feat(pipeline): add batch processing for scene graph construction

mini-nav/utils/__init__.py

@@ -4,7 +4,7 @@ from .feature_extractor import (
     extract_single_image_feature,
     infer_vector_dim,
 )
-from .image import segment_image, extract_masked_region
+from .image import extract_masked_region, segment_image, segment_image_dataset
 from .model import get_dino_dim, load_dino_model, load_hash_compressor, load_sam_model
 
 __all__ = [
@@ -14,6 +14,7 @@ __all__ = [
     "extract_single_image_feature",
     "extract_batch_features",
     "segment_image",
+    "segment_image_dataset",
     "extract_masked_region",
     "load_dino_model",
     "load_sam_model",

mini-nav/utils/image.py

@@ -1,4 +1,4 @@
-from typing import Any
+from typing import Any, Sequence
 
 import numpy as np
 from PIL import Image
@@ -64,6 +64,26 @@ def segment_image(
     return sorted_masks[:max_masks]
 
 
+def segment_image_dataset(
+    mask_generator: Any,
+    images: Sequence[Image.Image],
+    min_area: int = 32 * 32,
+    max_masks: int = 5,
+    points_per_batch: int = 64,
+) -> list[list[dict[str, Any]]]:
+    image_list = list(images)
+    return [
+        segment_image(
+            mask_generator,
+            image,
+            min_area=min_area,
+            max_masks=max_masks,
+            points_per_batch=points_per_batch,
+        )
+        for image in image_list
+    ]
+
+
 def _to_numpy_mask_array(mask_like: Any) -> np.ndarray | None:
     if mask_like is None:
         return None