"""
NVIDIA OSMO 工作流定义示例
适配 IMS 座舱数据合成管道

关键概念：
1. OSMO YAML 定义管道阶段
2. 每个 stage 运行在独立容器中
3. 支持 GPU 池调度
4. 输出自动版本管理
"""

# OSMO 工作流 YAML（概念示例）
OSMO_WORKFLOW = """
# IMS Cabin Data Generation Pipeline
name: ims_cabin_sdg
description: >-
  End-to-end synthetic data generation for IMS (In-cabin Monitoring System)
  models. Generates DMS, OMS, CPD training data from simulated cabin scenes.

stages:
  # Stage 1: 环境重建（可选，用于真实座舱数字化）
  - name: nurec_reconstruct
    type: interactive
    image: nvcr.io/nvidia/omniverse/nurec:latest
    gpu: 1
    commands:
      - nurec ingest --input ${RAW_SENSOR_DATA}
      - nurec reconstruct --output /workspace/scene.usd
    outputs:
      - scene.usd
    
  # Stage 2: 仿真场景组装
  - name: isaac_sim_assemble
    type: batch
    image: nvcr.io/nvidia/isaac/sim:5.1.0
    gpu: 1
    depends_on: [nurec_reconstruct]
    commands:
      # 加载座舱环境
      - isaac-sim load-scene /workspace/scene.usd
      # 添加 SimReady 乘员资产
      - isaac-sim add-asset --category human --variant adult_male
      - isaac-sim add-asset --category human --variant child_5yo
      - isaac-sim add-asset --category human --variant infant_rear_facing
      # 配置传感器
      - isaac-sim configure-camera --position dms_mount --resolution 1280x800
      - isaac-sim configure-radar --freq 60ghz --position headliner
      - isaac-sim configure-ir --position steering_column
    outputs:
      - scene_configured.usd
    
  # Stage 3: MobilityGen 轨迹生成
  - name: mobility_gen
    type: batch
    image: nvcr.io/nvidia/isaac/sim:5.1.0
    gpu: 1
    depends_on: [isaac_sim_assemble]
    commands:
      # 生成乘员行为轨迹
      - mobility-gen create-map --scene scene_configured.usd
      - mobility-gen record-trajectory \\
          --num-trajectories 1000 \\
          --behaviors normal_driving,fatigue,distraction,phone_use,child_moving
      - mobility-gen replay-and-render \\
          --sensors camera,radar,ir \\
          --output /workspace/raw_data
    outputs:
      - raw_data/
    
  # Stage 4: Cosmos 数据增强
  - name: cosmos_augment
    type: batch
    image: nvcr.io/nvidia/cosmos/transfer:1.0
    gpu: 4
    depends_on: [mobility_gen]
    commands:
      # 视觉多样性增强
      - cosmos-augment \\
          --input /workspace/raw_data \\
          --variations lighting,weather,interior_color,passenger_appearance \\
          --augmentation-factor 10 \\
          --output /workspace/augmented_data
    outputs:
      - augmented_data/
    
  # Stage 5: 自动标注与数据集打包
  - name: auto_annotate
    type: batch
    image: nvcr.io/nvidia/isaac/repacker:5.1.0
    gpu: 1
    depends_on: [cosmos_augment]
    commands:
      # 从仿真 ground truth 自动生成标注
      - isaac-annotate \\
          --input /workspace/augmented_data \\
          --tasks face_detection,gaze_estimation,pose_estimation,occupant_classification \\
          --format coco,yolo,onnx
      - isaac-package-dataset \\
          --output /workspace/ims_dataset_v1
    outputs:
      - ims_dataset_v1/
"""

# 管道执行示例
import subprocess
import json

class OSMOPipeline:
    """
    OSMO 管道执行器
    
    在实际部署中，OSMO CLI 管理这些操作
    这里展示 Python API 接口概念
    """
    
    def __init__(self, workflow_yaml: str, pool_name: str = 'gpu-pool'):
        self.workflow = workflow_yaml
        self.pool = pool_name
        self.stages = [
            'nurec_reconstruct',
            'isaac_sim_assemble', 
            'mobility_gen',
            'cosmos_augment',
            'auto_annotate'
        ]
    
    def submit(self):
        """提交工作流到 OSMO"""
        print(f"提交 OSMO 工作流...")
        # osmo workflow submit ims_cabin_sdg.yaml --pool gpu-pool
        workflow_id = f"ims-sdg-{hash(self.workflow) % 10000:04d}"
        print(f"工作流ID: {workflow_id}")
        return workflow_id
    
    def monitor(self, workflow_id: str):
        """监控工作流进度"""
        # osmo workflow status <workflow_id>
        stages_status = {
            'nurec_reconstruct': 'completed',
            'isaac_sim_assemble': 'completed',
            'mobility_gen': 'running',
            'cosmos_augment': 'pending',
            'auto_annotate': 'pending'
        }
        return stages_status
    
    def estimate_cost(self, num_scenes: int, augmentation: int):
        """
        估算数据生成成本
        
        1 scene → 1000 trajectories → 10x augmentation = 10000 samples
        """
        samples_per_scene = 1000 * augmentation
        total_samples = num_scenes * samples_per_scene
        
        # GPU 小时成本
        gpu_hours_per_scene = 0.5  # 粗略估算
        total_gpu_hours = num_scenes * gpu_hours_per_scene
        gpu_cost_per_hour = 2.0  # 云端RTX
        total_cost = total_gpu_hours * gpu_cost_per_hour
        
        return {
            'total_samples': total_samples,
            'gpu_hours': total_gpu_hours,
            'estimated_cost_usd': total_cost,
            'cost_per_sample': total_cost / total_samples
        }


# 测试
if __name__ == "__main__":
    pipeline = OSMOPipeline(OSMO_WORKFLOW)
    
    # 提交
    wf_id = pipeline.submit()
    
    # 成本估算
    estimate = pipeline.estimate_cost(num_scenes=50, augmentation=10)
    print(f"\n=== 50场景 × 10x增强 成本估算 ===")
    print(f"总样本数: {estimate['total_samples']:,}")
    print(f"GPU小时: {estimate['gpu_hours']}")
    print(f"总成本: ${estimate['estimated_cost_usd']:.2f}")
    print(f"每样本成本: ${estimate['cost_per_sample']:.6f}")
    
    print(f"\n=== 管道阶段 ===")
    for stage in pipeline.stages:
        print(f"  {stage}")

"""
NuRec 座舱重建流程
将真实座舱数字化为 OpenUSD 格式仿真环境

应用场景：
1. 重建真实车辆座舱 → 在仿真中生成训练数据
2. 多车型座舱数字化 → 跨车型数据覆盖
3. 特定场景重建（事故现场）→ 罕见场景数据生成
"""

import numpy as np

class NuRecCabinReconstructor:
    """
    NuRec 座舱重建器
    
    工作流：
    1. 多角度手机视频拍摄座舱
    2. NuRec 神经渲染重建 3D 场景
    3. 导出为 OpenUSD 格式
    4. 在 Isaac Sim 中加载并配置
    """
    
    def __init__(self, config: dict):
        self.reconstruction_method = config.get('method', '3dgut')  # 3DGS/NeRF/3DGUT
        self.quality = config.get('quality', 'high')  # low/medium/high
        self.output_format = 'usd'  # OpenUSD
        
    def capture_guide(self, vehicle_type: str = 'sedan') -> dict:
        """
        生成座舱拍摄指南
        
        不同车型需要不同的拍摄路径
        """
        guides = {
            'sedan': {
                'captures': 40,
                'paths': [
                    'driver_seat_360',
                    'passenger_seat_360', 
                    'rear_left_360',
                    'rear_right_360',
                    'dashboard_pan',
                    'headliner_pan',
                    'floor_pan',
                ],
                'duration_min': 8,
                'tips': '确保覆盖所有角度，包括脚部和顶部'
            },
            'suv': {
                'captures': 55,
                'paths': [
                    'driver_seat_360',
                    'passenger_seat_360',
                    'rear_left_360',
                    'rear_center_360',
                    'rear_right_360',
                    'third_row_360',
                    'dashboard_pan',
                    'headliner_pan',
                    'floor_pan',
                ],
                'duration_min': 12,
            }
        }
        return guides.get(vehicle_type, guides['sedan'])
    
    def reconstruct(self, capture_data_path: str) -> dict:
        """
        执行 3D 重建
        
        返回重建质量和场景信息
        """
        # NuRec 命令
        # nurec ingest --input <data_path>
        # nurec reconstruct --method 3dgut --quality high
        # nurec export --format usd --output scene.usd
        
        return {
            'output_file': 'cabin_scene.usd',
            'reconstruction_method': self.reconstruction_method,
            'quality_score': 0.92,  # 0-1
            'coverage': 0.88,  # 0-1, 座舱覆盖率
            'file_size_mb': 450,
            'gpu_hours': 2.5,
            'loadable_in_isaac': True,
        }

"""
MobilityGen 座舱数据生成
基于 Isaac Sim 生成多行为模式的座舱感知数据

核心价值：
1. 自动生成多样化驾驶行为数据（疲劳/分心/正常）
2. 多角度、多光照条件
3. Ground truth 自动标注（无需人工标注）
4. 10x-100x 成本降低 vs 真实数据采集
"""

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

@dataclass
class CabinBehavior:
    """座舱行为定义"""
    name: str
    duration_sec: int
    description: str
    head_movement: str  # 'normal', 'drowsy', 'distracted', 'aggressive'
    body_movement: str  # 'still', 'fidgeting', 'reaching'
    eye_state: str  # 'open', 'closing', 'closed', 'rapid_blink'
    hand_state: str  # 'on_wheel', 'phone', 'off_wheel', 'gesturing'
    passenger_count: int = 1
    passenger_types: list = None  # ['adult_male', 'child_5yo', 'infant']

class CabinMobilityGenerator:
    """
    座舱 MobilityGen 生成器
    
    生成 IMS 训练数据：
    - DMS: 驾驶员面部、眼动、头部姿态
    - OMS: 乘员检测、姿态、安全带
    - CPD: 后排儿童检测
    """
    
    # 预定义行为库
    BEHAVIOR_LIBRARY = {
        'normal_driving': CabinBehavior(
            name='normal_driving',
            duration_sec=300,
            description='正常驾驶，视线前方，偶尔看后视镜',
            head_movement='normal',
            body_movement='still',
            eye_state='open',
            hand_state='on_wheel',
            passenger_count=1,
            passenger_types=['adult_male']
        ),
        'fatigue_level_1': CabinBehavior(
            name='fatigue_level_1',
            duration_sec=180,
            description='轻度疲劳，眨眼频率增加，偶尔打哈欠',
            head_movement='normal',
            body_movement='still',
            eye_state='rapid_blink',
            hand_state='on_wheel',
        ),
        'fatigue_level_2': CabinBehavior(
            name='fatigue_level_2',
            duration_sec=120,
            description='中度疲劳，PERCLOS升高，点头',
            head_movement='drowsy',
            body_movement='still',
            eye_state='closing',
            hand_state='on_wheel',
        ),
        'fatigue_level_3': CabinBehavior(
            name='fatigue_level_3',
            duration_sec=60,
            description='严重疲劳，微睡眠，头部下垂',
            head_movement='drowsy',
            body_movement='still',
            eye_state='closed',
            hand_state='on_wheel',
        ),
        'phone_use_texting': CabinBehavior(
            name='phone_use_texting',
            duration_sec=60,
            description='低头看手机打字，视线离开前方',
            head_movement='distracted',
            body_movement='still',
            eye_state='open',
            hand_state='phone',
        ),
        'phone_use_calling': CabinBehavior(
            name='phone_use_calling',
            duration_sec=120,
            description='手持电话通话，单手方向盘',
            head_movement='distracted',
            body_movement='still',
            eye_state='open',
            hand_state='phone',
        ),
        'child_rear_normal': CabinBehavior(
            name='child_rear_normal',
            duration_sec=300,
            description='后排儿童正常坐姿',
            head_movement='normal',
            body_movement='fidgeting',
            eye_state='open',
            hand_state='off_wheel',
            passenger_count=2,
            passenger_types=['adult_male', 'child_5yo']
        ),
        'child_rear_sleeping': CabinBehavior(
            name='child_rear_sleeping',
            duration_sec=600,
            description='后排儿童睡着',
            head_movement='drowsy',
            body_movement='still',
            eye_state='closed',
            hand_state='off_wheel',
            passenger_count=2,
            passenger_types=['adult_male', 'child_5yo']
        ),
        'infant_rear_facing': CabinBehavior(
            name='infant_rear_facing',
            duration_sec=600,
            description='后排反向安全座椅中的婴儿',
            head_movement='normal',
            body_movement='still',
            eye_state='open',
            hand_state='off_wheel',
            passenger_count=2,
            passenger_types=['adult_male', 'infant']
        ),
        'passenger_reclined': CabinBehavior(
            name='passenger_reclined',
            duration_sec=300,
            description='副驾斜躺姿态（OOP场景）',
            head_movement='drowsy',
            body_movement='still',
            eye_state='closed',
            hand_state='off_wheel',
            passenger_count=2,
            passenger_types=['adult_male', 'adult_female']
        ),
    }
    
    def generate_dataset(self, behavior_names: List[str],
                          sensors: List[str],
                          variations: dict) -> dict:
        """
        生成训练数据集
        
        Args:
            behavior_names: 行为列表
            sensors: 传感器列表 ['camera', 'radar', 'ir']
            variations: 变化参数 {
                'lighting': ['day', 'night', 'tunnel'],
                'interior_color': ['black', 'beige', 'gray'],
                'driver_gender': ['male', 'female'],
                'driver_age': ['young', 'middle', 'elderly'],
            }
        
        Returns:
            dataset_info
        """
        total_clips = 0
        total_frames = 0
        estimated_size_gb = 0
        
        for behavior_name in behavior_names:
            behavior = self.BEHAVIOR_LIBRARY.get(behavior_name)
            if not behavior:
                continue
            
            # 变化组合数
            num_variations = 1
            for key, vals in variations.items():
                num_variations *= len(vals)
            
            # 每个行为在每个变化下的片段数
            clips_per_variation = 10  # 可调
            clips = clips_per_variation * num_variations
            frames = clips * behavior.duration_sec * 30  # 30fps
            
            total_clips += clips
            total_frames += frames
            
            # 估算大小（每帧 ~2MB for RGB+Depth+IR）
            size_gb = frames * 2 / 1024
        
        return {
            'behaviors': behavior_names,
            'sensors': sensors,
            'total_clips': total_clips,
            'total_frames': total_frames,
            'estimated_size_gb': total_frames * 2 / (1024**3),
            'gpu_hours': total_clips * 0.05,  # ~3min per clip
            'auto_labeled': True,
            'ground_truth': ['face_bbox', 'landmarks', 'gaze_vector', 
                            'body_pose', 'occupant_class', 'belt_status'],
        }


# 测试
if __name__ == "__main__":
    gen = CabinMobilityGenerator()
    
    print("=== 可用行为库 ===")
    for name, behavior in gen.BEHAVIOR_LIBRARY.items():
        print(f"  {name}: {behavior.description}")
    
    # 生成数据集
    behaviors = [
        'normal_driving',
        'fatigue_level_1', 'fatigue_level_2', 'fatigue_level_3',
        'phone_use_texting', 'phone_use_calling',
        'child_rear_normal', 'child_rear_sleeping',
        'infant_rear_facing',
        'passenger_reclined',
    ]
    
    sensors = ['camera', 'radar', 'ir']
    variations = {
        'lighting': ['day', 'night', 'tunnel', 'sunset'],
        'interior_color': ['black', 'beige'],
        'driver_gender': ['male', 'female'],
        'driver_age': ['young', 'middle', 'elderly'],
    }
    
    dataset = gen.generate_dataset(behaviors, sensors, variations)
    
    print(f"\n=== 数据集估算 ===")
    print(f"行为数: {len(behaviors)}")
    print(f"传感器: {sensors}")
    print(f"变化组合: {4*2*2*3} = 48")
    print(f"总片段: {dataset['total_clips']:,}")
    print(f"总帧数: {dataset['total_frames']:,}")
    print(f"估计大小: {dataset['estimated_size_gb']:.1f} GB")
    print(f"GPU小时: {dataset['gpu_hours']:.1f}")
    print(f"自动标注: {'是' if dataset['auto_labeled'] else '否'}")
    print(f"Ground truth: {dataset['ground_truth']}")