Isaac Sim 3（学习笔记5.8）-编程知识

Isaac Sim 利用深度学习获取mask掩码图

参考内容

Kubernetes官网

在 Linux 系统中安装并设置 kubectl | Kubernetes准备开始 kubectl 版本和集群版本之间的差异必须在一个小版本号内。例如：v1.30 版本的客户端能与 v1.29、 v1.30 和 v1.31 版本的控制面通信。用最新兼容版的 kubectl 有助于避免不可预见的问题。在 Linux 系统中安装 kubectl 在 Linux 系统中安装 kubectl 有如下几种方法：用 curl 在 Linux 系统中安装 kubectl 用原生包管理工具安装用其他包管理工具安装用 curl 在 Linux 系统中安装 kubectl 用以下命令下载最新发行版：x86-64 ARM64 curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl" curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/arm64/kubectl" 说明：如需下载某个指定的版本，请用指定版本号替换该命令的这一部分： $(curl -L -s https://dl.k8s.io/release/stable.txt)。例如，要在 Linux x86-64 中下载 1.30.0 版本，请输入：curl -LO https://dl.k8s.io/release/v1.30.0/bin/linux/amd64/kubectl 对于 Linux ARM64 来说，请输入：https://kubernetes.io/zh-cn/docs/tasks/tools/install-kubectl-linux/使用Isaac Sim进行DNN图像分割教程

Tutorial for DNN Image Segmentation with Isaac Sim — isaac_ros_docs documentationhttps://nvidia-isaac-ros.github.io/concepts/segmentation/unet/tutorial_isaac_sim.html设置开发环境等

isaac_ros_unet — isaac_ros_docs documentationhttps://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_image_segmentation/isaac_ros_unet/index.html#quickstartDeveloper Environment Setup — isaac_ros_docs documentationhttps://nvidia-isaac-ros.github.io/getting_started/dev_env_setup.html安装NVIDIA容器工具包

Installing the NVIDIA Container Toolkit — NVIDIA Container Toolkit 1.15.0 documentationhttps://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#prerequisites

一.设置Docker、安装NVIDIA工具包、容器部署

这里我在别的地方写过，在下方链接的第二章内容中

Ubuntu20.04 ISAAC SIM仿真下载使用流程_ubuntu启动isaac sim后一直有rtx loading界面-CSDN博客文章浏览阅读1.4k次，点赞38次，收藏30次。Ubuntu20.04 ISAAC SIM仿真下载使用流程机器：华硕天选X2024显卡：4060Tiubuntu20.04安装显卡驱动版本：525.85.05_ubuntu启动isaac sim后一直有rtx loading界面https://blog.csdn.net/weixin_61044335/article/details/137866781?spm=1001.2014.3001.5501说实在的我不知道为什么nvidia不同文档写的下载的内容不一样，哪个是必要，哪个是不必要也不清晰，没办法我先把以前做过的东西放在这儿，我再去跟着官网走一遍捋一捋

二.跟着官网再走一次

我因为想拿到图像分割的掩码，于是跟着官网走Tutorial for DNN Image Segmentation with Isaac Sim — isaac_ros_docs documentation

提示要先完成isaac_ros_unet — isaac_ros_docs documentation 中1~9的内容，于是我又去这个网站

这里又要我根据 Developer Environment Setup — isaac_ros_docs documentation 说明来设置开发环境，好吧继续

在上文链接中我已经配置好了docker，继续走下一步

接下来是https://nvidia-isaac-ros.github.io/getting_started/dev_env_setup.html中的环境设置部分：

On x86_64 platforms:
1. Install the nvidia-container-toolkit using the instructions.
2. Configure nvidia-container-toolkit for Docker using the instructions.
On Jetson platforms: Follow this instruction to first set your Jetson up with SSD, then come back to this document and resume from Step 2.

Restart Docker:

sudo systemctl daemon-reload && sudo systemctl restart docker

Install Git LFS to pull down all large files:

sudo apt-get install git-lfs

git lfs install --skip-repo

Create a ROS 2 workspace for experimenting with Isaac ROS:

For Jetson setup with SSD as optional storage:

mkdir -p  /ssd/workspaces/isaac_ros-dev/src
echo "export ISAAC_ROS_WS=/ssd/workspaces/isaac_ros-dev/" >> ~/.bashrc
source ~/.bashrc

mkdir -p  ~/workspaces/isaac_ros-dev/src
echo "export ISAAC_ROS_WS=${HOME}/workspaces/isaac_ros-dev/" >> ~/.bashrc
source ~/.bashrc

We expect to use the ISAAC_ROS_WS environmental variable to refer to this ROS 2 workspace directory, in the future.

To further customize your development environment, check out this guide.

很好走完了，接下来去做图像分割的前置步骤：

Clone isaac_ros_common and this repository under ${ISAAC_ROS_WS}/src.

cd ${ISAAC_ROS_WS}/src

git clone https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_common.git

git clone https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_image_segmentation.git

Pull down a ROS Bag of sample data:

cd ${ISAAC_ROS_WS}/src/isaac_ros_image_segmentation && \git lfs pull -X "" -I "resources/rosbags/"

Launch the Docker container using the run_dev.sh script:

cd ${ISAAC_ROS_WS}/src/isaac_ros_common && \./scripts/run_dev.sh

Install this package’s dependencies.

sudo apt-get install -y ros-humble-isaac-ros-unet ros-humble-isaac-ros-triton ros-humble-isaac-ros-dnn-image-encoder

Download the PeopleSemSegNet ShuffleSeg ETLT file and the int8 inference mode cache file:

mkdir -p /tmp/models/peoplesemsegnet_shuffleseg/1 && \cd /tmp/models/peoplesemsegnet_shuffleseg && \wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/peoplesemsegnet/versions/deployable_shuffleseg_unet_v1.0/files/peoplesemsegnet_shuffleseg_etlt.etlt && \wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/peoplesemsegnet/versions/deployable_shuffleseg_unet_v1.0/files/peoplesemsegnet_shuffleseg_cache.txt

Convert the ETLT file to a TensorRT plan file:

/opt/nvidia/tao/tao-converter -k tlt_encode -d 3,544,960 -p input_2:0,1x3x544x960,1x3x544x960,1x3x544x960 -t int8 -c peoplesemsegnet_shuffleseg_cache.txt -e /tmp/models/peoplesemsegnet_shuffleseg/1/model.plan -o argmax_1 peoplesemsegnet_shuffleseg_etlt.etlt

Create a file called /tmp/models/peoplesemsegnet_shuffleseg/config.pbtxt by copying the sample Triton config file:

cp /workspaces/isaac_ros-dev/src/isaac_ros_image_segmentation/resources/peoplesemsegnet_shuffleseg_config.pbtxt /tmp/models/peoplesemsegnet_shuffleseg/config.pbtxt

Run the following launch files to spin up a demo of this package:

ros2 launch isaac_ros_unet isaac_ros_unet_triton.launch.py model_name:=peoplesemsegnet_shuffleseg model_repository_paths:=['/tmp/models'] input_binding_names:=['input_2:0'] output_binding_names:=['argmax_1'] network_output_type:='argmax' input_image_width:=1200 input_image_height:=632

Then open another terminal, and enter the Docker container again:

cd ${ISAAC_ROS_WS}/src/isaac_ros_common && \./scripts/run_dev.sh

Then, play the ROS bag:

ros2 bag play -l src/isaac_ros_image_segmentation/resources/rosbags/unet_sample_data/

Visualize and validate the output of the package by launching rqt_image_view in another terminal: In a third terminal, enter the Docker container again:
```
cd ${ISAAC_ROS_WS}/src/isaac_ros_common && \./scripts/run_dev.sh
```
Then launch rqt_image_view:
```
ros2 run rqt_image_view rqt_image_view
```
Then inside the rqt_image_view GUI, change the topic to /unet/colored_segmentation_mask to view a colorized segmentation mask.