[Cómo usar Omniverse] Use código para generar escenas y sintetizar conjuntos de datos de imágenes

Esta vez es usar código para generar un almacén logístico y sintetizar conjuntos de datos de imágenes.

import os
import omni
from pxr import Usd, UsdGeom, Gf, UsdShade
from omni.isaac.synthetic_utils import SyntheticDataHelper

Establecer parámetros para guardar conjuntos de datos de imágenes

output_folder = "dataset/images"  # 图像数据集保存的文件夹路径
image_width = 640  # 图像宽度
image_height = 480  # 图像高度
num_images = 100  # 生成的图像数量

Crear escena de almacén logístico

def create_logistics_warehouse_scene():
    stage = omni.usd.get_context().get_stage()

# 创建物流仓库模型
warehouse_prim = stage.DefinePrim('/World/Warehouse', 'Xform')
warehouse_model = UsdGeom.Mesh.Define(stage, '/World/Warehouse/WarehouseModel')
# 设置物流仓库模型的位置、旋转等属性

# 添加其他物体、光照等场景元素
# ...

Inicializar el objeto SyntheticDataHelper

synthetic_helper = SyntheticDataHelper()
synthetic_helper.set_output_image_folder(output_folder)
synthetic_helper.set_output_image_size(image_width, image_height)

Crear escena de almacén logístico

create_logistics_warehouse_scene()

Genere conjuntos de datos de imágenes en un bucle

for i in range(num_images):

# 渲染场景并获取图像

image = synthetic_helper.render_image()

# 保存图像到指定文件夹
image_path = os.path.join(output_folder, f"image_{i}.png")
image.save(image_path)
# 输出生成进度
print(f"Generated image {i+1}/{num_images}: {image_path}")

print("Image dataset generation complete!")

Código completo:

```python
import os
import omni
from pxr import Usd, UsdGeom, Gf, UsdShade
from omni.isaac.synthetic_utils import SyntheticDataHelper

# 设置保存图像数据集的参数
output_folder = "dataset/images"  # 图像数据集保存的文件夹路径
image_width = 640  # 图像宽度
image_height = 480  # 图像高度
num_images = 100  # 生成的图像数量

# 创建物流仓库场景
def create_logistics_warehouse_scene():
    stage = omni.usd.get_context().get_stage()

    # 创建物流仓库模型
    warehouse_prim = stage.DefinePrim('/World/Warehouse', 'Xform')
    warehouse_model = UsdGeom.Mesh.Define(stage, '/World/Warehouse/WarehouseModel')
    # 设置物流仓库模型的位置、旋转等属性

    # 添加其他物体、光照等场景元素
    # ...

# 初始化SyntheticDataHelper对象
synthetic_helper = SyntheticDataHelper()
synthetic_helper.set_output_image_folder(output_folder)
synthetic_helper.set_output_image_size(image_width, image_height)

# 创建物流仓库场景
create_logistics_warehouse_scene()

# 循环生成图像数据集
for i in range(num_images):
    # 渲染场景并获取图像
    image = synthetic_helper.render_image()
    
    # 保存图像到指定文件夹
    image_path = os.path.join(output_folder, f"image_{i}.png")
    image.save(image_path)
    
    # 输出生成进度
    print(f"Generated image {i+1}/{num_images}: {image_path}")

print("Image dataset generation complete!")