DMS墨镜鲁棒性方案：940nm红外穿透技术

问题背景

行业痛点

UNECE 2024测试报告发现：

“When wearing sunglasses, drivers were able to stay distracted indefinitely without receiving any warning from the DMS.”

这是DMS系统面临的最严重缺陷之一。

技术原因

方案	波长	墨镜穿透性	问题
RGB摄像头	380-780nm	❌ 完全阻挡	无法检测
850nm红外	850nm	⚠️ 部分穿透	依赖镜片材质
940nm红外	940nm	✅ 完全穿透	需高功率补光

940nm红外技术详解

1. 墨镜透光率对比

"""
不同材质墨镜的红外透光率

数据来源：光学器件厂商测试报告
"""

sunglass_transmission = {
    "偏光墨镜（Polarized）": {
        "visible": 12,      # % 可见光
        "850nm": 45,        # % 850nm红外
        "940nm": 85         # % 940nm红外
    },
    "渐变墨镜（Gradient）": {
        "visible": 25,
        "850nm": 65,
        "940nm": 92
    },
    "镜面墨镜（Mirrored）": {
        "visible": 8,
        "850nm": 30,
        "940nm": 78
    },
    "防蓝光墨镜": {
        "visible": 35,
        "850nm": 70,
        "940nm": 95
    }
}

2. 940nm系统架构

import numpy as np
from typing import Tuple, Optional

class IR940nmEyeTracker:
    """
    940nm红外眼动追踪系统
    
    优势：
    1. 墨镜穿透率 > 80%
    2. 人眼不可见，不干扰驾驶
    3. 抗可见光干扰强
    
    劣势：
    1. 需要高功率补光
    2. 成本较高
    """
    
    def __init__(self, 
                 ir_power: float = 120,  # mW/sr 辐射强度
                 sensor_type: str = "RGB-IR"):
        self.ir_power = ir_power
        self.sensor_type = sensor_type
        
        # 眼睛检测参数
        self.pupil_threshold = 0.25  # 瞳孔/虹膜对比度阈值
        self.glint_threshold = 0.85  # 光斑亮度阈值
        
    def estimate_signal_quality(self, 
                                 sunglass_type: str,
                                 distance: float = 0.6) -> Tuple[float, str]:
        """
        估算信号质量
        
        Args:
            sunglass_type: 墨镜类型
            distance: 驾驶员距离摄像头距离（米）
        
        Returns:
            quality: 信号质量评分 (0-1)
            status: 状态描述
        """
        # 获取透光率
        transmission = sunglass_transmission.get(sunglass_type, 
                                                  sunglass_transmission["偏光墨镜（Polarized）"])
        ir_trans = transmission["940nm"] / 100
        
        # 距离衰减（平方反比）
        distance_factor = (0.5 / distance) ** 2
        
        # 红外功率影响
        power_factor = min(1.0, self.ir_power / 100)
        
        # 综合信号质量
        quality = ir_trans * distance_factor * power_factor
        
        # 判断状态
        if quality > 0.6:
            status = "优质信号，可靠检测"
        elif quality > 0.4:
            status = "中等信号，可检测"
        elif quality > 0.2:
            status = "弱信号，精度下降"
        else:
            status = "信号不足，需降级"
        
        return quality, status
    
    def detect_pupil_glint_ratio(self, 
                                  image: np.ndarray,
                                  ir_intensity: np.ndarray) -> Tuple[float, float, bool]:
        """
        检测瞳孔-光斑比（PGR）
        
        这是940nm系统的核心检测方法
        
        Args:
            image: IR图像
            ir_intensity: IR强度分布
        
        Returns:
            pupil_radius: 瞳孔半径（像素）
            glint_brightness: 光斑亮度
            valid: 是否有效检测
        """
        # 找光斑（最亮点）
        glint_pos = np.unravel_index(np.argmax(ir_intensity), ir_intensity.shape)
        glint_brightness = ir_intensity[glint_pos]
        
        # 在光斑附近找瞳孔（暗区域）
        search_radius = 50
        y, x = glint_pos
        
        pupil_region = image[
            max(0, y-search_radius):min(image.shape[0], y+search_radius),
            max(0, x-search_radius):min(image.shape[1], x+search_radius)
        ]
        
        if pupil_region.size == 0:
            return 0, 0, False
        
        # 瞳孔是暗区域
        pupil_threshold = np.percentile(pupil_region, 10)
        pupil_mask = pupil_region < pupil_threshold
        
        # 计算瞳孔半径
        pupil_area = np.sum(pupil_mask)
        pupil_radius = np.sqrt(pupil_area / np.pi)
        
        # 判断是否有效
        contrast_ratio = glint_brightness / (np.mean(pupil_region[pupil_mask]) + 1)
        valid = contrast_ratio > 3.0  # 对比度阈值
        
        return pupil_radius, glint_brightness, valid


class DualWavelengthSystem:
    """
    双波长系统：850nm + 940nm
    
    策略：
    - 正常情况用850nm（功耗低）
    - 检测到墨镜时切换940nm
    """
    
    def __init__(self):
        self.current_wavelength = 850
        self.ir850_power = 80    # mW/sr
        self.ir940_power = 120   # mW/sr
        
    def detect_sunglasses(self, 
                          visible_image: np.ndarray,
                          ir850_image: np.ndarray) -> bool:
        """
        检测是否佩戴墨镜
        
        原理：比较可见光和IR图像的眼部区域亮度
        """
        # 提取眼部区域
        eye_region_vis = self._extract_eye_region(visible_image)
        eye_region_ir = self._extract_eye_region(ir850_image)
        
        if eye_region_vis is None or eye_region_ir is None:
            return False
        
        # 计算亮度比
        vis_brightness = np.mean(eye_region_vis)
        ir_brightness = np.mean(eye_region_ir)
        
        # 如果可见光很暗但IR正常，说明有墨镜
        ratio = ir_brightness / (vis_brightness + 1)
        
        return ratio > 5.0
    
    def _extract_eye_region(self, image: np.ndarray) -> Optional[np.ndarray]:
        """简化：假设已有眼睛检测"""
        # 实际应用需要人脸/眼睛检测
        h, w = image.shape[:2]
        # 模拟：取图像中心上1/3区域
        eye_region = image[int(h*0.3):int(h*0.5), int(w*0.3):int(w*0.7)]
        return eye_region if eye_region.size > 0 else None
    
    def select_wavelength(self, 
                          visible_image: np.ndarray,
                          ir850_image: np.ndarray) -> int:
        """
        选择最佳波长
        
        Returns:
            wavelength: 850 或 940
        """
        if self.detect_sunglasses(visible_image, ir850_image):
            self.current_wavelength = 940
            return 940
        else:
            self.current_wavelength = 850
            return 850


# ============ 完整墨镜鲁棒性方案 ============

class SunglassRobustDMS:
    """
    墨镜鲁棒性DMS系统
    
    多层次应对策略：
    1. 940nm红外穿透
    2. 头部姿态替代
    3. 融合决策
    """
    
    def __init__(self):
        self.ir940 = IR940nmEyeTracker(ir_power=120)
        self.dual_wavelength = DualWavelengthSystem()
        
    def process_frame(self, 
                      visible: np.ndarray,
                      ir850: np.ndarray,
                      ir940: np.ndarray,
                      face_landmarks: np.ndarray) -> dict:
        """
        处理单帧
        
        Args:
            visible: 可见光图像
            ir850: 850nm红外图像
            ir940: 940nm红外图像
            face_landmarks: 面部关键点
        
        Returns:
            result: {
                'eye_detected': bool,
                'gaze_direction': tuple,
                'perclos': float,
                'confidence': float,
                'mode': str
            }
        """
        # 检测墨镜
        has_sunglasses = self.dual_wavelength.detect_sunglasses(visible, ir850)
        
        if has_sunglasses:
            # 使用940nm
            pupil_r, glint_b, valid = self.ir940.detect_pupil_glint_ratio(
                ir940, ir940
            )
            
            if valid:
                # 可以通过PGR计算视线
                gaze = self._estimate_gaze_from_pgr(pupil_r, glint_b)
                return {
                    'eye_detected': True,
                    'gaze_direction': gaze,
                    'perclos': 0.0,  # 需要序列数据
                    'confidence': 0.85,
                    'mode': 'ir940_pgr'
                }
            else:
                # 降级：使用头部姿态
                head_pose = self._estimate_head_pose(face_landmarks)
                return {
                    'eye_detected': False,
                    'gaze_direction': head_pose,
                    'perclos': 0.0,
                    'confidence': 0.6,
                    'mode': 'head_pose_fallback'
                }
        else:
            # 使用850nm（正常DMS流程）
            # ... 正常眼动检测代码 ...
            return {
                'eye_detected': True,
                'gaze_direction': (0, 0),
                'perclos': 0.0,
                'confidence': 0.95,
                'mode': 'ir850_normal'
            }
    
    def _estimate_gaze_from_pgr(self, pupil_r: float, glint_b: float) -> Tuple[float, float]:
        """
        从瞳孔-光斑比估计视线
        
        简化版本，实际需要标定
        """
        # PGR方法：瞳孔和光斑的相对位置决定视线
        # 这里简化返回
        return (0, 0)
    
    def _estimate_head_pose(self, landmarks: np.ndarray) -> Tuple[float, float]:
        """
        从面部关键点估计头部姿态
        
        降级方案：当眼动不可靠时使用
        """
        # 简化：使用鼻尖和眼角的位置关系
        return (0, 0)


# ============ 实际测试 ============

if __name__ == "__main__":
    # 初始化
    tracker = IR940nmEyeTracker(ir_power=120)
    
    print("=" * 60)
    print("940nm红外墨镜穿透测试")
    print("=" * 60)
    
    # 测试不同墨镜
    sunglass_types = [
        "偏光墨镜（Polarized）",
        "渐变墨镜（Gradient）",
        "镜面墨镜（Mirrored）",
        "防蓝光墨镜"
    ]
    
    for sg in sunglass_types:
        quality, status = tracker.estimate_signal_quality(sg, distance=0.6)
        print(f"\n{sg}:")
        print(f"  信号质量: {quality:.2f}")
        print(f"  状态: {status}")
    
    # 双波长系统测试
    print("\n" + "=" * 60)
    print("双波长系统测试")
    print("=" * 60)
    
    dual = DualWavelengthSystem()
    
    # 模拟：有墨镜的情况
    visible = np.random.randint(20, 50, (480, 640))  # 可见光很暗
    ir850 = np.random.randint(100, 200, (480, 640))  # IR正常
    
    wavelength = dual.select_wavelength(visible, ir850)
    print(f"\n检测到墨镜，切换波长: {wavelength}nm")
    
    # 完整系统测试
    print("\n" + "=" * 60)
    print("完整墨镜鲁棒性DMS测试")
    print("=" * 60)
    
    dms = SunglassRobustDMS()
    
    # 模拟输入
    ir940 = np.random.randint(80, 150, (480, 640))
    landmarks = np.random.rand(68, 2)
    
    result = dms.process_frame(visible, ir850, ir940, landmarks)
    print(f"\n检测结果:")
    print(f"  模式: {result['mode']}")
    print(f"  置信度: {result['confidence']:.2f}")
    print(f"  眼睛检测: {result['eye_detected']}")