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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 SenBenchDFC2020(NonGeoDataset):
url = None
#base_dir = 'all_imgs'
splits = ('train', 'val', 'test')
label_filenames = {
'train': 'dfc-train-new.csv',
'val': 'dfc-val-new.csv',
'test': 'dfc-test-new.csv',
}
s1_band_names = (
'VV', 'VH'
)
s2_band_names = (
'B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B10', 'B11', 'B12'
)
rgb_band_names = ('B04', 'B03', 'B02')
Cls_index = {
'Background': 0, # to be ignored
'Forest': 1,
'Shrubland': 2,
'Savanna': 3, # none, to be ignored
'Grassland': 4,
'Wetland': 5,
'Cropland': 6,
'Urban/Built-up': 7,
'Snow/Ice': 8, # none, to be ignored
'Barren': 9,
'Water': 10
}
cls_mapping = {0:255, 1:0, 2:1, 3:255, 4:2, 5:3, 6:4, 7:5, 8:255, 9:6, 10:7} # 8 valid classes
def __init__(
self,
root: Path = 'data',
split: str = 'train',
bands: Sequence[str] = s2_band_names,
modality = 's2',
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.modality = modality
if self.modality== 's1':
self.all_band_names = self.s1_band_names
else:
self.all_band_names = self.s2_band_names
self.band_indices = [(self.all_band_names.index(b)+1) for b in bands if b in self.all_band_names]
self.img_dir = os.path.join(self.root, modality)
self.label_dir = os.path.join(self.root, 'dfc')
self.label_csv = os.path.join(self.root, self.label_filenames[split])
self.label_fnames = []
with open(self.label_csv, 'r') as f:
lines = f.readlines()
for line in lines:
fname = line.strip()
self.label_fnames.append(fname)
#self.reference_date = date(1970, 1, 1)
self.patch_area = (16*10/1000)**2 # patchsize 8 pix, gsd 300m
def __len__(self):
return len(self.label_fnames)
def __getitem__(self, index):
images, meta_infos = self._load_image(index)
label = self._load_target(index)
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):
label_fname = self.label_fnames[index]
img_fname = label_fname.replace('dfc',self.modality)
img_path = os.path.join(self.img_dir, img_fname)
with rasterio.open(img_path) as src:
img = src.read(self.band_indices).astype('float32')
img = torch.from_numpy(img)
# # 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(s3_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([np.nan, np.nan, np.nan, self.patch_area]).astype(np.float32)
meta_info = torch.from_numpy(meta_info)
return img, meta_info
def _load_target(self, index):
label_fname = self.label_fnames[index]
label_path = os.path.join(self.label_dir, label_fname)
with rasterio.open(label_path) as src:
label = src.read(1)
# label[label==0] = 256
# label = label - 1
label_remap = label.copy()
for orig_label, new_label in self.cls_mapping.items():
label_remap[label == orig_label] = new_label
labels = torch.from_numpy(label_remap).long()
return labels
class SegDataAugmentation(torch.nn.Module):
def __init__(self, split, size, band_stats):
super().__init__()
if band_stats is not None:
mean = band_stats['mean']
std = band_stats['std']
else:
mean = [0.0]
std = [1.0]
mean = torch.Tensor(mean)
std = torch.Tensor(std)
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.RandomResizedCrop(size=size, scale=(0.8,1.0)),
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 SenBenchDFC2020Dataset:
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.modality = config.modality
self.band_stats = config.band_stats
def create_dataset(self):
train_transform = SegDataAugmentation(split="train", size=self.img_size, band_stats=self.band_stats)
eval_transform = SegDataAugmentation(split="test", size=self.img_size, band_stats=self.band_stats)
dataset_train = SenBenchDFC2020(
root=self.root_dir, split="train", bands=self.bands, modality=self.modality, transforms=train_transform
)
dataset_val = SenBenchDFC2020(
root=self.root_dir, split="val", bands=self.bands, modality=self.modality, transforms=eval_transform
)
dataset_test = SenBenchDFC2020(
root=self.root_dir, split="test", bands=self.bands, modality=self.modality, transforms=eval_transform
)
return dataset_train, dataset_val, dataset_test |