fix: replace facial landmarks with robust bounding boxes in face_verify

api_routes.py

@@ -4561,17 +4561,13 @@ async def face_similarity_verification(
         img1_region = facial_areas.get("img1", {})
         img2_region = facial_areas.get("img2", {})
 
-        # 确保分析器已初始化，用于绘制特征点
-        if analyzer is None:
-            _ensure_analyzer()
-
-        def _apply_landmarks_on_original(
+        def _apply_box_on_original(
             source_image: np.ndarray,
             region: dict,
             label: str,
         ) -> Tuple[np.ndarray, bool]:
-            if analyzer is None or not region:
-                return source_image, False
+            if not region:
+                return source_image.copy(), False
             try:
                 x = max(0, region.get("x", 0))
                 y = max(0, region.get("y", 0))
@@ -4579,19 +4575,21 @@ async def face_similarity_verification(
                 h = region.get("h", 0)
                 x_end = min(source_image.shape[1], x + w)
                 y_end = min(source_image.shape[0], y + h)
+                
                 if x_end <= x or y_end <= y:
-                    return source_image, False
+                    return source_image.copy(), False
+                
                 result_img = source_image.copy()
-                face_region = result_img[y:y_end, x:x_end]
-                face_with_landmarks = analyzer.facial_analyzer.draw_facial_landmarks(face_region)
-                result_img[y:y_end, x:x_end] = face_with_landmarks
+                # 绘制绿色矩形框，厚度根据图片尺寸动态调整
+                thickness = max(2, int(round(min(source_image.shape[:2]) / 200)))
+                cv2.rectangle(result_img, (x, y), (x_end, y_end), (0, 255, 0), thickness)
                 return result_img, True
             except Exception as exc:
-                logger.warning(f"Failed to draw facial landmarks on original image {label}: {exc}")
-                return source_image, False
+                logger.warning(f"Failed to draw face box on original image {label}: {exc}")
+                return source_image.copy(), False
 
-        original_output_img1, original1_has_landmarks = _apply_landmarks_on_original(image1, img1_region, "1")
-        original_output_img2, original2_has_landmarks = _apply_landmarks_on_original(image2, img2_region, "2")
+        original_output_img1, original1_has_box = _apply_box_on_original(image1, img1_region, "1")
+        original_output_img2, original2_has_box = _apply_box_on_original(image2, img2_region, "2")
 
         if save_image_high_quality(original_output_img1, original_path1, quality=SAVE_QUALITY):
             await _record_output_file(
@@ -4600,8 +4598,8 @@ async def face_similarity_verification(
                 category="original",
                 extra={
                     "source": "face_verify",
-                    "role": "original1_landmarks" if original1_has_landmarks else "original1",
-                    "with_landmarks": original1_has_landmarks,
+                    "role": "original1",
+                    "has_box": original1_has_box,
                 },
             )
         if save_image_high_quality(original_output_img2, original_path2, quality=SAVE_QUALITY):
@@ -4611,8 +4609,8 @@ async def face_similarity_verification(
                 category="original",
                 extra={
                     "source": "face_verify",
-                    "role": "original2_landmarks" if original2_has_landmarks else "original2",
-                    "with_landmarks": original2_has_landmarks,
+                    "role": "original2",
+                    "has_box": original2_has_box,
                 },
             )
 
@@ -4635,39 +4633,26 @@ async def face_similarity_verification(
 
                 face_path1 = os.path.join(IMAGES_DIR, face_filename1)
                 face_path2 = os.path.join(IMAGES_DIR, face_filename2)
-                # 根据分析器可用性决定是否绘制特征点，仅保存最终版本一次
-                def _prepare_face_image(face_img, face_index):
-                    if analyzer is None:
-                        return face_img, False
-                    try:
-                        return analyzer.facial_analyzer.draw_facial_landmarks(face_img.copy()), True
-                    except Exception as exc:
-                        logger.warning(f"Failed to draw facial landmarks on face{face_index}: {exc}")
-                        return face_img, False
-
-                face_output_img1, face1_has_landmarks = _prepare_face_image(face_img1, 1)
-                face_output_img2, face2_has_landmarks = _prepare_face_image(face_img2, 2)
-
-                if save_image_high_quality(face_output_img1, face_path1, quality=SAVE_QUALITY):
+                
+                # 直接保存裁剪图，不进行特征点绘制
+                if save_image_high_quality(face_img1, face_path1, quality=SAVE_QUALITY):
                     await _record_output_file(
                         file_path=face_path1,
                         nickname=nickname,
                         category="face",
                         extra={
                             "source": "face_verify",
-                            "role": "face1_landmarks" if face1_has_landmarks else "face1",
-                            "with_landmarks": face1_has_landmarks,
+                            "role": "face1",
                         },
                     )
-                if save_image_high_quality(face_output_img2, face_path2, quality=SAVE_QUALITY):
+                if save_image_high_quality(face_img2, face_path2, quality=SAVE_QUALITY):
                     await _record_output_file(
                         file_path=face_path2,
                         nickname=nickname,
                         category="face",
                         extra={
                             "source": "face_verify",
-                            "role": "face2_landmarks" if face2_has_landmarks else "face2",
-                            "with_landmarks": face2_has_landmarks,
+                            "role": "face2",
                         },
                     )
             except Exception as e: