Update src/ai_processor.py

src/ai_processor.py

@@ -232,7 +232,6 @@ initialize_cpu_models()
 setup_knowledge_base()
 
 # ---------- Calibration helpers ----------
-# ---- Adaptive thresholding for model prob map ----
 def _adaptive_prob_threshold(p: np.ndarray) -> float:
     """
     Pick a threshold that avoids tiny blobs while not swallowing skin.
@@ -244,20 +243,30 @@ def _adaptive_prob_threshold(p: np.ndarray) -> float:
     p01 = np.clip(p.astype(np.float32), 0, 1)
     p255 = (p01 * 255).astype(np.uint8)
 
-    # Otsu
-    _, thr_otsu = cv2.threshold(p255, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-    thr_otsu = np.clip(thr_otsu / 255.0, 0.25, 0.65)
+    # Otsu → use the returned scalar threshold (ret), NOT the image
+    ret_otsu, _dst = cv2.threshold(p255, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    thr_otsu = float(np.clip(ret_otsu / 255.0, 0.25, 0.65))
 
     # Percentile (90th)
     thr_pctl = float(np.clip(np.percentile(p01, 90), 0.25, 0.65))
 
-    # Prefer the threshold that yields an area fraction in [0.005..0.20]
-    def area_frac(thr):
+    # Area fraction helper
+    def area_frac(thr: float) -> float:
         return float((p01 >= thr).sum()) / float(p01.size)
 
     af_otsu = area_frac(thr_otsu)
     af_pctl = area_frac(thr_pctl)
 
+    # Score: prefer ~3–10% coverage
+    def score(af: float) -> float:
+        target_low, target_high = 0.03, 0.10
+        if af < target_low: return abs(af - target_low) * 3.0
+        if af > target_high: return abs(af - target_high) * 1.5
+        return 0.0
+
+    return thr_otsu if score(af_otsu) <= score(af_pctl) else thr_pctl
+
+
     # Score: closeness to a target area fraction (aim ~3–10%)
     def score(af):
         target_low, target_high = 0.03, 0.10