Install nvdec and nvh264enc as GStreamer plugins

$ sudo apt-get install gtk-doc-tools

$ git clone -b 1.14.5 https://github.com/GStreamer/gst-plugins-bad.git

download NVIDIA Video Codec SDK

$ unzip Video_Codec_SDK_11.1.5.zip

$ cp Video_Codec_SDK_11.1.5/Interface/nvEncodeAPI.h gst-plugins-bad/sys/nvenc/

$ cp Video_Codec_SDK_11.1.5/Interface/cuviddec.h gst-plugins-bad/sys/nvdec/

$ cp Video_Codec_SDK_11.1.5/Interface/nvcuvid.h gst-plugins-bad/sys/nvdec/

$ cd gst-plugins-bad/

$ ./autogen.sh --with-cuda-prefix="/usr/local/cuda"

$ cd sys/nvenc/

$ make

$ sudo cp .libs/libgstnvenc.so /usr/lib/x86_64-linux-gnu/gstreamer-1.0/

$ cd ../nvdec

$ make

$ sudo cp .libs/libgstnvdec.so /usr/lib/x86_64-linux-gnu/gstreamer-1.0/

$ gst-inspect-1.0 nvh264enc

$ gst-inspect-1.0 nvdec

Ubuntu 加入 RamDisk

$ sudo mkdir /mnt/ramdisk

$ sudo chmod 1777 /mnt/ramdisk

$ sudo vi /etc/fstab

tmpfs /mnt/ramdisk tmpfs rw,size=1G 0 0

$ sudo reboot

set frpc as service in Ubuntu

vi /etc/systemd/system/frpc.service

[Unit]

Description=frp client

Wants=network-online.target

After=network.target network-online.target

[Service]

ExecStart=/path_to/frpc -c /path_to/frpc.ini

[Install]

WantedBy=multi-user.target

$ sudo systemctl daemon-reload

$ sudo systemctl enable frpc

$ sudo systemctl start frpc

$ sudo systemctl status frpc

$ sudo systemctl staop frpc

GStreamer 使用 rstp

URI="rtsp://user:password@192.168.1.123:554/cam/realmonitor?channel=1&subtype=0"

使用 playbin

$ gst-launch-1.0 -v playbin uri=$URI

$ gst-launch-1.0 -v playbin uri=$URI uridecodebin0::source::latency=3000::video-sink=autovideosink

使用 uridecodebin

$ gst-launch-1.0 uridecodebin uri=$URI name=d d. ! nvvideoconvert ! nveglglessink

$ gst-launch-1.0 uridecodebin uri=$URI name=d d. ! nvvideoconvert ! nveglglessink d. ! queue ! audioconvert ! audioresample ! autoaudiosink

使用 decodebin

$ gst-launch-1.0 rtspsrc location=$URI ! application/x-rtp, media=video ! decodebin ! nvvideoconvert ! nveglglessink

$ gst-launch-1.0 rtspsrc location=$URI ! application/x-rtp, media=video ! decodebin name=d d. ! queue ! nvvideoconvert ! nveglglessink d. ! queue ! audioconvert ! audioresample ! autoaudiosink

自行編寫

$ gst-launch-1.0 rtspsrc location=$URI ! application/x-rtp, media=video ! queue ! rtph265depay ! h265parse ! nvv4l2decoder ! nvvideoconvert ! nveglglessink

$ gst-launch-1.0 rtspsrc location=$URI name=d d. ! application/x-rtp, media=video ! queue ! rtph265depay ! h265parse ! nvv4l2decoder ! nvvideoconvert ! nveglglessink d. ! queue ! application/x-rtp, media=audio ! rtppcmadepay ! alawdec ! autoaudiosink

存檔

$ gst-launch-1.0 -e rtspsrc location=$URI ! application/x-rtp, media=video ! queue ! rtph265depay ! h265parse ! matroskamux ! filesink location=aa.mkv

存檔並顯示

$ gst-launch-1.0 -e rtspsrc location=$URI ! application/x-rtp, media=video ! queue ! rtph265depay ! h265parse ! tee name=t  t. ! queue ! avdec_h265 ! videoconvert ! autovideosink t. ! queue ! matroskamux ! filesink location=aa.mkv

存檔並顯示(因使用 nvv4l2decoder，所以要在 h265parse 前 tee)

$ gst-launch-1.0 -e rtspsrc location=$URI ! application/x-rtp, media=video ! queue ! rtph265depay ! tee name=t  t. ! queue ! h265parse ! nvv4l2decoder ! nvvideoconvert ! nveglglessink t. ! queue ! h265parse ! matroskamux ! filesink location=aa.mkv

常用

nveglglessink nvoverlaysink autovideosink xvimagesink fakesink

nvvideoconvert videoconvert

nvv4l2decoder avdec_h265

Jetson 使用 nveglglessink 前要加 nvegltransform

gst-inspect-1.0

Ubuntu 安裝 DeepStream

$ sudo apt-get update
$ sudo apt-get upgrade

$ sudo apt-get install python3-pip

$ cd /usr/bin

$ sudo ln -s python3 python

參考 DeepStream Quickstart Guide

一般顯卡參考 dGPU Setup for Ubuntu 段落

依據文件要求安裝 NVIDIA driver 470.63.01

但是據上次安裝經驗不要使用 NVIDIA-Linux-x86_64-470.63.01.run

$ ubuntu-drivers devices

$ sudo apt-get install nvidia-driver-470

$ sudo reboot

依據文件說明安裝 CUDA Toolkit 11.4 Update 1

$ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin

$ sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600

$ sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub

sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"

$ sudo apt-get update

$ sudo apt-get -y install cuda

依據文件安裝 TensorRT 8.0.1

$ echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda-repo.list

$ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub

$ sudo apt-key add 7fa2af80.pub

$ sudo apt-get update

依據鏈結下載 TensorRT 8.0.1 GA for Ubuntu 18.04 and CUDA 11.3 DEB

$ sudo dpkg -i nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626_1-1_amd64.deb

$ sudo apt-key add /var/nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626/7fa2af80.pub

$ sudo apt-get update

$ sudo apt-get install -y libnvinfer8=8.0.1-1+cuda11.3 

$ sudo apt-get install -y libnvinfer-plugin8=8.0.1-1+cuda11.3 

$ sudo apt-get install -y libnvparsers8=8.0.1-1+cuda11.3 

$ sudo apt-get install -y libnvonnxparsers8=8.0.1-1+cuda11.3 

$ sudo apt-get install -y libnvinfer-bin=8.0.1-1+cuda11.3 

$ sudo apt-get install -y libnvinfer-dev=8.0.1-1+cuda11.3 

$ sudo apt-get install -y libnvinfer-plugin-dev=8.0.1-1+cuda11.3 

$ sudo apt-get install -y libnvparsers-dev=8.0.1-1+cuda11.3 

$ sudo apt-get install -y libnvonnxparsers-dev=8.0.1-1+cuda11.3 

$ sudo apt-get install -y libnvinfer-samples=8.0.1-1+cuda11.3 

$ sudo apt-get install -y libnvinfer-doc=8.0.1-1+cuda11.3

安裝 librdkafka

$ git clone https://github.com/edenhill/librdkafka.git

$ cd librdkafka/

$ git reset --hard 7101c2310341ab3f4675fc565f64f0967e135a6a

$ .configure

$ make

$ sudo make install

$ sudo mkdir -p /opt/nvidia/deepstream/deepstream-6.0/lib

$ sudo cp /usr/local/lib/librdkafka* /opt/nvidia/deepstream/deepstream-6.0/lib

依據鏈結下載 Deepstream 6.0 dGPU Debian package

$ sudo apt-get install ./deepstream-6.0_6.0.0-1_amd64.deb

$ rm ${HOME}/.cache/gstreamer-1.0/registry.x86_64.bin

$ cd /opt/nvidia/deepstream/deepstream-6.0/samples/configs/deepstream-app/

$ deepstream-app -c source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt

安裝 Docker

$ sudo apt-get update

$ sudo apt-get install -y ca-certificates

$ sudo apt-get install -y curl

$ sudo apt-get install -y gnupg

$ sudo apt-get install -y lsb-release

$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg

$ echo \

$   "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu \

$   $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

$ sudo apt-get update

$ sudo apt-get install docker-ce docker-ce-cli containerd.io

$ sudo docker run hello-world

$ sudo docker docker image list

$ sudo docker image rm hello-world

安裝 Nvidia Container Toolkit

參考文件 Nvidia Container Toolkit Installation Guide

$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \

   && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \

   && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

$ curl -s -L https://nvidia.github.io/nvidia-container-runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list

$ sudo apt-get update

$ sudo apt-get install -y nvidia-docker2

$ sudo systemctl restart docker

$ sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi

在 Docker 內，執行 deepstream-app

參考 Docker Containers 下載 docker image

$ docker pull nvcr.io/nvidia/deepstream:6.0-samples

參考 Nvidia NGC DeepStream 執行程式

$ xhost +

$ sudo docker run --gpus '"'device=0'"' -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -w /opt/nvidia/deepstream/deepstream-6.0 nvcr.io/nvidia/deepstream:6.0-samples

# cd samples/configs/deepstream-app

# deepstream-app -c source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt

Ubuntu 安裝 TeamViewer

到官方網站下載 Ubuntu 安裝 deb

$ sudo apt install gdebi-core

$ sudo gdebi teamviewer_15.25.5_amd64.deb

$ teamviewer

Ubuntu 不接螢幕，使用遠端桌面

$ sudo apt-get install xserver-xorg-video-dummy-hwe-18.04

$ sudo vi /usr/share/X11/xorg.conf.d/xorg.conf

Section "Device"    Identifier  "Configured Video Device"

    Driver      "dummy"

    VideoRam 256000

EndSection

 

 

Section "Monitor"

    Identifier  "Configured Monitor"

    HorizSync 5.0 - 1000.0

    VertRefresh 5.0 - 200.0

    ModeLine "1920x1080" 148.50 1920 2448 2492 2640 1080 1084 1089 1125 +Hsync +Vsync

EndSection

 

 

Section "Screen"

    Identifier  "Default Screen"

    Monitor     "Configured Monitor"

    Device      "Configured Video Device"

    DefaultDepth 24

    SubSection "Display"

    Depth 24

    Modes "1920x1080"

    EndSubSection

EndSection

因為要 login 才能有桌面，所以

start/settings/Details/Users

Automatic Login: ON

DeepStream 因為沒接實體螢幕

sink 不能使用 EglSink，只能使用 FakeSink or File

Ubuntu 安裝時出現錯誤

因為 nvidia 顯卡驅動問題出現

nouveau DRM: failed to create kernel channel, -22

等錯誤

進 GRUB menu 介面時，移到 Install Ubuntu，按 e 進入編輯環境

游標移到 linux 行，將行尾的 --- 改成 nomodeset

按 F10 開機

正常安裝後，系統顯示拔走 usb，按 Enter

按 Esc(or Shift)，進入 GRUB menu

(若按太多下，會進入 grub>，輸入 normal，按 Enter 後馬上按 Esc，回到 GRUB menu)

移到 Ubuntu，按 e 進入編輯環境

尋找 quiet splash 後面加入 nomodeset

按 F10 開機

python onvif

https://github.com/quatanium/python-onvif

只適用 python2

https://github.com/FalkTannhaeuser/python-onvif-zeep

才能在 python3 使用