upload eurosat-ms and eurosat-sar

eurosat_s1sar/dataset_eu.py

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+import numpy as np
+import torch
+from torch.utils.data import Dataset, DataLoader, Subset
+from pathlib import Path
+import os
+import rasterio
+import cv2
+import pdb
+from pyproj import Transformer
+
+
+EXTENSIONS = (".jpg", ".jpeg", ".png", ".ppm", ".bmp", ".pgm", ".tif", ".tiff", ".webp")
+ALL_BANDS = ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B09', 'B10', 'B11', 'B12', 'B8A']
+S2A_BANDS = ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B09', 'B11', 'B12', 'B8A']
+RGB_BANDS = ['B04', 'B03', 'B02']
+S1_BANDS = ['VV', 'VH']
+
+### SSL4EO stats
+BAND_STATS = {
+    'mean': {
+        'B01': 1353.72696296,
+        'B02': 1117.20222222,
+        'B03': 1041.8842963,
+        'B04': 946.554,
+        'B05': 1199.18896296,
+        'B06': 2003.00696296,
+        'B07': 2374.00874074,
+        'B08': 2301.22014815,
+        'B8A': 2599.78311111,
+        'B09': 732.18207407,
+        'B10': 12.09952894,
+        'B11': 1820.69659259,
+        'B12': 1118.20259259,
+        'VV': -12.54847273,
+        'VH': -20.19237134
+    },
+    'std': {
+        'B01': 897.27143653,
+        'B02': 736.01759721,
+        'B03': 684.77615743,
+        'B04': 620.02902871,
+        'B05': 791.86263829,
+        'B06': 1341.28018273,
+        'B07': 1595.39989386,
+        'B08': 1545.52915718,
+        'B8A': 1750.12066835,
+        'B09': 475.11595216,
+        'B10': 98.26600935,
+        'B11': 1216.48651476,
+        'B12': 736.6981037,
+        'VV': 5.25697717,
+        'VH': 5.91150917
+    }
+}
+
+# BAND_STATS_S1 = {
+#     'mean': {
+#         'VV': -12.54847273,
+#         'VH': -20.19237134
+#     },
+#     'std': {
+#         'VV': 5.25697717,
+#         'VH': 5.91150917
+#     }
+# }
+
+
+def is_valid_file(filename):
+    return filename.lower().endswith(EXTENSIONS)
+
+def normalize(img, mean, std):
+    min_value = mean - 2 * std
+    max_value = mean + 2 * std
+    img = (img - min_value) / (max_value - min_value) * 255.0
+    img = np.clip(img, 0, 255).astype(np.uint8)
+    #img = (img - min_value) / (max_value - min_value)
+    #img = np.clip(img, 0, 1).astype(np.float32)
+    return img
+
+class EurosatDataset(Dataset):
+
+    def __init__(self, root, bands='B2', split='train', transform=None, normalize=False, meta=False):
+        self.root = Path(root,split)
+        self.transform = transform
+        if bands=='B13':
+            self.bands = ALL_BANDS
+        elif bands=='B12':
+            self.bands = S2A_BANDS
+        elif bands=='RGB':
+            self.bands = RGB_BANDS
+        elif bands=='B2':
+            self.bands = S1_BANDS
+            
+        self.normalize = normalize
+            
+        self.classes = sorted([d.name for d in self.root.iterdir() if d.is_dir()])
+        self.class_to_idx = {cls_name: i for i, cls_name in enumerate(self.classes)}
+
+        self.samples = []
+        self.targets = []
+
+        #pdb.set_trace()
+        for froot, _, fnames in sorted(os.walk(self.root, followlinks=True)):
+            for fname in sorted(fnames):
+                if is_valid_file(fname):
+                    path = os.path.join(froot, fname)
+                    self.samples.append(path)
+                    target = self.class_to_idx[Path(path).parts[-2]]
+                    self.targets.append(target)
+        #print(self.root)
+        #print(f"Found {len(self.samples)} images belonging to {len(self.classes)} classes")
+        self.meta = meta
+
+    def __getitem__(self, index):
+        path = self.samples[index]
+        target = self.targets[index]
+        
+        with rasterio.open(path) as f:
+            if self.bands == ALL_BANDS:
+                array = f.read().astype(np.int16)
+            elif self.bands == S2A_BANDS:
+                array = f.read((1,2,3,4,5,6,7,8,9,11,12,13)).astype(np.int16)
+            elif self.bands == RGB_BANDS:
+                array = f.read((4,3,2)).astype(np.int16)
+            elif self.bands == S1_BANDS:
+                array = f.read().astype(np.float32)
+                            
+            img = array.transpose(1, 2, 0)
+
+            if self.meta:
+                # get lon, lat, time
+                cx,cy = f.xy(f.height // 2, f.width // 2)
+                # convert to lon, lat
+                crs_transformer = Transformer.from_crs(f.crs, 'epsg:4326')
+                lon, lat = crs_transformer.transform(cx,cy)
+                # no time
+                meta_info = np.array([lon, lat, 0, 0]).astype(np.float32)
+                #meta_info = np.array([0, 0, 0, 0]).astype(np.float32)
+                #meta_info = np.array([np.nan, np.nan, np.nan, np.nan]).astype(np.float32)
+
+        channels = []
+        
+        for i,b in enumerate(self.bands):
+            ch = img[:,:,i]
+            if self.normalize:
+                ch = normalize(ch, mean=BAND_STATS['mean'][b], std=BAND_STATS['std'][b])
+            elif self.bands == S2A_BANDS:
+                ch = (ch / 10000.0 * 255.0).astype('uint8')
+
+            if b=='B8A': # EuSAT band order is different than SSL4EO
+                channels.insert(8,ch)
+            else:
+                channels.append(ch)
+        #img = np.dstack(channels)
+        img = np.stack(channels, axis=0).astype('float32') / 255.0
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.meta:
+            return img, target, meta_info
+        else:
+            return img, target
+
+    def __len__(self):
+        return len(self.samples)
+
+
+class Subset(Dataset):
+    r"""
+    Subset of a dataset at specified indices.
+
+    Arguments:
+        dataset (Dataset): The whole Dataset
+        indices (sequence): Indices in the whole set selected for subset
+    """
+    def __init__(self, dataset, indices, transform=None):
+        self.dataset = dataset
+        self.indices = indices
+        self.transform = transform
+
+    def __getitem__(self, idx):
+        im, target = self.dataset[self.indices[idx]]
+        if self.transform:
+            im = self.transform(im)
+        return im, target
+
+    def __len__(self):
+        return len(self.indices)

eurosat_s1sar/eurosat_sar.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3556e8fe70f2043d5a19ebbdc0ec77fadc4e55633307170e0a10b08fb4f47696
+size 922582952

eurosat_s2ms/dataset_eu.py

@@ -0,0 +1,189 @@
+import numpy as np
+import torch
+from torch.utils.data import Dataset, DataLoader, Subset
+from pathlib import Path
+import os
+import rasterio
+import cv2
+import pdb
+from pyproj import Transformer
+
+
+EXTENSIONS = (".jpg", ".jpeg", ".png", ".ppm", ".bmp", ".pgm", ".tif", ".tiff", ".webp")
+ALL_BANDS = ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B09', 'B10', 'B11', 'B12', 'B8A']
+S2A_BANDS = ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B09', 'B11', 'B12', 'B8A']
+RGB_BANDS = ['B04', 'B03', 'B02']
+S1_BANDS = ['VV', 'VH']
+
+### SSL4EO stats
+BAND_STATS = {
+    'mean': {
+        'B01': 1353.72696296,
+        'B02': 1117.20222222,
+        'B03': 1041.8842963,
+        'B04': 946.554,
+        'B05': 1199.18896296,
+        'B06': 2003.00696296,
+        'B07': 2374.00874074,
+        'B08': 2301.22014815,
+        'B8A': 2599.78311111,
+        'B09': 732.18207407,
+        'B10': 12.09952894,
+        'B11': 1820.69659259,
+        'B12': 1118.20259259,
+        'VV': -12.54847273,
+        'VH': -20.19237134
+    },
+    'std': {
+        'B01': 897.27143653,
+        'B02': 736.01759721,
+        'B03': 684.77615743,
+        'B04': 620.02902871,
+        'B05': 791.86263829,
+        'B06': 1341.28018273,
+        'B07': 1595.39989386,
+        'B08': 1545.52915718,
+        'B8A': 1750.12066835,
+        'B09': 475.11595216,
+        'B10': 98.26600935,
+        'B11': 1216.48651476,
+        'B12': 736.6981037,
+        'VV': 5.25697717,
+        'VH': 5.91150917
+    }
+}
+
+# BAND_STATS_S1 = {
+#     'mean': {
+#         'VV': -12.54847273,
+#         'VH': -20.19237134
+#     },
+#     'std': {
+#         'VV': 5.25697717,
+#         'VH': 5.91150917
+#     }
+# }
+
+
+def is_valid_file(filename):
+    return filename.lower().endswith(EXTENSIONS)
+
+def normalize(img, mean, std):
+    min_value = mean - 2 * std
+    max_value = mean + 2 * std
+    img = (img - min_value) / (max_value - min_value) * 255.0
+    img = np.clip(img, 0, 255).astype(np.uint8)
+    #img = (img - min_value) / (max_value - min_value)
+    #img = np.clip(img, 0, 1).astype(np.float32)
+    return img
+
+class EurosatDataset(Dataset):
+
+    def __init__(self, root, bands='B2', split='train', transform=None, normalize=False, meta=False):
+        self.root = Path(root,split)
+        self.transform = transform
+        if bands=='B13':
+            self.bands = ALL_BANDS
+        elif bands=='B12':
+            self.bands = S2A_BANDS
+        elif bands=='RGB':
+            self.bands = RGB_BANDS
+        elif bands=='B2':
+            self.bands = S1_BANDS
+            
+        self.normalize = normalize
+            
+        self.classes = sorted([d.name for d in self.root.iterdir() if d.is_dir()])
+        self.class_to_idx = {cls_name: i for i, cls_name in enumerate(self.classes)}
+
+        self.samples = []
+        self.targets = []
+
+        #pdb.set_trace()
+        for froot, _, fnames in sorted(os.walk(self.root, followlinks=True)):
+            for fname in sorted(fnames):
+                if is_valid_file(fname):
+                    path = os.path.join(froot, fname)
+                    self.samples.append(path)
+                    target = self.class_to_idx[Path(path).parts[-2]]
+                    self.targets.append(target)
+        #print(self.root)
+        #print(f"Found {len(self.samples)} images belonging to {len(self.classes)} classes")
+        self.meta = meta
+
+    def __getitem__(self, index):
+        path = self.samples[index]
+        target = self.targets[index]
+        
+        with rasterio.open(path) as f:
+            if self.bands == ALL_BANDS:
+                array = f.read().astype(np.int16)
+            elif self.bands == S2A_BANDS:
+                array = f.read((1,2,3,4,5,6,7,8,9,11,12,13)).astype(np.int16)
+            elif self.bands == RGB_BANDS:
+                array = f.read((4,3,2)).astype(np.int16)
+            elif self.bands == S1_BANDS:
+                array = f.read().astype(np.float32)
+                            
+            img = array.transpose(1, 2, 0)
+
+            if self.meta:
+                # get lon, lat, time
+                cx,cy = f.xy(f.height // 2, f.width // 2)
+                # convert to lon, lat
+                crs_transformer = Transformer.from_crs(f.crs, 'epsg:4326')
+                lon, lat = crs_transformer.transform(cx,cy)
+                # no time
+                meta_info = np.array([lon, lat, 0, 0]).astype(np.float32)
+                #meta_info = np.array([0, 0, 0, 0]).astype(np.float32)
+                #meta_info = np.array([np.nan, np.nan, np.nan, np.nan]).astype(np.float32)
+
+        channels = []
+        
+        for i,b in enumerate(self.bands):
+            ch = img[:,:,i]
+            if self.normalize:
+                ch = normalize(ch, mean=BAND_STATS['mean'][b], std=BAND_STATS['std'][b])
+            elif self.bands == S2A_BANDS:
+                ch = (ch / 10000.0 * 255.0).astype('uint8')
+
+            if b=='B8A': # EuSAT band order is different than SSL4EO
+                channels.insert(8,ch)
+            else:
+                channels.append(ch)
+        #img = np.dstack(channels)
+        img = np.stack(channels, axis=0).astype('float32') / 255.0
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.meta:
+            return img, target, meta_info
+        else:
+            return img, target
+
+    def __len__(self):
+        return len(self.samples)
+
+
+class Subset(Dataset):
+    r"""
+    Subset of a dataset at specified indices.
+
+    Arguments:
+        dataset (Dataset): The whole Dataset
+        indices (sequence): Indices in the whole set selected for subset
+    """
+    def __init__(self, dataset, indices, transform=None):
+        self.dataset = dataset
+        self.indices = indices
+        self.transform = transform
+
+    def __getitem__(self, idx):
+        im, target = self.dataset[self.indices[idx]]
+        if self.transform:
+            im = self.transform(im)
+        return im, target
+
+    def __len__(self):
+        return len(self.indices)

eurosat_s2ms/eurosat_ms.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:201940af1e4e2f40ae2b26491f059a7efd389233ffcfd66a5e27727d2fe92745
+size 2027392300