尝试从卡点rtsp中移除算法

biz/checkpoint/checkpoint_biz.py

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+
+import cv2
+import numpy as np
+from typing import Dict, Any
+
+# -------------------------- Kadian 检测相关导入 --------------------------
+from algorithm.checkpoint.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX          # 主检测模型（人/车/后备箱/手机）
+from algorithm.checkpoint.npu_yolo_pose_onnx import YOLOv8_Pose_ONNX                 # Pose 专用模型
+from yolox.tracker.byte_tracker import BYTETracker
+
+
+# ========================= 配置区 =========================
+# Kadian 模型路径与ROI（可根据实际情况修改）
+DETECT_MODEL_PATH = 'YOLO_Weight/car_opentrunk_person_phone.onnx'
+POSE_MODEL_PATH = 'YOLO_Weight/yolov8l-pose.onnx'
+
+# 默认相对ROI（与原文件一致）
+#ROI_RELATIVE = np.array([
+#    [0.10989583333333333, 0.006481481481481481],
+#    [0.421875, 0.005555555555555556],
+#    [0.9921875, 0.9888888888888889],
+#    [0.3411458333333333, 0.9861111111111112]
+#])
+
+ROI_RELATIVE=np.array([
+  [0.15,0.001],
+  [0.5,0.001],
+  [1.0,0.8],
+  [0.35,1.0]
+])
+
+
+ALERT_PUSH_INTERVAL = 5.0
+
+# 输入尺寸
+PERSON_CAR_INPUT_SIZE = 640
+POSE_INPUT_SIZE = 640
+
+
+RTSP_TARGET_FPS = 10.0
+
+# ========================= Kadian TrafficMonitor（精简版，专为服务设计） =========================
+class KadianDetector:
+    def __init__(self, roi_points=ROI_RELATIVE):
+        # 模型加载
+        self.detector = YOLOv8_ONNX(DETECT_MODEL_PATH, conf_threshold=0.25, iou_threshold=0.45,
+                                    input_size=PERSON_CAR_INPUT_SIZE)
+        self.pose_detector = YOLOv8_Pose_ONNX(POSE_MODEL_PATH, conf_threshold=0.7, iou_threshold=0.6,
+                                              input_size=POSE_INPUT_SIZE)
+
+        # Tracker
+        class TrackerArgs:
+            track_thresh = 0.25
+            track_buffer = 30
+            match_thresh = 0.8
+            mot20 = False
+        self.tracker = BYTETracker(TrackerArgs(), frame_rate=10.0)
+
+        self.track_role = {}
+
+        self.fps = RTSP_TARGET_FPS
+
+        # ROI 处理（支持相对/绝对）
+        #self.roi_points = roi_points.astype(np.int32)
+        self.roi_points = np.array(roi_points, dtype=np.float64) if roi_points is not None else None
+
+        # ==========================================
+        # 超参数设置 (Hyperparameters)
+        # ==========================================
+
+        # 1. 业务判定时间阈值
+        self.TIME_THRESHOLD_ONLY_ONE = 3.0  # 单人单检判定时长
+        self.TIME_THRESHOLD_NOBODY = 2.0  # 无人检查判定时长
+
+        # 后备箱检查判定阈值
+        self.TIME_THRESHOLD_TRUNK_OPEN = 0.5
+
+        # 新增：手机检测判定阈值
+        self.TIME_THRESHOLD_PHONE = 1.0  # 手机检测持续1秒（30帧 @30fps）
+        self.TIME_TOLERANCE_PHONE = 0.5  # 手机丢失缓冲时间（防抖动）
+
+        # 新增：制服检测判定阈值
+        self.TIME_THRESHOLD_UNIFORM = 1.0  # 制服不合规判定时长
+        self.TIME_TOLERANCE_UNIFORM = 0.5  # 制服合规恢复缓冲时间
+
+        # 车辆最小停留时间阈值 (小于此时间视为无人检查/直接通过)
+        self.TIME_THRESHOLD_CAR_MIN_DURATION = 3.0
+
+        # 2. Person 丢帧缓冲
+        self.TIME_TOLERANCE_PERSON = 1.0
+
+        # 3. Car 丢帧/ID维持缓冲
+        self.TIME_TOLERANCE_CAR = 0.5
+
+        # --- 计算对应的帧数阈值 ---
+        self.frame_thresh_one = int(self.TIME_THRESHOLD_ONLY_ONE * self.fps)
+        self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps)
+        self.frame_thresh_trunk_valid = int(self.TIME_THRESHOLD_TRUNK_OPEN * self.fps)
+
+        # 新增：手机检测帧数阈值
+        self.frame_thresh_phone = int(self.TIME_THRESHOLD_PHONE * self.fps)
+        self.frame_buffer_phone = int(self.TIME_TOLERANCE_PHONE * self.fps)
+
+        # 新增：制服检测帧数阈值
+        self.frame_thresh_uniform = int(self.TIME_THRESHOLD_UNIFORM * self.fps)
+        self.frame_buffer_uniform = int(self.TIME_TOLERANCE_UNIFORM * self.fps)
+
+        self.frame_thresh_car_min_duration = int(self.TIME_THRESHOLD_CAR_MIN_DURATION * self.fps)
+
+        self.frame_buffer_limit_person = int(self.TIME_TOLERANCE_PERSON * self.fps)
+        self.frame_buffer_limit_car = int(self.TIME_TOLERANCE_CAR * self.fps)
+
+        print(f"\n超参数设置:")
+        print(f"  FPS: {self.fps:.2f}")
+        print(f"  判定 'Only One' / 'Nobody' 需连续: {self.frame_thresh_one} 帧")
+        print(f"  判定 'Trunk Checked' 需累计检测: {self.frame_thresh_trunk_valid} 帧")
+        print(f"  判定 'Phone Detected' 需累计检测: {self.frame_thresh_phone} 帧")
+        print(f"  手机丢失缓冲帧数: {self.frame_buffer_phone} 帧")
+        print(f"  判定 'Uniform Invalid' 需连续检测: {self.frame_thresh_uniform} 帧")
+        print(f"  制服合规恢复缓冲帧数: {self.frame_buffer_uniform} 帧")
+        print(f"  判定 'Too Fast' (视为Nobody) 最小停留: {self.frame_thresh_car_min_duration} 帧")
+
+
+        self.current_frame_idx = 0
+        self.cnt_frame_one_person = 0
+        self.cnt_frame_nobody = 0
+        self.cnt_missing_buffer_person = 0
+
+        # 手机检测状态变量（独立于车辆）
+        self.phone_detection_frames = 0  # 连续检测到手机的帧数
+        self.phone_missing_frames = 0  # 连续未检测到手机的帧数
+        self.phone_alert_active = False  # 手机报警是否激活
+
+        # 新增：制服检测状态变量
+        self.pose_person_count = 0  # 骨骼点模型检测的ROI内人员数量
+        self.uniform_alert_active = False  # 制服报警是否激活
+        self.uniform_detection_frames = 0  # 连续检测到制服不合规的帧数
+        self.uniform_recovery_frames = 0  # 连续恢复合规的帧数
+
+        # 车辆注册表 (字典)
+        self.roi_car_registry = {}
+
+        # 违规车辆记录 (后备箱未检)
+        self.unchecked_trunk_alerts = {}
+
+        # 违规车辆记录 (通过过快 -> 归类为 Nobody)
+        self.fast_pass_alerts = {}
+
+    def _get_roi_points(self, frame_width: int, frame_height: int):
+        """
+        每帧动态计算正确的 ROI 绝对坐标，并确保类型为 np.int32
+        用于 pointPolygonTest 和 polylines
+        """
+        if self.roi_points is None:
+            # 使用默认相对坐标
+            default_rel = np.array([
+                [0.15, 0.01],
+                [0.45, 0.01],
+                [0.95, 0.95],
+                [0.35, 0.95]
+            ], dtype=np.float64)
+            roi_abs = default_rel * np.array([frame_width, frame_height])
+        else:
+            if self.roi_points.max() <= 1.0:
+                # 相对坐标 → 转换为绝对
+                roi_abs = self.roi_points * np.array([frame_width, frame_height])
+            else:
+                # 绝对坐标，直接使用
+                roi_abs = self.roi_points.copy()
+
+        # 强制转为 int32（关键！解决 OpenCV 断言错误）
+        return roi_abs.astype(np.int32)
+
+    def check_point_in_roi(self,roi_points, point):
+        return cv2.pointPolygonTest(roi_points, point, False) >= 0
+
+    def compute_iou(self,boxA, boxB):
+        # box = [x1, y1, x2, y2]
+        xA = max(boxA[0], boxB[0])
+        yA = max(boxA[1], boxB[1])
+        xB = min(boxA[2], boxB[2])
+        yB = min(boxA[3], boxB[3])
+
+        interW = max(0, xB - xA)
+        interH = max(0, yB - yA)
+        interArea = interW * interH
+
+        boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
+        boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
+
+        unionArea = boxAArea + boxBArea - interArea
+        if unionArea == 0:
+            return 0.0
+
+        return interArea / unionArea
+
+    def draw_alert(self, frame, text, color=(0, 0, 255), sub_text=None, offset_y=0):
+        """在右上角绘制警告文字 (支持垂直偏移，防止文字重叠)"""
+        font_scale = 1.5
+        thickness = 3
+        font = cv2.FONT_HERSHEY_SIMPLEX
+
+        (text_w, text_h), _ = cv2.getTextSize(text, font, font_scale, thickness)
+        x = self.width - text_w - 20
+        y = 50 + text_h + offset_y  # 增加 Y 轴偏移
+
+        cv2.rectangle(frame, (x - 10, y - text_h - 10), (x + text_w + 10, y + 10), (0, 0, 0), -1)
+        cv2.putText(frame, text, (x, y), font, font_scale, color, thickness)
+
+        if sub_text:
+            cv2.putText(frame, sub_text, (x, y + 40), font, 0.7, (200, 200, 200), 2)
+
+    def is_point_in_box(self, point, box):
+        px, py = point
+        x1, y1, x2, y2 = box
+        return x1 < px < x2 and y1 < py < y2
+
+
+    def process_frame(self, frame, camera_id: int, timestamp: float) -> Dict[str, Any]:
+        h, w = frame.shape[:2]
+        self.width, self.height = w, h
+
+        self.current_frame_idx += 1
+        # ========= 每帧动态获取正确的 ROI（int32）=========
+        roi_points_int32 = self._get_roi_points(w, h)  # shape: (4, 2), dtype: int32
+        roi_points_draw = roi_points_int32.reshape((-1, 1, 2))  # shape: (4, 1, 2) 用于绘制
+
+
+        current_time_sec = timestamp
+
+        pose_results = self.pose_detector(frame)
+
+        # ========= 主检测 =========
+        detections = self.detector(frame)
+
+        dets_xyxy = []
+        dets_roles = []
+        dets_for_tracker = []
+
+        # ========= 当前帧所有警告列表（关键改动）==========
+        current_frame_alerts = []  # 每帧清空，重新收集
+
+        if detections:
+            for det in detections:
+                x1, y1, x2, y2, conf, cls_id = det  # x1, y1, x2, y2为角点坐标，x1 y1为左上角，x2 y2为右下角
+                dets_xyxy.append([x1, y1, x2, y2])
+                dets_for_tracker.append([x1, y1, x2, y2, conf])
+                if cls_id == 0:
+                    dets_roles.append("car")
+
+                elif cls_id == 1:
+                    dets_roles.append("opentrunk")
+
+                elif cls_id == 2:
+                    dets_roles.append("person")
+
+                elif cls_id == 3:
+                    dets_roles.append("phone")
+            # print(f'dets_roles: {dets_roles}')
+
+        dets = np.array(dets_for_tracker, dtype=np.float32) if len(dets_for_tracker) else np.empty((0, 5))
+
+        tracks = self.tracker.update(
+            dets,
+            [self.height, self.width],
+            [self.height, self.width]
+        )
+        # print("tracks: {}".format(tracks))
+        # 绘制骨骼
+        frame = YOLOv8_Pose_ONNX.draw_keypoints(frame, pose_results)
+        # ========= 绘制 ROI =========
+        cv2.polylines(frame, [roi_points_draw], isClosed=True, color=(255, 0, 0), thickness=3)
+
+        # ========= 单帧统计变量 =========
+        current_roi_person_count = 0
+        current_roi_trunk_count = 0
+        current_roi_phone_count = 0
+
+        # 临时存储本帧的目标，用于后续关联分析
+        current_cars = []  # {'id':, 'box':}
+        current_trunks = []  # (cx, cy)
+
+        for t in tracks:
+            # print("t: {}".format(t))
+            tid = t.track_id
+            # cls_id = -1
+
+            # IoU 匹配角色
+            # if tid not in track_role and dets_xyxy:
+            REVALIDATE_FRAME_INTERVAL  = 10
+            #if tid not in self.track_role:
+            if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (tid not in self.track_role):
+                best_iou = 0
+                best_role = "unknown"
+
+                t_box = list(map(float, t.tlbr))  # [x1,y1,x2,y2]
+
+                for i, box in enumerate(dets_xyxy):
+                    iou_val = self.compute_iou(t_box, box)
+                    if iou_val > best_iou:
+                        best_iou = iou_val
+                        best_role = dets_roles[i]
+                if best_iou > 0.1:
+                    self.track_role[tid] = best_role
+                else:
+                    self.track_role[tid] = "unknown"
+
+            role = self.track_role.get(tid, "unknown")
+            cls_id = -1
+            if role == "car":
+                cls_id = 0
+            elif role == "opentrunk":
+                cls_id = 1
+            elif role == "person":
+                cls_id = 2
+            elif role == "phone":
+                cls_id = 3
+            # print("tid: {}, role: {}, cls: {}".format(tid, role,cls_id))
+
+            x1, y1, x2, y2 = map(int, t.tlbr)
+
+            cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
+
+            color = None
+            label = None
+
+            if self.check_point_in_roi(roi_points_int32,(cx, cy)):
+                if cls_id == 0:  # Car
+                    color = (0, 255, 0)
+
+                    current_cars.append({'id': tid, 'box': [x1, y1, x2, y2]})
+
+                    if tid not in self.roi_car_registry:
+                        self.roi_car_registry[tid] = {
+                            'first_seen': self.current_frame_idx,
+                            'last_seen': self.current_frame_idx,
+                            'trunk_frames': 0,
+                            'is_checked': False,
+                        }
+                    else:
+                        self.roi_car_registry[tid]['last_seen'] = self.current_frame_idx
+
+                    label = f"Car:{tid} IN"
+
+                elif cls_id == 1:  # Opentrunk
+                    current_roi_trunk_count += 1
+                    color = (255, 165, 0)
+                    current_trunks.append((cx, cy))
+                    label = "OpenTrunk IN"
+
+                elif cls_id == 2:  # Person
+                    current_roi_person_count += 1
+                    color = (255, 0, 255)
+                    label = "Person IN"
+
+                elif cls_id == 3:  # Phone（主模型已支持）
+                    current_roi_phone_count += 1
+                    color = (0, 0, 139)
+
+                else:
+                    color = (255, 255, 255)
+                    label = "Unknown"
+
+                # label = f"ID:{tid} IN"
+
+                # 特殊显示: 如果这辆车已经合格，框变蓝色
+                if cls_id == 0 and tid in self.roi_car_registry and self.roi_car_registry[tid][
+                    'is_checked']:
+                    color = (255, 255, 0)  # Cyan for checked cars
+                    label += " (Checked)"
+            else:
+                color = (0, 0, 255)
+                label = "OUT"
+
+            cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
+            cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
+
+        # ==========================================
+        # 4. 从骨骼点模型中统计ROI内人员数量
+        # ==========================================
+        self.pose_person_count = 0
+        # if pose_results[0].boxes is not None:
+        #     pose_boxes = pose_results[0].boxes
+        #     for box in pose_boxes:
+        #         # 获取人体检测框的中心点
+        #         x1, y1, x2, y2 = map(int, box.xyxy[0])
+        #         cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
+        #
+        #         # 判断中心点是否在ROI内
+        #         if self.check_point_in_roi((cx, cy)):
+        #             self.pose_person_count += 1
+
+        if pose_results:
+            for pose in pose_results:
+
+                x1, y1, x2, y2 = pose['bbox'][0], pose['bbox'][1], pose['bbox'][2], pose['bbox'][3]
+                cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
+                # 判断中心点是否在ROI内
+                if self.check_point_in_roi(roi_points_int32,(cx, cy)):
+                    self.pose_person_count += 1
+
+        # ==========================================
+        # 5. 关联分析: 哪个后备箱属于哪辆车?
+        # ==========================================
+        for car_info in current_cars:
+            c_id = car_info['id']
+            c_box = car_info['box']
+
+            trunk_found_for_this_car = False
+            for t_pt in current_trunks:
+                if self.is_point_in_box(t_pt, c_box):
+                    trunk_found_for_this_car = True
+                    break
+
+            if trunk_found_for_this_car:
+                self.roi_car_registry[c_id]['trunk_frames'] += 1
+                if self.roi_car_registry[c_id]['trunk_frames'] >= self.frame_thresh_trunk_valid:
+                    self.roi_car_registry[c_id]['is_checked'] = True
+
+        # ==========================================
+        # 6. 独立的手机检测逻辑（不与车辆绑定）
+        # ==========================================
+        if current_roi_phone_count > 0:
+            # 检测到手机框
+            self.phone_detection_frames += 1
+            self.phone_missing_frames = 0  # 重置丢失计数器
+
+            # 当检测累计达到阈值时，激活报警
+            if self.phone_detection_frames >= self.frame_thresh_phone:
+                self.phone_alert_active = True
+        else:
+            # 未检测到手机框
+            self.phone_missing_frames += 1
+
+            # 如果之前检测到手机，重置检测计数器
+            if self.phone_detection_frames > 0:
+                # 只有在连续丢失超过缓冲帧数时才重置
+                if self.phone_missing_frames >= self.frame_buffer_phone:
+                    self.phone_detection_frames = 0
+                    self.phone_alert_active = False
+            else:
+                # 从未检测到手机，保持状态
+                pass
+
+        # ==========================================
+        # 7. 制服检测逻辑（比较两个模型的人员数量）
+        # ==========================================
+        # 比较骨骼点模型和业务检测模型的人员数量
+        uniform_invalid = False
+
+        if self.pose_person_count > current_roi_person_count:
+            # 骨骼点模型检测到的人员多于业务检测模型
+            # 说明有人没穿执勤制服
+            uniform_invalid = True
+            self.uniform_detection_frames += 1
+            self.uniform_recovery_frames = 0  # 重置恢复计数器
+
+            # 当连续检测不合规达到阈值时，激活报警
+            if self.uniform_detection_frames >= self.frame_thresh_uniform:
+                self.uniform_alert_active = True
+        else:
+            # 人员数量匹配或业务模型检测更多（理论上不会）
+            self.uniform_recovery_frames += 1
+
+            # 如果之前有不合规检测，检查是否需要关闭报警
+            if self.uniform_detection_frames > 0:
+                # 只有在连续合规超过缓冲帧数时才重置
+                if self.uniform_recovery_frames >= self.frame_buffer_uniform:
+                    self.uniform_detection_frames = 0
+                    self.uniform_alert_active = False
+            else:
+                # 从未检测到不合规，保持状态
+                pass
+
+        # ==========================================
+        # 8. 维护车辆注册表 & 生成离场报警
+        # ==========================================
+        active_car_ids = []
+        cars_to_remove = []
+
+        for car_id, info in self.roi_car_registry.items():
+            last_seen = info['last_seen']
+
+            if (self.current_frame_idx - last_seen) <= self.frame_buffer_limit_car:
+                active_car_ids.append(car_id)
+            else:
+                cars_to_remove.append(car_id)
+
+        # 执行删除 并 检查违规
+        for car_id in cars_to_remove:
+            car_info = self.roi_car_registry[car_id]
+
+            duration_frames = car_info['last_seen'] - car_info['first_seen']
+
+            # 情况1：通过时间太短 -> 归类为 Nobody (Too Fast)
+            if duration_frames < self.frame_thresh_car_min_duration:
+                print(f"ALARM: Car {car_id} passed too fast -> Regarded as Nobody Checked!")
+                self.fast_pass_alerts[car_id] = self.current_frame_idx + int(3.0 * self.fps)
+
+            # 情况2：时间够长，但没检查后备箱 -> Unchecked Trunk
+            elif not car_info['is_checked']:
+                print(f"ALARM: Car {car_id} left without checking trunk!")
+                self.unchecked_trunk_alerts[car_id] = self.current_frame_idx + int(3.0 * self.fps)
+
+            del self.roi_car_registry[car_id]
+
+        effective_car_count = len(active_car_ids)
+
+        # ==========================================
+        # 9. 业务逻辑判定 (Only One / Nobody)
+        # ==========================================
+        status_text = ""
+
+        if effective_car_count > 0:
+            # --- Only One ---
+            if current_roi_person_count == 1:
+                self.cnt_frame_one_person += 1
+                self.cnt_missing_buffer_person = 0
+                self.cnt_frame_nobody = 0
+
+            # --- Nobody ---
+            elif current_roi_person_count == 0:
+                if self.cnt_frame_one_person > 0 and self.cnt_missing_buffer_person < self.frame_buffer_limit_person:
+                    self.cnt_frame_one_person += 1
+                    self.cnt_missing_buffer_person += 1
+                    self.cnt_frame_nobody = 0
+                    status_text = f"Person Buffer ({self.cnt_missing_buffer_person}/{self.frame_buffer_limit_person})"
+                else:
+                    self.cnt_frame_one_person = 0
+                    self.cnt_missing_buffer_person = 0
+                    self.cnt_frame_nobody += 1
+            else:
+                self.cnt_frame_one_person = 0
+                self.cnt_missing_buffer_person = 0
+                self.cnt_frame_nobody = 0
+        else:
+            self.cnt_frame_one_person = 0
+            self.cnt_missing_buffer_person = 0
+            self.cnt_frame_nobody = 0
+
+        # ==========================================
+        # 10. 显示报警 (UI分层优化)
+        # ==========================================
+
+        # 更新调试信息，包含所有检测状态
+        phone_status = f"Phone: {current_roi_phone_count}"
+        if self.phone_alert_active:
+            phone_status += " (ALERT)"
+
+        uniform_status = f"Uniform: Pose={self.pose_person_count}, Model={current_roi_person_count}"
+        if self.uniform_alert_active:
+            uniform_status += " (INVALID!)"
+
+        debug_info = f"Cars: {len(active_car_ids)} | Person: {current_roi_person_count} | Trunk: {current_roi_trunk_count} | {phone_status}"
+        cv2.putText(frame, debug_info, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+        cv2.putText(frame, uniform_status, (20, 70), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+
+        # 使用 offset 实现报警堆叠，防止遮挡
+        alert_offset = 0
+
+        # 第一层：实时状态 (Real-time Status)
+        # ------------------------------------------------
+        # A. 显示 Only One
+        if self.cnt_frame_one_person >= self.frame_thresh_one:
+            current_frame_alerts.append(
+                {
+                    'time': current_time_sec,
+                    'action': "Only One",
+                }
+            )
+            self.draw_alert(frame, "Only One", (0, 255, 255), status_text, offset_y=alert_offset)
+            alert_offset += 100
+
+        # B. 显示 Nobody (实时状态)
+        elif self.cnt_frame_nobody >= self.frame_thresh_nobody:
+            current_frame_alerts.append(
+                {
+                    'time': current_time_sec,
+                    'action': "Nobody",
+                }
+            )
+            self.draw_alert(frame, "Nobody", (0, 0, 255), offset_y=alert_offset)
+            alert_offset += 100
+
+        # C. 显示 Trunk Checked (在车辆存活期间)
+        for car_id in active_car_ids:
+            if car_id in self.roi_car_registry and self.roi_car_registry[car_id]['is_checked']:
+                current_frame_alerts.append(
+                    {
+                        'time': current_time_sec,
+                        'action': "Trunk Checked",
+                    }
+                )
+                self.draw_alert(frame, "Trunk Checked!!", (0, 255, 0), offset_y=alert_offset)
+                alert_offset += 100
+                break  # 只显示一次
+
+        # D. 显示 Playing Phone（独立检测，不与车辆绑定）
+        if self.phone_alert_active:
+            # 可以显示检测的持续时间
+            duration_seconds = self.phone_detection_frames / self.fps
+            sub_text = f"Detected for {duration_seconds:.1f}s"
+            current_frame_alerts.append(
+                {
+                    'time': current_time_sec,
+                    'action': "Playing Phone",
+                }
+            )
+            self.draw_alert(frame, "Playing Phone", (255, 0, 0), sub_text, offset_y=alert_offset)
+            alert_offset += 100
+
+        # E. 新增：显示 Unvaild Uniform!!
+        if self.uniform_alert_active:
+            # 显示具体数量差异
+            diff = self.pose_person_count - current_roi_person_count
+            sub_text = f"Missing {diff} uniform(s)"
+            current_frame_alerts.append(
+                {
+                    'time': current_time_sec,
+                    'action': "Unvaild Uniform!!",
+                }
+            )
+            self.draw_alert(frame, "Unvaild Uniform!!", (255, 165, 0), sub_text, offset_y=alert_offset)
+            alert_offset += 100
+
+        # 第二层：离场违规 (Post-Event Alerts)
+        # ------------------------------------------------
+
+        # F. 显示 Unchecked Trunk
+        expired_alerts = [cid for cid, end_frame in self.unchecked_trunk_alerts.items() if
+                          self.current_frame_idx > end_frame]
+        for cid in expired_alerts:
+            del self.unchecked_trunk_alerts[cid]
+
+        if len(self.unchecked_trunk_alerts) > 0:
+            alert_text = f"Unchecked Trunk! (ID:{list(self.unchecked_trunk_alerts.keys())})"
+            current_frame_alerts.append(
+                {
+                    'time': current_time_sec,
+                    'action': "Unchecked Trunk",
+                }
+            )
+            self.draw_alert(frame, alert_text, (0, 0, 255), offset_y=alert_offset)
+            alert_offset += 100
+
+        # G. 显示 Nobody (离场结果)
+        expired_fast_alerts = [cid for cid, end_frame in self.fast_pass_alerts.items() if
+                               self.current_frame_idx > end_frame]
+        for cid in expired_fast_alerts:
+            del self.fast_pass_alerts[cid]
+
+        if len(self.fast_pass_alerts) > 0:
+            alert_text = f"Nobody (ID:{list(self.fast_pass_alerts.keys())})"
+            current_frame_alerts.append(
+                {
+                    'time': current_time_sec,
+                    'action': "Nobody",
+                }
+            )
+            self.draw_alert(frame, alert_text, (0, 0, 255), offset_y=alert_offset)
+            alert_offset += 100
+
+        return {
+            "image": frame,
+
+            "alerts":current_frame_alerts
+        }

rtsp_service_ws_kadian.py

@@ -3,7 +3,7 @@
 # 支持多路RTSP、抽帧、分段保存MP4、WebSocket推送图像与告警
 
 import cv2
-import numpy as np
+
 import os
 import time
 import threading
@@ -15,50 +15,15 @@ import asyncio
 import websockets
 from dataclasses import dataclass
 from typing import Dict, Any
+
+from biz.checkpoint.checkpoint_biz import KadianDetector, RTSP_TARGET_FPS, ALERT_PUSH_INTERVAL
 from test_cam import get_camera_preview_url
 
-# -------------------------- Kadian 检测相关导入 --------------------------
-from algorithm.checkpoint.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX          # 主检测模型（人/车/后备箱/手机）
-from algorithm.checkpoint.npu_yolo_pose_onnx import YOLOv8_Pose_ONNX                 # Pose 专用模型
-from yolox.tracker.byte_tracker import BYTETracker
-
-
-# ========================= 配置区 =========================
-# Kadian 模型路径与ROI（可根据实际情况修改）
-DETECT_MODEL_PATH = 'YOLO_Weight/car_opentrunk_person_phone.onnx'
-POSE_MODEL_PATH = 'YOLO_Weight/yolov8l-pose.onnx'
-
-# 默认相对ROI（与原文件一致）
-#ROI_RELATIVE = np.array([
-#    [0.10989583333333333, 0.006481481481481481],
-#    [0.421875, 0.005555555555555556],
-#    [0.9921875, 0.9888888888888889],
-#    [0.3411458333333333, 0.9861111111111112]
-#])
-
-
-ROI_RELATIVE=np.array([
-  [0.15,0.001],
-  [0.5,0.001],
-  [1.0,0.8],
-  [0.35,1.0]
-])
-
-ALERT_PUSH_INTERVAL = 5.0
-
-# 输入尺寸
-PERSON_CAR_INPUT_SIZE = 640
-POSE_INPUT_SIZE = 640
-
-# RTSP 服务配置
-RTSP_TARGET_FPS = 10.0
 WS_HOST = "0.0.0.0"
 WS_PORT = 8765
-WS_PORT_2 = 8764  # 新增：第二个WebSocket端口
 
 # WebSocket 客户端集合
 ws_clients = set()
-ws_clients_2 = set()  # 新增：第二个WebSocket客户端集合
 
 
 # ========================= 数据结构 =========================
@@ -70,633 +35,6 @@ class CameraConfig:
     rtsp_url: str
 
 
-# ========================= Kadian TrafficMonitor（精简版，专为服务设计） =========================
-class KadianDetector:
-    def __init__(self, roi_points=ROI_RELATIVE):
-        # 模型加载
-        self.detector = YOLOv8_ONNX(DETECT_MODEL_PATH, conf_threshold=0.25, iou_threshold=0.45,
-                                    input_size=PERSON_CAR_INPUT_SIZE)
-        self.pose_detector = YOLOv8_Pose_ONNX(POSE_MODEL_PATH, conf_threshold=0.7, iou_threshold=0.6,
-                                              input_size=POSE_INPUT_SIZE)
-
-        # Tracker
-        class TrackerArgs:
-            track_thresh = 0.25
-            track_buffer = 30
-            match_thresh = 0.8
-            mot20 = False
-        self.tracker = BYTETracker(TrackerArgs(), frame_rate=10.0)
-
-        self.track_role = {}
-
-        self.fps = RTSP_TARGET_FPS
-
-        # ROI 处理（支持相对/绝对）
-        #self.roi_points = roi_points.astype(np.int32)
-        self.roi_points = np.array(roi_points, dtype=np.float64) if roi_points is not None else None
-
-        # ==========================================
-        # 超参数设置 (Hyperparameters)
-        # ==========================================
-
-        # 1. 业务判定时间阈值
-        self.TIME_THRESHOLD_ONLY_ONE = 3.0  # 单人单检判定时长
-        self.TIME_THRESHOLD_NOBODY = 2.0  # 无人检查判定时长
-
-        # 后备箱检查判定阈值
-        self.TIME_THRESHOLD_TRUNK_OPEN = 0.5
-
-        # 新增：手机检测判定阈值
-        self.TIME_THRESHOLD_PHONE = 1.0  # 手机检测持续1秒（30帧 @30fps）
-        self.TIME_TOLERANCE_PHONE = 0.5  # 手机丢失缓冲时间（防抖动）
-
-        # 新增：制服检测判定阈值
-        self.TIME_THRESHOLD_UNIFORM = 1.0  # 制服不合规判定时长
-        self.TIME_TOLERANCE_UNIFORM = 0.5  # 制服合规恢复缓冲时间
-
-        # 车辆最小停留时间阈值 (小于此时间视为无人检查/直接通过)
-        self.TIME_THRESHOLD_CAR_MIN_DURATION = 3.0
-
-        # 2. Person 丢帧缓冲
-        self.TIME_TOLERANCE_PERSON = 1.0
-
-        # 3. Car 丢帧/ID维持缓冲
-        self.TIME_TOLERANCE_CAR = 0.5
-
-        # --- 计算对应的帧数阈值 ---
-        self.frame_thresh_one = int(self.TIME_THRESHOLD_ONLY_ONE * self.fps)
-        self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps)
-        self.frame_thresh_trunk_valid = int(self.TIME_THRESHOLD_TRUNK_OPEN * self.fps)
-
-        # 新增：手机检测帧数阈值
-        self.frame_thresh_phone = int(self.TIME_THRESHOLD_PHONE * self.fps)
-        self.frame_buffer_phone = int(self.TIME_TOLERANCE_PHONE * self.fps)
-
-        # 新增：制服检测帧数阈值
-        self.frame_thresh_uniform = int(self.TIME_THRESHOLD_UNIFORM * self.fps)
-        self.frame_buffer_uniform = int(self.TIME_TOLERANCE_UNIFORM * self.fps)
-
-        self.frame_thresh_car_min_duration = int(self.TIME_THRESHOLD_CAR_MIN_DURATION * self.fps)
-
-        self.frame_buffer_limit_person = int(self.TIME_TOLERANCE_PERSON * self.fps)
-        self.frame_buffer_limit_car = int(self.TIME_TOLERANCE_CAR * self.fps)
-
-        print(f"\n超参数设置:")
-        print(f"  FPS: {self.fps:.2f}")
-        print(f"  判定 'Only One' / 'Nobody' 需连续: {self.frame_thresh_one} 帧")
-        print(f"  判定 'Trunk Checked' 需累计检测: {self.frame_thresh_trunk_valid} 帧")
-        print(f"  判定 'Phone Detected' 需累计检测: {self.frame_thresh_phone} 帧")
-        print(f"  手机丢失缓冲帧数: {self.frame_buffer_phone} 帧")
-        print(f"  判定 'Uniform Invalid' 需连续检测: {self.frame_thresh_uniform} 帧")
-        print(f"  制服合规恢复缓冲帧数: {self.frame_buffer_uniform} 帧")
-        print(f"  判定 'Too Fast' (视为Nobody) 最小停留: {self.frame_thresh_car_min_duration} 帧")
-
-
-        self.current_frame_idx = 0
-        self.cnt_frame_one_person = 0
-        self.cnt_frame_nobody = 0
-        self.cnt_missing_buffer_person = 0
-
-        # 手机检测状态变量（独立于车辆）
-        self.phone_detection_frames = 0  # 连续检测到手机的帧数
-        self.phone_missing_frames = 0  # 连续未检测到手机的帧数
-        self.phone_alert_active = False  # 手机报警是否激活
-
-        # 新增：制服检测状态变量
-        self.pose_person_count = 0  # 骨骼点模型检测的ROI内人员数量
-        self.uniform_alert_active = False  # 制服报警是否激活
-        self.uniform_detection_frames = 0  # 连续检测到制服不合规的帧数
-        self.uniform_recovery_frames = 0  # 连续恢复合规的帧数
-
-        # 车辆注册表 (字典)
-        self.roi_car_registry = {}
-
-        # 违规车辆记录 (后备箱未检)
-        self.unchecked_trunk_alerts = {}
-
-        # 违规车辆记录 (通过过快 -> 归类为 Nobody)
-        self.fast_pass_alerts = {}
-
-    def _get_roi_points(self, frame_width: int, frame_height: int):
-        """
-        每帧动态计算正确的 ROI 绝对坐标，并确保类型为 np.int32
-        用于 pointPolygonTest 和 polylines
-        """
-        if self.roi_points is None:
-            # 使用默认相对坐标
-            default_rel = np.array([
-                [0.15, 0.01],
-                [0.45, 0.01],
-                [0.95, 0.95],
-                [0.35, 0.95]
-            ], dtype=np.float64)
-            roi_abs = default_rel * np.array([frame_width, frame_height])
-        else:
-            if self.roi_points.max() <= 1.0:
-                # 相对坐标 → 转换为绝对
-                roi_abs = self.roi_points * np.array([frame_width, frame_height])
-            else:
-                # 绝对坐标，直接使用
-                roi_abs = self.roi_points.copy()
-
-        # 强制转为 int32（关键！解决 OpenCV 断言错误）
-        return roi_abs.astype(np.int32)
-
-    def check_point_in_roi(self,roi_points, point):
-        return cv2.pointPolygonTest(roi_points, point, False) >= 0
-
-    def compute_iou(self,boxA, boxB):
-        # box = [x1, y1, x2, y2]
-        xA = max(boxA[0], boxB[0])
-        yA = max(boxA[1], boxB[1])
-        xB = min(boxA[2], boxB[2])
-        yB = min(boxA[3], boxB[3])
-
-        interW = max(0, xB - xA)
-        interH = max(0, yB - yA)
-        interArea = interW * interH
-
-        boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
-        boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
-
-        unionArea = boxAArea + boxBArea - interArea
-        if unionArea == 0:
-            return 0.0
-
-        return interArea / unionArea
-
-    def draw_alert(self, frame, text, color=(0, 0, 255), sub_text=None, offset_y=0):
-        """在右上角绘制警告文字 (支持垂直偏移，防止文字重叠)"""
-        font_scale = 1.5
-        thickness = 3
-        font = cv2.FONT_HERSHEY_SIMPLEX
-
-        (text_w, text_h), _ = cv2.getTextSize(text, font, font_scale, thickness)
-        x = self.width - text_w - 20
-        y = 50 + text_h + offset_y  # 增加 Y 轴偏移
-
-        cv2.rectangle(frame, (x - 10, y - text_h - 10), (x + text_w + 10, y + 10), (0, 0, 0), -1)
-        cv2.putText(frame, text, (x, y), font, font_scale, color, thickness)
-
-        if sub_text:
-            cv2.putText(frame, sub_text, (x, y + 40), font, 0.7, (200, 200, 200), 2)
-
-    def is_point_in_box(self, point, box):
-        px, py = point
-        x1, y1, x2, y2 = box
-        return x1 < px < x2 and y1 < py < y2
-
-
-    def process_frame(self, frame, camera_id: int, timestamp: float) -> Dict[str, Any]:
-        h, w = frame.shape[:2]
-        self.width, self.height = w, h
-
-        self.current_frame_idx += 1
-        # ========= 每帧动态获取正确的 ROI（int32）=========
-        roi_points_int32 = self._get_roi_points(w, h)  # shape: (4, 2), dtype: int32
-        roi_points_draw = roi_points_int32.reshape((-1, 1, 2))  # shape: (4, 1, 2) 用于绘制
-
-
-        current_time_sec = timestamp
-
-        pose_results = self.pose_detector(frame)
-
-        # ========= 主检测 =========
-        detections = self.detector(frame)
-
-        dets_xyxy = []
-        dets_roles = []
-        dets_for_tracker = []
-
-        # ========= 当前帧所有警告列表（关键改动）==========
-        current_frame_alerts = []  # 每帧清空，重新收集
-
-        if detections:
-            for det in detections:
-                x1, y1, x2, y2, conf, cls_id = det  # x1, y1, x2, y2为角点坐标，x1 y1为左上角，x2 y2为右下角
-                dets_xyxy.append([x1, y1, x2, y2])
-                dets_for_tracker.append([x1, y1, x2, y2, conf])
-                if cls_id == 0:
-                    dets_roles.append("car")
-
-                elif cls_id == 1:
-                    dets_roles.append("opentrunk")
-
-                elif cls_id == 2:
-                    dets_roles.append("person")
-
-                elif cls_id == 3:
-                    dets_roles.append("phone")
-            # print(f'dets_roles: {dets_roles}')
-
-        dets = np.array(dets_for_tracker, dtype=np.float32) if len(dets_for_tracker) else np.empty((0, 5))
-
-        tracks = self.tracker.update(
-            dets,
-            [self.height, self.width],
-            [self.height, self.width]
-        )
-        # print("tracks: {}".format(tracks))
-        # 绘制骨骼
-        frame = YOLOv8_Pose_ONNX.draw_keypoints(frame, pose_results)
-        # ========= 绘制 ROI =========
-        cv2.polylines(frame, [roi_points_draw], isClosed=True, color=(255, 0, 0), thickness=3)
-
-        # ========= 单帧统计变量 =========
-        current_roi_person_count = 0
-        current_roi_trunk_count = 0
-        current_roi_phone_count = 0
-
-        # 临时存储本帧的目标，用于后续关联分析
-        current_cars = []  # {'id':, 'box':}
-        current_trunks = []  # (cx, cy)
-
-        for t in tracks:
-            # print("t: {}".format(t))
-            tid = t.track_id
-            # cls_id = -1
-
-            # IoU 匹配角色
-            # if tid not in track_role and dets_xyxy:
-            REVALIDATE_FRAME_INTERVAL  = 10
-            #if tid not in self.track_role:
-            if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (tid not in self.track_role):
-                best_iou = 0
-                best_role = "unknown"
-
-                t_box = list(map(float, t.tlbr))  # [x1,y1,x2,y2]
-
-                for i, box in enumerate(dets_xyxy):
-                    iou_val = self.compute_iou(t_box, box)
-                    if iou_val > best_iou:
-                        best_iou = iou_val
-                        best_role = dets_roles[i]
-                if best_iou > 0.1:
-                    self.track_role[tid] = best_role
-                else:
-                    self.track_role[tid] = "unknown"
-
-            role = self.track_role.get(tid, "unknown")
-            cls_id = -1
-            if role == "car":
-                cls_id = 0
-            elif role == "opentrunk":
-                cls_id = 1
-            elif role == "person":
-                cls_id = 2
-            elif role == "phone":
-                cls_id = 3
-            # print("tid: {}, role: {}, cls: {}".format(tid, role,cls_id))
-
-            x1, y1, x2, y2 = map(int, t.tlbr)
-
-            cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
-
-            color = None
-            label = None
-
-            if self.check_point_in_roi(roi_points_int32,(cx, cy)):
-                if cls_id == 0:  # Car
-                    color = (0, 255, 0)
-
-                    current_cars.append({'id': tid, 'box': [x1, y1, x2, y2]})
-
-                    if tid not in self.roi_car_registry:
-                        self.roi_car_registry[tid] = {
-                            'first_seen': self.current_frame_idx,
-                            'last_seen': self.current_frame_idx,
-                            'trunk_frames': 0,
-                            'is_checked': False,
-                        }
-                    else:
-                        self.roi_car_registry[tid]['last_seen'] = self.current_frame_idx
-
-                    label = f"Car:{tid} IN"
-
-                elif cls_id == 1:  # Opentrunk
-                    current_roi_trunk_count += 1
-                    color = (255, 165, 0)
-                    current_trunks.append((cx, cy))
-                    label = "OpenTrunk IN"
-
-                elif cls_id == 2:  # Person
-                    current_roi_person_count += 1
-                    color = (255, 0, 255)
-                    label = "Person IN"
-
-                elif cls_id == 3:  # Phone（主模型已支持）
-                    current_roi_phone_count += 1
-                    color = (0, 0, 139)
-
-                else:
-                    color = (255, 255, 255)
-                    label = "Unknown"
-
-                # label = f"ID:{tid} IN"
-
-                # 特殊显示: 如果这辆车已经合格，框变蓝色
-                if cls_id == 0 and tid in self.roi_car_registry and self.roi_car_registry[tid][
-                    'is_checked']:
-                    color = (255, 255, 0)  # Cyan for checked cars
-                    label += " (Checked)"
-            else:
-                color = (0, 0, 255)
-                label = "OUT"
-
-            cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
-            cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
-
-        # ==========================================
-        # 4. 从骨骼点模型中统计ROI内人员数量
-        # ==========================================
-        self.pose_person_count = 0
-        # if pose_results[0].boxes is not None:
-        #     pose_boxes = pose_results[0].boxes
-        #     for box in pose_boxes:
-        #         # 获取人体检测框的中心点
-        #         x1, y1, x2, y2 = map(int, box.xyxy[0])
-        #         cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
-        #
-        #         # 判断中心点是否在ROI内
-        #         if self.check_point_in_roi((cx, cy)):
-        #             self.pose_person_count += 1
-
-        if pose_results:
-            for pose in pose_results:
-
-                x1, y1, x2, y2 = pose['bbox'][0], pose['bbox'][1], pose['bbox'][2], pose['bbox'][3]
-                cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
-                # 判断中心点是否在ROI内
-                if self.check_point_in_roi(roi_points_int32,(cx, cy)):
-                    self.pose_person_count += 1
-
-        # ==========================================
-        # 5. 关联分析: 哪个后备箱属于哪辆车?
-        # ==========================================
-        for car_info in current_cars:
-            c_id = car_info['id']
-            c_box = car_info['box']
-
-            trunk_found_for_this_car = False
-            for t_pt in current_trunks:
-                if self.is_point_in_box(t_pt, c_box):
-                    trunk_found_for_this_car = True
-                    break
-
-            if trunk_found_for_this_car:
-                self.roi_car_registry[c_id]['trunk_frames'] += 1
-                if self.roi_car_registry[c_id]['trunk_frames'] >= self.frame_thresh_trunk_valid:
-                    self.roi_car_registry[c_id]['is_checked'] = True
-
-        # ==========================================
-        # 6. 独立的手机检测逻辑（不与车辆绑定）
-        # ==========================================
-        if current_roi_phone_count > 0:
-            # 检测到手机框
-            self.phone_detection_frames += 1
-            self.phone_missing_frames = 0  # 重置丢失计数器
-
-            # 当检测累计达到阈值时，激活报警
-            if self.phone_detection_frames >= self.frame_thresh_phone:
-                self.phone_alert_active = True
-        else:
-            # 未检测到手机框
-            self.phone_missing_frames += 1
-
-            # 如果之前检测到手机，重置检测计数器
-            if self.phone_detection_frames > 0:
-                # 只有在连续丢失超过缓冲帧数时才重置
-                if self.phone_missing_frames >= self.frame_buffer_phone:
-                    self.phone_detection_frames = 0
-                    self.phone_alert_active = False
-            else:
-                # 从未检测到手机，保持状态
-                pass
-
-        # ==========================================
-        # 7. 制服检测逻辑（比较两个模型的人员数量）
-        # ==========================================
-        # 比较骨骼点模型和业务检测模型的人员数量
-        uniform_invalid = False
-
-        if self.pose_person_count > current_roi_person_count:
-            # 骨骼点模型检测到的人员多于业务检测模型
-            # 说明有人没穿执勤制服
-            uniform_invalid = True
-            self.uniform_detection_frames += 1
-            self.uniform_recovery_frames = 0  # 重置恢复计数器
-
-            # 当连续检测不合规达到阈值时，激活报警
-            if self.uniform_detection_frames >= self.frame_thresh_uniform:
-                self.uniform_alert_active = True
-        else:
-            # 人员数量匹配或业务模型检测更多（理论上不会）
-            self.uniform_recovery_frames += 1
-
-            # 如果之前有不合规检测，检查是否需要关闭报警
-            if self.uniform_detection_frames > 0:
-                # 只有在连续合规超过缓冲帧数时才重置
-                if self.uniform_recovery_frames >= self.frame_buffer_uniform:
-                    self.uniform_detection_frames = 0
-                    self.uniform_alert_active = False
-            else:
-                # 从未检测到不合规，保持状态
-                pass
-
-        # ==========================================
-        # 8. 维护车辆注册表 & 生成离场报警
-        # ==========================================
-        active_car_ids = []
-        cars_to_remove = []
-
-        for car_id, info in self.roi_car_registry.items():
-            last_seen = info['last_seen']
-
-            if (self.current_frame_idx - last_seen) <= self.frame_buffer_limit_car:
-                active_car_ids.append(car_id)
-            else:
-                cars_to_remove.append(car_id)
-
-        # 执行删除 并 检查违规
-        for car_id in cars_to_remove:
-            car_info = self.roi_car_registry[car_id]
-
-            duration_frames = car_info['last_seen'] - car_info['first_seen']
-
-            # 情况1：通过时间太短 -> 归类为 Nobody (Too Fast)
-            if duration_frames < self.frame_thresh_car_min_duration:
-                print(f"ALARM: Car {car_id} passed too fast -> Regarded as Nobody Checked!")
-                self.fast_pass_alerts[car_id] = self.current_frame_idx + int(3.0 * self.fps)
-
-            # 情况2：时间够长，但没检查后备箱 -> Unchecked Trunk
-            elif not car_info['is_checked']:
-                print(f"ALARM: Car {car_id} left without checking trunk!")
-                self.unchecked_trunk_alerts[car_id] = self.current_frame_idx + int(3.0 * self.fps)
-
-            del self.roi_car_registry[car_id]
-
-        effective_car_count = len(active_car_ids)
-
-        # ==========================================
-        # 9. 业务逻辑判定 (Only One / Nobody)
-        # ==========================================
-        status_text = ""
-
-        if effective_car_count > 0:
-            # --- Only One ---
-            if current_roi_person_count == 1:
-                self.cnt_frame_one_person += 1
-                self.cnt_missing_buffer_person = 0
-                self.cnt_frame_nobody = 0
-
-            # --- Nobody ---
-            elif current_roi_person_count == 0:
-                if self.cnt_frame_one_person > 0 and self.cnt_missing_buffer_person < self.frame_buffer_limit_person:
-                    self.cnt_frame_one_person += 1
-                    self.cnt_missing_buffer_person += 1
-                    self.cnt_frame_nobody = 0
-                    status_text = f"Person Buffer ({self.cnt_missing_buffer_person}/{self.frame_buffer_limit_person})"
-                else:
-                    self.cnt_frame_one_person = 0
-                    self.cnt_missing_buffer_person = 0
-                    self.cnt_frame_nobody += 1
-            else:
-                self.cnt_frame_one_person = 0
-                self.cnt_missing_buffer_person = 0
-                self.cnt_frame_nobody = 0
-        else:
-            self.cnt_frame_one_person = 0
-            self.cnt_missing_buffer_person = 0
-            self.cnt_frame_nobody = 0
-
-        # ==========================================
-        # 10. 显示报警 (UI分层优化)
-        # ==========================================
-
-        # 更新调试信息，包含所有检测状态
-        phone_status = f"Phone: {current_roi_phone_count}"
-        if self.phone_alert_active:
-            phone_status += " (ALERT)"
-
-        uniform_status = f"Uniform: Pose={self.pose_person_count}, Model={current_roi_person_count}"
-        if self.uniform_alert_active:
-            uniform_status += " (INVALID!)"
-
-        debug_info = f"Cars: {len(active_car_ids)} | Person: {current_roi_person_count} | Trunk: {current_roi_trunk_count} | {phone_status}"
-        cv2.putText(frame, debug_info, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
-        cv2.putText(frame, uniform_status, (20, 70), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
-
-        # 使用 offset 实现报警堆叠，防止遮挡
-        alert_offset = 0
-
-        # 第一层：实时状态 (Real-time Status)
-        # ------------------------------------------------
-        # A. 显示 Only One
-        if self.cnt_frame_one_person >= self.frame_thresh_one:
-            current_frame_alerts.append(
-                {
-                    'time': current_time_sec,
-                    'action': "Only One",
-                }
-            )
-            self.draw_alert(frame, "Only One", (0, 255, 255), status_text, offset_y=alert_offset)
-            alert_offset += 100
-
-        # B. 显示 Nobody (实时状态)
-        elif self.cnt_frame_nobody >= self.frame_thresh_nobody:
-            current_frame_alerts.append(
-                {
-                    'time': current_time_sec,
-                    'action': "Nobody",
-                }
-            )
-            self.draw_alert(frame, "Nobody", (0, 0, 255), offset_y=alert_offset)
-            alert_offset += 100
-
-        # C. 显示 Trunk Checked (在车辆存活期间)
-        for car_id in active_car_ids:
-            if car_id in self.roi_car_registry and self.roi_car_registry[car_id]['is_checked']:
-                current_frame_alerts.append(
-                    {
-                        'time': current_time_sec,
-                        'action': "Trunk Checked",
-                    }
-                )
-                self.draw_alert(frame, "Trunk Checked!!", (0, 255, 0), offset_y=alert_offset)
-                alert_offset += 100
-                break  # 只显示一次
-
-        # D. 显示 Playing Phone（独立检测，不与车辆绑定）
-        if self.phone_alert_active:
-            # 可以显示检测的持续时间
-            duration_seconds = self.phone_detection_frames / self.fps
-            sub_text = f"Detected for {duration_seconds:.1f}s"
-            current_frame_alerts.append(
-                {
-                    'time': current_time_sec,
-                    'action': "Playing Phone",
-                }
-            )
-            self.draw_alert(frame, "Playing Phone", (255, 0, 0), sub_text, offset_y=alert_offset)
-            alert_offset += 100
-
-        # E. 新增：显示 Unvaild Uniform!!
-        if self.uniform_alert_active:
-            # 显示具体数量差异
-            diff = self.pose_person_count - current_roi_person_count
-            sub_text = f"Missing {diff} uniform(s)"
-            current_frame_alerts.append(
-                {
-                    'time': current_time_sec,
-                    'action': "Unvaild Uniform!!",
-                }
-            )
-            self.draw_alert(frame, "Unvaild Uniform!!", (255, 165, 0), sub_text, offset_y=alert_offset)
-            alert_offset += 100
-
-        # 第二层：离场违规 (Post-Event Alerts)
-        # ------------------------------------------------
-
-        # F. 显示 Unchecked Trunk
-        expired_alerts = [cid for cid, end_frame in self.unchecked_trunk_alerts.items() if
-                          self.current_frame_idx > end_frame]
-        for cid in expired_alerts:
-            del self.unchecked_trunk_alerts[cid]
-
-        if len(self.unchecked_trunk_alerts) > 0:
-            alert_text = f"Unchecked Trunk! (ID:{list(self.unchecked_trunk_alerts.keys())})"
-            current_frame_alerts.append(
-                {
-                    'time': current_time_sec,
-                    'action': "Unchecked Trunk",
-                }
-            )
-            self.draw_alert(frame, alert_text, (0, 0, 255), offset_y=alert_offset)
-            alert_offset += 100
-
-        # G. 显示 Nobody (离场结果)
-        expired_fast_alerts = [cid for cid, end_frame in self.fast_pass_alerts.items() if
-                               self.current_frame_idx > end_frame]
-        for cid in expired_fast_alerts:
-            del self.fast_pass_alerts[cid]
-
-        if len(self.fast_pass_alerts) > 0:
-            alert_text = f"Nobody (ID:{list(self.fast_pass_alerts.keys())})"
-            current_frame_alerts.append(
-                {
-                    'time': current_time_sec,
-                    'action': "Nobody",
-                }
-            )
-            self.draw_alert(frame, alert_text, (0, 0, 255), offset_y=alert_offset)
-            alert_offset += 100
-
-        return {
-            "image": frame,
-
-            "alerts":current_frame_alerts
-        }
-
 # ========================= WebSocket 服务线程 =========================
 class WebSocketSender(threading.Thread):
     def __init__(self, send_queue: queue.Queue, stop_event: threading.Event):
@@ -738,46 +76,6 @@ class WebSocketSender(threading.Thread):
         asyncio.run(self._run_async())
 
 
-# ========================= WebSocket 服务线程2 =========================
-class WebSocketSender2(threading.Thread):
-    def __init__(self, send_queue: queue.Queue, stop_event: threading.Event):
-        super().__init__(daemon=True)
-        self.send_queue = send_queue
-        self.stop_event = stop_event
-
-    async def _ws_handler(self, websocket):
-        ws_clients_2.add(websocket)
-        try:
-            async for _ in websocket:
-                pass
-        finally:
-            ws_clients_2.discard(websocket)
-
-    async def _broadcaster(self):
-        while not self.stop_event.is_set():
-            try:
-                msg = await asyncio.to_thread(self.send_queue.get, timeout=0.5)
-            except queue.Empty:
-                continue
-            data = json.dumps(msg)
-            dead = []
-            for ws in list(ws_clients_2):
-                try:
-                    await ws.send(data)
-                except:
-                    dead.append(ws)
-            for ws in dead:
-                ws_clients_2.discard(ws)
-            self.send_queue.task_done()
-
-    async def _run_async(self):
-        async with websockets.serve(self._ws_handler, WS_HOST, WS_PORT_2):
-            print(f"[INFO] WebSocket server 2 started at ws://{WS_HOST}:{WS_PORT_2}")
-            await self._broadcaster()
-
-    def run(self):
-        asyncio.run(self._run_async())
-
 
 # ========================= RTSP 抓流线程 =========================
 class RTSPCaptureWorker(threading.Thread):
@@ -924,11 +222,10 @@ class RTSPCaptureWorker(threading.Thread):
 
 # ========================= 帧处理线程 =========================
 class FrameProcessorWorker(threading.Thread):
-    def __init__(self, raw_queue: queue.Queue, ws_queue: queue.Queue, ws_queue_2: queue.Queue, stop_event: threading.Event):
+    def __init__(self, raw_queue: queue.Queue, ws_queue: queue.Queue, stop_event: threading.Event):
         super().__init__(daemon=True)
         self.raw_queue = raw_queue
         self.ws_queue = ws_queue
-        self.ws_queue_2 = ws_queue_2  # 新增：第二个WebSocket队列
         self.stop_event = stop_event
 
         self.last_ts: Dict[int, float] = {}
@@ -1011,8 +308,8 @@ class FrameProcessorWorker(threading.Thread):
                 }
                 try:
                     self.ws_queue.put(msg, timeout=1.0)
-                    if push_actions and len(push_actions) > 0:
+                    # if push_actions and len(push_actions) > 0:
-                        self.ws_queue_2.put(msg, timeout=1.0)
+                    #     self.ws_queue_2.put(msg, timeout=1.0)
                 except queue.Full:
                     print("[WARN] ws_send_queue full, drop frame message")
 
@@ -1030,16 +327,13 @@ class RTSPService:
         self.stop_event = threading.Event()
         self.raw_queue = queue.Queue(maxsize=500)
         self.ws_queue = queue.Queue(maxsize=1000)
-        self.ws_queue_2 = queue.Queue(maxsize=1000)  # 新增：第二个WebSocket队列
 
         self.capture_workers = []
-        self.processor = FrameProcessorWorker(self.raw_queue, self.ws_queue, self.ws_queue_2, self.stop_event)
+        self.processor = FrameProcessorWorker(self.raw_queue, self.ws_queue, self.stop_event)
         self.ws_sender = WebSocketSender(self.ws_queue, self.stop_event)
-        self.ws_sender_2 = WebSocketSender2(self.ws_queue_2, self.stop_event)  # 新增：第二个WebSocket发送器
 
     def start(self):
         self.ws_sender.start()
-        self.ws_sender_2.start()  # 新增：启动第二个WebSocket服务
         self.processor.start()
         for cam in self.cameras:
             w = RTSPCaptureWorker(cam, self.raw_queue, self.stop_event)
@@ -1051,12 +345,10 @@ class RTSPService:
         self.stop_event.set()
         self.raw_queue.join()
         self.ws_queue.join()
-        self.ws_queue_2.join()  # 新增：等待第二个WebSocket队列
         for w in self.capture_workers:
             w.join(timeout=2.0)
         self.processor.join(timeout=2.0)
         self.ws_sender.join(timeout=2.0)
-        self.ws_sender_2.join(timeout=2.0)  # 新增：等待第二个WebSocket发送器
         print("[INFO] Service stopped")