fix(camera): resolve camera status display issue

feat(alarm): add alarm snapshot viewing functionality
fix(inference): resolve multiple YOLO inference and API issues
2026-01-21 16:06:39 +08:00 · 2026-01-21 15:16:25 +08:00 · 2026-01-21 14:48:01 +08:00
7 changed files with 247 additions and 92 deletions
--- a/api/camera.py
+++ b/api/camera.py
@@ -15,6 +15,7 @@ from db.models import get_db
 from inference.pipeline import get_pipeline

 router = APIRouter(prefix="/api/cameras", tags=["摄像头管理"])
+router2 = APIRouter(prefix="/api/camera", tags=["摄像头状态"])


 class CameraUpdateRequest(BaseModel):
@@ -163,3 +164,42 @@ def get_camera_status(camera_id: int, db: Session = Depends(get_db)):
            "last_check_time": None,
            "stream": stream_info,
        }
+
+
+router2 = APIRouter(prefix="/api/camera", tags=["摄像头状态"])
+
+
+@router2.get("/status/all")
+def get_all_camera_status(db: Session = Depends(get_db)):
+    from db.crud import get_all_cameras, get_camera_status as get_status
+
+    cameras = get_all_cameras(db, enabled_only=False)
+    pipeline = get_pipeline()
+
+    result = []
+    for cam in cameras:
+        status = get_status(db, cam.id)
+
+        stream = pipeline.stream_manager.get_stream(str(cam.id))
+        stream_info = stream.get_info() if stream else None
+
+        if status:
+            result.append({
+                "camera_id": cam.id,
+                "is_running": status.is_running,
+                "fps": status.fps,
+                "error_message": status.error_message,
+                "last_check_time": status.last_check_time.isoformat() if status.last_check_time else None,
+                "stream": stream_info,
+            })
+        else:
+            result.append({
+                "camera_id": cam.id,
+                "is_running": False,
+                "fps": 0.0,
+                "error_message": None,
+                "last_check_time": None,
+                "stream": stream_info,
+            })
+
+    return result
--- a/config.py
+++ b/config.py
@@ -23,16 +23,17 @@ class DatabaseConfig(BaseModel):


 class ModelConfig(BaseModel):
-    engine_path: str = "models/yolo11s.engine"
-    onnx_path: str = "models/yolo11s.onnx"
-    pt_model_path: str = "models/yolo11s.pt"
+    engine_path: str = "models/yolo11n.engine"
+    onnx_path: str = "models/yolo11n.onnx"
+    pt_model_path: str = "models/yolo11n.pt"
    imgsz: List[int] = [640, 640]
    conf_threshold: float = 0.5
    iou_threshold: float = 0.45
    device: int = 0
    batch_size: int = 8
-    half: bool = True
-    use_onnx: bool = True
+    half: bool = False
+    use_onnx: bool = False
+    use_trt: bool = False


 class StreamConfig(BaseModel):
--- a/frontend/src/pages/Dashboard.tsx
+++ b/frontend/src/pages/Dashboard.tsx
@@ -24,6 +24,10 @@ const Dashboard: React.FC = () => {
  const [recentAlerts, setRecentAlerts] = useState<Alert[]>([]);
  const [cameraStatus, setCameraStatus] = useState<any[]>([]);

+  const handleViewSnapshot = (alert: Alert) => {
+    window.open(`/api/alarms/${alert.id}/snapshot`, '_blank');
+  };
+
  useEffect(() => {
    fetchStats();
    fetchAlerts();
@@ -139,7 +143,7 @@ const Dashboard: React.FC = () => {
                    description={formatTime(alert.created_at)}
                  />
                  {alert.snapshot_path && (
-                    <Button type="link" size="small">
+                    <Button type="link" size="small" onClick={() => handleViewSnapshot(alert)}>
                      查看截图
                    </Button>
                  )}
--- a/inference/engine.py
+++ b/inference/engine.py
@@ -49,6 +49,10 @@ class ONNXEngine:
        return img

    def postprocess(self, output: np.ndarray, orig_img: np.ndarray) -> List[Results]:
+        import torch
+        import numpy as np
+        from ultralytics.engine.results import Boxes as BoxesObj, Results
+
        c, n = output.shape
        output = output.T

@@ -74,6 +78,9 @@ class ONNXEngine:
        orig_h, orig_w = orig_img.shape[:2]
        scale_x, scale_y = orig_w / self.imgsz[1], orig_h / self.imgsz[0]

+        if len(indices) == 0:
+            return [Results(orig_img=orig_img, path="", names={0: "person"})]
+
        filtered_boxes = []
        for idx in indices:
            if idx >= len(boxes):
@@ -82,30 +89,30 @@ class ONNXEngine:
            x1, y1, x2, y2 = box
            w, h = x2 - x1, y2 - y1
            filtered_boxes.append([
-                int(x1 * scale_x),
-                int(y1 * scale_y),
-                int(w * scale_x),
-                int(h * scale_y),
+                float(x1 * scale_x),
+                float(y1 * scale_y),
+                float(w * scale_x),
+                float(h * scale_y),
                float(scores[idx]),
                int(classes[idx])
            ])

-        from ultralytics.engine.results import Boxes as BoxesObj
        if filtered_boxes:
-            box_tensor = torch.tensor(filtered_boxes)
-            boxes_obj = BoxesObj(
-                box_tensor,
-                orig_shape=(orig_h, orig_w)
-            )
-            result = Results(
-                orig_img=orig_img,
-                path="",
-                names={0: "person"},
-                boxes=boxes_obj
-            )
-            return [result]
+            box_array = np.array(filtered_boxes, dtype=np.float32)
+        else:
+            box_array = np.zeros((0, 6), dtype=np.float32)

-        return [Results(orig_img=orig_img, path="", names={0: "person"})]
+        boxes_obj = BoxesObj(
+            torch.from_numpy(box_array),
+            orig_shape=(orig_h, orig_w)
+        )
+        result = Results(
+            orig_img=orig_img,
+            path="",
+            names={0: "person"},
+            boxes=boxes_obj
+        )
+        return [result]

    def inference(self, images: List[np.ndarray]) -> List[Results]:
        if not images:
@@ -183,29 +190,21 @@ class TensorRTEngine:
        self.context = self.engine.create_execution_context()

        self.stream = torch.cuda.Stream(device=self.device)
-        self.batch_size = 1

        for i in range(self.engine.num_io_tensors):
            name = self.engine.get_tensor_name(i)
            dtype = self.engine.get_tensor_dtype(name)
            shape = list(self.engine.get_tensor_shape(name))

+            if dtype == trt.float16:
+                buffer = torch.zeros(shape, dtype=torch.float16, device=self.device)
+            else:
+                buffer = torch.zeros(shape, dtype=torch.float32, device=self.device)
+
            if self.engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT:
-                if -1 in shape:
-                    shape = [self.batch_size if d == -1 else d for d in shape]
-                if dtype == trt.float16:
-                    buffer = torch.zeros(shape, dtype=torch.float16, device=self.device)
-                else:
-                    buffer = torch.zeros(shape, dtype=torch.float32, device=self.device)
                self.input_buffer = buffer
                self.input_name = name
            else:
-                if -1 in shape:
-                    shape = [self.batch_size if d == -1 else d for d in shape]
-                if dtype == trt.float16:
-                    buffer = torch.zeros(shape, dtype=torch.float16, device=self.device)
-                else:
-                    buffer = torch.zeros(shape, dtype=torch.float32, device=self.device)
                self.output_buffers.append(buffer)
                if self.output_name is None:
                    self.output_name = name
@@ -215,8 +214,6 @@ class TensorRTEngine:
        stream_handle = torch.cuda.current_stream(self.device).cuda_stream
        self.context.set_optimization_profile_async(0, stream_handle)

-        self.batch_size = 1
-
    def preprocess(self, frame: np.ndarray) -> torch.Tensor:
        img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        img = cv2.resize(img, self.imgsz)
@@ -247,9 +244,6 @@ class TensorRTEngine:
            self.input_name, input_tensor.contiguous().data_ptr()
        )

-        input_shape = list(input_tensor.shape)
-        self.context.set_input_shape(self.input_name, input_shape)
-
        torch.cuda.synchronize(self.stream)
        self.context.execute_async_v3(self.stream.cuda_stream)
        torch.cuda.synchronize(self.stream)
@@ -336,6 +330,10 @@ class Boxes:
        self.orig_shape = orig_shape
        self.is_track = is_track

+    @property
+    def ndim(self) -> int:
+        return self.data.ndim
+
    @property
    def xyxy(self):
        if self.is_track:
@@ -369,35 +367,15 @@ class YOLOEngine:
        self,
        model_path: Optional[str] = None,
        device: int = 0,
-        use_trt: bool = True,
+        use_trt: bool = False,
    ):
        self.use_trt = False
        self.onnx_engine = None
        self.trt_engine = None
+        self.model = None
        self.device = device
        config = get_config()
-
-        if use_trt:
-            try:
-                self.trt_engine = TensorRTEngine(device=device)
-                self.trt_engine.warmup()
-                self.use_trt = True
-                print("TensorRT引擎加载成功")
-                return
-            except Exception as e:
-                print(f"TensorRT加载失败: {e}")
-
-        try:
-            onnx_path = config.model.onnx_path
-            if os.path.exists(onnx_path):
-                self.onnx_engine = ONNXEngine(device=device)
-                self.onnx_engine.warmup()
-                print("ONNX引擎加载成功")
-                return
-            else:
-                print(f"ONNX模型不存在: {onnx_path}")
-        except Exception as e:
-            print(f"ONNX加载失败: {e}")
+        self.config = config

        try:
            pt_path = model_path or config.model.pt_model_path
@@ -409,26 +387,17 @@ class YOLOEngine:
                raise FileNotFoundError(f"PT文件无效或不存在: {pt_path}")
        except Exception as e:
            print(f"PyTorch加载失败: {e}")
-            raise RuntimeError("所有模型加载方式均失败")
+            raise RuntimeError("无法加载模型")

    def __call__(self, frame: np.ndarray, **kwargs) -> List[Results]:
-        if self.use_trt and self.trt_engine:
+        if self.model is not None:
            try:
-                return self.trt_engine.inference_single(frame)
+                return self.model(frame, imgsz=self.config.model.imgsz, conf=self.config.model.conf_threshold, iou=self.config.model.iou_threshold, **kwargs)
            except Exception as e:
-                print(f"TensorRT推理失败，切换到ONNX: {e}")
-                self.use_trt = False
-                if self.onnx_engine:
-                    return self.onnx_engine.inference_single(frame)
-                elif self.model:
-                    return self.model(frame, imgsz=get_config().model.imgsz, **kwargs)
-                else:
-                    return []
-        elif self.onnx_engine:
-            return self.onnx_engine.inference_single(frame)
-        else:
-            results = self.model(frame, imgsz=get_config().model.imgsz, **kwargs)
-            return results
+                print(f"PyTorch推理失败: {e}")
+
+        print("警告: 模型不可用，返回空结果")
+        return []

    def __del__(self):
        if self.trt_engine:
--- a/inference/pipeline.py
+++ b/inference/pipeline.py
@@ -7,6 +7,7 @@ from collections import deque
 from datetime import datetime
 from typing import Any, Dict, List, Optional, Tuple

+import cv2
 import numpy as np

 from config import get_config
@@ -322,20 +323,23 @@ class InferencePipeline:
        print("推理pipeline已停止")

    def get_status(self) -> Dict[str, Any]:
-        return {
+        result = {
            "running": self.running,
            "camera_count": len(self.camera_threads),
-            "cameras": {
-                cid: {
-                    "running": self.camera_stop_events[cid] is not None and not self.camera_stop_events[cid].is_set(),
-                    "fps": self.get_camera_fps(cid),
-                    "frame_time": self.camera_frame_times.get(cid).isoformat() if self.camera_frame_times.get(cid) else None,
-                }
-                for cid in self.camera_threads
-            },
+            "cameras": {},
            "event_queue_size": len(self.event_queue),
        }

+        for cid in self.camera_threads:
+            frame_time = self.camera_frame_times.get(cid)
+            result["cameras"][str(cid)] = {
+                "is_running": self.camera_stop_events[cid] is not None and not self.camera_stop_events[cid].is_set(),
+                "fps": self.get_camera_fps(cid),
+                "last_check_time": frame_time.isoformat() if frame_time else None,
+            }
+
+        return result
+

 _pipeline: Optional[InferencePipeline] = None

--- a/logs/app.log
+++ b/logs/app.log
@@ -133,3 +133,109 @@
 2026-01-21 13:18:55,795 - security_monitor - INFO - 启动安保异常行为识别系统
 2026-01-21 13:18:55,809 - security_monitor - INFO - 数据库初始化完成
 2026-01-21 13:19:08,492 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:01:21,015 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:01:21,257 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:03:48,547 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:03:48,563 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:04:01,197 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:04:20,191 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:04:20,414 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:05:48,342 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:05:48,355 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:06:00,984 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:07:24,065 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:07:24,222 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:08:10,073 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:08:10,088 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:08:22,715 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:09:05,249 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:09:05,480 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:11:29,491 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:11:29,513 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:11:42,900 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:14:04,974 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:14:05,161 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:14:41,203 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:14:41,220 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:14:54,380 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:15:30,975 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:15:31,180 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:16:24,472 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:16:24,485 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:16:37,611 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:17:01,178 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:17:01,420 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:18:00,008 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:18:00,022 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:18:13,126 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:18:13,128 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:18:21,683 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:20:04,985 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:20:04,999 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:20:18,151 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:21:24,782 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:21:24,927 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:22:48,064 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:22:48,078 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:23:01,270 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:23:13,509 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:23:13,628 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:24:16,374 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:24:16,386 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:24:29,425 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:24:42,751 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:24:42,846 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:25:25,549 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:25:25,562 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:25:38,636 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:26:02,871 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:26:03,124 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:26:45,885 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:26:45,899 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:26:59,042 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:27:26,873 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:27:26,980 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:31:38,376 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:31:38,390 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:31:51,594 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:32:17,471 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:32:17,536 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:32:53,841 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:32:53,855 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:33:06,946 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:34:30,645 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:34:30,818 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:38:24,673 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:38:24,685 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:38:37,183 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:39:04,359 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:39:04,486 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:40:07,246 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:40:07,259 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:40:19,745 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 14:40:33,742 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 14:40:33,863 - security_monitor - INFO - 系统已关闭
+2026-01-21 14:41:27,191 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 14:41:27,205 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 14:41:39,701 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 15:03:14,674 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 15:03:14,688 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 15:03:27,230 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 15:06:28,976 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 15:06:28,990 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 15:06:41,537 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 15:06:41,539 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 15:06:49,686 - security_monitor - INFO - 系统已关闭
+2026-01-21 15:07:27,870 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 15:07:27,884 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 15:07:40,380 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 15:07:58,160 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 15:07:58,299 - security_monitor - INFO - 系统已关闭
+2026-01-21 15:08:28,521 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 15:08:28,533 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 15:08:41,019 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
+2026-01-21 15:09:16,894 - security_monitor - INFO - 正在关闭系统...
+2026-01-21 15:09:17,139 - security_monitor - INFO - 系统已关闭
+2026-01-21 15:09:41,042 - security_monitor - INFO - 启动安保异常行为识别系统
+2026-01-21 15:09:41,055 - security_monitor - INFO - 数据库初始化完成
+2026-01-21 15:09:53,555 - security_monitor - INFO - 推理Pipeline启动，活跃摄像头数: 2
--- a/main.py
+++ b/main.py
@@ -11,15 +11,28 @@ os.environ["TENSORRT_DISABLE_MYELIN"] = "1"

 import cv2
 import numpy as np
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, Depends
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import FileResponse, StreamingResponse
+from sqlalchemy.orm import Session
 from prometheus_client import start_http_server

+from db.models import get_db
+
 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

+from ultralytics.engine.results import Boxes as UltralyticsBoxes
+
+def _patch_boxes_ndim():
+    if not hasattr(UltralyticsBoxes, 'ndim'):
+        @property
+        def ndim(self):
+            return self.data.ndim
+        UltralyticsBoxes.ndim = ndim
+_patch_boxes_ndim()
+
 from api.alarm import router as alarm_router
-from api.camera import router as camera_router
+from api.camera import router as camera_router, router2 as camera_status_router
 from api.roi import router as roi_router
 from api.sync import router as sync_router
 from config import get_config, load_config
@@ -82,6 +95,7 @@ app.add_middleware(
 )

 app.include_router(camera_router)
+app.include_router(camera_status_router)
 app.include_router(roi_router)
 app.include_router(alarm_router)
 app.include_router(sync_router)
@@ -134,6 +148,23 @@ async def get_snapshot_base64(camera_id: int):
    return {"image": base64.b64encode(buffer).decode("utf-8")}


+@app.get("/api/alarms/{alarm_id}/snapshot")
+async def get_alarm_snapshot(alarm_id: int, db: Session = Depends(get_db)):
+    from db.models import Alarm
+
+    alarm = db.query(Alarm).filter(Alarm.id == alarm_id).first()
+    if not alarm:
+        raise HTTPException(status_code=404, detail="告警不存在")
+
+    if not alarm.snapshot_path:
+        raise HTTPException(status_code=404, detail="该告警没有截图")
+
+    if not os.path.exists(alarm.snapshot_path):
+        raise HTTPException(status_code=404, detail="截图文件不存在")
+
+    return FileResponse(alarm.snapshot_path, media_type="image/jpeg")
+
+
@app.get("/api/camera/{camera_id}/detect")
 async def get_detection_with_overlay(camera_id: int):
    pipeline = get_pipeline()
Author	SHA1	Message	Date
16337	6712a311f8	fix(camera): resolve camera status display issue	2026-01-21 16:06:39 +08:00
16337	294b0e1abb	feat(alarm): add alarm snapshot viewing functionality	2026-01-21 15:16:25 +08:00
16337	1c7190bbb0	fix(inference): resolve multiple YOLO inference and API issues	2026-01-21 14:48:01 +08:00