Update SSL4EO-L-Benchmark.py

SSL4EO-L-Benchmark.py

@@ -30,6 +30,8 @@ num_classes = {
     'oli_sr_nlcd': 21,
     'oli_tirs_toa_cdl': 134,
     'oli_tirs_toa_nlcd': 21,
+    "etm_oli_toa_nlcd": 21,
+    "etm_oli_toa_cdl": 134,
 }
 
 num_channels = {
@@ -41,6 +43,8 @@ num_channels = {
     'oli_sr_nlcd': 7,
     'oli_tirs_toa_cdl': 11,
     'oli_tirs_toa_nlcd': 11,
+    "etm_oli_toa_nlcd": 20,
+    "etm_oli_toa_cdl": 20,
 }
 
 MEAN = [0]
@@ -55,6 +59,8 @@ metadata = { # TODO: check if info below is correct or not
     'oli_sr_nlcd': {"bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7"], "channel_wv": [443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0], "mean": MEAN * 7, 'std': STD * 7},
     'oli_tirs_toa_cdl': {"bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B9", "B10", "B11"], "channel_wv": [443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0, 590.0, 1735.0, 10800.0, 12000.0], "mean": MEAN * 11, 'std': STD * 11},
     'oli_tirs_toa_nlcd': {"bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B9", "B10", "B11"], "channel_wv": [443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0, 590.0, 1735.0, 10800.0, 12000.0], "mean": MEAN * 11, 'std': STD * 11},
+    "etm_oli_toa_nlcd": {"bands":["B1E", "B2E", "B3E", "B4E", "B5E", "B6LE", "B6HE", "B7E", "B8E", "B1O", "B2O", "B3O", "B4O", "B5O", "B6O", "B7O", "B8O", "B9O", "B10O", "B11O"], "channel_wv": [485.0, 560.0, 660.0, 835.0, 1650.0, 10900.0, 10900.0, 2220.0, 710.0, 443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0, 590.0, 1735.0, 10800.0, 12000.0], "mean": MEAN * 20, 'std': STD * 20},
+    "etm_oli_toa_cdl": {"bands":["B1E", "B2E", "B3E", "B4E", "B5E", "B6LE", "B6HE", "B7E", "B8E", "B1O", "B2O", "B3O", "B4O", "B5O", "B6O", "B7O", "B8O", "B9O", "B10O", "B11O"], "channel_wv": [485.0, 560.0, 660.0, 835.0, 1650.0, 10900.0, 10900.0, 2220.0, 710.0, 443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0, 590.0, 1735.0, 10800.0, 12000.0], "mean": MEAN * 20, 'std': STD * 20},
 }
 
 class SSL4EOLBenchmarkDataset(datasets.GeneratorBasedBuilder):
@@ -146,13 +152,28 @@ class SSL4EOLBenchmarkDataset(datasets.GeneratorBasedBuilder):
         spatial_resolution = self.spatial_resolution
 
         data_dir = os.path.join(data_dir, "SSL4EOLBenchmark")
-        metadata = pd.read_csv(os.path.join(data_dir, f"metadata_{self.config.name}.csv"))
+        if self.config.name in ["etm_oli_toa_nlcd", "etm_oli_toa_cdl"]:
+            product = self.config.name.split('_')[-1] # nlcd / cdl
+            metadata_oli = pd.read_csv(os.path.join(data_dir, f"metadata_oli_tirs_toa_{product}.csv"))
+            metadata_etm = pd.read_csv(os.path.join(data_dir, f"metadata_etm_toa_{product}.csv"))
+        
+            metadata = self.sort_and_create_new_csv(metadata_oli, metadata_etm)
+        else:
+            metadata = pd.read_csv(os.path.join(data_dir, f"metadata_{self.config.name}.csv"))
+        
         metadata = metadata[metadata["split"] == split].reset_index(drop=True)
 
         for index, row in metadata.iterrows():
             optical_path = os.path.join(data_dir, row.optical_path)
             optical = self._read_image(optical_path).astype(np.float32) # CxHxW
 
+            try:
+                optical_path_etm = os.path.join(data_dir, row.optical_path_etm)
+                optical_etm = self._read_image(optical_path_etm).astype(np.float32)
+                optical = np.concatenate((optical_etm, optical), axis=0)
+            except:
+                pass
+
             label_path = os.path.join(data_dir, row.label_path)
             label = self._read_image(label_path).astype(np.int32)
             label = self.ordinal_map[label]
@@ -180,4 +201,35 @@ class SSL4EOLBenchmarkDataset(datasets.GeneratorBasedBuilder):
         if len(image.shape) == 3:
             image = np.transpose(image, (2, 0, 1))
 
-        return image
+        return image
+    
+    def sort_and_create_new_csv(metadata_oli, metadata_etm):
+        def extract_number(optical_path):
+            return optical_path.split('/')[1]
+        
+        metadata_oli['number'] = metadata_oli['optical_path'].apply(extract_number) # a number
+        metadata_etm['number'] = metadata_etm['optical_path'].apply(extract_number) # a number
+        
+        metadata_oli = metadata_oli.sort_values('number').reset_index(drop=True)
+        metadata_etm = metadata_etm.sort_values('number').reset_index(drop=True)
+        
+        new_rows = []
+        
+        i, j = 0, 0
+        while i < len(metadata_oli) and j < len(metadata_etm):
+            if metadata_oli.loc[i, 'number'] == metadata_etm.loc[j, 'number']:
+                new_rows.append({
+                    'split': metadata_oli.loc[i, 'split'],
+                    'optical_path_etm': metadata_etm.loc[j, 'optical_path'],
+                    'optical_path': metadata_oli.loc[i, 'optical_path'],
+                    'label_path': metadata_oli.loc[i, 'label_path']
+                })
+                i += 1
+                j += 1
+            elif metadata_oli.loc[i, 'number'] < metadata_etm.loc[j, 'number']:
+                i += 1
+            else:
+                j += 1
+        
+        new_df = pd.DataFrame(new_rows, columns=['split', 'optical_path_etm', 'optical_path', 'label_path'])
+        return new_df