []
if isinstance(self.DATA_URL, list):
try:
downloaded_files = dl_manager.download(self.DATA_URL)
print(f"downloaded files: {downloaded_files}")
combined_file = os.path.join(dl_manager.download_config.cache_dir, "combined.tar.gz")
print(f"copying files to {combined_file}")
target_dir = os.path.dirname(combined_file)
os.makedirs(target_dir, exist_ok=True) # Create only the directory
with open(combined_file, 'wb') as outfile:
counter = 0
for part_file in tqdm(downloaded_files, desc="Copying files", unit="file"):
# print(f"copying {counter}-th file: {part_file}")
with open(part_file, 'rb') as infile:
shutil.copyfileobj(infile, outfile)
counter += 1
print(f"extacting from {combined_file}")
data_dir = dl_manager.extract(combined_file)
os.remove(combined_file)
print(f"data_dir: {data_dir}")
except Exception as e:
print("setting data_dir to None")
data_dir = None
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"])
radar_channel_wv = np.array(self.metadata["s1"]["channel_wv"])
spatial_resolution = self.spatial_resolution
data_dir = os.path.join(data_dir, "BigEarthNet")
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
radar_path = os.path.join(data_dir, row.radar_path)
radar = self._read_image(radar_path).astype(np.float32)
label_path = os.path.join(data_dir, row.label_path)
label = self._load_label(label_path)
sample = {
"optical": optical,
"radar": radar,
"optical_channel_wv": optical_channel_wv,
"radar_channel_wv": radar_channel_wv,
"label": label,
"spatial_resolution": spatial_resolution,
}
yield f"{index}", sample
def _load_label(self, label_path):
with open(label_path) as f:
labels = json.load(f)['labels']
indices =[self.class2idx[label] for label in labels]
indices_optional = [label_converter.get(idx) for idx in indices]
indices = [idx for idx in indices_optional if idx is not None]
label = np.zeros(19, dtype=np.int64)
label[indices] = 1
return label
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)
image = np.transpose(image, (2, 0, 1))
return image |