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