SegMunich / SegMunich.py
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import os
import json
import shutil
import datasets
import tifffile
import pandas as pd
import numpy as np
S2_MEAN = [752.40087073, 884.29673756, 1144.16202635, 1297.47289228, 1624.90992062, 2194.6423161, 2422.21248945, 2581.64687018, 2368.51236873, 1805.06846033]
S2_STD = [1108.02887453, 1155.15170768, 1183.6292542, 1368.11351514, 1370.265037, 1355.55390699, 1416.51487101, 1439.3086061, 1455.52084939, 1343.48379601]
class SegMunichDataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
DATA_URL = "https://huggingface.co/datasets/GFM-Bench/SegMunich/resolve/main/SegMunich.zip"
metadata = {
"s2c": {
"bands": ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8A", "B11", "B12"],
"channel_wv": [442.7, 492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 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 = 128
spatial_resolution = 10
NUM_CLASSES = 13
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.Array2D(shape=(self.HEIGHT, self.WIDTH), dtype="int32"),
"optical_channel_wv": datasets.Sequence(datasets.Value("float32")),
"spatial_resolution": datasets.Value("int32"),
}),
)
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 = self.metadata["s2c"]["channel_wv"]
spatial_resolution = self.spatial_resolution
data_dir = os.path.join(data_dir, "SegMunich")
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_path = os.path.join(data_dir, row.label_path)
label = self._read_image(label_path).astype(np.int32)
label[label == 21] = 1
label[label == 22] = 2
label[label == 23] = 3
label[label == 31] = 4
label[label == 32] = 6
label[label == 33] = 7
label[label == 41] = 8
label[label == 13] = 9
label[label == 14] = 10
sample = {
"optical": optical,
"optical_channel_wv": optical_channel_wv,
"label": label,
"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