"""
NVIDIA NuRec 概念流程：从真实传感器数据到合成场景

虽然 NuRec 具体模型未开源，但可以复现其概念流程：
1. 多传感器数据采集
2. 神经辐射场（NeRF/3DGS）重建
3. 场景编辑（视角/光照/对象变换）
4. 新视角渲染
"""

import numpy as np
from dataclasses import dataclass, field
from typing import List, Optional
from enum import Enum


class SensorType(Enum):
    RGB_CAMERA = "rgb_camera"
    IR_CAMERA = "ir_camera"
    RADAR = "radar"
    LIDAR = "lidar"
    UWB = "uwb"


@dataclass
class SensorFrame:
    """单帧传感器数据"""
    sensor_type: SensorType
    timestamp: float
    data: np.ndarray  # 图像/点云/雷达数据
    extrinsics: np.ndarray  # 4x4 外参矩阵
    intrinsics: Optional[np.ndarray] = None  # 3x3 内参矩阵


class NeuralReconstructionPipeline:
    """
    NuRec 概念流程：神经场景重建管道
    
    步骤：
    1. 传感器数据预处理
    2. 位姿估计（COLMAP/SfM）
    3. 神经辐射场训练（3D Gaussian Splatting）
    4. 场景编辑
    5. 新视角渲染
    """
    
    def __init__(self, scene_bounds: tuple = (0, 5, 0, 3, 0, 2.5)):
        """
        Args:
            scene_bounds: 座舱场景边界 (x_min, x_max, y_min, y_max, z_min, z_max)
                          典型座舱：5m x 3m x 2.5m
        """
        self.scene_bounds = scene_bounds
        self.frames: List[SensorFrame] = []
        self.point_cloud = None
        self.gaussian_model = None
    
    def add_frame(self, frame: SensorFrame):
        """添加传感器帧"""
        self.frames.append(frame)
    
    def estimate_poses(self):
        """
        多视角位姿估计
        
        使用 COLMAP 风格的 SfM（Structure from Motion）
        """
        num_frames = len(self.frames)
        print(f"  帧数: {num_frames}")
        
        # 模拟位姿估计
        poses = []
        for i, frame in enumerate(self.frames):
            # 模拟相机位姿（围绕座舱轨道）
            angle = 2 * np.pi * i / num_frames
            t = np.array([
                2.5 + 1.5 * np.cos(angle),
                1.5 + 0.5 * np.sin(angle),
                1.2
            ])
            
            R = np.eye(4)
            R[:3, :3] = np.array([
                [np.cos(angle), 0, np.sin(angle)],
                [0, 1, 0],
                [-np.sin(angle), 0, np.cos(angle)]
            ])
            R[:3, 3] = t
            
            poses.append(R)
        
        print(f"  估计位姿: {len(poses)} 个")
        return poses
    
    def train_gaussian_model(self, poses):
        """
        3D Gaussian Splatting 训练
        
        概念模型：
        - 每个高斯球：位置 + 协方差 + 不透明度 + 球谐系数
        - 渲染：alpha 混合
        """
        # 模拟初始化 50000 个高斯球
        num_gaussians = 50000
        
        # 随机初始化在座舱空间内
        positions = np.random.uniform(
            low=[self.scene_bounds[0], self.scene_bounds[2], self.scene_bounds[4]],
            high=[self.scene_bounds[1], self.scene_bounds[3], self.scene_bounds[5]],
            size=(num_gaussians, 3)
        )
        
        # 协方差（各向同性初始化）
        scales = np.random.uniform(0.001, 0.01, size=(num_gaussians, 3))
        rotations = np.tile(np.array([1, 0, 0, 0]), (num_gaussians, 1))
        
        # 不透明度
        opacities = np.random.uniform(0.01, 0.5, size=num_gaussians)
        
        # 球谐系数（模拟颜色）
        sh_coeffs = np.random.randn(num_gaussians, 3, 3)  # 3阶球谐
        
        print(f"  高斯球数: {num_gaussians}")
        print(f"  训练迭代: ~30000 steps")
        
        return {
            'positions': positions,
            'scales': scales,
            'rotations': rotations,
            'opacities': opacities,
            'sh_coeffs': sh_coeffs
        }
    
    def render_novel_view(self, model, camera_pose: np.ndarray,
                          resolution=(1920, 1080)):
        """
        从新视角渲染场景
        
        Args:
            model: 训练好的高斯模型
            camera_pose: 4x4 相机位姿
            resolution: (width, height)
            
        Returns:
            rendered_image: shape=(H, W, 3)
        """
        H, W = resolution[1], resolution[0]
        
        # 模拟渲染（实际使用 CUDA 加速的光栅化）
        image = np.zeros((H, W, 3), dtype=np.float32)
        
        # 简化的 alpha 混合渲染
        positions = model['positions']
        opacities = model['opacities']
        sh_coeffs = model['sh_coeffs']
        
        # 投影到图像平面（简化版）
        fx = fy = 1000  # 焦距
        cx, cy = W/2, H/2
        
        camera_center = camera_pose[:3, 3]
        camera_R = camera_pose[:3, :3]
        
        for i in range(0, len(positions), 100):  # 采样渲染
            world_pos = positions[i]
            cam_pos = camera_R.T @ (world_pos - camera_center)
            
            if cam_pos[2] <= 0:
                continue
            
            # 投影
            u = int(fx * cam_pos[0] / cam_pos[2] + cx)
            v = int(fy * cam_pos[1] / cam_pos[2] + cy)
            
            if 0 <= u < W and 0 <= v < H:
                color = np.clip(sh_coeffs[i, :, 0], 0, 1)
                alpha = opacities[i]
                image[v, u] = image[v, u] * (1 - alpha) + color * alpha
        
        return image


# ========== 测试 ==========

if __name__ == "__main__":
    pipeline = NeuralReconstructionPipeline()
    
    # 模拟添加 20 帧 DMS 摄像头数据
    for i in range(20):
        frame = SensorFrame(
            sensor_type=SensorType.RGB_CAMERA,
            timestamp=i * 0.1,
            data=np.random.randn(1080, 1920, 3),
            extrinsics=np.eye(4)
        )
        pipeline.add_frame(frame)
    
    print("=== NuRec 神经重建流程 ===")
    print("Step 1: 位姿估计")
    poses = pipeline.estimate_poses()
    
    print("\nStep 2: 3D Gaussian Splatting 训练")
    model = pipeline.train_gaussian_model(poses)
    
    print("\nStep 3: 新视角渲染")
    novel_pose = np.eye(4)
    novel_pose[:3, 3] = np.array([2.0, 1.5, 1.3])
    image = pipeline.render_novel_view(model, novel_pose)
    print(f"  渲染图像: {image.shape}")
    print(f"  非零像素: {np.count_nonzero(image.any(axis=2))}")

"""
NVIDIA Cosmos 世界模型概念框架

Cosmos 是"世界基础模型"（World Foundation Model）：
- 从真实数据学习世界动态规律
- 可以生成新的场景变体
- 支持条件生成（天气/光照/交通/传感器）

对座舱数据的启示：
- 从真实 DMS 数据生成新的疲劳/分心/酒驾场景
- 生成不同光照/遮挡/乘员组合
- 扩展训练数据集
"""

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


@dataclass
class CabinSceneConfig:
    """座舱场景配置"""
    # 乘员配置
    driver_present: bool = True
    driver_state: str = "normal"  # normal/fatigue/distraction/alcohol
    passenger_front: bool = False
    passenger_rear_left: bool = False
    passenger_rear_right: bool = False
    child_seat: bool = False
    
    # 环境条件
    lighting: str = "day"  # day/night/dusk/tunnel
    temperature: float = 22.0  # 摄氏度
    
    # 传感器配置
    camera_position: str = "dashboard"  # dashboard/overhead/A-pillar
    camera_type: str = "rgb-ir"  # rgb/rgb-ir/depth
    radar_enabled: bool = True
    
    # 场景动态
    vehicle_speed: float = 60.0  # km/h
    road_type: str = "highway"  # highway/urban/rural


class CosmosCabinGenerator:
    """
    Cosmos 座舱场景生成器概念
    
    使用世界模型生成多样化的座舱场景
    """
    
    def __init__(self):
        self.base_scenes = []
        self.generated_count = 0
    
    def add_base_scene(self, config: CabinSceneConfig):
        """添加真实采集的基础场景"""
        self.base_scenes.append(config)
    
    def generate_variants(self, base: CabinSceneConfig,
                          num_variants: int = 10) -> List[CabinSceneConfig]:
        """
        从基础场景生成变体
        
        策略：
        1. 改变环境条件（光照/温度）
        2. 改变乘员组合
        3. 改变驾驶员状态
        4. 改变传感器配置
        """
        variants = []
        
        # 1. 光照变体
        lightings = ["day", "night", "dusk", "tunnel", "dawn"]
        for light in lightings:
            if light != base.lighting:
                v = CabinSceneConfig(**{**base.__dict__, 'lighting': light})
                variants.append(v)
        
        # 2. 驾驶员状态变体
        states = ["normal", "fatigue", "distraction", "alcohol"]
        for state in states:
            if state != base.driver_state:
                v = CabinSceneConfig(**{**base.__dict__, 'driver_state': state})
                variants.append(v)
        
        # 3. 乘员组合变体
        # 只有驾驶员
        v1 = CabinSceneConfig(**{**base.__dict__})
        v1.passenger_front = False
        v1.passenger_rear_left = False
        v1.passenger_rear_right = False
        variants.append(v1)
        
        # 驾驶员 + 前排乘客
        v2 = CabinSceneConfig(**{**base.__dict__})
        v2.passenger_front = True
        variants.append(v2)
        
        # 全座
        v3 = CabinSceneConfig(**{**base.__dict__})
        v3.passenger_front = True
        v3.passenger_rear_left = True
        v3.passenger_rear_right = True
        variants.append(v3)
        
        # 有儿童座椅
        v4 = CabinSceneConfig(**{**base.__dict__})
        v4.child_seat = True
        v4.passenger_rear_left = True
        variants.append(v4)
        
        # 4. 传感器位置变体
        positions = ["dashboard", "overhead", "A-pillar"]
        for pos in positions:
            if pos != base.camera_position:
                v = CabinSceneConfig(**{**base.__dict__, 'camera_position': pos})
                variants.append(v)
        
        # 取前 num_variants 个
        np.random.shuffle(variants)
        self.generated_count += len(variants[:num_variants])
        return variants[:num_variants]
    
    def generate_corner_cases(self) -> List[CabinSceneConfig]:
        """生成 corner case 场景"""
        corner_cases = [
            # 驾驶员突发疾病
            CabinSceneConfig(
                driver_state="fatigue",
                lighting="night",
                vehicle_speed=100,
                road_type="highway"
            ),
            # 儿童单独在后排（CPD场景）
            CabinSceneConfig(
                driver_present=False,
                child_seat=True,
                passenger_rear_left=True,
                lighting="day",
                vehicle_speed=0
            ),
            # 酒驾 + 夜间
            CabinSceneConfig(
                driver_state="alcohol",
                lighting="night",
                vehicle_speed=60,
                road_type="urban"
            ),
            # 隧道中分心
            CabinSceneConfig(
                driver_state="distraction",
                lighting="tunnel",
                vehicle_speed=80,
                road_type="highway"
            ),
            # 儿童在后备箱（极端case）
            CabinSceneConfig(
                driver_present=False,
                child_seat=False,
                lighting="day",
                vehicle_speed=0,
                camera_position="overhead"
            ),
        ]
        return corner_cases


# ========== 测试 ==========

if __name__ == "__main__":
    generator = CosmosCabinGenerator()
    
    # 基础场景：白天高速，驾驶员正常
    base = CabinSceneConfig(
        driver_present=True,
        driver_state="normal",
        lighting="day",
        vehicle_speed=100,
        road_type="highway",
        camera_position="dashboard"
    )
    generator.add_base_scene(base)
    
    # 生成变体
    variants = generator.generate_variants(base, num_variants=10)
    print(f"=== Cosmos 场景生成 ===")
    print(f"基础场景: 1 个")
    print(f"生成变体: {len(variants)} 个")
    for i, v in enumerate(variants):
        print(f"  变体{i+1}: "
              f"光照={v.lighting}, "
              f"状态={v.driver_state}, "
              f"前排乘客={v.passenger_front}, "
              f"儿童座椅={v.child_seat}")
    
    # 生成 corner cases
    corners = generator.generate_corner_cases()
    print(f"\nCorner Cases: {len(corners)} 个")
    for i, c in enumerate(corners):
        print(f"  CC{i+1}: "
              f"驾驶员={c.driver_present}, "
              f"状态={c.driver_state}, "
              f"光照={c.lighting}, "
              f"儿童座椅={c.child_seat}")

# IMS NuRec 数据合成管道
pipeline = [
    "1. 采集真实座舱多视角数据（5-10个摄像头）",
    "2. NuRec 神经重建座舱 3D 场景",
    "3. 编辑：改变乘员/姿态/光照/遮挡",
    "4. 渲染新视角的 DMS/OMS 图像",
    "5. 自动标注（3D->2D投影）",
    "6. 生成训练数据集",
]