fix

api_routes.py

@@ -147,7 +147,7 @@ if DEEPFACE_AVAILABLE:
                     outputs = self.model(img, training=False)
                     embeddings = _safe_tensor_to_numpy(outputs)
                 except Exception as call_exc:
-                    logger.debug(f"DeepFace forward fallback self.model 调用失败，改用 predict: {call_exc}")
+                    logger.info(f"DeepFace forward fallback self.model 调用失败，改用 predict: {call_exc}")
 
                 if embeddings is None:
                     # Keras 3 调用 self.model(...) 可能返回SymbolicTensor，退回 predict

app.py

@@ -73,7 +73,7 @@ async def lifespan(app: FastAPI):
     logger.info(f"DeepFace available: {DEEPFACE_AVAILABLE}")
     logger.info(f"YOLO available: {YOLO_AVAILABLE}")
     logger.info(f"MediaPipe available: {DLIB_AVAILABLE}")
-    logger.debug(f"Archive directory: {IMAGES_DIR}")
+    logger.info(f"Archive directory: {IMAGES_DIR}")
     os.makedirs(OUTPUT_DIR, exist_ok=True)
 
     # 初始化数据库连接池

cleanup_scheduler.py

@@ -74,14 +74,14 @@ class ImageCleanupScheduler:
                             deleted_files.append(os.path.basename(file_path))
                             total_size_deleted += file_size
 
-                            logger.debug(f"Deleting expired file: {os.path.basename(file_path)} ")
+                            logger.info(f"Deleting expired file: {os.path.basename(file_path)} ")
 
                     except (OSError, IOError) as e:
                         logger.error(f"Failed to delete file {os.path.basename(file_path)}: {e}")
                         continue
 
                 logger.info(f"Cleanup completed! Deleted {len(deleted_files)} files, ")
-                logger.debug(f"Deleted file list: {', '.join(deleted_files[:10])}")
+                logger.info(f"Deleted file list: {', '.join(deleted_files[:10])}")
             else:
                 logger.info("Cleanup completed! No expired files found to clean")
 

face_analyzer.py

@@ -194,7 +194,7 @@ class EnhancedFaceAnalyzer:
                             # 获取边界框坐标 (xyxy格式)
                             x1, y1, x2, y2 = box.xyxy[0].cpu().numpy().astype(int)
                             confidence = float(box.conf[0])
-                            logger.debug(
+                            logger.info(
                                 f"detect class_id={class_id}, confidence={confidence}"
                             )
                             # 基本边界检查
@@ -812,7 +812,7 @@ class EnhancedFaceAnalyzer:
 
         # 复制原图用于绘制
         annotated_image = image.copy()
-        logger.debug(
+        logger.info(
             f"Input annotated_image shape: {annotated_image.shape}, dtype: {annotated_image.dtype}, ndim: {annotated_image.ndim}"
         )
         # 分析每张人脸
@@ -952,7 +952,7 @@ class EnhancedFaceAnalyzer:
                 save_image_force_compress(
                     face_cropped, cropped_face_path, max_size_kb=100
                 )
-                logger.debug(f"cropped face: {cropped_face_path}")
+                logger.info(f"cropped face: {cropped_face_path}")
             except Exception as e:
                 logger.error(f"Failed to save cropped face {cropped_face_path}: {e}")
                 cropped_face_filename = None

facial_analyzer.py

@@ -518,7 +518,7 @@ class FacialFeatureAnalyzer:
         基于多个美学比例和对称性指标
         """
         try:
-            logger.debug(f"face landmarks={len(landmarks)}")
+            logger.info(f"face landmarks={len(landmarks)}")
             if len(landmarks) < 68:  # 假设使用68点面部关键点
                 return 6.21
 
@@ -533,37 +533,37 @@ class FacialFeatureAnalyzer:
 
             # 黄金比例评分 (权重: 20%)
             golden_score = self._calculate_golden_ratios(face_measurements)
-            logger.debug(f"Golden ratio score={golden_score}")
+            logger.info(f"Golden ratio score={golden_score}")
             scores.append(("golden_ratio", golden_score, 0.10))
 
             # 对称性评分 (权重: 25%)
             symmetry_score = self._calculate_facial_symmetry(face_measurements, points)
-            logger.debug(f"Symmetry score={symmetry_score}")
+            logger.info(f"Symmetry score={symmetry_score}")
             scores.append(("symmetry", symmetry_score, 0.40))
 
             # 三庭五眼比例 (权重: 20%)
             proportion_score = self._calculate_classical_proportions(face_measurements)
-            logger.debug(f"Three courts five eyes ratio={proportion_score}")
+            logger.info(f"Three courts five eyes ratio={proportion_score}")
             scores.append(("proportions", proportion_score, 0.05))
 
             # 五官间距协调性 (权重: 15%)
             spacing_score = self._calculate_feature_spacing(face_measurements)
-            logger.debug(f"Facial feature spacing harmony={spacing_score}")
+            logger.info(f"Facial feature spacing harmony={spacing_score}")
             scores.append(("spacing", spacing_score, 0))
 
             # 面部轮廓协调性 (权重: 10%)
             contour_score = self._calculate_contour_harmony(points)
-            logger.debug(f"Facial contour harmony={contour_score}")
+            logger.info(f"Facial contour harmony={contour_score}")
             scores.append(("contour", contour_score, 0.05))
 
             # 眼鼻口比例协调性 (权重: 10%)
             feature_score = self._calculate_feature_proportions(face_measurements)
-            logger.debug(f"Eye-nose-mouth proportion harmony={feature_score}")
+            logger.info(f"Eye-nose-mouth proportion harmony={feature_score}")
             scores.append(("features", feature_score, 0.40))
 
             # 加权平均计算最终得分
             final_score = sum(score * weight for _, score, weight in scores)
-            logger.debug(f"Weighted average final score={final_score}")
+            logger.info(f"Weighted average final score={final_score}")
             return max(0, min(10, final_score))
 
         except Exception as e:
@@ -879,7 +879,7 @@ class FacialFeatureAnalyzer:
         try:
             # 复制原图用于绘制
             annotated_image = face_image.copy()
-            
+
             # MediaPipe需要RGB图像
             rgb_image = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
 
@@ -900,7 +900,7 @@ class FacialFeatureAnalyzer:
                 y = int(landmark.y * h)
                 # 绘制小圆点表示关键点
                 cv2.circle(annotated_image, (x, y), 1, (0, 255, 0), -1)
-                
+
                 # 绘制十字标记
                 cv2.line(annotated_image, (x-2, y), (x+2, y), (0, 255, 0), 1)
                 cv2.line(annotated_image, (x, y-2), (x, y+2), (0, 255, 0), 1)

utils.py

@@ -609,7 +609,7 @@ def save_image_force_compress(
             if len(encoded_img) <= max_bytes:
                 with open(output_path, "wb") as f:
                     f.write(encoded_img)
-                logger.debug(
+                logger.info(
                     f"压缩后图像大小: {len(encoded_img) / 1024:.2f} KB，scale={scale:.2f}, quality={quality}"
                 )
                 upload_file_to_bos(output_path)
@@ -646,7 +646,7 @@ def move_file_to_archive(file_path: str):
         filename = os.path.basename(file_path)
         destination = os.path.join(IMAGES_DIR, filename)
         shutil.move(file_path, destination)
-        logger.debug(f"Moved file to archive: {destination}")
+        logger.info(f"Moved file to archive: {destination}")
     except Exception as error:
         logger.error(f"Failed to move file {file_path} to archive: {error}")
 

vector_store.py

@@ -102,7 +102,7 @@ def save_index():
         faiss.write_index(index, FAISS_INDEX_PATH)
         with open(ID_MAP_PATH, "wb") as f:
             pickle.dump(id_map, f)
-        logger.debug("Vector index saved")
+        logger.info("Vector index saved")
     except Exception as e:
         logger.error(f"Failed to save vector index: {e}")
 

wx_access_token.py

@@ -33,7 +33,7 @@ async def get_access_token() -> Optional[str]:
                             time.time() + result.get("expires_in", 7200) - 300
                         )
                         expires_time = access_token_cache["expires_at"]
-                        logger.debug(
+                        logger.info(
                             f"成功获取 stable access_token expires_time={expires_time}"
                         )
                         return result["access_token"]