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import torch
from torch.utils.data import DataLoader, Dataset
import cv2
import os
import rasterio
import numpy as np
from pyproj import Transformer
from datetime import date
class S5P_EEAAirQualityDataset(Dataset):
'''
1973/494 train/test air quality dataset for NO2 and O3, measure from S5P, label from EEA
annual: 1x56x56x1 annual avg
seasonal: 4x56x56x1 seasonal avg
s5p nodata: -inf
label nodata: -3.4e38 # this needs to be masked out for loss and metric calculation
'''
def __init__(self, root_dir, modality='no2', mode='annual', split='train', meta=False):
self.root_dir = root_dir
self.mode = mode
self.modality = modality
if self.mode == 'annual':
mode_dir = 's5p_annual'
elif self.mode == 'seasonal':
mode_dir = 's5p_seasonal'
self.img_dir = os.path.join(root_dir, modality, split, mode_dir)
self.label_dir = os.path.join(root_dir, modality, split, 'label_annual')
self.fnames = sorted(os.listdir(self.label_dir))
self.meta = meta
if self.meta:
self.reference_date = date(1970, 1, 1)
def __len__(self):
return len(self.fnames)
def __getitem__(self, idx):
fname = self.fnames[idx] # label filename
label_path = os.path.join(self.label_dir, fname)
img_path = os.path.join(self.img_dir, fname.replace('.tif', ''))
img_fnames = os.listdir(img_path)
img_paths = []
for img_fname in img_fnames:
img_paths.append(os.path.join(img_path, img_fname))
# img
imgs = []
meta_infos = []
for img_path in img_paths:
with rasterio.open(img_path) as src:
img = src.read(1)
img = cv2.resize(img, (56,56), interpolation=cv2.INTER_CUBIC)
img[np.isnan(img)] = 0
img = torch.from_numpy(img).float()
img = img.unsqueeze(0)
if self.meta:
cx,cy = src.xy(src.height // 2, src.width // 2)
crs_transformer = Transformer.from_crs(src.crs, 'epsg:4326')
lon, lat = crs_transformer.transform(cx,cy)
#lon, lat = cx, cy
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, 0]).astype(np.float32)
else:
meta_info = np.array([np.nan,np.nan,np.nan,np.nan]).astype(np.float32)
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)
img_paths.append(img_path)
meta_infos.append(meta_info)
# label
with rasterio.open(label_path) as src:
label = src.read(1)
label = cv2.resize(label, (56,56), interpolation=cv2.INTER_CUBIC) # 0-650
label[label<-1e10] = np.nan
label[label>1e10] = np.nan
#label[np.isnan(label)] = -1e10
label = torch.from_numpy(label.astype('float32'))
#nan_mask = (label > -1e10)
if self.mode == 'annual':
return imgs[0], meta_infos[0], label#, nan_mask #,label_path
elif self.mode == 'seasonal':
return imgs[0], imgs[1], imgs[2], imgs[3], meta_infos[0], meta_infos[1], meta_infos[2], meta_infos[3], label#, nan_mask #,label_path
if __name__ == '__main__':
dataset = S5P_EEAAirQualityDataset(root_dir='./airquality_s5p', modality='no2', mode='annual', split='train')
dataloader = DataLoader(dataset, batch_size=1, shuffle=False)
for i, data in enumerate(dataloader):
print(data[0].shape, data[1].shape, data[2].shape, data[3].shape)
break |