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import kornia as K
import torch
from torchgeo.datasets.geo import NonGeoDataset
import os
from collections.abc import Callable, Sequence
from torch import Tensor
import numpy as np
import rasterio
import cv2
from pyproj import Transformer
from datetime import date
from typing import TypeAlias, ClassVar
import pathlib
import logging
logging.getLogger("rasterio").setLevel(logging.ERROR)
Path: TypeAlias = str | os.PathLike[str]
class SenBenchLC100S3Seg(NonGeoDataset):
url = None
#base_dir = 'all_imgs'
splits = ('train', 'val', 'test')
# label_filenames = {
# 'train': 'lc100_multilabel-train.csv',
# 'val': 'lc100_multilabel-val.csv',
# 'test': 'lc100_multilabel-test.csv',
# }
static_filenames = {
'train': 'static_fnames-train.csv',
'val': 'static_fnames-val.csv',
'test': 'static_fnames-test.csv',
}
all_band_names = (
'Oa01_radiance', 'Oa02_radiance', 'Oa03_radiance', 'Oa04_radiance', 'Oa05_radiance', 'Oa06_radiance', 'Oa07_radiance',
'Oa08_radiance', 'Oa09_radiance', 'Oa10_radiance', 'Oa11_radiance', 'Oa12_radiance', 'Oa13_radiance', 'Oa14_radiance',
'Oa15_radiance', 'Oa16_radiance', 'Oa17_radiance', 'Oa18_radiance', 'Oa19_radiance', 'Oa20_radiance', 'Oa21_radiance',
)
all_band_scale = (
0.0139465,0.0133873,0.0121481,0.0115198,0.0100953,0.0123538,0.00879161,
0.00876539,0.0095103,0.00773378,0.00675523,0.0071996,0.00749684,0.0086512,
0.00526779,0.00530267,0.00493004,0.00549962,0.00502847,0.00326378,0.00324118)
rgb_bands = ('Oa08_radiance', 'Oa06_radiance', 'Oa04_radiance')
LC100_CLSID = {
0: 0, # unknown
20: 1,
30: 2,
40: 3,
50: 4,
60: 5,
70: 6,
80: 7,
90: 8,
100: 9,
111: 10,
112: 11,
113: 12,
114: 13,
115: 14,
116: 15,
121: 16,
122: 17,
123: 18,
124: 19,
125: 20,
126: 21,
200: 22, # ocean
}
def __init__(
self,
root: Path = 'data',
split: str = 'train',
bands: Sequence[str] = all_band_names,
mode = 'static',
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.bands = bands
self.band_indices = [(self.all_band_names.index(b)+1) for b in bands if b in self.all_band_names]
self.mode = mode
self.img_dir = os.path.join(self.root, 's3_olci')
#self.lc100_cls = os.path.join(self.root, self.label_filenames[split])
self.label_dir = os.path.join(self.root, 'lc100')
if self.mode == 'static':
self.static_csv = os.path.join(self.root, self.static_filenames[split])
with open(self.static_csv, 'r') as f:
lines = f.readlines()
self.static_img = {}
for line in lines:
pid = line.strip().split(',')[0]
img_fname = line.strip().split(',')[1]
self.static_img[pid] = img_fname
self.pids = list(self.static_img.keys())
self.reference_date = date(1970, 1, 1)
self.patch_area = (16*10/1000)**2 # patchsize 16 pix, gsd 10m
def __len__(self):
return len(self.pids)
def __getitem__(self, index):
images, meta_infos = self._load_image(index)
#meta_info = np.array([coord[0], coord[1], np.nan, self.patch_area]).astype(np.float32)
label = self._load_target(index)
if self.mode == 'static':
sample = {'image': images[0], 'mask': label, 'meta': meta_infos[0]}
elif self.mode == 'series':
sample = {'image': images, 'mask': label, 'meta': meta_infos}
if self.transforms is not None:
sample = self.transforms(sample)
return sample
def _load_image(self, index):
pid = self.pids[index]
s3_path = os.path.join(self.img_dir, pid)
if self.mode == 'static':
img_fname = self.static_img[pid]
s3_paths = [os.path.join(s3_path, img_fname)]
else:
img_fnames = os.listdir(s3_path)
s3_paths = []
for img_fname in img_fnames:
s3_paths.append(os.path.join(s3_path, img_fname))
imgs = []
img_paths = []
meta_infos = []
for img_path in s3_paths:
with rasterio.open(img_path) as src:
img = src.read()
img[np.isnan(img)] = 0
chs = []
for b in range(21):
ch = img[b]*self.all_band_scale[b]
#ch = cv2.resize(ch, (96,96), interpolation=cv2.INTER_CUBIC)
ch = cv2.resize(ch, (282,282), interpolation=cv2.INTER_CUBIC)
chs.append(ch)
img = np.stack(chs)
img = torch.from_numpy(img).float()
# get lon, lat
cx,cy = src.xy(src.height // 2, src.width // 2)
if src.crs.to_string() != 'EPSG:4326':
# convert to lon, lat
crs_transformer = Transformer.from_crs(src.crs, 'epsg:4326', always_xy=True)
lon, lat = crs_transformer.transform(cx,cy)
else:
lon, lat = cx, cy
# get time
img_fname = os.path.basename(img_path)
date_str = img_fname.split('_')[1][:8]
date_obj = date(int(date_str[:4]), int(date_str[4:6]), int(date_str[6:8]))
delta = (date_obj - self.reference_date).days
meta_info = np.array([lon, lat, delta, self.patch_area]).astype(np.float32)
meta_info = torch.from_numpy(meta_info)
imgs.append(img)
img_paths.append(img_path)
meta_infos.append(meta_info)
if self.mode == 'series':
# pad to 4 images if less than 4
while len(imgs) < 4:
imgs.append(img)
img_paths.append(img_path)
meta_infos.append(meta_info)
return imgs, meta_infos
def _load_target(self, index):
pid = self.pids[index]
label_path = os.path.join(self.label_dir, pid+'.tif')
with rasterio.open(label_path) as src:
label = src.read(1)
label = cv2.resize(label, (282,282), interpolation=cv2.INTER_NEAREST) # 0-650
label_new = np.zeros_like(label)
for k,v in self.LC100_CLSID.items():
label_new[label==k] = v
labels = torch.from_numpy(label_new.astype('int64'))
return labels
class SegDataAugmentation(torch.nn.Module):
def __init__(self, split, size):
super().__init__()
mean = torch.Tensor([0.0])
std = torch.Tensor([1.0])
self.norm = K.augmentation.Normalize(mean=mean, std=std)
if split == "train":
self.transform = K.augmentation.AugmentationSequential(
K.augmentation.Resize(size=size, align_corners=True),
K.augmentation.RandomRotation(degrees=90, p=0.5, align_corners=True),
K.augmentation.RandomHorizontalFlip(p=0.5),
K.augmentation.RandomVerticalFlip(p=0.5),
data_keys=["input", "mask"],
)
else:
self.transform = K.augmentation.AugmentationSequential(
K.augmentation.Resize(size=size, align_corners=True),
data_keys=["input", "mask"],
)
@torch.no_grad()
def forward(self, batch: dict[str,]):
"""Torchgeo returns a dictionary with 'image' and 'label' keys, but engine expects a tuple"""
x,mask = batch["image"], batch["mask"]
x = self.norm(x)
x_out, mask_out = self.transform(x, mask)
return x_out.squeeze(0), mask_out.squeeze(0).squeeze(0), batch["meta"]
class SenBenchLC100S3SegDataset:
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
self.mode = config.mode
def create_dataset(self):
train_transform = SegDataAugmentation(split="train", size=self.img_size)
eval_transform = SegDataAugmentation(split="test", size=self.img_size)
dataset_train = SenBenchLC100S3Seg(
root=self.root_dir, split="train", bands=self.bands, mode=self.mode, transforms=train_transform
)
dataset_val = SenBenchLC100S3Seg(
root=self.root_dir, split="val", bands=self.bands, mode=self.mode, transforms=eval_transform
)
dataset_test = SenBenchLC100S3Seg(
root=self.root_dir, split="test", bands=self.bands, mode=self.mode, transforms=eval_transform
)
return dataset_train, dataset_val, dataset_test |