Jetson

jetson 每个产品都有一套 developer kit 和一套 module，引用自 FAQ，developer kit 用于研发阶段，module 用于生产环境。

FYI:

Flash

刷机有两种方式，

Nvidia Sdk manager

Command-Line Install :: NVIDIA SDK Manager Documentation

Manual

NVIDIA Jetson Linux Developer Guide

安装环境

Host

Mac mini
macOS 11.1
VirtualBox 6.1.16
VirtualBox Extension Pack 6.1.16
Ubuntu-18.04.5-live-server-amd64.iso

Vbox 需要支持 USB2/USB3，所以需要安装扩展包：Downloads – Oracle VM VirtualBox

手动刷机更不需要 gui 了，所以选择 server 版本的 ubuntu，省点资源：Ubuntu 18.04.5 LTS (Bionic Beaver)

Jetson

Jetson agx xavier 32GB(P2888-004)
L4T R32.4.4 archive | NVIDIA Developer

安装 Ubuntu

创建虚拟机需要注意:

选择 usb3 的支持
硬盘镜像选择 30GB，基于 sdkmanager 安装 jetson 的文档提及可用空间需要大于 25GB，所以 30 GB 肯定是够的。

手动安装只安装 l4t，实际肯定不用 30GB，但 10 GB 肯定是不够的，别问为什么。

成功刷机后的空间情况：

$ df -h
Filesystem                         Size  Used Avail Use% Mounted on
udev                               462M     0  462M   0% /dev
tmpfs                               99M  736K   98M   1% /run
/dev/mapper/ubuntu--vg-ubuntu--lv   20G   19G   76M 100% /
tmpfs                              493M     0  493M   0% /dev/shm
tmpfs                              5.0M     0  5.0M   0% /run/lock
tmpfs                              493M     0  493M   0% /sys/fs/cgroup
/dev/sda2                          976M   78M  832M   9% /boot
tmpfs                               99M     0   99M   0% /run/user/1000
$ lsblk
lsblk
NAME                      MAJ:MIN RM  SIZE RO TYPE MOUNTPOINT
sda                         8:0    0   30G  0 disk
├─sda1                      8:1    0    1M  0 part
├─sda2                      8:2    0    1G  0 part /boot
└─sda3                      8:3    0   29G  0 part
  └─ubuntu--vg-ubuntu--lv 253:0    0   20G  0 lvm  /
sr0                        11:0    1 1024M  0 rom

添加光驱镜像 ubuntu-18.04.5-live-server-amd64.iso 后，启动虚拟机便开始安装流程。主要注意把镜像替换为

https://mirrors.tuna.tsinghua.edu.cn/ubuntu

进入系统后，需要安装

sudo apt-get install qemu-user-static

Host 便配置完成。

下载 l4t archive

下载页 L4T R32.4.4 archive | NVIDIA Developer

下载

配置环境变量，可配可不配，主要是文档提及的文件名容易造成混淆。

export L4T_RELEASE_PACKAGE=Tegra186_Linux_R32.4.4_aarch64.tbz2  # 就是下载的 BSP 文件
export SAMPLE_FS_PACKAGE=Tegra_Linux_Sample-Root-Filesystem_R32.4.4_aarch64.tbz2
export export BOARD=jetson-agx-xavier-devkit

解压文件

$ tar xf ${L4T_RELEASE_PACKAGE}
$ cd Linux_for_Tegra/rootfs/
$ sudo tar xpf ../../${SAMPLE_FS_PACKAGE}
$ cd ..
$ sudo ./apply_binaries.sh

usb 需要重启虚拟机才能认到
文档未提及需要 python 可执行。ubuntu 只有 python3，需要手动安装一些 python,或者链接一下python3

第一次执行 flash.sh，因为本地找不到 python 可执行文件，所以报错。安装 python2 后重新执行，提示

Error: probing the target board failed.
Flash Jetson TX2 : Make sure the target board is connected through
Flash Jetson TX2 : USB port and is in recovery mode.

主要问题是因为 sudo ./bootloader/tegrarcm_v2 --uid 报错：

Failed to read UID

根据这个回帖，https://forums.developer.nvidia.com/t/sdk-manager-could-not-detect-target-hardware/71797/24 发现，是第一次 python 报错导致，信息被读取，下一次 flash 就会读不到信息。断电重新进入一次 Force Recovery Mode。便可以正常刷机。差点以为是不支持 VM

不过貌似也有办法支持其他 linux：https://forums.developer.nvidia.com/t/using-jetpack-from-arch-linux/44979

确保所有条件没问题，便可以开始刷机。

进入 Force Recovery mode，设备关机，按住 Force Recovery 键（与电源键相邻），再开机。
链接 USB
sudo ./flash.sh ${BOARD} mmcblk0p1

换源

备份

sudo cp /etc/apt/sources.list /etc/apt/sources.list.origin

内容替换为：

deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic main multiverse restricted universe
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-security main multiverse restricted universe
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-updates main multiverse restricted universe
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-backports main multiverse restricted universe
deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic main multiverse restricted universe
deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-security main multiverse restricted universe
deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-updates main multiverse restricted universe
deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-backports main multiverse restricted universe

更新系统

sudo apt-get update
sudo apt-get upgrade

见 How to Install Debian Packages with OTA Update

display manager 更新为 lightdm

dpkg --configure lightdm

补完 jetpack

 sudo apt install nvidia-jetpack

见 1.3. Package Management Tool

Headless Jetson

tigervnc 等一般 vncserver 方案

一个 vncserver 对应一个 xsession，不与当前显示器共享 xsession。

当是这方案不支持 EGL

Unfortunately, if there is no monitor case, then VNC cannot work. You could try to use RDP (remote desktop protocol) application and it should work. However, EGL seems not able to run with rdp backend. Thus, if you want to render something by using EGL, it would not work.

x11vnc

Dummy Monitor 不能生效，且不能使用 nvidia 驱动
尝试 hdmi 欺骗器 TODO

开机自动启用 x11vnc

login manager 自动登陆
先连到 loginmanager 的 xsession(Dispaly=:0)，手动登陆后再，连到桌面（Display=:1）:https://askubuntu.com/a/1036507

X11 Forwarding

转发远端的 xclient 链接到本地的 xserver

macos

xquartz

Vino

折腾了好久之后才发现官方有文档 Setting Up VNC | NVIDIA Developer

官方用的是 ubuntu 自带的 vino。与 x11vnc 一样是屏幕共享。需要等本地登陆后才能连，也可配置自动登陆。不插显示器也支持，不过默认分辨率有点小

问题：卡，没有 x11vnc 流畅

切换到 lightdm 后流畅度改善不少。

默认分辨率太小，需要修改 /etc/X11/xorg.conf

Section "Screen"
   Identifier    "Default Screen"
   Monitor        "Configured Monitor"
   Device        "Default Device"
   SubSection "Display"
       Depth    24
       Virtual 1440 900
   EndSubSection
EndSection

ZED

确保安装了

Cython

距离识别模式

STANDARD，效率好，检测边界，存在黑洞（无法获取数据的点）
FILL，生产稠密的距离图，不支持 Point Cloud，

Dr=Z^2*alpha, where Dr is depth resolution, Z the distance and alpha a constant.

zed-gstreamer

  gir1.2-gst-rtsp-server-1.0 gstreamer1.0-doc gstreamer1.0-qt5 libglib2.0-doc libgstrtspserver-1.0-0 libgstrtspserver-1.0-dev

pytorch

PyTorch for Jetson - version 1.7.0 now available - Jetson & Embedded Systems / Jetson Nano - NVIDIA Developer Forums

cmake python3

https://github.com/pytorch/vision/issues/2565

cmake

系统自带版本太旧，编译 torchvison 不支持 python3。删除系统的版本，再从源码重新编译。

sudo apt remove cmake

确保安装 openssl 依赖

sudo apt install libssl-dev

下载

opencv

自带的 opencv 并不支持 CUDA，cv2.getBuildInfomation()：

General configuration for OpenCV 4.1.1 =====================================
  Version control:               4.1.1-2-gd5a58aa75

  Platform:
    Timestamp:                   2019-12-13T17:25:11Z
    Host:                        Linux 4.9.140-tegra aarch64
    CMake:                       3.10.2
    CMake generator:             Unix Makefiles
    CMake build tool:            /usr/bin/make
    Configuration:               Release

  CPU/HW features:
    Baseline:                    NEON FP16
      required:                  NEON
      disabled:                  VFPV3

  ....

OpenCV modules:
    To be built:                 calib3d core dnn features2d flann gapi highgui imgcodecs imgproc ml objdetect photo python2 python3 stitching ts video videoio
    Disabled:                    world
    Disabled by dependency:      -
    Unavailable:                 java js
    Applications:                tests perf_tests examples apps
    Documentation:               NO
    Non-free algorithms:         NO

需要自己重新编译：Install OpenCV 4.5 on Jetson Nano - Q-engineering

# reveal the CUDA location
sudo sh -c "echo '/usr/local/cuda/lib64' >> /etc/ld.so.conf.d/nvidia-tegra.conf"
sudo ldconfig
# third-party libraries
sudo apt-get install build-essential cmake git unzip pkg-config
sudo apt-get install libjpeg-dev libpng-dev libtiff-dev
sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install libgtk2.0-dev libcanberra-gtk*
sudo apt-get install python3-dev python3-numpy python3-pip
sudo apt-get install libxvidcore-dev libx264-dev libgtk-3-dev
sudo apt-get install libtbb2 libtbb-dev libdc1394-22-dev
sudo apt-get install libv4l-dev v4l-utils
sudo apt-get install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev
sudo apt-get install libavresample-dev libvorbis-dev libxine2-dev
sudo apt-get install libfaac-dev libmp3lame-dev libtheora-dev
sudo apt-get install libopencore-amrnb-dev libopencore-amrwb-dev
sudo apt-get install libopenblas-dev libatlas-base-dev libblas-dev
sudo apt-get install liblapack-dev libeigen3-dev gfortran
sudo apt-get install libhdf5-dev protobuf-compiler
sudo apt-get install libprotobuf-dev libgoogle-glog-dev libgflags-dev
# qt5
sudo apt-get install qt5-default

下载源码：

# download the latest version
$ cd ~
$ wget -O opencv.zip https://github.com/opencv/opencv/archive/4.5.0.zip
$ wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/4.5.0.zip
# unpack
$ unzip opencv.zip
$ unzip opencv_contrib.zip

$ mv opencv-4.5.0 opencv
$ mv opencv_contrib-4.5.0 opencv_contrib
# clean up the zip files
$ rm opencv.zip
$ rm opencv_contrib.zip

$ cd ~/opencv
$ mkdir build
$ cd build

确保 CUDA_ARCH_BIN 改成 agx xavier 的版本默认安装位置是 /usr/local，改成/usr是为了和本机版本共存。

cmake -D CMAKE_BUILD_TYPE=RELEASE \
        -D CMAKE_INSTALL_PREFIX=/usr \
        -D OPENCV_EXTRA_MODULES_PATH=~/Playground/opencv_contrib/modules \
        -D EIGEN_INCLUDE_PATH=/usr/include/eigen3 \
        -D WITH_CUDA=ON \
        -D CUDA_ARCH_BIN=7.2 \
        -D CUDA_ARCH_PTX="" \
        -D WITH_CUDNN=ON \
        -D WITH_CUBLAS=ON \
        -D ENABLE_FAST_MATH=ON \
        -D CUDA_FAST_MATH=ON \
        -D OPENCV_DNN_CUDA=ON \
        -D ENABLE_NEON=ON \
        -D WITH_QT=ON \
        -D WITH_OPENMP=ON \
        -D WITH_OPENGL=ON \
        -D BUILD_TIFF=ON \
        -D WITH_FFMPEG=ON \
        -D WITH_GSTREAMER=ON \
        -D WITH_TBB=ON \
        -D BUILD_TBB=ON \
        -D BUILD_TESTS=OFF \
        -D WITH_EIGEN=ON \
        -D WITH_V4L=ON \
        -D WITH_LIBV4L=ON \
        -D OPENCV_ENABLE_NONFREE=ON \
        -D INSTALL_C_EXAMPLES=OFF \
        -D INSTALL_PYTHON_EXAMPLES=OFF \
        -D BUILD_NEW_PYTHON_SUPPORT=ON \
        -D BUILD_opencv_python3=TRUE \
        -D OPENCV_GENERATE_PKGCONFIG=ON \
        -D BUILD_EXAMPLES=ON ..

编译

make -j4


$ sudo rm -r /usr/include/opencv4/opencv2 # 清理 old packages
$ sudo make install
$ sudo ldconfig
# cleaning (frees 300 KB)
$ make clean

测试一下：

General configuration for OpenCV 4.5.1 =====================================
  Version control:               unknown

  Extra modules:
    Location (extra):            /home/tx/Playground/opencv_contrib/modules
    Version control (extra):     unknown

  Platform:
    Timestamp:                   2020-12-29T03:01:07Z
    Host:                        Linux 4.9.140-tegra aarch64
    CMake:                       3.19.2
    CMake generator:             Unix Makefiles
    CMake build tool:            /usr/bin/make
    Configuration:               RELEASE

  CPU/HW features:
    Baseline:                    NEON FP16
      required:                  NEON

  ...

  OpenCV modules:
    To be built:                 alphamat aruco bgsegm bioinspired calib3d ccalib core cudaarithm cudabgsegm cudacodec cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev cvv datasets dnn dnn_objdetect dnn_superres dpm face features2d flann freetype fuzzy gapi hdf hfs highgui img_hash imgcodecs imgproc intensity_transform line_descriptor mcc ml objdetect optflow phase_unwrapping photo plot python3 quality rapid reg rgbd saliency sfm shape stereo stitching structured_light superres surface_matching text tracking ts video videoio videostab xfeatures2d ximgproc xobjdetect xphoto
    Disabled:                    world
    Disabled by dependency:      -
    Unavailable:                 cnn_3dobj java julia matlab ovis python2 viz
    Applications:                perf_tests examples apps
    Documentation:               NO
    Non-free algorithms:         YES




cv2.cuda.getCudaEnabledDeviceCount()
1

gstreamer

安装需要参考 Accelerated GStreamer

sudo add-apt-repository universe
sudo add-apt-repository multiverse
sudo apt-get update
sudo apt-get install gstreamer1.0-tools gstreamer1.0-alsa \
  gstreamer1.0-plugins-base gstreamer1.0-plugins-good \
  gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly \
  gstreamer1.0-libav
sudo apt-get install libgstreamer1.0-dev \
  libgstreamer-plugins-base1.0-dev \
  libgstreamer-plugins-good1.0-dev \
  libgstreamer-plugins-bad1.0-dev

opencv 才能支持 CAP_GSTREAMER，jetcam 才能正常使用

cv2.getBuildInfomation() 输出有：

Video I/O:
    FFMPEG:                      YES
      avcodec:                   YES (57.107.100)
      avformat:                  YES (57.83.100)
      avutil:                    YES (55.78.100)
      swscale:                   YES (4.8.100)
      avresample:                NO
    GStreamer:                   YES (1.14.5)
    v4l/v4l2:                    YES (linux/videodev2.h)

videoparse
videoconvert
queue

trt_pose

jupyter 无法显示图片 https://stackoverflow.com/questions/61834868/ipython-display-display-not-showing-image-in-jupyter-notebook

python3 -m pip install ipywidgets
jupyter nbextension enable --py widgetsnbextension
# 需要安装 jupyter-lab 和 nodejs > 12
jupyter labextension install @jupyter-widgets/jupyterlab-manager