Create prepare.py

prepare.py

@@ -0,0 +1,93 @@
+from datasets import Dataset, DatasetDict, Features, Image, Value
+import os
+
+# Script for preparing the dataset from a local directory.
+
+def load_ai4mars_dataset(data_dir):
+    # Define features
+    features = Features({
+        'image': Image(decode=True),
+        'label_mask': Image(decode=True),
+        'rover_mask': Image(decode=True),
+        'range_mask': Image(decode=True),
+        'has_masks': Value(dtype='bool'),
+        'has_labels': Value(dtype='bool')
+    })
+    
+    dataset_dict = {}
+    train_data = {
+        'image': [],
+        'label_mask': [],
+        'rover_mask': [],
+        'range_mask': [],
+        'has_masks': [],
+        'has_labels': []
+    }
+    
+    # Training data paths
+    train_img_dir = os.path.join(data_dir, 'msl/images/edr')
+    train_label_dir = os.path.join(data_dir, 'msl/labels/train')
+    train_mxy_dir = os.path.join(data_dir, 'msl/images/mxy')
+    train_range_dir = os.path.join(data_dir, 'msl/images/rng-30m')
+    
+    without_labels = 0
+    without_masks = 0
+
+    for img_name in os.listdir(train_img_dir):
+        base_name = os.path.splitext(img_name)[0]
+        img_path = os.path.join(train_img_dir, img_name)
+        label_path = os.path.join(train_label_dir, f"{base_name}.png")
+        rover_path = os.path.join(train_mxy_dir, f"{base_name}.png").replace('EDR', 'MXY')
+        range_path = os.path.join(train_range_dir, f"{base_name}.png").replace('EDR', 'RNG')
+
+        # Always add the image
+        train_data['image'].append(img_path)
+        
+        # Check if label files exist
+        has_labels = os.path.exists(label_path)
+        has_masks = os.path.exists(rover_path) and os.path.exists(range_path)
+        without_labels += 1 if not has_labels else 0
+        without_masks += 1 if not has_masks else 0
+        train_data['has_labels'].append(has_labels)
+        train_data['has_masks'].append(has_masks)
+        
+        # Add paths if they exist, None if they don't
+        train_data['label_mask'].append(label_path if os.path.exists(label_path) else None)
+        train_data['rover_mask'].append(rover_path if os.path.exists(rover_path) else None)
+        train_data['range_mask'].append(range_path if os.path.exists(range_path) else None)
+    
+
+    print(f"Training data without labels: {without_labels}")
+    print(f"Training data without masks: {without_masks}")
+    dataset_dict['train'] = Dataset.from_dict(train_data, features=features)
+    
+    # Load test data for each agreement level
+    for agreement in ['min1', 'min2', 'min3']:
+        test_data = {
+            'image': [],
+            'label_mask': [],
+            'rover_mask': [],
+            'range_mask': [],
+            'has_masks': [],
+            'has_labels': []
+        }
+        
+        test_label_dir = os.path.join(data_dir, f'msl/labels/test/masked-gold-{agreement}-100agree')
+
+        for label_name in os.listdir(test_label_dir):
+            base_name = os.path.splitext(label_name)[0]
+            img_path = os.path.join(data_dir, 'msl/images/edr', f"{base_name[:-len('_merged')]}.JPG")
+
+            if os.path.exists(img_path):
+                test_data['image'].append(img_path)
+                test_data['label_mask'].append(os.path.join(test_label_dir, label_name))
+                test_data['rover_mask'].append(os.path.join(train_mxy_dir, f"{base_name.replace('_merged', '').replace('EDR', 'MXY')}.png"))
+                test_data['range_mask'].append(os.path.join(train_range_dir, f"{base_name.replace('_merged', '').replace('EDR', 'RNG')}.png"))
+                test_data['has_labels'].append(True)
+                test_data['has_masks'].append(True)
+        
+        dataset_dict[f'test_{agreement}'] = Dataset.from_dict(test_data, features=features)
+    
+    return DatasetDict(dataset_dict)
+
+dataset = load_ai4mars_dataset("./ai4mars-dataset-merged-0.1")