chore: 注释掉人群聚集检测算法

- 注释 CrowdDetectionAlgorithm 类
- 注释 AlgorithmManager 中的 crowd_detection 相关代码
- 保留代码以便后续需要时启用

当前仅保留2个算法：leave_post、intrusion

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

algorithms.py

@@ -306,82 +306,82 @@ class IntrusionAlgorithm:
         self.detection_start.clear()
 
 
-class CrowdDetectionAlgorithm:
-    """人群聚集检测算法"""
-
-    def __init__(
-        self,
-        max_count: int = 10,
-        cooldown_seconds: int = 300,
-        target_class: Optional[str] = "person",
-    ):
-        self.max_count = max_count
-        self.cooldown_seconds = cooldown_seconds
-        self.target_class = target_class
-
-        self.last_alert_time: Dict[str, datetime] = {}
-        self.alert_triggered: Dict[str, bool] = {}
-
-    def _check_detection_in_roi(self, detection: Dict, roi_id: str) -> bool:
-        matched_rois = detection.get("matched_rois", [])
-        for roi in matched_rois:
-            if roi.get("roi_id") == roi_id:
-                return True
-        return False
-
-    def _check_target_class(self, detection: Dict, target_class: Optional[str]) -> bool:
-        if not target_class:
-            return True
-        return detection.get("class") == target_class
-
-    def _get_bboxes(self, tracks: List[Dict], roi_id: str) -> List[List[float]]:
-        bboxes = []
-        for det in tracks:
-            if self._check_detection_in_roi(det, roi_id) and self._check_target_class(det, self.target_class):
-                bboxes.append(det.get("bbox", []))
-        return bboxes
-
-    def process(
-        self,
-        roi_id: str,
-        camera_id: str,
-        tracks: List[Dict],
-        current_time: Optional[datetime] = None,
-    ) -> List[Dict]:
-        current_time = current_time or datetime.now()
-        key = f"{camera_id}_{roi_id}"
-
-        person_count = 0
-        for det in tracks:
-            if self._check_detection_in_roi(det, roi_id) and self._check_target_class(det, self.target_class):
-                person_count += 1
-
-        if person_count <= self.max_count:
-            self.alert_triggered[key] = False
-            return []
-
-        if self.alert_triggered.get(key, False):
-            elapsed_since_alert = (current_time - self.last_alert_time.get(key, datetime.min)).total_seconds()
-            if elapsed_since_alert < self.cooldown_seconds:
-                return []
-            self.alert_triggered[key] = False
-
-        bboxes = self._get_bboxes(tracks, roi_id)
-        self.last_alert_time[key] = current_time
-        self.alert_triggered[key] = True
-
-        return [{
-            "roi_id": roi_id,
-            "camera_id": camera_id,
-            "bbox": bboxes[0] if bboxes else [],
-            "alert_type": "crowd_detection",
-            "message": f"检测到人群聚集，当前人数: {person_count}",
-            "count": person_count,
-        }]
-
-    def reset(self):
-        self.last_alert_time.clear()
-        self.alert_triggered.clear()
+# class CrowdDetectionAlgorithm:
+#     """人群聚集检测算法 - 暂时注释，后续需要时再启用"""
+#
+#     def __init__(
+#         self,
+#         max_count: int = 10,
+#         cooldown_seconds: int = 300,
+#         target_class: Optional[str] = "person",
+#     ):
+#         self.max_count = max_count
+#         self.cooldown_seconds = cooldown_seconds
+#         self.target_class = target_class
+#
+#         self.last_alert_time: Dict[str, datetime] = {}
+#         self.alert_triggered: Dict[str, bool] = {}
+#
+#     def _check_detection_in_roi(self, detection: Dict, roi_id: str) -> bool:
+#         matched_rois = detection.get("matched_rois", [])
+#         for roi in matched_rois:
+#             if roi.get("roi_id") == roi_id:
+#                 return True
+#         return False
+#
+#     def _check_target_class(self, detection: Dict, target_class: Optional[str]) -> bool:
+#         if not target_class:
+#             return True
+#         return detection.get("class") == target_class
+#
+#     def _get_bboxes(self, tracks: List[Dict], roi_id: str) -> List[List[float]]:
+#         bboxes = []
+#         for det in tracks:
+#             if self._check_detection_in_roi(det, roi_id) and self._check_target_class(det, self.target_class):
+#                 bboxes.append(det.get("bbox", []))
+#         return bboxes
+#
+#     def process(
+#         self,
+#         roi_id: str,
+#         camera_id: str,
+#         tracks: List[Dict],
+#         current_time: Optional[datetime] = None,
+#     ) -> List[Dict]:
+#         current_time = current_time or datetime.now()
+#         key = f"{camera_id}_{roi_id}"
+#
+#         person_count = 0
+#         for det in tracks:
+#             if self._check_detection_in_roi(det, roi_id) and self._check_target_class(det, self.target_class):
+#                 person_count += 1
+#
+#         if person_count <= self.max_count:
+#             self.alert_triggered[key] = False
+#             return []
+#
+#         if self.alert_triggered.get(key, False):
+#             elapsed_since_alert = (current_time - self.last_alert_time.get(key, datetime.min)).total_seconds()
+#             if elapsed_since_alert < self.cooldown_seconds:
+#                 return []
+#             self.alert_triggered[key] = False
+#
+#         bboxes = self._get_bboxes(tracks, roi_id)
+#         self.last_alert_time[key] = current_time
+#         self.alert_triggered[key] = True
+#
+#         return [{
+#             "roi_id": roi_id,
+#             "camera_id": camera_id,
+#             "bbox": bboxes[0] if bboxes else [],
+#             "alert_type": "crowd_detection",
+#             "message": f"检测到人群聚集，当前人数: {person_count}",
+#             "count": person_count,
+#         }]
+#
+#     def reset(self):
+#         self.last_alert_time.clear()
+#         self.alert_triggered.clear()
 
 
 class AlgorithmManager:
@@ -403,11 +403,11 @@ class AlgorithmManager:
                 "confirm_seconds": 5,
                 "target_class": None,
             },
-            "crowd_detection": {
-                "max_count": 10,
-                "cooldown_seconds": 300,
-                "target_class": "person",
-            },
+            # "crowd_detection": {
+            #     "max_count": 10,
+            #     "cooldown_seconds": 300,
+            #     "target_class": "person",
+            # },
         }
         
         self._pubsub = None
@@ -635,13 +635,13 @@ class AlgorithmManager:
                 confirm_seconds=algo_params.get("confirm_seconds", 5),
                 target_class=algo_params.get("target_class"),
             )
-        elif algorithm_type == "crowd_detection":
-            from algorithms import CrowdDetectionAlgorithm
-            self.algorithms[roi_id][key]["crowd_detection"] = CrowdDetectionAlgorithm(
-                max_count=algo_params.get("max_count", 10),
-                cooldown_seconds=algo_params.get("cooldown_seconds", 300),
-                target_class=algo_params.get("target_class", "person"),
-            )
+        # elif algorithm_type == "crowd_detection":
+        #     from algorithms import CrowdDetectionAlgorithm
+        #     self.algorithms[roi_id][key]["crowd_detection"] = CrowdDetectionAlgorithm(
+        #         max_count=algo_params.get("max_count", 10),
+        #         cooldown_seconds=algo_params.get("cooldown_seconds", 300),
+        #         target_class=algo_params.get("target_class", "person"),
+        #     )
 
         self._registered_keys.add(cache_key)