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import kornia.augmentation as K
import torch
from torchgeo.datasets import EuroSAT
import os
from collections.abc import Callable, Sequence
from torch import Tensor
import numpy as np
import rasterio
from pyproj import Transformer
from typing import TypeAlias, ClassVar
import pathlib
Path: TypeAlias = str | os.PathLike[str]
class SenBenchEuroSATS1(EuroSAT):
url = None
base_dir = 'all_imgs'
splits = ('train', 'val', 'test')
split_filenames: ClassVar[dict[str, str]] = {
'train': 'eurosat-train.txt',
'val': 'eurosat-val.txt',
'test': 'eurosat-test.txt',
}
all_band_names = ('VV','VH')
def __init__(
self,
root: Path = 'data',
split: str = 'train',
bands: Sequence[str] = ['VV','VH'],
transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
download: bool = False,
) -> None:
self.root = root
self.transforms = transforms
self.download = download
#self.checksum = checksum
assert split in ['train', 'val', 'test']
self._validate_bands(bands)
self.bands = bands
self.band_indices = [(self.all_band_names.index(b)+1) for b in bands if b in self.all_band_names]
self._verify()
self.valid_fns = []
self.classes = []
with open(os.path.join(self.root, self.split_filenames[split])) as f:
for fn in f:
self.valid_fns.append(fn.strip().replace('.jpg', '.tif'))
cls_name = fn.strip().split('_')[0]
if cls_name not in self.classes:
self.classes.append(cls_name)
self.classes = sorted(self.classes)
self.root = os.path.join(self.root, self.base_dir)
#root_path = pathlib.Path(root,split)
#self.classes = sorted([d.name for d in root_path.iterdir() if d.is_dir()])
self.class_to_idx = {cls_name: i for i, cls_name in enumerate(self.classes)}
self.patch_area = (16*10/1000)**2 # patchsize 16 pix, gsd 10m
def __len__(self):
return len(self.valid_fns)
def __getitem__(self, index):
image, coord, label = self._load_image(index)
meta_info = np.array([coord[0], coord[1], np.nan, self.patch_area]).astype(np.float32)
sample = {'image': image, 'label': label, 'meta': torch.from_numpy(meta_info)}
if self.transforms is not None:
sample = self.transforms(sample)
return sample
def _load_image(self, index):
fname = self.valid_fns[index]
dirname = fname.split('_')[0]
img_path = os.path.join(self.root, dirname, fname)
target = self.class_to_idx[dirname]
with rasterio.open(img_path) as src:
image = src.read(self.band_indices).astype('float32')
cx,cy = src.xy(src.height // 2, src.width // 2)
if src.crs.to_string() != 'EPSG:4326':
crs_transformer = Transformer.from_crs(src.crs, 'epsg:4326', always_xy=True)
lon, lat = crs_transformer.transform(cx,cy)
else:
lon, lat = cx, cy
return torch.from_numpy(image), (lon,lat), target
class ClsDataAugmentation(torch.nn.Module):
BAND_STATS = {
'mean': {
# 'B01': 1353.72696296,
# 'B02': 1117.20222222,
# 'B03': 1041.8842963,
# 'B04': 946.554,
# 'B05': 1199.18896296,
# 'B06': 2003.00696296,
# 'B07': 2374.00874074,
# 'B08': 2301.22014815,
# 'B8A': 2599.78311111,
# 'B09': 732.18207407,
# 'B10': 12.09952894,
# 'B11': 1820.69659259,
# 'B12': 1118.20259259,
'VV': -12.54847273,
'VH': -20.19237134
},
'std': {
# 'B01': 897.27143653,
# 'B02': 736.01759721,
# 'B03': 684.77615743,
# 'B04': 620.02902871,
# 'B05': 791.86263829,
# 'B06': 1341.28018273,
# 'B07': 1595.39989386,
# 'B08': 1545.52915718,
# 'B8A': 1750.12066835,
# 'B09': 475.11595216,
# 'B10': 98.26600935,
# 'B11': 1216.48651476,
# 'B12': 736.6981037,
'VV': 5.25697717,
'VH': 5.91150917
}
}
def __init__(self, split, size, bands):
super().__init__()
mean = []
std = []
for band in bands:
mean.append(self.BAND_STATS['mean'][band])
std.append(self.BAND_STATS['std'][band])
mean = torch.Tensor(mean)
std = torch.Tensor(std)
if split == "train":
self.transform = torch.nn.Sequential(
K.Normalize(mean=mean, std=std),
K.Resize(size=size, align_corners=True),
K.RandomHorizontalFlip(p=0.5),
K.RandomVerticalFlip(p=0.5),
)
else:
self.transform = torch.nn.Sequential(
K.Normalize(mean=mean, std=std),
K.Resize(size=size, align_corners=True),
)
@torch.no_grad()
def forward(self, batch: dict[str,]):
"""Torchgeo returns a dictionary with 'image' and 'label' keys, but engine expects a tuple"""
x_out = self.transform(batch["image"]).squeeze(0)
return x_out, batch["label"], batch["meta"]
class SenBenchEuroSATS1Dataset:
def __init__(self, config):
self.dataset_config = config
self.img_size = (config.image_resolution, config.image_resolution)
self.root_dir = config.data_path
self.bands = config.band_names
def create_dataset(self):
train_transform = ClsDataAugmentation(split="train", size=self.img_size, bands=self.bands)
eval_transform = ClsDataAugmentation(split="test", size=self.img_size, bands=self.bands)
dataset_train = SenBenchEuroSATS1(
root=self.root_dir, split="train", bands=self.bands, transforms=train_transform
)
dataset_val = SenBenchEuroSATS1(
root=self.root_dir, split="val", bands=self.bands, transforms=eval_transform
)
dataset_test = SenBenchEuroSATS1(
root=self.root_dir, split="test", bands=self.bands, transforms=eval_transform
)
return dataset_train, dataset_val, dataset_test |