Update utils/segmentation.py

utils/segmentation.py

@@ -8,8 +8,9 @@
 -------
 segment_person_hq(image, predictor, fallback_enabled=True) → np.ndarray
 segment_person_hq_original(image, predictor, fallback_enabled=True) → np.ndarray
+SegmentationError - Custom exception for segmentation errors
 
-Everything else is prefixed “_” and considered private.
+Everything else is prefixed "_" and considered private.
 """
 
 from __future__ import annotations
@@ -22,6 +23,13 @@
 
 log = logging.getLogger(__name__)
 
+# ============================================================================
+# CUSTOM EXCEPTION
+# ============================================================================
+class SegmentationError(Exception):
+    """Custom exception for segmentation-related errors"""
+    pass
+
 # ============================================================================
 # TUNABLE CONSTANTS
 # ============================================================================
@@ -40,6 +48,7 @@
 __all__ = [
     "segment_person_hq",
     "segment_person_hq_original",
+    "SegmentationError",
 ]
 
 # ============================================================================
@@ -56,7 +65,7 @@ def segment_person_hq(image: np.ndarray, predictor: Any, fallback_enabled: bool
         return segment_person_hq_original(image, predictor, fallback_enabled)
 
     if image is None or image.size == 0:
-        raise ValueError("Invalid input image")
+        raise SegmentationError("Invalid input image")
 
     # 1) — SAM-2 path -------------------------------------------------------
     if predictor and hasattr(predictor, "set_image") and hasattr(predictor, "predict"):
@@ -93,7 +102,7 @@ def segment_person_hq_original(image: np.ndarray, predictor: Any, fallback_enabl
     Very first implementation kept for rollback.  Fewer smarts, still robust.
     """
     if image is None or image.size == 0:
-        raise ValueError("Invalid input image")
+        raise SegmentationError("Invalid input image")
 
     try:
         if predictor and hasattr(predictor, "set_image") and hasattr(predictor, "predict"):
@@ -290,4 +299,4 @@ def _geometric_person_mask(image: np.ndarray) -> np.ndarray:
 
 def _auto_refine_mask_iteratively(image, mask, predictor, max_iterations=1):
     # Simple one-pass hook (full version lives in refinement.py)
-    return mask
+    return mask