"""
NVIDIA Isaac Sim 座舱数据合成脚本
生成DMS/OMS训练数据

依赖: Isaac Sim >= 4.5, Omniverse >= 2026.1
运行环境: RTX 4090 / A100 / H100

输出格式: COCO标注 + RGB + 深度 + 分割
"""

import numpy as np
from dataclasses import dataclass
from typing import List, Dict, Tuple
import json

@dataclass
class CabinSceneConfig:
    """座舱场景配置"""
    # 摄像头配置
    camera_position: Tuple[float, float, float]  # (x, y, z) in meters
    camera_rotation: Tuple[float, float, float]  # (roll, pitch, yaw) in degrees
    camera_fov: float = 90.0  # 视场角
    camera_resolution: Tuple[int, int] = (1920, 1200)  # 宽×高
    
    # 乘员配置
    num_occupants: int = 2  # 驾驶员 + 乘客
    driver_pose: str = "normal"  # normal/reclined/leaning/sleeping
    passenger_pose: str = "normal"
    
    # 光照配置
    light_condition: str = "daylight"  # daylight/sunset/night_ir/night_lowlight
    light_intensity: float = 500.0  # lux
    
    # 车辆内饰
    cabin_type: str = "sedan"  # sedan/suv/truck
    interior_color: str = "black"
    
    # 数据增强
    use_domain_randomization: bool = True
    random_texture: bool = True
    random_lighting: bool = True


class CabinDataGenerator:
    """
    座舱合成数据生成器
    基于Isaac Sim + Omniverse
    
    数据生成流程:
    1. 加载座舱3D场景 (OpenUSD)
    2. 加载Metahuman乘员模型
    3. 设置姿态/表情
    4. 配置摄像头和光照
    5. 渲染 + 自动标注
    6. 域随机化 → 重复
    """
    
    # 疲劳表情参数
    FATIGUE_EXPRESSIONS = {
        'normal': {'eye_openness': 0.8, 'brow_height': 0.5, 'mouth_open': 0.1},
        'drowsy': {'eye_openness': 0.3, 'brow_height': 0.2, 'mouth_open': 0.2},
        'microsleep': {'eye_openness': 0.0, 'brow_height': 0.1, 'mouth_open': 0.3},
        'yawning': {'eye_openness': 0.5, 'brow_height': 0.3, 'mouth_open': 0.9},
    }
    
    # 姿态参数
    POSE_CONFIGS = {
        'normal': {'torso_angle': 0, 'head_pitch': 0, 'head_yaw': 0},
        'reclined': {'torso_angle': 35, 'head_pitch': -15, 'head_yaw': 0},
        'leaning_fwd': {'torso_angle': -20, 'head_pitch': 25, 'head_yaw': 0},
        'phone': {'torso_angle': 5, 'head_pitch': 10, 'head_yaw': -30},
        'sleeping': {'torso_angle': 15, 'head_pitch': 30, 'head_yaw': 0},
        'feet_dash': {'torso_angle': -10, 'head_pitch': -5, 'head_yaw': 0},
    }
    
    def __init__(self, scene_config: CabinSceneConfig):
        self.config = scene_config
        self.scene_loaded = False
        self.metahuman_loaded = False
        
    def generate_batch(
        self, 
        num_samples: int = 1000,
        output_dir: str = "output/cabin_synthetic"
    ) -> Dict:
        """
        批量生成合成数据
        
        Args:
            num_samples: 生成样本数
            output_dir: 输出目录
            
        Returns:
            stats: 生成统计
        """
        stats = {
            'total_samples': 0,
            'by_activity': {},
            'by_lighting': {},
            'by_occupant': {},
            'label_distribution': {},
        }
        
        activities = list(self.POSE_CONFIGS.keys())
        lightings = ['daylight', 'sunset', 'night_ir', 'night_lowlight']
        
        for i in range(num_samples):
            # 域随机化
            activity = activities[i % len(activities)]
            lighting = lightings[i % len(lightings)]
            
            # 更新配置
            self.config.driver_pose = activity
            self.config.light_condition = lighting
            
            if self.config.use_domain_randomization:
                # 随机化摄像头位置
                self.config.camera_position = (
                    np.random.uniform(1.8, 2.2),
                    np.random.uniform(-0.1, 0.1),
                    np.random.uniform(1.1, 1.3)
                )
                # 随机化光照强度
                if lighting == 'daylight':
                    self.config.light_intensity = np.random.uniform(400, 600)
                elif lighting == 'night_lowlight':
                    self.config.light_intensity = np.random.uniform(5, 20)
                elif lighting == 'night_ir':
                    self.config.light_intensity = np.random.uniform(0, 5)  # IR补光
                else:
                    self.config.light_intensity = np.random.uniform(100, 300)
            
            # 渲染并生成标注
            sample = self._render_and_annotate()
            
            stats['total_samples'] += 1
            stats['by_activity'][activity] = stats['by_activity'].get(activity, 0) + 1
            stats['by_lighting'][lighting] = stats['by_lighting'].get(lighting, 0) + 1
            
        return stats
    
    def _render_and_annotate(self) -> dict:
        """
        渲染场景并生成自动标注
        
        Ground Truth来源：
        - 面部关键点: Metahuman控制参数 → 2D投影
        - 行为标签: 姿态配置 → 类别标签
        - 深度图: RTX渲染深度通道
        - 分割图: 实例分割自动生成
        """
        # 在实际Isaac Sim中，这里会调用:
        # self.world.update_scene(self.config)
        # rgb = self.camera.get_rgba()
        # depth = self.camera.get_depth()
        # segmentation = self.camera.get_segmentation()
        # keypoints = self.metahuman.get_facial_keypoints_2d()
        
        pose_params = self.POSE_CONFIGS.get(self.config.driver_pose, {})
        expr_params = self.FATIGUE_EXPRESSIONS.get(
            'drowsy' if 'sleep' in self.config.driver_pose else 'normal', {}
        )
        
        return {
            'rgb_image': f"render_{self.config.driver_pose}_{self.config.light_condition}.png",
            'depth_map': f"depth_{self.config.driver_pose}_{self.config.light_condition}.exr",
            'segmentation': f"seg_{self.config.driver_pose}_{self.config.light_condition}.png",
            'keypoints_2d': f"kp_{self.config.driver_pose}_{self.config.light_condition}.json",
            'keypoints_3d': f"kp3d_{self.config.driver_pose}_{self.config.light_condition}.json",
            'label': self.config.driver_pose,
            'lighting': self.config.light_condition,
            'camera_config': {
                'position': self.config.camera_position,
                'fov': self.config.camera_fov,
                'resolution': self.config.camera_resolution,
            },
            'pose_params': pose_params,
            'expression_params': expr_params,
        }


# ==================== 生成计划 ====================
if __name__ == "__main__":
    config = CabinSceneConfig(
        camera_position=(2.0, 0.0, 1.2),
        camera_rotation=(0, 0, 0),
        camera_fov=90.0,
        camera_resolution=(1920, 1200),
        num_occupants=2,
        driver_pose="normal",
        passenger_pose="normal",
        light_condition="daylight",
        light_intensity=500.0,
        cabin_type="sedan",
        interior_color="black",
        use_domain_randomization=True,
    )
    
    gen = CabinDataGenerator(config)
    
    # 生成10,000张合成训练数据
    stats = gen.generate_batch(num_samples=10000)
    
    print("=" * 60)
    print("座舱合成数据生成统计")
    print("=" * 60)
    print(f"总样本数: {stats['total_samples']}")
    print(f"\n按活动分类:")
    for act, count in sorted(stats['by_activity'].items()):
        print(f"  {act}: {count} ({count/stats['total_samples']*100:.1f}%)")
    print(f"\n按光照条件:")
    for light, count in sorted(stats['by_lighting'].items()):
        print(f"  {light}: {count} ({count/stats['total_samples']*100:.1f}%)")

# 验证合成数据有效性的标准流程

def validate_synthetic_data(
    real_test_set, 
    synth_train_set, 
    model_architecture
):
    """
    1. 基线: 仅真实数据训练 → 真实测试
    2. 增量: 合成+真实训练 → 真实测试
    3. 零样本: 仅合成训练 → 真实测试
    
    有效性指标：
    - 增量 > 基线 → 合成数据有正向贡献
    - 零样本 > 50%基线 → 合成数据质量可用
    - 增量 - 基线 > 5% → 值得纳入管道
    """
    results = {
        'baseline': train_and_eval(
            model_architecture, 
            train_data='real_only',
            test_data=real_test_set
        ),
        'incremental': train_and_eval(
            model_architecture,
            train_data='real + synthetic',
            test_data=real_test_set
        ),
        'zero_shot': train_and_eval(
            model_architecture,
            train_data='synthetic_only',
            test_data=real_test_set
        ),
    }
    
    # 有效性判断
    valid = results['incremental'] > results['baseline'] * 1.05
    quality = results['zero_shot'] > results['baseline'] * 0.50
    
    return {
        'results': results,
        'effective': valid,
        'quality_ok': quality,
        'recommendation': 'continue' if valid else 'improve_generation'
    }