class ZonalDMSPipeline:
    """
    区域架构下的DMS部署方案
    
    三层处理：
    1. 边缘节点：原始视频→特征提取（<5ms）
    2. 区域聚合器：多传感器特征融合（<10ms）
    3. 中央计算：跨区域决策+上下文（<20ms）
    
    总延迟目标：<50ms端到端
    """
    
    class EdgeNode:
        """边缘节点（摄像头侧）"""
        
        def __init__(self, name, sensor_type):
            self.name = name
            self.sensor = sensor_type
            self.models = {
                'face_detect': None,     # 轻量级人脸检测
                'eye_detect': None,      # 眼睛关键点
                'head_pose': None,       # 头部姿态
                'gaze_est': None,       # 视线估计
            }
        
        def process(self, frame):
            """
            边缘推理：提取关键特征
            
            输出：特征向量（非原始视频）
            带宽需求：特征 << 原始视频
            """
            features = {}
            
            # 人脸检测（轻量模型 ~2MB）
            faces = self.models['face_detect'](frame)
            if len(faces) == 0:
                return {'face_detected': False}
            
            # 眼睛关键点（~1MB模型）
            eyes = self.models['eye_detect'](frame, faces)
            
            # 头部姿态（~0.5MB模型）
            head_pose = self.models['head_pose'](frame, faces)
            
            # 视线估计（~3MB模型）
            gaze = self.models['gaze_est'](frame, eyes)
            
            return {
                'face_detected': True,
                'eye_features': eyes,
                'head_pose': head_pose,
                'gaze': gaze,
                'timestamp': frame['timestamp']
            }
    
    class ZonalAggregator:
        """区域聚合器"""
        
        def __init__(self, zone_name):
            self.zone = zone_name
            self.edge_nodes = []
        
        def add_node(self, node):
            self.edge_nodes.append(node)
        
        def aggregate(self, edge_results):
            """
            融合同区域多传感器特征
            
            例：驾驶员区域可能包含
            - DMS摄像头（正面）
            - 侧方摄像头（辅助视角）
            - 方向盘传感器
            """
            fused = {
                'zone': self.zone,
                'confidence': 0,
                'features': {}
            }
            
            # 加权融合
            valid = [r for r in edge_results if r.get('face_detected')]
            if valid:
                fused['features'] = valid[0]  # 简化：取最佳
                fused['confidence'] = 0.9
            else:
                fused['features'] = {'face_detected': False}
                fused['confidence'] = 0.0
            
            return fused
    
    class CentralBrain:
        """中央计算单元"""
        
        def __init__(self):
            self.zones = {}
            self.context = {
                'driving_scenario': None,
                'history': [],
                'user_profile': None
            }
        
        def decide(self, zonal_results):
            """
            跨区域决策+上下文融合
            """
            driver_zone = zonal_results.get('driver_zone', {})
            
            # DMS决策
            if not driver_zone.get('features', {}).get('face_detected'):
                return {'status': 'no_driver', 'action': 'alert'}
            
            gaze = driver_zone['features'].get('gaze', {})
            head_pose = driver_zone['features'].get('head_pose', {})
            
            # 上下文感知决策
            status = self._evaluate_state(gaze, head_pose)
            
            # 更新历史
            self.context['history'].append(status)
            if len(self.context['history']) > 300:
                self.context['history'].pop(0)
            
            return {
                'status': status,
                'action': self._select_action(status),
                'confidence': driver_zone.get('confidence', 0)
            }
        
        def _evaluate_state(self, gaze, head_pose):
            """状态评估"""
            gaze_on_road = gaze.get('on_road', True)
            if not gaze_on_road:
                return 'distracted'
            return 'normal'
        
        def _select_action(self, status):
            """选择干预动作"""
            actions = {
                'normal': 'none',
                'distracted': 'visual_warning',
                'drowsy': 'audio_warning',
                'unresponsive': 'emergency_stop'
            }
            return actions.get(status, 'none')


# 测试
if __name__ == "__main__":
    pipeline = ZonalDMSPipeline()
    
    # 创建边缘节点
    dms_cam = pipeline.EdgeNode('DMS摄像头', 'RGB-IR')
    side_cam = pipeline.EdgeNode('侧方摄像头', 'RGB')
    
    # 创建区域聚合器
    driver_zone = pipeline.ZonalAggregator('driver_zone')
    driver_zone.add_node(dms_cam)
    driver_zone.add_node(side_cam)
    
    # 创建中央大脑
    brain = pipeline.CentralBrain()
    
    # 模拟处理流程
    frame = {'timestamp': 0, 'data': 'mock'}
    
    # 边缘处理
    dms_result = dms_cam.process(frame)
    side_result = side_cam.process(frame)
    
    # 区域聚合
    zonal_result = driver_zone.aggregate([dms_result, side_result])
    
    # 中央决策
    decision = brain.decide({'driver_zone': zonal_result})
    
    print("区域架构DMS处理流程:")
    print(f"  边缘节点: {dms_result.get('face_detected', 'N/A')}")
    print(f"  区域聚合: zone={zonal_result['zone']}, conf={zonal_result['confidence']}")
    print(f"  中央决策: status={decision['status']}, action={decision['action']}")