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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
import pdb
logging.getLogger("rasterio").setLevel(logging.ERROR)
Path: TypeAlias = str | os.PathLike[str]
class SenBenchAirQualityS5P(NonGeoDataset):
url = None
splits = ('train', 'val', 'test')
split_fnames = {
'train': 'train.csv',
'val': 'val.csv',
'test': 'test.csv',
}
def __init__(
self,
root: Path = 'data',
split: str = 'train',
modality = 'no2', # or 'o3'
mode = 'annual', # or 'seasonal'
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.modality = modality
self.mode = mode
if self.mode == 'annual':
mode_dir = 's5p_annual'
elif self.mode == 'seasonal':
mode_dir = 's5p_seasonal'
self.img_dir = os.path.join(root, modality, mode_dir)
self.label_dir = os.path.join(root, modality, 'label_annual')
self.split_csv = os.path.join(self.root, modality, self.split_fnames[split])
with open(self.split_csv, 'r') as f:
lines = f.readlines()
self.pids = []
for line in lines:
self.pids.append(line.strip())
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 == 'annual':
sample = {'image': images[0], 'groudtruth': label, 'meta': meta_infos[0]}
elif self.mode == 'seasonal':
sample = {'image': images, 'groudtruth': label, 'meta': meta_infos}
#pdb.set_trace()
if self.transforms is not None:
sample = self.transforms(sample)
return sample
def _load_image(self, index):
pid = self.pids[index]
s5p_path = os.path.join(self.img_dir, pid)
img_fnames = os.listdir(s5p_path)
s5p_paths = []
for img_fname in img_fnames:
s5p_paths.append(os.path.join(s5p_path, img_fname))
imgs = []
meta_infos = []
for img_path in s5p_paths:
with rasterio.open(img_path) as src:
img = src.read(1)
img[np.isnan(img)] = 0
img = cv2.resize(img, (56,56), interpolation=cv2.INTER_CUBIC)
img = torch.from_numpy(img).float()
img = img.unsqueeze(0)
# 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('_')[0][:10]
date_obj = date(int(date_str[:4]), int(date_str[5:7]), int(date_str[8:10]))
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)
meta_infos.append(meta_info)
if self.mode == 'seasonal':
# pad to 4 images if less than 4
while len(imgs) < 4:
imgs.append(img)
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, (56,56), interpolation=cv2.INTER_NEAREST) # 0-650
# label contains -inf
#pdb.set_trace()
label[label<-1e10] = np.nan
label[label>1e10] = np.nan
label = torch.from_numpy(label.astype('float32'))
return label
class RegDataAugmentation(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", "input"],
)
else:
self.transform = K.augmentation.AugmentationSequential(
K.augmentation.Resize(size=size, align_corners=True),
data_keys=["input", "input"],
)
@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["groudtruth"]
x = self.norm(x)
x_out, mask_out = self.transform(x, mask.unsqueeze(0))
return x_out.squeeze(0), mask_out.squeeze(0), batch["meta"]
class SenBenchAirQualityS5PDataset:
def __init__(self, config):
self.dataset_config = config
self.img_size = (config.image_resolution, config.image_resolution)
self.root_dir = config.data_path
self.modality = config.modality
self.mode = config.mode
def create_dataset(self):
train_transform = RegDataAugmentation(split="train", size=self.img_size)
eval_transform = RegDataAugmentation(split="test", size=self.img_size)
dataset_train = SenBenchAirQualityS5P(
root=self.root_dir, split="train", modality=self.modality, mode=self.mode, transforms=train_transform
)
dataset_val = SenBenchAirQualityS5P(
root=self.root_dir, split="val", modality=self.modality, mode=self.mode, transforms=eval_transform
)
dataset_test = SenBenchAirQualityS5P(
root=self.root_dir, split="test", modality=self.modality, mode=self.mode, transforms=eval_transform
)
return dataset_train, dataset_val, dataset_test |