import numpy as np
from sklearn.ensemble import RandomForestRegressor
from typing import Tuple, Dict
import cv2

class ThermalComfortSystem:
    """
    非接触驾驶员热舒适感知系统
    
    论文: Building and Environment, 2025
    核心: 红外热成像 → YOLO面部检测 → RF TSV → 模糊HVAC
    
    TSV: -3(冷) -2(凉) -1(微凉) 0(适中) 1(微暖) 2(暖) 3(热)
    """
    
    # 面部热敏感区域权重
    ROI_WEIGHTS = {
        'forehead': 0.35,
        'nose_tip': 0.25,
        'cheek': 0.20,
        'temple': 0.15,
        'chin': 0.05
    }
    
    # TSV标尺
    TSV_SCALE = {
        -3: 'cold', -2: 'cool', -1: 'slightly_cool',
        0: 'neutral',
        1: 'slightly_warm', 2: 'warm', 3: 'hot'
    }
    
    def __init__(self):
        # 面部检测（可替换为YOLO）
        self.face_cascade = cv2.CascadeClassifier(
            cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
        )
        
        # TSV预测模型
        self.tsv_model = RandomForestRegressor(
            n_estimators=100, max_depth=10, random_state=42
        )
        
        # PMV模型参数
        self.pmv_params = {
            'met': 1.0,    # 代谢率（坐着驾驶）
            'clo': 0.5,    # 衣服热阻（夏季）
            'air_vel': 0.2, # 空气流速
        }
        
    def extract_facial_temperatures(self, thermal_image: np.ndarray) -> dict:
        """
        从红外热像提取面部各区域温度
        
        Args:
            thermal_image: 红外图像（温度矩阵, °C）
            
        Returns:
            dict: 各区域平均温度
        """
        # 转换为伪彩用于面部检测
        normalized = ((thermal_image - 20) / (40 - 20) * 255).astype(np.uint8)
        colored = cv2.applyColorMap(normalized, cv2.COLORMAP_JET)
        gray = cv2.cvtColor(colored, cv2.COLOR_BGR2GRAY)
        
        faces = self.face_cascade.detectMultiScale(gray, 1.3, 5)
        
        if len(faces) == 0:
            return {'error': 'no_face_detected'}
        
        x, y, w, h = faces[0]
        
        # 提取各区域
        forehead = thermal_image[y:y+h//4, x+w//4:x+3*w//4]
        nose = thermal_image[y+h//4:y+3*h//8, x+3*w//8:x+5*w//8]
        cheek_l = thermal_image[y+h//4:y+h//2, x:x+w//4]
        cheek_r = thermal_image[y+h//4:y+h//2, x+3*w//4:x+w]
        cheek = np.concatenate([cheek_l.ravel(), cheek_r.ravel()])
        temple = thermal_image[y:y+h//3, x+w-10:x+w]
        chin = thermal_image[y+3*h//4:y+h, x+w//4:x+3*w//4]
        
        return {
            'forehead': np.mean(forehead),
            'nose_tip': np.mean(nose),
            'cheek': np.mean(cheek),
            'temple': np.mean(temple),
            'chin': np.mean(chin)
        }
    
    def predict_tsv(self, facial_temps: dict, 
                    ambient_temp: float = 24,
                    humidity: float = 50) -> float:
        """
        预测热感觉投票（TSV）
        
        Args:
            facial_temps: 面部各区域温度
            ambient_temp: 环境温度
            humidity: 湿度
            
        Returns:
            tsv: -3 to +3
        """
        if 'error' in facial_temps:
            # 面部检测失败→使用PMV模型
            return self._compute_pmv(ambient_temp, humidity)
        
        # 特征向量
        features = np.array([
            facial_temps['forehead'],
            facial_temps['nose_tip'],
            facial_temps['cheek'],
            facial_temps['temple'],
            facial_temps['chin'],
            ambient_temp,
            humidity,
            # 鼻尖-额头温差（热舒适敏感指标）
            facial_temps['forehead'] - facial_temps['nose_tip'],
        ]).reshape(1, -1)
        
        tsv = self.tsv_model.predict(features)[0]
        return np.clip(tsv, -3, 3)
    
    def _compute_pmv(self, air_temp: float, humidity: float) -> float:
        """
        PMV (Predicted Mean Vote) 物理模型
        用于面部检测失败时的降级方案
        """
        # 简化PMV计算
        # 完整版本需考虑辐射温度、衣服热阻、代谢率、空气流速
        tr = air_temp  # 假设辐射温度=空气温度
        pa = humidity / 100 * 2.338  # 水蒸气分压
        
        # 简化热负荷
        L = 0.303 * np.exp(-0.036 * 58.2) - 0.028  # 代谢率58.2 W/m²
        pmv = (0.303 * L) * (
            58.2 - 0.35 * (air_temp - 30) - 
            0.42 * (58.2 - 58.15) - 
            0.0023 * (58.2) * (44 - pa) - 
            0.0014 * 58.2 * (34 - air_temp) - 
            1.7 * 0.1 * (1 - 0.01 * humidity)
        )
        return np.clip(pmv, -3, 3)
    
    def compute_hvac_control(self, tsv: float, current_hvac: dict) -> dict:
        """
        模糊逻辑HVAC控制
        
        Args:
            tsv: 当前TSV
            current_hvac: 当前HVAC状态
            
        Returns:
            dict: 新HVAC设置
        """
        # 模糊规则
        if tsv <= -2:  # 冷
            return {
                'target_temp': min(current_hvac.get('target_temp', 22) + 2, 28),
                'fan_speed': min(current_hvac.get('fan_speed', 3) + 2, 8),
                'mode': 'heating',
                'air_direction': 'feet'
            }
        elif tsv <= -1:  # 凉
            return {
                'target_temp': current_hvac.get('target_temp', 22) + 1,
                'fan_speed': current_hvac.get('fan_speed', 3) + 1,
                'mode': 'heating',
                'air_direction': 'feet'
            }
        elif tsv <= 1:  # 适中
            return current_hvac  # 不变
        elif tsv <= 2:  # 暖
            return {
                'target_temp': max(current_hvac.get('target_temp', 22) - 1, 18),
                'fan_speed': current_hvac.get('fan_speed', 3) + 1,
                'mode': 'cooling',
                'air_direction': 'face'
            }
        else:  # 热
            return {
                'target_temp': max(current_hvac.get('target_temp', 22) - 2, 16),
                'fan_speed': min(current_hvac.get('fan_speed', 3) + 2, 8),
                'mode': 'cooling',
                'air_direction': 'face'
            }


# 实际测试
if __name__ == "__main__":
    system = ThermalComfortSystem()
    
    # 模拟红外热像
    np.random.seed(42)
    thermal = np.random.normal(33, 2, (120, 160))
    
    # 提取面部温度
    temps = {
        'forehead': 34.5,
        'nose_tip': 31.2,  # 鼻尖偏冷→可能冷
        'cheek': 33.0,
        'temple': 34.8,
        'chin': 33.5
    }
    
    # 预测TSV
    tsv = system.predict_tsv(temps, ambient_temp=20, humidity=50)
    
    print("=== 驾驶员热舒适感知 ===")
    print(f"面部温度: {temps}")
    print(f"环境温度: 20°C, 湿度: 50%")
    print(f"预测TSV: {tsv:.1f} ({system.TSV_SCALE.get(int(tsv), 'unknown')})")
    
    # HVAC控制
    current_hvac = {'target_temp': 22, 'fan_speed': 3, 'mode': 'auto'}
    new_hvac = system.compute_hvac_control(tsv, current_hvac)
    
    print(f"\n当前HVAC: {current_hvac}")
    print(f"建议HVAC: {new_hvac}")
    
    print(f"\n=== 论文性能 ===")
    print(f"  RF TSV准确率: >85%")
    print(f"  能耗节省: 12-18%")
    print(f"  响应延迟: <2秒")
    print(f"  遮挡降级: 自动切换PMV模型")