Initial commit: Video detection platform with YOLO models

Features:
- Fire detection (YOLOv10)
- Helmet detection (YOLOv8)
- Crowd detection (YOLOv8)
- Smoking detection (YOLOv8)
- Loitering detection (YOLOv8)

Tech Stack:
- Frontend: Vue 3 + Vite + Element Plus
- Backend: FastAPI + WebSocket
- Monorepo: pnpm workspace + Turbo
- Docker support included

apps/server/services/model_service.py

@@ -0,0 +1,115 @@
+import os
+import logging
+from ultralytics import YOLO
+from typing import Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+class ModelService:
+    def __init__(self):
+        self.models: Dict[str, YOLO] = {}
+        # 基础路径：从 apps/server/services/model_service.py 到 jc-video-web 根目录
+        base_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
+
+        self.model_configs = {
+            'fire_detection': {
+                'path': os.path.join(base_dir, 'models', 'fire_detection', 'best.pt'),
+                'type': 'yolov10',
+                'classes': ['Fire', 'Smoke'],
+                'labels': {'Fire': '火焰', 'Smoke': '烟雾'},
+                'size': '61MB',
+                'description': '基于YOLOv10的火灾烟雾检测模型',
+                'name': '火灾检测'
+            },
+            'helmet_detection': {
+                'path': os.path.join(base_dir, 'models', 'helmet_detection', 'yolov8n.pt'),
+                'type': 'yolov8',
+                'classes': ['person', 'helmet'],
+                'labels': {'person': '人员', 'helmet': '安全帽'},
+                'size': '6MB',
+                'description': '基于YOLOv8的安全帽检测模型',
+                'name': '安全帽检测'
+            },
+            'crowd_detection': {
+                'path': os.path.join(base_dir, 'models', 'crowd_detection', 'yolov8l.pt'),
+                'type': 'yolov8',
+                'classes': ['person'],
+                'labels': {'person': '人员'},
+                'size': '100MB',
+                'description': '基于YOLOv8的人群聚集检测模型',
+                'name': '人群检测'
+            },
+            'smoking_detection': {
+                'path': os.path.join(base_dir, 'models', 'smoking_detection', 'smoking_yolov8n.pt'),
+                'type': 'yolov8',
+                'classes': ['cigarette', 'smoke'],
+                'labels': {'cigarette': '香烟', 'smoke': '烟雾'},
+                'size': '6MB',
+                'description': '基于YOLOv8的抽烟检测模型',
+                'name': '抽烟检测'
+            },
+            'loitering_detection': {
+                'path': os.path.join(base_dir, 'models', 'loitering_detection', 'yolov8n.pt'),
+                'type': 'yolov8',
+                'classes': ['person'],
+                'labels': {'person': '人员'},
+                'size': '6MB',
+                'description': '基于YOLOv8的徘徊检测模型',
+                'name': '徘徊检测'
+            }
+        }
+    
+    def get_available_models(self) -> List[Dict]:
+        available_models = []
+        for model_id, config in self.model_configs.items():
+            if os.path.exists(config['path']):
+                available_models.append({
+                    'id': model_id,
+                    'name': config['name'],
+                    'description': config['description'],
+                    'classes': config['classes'],
+                    'labels': config['labels'],
+                    'size': config['size'],
+                    'type': config['type']
+                })
+            else:
+                logger.warning(f"模型文件不存在: {config['path']}")
+        return available_models
+    
+    async def load_model(self, model_id: str) -> Optional[YOLO]:
+        if model_id not in self.model_configs:
+            logger.error(f"未知模型ID: {model_id}")
+            return None
+        
+        if model_id in self.models:
+            logger.info(f"模型已加载: {model_id}")
+            return self.models[model_id]
+        
+        config = self.model_configs[model_id]
+
+        # 处理 YOLO 模型
+        model_path = config['path']
+        
+        if not os.path.exists(model_path):
+            logger.error(f"模型文件不存在: {model_path}")
+            return None
+        
+        try:
+            logger.info(f"正在加载模型: {model_id} from {model_path}")
+            model = YOLO(model_path)
+            self.models[model_id] = model
+            logger.info(f"模型加载成功: {model_id}")
+            return model
+        except Exception as e:
+            logger.error(f"模型加载失败: {model_id}, 错误: {e}")
+            return None
+    
+    def get_model(self, model_id: str) -> Optional[YOLO]:
+        return self.models.get(model_id)
+    
+    async def unload_model(self, model_id: str) -> bool:
+        if model_id in self.models:
+            del self.models[model_id]
+            logger.info(f"模型已卸载: {model_id}")
+            return True
+        return False