feat(feature-compressor): add DINOv2 feature extraction and compression pipeline

mini-nav/feature_compressor/examples/batch_processing.py

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+"""Batch processing example for DINOv2 Feature Compressor."""
+
+import sys
+from pathlib import Path
+
+# Add parent to path for imports
+sys.path.insert(0, str(Path(__file__).parent.parent.parent))
+
+from dino_feature_compressor import DINOv2FeatureExtractor
+
+
+def main():
+    # Initialize extractor
+    print("Initializing DINOv2FeatureExtractor...")
+    extractor = DINOv2FeatureExtractor()
+
+    # Create a test directory with sample images
+    # In practice, use your own directory
+    image_dir = "/tmp/test_images"
+    Path(image_dir).mkdir(parents=True, exist_ok=True)
+
+    # Create 3 test images
+    print("Creating test images...")
+    import numpy as np
+    from PIL import Image
+
+    for i in range(3):
+        img_array = np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8)
+        img = Image.fromarray(img_array)
+        img.save(f"{image_dir}/test_{i}.jpg")
+    print(f"Created 3 test images in {image_dir}")
+
+    # Process batch
+    print("\nProcessing images in batch...")
+    results = extractor.process_batch(image_dir, batch_size=2, save_features=True)
+
+    print(f"\n=== Batch Processing Results ===")
+    print(f"Processed {len(results)} images")
+
+    for i, result in enumerate(results):
+        print(f"\nImage {i + 1}: {result['metadata']['image_path']}")
+        print(f"  Compressed shape: {result['compressed_features'].shape}")
+        print(f"  Feature norm: {result['metadata']['feature_norm']:.4f}")
+
+    print("\nDone! Features saved to outputs/ directory.")
+
+
+if __name__ == "__main__":
+    main()