新增base_detector，将帧回溯放入其中

biz/base_detector.py

@@ -0,0 +1,62 @@
+from collections import deque
+from typing import Optional
+import numpy as np
+
+
+class BaseDetector:
+    """
+    检测器基类
+    提供通用的帧回溯缓存功能，子类可按需使用
+    """
+
+    def __init__(self):
+        # 帧回溯缓存（子类需要时调用 init_frame_buffer 初始化）
+        self._frame_buffer: Optional[deque] = None
+
+    def init_frame_buffer(self, buffer_seconds: float, fps: float):
+        """
+        初始化帧回溯缓存队列
+
+        Args:
+            buffer_seconds: 需要缓存的时间长度（秒）
+            fps: 视频帧率
+        """
+        maxlen = int(buffer_seconds * fps)
+        self._frame_buffer = deque(maxlen=maxlen)
+
+    def append_frame(self, frame: np.ndarray, timestamp: float):
+        """
+        将当前帧入队缓存
+
+        Args:
+            frame: 当前帧图像
+            timestamp: 当前帧的时间戳
+        """
+        if self._frame_buffer is not None:
+            self._frame_buffer.append({
+                'timestamp': timestamp,
+                'frame': frame.copy(),
+            })
+
+    def find_target_frame(self, target_time_sec: float) -> Optional[np.ndarray]:
+        """
+        在帧缓存中找到最接近目标时间的帧
+
+        Args:
+            target_time_sec: 目标时间戳
+
+        Returns:
+            最接近目标时间的帧图像，缓存为空则返回 None
+        """
+        if self._frame_buffer is None or len(self._frame_buffer) == 0:
+            return None
+
+        target_frame = None
+        min_time_diff = float('inf')
+        for buffered in self._frame_buffer:
+            time_diff = abs(buffered['timestamp'] - target_time_sec)
+            if time_diff < min_time_diff:
+                min_time_diff = time_diff
+                target_frame = buffered['frame']
+
+        return target_frame

biz/checkpoint/checkpoint_biz.py

@@ -1,16 +1,14 @@
 import cv2
 import numpy as np
 from typing import Dict, Any
-import threading
-import queue
-from collections import deque
 
 
 from biz.base_frame_processor import BaseFrameProcessorWorker
+from biz.base_detector import BaseDetector
 
 # -------------------------- Kadian 检测相关导入 --------------------------
 from algorithm.common.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX          # 主检测模型（人/车/后备箱/手机）
-from algorithm.common.npu_yolo_pose_onnx import YOLOv8_Pose_ONNX                 # Pose 专用模型
+# from algorithm.common.npu_yolo_pose_onnx import YOLOv8_Pose_ONNX                 # Pose 专用模型
 from yolox.tracker.byte_tracker import BYTETracker
 
 from utils.logger import get_logger
@@ -55,8 +53,9 @@ PERSON_CAR_INPUT_SIZE = 640
 RTSP_TARGET_FPS = 10.0
 
 # ========================= Kadian TrafficMonitor（精简版，专为服务设计） =========================
-class KadianDetector:
+class KadianDetector(BaseDetector):
     def __init__(self, params=None):
+        super().__init__()
         # 摄像头额外参数
         self.params = params if params is not None else {}
 
@@ -147,8 +146,8 @@ class KadianDetector:
         self.nobody_frames = 0  # 累计无人在场帧数
         self.only_one_frames = 0  # 累计单人在场帧数
 
-        self.max_car_frames = int((15.0 + self.TIME_TOLERANCE_CAR) * self.fps)  # 
-        self.frame_buffer_ignore_untrunk = deque(maxlen=self.max_car_frames)
+        buffer_seconds = 15.0 + self.TIME_TOLERANCE_CAR
+        self.init_frame_buffer(buffer_seconds, self.fps)
 
         self.untrunk_rollback_time = 12.0    # 未检查后备箱需要回溯的时间
         self.ignored_rollback_time = 12.0    # 漏检需要回溯的时间
@@ -219,21 +218,6 @@ class KadianDetector:
         x1, y1, x2, y2 = box
         return x1 < px < x2 and y1 < py < y2
 
-    def find_target_frame(self, target_time_sec):
-
-        target_frame = None
-        min_time_diff = float('inf')
-        for buffered in self.frame_buffer_ignore_untrunk:
-            time_diff = abs(buffered['timestamp'] - target_time_sec)
-            if time_diff < min_time_diff:
-                min_time_diff = time_diff
-                target_frame = buffered['frame']
-        # 如果没找到，返回最早的帧
-        if target_frame is None and len(self.frame_buffer_ignore_untrunk) > 0:
-            target_frame = self.frame_buffer_ignore_untrunk[0]['frame']
-
-        return target_frame
-
     def process_frame(self, frame, camera_id: int, timestamp: float) -> Dict[str, Any]:
         h, w = frame.shape[:2]
         self.width, self.height = w, h
@@ -402,11 +386,7 @@ class KadianDetector:
             cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
 
         # 每帧保存到缓存（移到循环外，确保每帧只写入一次）
-        self.frame_buffer_ignore_untrunk.append({
-            'frame_idx': self.current_frame_idx,
-            'timestamp': current_time_sec,
-            'frame': frame.copy(),
-        })
+        self.append_frame(frame, current_time_sec)
 
         # ==========================================
         # 关联分析: 哪个后备箱属于哪辆车?