"""
FORVIA HELLA 无镜3D AR-HUD 内容生成框架
核心: 计算光学 + 3D光场 + 软件定义显示
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Dict, List, Tuple

class ComputationalOpticsEngine(nn.Module):
    """
    计算光学引擎
    
    将2D渲染内容转换为3D光场编码
    
    Distance Technologies 核心技术: AI-based computational optics
    """
    
    def __init__(self, num_views: int = 9, resolution: Tuple[int, int] = (1920, 720)):
        super().__init__()
        self.num_views = num_views  # 光场视角数
        self.resolution = resolution
        
        # 深度估计
        self.depth_net = nn.Sequential(
            nn.Conv2d(3, 64, 3, 2, 1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, 128, 3, 2, 1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Conv2d(128, 64, 3, 1, 1),
            nn.Sigmoid(),
        )
        
        # 视差生成
        self.parallax_net = nn.Sequential(
            nn.Conv2d(64, 128, 3, 1, 1),
            nn.ReLU(),
            nn.Conv2d(128, num_views - 1, 1),  # N-1个视差图
        )
        
    def forward(self, content_2d: torch.Tensor) -> Dict:
        """
        将2D内容转换为3D光场
        
        Args:
            content_2d: (B, 3, H, W) 2D渲染内容
            
        Returns:
            light_field: {
                'views': (B, num_views, 3, H, W),  # 多视角图像
                'depth': (B, 1, H, W),              # 深度图
                'disparity': (B, num_views-1, H, W), # 视差图
            }
        """
        # 深度估计
        depth_feat = self.depth_net(content_2d)
        depth = depth_feat[:, :1]  # (B, 1, H/4, W/4)
        
        # 视差生成
        disparity = self.parallax_net(depth_feat)  # (B, N-1, H/4, W/4)
        
        # 基于视差生成多视角
        views = [content_2d]
        for i in range(self.num_views - 1):
            # 简化: 水平偏移
            shift = disparity[:, i:i+1].expand(-1, 3, -1, -1)
            shifted = torch.roll(content_2d, 
                                shifts=int((i + 1) * 5), 
                                dims=3)
            views.append(shifted)
        
        return {
            'views': torch.stack(views, dim=1),
            'depth': depth,
            'disparity': disparity,
        }


class ARContentGenerator(nn.Module):
    """
    AR内容生成器
    
    生成与路面环境空间对齐的3D AR内容
    
    应用: 导航、车道、危险警告等
    """
    
    # AR内容类型
    CONTENT_TYPES = {
        'navigation_arrow': {'depth': 15.0, 'color': (0, 1, 0)},
        'lane_highlight': {'depth': 20.0, 'color': (0, 0.8, 1)},
        'hazard_warning': {'depth': 30.0, 'color': (1, 0.2, 0)},
        'exit_sign': {'depth': 50.0, 'color': (1, 1, 0)},
        'speed_display': {'depth': 5.0, 'color': (1, 1, 1)},
    }
    
    def __init__(self):
        super().__init__()
        self.optics = ComputationalOpticsEngine()
        
    def generate_nav_overlay(self, road_scene: torch.Tensor,
                              nav_data: Dict) -> torch.Tensor:
        """
        生成导航AR叠加
        
        Args:
            road_scene: (B, 3, H, W) 道路场景
            nav_data: {
                'route_points': List[(x, y)],  # 路线点
                'lanes': List[int],            # 车道索引
                'exit': Optional[int],         # 出口
            }
            
        Returns:
            ar_overlay: (B, 3, H, W) AR叠加层
        """
        B, C, H, W = road_scene.shape
        overlay = torch.zeros_like(road_scene)
        
        for i in range(B):
            # 导航箭头
            for pt in nav_data.get('route_points', []):
                x, y = int(pt[0] * W), int(pt[1] * H)
                if 0 <= x < W and 0 <= y < H:
                    self._draw_arrow(overlay[i], x, y, 
                                    color=self.CONTENT_TYPES['navigation_arrow']['color'])
            
            # 车道高亮
            for lane_idx in nav_data.get('lanes', []):
                self._highlight_lane(overlay[i], lane_idx, W, H)
        
        return overlay
    
    def _draw_arrow(self, img, x, y, color):
        """绘制导航箭头"""
        for dy in range(-20, 21):
            for dx in range(-10, 11):
                px, py = x + dx, y + dy
                if 0 <= px < img.shape[2] and 0 <= py < img.shape[1]:
                    if abs(dx) < 10 - abs(dy) / 4:
                        for c in range(3):
                            img[c, py, px] = color[c]
    
    def _highlight_lane(self, img, lane_idx, W, H):
        """高亮车道"""
        lane_width = W // 4
        x_start = lane_idx * lane_width
        x_end = x_start + lane_width
        for y in range(H // 2, H):
            for x in range(x_start, x_end):
                if 0 <= x < W:
                    img[1, y, x] = torch.clamp(img[1, y, x] + 0.3, max=1.0)


class DriverDistractionAnalyzer(nn.Module):
    """
    驾驶员分心分析器
    
    AR-HUD与DMS协同: 通过AR内容引导注意力，减少分心
    
    关键: AR-HUD使驾驶员视线不离路，DMS验证效果
    """
    
    def __init__(self):
        super().__init__()
        self.gaze_zones = [
            'road_ahead',     # 前方道路
            'ar_hud',         # AR-HUD区域
            'dashboard',      # 仪表盘
            'center_screen',  # 中控屏
            'side_mirror',    # 后视镜
            'off_road',       # 离路
        ]
        
    def analyze_attention(self, gaze_direction: torch.Tensor) -> Dict:
        """
        分析注意力分布
        
        Args:
            gaze_direction: (B, 2) 视线方向
            
        Returns:
            analysis: {
                'primary_zone': str,
                'attention_score': float,
                'distraction_risk': str,
                'ar_enhancement': str,
            }
        """
        B = gaze_direction.shape[0]
        results = []
        
        for i in range(B):
            dx, dy = gaze_direction[i]
            
            # 区域分类
            if abs(dx) < 0.1 and abs(dy) < 0.1:
                zone = 'road_ahead'
                risk = 'low'
                ar_suggestion = '导航箭头保持显示'
            elif abs(dx) < 0.15 and abs(dy) < 0.15:
                zone = 'ar_hud'
                risk = 'low'
                ar_suggestion = 'AR内容已关注'
            elif dy < -0.2:
                zone = 'dashboard'
                risk = 'medium'
                ar_suggestion = '将仪表信息投射到AR-HUD'
            elif abs(dx) > 0.3:
                zone = 'center_screen'
                risk = 'high'
                ar_suggestion = '将中控内容投射到AR-HUD'
            else:
                zone = 'off_road'
                risk = 'high'
                ar_suggestion = '增强AR警告显示'
            
            results.append({
                'primary_zone': zone,
                'attention_score': 1.0 if zone in ['road_ahead', 'ar_hud'] else 0.5,
                'distraction_risk': risk,
                'ar_enhancement': ar_suggestion,
            })
        
        return {'results': results}


# ========== 测试 ==========
if __name__ == "__main__":
    torch.manual_seed(42)
    
    # 测试1: AR内容生成
    print("=" * 60)
    print("测试1: 3D AR内容生成")
    content = torch.randn(2, 3, 480, 960)  # 2D渲染内容
    engine = ComputationalOpticsEngine(num_views=9)
    lf = engine(content)
    print(f"  多视角: {lf['views'].shape}")
    print(f"  深度图: {lf['depth'].shape}")
    print(f"  视差图: {lf['disparity'].shape}")
    
    # 测试2: 导航AR叠加
    print("\n测试2: 导航AR叠加")
    gen = ARContentGenerator()
    road = torch.randn(1, 3, 480, 960)
    nav_data = {
        'route_points': [(0.5, 0.7), (0.6, 0.6), (0.7, 0.5)],
        'lanes': [1],
    }
    overlay = gen.generate_nav_overlay(road, nav_data)
    print(f"  叠加层: {overlay.shape}")
    print(f"  非零像素: {(overlay > 0).sum().item()}")
    
    # 测试3: 分心分析
    print("\n测试3: AR-HUD与DMS协同分心分析")
    analyzer = DriverDistractionAnalyzer()
    
    test_gazes = [
        torch.tensor([[0.0, 0.0]]),   # 前方
        torch.tensor([[0.12, 0.1]]),  # AR-HUD
        torch.tensor([[0.5, -0.3]]),   # 中控屏
    ]
    
    for g in test_gazes:
        result = analyzer.analyze(g)
        r = result['results'][0]
        print(f"  视线({g[0].tolist()}): {r['primary_zone']}, "
              f"风险={r['distraction_risk']}, 建议={r['ar_enhancement']}")
    
    print("\n✅ 测试通过")