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| """
NVIDIA Cosmos 3 座舱场景生成管道
依赖: Cosmos 3 API (GTC 2025后开放)
用途: 生成DMS/OMS训练数据
核心思路:
1. 定义场景提示词模板
2. Cosmos生成场景变体
3. Isaac Sim验证+标注
4. 输出训练数据
"""
from dataclasses import dataclass
from typing import List, Dict, Optional
import json
@dataclass
class CosmosPrompt:
"""Cosmos场景生成提示词"""
driver_state: str
fatigue_level: str = ""
distraction_type: str = ""
body_type: str = "medium_male"
age_group: str = "adult"
lighting: str = "day_clear"
weather: str = "clear"
camera_type: str = "dms_standard"
camera_angle: str = "front"
def to_prompt(self) -> str:
"""生成文本提示词"""
parts = [
f"interior view of a car cabin from {self.camera_type} camera",
f"driver in {self.driver_state} state",
]
if self.fatigue_level:
parts.append(f"fatigue level: {self.fatigue_level}")
if self.distraction_type:
parts.append(f"distraction: {self.distraction_type}")
parts.append(f"driver body type: {self.body_type}")
parts.append(f"age: {self.age_group}")
parts.append(f"lighting: {self.lighting}")
parts.append(f"weather outside: {self.weather}")
return ", ".join(parts)
class CosmosCabinDataPipeline:
"""
Cosmos 3 + Isaac Sim 融合数据生成管道
"""
def __init__(self):
self.prompt_templates = self._init_templates()
def _init_templates(self) -> List[CosmosPrompt]:
"""初始化场景模板"""
templates = []
for body in ['small_female', 'medium_male', 'large_male']:
for level in ['mild', 'moderate', 'severe']:
for light in ['day_clear', 'night_urban', 'tunnel', 'sunset']:
templates.append(CosmosPrompt(
driver_state='fatigue',
fatigue_level=level,
body_type=body,
lighting=light,
))
for body in ['small_female', 'medium_male', 'large_male']:
for dist in ['phone_texting', 'phone_ear', 'reach_passenger', 'turn_back']:
for light in ['day_clear', 'night_urban']:
templates.append(CosmosPrompt(
driver_state='distracted',
distraction_type=dist,
body_type=body,
lighting=light,
))
for body in ['small_female', 'medium_male', 'large_male', 'child']:
for oop in ['forward_bent', 'side_leaning', 'feet_up', 'head_on_wheel']:
templates.append(CosmosPrompt(
driver_state='oop',
body_type=body,
lighting='day_clear',
))
return templates
def generate_dataset_spec(self) -> Dict:
"""生成数据集规格"""
n_templates = len(self.prompt_templates)
variants_per_template = 10
frames_per_variant = 300
total_images = n_templates * variants_per_template * frames_per_variant
return {
'total_templates': n_templates,
'variants_per_template': variants_per_template,
'frames_per_variant': frames_per_variant,
'total_images': total_images,
'estimated_size_gb': total_images * 2 / 1024,
'generation_time_hours': total_images / (1000 * 3600),
'cost_estimate_usd': total_images / (1000 * 3600) * 3,
}
def to_isaac_sim_config(self, cosmos_output: Dict) -> Dict:
"""将Cosmos输出转换为Isaac Sim验证配置"""
return {
'scene_type': cosmos_output.get('driver_state', 'normal'),
'body_type': cosmos_output.get('body_type', 'medium_male'),
'lighting': cosmos_output.get('lighting', 'day_clear'),
'camera_config': 'dms_standard',
'validation_checks': [
'face_visible',
'eyes_detectable',
'posture_correct',
'lighting_adequate',
'no_artifacts',
],
'auto_label': True,
'label_classes': [
'face', 'eyes', 'mouth', 'hands',
'steering_wheel', 'phone', 'seatbelt',
],
}
if __name__ == "__main__":
pipeline = CosmosCabinDataPipeline()
spec = pipeline.generate_dataset_spec()
print("=== Cosmos + Isaac 数据生成规格 ===")
for k, v in spec.items():
if isinstance(v, float):
print(f" {k}: {v:.1f}")
else:
print(f" {k}: {v}")
print(f"\n=== 前5个场景提示词 ===")
for i, t in enumerate(pipeline.prompt_templates[:5]):
print(f" [{i}] {t.to_prompt()}")
example_output = {
'driver_state': 'fatigue',
'body_type': 'medium_male',
'lighting': 'night_urban',
}
isaac_config = pipeline.to_isaac_sim_config(example_output)
print(f"\n=== Isaac Sim验证配置 ===")
print(json.dumps(isaac_config, indent=2))
|