feat: 支持按算法独立配置置信度阈值

通过 ALGO_CONF_{ALGO_CODE} 环境变量为每个算法设置独立的 conf_threshold，
未配置的算法回退到全局 CONF_THRESHOLD。推理过程零改动，仅后处理过滤阶段
按 bind.algo_code 使用对应阈值。

当前配置：离岗=0.4（降低漏检），入侵=0.5（减少误报）。

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

core/postprocessor.py

@@ -35,7 +35,7 @@ class NMSProcessor:
     可选 GPU 加速 (torchvision.ops.nms)
     """
     
-    def __init__(self, nms_threshold: float = 0.45, use_gpu: bool = False):
+    def __init__(self, nms_threshold: float = 0.5, use_gpu: bool = False):
         self.nms_threshold = nms_threshold
         self.use_gpu = use_gpu and _HAS_TORCH
         self._logger = get_logger("postprocessor")
@@ -162,7 +162,7 @@ class NMSProcessor:
         boxes: np.ndarray,
         scores: np.ndarray,
         class_ids: np.ndarray,
-        conf_threshold: float = 0.5,
+        conf_threshold: float = 0.45,
         max_output_size: int = 300
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """
@@ -579,8 +579,8 @@ class PostProcessor:
                 "alert_cooldown": 300,
             }
         
-        self.nms_threshold = config.get("nms_threshold", 0.45)
-        self.conf_threshold = config.get("conf_threshold", 0.5)
+        self.nms_threshold = config.get("nms_threshold", 0.5)
+        self.conf_threshold = config.get("conf_threshold", 0.45)
         
         MAX_DETECTIONS = 8400
         self._buffer_xyxy = np.zeros((MAX_DETECTIONS, 4), dtype=np.float32)
@@ -707,22 +707,24 @@ class PostProcessor:
         batch_outputs: List[np.ndarray],
         batch_size: int,
         conf_threshold: Optional[float] = None,
-        nms_threshold: Optional[float] = None
+        nms_threshold: Optional[float] = None,
+        per_item_conf_thresholds: Optional[List[float]] = None,
     ) -> List[Tuple[np.ndarray, np.ndarray, np.ndarray]]:
         """
         批量处理多 ROI 检测结果
-        
+
         Args:
             batch_outputs: 模型输出 (可能是 [output] 或单个 batch 数组)
             batch_size: batch 大小
             conf_threshold: 置信度阈值
             nms_threshold: NMS阈值
-        
+            per_item_conf_thresholds: 每个 batch item 独立的置信度阈值列表，
+                长度必须等于 batch_size。传入时忽略 conf_threshold 参数。
+
         Returns:
             List of (检测框, 置信度, 类别ID) per ROI
         """
-        if conf_threshold is None:
-            conf_threshold = self.conf_threshold
+        default_conf = conf_threshold if conf_threshold is not None else self.conf_threshold
         if nms_threshold is None:
             nms_threshold = self.nms_threshold
         
@@ -793,11 +795,16 @@ class PostProcessor:
             self._buffer_xyxy[:valid_count, 2] = boxes[:, 0] + boxes[:, 2] / 2
             self._buffer_xyxy[:valid_count, 3] = boxes[:, 1] + boxes[:, 3] / 2
             
+            item_conf = (
+                per_item_conf_thresholds[batch_idx]
+                if per_item_conf_thresholds is not None
+                else default_conf
+            )
             keep_boxes, keep_scores, keep_classes = nms_processor.process_with_confidence_filter(
                 self._buffer_xyxy[:valid_count],
                 scores_coarse.astype(np.float32),
                 class_ids,
-                conf_threshold
+                item_conf
             )
             
             results.append((keep_boxes, keep_scores, keep_classes))