Always return mask image; apply mask to anomaly map to reduce background bleed

app.py

@@ -93,17 +93,21 @@ def infer(ref_files, test_file, use_sam3, sam_prompt):
     model.fit(ref_paths, n_ref=len(ref_paths))
     image, amap, score = model.predict(test_path)
 
-    fg_mask_img = None
+    # Default empty mask for UI (in case SAM3 is off or fails)
+    fg_mask_img = Image.fromarray(np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8))
     if segmenter is not None:
         # Compute full-resolution mask for visualization and for suppressing background
         fg_mask = segmenter.get_object_mask(image)
+        # Resize mask to anomaly map resolution
+        fg_mask_resized = fg_mask
         if fg_mask.shape != amap.shape:
-            fg_mask = cv2.resize(
+            fg_mask_resized = cv2.resize(
                 fg_mask.astype(np.float32),
                 (amap.shape[1], amap.shape[0]),
                 interpolation=cv2.INTER_NEAREST,
             ) > 0.5
-        amap = amap * fg_mask  # zero-out background to reduce false positives
+        # Apply mask to anomaly map to suppress background leakage
+        amap = amap * fg_mask_resized
         fg_mask_img = Image.fromarray((fg_mask.astype(np.uint8) * 255))
 
     overlay = _make_overlay(image, amap)