feat: upgrade pipeline UI — binary seg + multiclass + PWAT raw vs adjusted

pipeline.py

@@ -23,6 +23,9 @@ class AnalysisResult:
     """Complete DFU analysis result from a single image."""
     # Segmentation
     class_distribution: dict = field(default_factory=dict)  # {class_name: percentage}
+    classmap: Optional[np.ndarray] = field(default=None, repr=False)  # (H,W) uint8
+    probs: Optional[np.ndarray] = field(default=None, repr=False)     # (4,H,W) float32
+    ulcer_mask: Optional[np.ndarray] = field(default=None, repr=False)  # (H,W) uint8
     # Fitzpatrick
     fitzpatrick: Optional[FitzpatrickResult] = None
     # PWAT
@@ -147,23 +150,42 @@ class WoundNetB7Pipeline:
 
         return AnalysisResult(
             class_distribution=class_dist,
+            classmap=classmap,
+            probs=seg["probs"],
+            ulcer_mask=ulcer_mask,
             fitzpatrick=fitz,
             pwat=pwat,
             image_size=(h, w),
             device=str(self.device),
         )
 
-    def visualize(self, img_bgr: np.ndarray, result: AnalysisResult) -> np.ndarray:
-        """Create overlay visualization of segmentation result."""
-        h, w = img_bgr.shape[:2]
-        seg = segment(self.seg_model, img_bgr, self.device, use_tta=False)
-        classmap = seg["classmap"]
-
+    def visualize_binary(self, img_bgr: np.ndarray, result: AnalysisResult) -> np.ndarray:
+        """Create binary ulcer segmentation overlay (ulcer only, red mask)."""
+        img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB).astype(np.float32)
+        overlay = img_rgb.copy()
+        if result.ulcer_mask is not None:
+            ulcer_bool = result.ulcer_mask > 127
+            if np.any(ulcer_bool):
+                overlay[ulcer_bool] = overlay[ulcer_bool] * 0.4 + np.array([255, 0, 0], dtype=np.float32) * 0.6
+                overlay_u8 = np.clip(overlay, 0, 255).astype(np.uint8)
+                contours, _ = cv2.findContours(
+                    ulcer_bool.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+                )
+                cv2.drawContours(overlay_u8, contours, -1, (255, 255, 255), 2)
+                return overlay_u8
+        return np.clip(overlay, 0, 255).astype(np.uint8)
+
+    def visualize_multiclass(self, img_bgr: np.ndarray, result: AnalysisResult) -> np.ndarray:
+        """Create multiclass segmentation overlay using cached classmap."""
         overlay = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB).astype(np.float32)
-        for cid, color in CLASS_COLORS.items():
-            if cid == 0:
-                continue
-            mask = classmap == cid
-            overlay[mask] = overlay[mask] * 0.5 + np.array(color, dtype=np.float32) * 0.5
-
+        if result.classmap is not None:
+            for cid, color in CLASS_COLORS.items():
+                if cid == 0:
+                    continue
+                mask = result.classmap == cid
+                overlay[mask] = overlay[mask] * 0.5 + np.array(color, dtype=np.float32) * 0.5
         return overlay.astype(np.uint8)
+
+    def visualize(self, img_bgr: np.ndarray, result: AnalysisResult) -> np.ndarray:
+        """Create overlay visualization (backward compatible)."""
+        return self.visualize_multiclass(img_bgr, result)