upload cloud_s3olci dataset

cloud_s3olci/cloud_s3olci.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1ce5697ab3c60e43e92a24968d6dcb1b7080da0a8c9016782e8926a88a4733d8
+size 6991634480

cloud_s3olci/dataset_cloud_s3olci.py

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+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()
+            # convert nan pixels to 0
+            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