"""
传统DMS vs Metropolis Agentic Vision 对比

传统: 检测→规则→输出
Agentic: 理解→推理→决策→输出
"""

comparison = {
    "传统DMS管道": {
        "步骤": "人脸检测 → 关键点 → PERCLOS计算 → 阈值规则 → 警告",
        "决策方式": "if PERCLOS > 0.3: warn()",
        "上下文": "当前帧或短窗口",
        "适应性": "差(固定阈值)",
        "可解释性": "中(规则透明)",
        "开发效率": "低(每场景需新规则)",
        "部署": "轻量(INT8量化)",
    },
    "Metropolis Agentic管道": {
        "步骤": "Cosmos视觉理解 → Nemotron推理 → 自然语言决策",
        "决策方式": "LLM基于场景理解做综合判断",
        "上下文": "长时序(60s+)+多模态(视觉+车辆+历史)",
        "适应性": "高(自适应场景)",
        "可解释性": "高(自然语言解释决策)",
        "开发效率": "高(自然语言指令即可)",
        "部署": "重(Cosmos 4B+Nemotron 2B, 需Jetson Thor)",
    }
}

print("=== 传统DMS vs Agentic Vision ===\n")
for approach, spec in comparison.items():
    print(f"【{approach}】")
    for k, v in spec.items():
        print(f"  {k}: {v}")
    print()

"""
座舱 Agentic Vision 应用示例

使用自然语言指令驱动座舱安全决策
基于 Cosmos 3 Edge + Nemotron 2B
"""

agentic_prompts = {
    "疲劳检测": {
        "prompt": """
        你是座舱安全AI。分析当前驾驶员状态:
        - 眼睛开度: {eye_openness}
        - 眨眼频率: {blink_rate}/min
        - 头部姿态: {head_pose}
        - 驾驶时长: {drive_duration}
        
        判断疲劳等级(0-3)并决定是否需要警告。
        输出JSON: {{"level": 0-3, "action": "none|remind|warn|intervene", "reason": "..."}}
        """,
        "优势": "综合考虑多因素, 非单一阈值",
        "示例输出": '{"level": 2, "action": "warn", "reason": "PERCLOS 35%+驾驶3.5h+头部下垂3次, 疲劳累积"}'
    },
    "L3接管决策": {
        "prompt": """
        L3自动驾驶即将结束, 评估驾驶员接管准备度:
        - 过去60秒视线方向: {gaze_pattern}
        - 是否在看路: {eyes_on_road_ratio}
        - HRV状态: {hrv_metrics}
        - 当前路况复杂度: {road_complexity}
        
        驾驶员能否在10秒内安全接管?
        输出JSON: {{"ready": true/false, "confidence": 0-1, "action": "..."}}
        """,
        "优势": "多维度评估接管能力, 非简单是否检测",
        "示例输出": '{"ready": false, "confidence": 0.7, "action": "extend_autopilot_and_alert"}'
    },
    "OOP+气囊决策": {
        "prompt": """
        紧急情况: 前方碰撞预警0.5秒内
        评估乘员姿态并决定气囊模式:
        - 前排驾驶员: 躯干角度={driver_torso_angle}, 距方向盘={driver_distance}cm
        - 前排乘客: 躯干角度={passenger_torso_angle}
        - 后排: {rear_occupants}
        
        为每个气囊选择展开模式: low_power|standard|high_power|disable
        输出JSON: {{"airbags": {...}}}
        """,
        "优势": "实时姿态自适应气囊控制, 非固定档位",
        "示例输出": '{"airbags": {"driver": "low_power", "passenger": "standard", "rear": "disable"}}'
    },
    "CPD综合判断": {
        "prompt": """
        车辆已锁车, 检测后排是否有儿童遗留:
        - 摄像头: 检测到{count}个小型移动物体
        - 雷达: 检测到呼吸频率{breath_rate}/min
        - 温度: 车内{temp}°C
        - 时间: 已锁车{locked_duration}分钟
        
        判断是否有儿童遗留, 决定是否触发警报。
        """,
        "优势": "多传感器融合+场景理解, 非单一传感器判断",
        "示例输出": '{"child_detected": true, "confidence": 0.9, "action": "alert_owner_and_authorities"}'
    }
}

print("=== 座舱Agentic Vision应用 ===\n")
for app, spec in agentic_prompts.items():
    print(f"【{app}】")
    print(f"  优势: {spec['优势']}")
    print(f"  示例输出: {spec['示例输出'][:80]}...\n")

"""
Agentic Vision 座舱部署架构

分两层:
1. 视觉理解层: Cosmos 3 Edge (4B, Jetson Thor)
2. 推理决策层: Nemotron 2B (可与Cosmos同芯片)

总内存: ~9GB (Cosmos) + ~4GB (Nemotron) = ~13GB
Jetson Thor 64GB内存: 充裕
"""

deployment = {
    "硬件": "Jetson AGX Thor T5000 (64GB, 1000 TOPS)",
    "视觉模型": "Cosmos 3 Edge (4B参数, ~9GB BF16)",
    "推理模型": "Nemotron 2B (~4GB BF16)",
    "总内存": "~13GB / 64GB",
    "摄像头": "4×GMSL2 (DMS+OMS前排+OMS后排+后视)",
    "推理延迟": {
        "Cosmos视觉": "~1.5s/chunk (32帧@15Hz)",
        "Nemotron推理": "~0.5s/decision",
        "总延迟": "~2s (可接受, 非碰撞级决策)",
    },
    "碰撞级决策": {
        "方式": "传统快速管道(INT8量化模型, <30ms)",
        "说明": "气囊控制等碰撞级决策不用Agentic, 用快速确定性模型",
    },
    "Agentic适用": [
        "疲劳/分心综合评估 (延迟~2s可接受)",
        "L3接管准备度评估 (提前60s预警)",
        "CPD综合判断 (延迟~5s可接受)",
        "乘员姿态+安全带状态综合评估",
    ],
    "Agentic不适用": [
        "碰撞时刻气囊控制 (<50ms)",
        "紧急制动 (<100ms)",
        "翻滚检测 (<20ms)",
    ]
}

print("=== Agentic Vision 座舱部署 ===")
for k, v in deployment.items():
    if isinstance(v, dict):
        print(f"\n{k}:")
        for sk, sv in v.items():
            print(f"  {sk}: {sv}")
    elif isinstance(v, list):
        print(f"\n{k}:")
        for item in v:
            print(f"  - {item}")
    else:
        print(f"{k}: {v}")