refactor(compressors): reorganize SAM utilities into proposal module

mini-nav/compressors/pipeline.py

@@ -8,9 +8,13 @@ import torch.nn.functional as F
 from PIL import Image
 
 from .object_score import select_best_mask
+from .proposal import (
+    extract_masked_region,
+    generate_proposals,
+    generate_proposals_batch,
+)
 from utils import get_device
-from utils.image import extract_masked_region, segment_image, segment_image_dataset
-from utils.model import (
+from .model_loader import (
     get_dino_dim,
     load_dino_model,
     load_hash_compressor,
@@ -101,7 +105,7 @@ class HashPipeline(nn.Module):
         Returns:
             Masked image containing only the largest object, or original if no masks.
         """
-        masks = segment_image(
+        masks = generate_proposals(
             self.mask_generator,
             image,
             min_area=self.sam_min_mask_area,
@@ -122,7 +126,7 @@ class HashPipeline(nn.Module):
         images: Sequence[Image.Image],
     ) -> list[Image.Image]:
         image_list = list(images)
-        masks_dataset = segment_image_dataset(
+        masks_dataset = generate_proposals_batch(
             self.mask_generator,
             image_list,
             min_area=self.sam_min_mask_area,