"""
Tobii 单摄像头 DMS+OMS 架构概念实现

核心：一颗高规格 IR 摄像头同时覆盖驾驶员和乘员区域
关键挑战：FOV 需覆盖全舱，算法需同时处理两类任务
"""

from dataclasses import dataclass
from typing import Optional, Tuple
import numpy as np


@dataclass
class SingleCameraConfig:
    """单摄像头配置"""
    resolution: Tuple[int, int] = (1600, 1200)  # 2MP
    fps: int = 30
    fov_h: float = 120.0  # 水平视场角（度）
    fov_v: float = 90.0   # 垂直视场角
    wavelength: int = 940   # nm IR
    global_shutter: bool = True
    # 关键：宽 FOV 覆盖驾驶员+乘客
    # 高分辨率确保远距离乘员细节


@dataclass
class DMSResult:
    """DMS 检测结果"""
    face_detected: bool = False
    gaze_direction: Tuple[float, float] = (0.0, 0.0)  # (pitch, yaw)
    perclos: float = 0.0
    blink_rate: float = 0.0
    distraction_level: float = 0.0
    fatigue_level: float = 0.0
    head_pose: Tuple[float, float, float] = (0.0, 0.0, 0.0)
    identity: Optional[str] = None


@dataclass
class OMSResult:
    """OMS 检测结果"""
    seat_occupancy: dict = None  # {seat: bool}
    person_count: int = 0
    child_detected: bool = False
    seatbelt_status: dict = None  # {seat: bool}
    posture_classification: dict = None  # {seat: str}
    body_pose: dict = None  # {seat: keypoints}


class SingleCameraDMSOMS:
    """
    Tobii 式单摄像头 DMS+OMS 融合处理器
    
    架构：
    1. 统一图像采集 → 单摄像头宽 FOV
    2. ROI 分割 → 驾驶员区域 + 乘员区域
    3. 并行推理 → DMS 任务 + OMS 任务
    4. 融合输出 → 统一座舱状态
    """
    
    def __init__(self, config: SingleCameraConfig):
        self.config = config
        # ROI 定义（图像中的区域）
        self.dms_roi = (0.0, 0.3, 0.5, 1.0)  # 左侧 50%
        self.oms_roi = (0.3, 0.0, 1.0, 0.7)  # 右侧 70%
        # 重叠区域用于上下文关联
        
    def process_frame(self, frame: np.ndarray) -> tuple:
        """
        处理单帧图像，同时输出 DMS 和 OMS 结果
        
        Args:
            frame: shape=(H, W, C), IR 图像
            
        Returns:
            dms_result: DMS 检测结果
            oms_result: OMS 检测结果
        """
        H, W = frame.shape[:2]
        
        # ROI 提取
        dms_x1 = int(self.dms_roi[0] * W)
        dms_y1 = int(self.dms_roi[1] * H)
        dms_x2 = int(self.dms_roi[2] * W)
        dms_y2 = int(self.dms_roi[3] * H)
        dms_crop = frame[dms_y1:dms_y2, dms_x1:dms_x2]
        
        oms_x1 = int(self.oms_roi[0] * W)
        oms_y1 = int(self.oms_roi[1] * H)
        oms_x2 = int(self.oms_roi[2] * W)
        oms_y2 = int(self.oms_roi[3] * H)
        oms_crop = frame[oms_y1:oms_y2, oms_x1:oms_x2]
        
        # 并行推理（实际部署中共享 backbone）
        dms_result = self._run_dms(dms_crop)
        oms_result = self._run_oms(oms_crop)
        
        # 上下文融合
        if dms_result.face_detected and oms_result.person_count > 0:
            # 驾驶员和乘员都在 → 整体座舱状态
            pass
            
        return dms_result, oms_result
    
    def _run_dms(self, crop: np.ndarray) -> DMSResult:
        """DMS 推理（概念实现）"""
        return DMSResult(
            face_detected=True,
            gaze_direction=(5.0, -10.0),
            perclos=12.0,
            blink_rate=18,
            distraction_level=0.15,
            fatigue_level=0.20,
        )
    
    def _run_oms(self, crop: np.ndarray) -> OMSResult:
        """OMS 推理（概念实现）"""
        return OMSResult(
            seat_occupancy={'front_passenger': True, 'rear_left': False},
            person_count=1,
            child_detected=False,
            seatbelt_status={'front_passenger': True},
            posture_classification={'front_passenger': 'normal'},
        )


# ========== 测试 ==========
if __name__ == "__main__":
    config = SingleCameraConfig()
    processor = SingleCameraDMSOMS(config)
    
    # 模拟帧
    frame = np.random.randint(0, 255, (1200, 1600, 1), dtype=np.uint8)
    
    dms, oms = processor.process_frame(frame)
    
    print("=== 单摄像头 DMS+OMS 结果 ===")
    print(f"DMS:")
    print(f"  人脸检测: {dms.face_detected}")
    print(f"  视线方向: pitch={dms.gaze_direction[0]:.1f}°, "
          f"yaw={dms.gaze_direction[1]:.1f}°")
    print(f"  PERCLOS: {dms.perclos:.1f}%")
    print(f"  眨眼频率: {dms.blink_rate} 次/分")
    print(f"  分心等级: {dms.distraction_level:.2f}")
    print(f"  疲劳等级: {dms.fatigue_level:.2f}")
    print(f"OMS:")
    print(f"  座椅占用: {oms.seat_occupancy}")
    print(f"  乘员数: {oms.person_count}")
    print(f"  儿童检测: {oms.child_detected}")
    print(f"  安全带: {oms.seatbelt_status}")
    print(f"  姿态: {oms.posture_classification}")

"""
DMS 行业整合时间线（2024-2026）
"""

TIMELINE = [
    {
        'date': '2024',
        'event': 'Tobii 收购 Xperi AutoSense',
        'impact': 'Tobii 获得车载量产基础',
        'significance': '眼动追踪龙头进入车载市场'
    },
    {
        'date': '2025-08',
        'event': 'indie Semiconductor 收购 emotion3D',
        'impact': 'DMS 从独立软件变为芯片+软件整包',
        'significance': '纯软件公司生存空间缩小'
    },
    {
        'date': '2026',
        'event': 'Smart Eye 收购 Sightic',
        'impact': '扩展商用车队产品线',
        'significance': '前装+后装全覆盖'
    },
    {
        'date': '2026-09',
        'event': 'NOVELIC + Smart Eye 合作',
        'impact': 'Radar + Camera 融合方案',
        'significance': '传感器融合成为标配'
    },
    {
        'date': '2026-09',
        'event': 'Murata + Smart Eye 合作',
        'impact': '60GHz + Camera 融合',
        'significance': '多家雷达厂商选择 Smart Eye 软件'
    },
    {
        'date': '2026-09',
        'event': 'Tobii InCabin 展示单摄像头方案',
        'impact': 'DMS+OMS 合一验证量产',
        'significance': '成本优化路径确立'
    },
]

for item in TIMELINE:
    print(f"[{item['date']}] {item['event']}")
    print(f"  影响: {item['impact']}")
    print(f"  意义: {item['significance']}")
    print()

"""
IMS 单摄像头 DMS+OMS 算法架构建议

关键设计原则：
1. 共享 backbone，分支 head（降低计算量）
2. ROI 分割但保持上下文关联
3. 时序融合提升稳定性
"""

# 架构示意（伪代码）
"""
Input: IR Frame (1600×1200, 1ch)
  ↓
[共享 Backbone: MobileNetV3 / EfficientNet-Lite]
  ↓
  ├── [DMS Head]
  │   ├── Face Detection → 人脸/ landmarks
  │   ├── Gaze Estimation → 视线方向
  │   ├── Blink Detection → PERCLOS
  │   ├── Head Pose → 头部姿态
  │   └── Emotion Recognition → 情绪状态
  │
  ├── [OMS Head]
  │   ├── Seat Occupancy → 座椅占用
  │   ├── Person Detection → 乘员检测
  │   ├── Seatbelt Status → 安全带
  │   ├── Posture Classification → 姿态分类
  │   └── Child Detection → 儿童检测
  │
  └── [Fusion Head]
      ├── Context Understanding → 上下文理解
      └── Cabin State → 整体座舱状态
"""