"""
Co-MLOps + Cosmos 集成架构

搜索(Cosmos Reason) → 创建(Cosmos Predict) → 增强(Cosmos Transfer) → 标注(CoMET)
"""

cosmos_pipeline = {
    "Cosmos Reason": {
        "角色": "基于推理的数据搜索",
        "输入": "驾驶视频",
        "输出": "结构化JSON(场景描述/天气/时间/位置/行为)",
        "ISO标准": "ISO 34504:2024 场景标签",
        "IMS适配": "座舱视频自动标签: 驾驶员状态/乘客行为/光照条件",
        "优势": "PB级视频库的自然语言搜索"
    },
    "Cosmos Predict": {
        "角色": "边缘案例创建",
        "输入": "文本/图像/视频提示",
        "输出": "物理一致的未来世界状态视频",
        "IMS适配": "生成罕见座舱场景: 儿童遗留+异常姿态+紧急情况",
        "优势": "物理一致性,非随机生成"
    },
    "Cosmos Transfer": {
        "角色": "结构化数据增强",
        "输入": "HD地图/深度图/分割图/布局",
        "输出": "多样化驾驶视频",
        "IMS适配": "从座舱3D布局图生成多角度/多光照训练数据",
        "优势": "可控生成,保持场景结构"
    },
    "CoMET": {
        "角色": "自动标注",
        "输入": "摄像头+LiDAR数据",
        "输出": "全景分割/交通灯/3D检测",
        "模型数": "12个大模型集成",
        "IMS适配": "座舱视频自动标注: 人脸/身体/手势/物体"
    }
}

"""
IMS 座舱数据合成完整管线

基于 NVIDIA Cosmos 3 + Omniverse + Metahuman
"""

import json
from dataclasses import dataclass
from typing import List

@dataclass
class CabinSceneConfig:
    """座舱场景配置"""
    # 场景类型
    scene_type: str  # "normal_driving", "fatigue", "distraction", "cpd", "oop"
    # 乘客配置
    driver_age: int = 35
    driver_gender: str = "male"
    driver_ethnicity: str = "asian"
    passengers: List[dict] = None  # [{"age": 3, "position": "rear_left"}]
    # 环境条件
    lighting: str = "day"  # "day", "night", "dawn", "dusk", "tunnel"
    weather: str = "clear"  # "clear", "rain", "snow"
    # 传感器配置
    camera_config: str = "front_dms"  # "front_dms", "rear_oms", "side"
    camera_params: dict = None  # {"fov": 60, "resolution": [1280, 960]}
    # 场景动态
    duration_sec: int = 10
    fps: int = 30

class CabinDataSynthesisPipeline:
    """
    IMS 座舱数据合成管线
    
    Stage 1: 场景定义 (OpenUSD + Metahuman)
    Stage 2: 数据生成 (Cosmos Predict/Transfer)
    Stage 3: 自动标注 (Cosmos Reason + CoMET)
    Stage 4: 质量验证 (物理一致性 + 标注置信度)
    Stage 5: 数据集构建 (去重 + 多样性 + 平衡)
    """
    
    def __init__(self):
        self.stages = {
            "scene_definition": "Omniverse + OpenUSD + Metahuman",
            "data_generation": "Cosmos 3 Super (Predict/Transfer)",
            "auto_labeling": "Cosmos Reason + CoMET",
            "quality_validation": "Physics-IQ + 标注置信度",
            "dataset_construction": "相似性排序 + 不确定性估计"
        }
    
    def stage1_scene_definition(self, config: CabinSceneConfig) -> dict:
        """
        Stage 1: 使用 OpenUSD + Metahuman 定义场景
        
        - 在 Omniverse 中构建座舱3D环境
        - 使用 Metahuman 生成多样化乘员
        - 参数化配置: 光照/角度/乘员组合
        """
        usd_scene = {
            "environment": {
                "cabin_model": "suv_interior_v2.usd",
                "lighting_preset": config.lighting,
                "weather": config.weather,
            },
            "characters": [{
                "metahuman_id": f"mh_{config.driver_age}_{config.driver_gender}_{config.driver_ethnicity}",
                "position": "driver_seat",
                "animation": self._get_animation(config.scene_type),
                "skin_tone": "varied",
                "clothing": "casual"
            }],
            "sensors": [{
                "type": "camera",
                "config": config.camera_config,
                "params": config.camera_params or {"fov": 60, "resolution": [1280, 960]},
                "placement": "dashboard_center"
            }],
            "output": f"scene_{config.scene_type}_{config.lighting}.usd"
        }
        
        if config.passengers:
            for p in config.passengers:
                usd_scene["characters"].append({
                    "metahuman_id": f"mh_{p['age']}_{p.get('gender', 'random')}",
                    "position": p["position"],
                    "animation": "sleeping" if p["age"] < 10 else "normal",
                    "age_override": p["age"]
                })
        
        return usd_scene
    
    def stage2_data_generation(self, scene_usd: dict, config: CabinSceneConfig) -> dict:
        """
        Stage 2: 使用 Cosmos Predict/Transfer 生成数据
        
        - Cosmos Transfer: 从OpenUSD场景生成多角度视频
        - Cosmos Predict: 生成特定行为的未来状态
        """
        n_frames = config.duration_sec * config.fps
        
        generation_result = {
            "model": "Cosmos 3 Super",
            "input": scene_usd["output"],
            "output_format": "video_mp4",
            "resolution": config.camera_params["resolution"] if config.camera_params else [1280, 960],
            "frames": n_frames,
            "fps": config.fps,
            "physical_consistency": True,  # Cosmos保证物理一致性
            "output_files": [
                f"synthetic/{config.scene_type}_{config.lighting}_{i:04d}.mp4"
                for i in range(1)  # 简化,实际可生成多个变体
            ]
        }
        
        return generation_result
    
    def stage3_auto_labeling(self, generated_data: dict, config: CabinSceneConfig) -> dict:
        """
        Stage 3: 使用 Cosmos Reason + CoMET 自动标注
        
        - Cosmos Reason: 语义理解(驾驶员状态/乘客行为)
        - CoMET: 关键点/分割/检测标注
        """
        labels = {
            "scene_level": {
                "scene_type": config.scene_type,
                "lighting": config.lighting,
                "weather": config.weather,
                "num_occupants": 1 + len(config.passengers or []),
            },
            "frame_level": [],
            "detection": [],
            "keypoints": [],
            "segmentation": []
        }
        
        # 模拟自动标注输出
        n_frames = generated_data["frames"]
        for i in range(n_frames):
            labels["frame_level"].append({
                "frame_id": i,
                "driver_state": config.scene_type,
                "confidence": 0.92 + np.random.uniform(-0.05, 0.05),
                "perclos": 0.15 if config.scene_type == "fatigue" else 0.05,
                "gaze_direction": "forward" if config.scene_type == "normal" else "away",
            })
            
            labels["detection"].append({
                "frame_id": i,
                "objects": [
                    {"class": "face", "bbox": [100, 50, 200, 180], "confidence": 0.95},
                    {"class": "body", "bbox": [50, 150, 300, 500], "confidence": 0.93}
                ]
            })
            
            labels["keypoints"].append({
                "frame_id": i,
                "face_landmarks": 68,  # 68点人脸关键点
                "body_pose": 17,  # COCO 17点
                "confidence": 0.90
            })
        
        return labels
    
    def stage4_quality_validation(self, labels: dict) -> dict:
        """Stage 4: 质量验证"""
        return {
            "physical_consistency_pass": True,
            "avg_confidence": 0.93,
            "low_confidence_frames": 0,
            "label_coverage": 1.0,
            "passes_validation": True
        }
    
    def run(self, config: CabinSceneConfig) -> dict:
        """运行完整合成管线"""
        print(f"=== 座舱数据合成: {config.scene_type} ===")
        
        s1 = self.stage1_scene_definition(config)
        print(f"Stage 1 完成: 场景定义 ({s1['output']})")
        
        s2 = self.stage2_data_generation(s1, config)
        print(f"Stage 2 完成: 数据生成 ({s2['frames']} frames)")
        
        s3 = self.stage3_auto_labeling(s2, config)
        print(f"Stage 3 完成: 自动标注 ({len(s3['frame_level'])} frames)")
        
        s4 = self.stage4_quality_validation(s3)
        print(f"Stage 4 完成: 质量验证 (pass={s4['passes_validation']})")
        
        return {
            "config": config.__dict__,
            "scene": s1,
            "generation": s2,
            "labels": s3,
            "validation": s4
        }
    
    def _get_animation(self, scene_type: str) -> str:
        animations = {
            "normal_driving": "driving_attentive",
            "fatigue": "driving_drowsy_eyelids_dropping",
            "distraction": "looking_away_phone",
            "cpd": "sleeping_child",
            "oop": "leaning_forward_unusual"
        }
        return animations.get(scene_type, "idle")


import numpy as np

# ========== 测试: 生成多种场景 ==========
if __name__ == "__main__":
    pipeline = CabinDataSynthesisPipeline()
    
    configs = [
        CabinSceneConfig(
            scene_type="fatigue",
            lighting="night",
            camera_config="front_dms",
            duration_sec=10
        ),
        CabinSceneConfig(
            scene_type="cpd",
            lighting="day",
            passengers=[{"age": 2, "position": "rear_left", "gender": "random"}],
            camera_config="rear_oms",
            duration_sec=15
        ),
        CabinSceneConfig(
            scene_type="distraction",
            lighting="dawn",
            camera_config="front_dms",
            duration_sec=8
        ),
    ]
    
    results = []
    for cfg in configs:
        result = pipeline.run(cfg)
        results.append(result)
        print()
    
    print(f"=== 合成完成: {len(results)} 个场景 ===")
    print(f"总帧数: {sum(r['generation']['frames'] for r in results)}")

"""
OpenUSD 座舱场景定义示例

Omniverse 中构建可参数化的座舱环境
"""

# OpenUSD Python API 伪代码
usd_cabin_template = """
#usda 1.0
(
    defaultPrim = "CabinScene"
    metersPerUnit = 0.01
    upAxis = "Y"
)

def Xform "CabinScene" {
    def Xform "VehicleInterior" {
        # 座舱几何
        def Mesh "Dashboard" { ... }
        def Mesh "SteeringWheel" { ... }
        def Mesh "Seats" {
            # 4个座椅参数化
        }
        def Mesh "RearviewMirror" { ... }
    }
    
    def Xform "Lighting" {
        # 参数化光照
        custom string preset = "{lighting}"  # day/night/dawn/dusk
        def DomeLight "SunLight" {
            custom float intensity = {sun_intensity}
            custom color3f color = (1.0, 0.95, 0.9)
        }
        def SphereLight "DashboardIR" {
            custom float intensity = 0.3
            custom color3f color = (0.85, 0.0, 0.0)  # IR
        }
    }
    
    def Xform "Sensors" {
        def Camera "DMS_Front" {
            custom float2 resolution = (1280, 960)
            custom float focalLength = 6.0
            custom float2 sensorSize = (4.8, 3.6)
            custom float2 lensShift = (0.0, 0.0)
            # 安装位置: 仪表台中央, 面向驾驶员
            double3x4 xformOp:transform = ((1,0,0,0),(0,1,0,-0.3),(0,0,1,-1.5))
        }
        def Camera "OMS_Rear" {
            custom float2 resolution = (1280, 960)
            custom float focalLength = 4.0
            # 安装位置: 顶灯, 面向后排
        }
    }
    
    def Xform "Characters" {
        # Metahuman 角色引用
        def SkelRoot "Driver" {
            custom string metahuman_asset = "/Metahuman/{driver_id}"
            custom string animation = "{animation}"
            custom float age_override = {driver_age}
        }
        # 后排乘员(可选)
        def SkelRoot "Passenger_RearLeft" {
            # 条件性存在
        }
    }
}
"""