fixed unknown fields, SegFromer processor assigned, MediaPipe for lips

analyzers/color_analyzer.py

@@ -41,6 +41,18 @@ EYE_COLOR_RANGES = {
 HAIR_TEXTURE_CURLY_THRESHOLD = 25.0
 HAIR_TEXTURE_WAVY_THRESHOLD = 15.0
 
+# MediaPipe FaceMesh lip contours. Outer ring traces the lip border;
+# inner ring traces the mouth opening — subtract one from the other
+# to get just the lip flesh and avoid sampling teeth or tongue.
+MEDIAPIPE_LIP_OUTER = [
+    61, 146, 91, 181, 84, 17, 314, 405, 321, 375,
+    291, 409, 270, 269, 267, 0, 37, 39, 40, 185,
+]
+MEDIAPIPE_LIP_INNER = [
+    78, 95, 88, 178, 87, 14, 317, 402, 318, 324,
+    308, 415, 310, 311, 312, 13, 82, 81, 80, 191,
+]
+
 
 class ColorAnalyzer:
     def __init__(self):
@@ -66,6 +78,14 @@ class ColorAnalyzer:
         if lip_mask is not None:
             lip_mask = lip_mask.astype(bool)
 
+        # SegFormer human-parsing has no dedicated lip class, so the
+        # parser hands us an empty mask. Fall back to MediaPipe lip
+        # landmarks whenever the parser-derived mask is missing or tiny.
+        if (lip_mask is None or lip_mask.sum() < 50) and landmarks:
+            derived = self._lip_mask_from_landmarks(landmarks, h, w)
+            if derived is not None:
+                lip_mask = derived
+
         # ── Skin Tone ────────────────────────────────────────────────
         if skin_mask is not None and skin_mask.sum() > 100:
             skin_lab = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2LAB)
@@ -258,6 +278,33 @@ class ColorAnalyzer:
             return "wavy"
         return "straight"
 
+    # ------------------------------------------------------------------
+    # Lip mask helper
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _lip_mask_from_landmarks(
+        landmarks: list[dict], h: int, w: int
+    ) -> np.ndarray | None:
+        """Build a lip-flesh mask by filling outer lip contour minus inner."""
+        max_idx = max(MEDIAPIPE_LIP_OUTER + MEDIAPIPE_LIP_INNER)
+        if len(landmarks) <= max_idx:
+            return None
+
+        def _poly(indices: list[int]) -> np.ndarray:
+            return np.array(
+                [
+                    [int(landmarks[i]["x"] * w), int(landmarks[i]["y"] * h)]
+                    for i in indices
+                ],
+                dtype=np.int32,
+            )
+
+        mask = np.zeros((h, w), dtype=np.uint8)
+        cv2.fillPoly(mask, [_poly(MEDIAPIPE_LIP_OUTER)], 255)
+        cv2.fillPoly(mask, [_poly(MEDIAPIPE_LIP_INNER)], 0)
+        return mask.astype(bool)
+
     # ------------------------------------------------------------------
     # Eye color helpers
     # ------------------------------------------------------------------

analyzers/parsing_analyzer.py

@@ -63,6 +63,7 @@ class ParsingAnalyzer:
         self.processor = None
         self.model = None
         try:
+            self.processor = SegformerImageProcessor.from_pretrained(MODEL_ID)
             self.model = SegformerForSemanticSegmentation.from_pretrained(MODEL_ID)
             self.model.to(self.device).eval()
         except Exception as exc: