| import torch |
| from torch.utils.data import Dataset, DataLoader |
| import os |
| import rasterio |
| import numpy as np |
| from datetime import date |
| from pyproj import Transformer |
|
|
| S3_OLCI_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] |
|
|
| Cls_index_binary = { |
| 'invalid': 0, |
| 'clear': 1, |
| 'cloud': 2, |
| } |
|
|
| Cls_index_multi = { |
| 'invalid': 0, |
| 'clear': 1, |
| 'cloud-sure': 2, |
| 'cloud-ambiguous': 3, |
| 'cloud shadow': 4, |
| 'snow and ice': 5, |
| } |
|
|
|
|
|
|
| class S3OLCI_CloudDataset(Dataset): |
| ''' |
| 1596/399 train/test images 256x256 |
| 21 bands |
| nodata: nan |
| |
| ''' |
| def __init__(self, root_dir, split='train', mode='multi', meta=True): |
| self.root_dir = root_dir |
| self.meta = meta |
|
|
| self.img_dir = os.path.join(root_dir, split, 's3_olci') |
| self.fpaths = os.listdir(self.img_dir) |
| self.fpaths = [f for f in self.fpaths if f.endswith('.tif')] |
|
|
| if mode == 'multi': |
| self.cloud_dir = os.path.join(root_dir, split, 'cloud_multi') |
| elif mode == 'binary': |
| self.cloud_dir = os.path.join(root_dir, split, 'cloud_binary') |
|
|
| if self.meta: |
| self.reference_date = date(1970, 1, 1) |
|
|
|
|
| def __len__(self): |
| return len(self.fpaths) |
| |
| def __getitem__(self, idx): |
| fpath = self.fpaths[idx] |
| fpath_img = os.path.join(self.img_dir, fpath) |
| fpath_cloud = os.path.join(self.cloud_dir, fpath) |
|
|
| with rasterio.open(fpath_img) as src: |
| img = src.read() |
| |
| img[np.isnan(img)] = 0 |
|
|
| for b in range(21): |
| img[b] = img[b] * S3_OLCI_SCALE[b] |
|
|
| 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) |
| img_fname = os.path.basename(fpath_img) |
| 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, np.nan]).astype(np.float32) |
| else: |
| meta_info = np.array([np.nan,np.nan,np.nan,np.nan]).astype(np.float32) |
|
|
| img = torch.from_numpy(img).float() |
|
|
| with rasterio.open(fpath_cloud) as src: |
| cloud = src.read(1) |
| cloud = torch.from_numpy(cloud).long() |
|
|
| return img, cloud, meta_info |
|
|
|
|
| |
| if __name__ == '__main__': |
| dataset = S3OLCI_CloudDataset(root_dir='./cloud_s3olci', split='train', mode='multi') |
| dataloader = DataLoader(dataset, batch_size=2, shuffle=False) |
| for img, cloud, meta in dataloader: |
| print(img.shape, cloud.shape, meta.shape) |
| break |
