cv2.CacadeClassifier error fix attempt

inference_utils.py

@@ -458,13 +458,11 @@ class StyleEmbedderApp:
         self._artist_names = None
         self._embeddings = None
         
-        # Face/Eye extractor (lazy load, uses CPU for detector to avoid CUDA init)
-        self._extractor = FaceEyeExtractor(
-            yolo_dir=_default_path('yolov5_anime') if yolo_dir is None else Path(yolo_dir),
-            weights_path=_default_path('yolov5x_anime.pt') if yolo_weights is None else Path(yolo_weights),
-            cascade_path=_default_path('anime-eyes-cascade.xml') if eyes_cascade is None else Path(eyes_cascade),
-            device='cpu',  # Always use CPU for detector to avoid CUDA init
-        )
+        # Face/Eye extractor - lazy load to avoid pickle issues with cv2.CascadeClassifier
+        self._extractor = None
+        self._extractor_yolo_dir = yolo_dir
+        self._extractor_yolo_weights = yolo_weights
+        self._extractor_eyes_cascade = eyes_cascade
         
         # Transform (no CUDA needed)
         self.transform = transforms.Compose([
@@ -583,12 +581,13 @@ class StyleEmbedderApp:
 
         if auto_face_image is None or auto_eye_image is None:
             try:
+                extractor = self._get_extractor()
                 if auto_face_image is None:
-                    auto_face_image = self._extractor.extract_face(full_image)
+                    auto_face_image = extractor.extract_face(full_image)
                 if auto_eye_image is None:
                     # Prefer detecting eyes from face if available.
                     if auto_face_image is not None:
-                        auto_eye_image = self._extractor.extract_eye_region(auto_face_image)
+                        auto_eye_image = extractor.extract_eye_region(auto_face_image)
             except Exception as e:
                 # If detector fails, proceed without branches.
                 print(f"[WARN] Auto face/eye extraction failed: {e}")
@@ -631,16 +630,28 @@ class StyleEmbedderApp:
         top_indices = np.argsort(similarities)[::-1][:top_k]
         return [(self._artist_names[i], float(similarities[i])) for i in top_indices]
     
-    def extract_crops(self, full_image: Image.Image) -> Tuple[Optional[Image.Image], Optional[Image.Image], str]:
-        """얼굴과 눈 자동 추출"""
+    def _get_extractor(self):
+        """Lazy load extractor to avoid pickle issues"""
+        if self._extractor is None:
+            self._extractor = FaceEyeExtractor(
+                yolo_dir=_default_path('yolov5_anime') if self._extractor_yolo_dir is None else Path(self._extractor_yolo_dir),
+                weights_path=_default_path('yolov5x_anime.pt') if self._extractor_yolo_weights is None else Path(self._extractor_yolo_weights),
+                cascade_path=_default_path('anime-eyes-cascade.xml') if self._extractor_eyes_cascade is None else Path(self._extractor_eyes_cascade),
+                device='cpu',  # Always use CPU for detector to avoid CUDA init
+            )
+        return self._extractor
+    
+    def _extract_crops_impl(self, full_image: Image.Image) -> Tuple[Optional[Image.Image], Optional[Image.Image], str]:
+        """얼굴과 눈 자동 추출 - 내부 구현"""
         if full_image is None:
             return None, None, "❌ 전체 이미지를 먼저 업로드해주세요."
         
         try:
-            face = self._extractor.extract_face(full_image)
+            extractor = self._get_extractor()
+            face = extractor.extract_face(full_image)
             eye = None
             if face is not None:
-                eye = self._extractor.extract_eye_region(face)
+                eye = extractor.extract_eye_region(face)
             
             status = "✅ 추출 완료:\n"
             status += f"- 얼굴: {'발견됨' if face else '발견되지 않음'}\n"
@@ -654,6 +665,12 @@ class StyleEmbedderApp:
         except Exception as e:
             return None, None, f"❌ 추출 실패: {str(e)}"
     
+    def extract_crops(self, full_image: Image.Image) -> Tuple[Optional[Image.Image], Optional[Image.Image], str]:
+        """얼굴과 눈 자동 추출 - Gradio용 래핑 함수"""
+        # Create extractor on-demand to avoid pickle issues
+        # The extractor will be created fresh each time, but _ensure_ready() handles caching
+        return self._extract_crops_impl(full_image)
+    
     def search(
         self,
         full_image: Image.Image,