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import os
import gzip
import json
import numpy as np
import rasterio
import re
from torch.utils.data import Dataset, DataLoader
import torch
#from cvtorchvision import cvtransforms
from kornia.augmentation import AugmentationSequential
import kornia
import argparse
class SSL4EO_S(Dataset):
def __init__(self, fnames_path, root_dir, modality=['s1_grd', 's2_toa', 's3_olci', 's5p_co', 's5p_no2', 's5p_so2', 's5p_o3', 'dem'], transform_s1=None, transform_s2=None, transform_s3=None, transform_s5p=None, transform_dem=None):
with gzip.open(fnames_path, 'rt', encoding='utf-8') as gz_file:
self.fnames_json = json.load(gz_file)
self.grid_ids = list(self.fnames_json.keys())
self.root_dir = root_dir
self.transform_s1 = transform_s1
self.transform_s2 = transform_s2
self.transform_s3 = transform_s3
self.transform_s5p = transform_s5p
self.transform_dem = transform_dem
self.modality = modality
def __len__(self):
return len(self.grid_ids)
def get_s1_s2(self,grid_id,modality):
arrays = []
meta_data = []
local_grids = list(self.fnames_json[grid_id][modality].keys())
grid_id_coord = self.fnames_json[grid_id]['grid_id_coord']
for local_grid in local_grids:
local_fpaths = self.fnames_json[grid_id][modality][local_grid]
imgs = []
meta = []
for local_fpath in local_fpaths:
with rasterio.open(os.path.join(self.root_dir, local_fpath)) as src:
img = src.read()
if modality=='s1_grd' and self.transform_s1:
#img = self.transform_s1(np.transpose(img, (1, 2, 0)))
img = torch.from_numpy(img).unsqueeze(0)
img = self.transform_s1(img).squeeze(0)
elif modality=='s2_toa' and self.transform_s2:
#img = self.transform_s2(np.transpose(img.astype(np.int16), (1, 2, 0)))
img = torch.from_numpy(img.astype(np.int16)).unsqueeze(0)
img = self.transform_s2(img.to(torch.float16)).squeeze(0)
imgs.append(img)
fname = local_fpath.split('/')[-1]
date = re.search(r'(\d{8})T', fname).group(1)
meta_info = f"{grid_id_coord}/{local_grid}/{date}"
meta.append(meta_info)
arrays.append(imgs)
meta_data.append(meta)
return arrays, meta_data
def get_s3(self,grid_id):
arrays = []
meta_data = []
fpaths = self.fnames_json[grid_id]['s3_olci']
grid_id_coord = self.fnames_json[grid_id]['grid_id_coord']
for fpath in fpaths:
with rasterio.open(os.path.join(self.root_dir, fpath)) as src:
img = src.read()
if self.transform_s3:
#img = self.transform_s3(np.transpose(img, (1, 2, 0)))
img = torch.from_numpy(img).unsqueeze(0)
img = self.transform_s3(img).squeeze(0)
arrays.append(img)
fname = fpath.split('/')[-1]
date = re.search(r'(\d{8})T', fname).group(1)
meta_info = f"{grid_id_coord}/{date}"
meta_data.append(meta_info)
return arrays, meta_data
def get_s5p(self,grid_id,modality):
arrays = []
meta_data = []
fpaths = self.fnames_json[grid_id][modality]
grid_id_coord = self.fnames_json[grid_id]['grid_id_coord']
for fpath in fpaths:
with rasterio.open(os.path.join(self.root_dir, fpath)) as src:
img = src.read()
if self.transform_s5p:
#img = self.transform_s5p(np.transpose(img, (1, 2, 0)))
img = torch.from_numpy(img).unsqueeze(0)
img = self.transform_s5p(img).squeeze(0)
arrays.append(img)
fname = fpath.split('/')[-1]
match = re.search(r'(\d{4})-(\d{2})-(\d{2})', fname)
date = f"{match.group(1)}{match.group(2)}{match.group(3)}"
meta_info = f"{grid_id_coord}/{date}"
meta_data.append(meta_info)
return arrays, meta_data
def get_dem(self,grid_id):
fpath = self.fnames_json[grid_id]['dem'][0]
with rasterio.open(os.path.join(self.root_dir, fpath)) as src:
img = src.read()
if self.transform_dem:
#img = self.transform_dem(np.transpose(img, (1, 2, 0)))
img = torch.from_numpy(img).unsqueeze(0)
img = self.transform_dem(img).squeeze(0)
return img
def __getitem__(self, idx):
grid_id = self.grid_ids[idx]
grid_id_coord = self.fnames_json[grid_id]['grid_id_coord']
sample = {}
meta_data = {}
# s1
if 's1_grd' in self.modality:
arr_s1, meta_s1 = self.get_s1_s2(grid_id,'s1_grd')
sample['s1_grd'] = arr_s1
meta_data['s1_grd'] = meta_s1
# s2
if 's2_toa' in self.modality:
arr_s2, meta_s2 = self.get_s1_s2(grid_id,'s2_toa')
sample['s2_toa'] = arr_s2
meta_data['s2_toa'] = meta_s2
# s3
if 's3_olci' in self.modality:
arr_s3, meta_s3 = self.get_s3(grid_id)
sample['s3_olci'] = arr_s3
meta_data['s3_olci'] = meta_s3
# s5p_co
if 's5p_co' in self.modality:
arr_s5p_co, meta_s5p_co = self.get_s5p(grid_id,'s5p_co')
sample['s5p_co'] = arr_s5p_co
meta_data['s5p_co'] = meta_s5p_co
# s5p_no2
if 's5p_no2' in self.modality:
arr_s5p_no2, meta_s5p_no2 = self.get_s5p(grid_id,'s5p_no2')
sample['s5p_no2'] = arr_s5p_no2
meta_data['s5p_no2'] = meta_s5p_no2
# s5p_o3
if 's5p_o3' in self.modality:
arr_s5p_o3, meta_s5p_o3 = self.get_s5p(grid_id,'s5p_o3')
sample['s5p_o3'] = arr_s5p_o3
meta_data['s5p_o3'] = meta_s5p_o3
# s5p_so2
if 's5p_so2' in self.modality:
arr_s5p_so2, meta_s5p_so2 = self.get_s5p(grid_id,'s5p_so2')
sample['s5p_so2'] = arr_s5p_so2
meta_data['s5p_so2'] = meta_s5p_so2
# dem
if 'dem' in self.modality:
arr_dem = self.get_dem(grid_id)
sample['dem'] = arr_dem
meta_data['dem'] = grid_id_coord
return sample, meta_data
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--fnames_path', type=str, default='data_loading/fnames.json.gz')
parser.add_argument('--root_dir', type=str, default='data_loading/data')
args = parser.parse_args()
# transform_s1 = cvtransforms.Compose([
# cvtransforms.CenterCrop(224),
# cvtransforms.ToTensor()
# ])
# transform_s2 = cvtransforms.Compose([
# cvtransforms.CenterCrop(224),
# cvtransforms.ToTensor()
# ])
# transform_s3 = cvtransforms.Compose([
# cvtransforms.CenterCrop(96),
# cvtransforms.ToTensor()
# ])
# transform_s5p = cvtransforms.Compose([
# cvtransforms.CenterCrop(28),
# cvtransforms.ToTensor()
# ])
# transform_dem = cvtransforms.Compose([
# cvtransforms.CenterCrop(960),
# cvtransforms.ToTensor()
# ])
transform_s1 = AugmentationSequential(
#kornia.augmentation.SmallestMaxSize(264),
kornia.augmentation.CenterCrop(224),
)
transform_s2 = AugmentationSequential(
#kornia.augmentation.SmallestMaxSize(264),
kornia.augmentation.CenterCrop(224),
)
transform_s3 = AugmentationSequential(
kornia.augmentation.SmallestMaxSize(96),
kornia.augmentation.CenterCrop(96),
)
transform_s5p = AugmentationSequential(
kornia.augmentation.SmallestMaxSize(28),
kornia.augmentation.CenterCrop(28),
)
transform_dem = AugmentationSequential(
kornia.augmentation.SmallestMaxSize(960),
kornia.augmentation.CenterCrop(960),
)
ssl4eo_s = SSL4EO_S(args.fnames_path, args.root_dir, transform_s1=transform_s1, transform_s2=transform_s2, transform_s3=transform_s3, transform_s5p=transform_s5p, transform_dem=transform_dem)
dataloader = DataLoader(ssl4eo_s, batch_size=1, shuffle=True, num_workers=0) # batch size can only be 1 because of varying number of images per grid
for i, (sample, meta_data) in enumerate(dataloader):
#print(i)
print('Grid ID:', meta_data['dem'][0])
print(sample.keys())
print(meta_data.keys())
print('### S1 GRD ###')
print('Number of s1 local patches:', len(meta_data['s1_grd']), ' ', 'Number of time stamps for first local patch:', len(meta_data['s1_grd'][0]))
print('Example for one image:', sample['s1_grd'][0][0].shape, meta_data['s1_grd'][0][0])
print('### S2 TOA ###')
print('Number of s2 local patches:', len(meta_data['s2_toa']), ' ', 'Number of time stamps for first local patch:', len(meta_data['s2_toa'][0]))
print('Example for one image:', sample['s2_toa'][0][0].shape, meta_data['s2_toa'][0][0])
print('### S3 OLCI ###')
print('Number of s3 time stamps:', len(meta_data['s3_olci']))
print('Example for one image:', sample['s3_olci'][0].shape, meta_data['s3_olci'][0])
print('### S5P ###')
print('Number of s5p time stamps for CO/NO2/O3/SO2:', len(meta_data['s5p_co']), len(meta_data['s5p_no2']), len(meta_data['s5p_o3']), len(meta_data['s5p_so2']))
print('Example for one CO image:', sample['s5p_co'][0].shape, meta_data['s5p_co'][0])
print('Example for one NO2 image:', sample['s5p_no2'][0].shape, meta_data['s5p_no2'][0])
print('Example for one O3 image:', sample['s5p_o3'][0].shape, meta_data['s5p_o3'][0])
print('Example for one SO2 image:', sample['s5p_so2'][0].shape, meta_data['s5p_so2'][0])
print('### DEM ###')
print('One DEM image for the grid:', sample['dem'].shape, meta_data['dem'][0])
break |