关于Ubuntu18.04/20.04安装后的一系列环境配置过程的总结_ubuntu18.04安装后

因某些原因，本博客以后将在以下网址更新~

关于Ubuntu18.04/20.04安装后的一系列环境配置过程的总结_安装好ubuntu18-CSDN博客文章浏览阅读854次。Updating本文所有用到的文件打包供大家下载（不含代码）{}：提取码:zwz4--来自百度网盘超级会员v4的分享。_安装好ubuntu18https://blog.csdn.net/M0rtzz/article/details/134085988Updating...

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目录

1.注意

2.更换国内源

3.设置/home文件夹下为英文

4.禁用Nouveau驱动

5.安装Nvidia驱动（有可能会损坏系统，如果损坏可以重装并看看网上的其他教程，除了这种安装方法还有其他安装方法，自行上网了解）

6.cuda安装：

7.cudnn安装：

8.安装ROS（有些图忘记截了）

9.安装opencv-3.4.16和opencv_contrib-3.4.16（Ubuntu18.04），Ubuntu20.04请装opencv-4.2.0及其扩展模块：

10.安装protobuf2.6.1

11.配置OpenBLAS

12.配置seetaface2工作空间

13.百度智能云

14.使在桌面上右键打开终端时进入Desktop目录

15.同步双系统时间

16.启动菜单的默认项

17.安装darknet版yolov3及darknet-ros工作空间

18.Azure Kinect SDK-v1.4.0的安装（Ubuntu18.04）

19.配置科大讯飞

20.配置realsense及realsense工作空间

21.配置Kinova机械臂工作空间

22.配置机器人导航（实体）

23.安装配置caffe

24.安装libfreenect2

25.安装vtk8.2.0及PCL1.9.1

26.安装CarlaUE4（必须是Carla的UE仓库里的carla分支才可以通过安装Carla时的编译）

27.安装Carla0.9.13（添加fisheye sensor模块）

---

本文所有用到的文件打包供大家下载（不含代码）{Updating}：

链接:

https://pan.baidu.com/s/1PgmWHKl8oyX_cWYx_uZJrg?pwd=zwz4

提取码:

zwz4
--来自百度网盘超级会员v4的分享

1.注意

刚进入系统一段时间，系统会通知更新到新版本系统（Ubuntu18.04），选择否，之后会询问是否更新系统组件（大概400mb），选择是。

阻止软件更新弹窗：

打开终端输入：

sudo chmod a-x /usr/bin/update-notifier

将关机时间从90秒换为5秒：

打开终端输入：

sudo gedit /etc/systemd/system.conf

 将：

#DefaultTimeoutStopSec=90s

改为：

DefaultTimeoutStopSec=5s

 保存退出，打开终端输入：

sudo systemctl daemon-reload

打开终端输入：

sudo gedit ~/.bashrc

# 找到以下代码段，修改如下：
if [ "$color_prompt" = yes ]; then
    #PS1='${debian_chroot:+($debian_chroot)}\[\033[01;32m\]\u@\h\[\033[00m\]:\[\033[01;34m\]\w\[\033[00m\]\$ '
    PS1='${debian_chroot:+($debian_chroot)}\[\033[01;32m\]\u@\h\[\033[00m\]:\[\033[01;34m\]\w$(git_branch)\[\033[00m\]\$ '
else
    #PS1='${debian_chroot:+($debian_chroot)}\u@\h:\w\$ '
    PS1='${debian_chroot:+($debian_chroot)}\u@\h:\W$(git_branch)\$ '
fi
 
# 在最后加入如下代码段：
git_branch()
{
   branch=`git rev-parse --abbrev-ref HEAD 2>/dev/null`
   if [ "${branch}" != "" ]
   then
       if [ "${branch}" = "(no branch)" ]
       then
           branch="(`git rev-parse --short HEAD`...)"
       fi
       #echo -e " \033[01;36m[$branch]\033[0m " # 天蓝色字体
       #echo -e " \033[46;37m[$branch]\033[0m "  # 天蓝色背景，白色字体
       echo -e " \033[1;43;37m[$branch]\033[0m " # 黄色背景，白色字体
   fi
}

之后保存退出

source ~/.bashrc

 这样就可以更清晰的显示git分支~

2.更换国内源

sudo gedit /etc/apt/sources.list

 将原本的注释掉，在最下方加入:

# 中科大源（Ubuntu 18.04）

deb https://mirrors.ustc.edu.cn/ubuntu/ bionic main restricted universe multiverse
deb-src https://mirrors.ustc.edu.cn/ubuntu/ bionic main restricted universe multiverse

deb https://mirrors.ustc.edu.cn/ubuntu/ bionic-security main restricted universe multiverse
deb-src https://mirrors.ustc.edu.cn/ubuntu/ bionic-security main restricted universe multiverse

deb https://mirrors.ustc.edu.cn/ubuntu/ bionic-updates main restricted universe multiverse
deb-src https://mirrors.ustc.edu.cn/ubuntu/ bionic-updates main restricted universe multiverse

deb https://mirrors.ustc.edu.cn/ubuntu/ bionic-backports main restricted universe multiverse
deb-src https://mirrors.ustc.edu.cn/ubuntu/ bionic-backports main restricted universe multiverse

## Not recommended
# deb https://mirrors.ustc.edu.cn/ubuntu/ bionic-proposed main restricted universe multiverse
# deb-src https://mirrors.ustc.edu.cn/ubuntu/ bionic-proposed main restricted universe multiverse

# 中科大源（Ubuntu 20.04）
deb https://mirrors.ustc.edu.cn/ubuntu/ focal main restricted universe multiverse
deb-src https://mirrors.ustc.edu.cn/ubuntu/ focal main restricted universe multiverse

deb https://mirrors.ustc.edu.cn/ubuntu/ focal-security main restricted universe multiverse
deb-src https://mirrors.ustc.edu.cn/ubuntu/ focal-security main restricted universe multiverse

deb https://mirrors.ustc.edu.cn/ubuntu/ focal-updates main restricted universe multiverse
deb-src https://mirrors.ustc.edu.cn/ubuntu/ focal-updates main restricted universe multiverse

deb https://mirrors.ustc.edu.cn/ubuntu/ focal-backports main restricted universe multiverse
deb-src https://mirrors.ustc.edu.cn/ubuntu/ focal-backports main restricted universe multiverse

## Not recommended
# deb https://mirrors.ustc.edu.cn/ubuntu/ focal-proposed main restricted universe multiverse
# deb-src https://mirrors.ustc.edu.cn/ubuntu/ focal-proposed main restricted universe multiverse

或（寻找属于自己的发行版）：

https://mirrors.ustc.edu.cn/repogen/https://mirrors.ustc.edu.cn/repogen/

sudo apt-get update

 anaconda镜像源（~/.condarc）:

channels:
  - defaults
show_channel_urls: true
default_channels:
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:
  conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  msys2: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  bioconda: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  menpo: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch-lts: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  simpleitk: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  deepmodeling: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/
  nvidia: https://mirrors.cernet.edu.cn/anaconda-extra/cloud/
envs_dirs:
  - /home/m0rtzz/Program_Files/anaconda3/envs

3.设置/home文件夹下为英文

export LANG=en_US

xdg-user-dirs-gtk-update

​编辑选择右边的Update Names

 之后执行以下语句：

export LANG=zh_CN

reboot

勾选不要在次询问我，并选择保留旧的名称

4.禁用Nouveau驱动

sudo gedit /etc/modprobe.d/blacklist.conf

输入

blacklist nouveau
options nouveau modeset=0

保存后关闭，打开终端，输入：

sudo update-initramfs -u

reboot

5.安装Nvidia驱动（有可能会损坏系统，如果损坏可以重装并看看网上的其他教程，除了这种安装方法还有其他安装方法，自行上网了解）

打开终端，输入：

sudo apt-get install gcc g++ make

sudo ubuntu-drivers devices

 寻找带有recommended的版本，输入

sudo apt-get install nvidia-driver-* nvidia-settings nvidia-prime

（*是你的版本号）

sudo apt-get update

sudo apt-get upgrade

reboot

 验证版本

nvidia-smi

6.cuda安装：

https://developer.nvidia.com/cuda-toolkit-archivehttps://developer.nvidia.com/cuda-toolkit-archive

选择和上一步nvidia-smi显示的cuda版本对应的进行安装，官方有教程

安装好之后打开终端输入

sudo gedit ~/.bashrc

 在最后输入

#cuda
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
export PATH=$PATH:/usr/local/cuda/bin
export CUDA_HOME=/usr/local/cuda  #cuda的软连接库,可以设置多版本共存指向

保存后关闭，打开终端，输入： 

source ~/.bashrc

sudo gedit /etc/profile

 在最后加入

#cuda
export PATH=/usr/local/cuda/bin:$PATH
export PATH=/usr/local/cuda/bin:$PATH

保存后关闭，打开终端，输入： 

 source /etc/profile

  验证cuda版本

nvcc -V

 

 安装成功！

7.cudnn安装：

cuDNN Archive | NVIDIA DeveloperExplore and download past releases from cuDNN GPU-accelerated primitive library for deep neural networks.https://developer.nvidia.com/rdp/cudnn-archive

同样需要选择与刚才安装cuda对应的版本下载，下载好后进入文件所在目录打开终端

tar -xvf cudnn-*-linux-x64-*.tgz 

打开终端：

sudo cp -r cuda/include/* /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/

sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*

sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.0.1 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.0.1 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.0.1 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.0.1 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.0.1 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.0.1 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
sudo ln -sf /usr/local/cuda-11.4/lib64/libcudnn.so.8 /usr/local/cuda-11.4/lib64/libcudnn.so.8.2.2

 验证是否安装成功

cat /usr/local/cuda/include/cudnn_version.h

 

8.安装ROS（有些图忘记截了）

设置中科大源

sudo sh -c '. /etc/lsb-release && echo "deb http://mirrors.ustc.edu.cn/ros/ubuntu/ `lsb_release -cs` main" > /etc/apt/sources.list.d/ros-latest.list'

设置公钥

sudo apt-key adv --keyserver 'hkp://keyserver.ubuntu.com:80' --recv-key C1CF6E31E6BADE8868B172B4F42ED6FBAB17C654

sudo apt update

sudo apt install ros-melodic-desktop-full

echo "source /opt/ros/melodic/setup.bash" >> ~/.bashrc
source ~/.bashrc

sudo apt install python-rosdep python-rosinstall python-rosinstall-generator python-wstool build-essential

sudo apt-get install python3-pip 

 使用阿里镜像源加速pip下载：

sudo pip3 install rosdepc -i https://mirrors.aliyun.com/pypi/simple/

sudo rosdepc init
rosdepc update

sudo chmod 777 -R ~/.ros/ 

roscore

   再新建两个终端，分别输入

rosrun turtlesim turtlesim_node

rosrun turtlesim turtle_teleop_key

 在rosrun turtlesim turtle_teleop_key所在终端点击一下任意位置，然后使用↕↔小键盘控制，看小海龟会不会动，如果会动则安装成功

9.安装opencv-3.4.16和opencv_contrib-3.4.16（Ubuntu18.04），Ubuntu20.04请装opencv-4.2.0及其扩展模块：

虽然使用cv_bridge时某些shared object有可能和ROS自带的opencv-3.2.0版本冲突，但实测安装3.2.0对cuda的兼容性太差导致无法使用深度相机，所以安装官网最近更新过的OpenCV3.4.16

 经尝试多版本Ubuntu和OpenCV，装Ubuntu20.04，ROS noetic和OpenCV4.2.0及其扩展模块才能解决将彩色图像转换为网络所需的输入Blob后前馈时抛出的（raised OpenCV exception，error: (-215:Assertion failed)等等）。

git clone -b 3.4.16 https://gitee.com/KylenWrt/opencv.git opencv-3.4.16

cd opencv-3.4.16

git clone -b 3.4.16 https://gitee.com/zsy26226/opencv_contrib.git opencv_contrib-3.4.16

  安装所需依赖库，打开终端，输入：

sudo add-apt-repository "deb http://security.ubuntu.com/ubuntu xenial-security main"
sudo apt update
sudo apt install libjasper1 libjasper-dev

sudo apt-get install build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libdc1394-22-dev liblapacke-dev checkinstall

sudo apt-get install liblapacke-dev checkinstall

进入opencv-3.4.16文件夹，打开终端，输入：

mkdir build

cd build

 接下来编译安装，注意此命令的OPENCV_EXTRA_MODULES_PATH=后边的路径是你电脑下的绝对路径，请自行修改

cmake -D CMAKE_BUILD_TYPE=RELEASE -D WITH_GTK_2_X=ON -D OPENCV_ENABLE_NONFREE=ON -D OPENCV_GENERATE_PKGCONFIG=YES -D OPENCV_EXTRA_MODULES_PATH=/home/m0rtzz/Program_Files/opencv-3.4.16/opencv_contrib-3.4.16/modules -D WITH_CUDA=ON -D WITH_CUDNN=ON -D WITH_FFMPEG=ON -D WITH_OPENGL=ON -D WITH_NVCUVID=ON -D -DENABLE_PRECOMPILED_HEADERS=OFF -D CMAKE_EXE_LINKER_FLAGS=-lcblas -DWITH_LAPACK=OFF -j16 ..

过程中会出现IPPICV: Download: ippicv_2020_lnx_intel64_20191018_general.tgz

 解决方法：

cd ../ && mkdir downloads 

cd downloads && pwd

 复制绝对路径后：

打开这个ippicv.cmake

 把绝对路径复制进去：

 然后把下面网址下载的文件cp进去就行了（或者开头百度云分享链接中自取~）

https://github.com/opencv/opencv_3rdparty/blob/ippicv/master_20191018/ippicv/ippicv_2020_lnx_intel64_20191018_general.tgzhttps://github.com/opencv/opencv_3rdparty/blob/ippicv/master_20191018/ippicv/ippicv_2020_lnx_intel64_20191018_general.tgz之后重新打开终端，输入：cmake（别忘了改路径）：

cmake -D CMAKE_BUILD_TYPE=RELEASE -D WITH_GTK_2_X=ON -D OPENCV_ENABLE_NONFREE=ON -D OPENCV_GENERATE_PKGCONFIG=YES -D OPENCV_EXTRA_MODULES_PATH=/home/m0rtzz/Program_Files/opencv-3.4.16/opencv_contrib-3.4.16/modules -D WITH_CUDA=ON -D WITH_CUDNN=ON -D WITH_FFMPEG=ON -D WITH_OPENGL=ON -D WITH_NVCUVID=ON -D -DENABLE_PRECOMPILED_HEADERS=OFF -D CMAKE_EXE_LINKER_FLAGS=-lcblas -DWITH_LAPACK=OFF -j16 ..

这些.i文件需要在国外下载，网上说下载好文件直接把他们放进相对应的目录下就行，实测不行（建议科学的上网，想试试网上说法的：

Reference：fatal error: boostdesc_bgm.i: No such file or directory_"atal error: boostdesc_bgm.i: no such file or dire_eziaowonder的博客-CSDN博客OpenCV4 编译 fatal error: boostdesc_bgm.i: No such file or directory环境编译参考错误原因数据参考环境硬件：树莓派 4B 4G系统：RasberryPi OS编译参考Raspberry pi 树莓派安装 OpenCV 4.1.2子豪兄教你在树莓派上安装OpenCV错误编译到 83% 左右的时候出现类似下图的错误(当时没截图，用 #1301 的部分出错报告)[ 80%] Building CXX object modules/xfhttps://blog.csdn.net/curious_undergather/article/details/111639199?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522167905082916800184134715%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fblog.%2522%257D&request_id=167905082916800184134715&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~rank_v31_ecpm-1-111639199-null-null.blog_rank_default&utm_term=boosetdesc_bgm.i&spm=1018.2226.3001.4450 文件的话，开头百度云分享链接里都有)

cmake -D CMAKE_BUILD_TYPE=RELEASE -D WITH_GTK_2_X=ON -D OPENCV_ENABLE_NONFREE=ON -D OPENCV_GENERATE_PKGCONFIG=YES -D OPENCV_EXTRA_MODULES_PATH=/home/m0rtzz/Program_Files/opencv-3.4.16/opencv_contrib-3.4.16/modules -D WITH_CUDA=ON -D WITH_CUDNN=ON -D WITH_FFMPEG=ON -D WITH_OPENGL=ON -D WITH_NVCUVID=ON -D -DENABLE_PRECOMPILED_HEADERS=OFF -D CMAKE_EXE_LINKER_FLAGS=-lcblas -DWITH_LAPACK=OFF -j16 ..

sudo make -j16

打开那个头文件，把报错所在行改为：

#include "lapacke.h"

sudo make -j16

sudo make install

sudo gedit /etc/ld.so.conf.d/opencv.conf

  加入

/usr/local/lib

保存后关闭，打开终端，输入： 

sudo ldconfig

sudo gedit /etc/bash.bashrc

加入

PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig
export PKG_CONFIG_PATH

保存后关闭，打开终端，输入： 

source /etc/bash.bashrc

测试

cd ../samples/cpp/example_cmake
cmake -j8 .
sudo make -j8
./opencv_example

安装成功！

设置cv_bridge的版本：

sudo gedit /opt/ros/melodic/share/cv_bridge/cmake/cv_bridgeConfig.cmake

# generated from catkin/cmake/template/pkgConfig.cmake.in
 
# append elements to a list and remove existing duplicates from the list
# copied from catkin/cmake/list_append_deduplicate.cmake to keep pkgConfig
# self contained
macro(_list_append_deduplicate listname)
  if(NOT "${ARGN}" STREQUAL "")
    if(${listname})
      list(REMOVE_ITEM ${listname} ${ARGN})
    endif()
 
    list(APPEND ${listname} ${ARGN})
  endif()
endmacro()
 
# append elements to a list if they are not already in the list
# copied from catkin/cmake/list_append_unique.cmake to keep pkgConfig
# self contained
macro(_list_append_unique listname)
  foreach(_item ${ARGN})
    list(FIND ${listname} ${_item} _index)
 
    if(_index EQUAL -1)
      list(APPEND ${listname} ${_item})
    endif()
  endforeach()
endmacro()
 
# pack a list of libraries with optional build configuration keywords
# copied from catkin/cmake/catkin_libraries.cmake to keep pkgConfig
# self contained
macro(_pack_libraries_with_build_configuration VAR)
  set(${VAR} "")
  set(_argn ${ARGN})
  list(LENGTH _argn _count)
  set(_index 0)
 
  while(${_index} LESS ${_count})
    list(GET _argn ${_index} lib)
 
    if("${lib}" MATCHES "^(debug|optimized|general)$")
      math(EXPR _index "${_index} + 1")
 
      if(${_index} EQUAL ${_count})
        message(FATAL_ERROR "_pack_libraries_with_build_configuration() the list of libraries '${ARGN}' ends with '${lib}' which is a build configuration keyword and must be followed by a library")
      endif()
 
      list(GET _argn ${_index} library)
      list(APPEND ${VAR} "${lib}${CATKIN_BUILD_CONFIGURATION_KEYWORD_SEPARATOR}${library}")
    else()
      list(APPEND ${VAR} "${lib}")
    endif()
 
    math(EXPR _index "${_index} + 1")
  endwhile()
endmacro()
 
# unpack a list of libraries with optional build configuration keyword prefixes
# copied from catkin/cmake/catkin_libraries.cmake to keep pkgConfig
# self contained
macro(_unpack_libraries_with_build_configuration VAR)
  set(${VAR} "")
 
  foreach(lib ${ARGN})
    string(REGEX REPLACE "^(debug|optimized|general)${CATKIN_BUILD_CONFIGURATION_KEYWORD_SEPARATOR}(.+)$" "\\1;\\2" lib "${lib}")
    list(APPEND ${VAR} "${lib}")
  endforeach()
endmacro()
 
if(cv_bridge_CONFIG_INCLUDED)
  return()
endif()
 
set(cv_bridge_CONFIG_INCLUDED TRUE)
 
# set variables for source/devel/install prefixes
if("FALSE" STREQUAL "TRUE")
  set(cv_bridge_SOURCE_PREFIX /tmp/binarydeb/ros-melodic-cv-bridge-1.13.1)
  set(cv_bridge_DEVEL_PREFIX /tmp/binarydeb/ros-melodic-cv-bridge-1.13.1/.obj-x86_64-linux-gnu/devel)
  set(cv_bridge_INSTALL_PREFIX "")
  set(cv_bridge_PREFIX ${cv_bridge_DEVEL_PREFIX})
else()
  set(cv_bridge_SOURCE_PREFIX "")
  set(cv_bridge_DEVEL_PREFIX "")
  set(cv_bridge_INSTALL_PREFIX /opt/ros/melodic)
  set(cv_bridge_PREFIX ${cv_bridge_INSTALL_PREFIX})
endif()
 
# warn when using a deprecated package
if(NOT "" STREQUAL "")
  set(_msg "WARNING: package 'cv_bridge' is deprecated")
 
  # append custom deprecation text if available
  if(NOT "" STREQUAL "TRUE")
    set(_msg "${_msg} ()")
  endif()
 
  message("${_msg}")
endif()
 
# flag project as catkin-based to distinguish if a find_package()-ed project is a catkin project
set(cv_bridge_FOUND_CATKIN_PROJECT TRUE)
 
# if(NOT "include;/usr/include;/usr/include/opencv " STREQUAL " ")
# set(cv_bridge_INCLUDE_DIRS "")
# set(_include_dirs "include;/usr/include;/usr/include/opencv")
if(NOT "include;/usr/local/include/opencv;/usr/local/include/opencv2 " STREQUAL " ")
  set(cv_bridge_INCLUDE_DIRS "")
  set(_include_dirs "include;/usr/local/include/opencv;/usr/local/include/opencv;/usr/local/include/;/usr/include")
 
  if(NOT "https://github.com/ros-perception/vision_opencv/issues " STREQUAL " ")
    set(_report "Check the issue tracker 'https://github.com/ros-perception/vision_opencv/issues' and consider creating a ticket if the problem has not been reported yet.")
  elseif(NOT "http://www.ros.org/wiki/cv_bridge " STREQUAL " ")
    set(_report "Check the website 'http://www.ros.org/wiki/cv_bridge' for information and consider reporting the problem.")
  else()
    set(_report "Report the problem to the maintainer 'Vincent Rabaud <vincent.rabaud@gmail.com>' and request to fix the problem.")
  endif()
 
  foreach(idir ${_include_dirs})
    if(IS_ABSOLUTE ${idir} AND IS_DIRECTORY ${idir})
      set(include ${idir})
    elseif("${idir} " STREQUAL "include ")
      get_filename_component(include "${cv_bridge_DIR}/../../../include" ABSOLUTE)
 
      if(NOT IS_DIRECTORY ${include})
        message(FATAL_ERROR "Project 'cv_bridge' specifies '${idir}' as an include dir, which is not found.  It does not exist in '${include}'.  ${_report}")
      endif()
    else()
      message(FATAL_ERROR "Project 'cv_bridge' specifies '${idir}' as an include dir, which is not found.  It does neither exist as an absolute directory nor in '\${prefix}/${idir}'.  ${_report}")
    endif()
 
    _list_append_unique(cv_bridge_INCLUDE_DIRS ${include})
  endforeach()
endif()
 
# set(libraries "cv_bridge;/usr/lib/x86_64-linux-gnu/libopencv_core.so.3.2.0;/usr/lib/x86_64-linux-gnu/libopencv_imgproc.so.3.2.0;/usr/lib/x86_64-linux-gnu/libopencv_imgcodecs.so.3.2.0")
set(libraries "cv_bridge;/usr/local/lib/libopencv_core.so.3.4.16;/usr/local/lib/libopencv_imgproc.so.3.4.16;/usr/local/lib/libopencv_imgcodecs.so.3.4.16")
 
foreach(library ${libraries})
  # keep build configuration keywords, target names and absolute libraries as-is
  if("${library}" MATCHES "^(debug|optimized|general)$")
    list(APPEND cv_bridge_LIBRARIES ${library})
  elseif(${library} MATCHES "^-l")
    list(APPEND cv_bridge_LIBRARIES ${library})
  elseif(${library} MATCHES "^-")
    # This is a linker flag/option (like -pthread)
    # There's no standard variable for these, so create an interface library to hold it
    if(NOT cv_bridge_NUM_DUMMY_TARGETS)
      set(cv_bridge_NUM_DUMMY_TARGETS 0)
    endif()
 
    # Make sure the target name is unique
    set(interface_target_name "catkin::cv_bridge::wrapped-linker-option${cv_bridge_NUM_DUMMY_TARGETS}")
 
    while(TARGET "${interface_target_name}")
      math(EXPR cv_bridge_NUM_DUMMY_TARGETS "${cv_bridge_NUM_DUMMY_TARGETS}+1")
      set(interface_target_name "catkin::cv_bridge::wrapped-linker-option${cv_bridge_NUM_DUMMY_TARGETS}")
    endwhile()
 
    add_library("${interface_target_name}" INTERFACE IMPORTED)
 
    if("${CMAKE_VERSION}" VERSION_LESS "3.13.0")
      set_property(
        TARGET
        "${interface_target_name}"
        APPEND PROPERTY
        INTERFACE_LINK_LIBRARIES "${library}")
    else()
      target_link_options("${interface_target_name}" INTERFACE "${library}")
    endif()
 
    list(APPEND cv_bridge_LIBRARIES "${interface_target_name}")
  elseif(TARGET ${library})
    list(APPEND cv_bridge_LIBRARIES ${library})
  elseif(IS_ABSOLUTE ${library})
    list(APPEND cv_bridge_LIBRARIES ${library})
  else()
    set(lib_path "")
    set(lib "${library}-NOTFOUND")
 
    # since the path where the library is found is returned we have to iterate over the paths manually
    foreach(path /opt/ros/melodic/lib;/opt/ros/melodic/lib)
      find_library(lib ${library}
        PATHS ${path}
        NO_DEFAULT_PATH NO_CMAKE_FIND_ROOT_PATH)
 
      if(lib)
        set(lib_path ${path})
        break()
      endif()
    endforeach()
 
    if(lib)
      _list_append_unique(cv_bridge_LIBRARY_DIRS ${lib_path})
      list(APPEND cv_bridge_LIBRARIES ${lib})
    else()
      # as a fall back for non-catkin libraries try to search globally
      find_library(lib ${library})
 
      if(NOT lib)
        message(FATAL_ERROR "Project '${PROJECT_NAME}' tried to find library '${library}'.  The library is neither a target nor built/installed properly.  Did you compile project 'cv_bridge'?  Did you find_package() it before the subdirectory containing its code is included?")
      endif()
 
      list(APPEND cv_bridge_LIBRARIES ${lib})
    endif()
  endif()
endforeach()
 
set(cv_bridge_EXPORTED_TARGETS "")
 
# create dummy targets for exported code generation targets to make life of users easier
foreach(t ${cv_bridge_EXPORTED_TARGETS})
  if(NOT TARGET ${t})
    add_custom_target(${t})
  endif()
endforeach()
 
set(depends "rosconsole;sensor_msgs")
 
foreach(depend ${depends})
  string(REPLACE " " ";" depend_list ${depend})
 
  # the package name of the dependency must be kept in a unique variable so that it is not overwritten in recursive calls
  list(GET depend_list 0 cv_bridge_dep)
  list(LENGTH depend_list count)
 
  if(${count} EQUAL 1)
    # simple dependencies must only be find_package()-ed once
    if(NOT ${cv_bridge_dep}_FOUND)
      find_package(${cv_bridge_dep} REQUIRED NO_MODULE)
    endif()
  else()
    # dependencies with components must be find_package()-ed again
    list(REMOVE_AT depend_list 0)
    find_package(${cv_bridge_dep} REQUIRED NO_MODULE ${depend_list})
  endif()
 
  _list_append_unique(cv_bridge_INCLUDE_DIRS ${${cv_bridge_dep}_INCLUDE_DIRS})
 
  # merge build configuration keywords with library names to correctly deduplicate
  _pack_libraries_with_build_configuration(cv_bridge_LIBRARIES ${cv_bridge_LIBRARIES})
  _pack_libraries_with_build_configuration(_libraries ${${cv_bridge_dep}_LIBRARIES})
  _list_append_deduplicate(cv_bridge_LIBRARIES ${_libraries})
 
  # undo build configuration keyword merging after deduplication
  _unpack_libraries_with_build_configuration(cv_bridge_LIBRARIES ${cv_bridge_LIBRARIES})
 
  _list_append_unique(cv_bridge_LIBRARY_DIRS ${${cv_bridge_dep}_LIBRARY_DIRS})
  list(APPEND cv_bridge_EXPORTED_TARGETS ${${cv_bridge_dep}_EXPORTED_TARGETS})
endforeach()
 
set(pkg_cfg_extras "cv_bridge-extras.cmake")
 
foreach(extra ${pkg_cfg_extras})
  if(NOT IS_ABSOLUTE ${extra})
    set(extra ${cv_bridge_DIR}/${extra})
  endif()
 
  include(${extra})
endforeach()

opencv-3.4.4cmake命令：

cmake -D CMAKE_BUILD_TYPE=BUILD -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_GTK_2_X=ON -D OPENCV_ENABLE_NONFREE=ON -D OPENCV_GENERATE_PKGCONFIG=YES -D OPENCV_EXTRA_MODULES_PATH=/home/m0rtzz/Program_Files/opencv-3.4.4/opencv_contrib-3.4.4/modules -D WITH_CUDA=ON -D WITH_CUDNN=ON -D WITH_FFMPEG=ON -D WITH_OPENGL=ON -D WITH_NVCUVID=ON -D -DENABLE_PRECOMPILED_HEADERS=OFF -D CMAKE_EXE_LINKER_FLAGS=-lcblas -DWITH_LAPACK=OFF -D WITH_OPENMP=ON  -D BUILD_TESTS=OFF -D WITH_OPENGL=ON -D BUILD_opencv_xfeatures2d=ON -D CUDA_nppicom_LIBRARY=stdc++ -DENABLE_PRECOMPILED_HEADERS=OFF -DENABLE_PRECOMPILED_HEADERS=OFF -D CUDA_ARCH_BIN=8.6 -D CUDA_nppicom_LIBRARY=stdc++ -D CUDA_GENERATION=Auto -D CUDA_HOST_COMPILER:FILEPATH=/usr/bin/gcc-7 -j16 ..

opencv-4.2.0cmake命令（Ubuntu20.04装这个）： 

cmake -D CMAKE_BUILD_TYPE=RELEASE -D OPENCV_GENERATE_PKGCONFIG=ON -D INSTALL_PYTHON_EXAMPLES=ON -D INSTALL_C_EXAMPLES=ON -D OPENCV_EXTRA_MODULES_PATH=/home/m0rtzz/Program_Files/opencv-4.2.0/opencv_contrib-4.2.0/modules -D WITH_V4L=ON -D WITH_QT=ON -D WITH_GTK=ON -D WITH_VTK=ON -D WITH_OPENGL=ON -D WITH_OPENMP=ON -D BUILD_EXAMPLES=ON -D WITH_CUDA=ON -D WITH_CUDNN=ON -D BUILD_TIFF=ON -D ENABLE_PRECOMPILED_HEADERS=OFF -D OPENCV_ENABLE_NONFREE=ON -D CUDA_GENERATION=Auto -D CUDA_CUDA_LIBRARY=/usr/local/cuda-11.7/lib64/stubs/libcuda.so -D CUDA_TOOLKIT_ROOT_DIR=0 -D CUDNN_LIBRARY=/usr/local/cuda-11.7/lib64/libcudnn.so -D WITH_ADE=OFF ..

或：

cmake -D CMAKE_BUILD_TYPE=RELEASE -D OPENCV_GENERATE_PKGCONFIG=ON -D INSTALL_PYTHON_EXAMPLES=ON -D INSTALL_C_EXAMPLES=ON -D OPENCV_EXTRA_MODULES_PATH=/home/m0rtzz/Program_Files/opencv-4.2.0/opencv_contrib-4.2.0/modules -D WITH_V4L=ON -D WITH_QT=ON -D WITH_GTK=ON -D WITH_VTK=ON -D WITH_OPENGL=ON -D WITH_OPENMP=ON -D BUILD_EXAMPLES=ON -D WITH_CUDA=ON -D WITH_CUDNN=ON -D BUILD_TIFF=ON -D ENABLE_PRECOMPILED_HEADERS=OFF -D OPENCV_ENABLE_NONFREE=ON -D CUDA_GENERATION=Auto -D WITH_ADE=OFF CUDA_CUDA_LIBRARY=true -D CUDA_nppicom_LIBRARY=true -j16 ..

 或：

cmake -D CMAKE_BUILD_TYPE=RELEASE -D OPENCV_GENERATE_PKGCONFIG=ON -D INSTALL_PYTHON_EXAMPLES=ON -D INSTALL_C_EXAMPLES=ON -D OPENCV_EXTRA_MODULES_PATH=/home/m0rtzz/Program_Files/opencv-4.2.0/opencv_contrib-4.2.0/modules -D WITH_V4L=ON -D WITH_QT=ON -D WITH_GTK=ON -D WITH_VTK=OFF -D WITH_OPENGL=ON -D WITH_OPENMP=ON -D BUILD_EXAMPLES=ON -D WITH_CUDA=ON -D WITH_CUDNN=ON -D BUILD_TIFF=ON -D ENABLE_PRECOMPILED_HEADERS=OFF -D OPENCV_ENABLE_NONFREE=ON -D CUDA_GENERATION=Auto -D WITH_ADE=OFF CUDA_CUDA_LIBRARY=true -D CUDNN_LIBRARY=/usr/local/cuda-11.8/lib64/libcudnn.so  -DCUDA_ARCH_BIN=Auto CUDA_ARCH_BIN=8.6 -D CUDA_nppicom_LIBRARY=stdc++ -D CUDA_GENERATION=Auto -D CUDA_HOST_COMPILER:FILEPATH=/usr/bin/gcc -j16 ..

+（解决CUDNN8.x编译报错）

https://github.com/opencv/opencv/pull/17685/fileshttps://github.com/opencv/opencv/pull/17685/files如果不执行以下几步，编译darknet_ros会报错:error:‘IplImage’之类的

sudo cp /usr/local/lib/pkgconfig/opencv4.pc /usr/lib/pkgconfig

cd /usr/lib/pkgconfig
sudo mv opencv4.pc opencv.pc

10.安装protobuf2.6.1

sudo apt-get install libtool

https://github.com/google/protobuf/releases/download/v2.6.1/protobuf-2.6.1.tar.gzhttps://github.com/google/protobuf/releases/download/v2.6.1/protobuf-2.6.1.tar.gz或镜像：

wget https://gitee.com/M0rtzz/protobuf-2.6.1/raw/master/protobuf-2.6.1.tar.gz

解压压缩包后进入文件夹，打开终端，输入：

./autogen.sh

./configure --prefix=/usr/local/protobuf 

sudo make -j16

 养成make check 的好习惯

sudo make check -j16

 sudo make install

sudo gedit /etc/profile

在最后加入：

#protobuf
export PATH=$PATH:/usr/local/protobuf/bin/
export PKG_CONFIG_PATH=/usr/local/protobuf/lib/pkgconfig/

保存后关闭，打开终端，输入： 

source /etc/profile

sudo gedit /etc/ld.so.conf

在最后一行输入：

/usr/local/protobuf/lib

保存后关闭，打开终端，输入： 

sudo ldconfig

最后验证版本：

protoc --version

11.配置OpenBLAS

sudo apt-get install gcc-arm-linux-gnueabihf libnewlib-arm-none-eabi libc6-dev-i386

OpenBLAS文件夹最上方百度网盘里有，或者绕开github用gitee进行克隆：

git clone https://gitee.com/HyperChao/OpenBLAS.git OpenBLAS

cd OpenBLAS

sudo apt-get install gfortran

sudo make FC=gfortran TARGET=ARMV8 -j16

sudo make PREFIX=/usr/local install

 查看版本

grep OPENBLAS_VERSION /usr/local/include/openblas_config.h 

12.配置seetaface2工作空间

sudo gedit ~/.bashrc

 在最后加入

source /home/m0rtzz/Workspaces/catkin_ws/devel/setup.bash

保存后关闭，打开终端，输入：  

source ~/.bashrc

解决办法：

终端输入： 

sudo gedit ~/.bashrc

加入工作空间下lib文件夹的路径

export LD_LIBRARY_PATH=/home/m0rtzz/Workspaces/catkin_ws/lib:$LD_LIBRARY_PATH

保存后关闭，打开终端，输入：  

source ~/.bashrc

 解决！

报错：

Gtk-Message: 15:22:30.610: Failed to load module "canberra-gtk-module"

下方第16小节最后有解决办法~

13.百度智能云

sudo apt-get install curl

include jsoncpp库的头文件改为

 #include "jsoncpp/json/json.h"

g++编译

g++ *.cpp -o * -lcurl -ljsoncpp

 运行

./*

14.使在桌面上右键打开终端时进入Desktop目录

Ubuntu – Details of source package gnome-terminal in bionichttps://packages.ubuntu.com/source/bionic/gnome-terminal

下载下图表格中的下边两个文件

 下载好gnome-terminal_3.28.1.orig.tar.xz文件之后解压出一个文件夹gnome-terminal-3.28.1，将gnome-terminal_3.28.1-1ubuntu1.debian.tar.xz 里面debian目录下的文件解压到之前解压出的gnome-terminal-3.28.1目录下

在此目录下打开终端

git apply patches/*.patch

安装依赖

sudo apt install  intltool  libvte-2.91-dev gsettings-desktop-schemas-dev uuid-dev libdconf-dev libpcre2-dev libgconf2-dev libxml2-utils  gnome-shell libnautilus-extension-dev itstool  yelp-tools pcre2-utils

 打开src/下的terminal-nautilus.c

找到

static inline gboolean
desktop_opens_home_dir (TerminalNautilus *nautilus)
{
#if 0
  return  _client_get_bool (gconf_client,
                                "/apps/nautilus-open-terminal/desktop_opens_home_dir",
                                NULL);
#endif
  return TRUE;
}

改为

static inline gboolean
desktop_opens_home_dir (TerminalNautilus *nautilus)
{
#if 0
  return  _client_get_bool (gconf_client,
                                "/apps/nautilus-open-terminal/desktop_opens_home_dir",
                                NULL);
#endif
  return FALSE;
}

 src下打开终端

cd ..

autoreconf --install

autoconf

./configure --prefix='/usr'

sudo make -j8

sudo make check -j8

sudo make install

reboot

之后在桌面上打开终端，看看是否生效 ，没生效打开终端

sudo cp /usr/lib/nautilus/extensions-3.0/libterminal-nautilus.so /usr/lib/x86_64-linux-gnu/nautilus/extensions-3.0/

reboot

问题解决！

15.同步双系统时间

sudo apt-get install ntpdate

sudo ntpdate time.windows.com

timedatectl set-local-rtc 1 --adjust-system-clock

16.启动菜单的默认项

sudo gedit /etc/default/grub

 改一下GRUB_DEFAULT=后边的数字，默认是0，windows是第n个就设置为 n-1

保存后关闭，打开终端，输入：  

sudo update-grub

reboot

 重启后问题解决~

17.安装darknet版yolov3及darknet-ros工作空间

git clone https://gitcode.net/xyll24/darknet.git darknet

cd darknet

sudo gedit Makefile

 修改以下前几行为：

GPU=1
CUDNN=1
CUDNN_HALF=1
OPENCV=1
AVX=0
OPENMP=1
LIBSO=1
ZED_CAMERA=0
ZED_CAMERA_v2_8=0

 然后修改NVCC=后边为nvcc路径：

NVCC=/usr/local/cuda-11.4/bin/nvcc

 之后保存退出后，打开终端，输入：

sudo gedit /etc/ld.so.conf.d/cuda.conf

 加入以下内容后保存退出：

/usr/local/cuda/lib64

 打开终端输入：

sudo ldconfig

sudo make -j16

./darknet

输出为：

usage: ./darknet <function>

 之后我们下载yolov3权重文件：

mkdir weights && cd ./weights && wget https://pjreddie.com/media/files/yolov3.weights 

正常wget太慢，我们使用mwget进行安装：

找一个你想安装mwget的地方打开终端，输入：

sudo apt install build-essential

sudo apt upgrade intltool

sudo apt install  libssl-dev

 之后：

wget http://jaist.dl.sourceforge.net/project/kmphpfm/mwget/0.1/mwget_0.1.0.orig.tar.bz2

tar -xjvf mwget_0.1.0.orig.tar.bz2

cd mwget_0.1.0.orig

./configure

sudo make -j8

sudo make install

 函数报错的话在文件夹中搜索httpplugin.h和ftpplugin.h中加入

#include <string.h>

保存后关闭，打开终端，输入：  

再次安装：

sudo make -j8

sudo make install

 之后mwget就安装成功了

我们用mwget多线程获取权重文件：

cd darknet/ && mkdir weights && cd weights/

mwget https://pjreddie.com/media/files/yolov3.weights -n16

上方命令是16线程获取 ，速度会快很多

到此为止darknet版yolov3就配置好了

下面我们测试一下：

./darknet detect cfg/yolov3.cfg weights/yolov3.weights data/dog.jpg

 输出以下就证明配置没有问题：

输出的最后一行报错：

Gtk-Message: 15:22:30.610: Failed to load module "canberra-gtk-module"

解决方法：

sudo apt-get install libcanberra-gtk*

 安装之后重新运行就不会报错了。 

配置 darknet-ros工作空间：

mkdir darknet-ros_test_ws && cd darknet-ros_test_ws/ && mkdir src

cd src/ && catkin_init_workspace

cd .. && catkin_make -j16

cd src/

git clone --recursive https://gitee.com/mirrors_leggedrobotics/darknet_ros.git darknet_ros 

 如果是OpenCV4：

git clone -b opencv4 --recursive https://github.com/kunaltyagi/darknet_ros.git darknet_ros
git branch -a
git checkout remotes/origin/opencv4
git submodule update --recursive

  如果是OpenCV4，视频流只有第一帧是RGB8编码格式，阅读源码后发现在show_image之前调用image.cpp中的rgbgr_image函数循环转换图像编码格式即可解决此问题：

// @file : image.cpp
void rgbgr_image(image im)
{
    int i;
    for(i = 0; i < im.w*im.h; ++i){
        float swap = im.data[i];
        im.data[i] = im.data[i+im.w*im.h*2];
        im.data[i+im.w*im.h*2] = swap;
    }
}

// @file : YoloObjectDetector.cpp
  void *YoloObjectDetector::displayInThread(void *ptr)
  {
    // NOTE: Modified by M0rtzz，Solved the problem of displaying video streams as bgr8
    rgbgr_image(buff_[(buffIndex_ + 1) % 3]);
    int c = show_image(buff_[(buffIndex_ + 1) % 3], "YOLO V3", waitKeyDelay_);
    if (c != -1)
      c = c % 256;
    if (c == 27)
    {
      demoDone_ = 1;
      return 0;
    }
    else if (c == 82)
    {
      demoThresh_ += .02;
    }
    else if (c == 84)
    {
      demoThresh_ -= .02;
      if (demoThresh_ <= .02)
        demoThresh_ = .02;
    }
    else if (c == 83)
    {
      demoHier_ += .02;
    }
    else if (c == 81)
    {
      demoHier_ -= .02;
      if (demoHier_ <= .0)
        demoHier_ = .0;
    }
    return 0;
  }

  若darknet_ros/darknet文件夹下为空，则：

cd darknet_ros && sudo rm -rf darknet

git clone https://github.com/alexeyab/darknet.git darknet

 catkin_make如果编译不过的话（error: ‘IplImage’之类的，之前装OpenCV提到过避免报错的方法），注意以下命令是只编译darknet-ros一个包，若工作空间下有多个包需要一起编译那么把命令中的darknet-ros删除重新执行即可：

catkin_make -j16 darknet_ros --cmake-args -DCMAKE_CXX_FLAGS=-DCV__ENABLE_C_API_CTORS

 如果报错nvcc fatal : Unsupported gpu architecture 'compute_30'之类的，是因为CUDA11已经不支持compute_30了，我们将darknet_ros/darknet/Makefile和darknet_ros/darknet_ros/CMakeLists.txt中含有 'compute_30'的行进行注释后重新catkin_make：

 

18.Azure Kinect SDK-v1.4.0的安装（Ubuntu18.04）

Reference： BkbK-的博客https://bokai.blog.csdn.net/article/details/119115883?spm=1001.2014.3001.5502

git clone -b v1.4.0 https://github.com/microsoft/Azure-Kinect-Sensor-SDK.git Azure-Kinect-Sensor-SDK-v1.4.0

 嫌太慢可以使用gitee镜像仓库克隆：

git clone -b v1.4.0 https://gitee.com/javenst/Azure-Kinect-Sensor-SDK.git Azure-Kinect-Sensor-SDK-v1.4.0

sudo dpkg --add-architecture amd64

sudo apt-get update

sudo apt install -y  pkg-config  ninja-build doxygen clang  gcc-multilib  g++-multilib python3 nasm cmake libgl1-mesa-dev libsoundio-dev libvulkan-dev libx11-dev libxcursor-dev libxinerama-dev libxrandr-dev libusb-1.0-0-dev libssl-dev libudev-dev mesa-common-dev uuid-dev

Index of /ubuntu/18.04/prod/pool/main/libk/https://packages.microsoft.com/ubuntu/18.04/prod/pool/main/libk/

从上面的网站下载 libk4a1.2 中 libk4a1.2_1.2.0_amd64.deb文件

 解压 .deb 文件，再解压内部的 data.tar.gz和control.tar.gz文件,并进入data文件夹，打开终端输入：

cd usr/lib/x86_64-linux-gnu
sudo cp libdepthengine.so.2.0 /usr/lib/x86_64-linux-gnu

随后进入下载好的 Azure-Kinect-Sensor-SDK-v1.4.0文件夹下打开终端输入

mkdir build && cd build
cmake -j8 .. -GNinja

注意此步过程中extern/libyuv/src克隆较慢原因是使用了google的网站，我们把对应文件的克隆url改为github的就能正常克隆了，在Azure-Kinect-Sensor-SDK-v1.4.0文件夹下键盘Ctrl+H显示隐藏文件，打开.gitmodules文件，修改libyuv的部分为：

[submodule "extern/libyuv/src"]
	path = extern/libyuv/src
	url = https://github.com/lemenkov/libyuv.git

保存后关闭

 之后打开.git文件夹下的config文件，修改libyuv的部分为：

[submodule "extern/libyuv/src"]
	active = true
	url = https://github.com/lemenkov/libyuv.git

接下来就能正常克隆了，但是速度还是很慢，请耐心等待~

保存后关闭，打开终端，输入： 

cmake -j8 .. -GNinja

克隆完成后为如图所示：

 之后输入：

sudo ninja -j8

 完成后如下：

 最后输入：

sudo ninja install

完成后如下：

 之后安装依赖：

sudo add-apt-repository ppa:ubuntu-toolchain-r/test

sudo apt-get update

sudo gedit /etc/apt/sources.list

 在最后一行加入：

##gcc-4.9
deb http://dk.archive.ubuntu.com/ubuntu/ xenial main
deb http://dk.archive.ubuntu.com/ubuntu/ xenial universe
##

保存后关闭，打开终端，输入：

sudo apt-get update

sudo apt-get install gcc-4.9

sudo apt-get upgrade libstdc++6

sudo cp /usr/lib/x86_64-linux-gnu/libk4a1.4/libdepthengine.so.2.0 /usr/lib

之后测试一下：

sudo ./bin/k4aviewer

 授予权限：

cd ../ && sudo cp scripts/99-k4a.rules /etc/udev/rules.d/.

 Ubuntu20.04，Reference：

https://blog.csdn.net/qq_42108414/article/details/129015474https://blog.csdn.net/qq_42108414/article/details/129015474

19.配置科大讯飞

SDK下载 - 科大讯飞api接口 - 讯飞开放平台科大讯飞api接口，为开发者免费提供：语音识别、语音合成、语音评测、声纹识别、人脸识别等SDK下载服务，一站式人机智能语音交互等解决方案。https://www.xfyun.cn/sdk/dispatcher

sudo apt-get install sox libsox-fmt-all pavucontrol

sudo gedit /usr/include/pcl-1.8/pcl/visualization/cloud_viewer.h

修改一下：

//line 199左右
private:
        /** \brief Private implementation. */
        struct CloudViewer_impl;
        //std::auto_ptr<CloudViewer_impl> impl_;
	    std::shared_ptr<CloudViewer_impl> impl_;
        
        boost::signals2::connection 
        registerMouseCallback (boost::function<void (const pcl::visualization::MouseEvent&)>);

 下载所需SDK,将libs/x64/libmsc.so文件拷贝至/usr/lib/下；修改~/.bashrc；

cmake_minimum_required(VERSION 3.0.2)
project(tts_voice_test)
SET(CMAKE_CXX_FLAGS "-std=c++0x")
find_package(k4a REQUIRED)
find_package(OpenCV REQUIRED)
find_package(catkin REQUIRED COMPONENTS
roscpp
rospy
std_msgs
cv_bridge
message_generation
)
 
generate_messages(
  DEPENDENCIES
  std_msgs
)
 
include_directories(
  ~/Workspaces/tts_test_ws/include
  ${catkin_INCLUDE_DIRS}
)
 
add_executable(tts_voice_test src/tts_voice_test.cpp)
 
target_link_libraries(tts_voice_test
  PRIVATE k4a::k4a
  ${OpenCV_LIBRARIES}
  ${PCL_LIBRARIES}
  ${catkin_LIBRARIES}
  ${catkin_LIBRARIES} -lcurl -ljsoncpp -lmsc -lrt -ldl -pthread
  ${catkin_LIBRARIES} /home/m0rtzz/Workspaces/tts_voice_test_ws/libs/x64/libmsc.so -ldl -pthread -lasound

打开终端：

catkin_make

若找不到asoundlib.h文件打开终端输入：

sudo apt-get install libasound2-dev

编译通过~

20.配置realsense及realsense工作空间

sudo apt-get install ros-melodic-realsense2-camera ros-melodic-rgbd-launch

 安装realsense sdk:

sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-key F6E65AC044F831AC80A06380C8B3A55A6F3EFCDE || sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-key F6E65AC044F831AC80A06380C8B3A55A6F3EFCDE

sudo add-apt-repository "deb https://librealsense.intel.com/Debian/apt-repo $(lsb_release -cs) main" -u

sudo apt-get update

 安装realsense lib

sudo apt-get install librealsense2-dkms librealsense2-utils

测试：

realsense-viewer

 下载lib并指定版本为v2.50.0，否则接下来会与realsense-ros版本冲突导致无法打开摄像头:

git clone -b v2.50.0 https://gitee.com/lhospitallky/librealsense.git librealsense-2.50.0

  安装依赖：

sudo apt-get install libudev-dev pkg-config libgtk-3-dev libusb-1.0-0-dev pkg-config libglfw3-dev

 进入刚才克隆的librealsense文件夹内：

cd librealsense-2.50.0/

./scripts/setup_udev_rules.sh

./scripts/patch-realsense-ubuntu-lts.sh

 注意：上面的命令可能执行过慢，请耐心等待，或者科学的上网~

  完成结果如下：

之后输入：

mkdir build && cd build

# @file : CMakeLists.txt
# NOTE: Modified by M0rtzz
LINK_LIBRARIES(-lcurl -lcrypto)

cmake -j8 ../ -DCMAKE_BUILD_TYPE=Release -DBUILD_EXAMPLES=true

 以下编译过慢，使用CPU最大线程进行make，速度会快很多：

sudo make -j16

sudo make install

测试：

cd examples/capture

./rs-capture

接下来我们配置realsense工作空间：

创建一个realsense_test_ws文件夹，进入文件夹下，打开终端：

mkdir src && cd src/

 下载功能包:

git clone -b ros1-legacy https://gitee.com/joosoo/realsense-ros.git realsense-ros

cd ..

catkin_make -j16 -DCATKIN_ENABLE_TESTING=False -DCMAKE_BUILD_TYPE=Release

catkin_make install

测试：

roslaunch realsense2_camera rs_camera.launch

 还没安摄像头~

21.配置Kinova机械臂工作空间

mkdir -p kinova_test_ws/src

cd kinova_test_ws/src 

catkin_init_workspace

cd ..

catkin_make

echo 'source /home/m0rtzz/Workspaces/kinova_test_ws/devel/setup.bash' >> ~/.bashrc

cd src/

git clone https://gitee.com/dva7777/kinova-ros.git kinova-ros

cd ..

安装缺少的moveit中相应的功能包 ：

sudo apt-get install ros-melodic-moveit-visual-tools ros-melodic-moveit-ros-planning-interface

catkin_make -j16

sudo cp src/kinova-ros/kinova_driver/udev/10-kinova-arm.rules /etc/udev/rules.d/

 安装Moveit和pr2：

sudo apt-get install ros-melodic-moveit ros-melodic-trac-ik ros-melodic-pr2*

完成~

22.配置机器人导航（实体）

安装 Arduino IDE:

https://www.arduino.cc/en/softwarehttps://www.arduino.cc/en/software

 下载Linux 64bit安装包

tar -xvf arduino-1.8.19-linux64.tar.xz

sudo mv arduino-1.8.19 /opt

cd /opt/arduino-1.8.19

sudo chmod +x install.sh

sudo ./install.sh

sudo apt install ros-melodic-move-base* ros-melodic-turtlebot3-* ros-melodic-dwa-local-planner

sudo apt-get install ros-melodic-joy ros-melodic-teleop-twist-joy ros-melodic-teleop-twist-keyboard ros-melodic-laser-proc ros-melodic-rgbd-launch ros-melodic-depthimage-to-laserscan ros-melodic-rosserial-arduino ros-melodic-rosserial-python ros-melodic-rosserial-server ros-melodic-rosserial-client ros-melodic-rosserial-msgs ros-melodic-amcl ros-melodic-map-server ros-melodic-move-base ros-melodic-urdf ros-melodic-xacro ros-melodic-compressed-image-transport ros-melodic-rqt-image-view ros-melodic-gmapping ros-melodic-navigation ros-melodic-interactive-markers

 安装 gmapping 包（用于构建地图）：

sudo apt-get install ros-melodic-gmapping

安装地图服务包（用于保存与读取地图）: 

sudo apt-get install ros-melodic-map-server

安装 navigation 包（用于定位以及路径规划）: 

sudo apt-get install ros-melodic-navigation

因tf和tf2迁移问题，需将工作空间内的所有global_costmap_params.yaml和local_costmap_params.yaml文件里的头几行去掉“/”,返回工作空间根目录下重新编译。

Reference：

http://wiki.ros.org/tf2/Migrationhttp://wiki.ros.org/tf2/Migration

首先创建实体导航工作空间：

mkdir -p navigation_entity_test_ws/src

cd navigation_entity_test_ws/src

catkin_create_pkg entity_test roscpp rospy std_msgs  gmapping map_server amcl move_base

cd .. && catkin_make

 查看一下文件目录，tree命令在下边的PS小节有讲怎么安装

tree .

cd src/ && catkin_create_pkg robot_start_test roscpp rospy std_msgs ros_arduino_python usb_cam rplidar_ros

cd robot_start_test/ && mkdir launch && cd launch && touch start_test.launch

<!--@File Name : start_test.launch 
    @Brief : 机器人启动文件：
        1.启动底盘
        2.启动激光雷达
        3.启动摄像头
 -->
 
<launch>
        <include file="$(find ros_arduino_python)/launch/arduino.launch" />
        <include file="$(find usb_cam)/launch/usb_cam-test.launch" />
        <include file="$(find rplidar_ros)/launch/rplidar.launch" />
</launch>

FIXME：Updating...

接下来创建机器人模型相关的功能包：

cd src/

catkin_create_pkg robot_description_test urdf xacro

 在功能包下新建 urdf 目录，编写具体的 urdf 文件（code命令是VSCode，没安装的小伙伴下边PS小节有下载网址~）:

cd robot_description_test/ && mkdir urdf

cd urdf/ && touch {robot.urdf.xacro,robot_base.urdf.xacro,robot_camera.urdf.xacro,robot_laser.urdf.xacro} && code robot.urdf.xacro

将下列代码粘贴进去：

<!-- File Name : robot.urdf.xacro -->
 
<robot name="robot_test" xmlns:xacro="http://wiki.ros.org/xacro">
 
    <xacro:include filename="robot_base.urdf.xacro" />
    <xacro:include filename="robot_camera.urdf.xacro" />
    <xacro:include filename="robot_laser.urdf.xacro" />
 
</robot>

 保存退出，打开终端输入：

code robot_base.urdf.xacro

将下列代码粘贴进去：

<!-- File Name : robot_base.urdf.xacro -->
 
<robot name="robot_test" xmlns:xacro="http://wiki.ros.org/xacro">
 
    <xacro:property name="footprint_radius" value="0.001" />
    <link name="base_footprint">
        <visual>
            <geometry>
                <sphere radius="${footprint_radius}" />
            </geometry>
        </visual>
    </link>
 
    <xacro:property name="base_radius" value="0.1" />
    <xacro:property name="base_length" value="0.08" />
    <xacro:property name="lidi" value="0.015" />
    <xacro:property name="base_joint_z" value="${base_length / 2 + lidi}" />
    <link name="base_link">
        <visual>
            <geometry>
                <cylinder radius="0.1" length="0.08" />
            </geometry>
 
            <origin xyz="0 0 0" rpy="0 0 0" />
 
            <material name="baselink_color">
                <color rgba="1.0 0.5 0.2 0.5" />
            </material>
        </visual>
 
    </link>
 
    <joint name="link2footprint" type="fixed">
        <parent link="base_footprint"  />
        <child link="base_link" />
        <origin xyz="0 0 0.055" rpy="0 0 0" />
    </joint>
 
    <xacro:property name="wheel_radius" value="0.0325" />
    <xacro:property name="wheel_length" value="0.015" />
    <xacro:property name="PI" value="3.1415927" />
    <xacro:property name="wheel_joint_z" value="${(base_length / 2 + lidi - wheel_radius) * -1}" />
 
    <xacro:macro name="wheel_func" params="wheel_name flag">
 
        <link name="${wheel_name}_wheel">
            <visual>
                <geometry>
                    <cylinder radius="${wheel_radius}" length="${wheel_length}" />
                </geometry>
 
                <origin xyz="0 0 0" rpy="${PI / 2} 0 0" />
 
                <material name="wheel_color">
                    <color rgba="0 0 0 0.3" />
                </material>
            </visual>
 
        </link>
 
        <joint name="${wheel_name}2link" type="continuous">
            <parent link="base_link"  />
            <child link="${wheel_name}_wheel" />
 
            <origin xyz="0 ${0.1 * flag} ${wheel_joint_z}" rpy="0 0 0" />
            <axis xyz="0 1 0" />
        </joint>
 
    </xacro:macro>
 
    <xacro:wheel_func wheel_name="left" flag="1" />
    <xacro:wheel_func wheel_name="right" flag="-1" />
 
    <xacro:property name="small_wheel_radius" value="0.0075" />
    <xacro:property name="small_joint_z" value="${(base_length / 2 + lidi - small_wheel_radius) * -1}" />
 
    <xacro:macro name="small_wheel_func" params="small_wheel_name flag">
        <link name="${small_wheel_name}_wheel">
            <visual>
                <geometry>
                    <sphere radius="${small_wheel_radius}" />
                </geometry>
 
                <origin xyz="0 0 0" rpy="0 0 0" />
 
                <material name="wheel_color">
                    <color rgba="0 0 0 0.3" />
                </material>
            </visual>
 
        </link>
 
        <joint name="${small_wheel_name}2link" type="continuous">
            <parent link="base_link"  />
            <child link="${small_wheel_name}_wheel" />
 
            <origin xyz="${0.08 * flag} 0 ${small_joint_z}" rpy="0 0 0" />
            <axis xyz="0 1 0" />
        </joint>
 
    </xacro:macro >
    <xacro:small_wheel_func small_wheel_name="front" flag="1"/>
    <xacro:small_wheel_func small_wheel_name="back" flag="-1"/>
 
</robot>

  保存退出，打开终端输入：

code robot_camera.urdf.xacro

 将下列代码粘贴进去：

<!-- File Name : robot_camera.urdf.xacro -->
 
<robot name="robot_test" xmlns:xacro="http://wiki.ros.org/xacro">
 
    <xacro:property name="camera_length" value="0.02" /> 
    <xacro:property name="camera_width" value="0.05" /> 
    <xacro:property name="camera_height" value="0.05" /> 
    <xacro:property name="joint_camera_x" value="0.08" />
    <xacro:property name="joint_camera_y" value="0" />
    <xacro:property name="joint_camera_z" value="${base_length / 2 + camera_height / 2}" />
 
    <link name="camera">
        <visual>
            <geometry>
                <box size="${camera_length} ${camera_width} ${camera_height}" />
            </geometry>
            <origin xyz="0 0 0" rpy="0 0 0" />
            <material name="black">
                <color rgba="0 0 0 0.8" />
            </material>
        </visual>
    </link>
 
    <joint name="camera2base" type="fixed">
        <parent link="base_link" />
        <child link="camera" />
        <origin xyz="${joint_camera_x} ${joint_camera_y} ${joint_camera_z}" rpy="0 0 0" />
    </joint>
 
</robot>

   保存退出，打开终端输入：

code robot_laser.urdf.xacro

 将下列代码粘贴进去：

<!-- File Name : robot_laser.urdf.xacro -->
 
<robot name="robot_test" xmlns:xacro="http://wiki.ros.org/xacro">
 
    <xacro:property name="support_radius" value="0.01" />
    <xacro:property name="support_length" value="0.15" />
 
    <xacro:property name="laser_radius" value="0.03" />
    <xacro:property name="laser_length" value="0.05" />
 
    <xacro:property name="joint_support_x" value="0" />
    <xacro:property name="joint_support_y" value="0" />
    <xacro:property name="joint_support_z" value="${base_length / 2 + support_length / 2}" />
 
    <xacro:property name="joint_laser_x" value="0" />
    <xacro:property name="joint_laser_y" value="0" />
    <xacro:property name="joint_laser_z" value="${support_length / 2 + laser_length / 2}" />
 
    <link name="support">
        <visual>
            <geometry>
                <cylinder radius="${support_radius}" length="${support_length}" />
            </geometry>
            <material name="yellow">
                <color rgba="0.8 0.5 0.0 0.5" />
            </material>
        </visual>
 
    </link>
 
    <joint name="support2base" type="fixed">
        <parent link="base_link" />
        <child link="support"/>
        <origin xyz="${joint_support_x} ${joint_support_y} ${joint_support_z}" rpy="0 0 0" />
    </joint>
    <link name="laser">
        <visual>
            <geometry>
                <cylinder radius="${laser_radius}" length="${laser_length}" />
            </geometry>
            <material name="black">
                <color rgba="0 0 0 0.5" />
            </material>
        </visual>
 
    </link>
 
    <joint name="laser2support" type="fixed">
        <parent link="support" />
        <child link="laser"/>
        <origin xyz="${joint_laser_x} ${joint_laser_y} ${joint_laser_z}" rpy="0 0 0" />
    </joint>
</robot>

保存退出，打开终端：

cd .. && mkdir launch

touch robot_test.launch && code robot_test.launch

将下列代码粘贴进去：

<!-- File Name : robot_test.launch -->
 
<launch>
    <param name="robot_description" command="$(find xacro)/xacro $(find robot_description_test)/urdf/robot.urdf.xacro" />
    <node pkg="joint_state_publisher" name="joint_state_publisher" type="joint_state_publisher" />
    <node pkg="robot_state_publisher" name="robot_state_publisher" type="robot_state_publisher" />
</launch>

 保存退出，打开终端：

cd ../../../ && echo 'source /home/m0rtzz/Workspaces/navigation_entity_test_ws/devel/setup.bash' >> ~/.bashrc && source ~/.bashrc

 测试一下：

roslaunch robot_description_test robot_test.launch

之后Ctrl+Alt+T打开一个新的终端，输入：

rviz

将 Fixed Frame设置为base_footprint：

 Add一个RobotModel：

 Add一个TF：

cd src/entity_test/ && mkdir launch && cd launch/ 

touch gmapping.launch && code gmapping.launch

将下列代码粘贴进去：

<!-- File Name : gmapping.launch -->
 
<launch>
    <node pkg="gmapping" type="slam_gmapping" name="slam_gmapping" output="screen">
      <remap from="scan" to="scan"/>
      <param name="base_frame" value="base_footprint"/><!--底盘坐标系-->
      <param name="odom_frame" value="odom"/> <!--里程计坐标系-->
      <param name="map_update_interval" value="5.0"/>
      <param name="maxUrange" value="16.0"/>
      <param name="sigma" value="0.05"/>
      <param name="kernelSize" value="1"/>
      <param name="lstep" value="0.05"/>
      <param name="astep" value="0.05"/>
      <param name="iterations" value="5"/>
      <param name="lsigma" value="0.075"/>
      <param name="ogain" value="3.0"/>
      <param name="lskip" value="0"/>
      <param name="srr" value="0.1"/>
      <param name="srt" value="0.2"/>
      <param name="str" value="0.1"/>
      <param name="stt" value="0.2"/>
      <param name="linearUpdate" value="1.0"/>
      <param name="angularUpdate" value="0.5"/>
      <param name="temporalUpdate" value="3.0"/>
      <param name="resampleThreshold" value="0.5"/>
      <param name="particles" value="30"/>
      <param name="xmin" value="-50.0"/>
      <param name="ymin" value="-50.0"/>
      <param name="xmax" value="50.0"/>
      <param name="ymax" value="50.0"/>
      <param name="delta" value="0.05"/>
      <param name="llsamplerange" value="0.01"/>
      <param name="llsamplestep" value="0.01"/>
      <param name="lasamplerange" value="0.005"/>
      <param name="lasamplestep" value="0.005"/>
    </node>
</launch>

cd .. && mkdir map

cd launch && touch map_save.launch && code map_save.launch

将下列代码粘贴进去：

<!-- File Name : map_save.launch -->
 
<launch>
    <arg name="filename" value="$(find entity_test)/map/nav" />
    <node name="map_save" pkg="map_server" type="map_saver" args="-f $(arg filename)" />
</launch>

 touch map_server.launch && code map_server.launch

 将下列代码粘贴进去：

<!-- File Name : map_server.launch -->
 
<launch>
    <!-- 设置地图的配置文件 -->
    <arg name="map" default="nav.yaml" />
    <!-- 运行地图服务器，并且加载设置的地图-->
    <node name="map_server" pkg="map_server" type="map_server" args="$(find entity_test)/map/$(arg map)"/>
</launch>

touch amcl.launch && code amcl.launch

 将下列代码粘贴进去：

<!-- File Name : amcl.launch -->
 
<launch>
  <node pkg="amcl" type="amcl" name="amcl" output="screen">
    <!-- Publish scans from best pose at a max of 10 Hz -->
    <param name="odom_model_type" value="diff"/><!-- 里程计模式为差分 -->
    <param name="odom_alpha5" value="0.1"/>
    <param name="transform_tolerance" value="0.2" />
    <param name="gui_publish_rate" value="10.0"/>
    <param name="laser_max_beams" value="30"/>
    <param name="min_particles" value="500"/>
    <param name="max_particles" value="5000"/>
    <param name="kld_err" value="0.05"/>
    <param name="kld_z" value="0.99"/>
    <param name="odom_alpha1" value="0.2"/>
    <param name="odom_alpha2" value="0.2"/>
    <!-- translation std dev, m -->
    <param name="odom_alpha3" value="0.8"/>
    <param name="odom_alpha4" value="0.2"/>
    <param name="laser_z_hit" value="0.5"/>
    <param name="laser_z_short" value="0.05"/>
    <param name="laser_z_max" value="0.05"/>
    <param name="laser_z_rand" value="0.5"/>
    <param name="laser_sigma_hit" value="0.2"/>
    <param name="laser_lambda_short" value="0.1"/>
    <param name="laser_lambda_short" value="0.1"/>
    <param name="laser_model_type" value="likelihood_field"/>
    <!-- <param name="laser_model_type" value="beam"/> -->
    <param name="laser_likelihood_max_dist" value="2.0"/>
    <param name="update_min_d" value="0.2"/>
    <param name="update_min_a" value="0.5"/>
 
    <param name="odom_frame_id" value="odom"/><!-- 里程计坐标系 -->
    <param name="base_frame_id" value="base_footprint"/><!-- 添加机器人基坐标系 -->
    <param name="global_frame_id" value="map"/><!-- 添加地图坐标系 -->
 
  </node>
</launch>

cd .. && mkdir param && cd param/ && touch {costmap_common_params.yaml,local_costmap_params.yaml,global_costmap_params.yaml,base_local_planner_params.yaml} && code .

 将下列几个代码分别粘贴进去：

# File Name : base_local_planner_params.yaml
 
TrajectoryPlannerROS:
 
# Robot Configuration Parameters
  max_vel_x: 0.5 # X 方向最大速度
  min_vel_x: 0.1 # X 方向最小速速
 
  max_vel_theta:  1.0 # 
  min_vel_theta: -1.0
  min_in_place_vel_theta: 1.0
 
  acc_lim_x: 1.0 # X 加速限制
  acc_lim_y: 0.0 # Y 加速限制
  acc_lim_theta: 0.6 # 角速度加速限制
 
# Goal Tolerance Parameters，目标公差
  xy_goal_tolerance: 0.10
  yaw_goal_tolerance: 0.05
 
# Differential-drive robot configuration
# 是否是全向移动机器人
  holonomic_robot: false
 
# Forward Simulation Parameters，前进模拟参数
  sim_time: 0.8
  vx_samples: 18
  vtheta_samples: 20
  sim_granularity: 0.05

# File Name : cost_common_params.yaml
 
#机器人几何参，如果机器人是圆形，设置 robot_radius,如果是其他形状设置 footprint
robot_radius: 0.12 #圆形
# footprint: [[-0.12, -0.12], [-0.12, 0.12], [0.12, 0.12], [0.12, -0.12]] #其他形状
 
obstacle_range: 3.0 # 用于障碍物探测，比如: 值为 3.0，意味着检测到距离小于 3 米的障碍物时，就会引入代价地图
raytrace_range: 3.5 # 用于清除障碍物，比如：值为 3.5，意味着清除代价地图中 3.5 米以外的障碍物
 
 
#膨胀半径，扩展在碰撞区域以外的代价区域，使得机器人规划路径避开障碍物
inflation_radius: 0.2
#代价比例系数，越大则代价值越小
cost_scaling_factor: 3.0
 
#地图类型
map_type: costmap
#导航包所需要的传感器
observation_sources: scan
#对传感器的坐标系和数据进行配置。这个也会用于代价地图添加和清除障碍物。例如，你可以用激光雷达传感器用于在代价地图添加障碍物，再添加kinect用于导航和清除障碍物。
scan: {sensor_frame: laser, data_type: LaserScan, topic: scan, marking: true, clearing: true}

# File Name : global_costmap_params.yaml
 
global_costmap:
  global_frame: map #地图坐标系
  robot_base_frame: base_footprint #机器人坐标系
  # 以此实现坐标变换
 
  update_frequency: 1.0 #代价地图更新频率
  publish_frequency: 1.0 #代价地图的发布频率
  transform_tolerance: 0.5 #等待坐标变换发布信息的超时时间
 
  static_map: true # 是否使用一个地图或者地图服务器来初始化全局代价地图，如果不使用静态地图，这个参数为false.

# File Name : local_costmap_params.yaml
 
local_costmap:
  global_frame: odom #里程计坐标系
  robot_base_frame: base_footprint #机器人坐标系
 
  update_frequency: 10.0 #代价地图更新频率
  publish_frequency: 10.0 #代价地图的发布频率
  transform_tolerance: 0.5 #等待坐标变换发布信息的超时时间
 
  static_map: false  #不需要静态地图，可以提升导航效果
  rolling_window: true #是否使用动态窗口，默认为false，在静态的全局地图中，地图不会变化
  width: 3 # 局部地图宽度 单位是 m
  height: 3 # 局部地图高度 单位是 m
  resolution: 0.05 # 局部地图分辨率 单位是 m，一般与静态地图分辨率保持一致

cd ../launch && touch move_base.launch && code move_base.launch

将下列代码粘贴进去： 

<!-- File Name : move_base.launch -->
 
<launch>
 
    <node pkg="move_base" type="move_base" respawn="false" name="move_base" output="screen" clear_params="true">
        <rosparam file="$(find nav)/param/costmap_common_params.yaml" command="load" ns="global_costmap" />
        <rosparam file="$(find nav)/param/costmap_common_params.yaml" command="load" ns="local_costmap" />
        <rosparam file="$(find nav)/param/local_costmap_params.yaml" command="load" />
        <rosparam file="$(find nav)/param/global_costmap_params.yaml" command="load" />
        <rosparam file="$(find nav)/param/base_local_planner_params.yaml" command="load" />
    </node>
 
</launch>

touch auto_slam.launch && code auto_slam.launch

将下列代码粘贴进去：

<!-- File Name : auto_slam.launch -->
 
<launch>
    <!-- 启动SLAM节点 -->
    <include file="$(find entity_test)/launch/gmapping.launch" />
    <!-- 运行move_base节点 -->
    <include file="$(find entity_test)/launch/move_base.launch" />
</launch>

23.安装配置caffe

Reference：

https://blog.csdn.net/weixin_39161727/article/details/120136500https://blog.csdn.net/weixin_39161727/article/details/120136500首先安装依赖：

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install libatlas-base-dev
sudo apt-get install python-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

 git clone https://gitee.com/quietbirds/caffe.git caffe

cd caffe/ && sudo cp Makefile.config.example Makefile.config

sudo gedit Makefile.config

## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!
 
# cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1
 
# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1
 
# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0
# This code is taken from https://github.com/sh1r0/caffe-android-lib
# USE_HDF5 := 0
 
# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
#	You should not set this flag if you will be reading LMDBs with any
#	possibility of simultaneous read and write
# ALLOW_LMDB_NOLOCK := 1
 
# Uncomment if you're using OpenCV 3
OPENCV_VERSION := 3
 
# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
CUSTOM_CXX := g++
 
# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr
 
# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility.
# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
# For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility.
CUDA_ARCH := #-gencode arch=compute_20,code=sm_20 \
		#-gencode arch=compute_20,code=sm_21 \
		#-gencode arch=compute_30,code=sm_30 \
		-gencode arch=compute_35,code=sm_35 \
		-gencode arch=compute_50,code=sm_50 \
		-gencode arch=compute_52,code=sm_52 \
		-gencode arch=compute_60,code=sm_60 \
		-gencode arch=compute_61,code=sm_61 \
		-gencode arch=compute_61,code=compute_61
 
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := open
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas
 
# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib
 
# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app
 
# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
PYTHON_INCLUDE := /usr/include/python2.7 \
		/usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
# ANACONDA_HOME := $(HOME)/anaconda
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
		# $(ANACONDA_HOME)/include/python2.7 \
		# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
 
# Uncomment to use Python 3 (default is Python 2)
 PYTHON_LIBRARIES := boost_python3 python3.6m
 PYTHON_INCLUDE := /usr/include/python3.6m \
                 /usr/lib/python3.6/dist-packages/numpy/core/include
 
# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib
 
# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib
 
# Uncomment to support layers written in Python (will link against Python libs)
WITH_PYTHON_LAYER := 1
 
# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial/
 
# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib
 
# NCCL acceleration switch (uncomment to build with NCCL)
# https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)
# USE_NCCL := 1
 
# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
 
# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
 
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1
 
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
 
# enable pretty build (comment to see full commands)
Q ?= @

sudo gedit Makefile

PROJECT := caffe
 
CONFIG_FILE := Makefile.config
# Explicitly check for the config file, otherwise make -k will proceed anyway.
ifeq ($(wildcard $(CONFIG_FILE)),)
$(error $(CONFIG_FILE) not found. See $(CONFIG_FILE).example.)
endif
include $(CONFIG_FILE)
 
BUILD_DIR_LINK := $(BUILD_DIR)
ifeq ($(RELEASE_BUILD_DIR),)
	RELEASE_BUILD_DIR := .$(BUILD_DIR)_release
endif
ifeq ($(DEBUG_BUILD_DIR),)
	DEBUG_BUILD_DIR := .$(BUILD_DIR)_debug
endif
 
DEBUG ?= 0
ifeq ($(DEBUG), 1)
	BUILD_DIR := $(DEBUG_BUILD_DIR)
	OTHER_BUILD_DIR := $(RELEASE_BUILD_DIR)
else
	BUILD_DIR := $(RELEASE_BUILD_DIR)
	OTHER_BUILD_DIR := $(DEBUG_BUILD_DIR)
endif
 
# All of the directories containing code.
SRC_DIRS := $(shell find * -type d -exec bash -c "find {} -maxdepth 1 \
	\( -name '*.cpp' -o -name '*.proto' \) | grep -q ." \; -print)
 
# The target shared library name
LIBRARY_NAME := $(PROJECT)
LIB_BUILD_DIR := $(BUILD_DIR)/lib
STATIC_NAME := $(LIB_BUILD_DIR)/lib$(LIBRARY_NAME).a
DYNAMIC_VERSION_MAJOR 		:= 1
DYNAMIC_VERSION_MINOR 		:= 0
DYNAMIC_VERSION_REVISION 	:= 0
DYNAMIC_NAME_SHORT := lib$(LIBRARY_NAME).so
#DYNAMIC_SONAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR)
DYNAMIC_VERSIONED_NAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)
DYNAMIC_NAME := $(LIB_BUILD_DIR)/$(DYNAMIC_VERSIONED_NAME_SHORT)
COMMON_FLAGS += -DCAFFE_VERSION=$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)
 
##############################
# Get all source files
##############################
# CXX_SRCS are the source files excluding the test ones.
CXX_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cpp" -name "*.cpp")
# CU_SRCS are the cuda source files
CU_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cu" -name "*.cu")
# TEST_SRCS are the test source files
TEST_MAIN_SRC := src/$(PROJECT)/test/test_caffe_main.cpp
TEST_SRCS := $(shell find src/$(PROJECT) -name "test_*.cpp")
TEST_SRCS := $(filter-out $(TEST_MAIN_SRC), $(TEST_SRCS))
TEST_CU_SRCS := $(shell find src/$(PROJECT) -name "test_*.cu")
GTEST_SRC := src/gtest/gtest-all.cpp
# TOOL_SRCS are the source files for the tool binaries
TOOL_SRCS := $(shell find tools -name "*.cpp")
# EXAMPLE_SRCS are the source files for the example binaries
EXAMPLE_SRCS := $(shell find examples -name "*.cpp")
# BUILD_INCLUDE_DIR contains any generated header files we want to include.
BUILD_INCLUDE_DIR := $(BUILD_DIR)/src
# PROTO_SRCS are the protocol buffer definitions
PROTO_SRC_DIR := src/$(PROJECT)/proto
PROTO_SRCS := $(wildcard $(PROTO_SRC_DIR)/*.proto)
# PROTO_BUILD_DIR will contain the .cc and obj files generated from
# PROTO_SRCS; PROTO_BUILD_INCLUDE_DIR will contain the .h header files
PROTO_BUILD_DIR := $(BUILD_DIR)/$(PROTO_SRC_DIR)
PROTO_BUILD_INCLUDE_DIR := $(BUILD_INCLUDE_DIR)/$(PROJECT)/proto
# NONGEN_CXX_SRCS includes all source/header files except those generated
# automatically (e.g., by proto).
NONGEN_CXX_SRCS := $(shell find \
	src/$(PROJECT) \
	include/$(PROJECT) \
	python/$(PROJECT) \
	matlab/+$(PROJECT)/private \
	examples \
	tools \
	-name "*.cpp" -or -name "*.hpp" -or -name "*.cu" -or -name "*.cuh")
LINT_SCRIPT := scripts/cpp_lint.py
LINT_OUTPUT_DIR := $(BUILD_DIR)/.lint
LINT_EXT := lint.txt
LINT_OUTPUTS := $(addsuffix .$(LINT_EXT), $(addprefix $(LINT_OUTPUT_DIR)/, $(NONGEN_CXX_SRCS)))
EMPTY_LINT_REPORT := $(BUILD_DIR)/.$(LINT_EXT)
NONEMPTY_LINT_REPORT := $(BUILD_DIR)/$(LINT_EXT)
# PY$(PROJECT)_SRC is the python wrapper for $(PROJECT)
PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp
PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so
PY$(PROJECT)_HXX := include/$(PROJECT)/layers/python_layer.hpp
# MAT$(PROJECT)_SRC is the mex entrance point of matlab package for $(PROJECT)
MAT$(PROJECT)_SRC := matlab/+$(PROJECT)/private/$(PROJECT)_.cpp
ifneq ($(MATLAB_DIR),)
	MAT_SO_EXT := $(shell $(MATLAB_DIR)/bin/mexext)
endif
MAT$(PROJECT)_SO := matlab/+$(PROJECT)/private/$(PROJECT)_.$(MAT_SO_EXT)
 
##############################
# Derive generated files
##############################
# The generated files for protocol buffers
PROTO_GEN_HEADER_SRCS := $(addprefix $(PROTO_BUILD_DIR)/, \
		$(notdir ${PROTO_SRCS:.proto=.pb.h}))
PROTO_GEN_HEADER := $(addprefix $(PROTO_BUILD_INCLUDE_DIR)/, \
		$(notdir ${PROTO_SRCS:.proto=.pb.h}))
PROTO_GEN_CC := $(addprefix $(BUILD_DIR)/, ${PROTO_SRCS:.proto=.pb.cc})
PY_PROTO_BUILD_DIR := python/$(PROJECT)/proto
PY_PROTO_INIT := python/$(PROJECT)/proto/__init__.py
PROTO_GEN_PY := $(foreach file,${PROTO_SRCS:.proto=_pb2.py}, \
		$(PY_PROTO_BUILD_DIR)/$(notdir $(file)))
# The objects corresponding to the source files
# These objects will be linked into the final shared library, so we
# exclude the tool, example, and test objects.
CXX_OBJS := $(addprefix $(BUILD_DIR)/, ${CXX_SRCS:.cpp=.o})
CU_OBJS := $(addprefix $(BUILD_DIR)/cuda/, ${CU_SRCS:.cu=.o})
PROTO_OBJS := ${PROTO_GEN_CC:.cc=.o}
OBJS := $(PROTO_OBJS) $(CXX_OBJS) $(CU_OBJS)
# tool, example, and test objects
TOOL_OBJS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o})
TOOL_BUILD_DIR := $(BUILD_DIR)/tools
TEST_CXX_BUILD_DIR := $(BUILD_DIR)/src/$(PROJECT)/test
TEST_CU_BUILD_DIR := $(BUILD_DIR)/cuda/src/$(PROJECT)/test
TEST_CXX_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o})
TEST_CU_OBJS := $(addprefix $(BUILD_DIR)/cuda/, ${TEST_CU_SRCS:.cu=.o})
TEST_OBJS := $(TEST_CXX_OBJS) $(TEST_CU_OBJS)
GTEST_OBJ := $(addprefix $(BUILD_DIR)/, ${GTEST_SRC:.cpp=.o})
EXAMPLE_OBJS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o})
# Output files for automatic dependency generation
DEPS := ${CXX_OBJS:.o=.d} ${CU_OBJS:.o=.d} ${TEST_CXX_OBJS:.o=.d} \
	${TEST_CU_OBJS:.o=.d} $(BUILD_DIR)/${MAT$(PROJECT)_SO:.$(MAT_SO_EXT)=.d}
# tool, example, and test bins
TOOL_BINS := ${TOOL_OBJS:.o=.bin}
EXAMPLE_BINS := ${EXAMPLE_OBJS:.o=.bin}
# symlinks to tool bins without the ".bin" extension
TOOL_BIN_LINKS := ${TOOL_BINS:.bin=}
# Put the test binaries in build/test for convenience.
TEST_BIN_DIR := $(BUILD_DIR)/test
TEST_CU_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
		$(foreach obj,$(TEST_CU_OBJS),$(basename $(notdir $(obj))))))
TEST_CXX_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
		$(foreach obj,$(TEST_CXX_OBJS),$(basename $(notdir $(obj))))))
TEST_BINS := $(TEST_CXX_BINS) $(TEST_CU_BINS)
# TEST_ALL_BIN is the test binary that links caffe dynamically.
TEST_ALL_BIN := $(TEST_BIN_DIR)/test_all.testbin
 
##############################
# Derive compiler warning dump locations
##############################
WARNS_EXT := warnings.txt
CXX_WARNS := $(addprefix $(BUILD_DIR)/, ${CXX_SRCS:.cpp=.o.$(WARNS_EXT)})
CU_WARNS := $(addprefix $(BUILD_DIR)/cuda/, ${CU_SRCS:.cu=.o.$(WARNS_EXT)})
TOOL_WARNS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o.$(WARNS_EXT)})
EXAMPLE_WARNS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o.$(WARNS_EXT)})
TEST_WARNS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o.$(WARNS_EXT)})
TEST_CU_WARNS := $(addprefix $(BUILD_DIR)/cuda/, ${TEST_CU_SRCS:.cu=.o.$(WARNS_EXT)})
ALL_CXX_WARNS := $(CXX_WARNS) $(TOOL_WARNS) $(EXAMPLE_WARNS) $(TEST_WARNS)
ALL_CU_WARNS := $(CU_WARNS) $(TEST_CU_WARNS)
ALL_WARNS := $(ALL_CXX_WARNS) $(ALL_CU_WARNS)
 
EMPTY_WARN_REPORT := $(BUILD_DIR)/.$(WARNS_EXT)
NONEMPTY_WARN_REPORT := $(BUILD_DIR)/$(WARNS_EXT)
 
##############################
# Derive include and lib directories
##############################
CUDA_INCLUDE_DIR := $(CUDA_DIR)/include
 
CUDA_LIB_DIR :=
# add <cuda>/lib64 only if it exists
ifneq ("$(wildcard $(CUDA_DIR)/lib64)","")
	CUDA_LIB_DIR += $(CUDA_DIR)/lib64
endif
CUDA_LIB_DIR += $(CUDA_DIR)/lib
 
INCLUDE_DIRS += $(BUILD_INCLUDE_DIR) ./src ./include
ifneq ($(CPU_ONLY), 1)
	INCLUDE_DIRS += $(CUDA_INCLUDE_DIR)
	LIBRARY_DIRS += $(CUDA_LIB_DIR)
	LIBRARIES := cudart cublas curand
endif
 
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial
 
# handle IO dependencies
USE_LEVELDB ?= 1
USE_LMDB ?= 1
# This code is taken from https://github.com/sh1r0/caffe-android-lib
USE_HDF5 ?= 1
USE_OPENCV ?= 1
 
ifeq ($(USE_LEVELDB), 1)
	LIBRARIES += leveldb snappy
endif
ifeq ($(USE_LMDB), 1)
	LIBRARIES += lmdb
endif
# This code is taken from https://github.com/sh1r0/caffe-android-lib
ifeq ($(USE_HDF5), 1)
	LIBRARIES += hdf5_hl hdf5
endif
ifeq ($(USE_OPENCV), 1)
	LIBRARIES += opencv_core opencv_highgui opencv_imgproc
 
	ifeq ($(OPENCV_VERSION), 3)
		LIBRARIES += opencv_imgcodecs
	endif
 
endif
PYTHON_LIBRARIES ?= boost_python python2.7
WARNINGS := -Wall -Wno-sign-compare
 
##############################
# Set build directories
##############################
 
DISTRIBUTE_DIR ?= distribute
DISTRIBUTE_SUBDIRS := $(DISTRIBUTE_DIR)/bin $(DISTRIBUTE_DIR)/lib
DIST_ALIASES := dist
ifneq ($(strip $(DISTRIBUTE_DIR)),distribute)
		DIST_ALIASES += distribute
endif
 
ALL_BUILD_DIRS := $(sort $(BUILD_DIR) $(addprefix $(BUILD_DIR)/, $(SRC_DIRS)) \
	$(addprefix $(BUILD_DIR)/cuda/, $(SRC_DIRS)) \
	$(LIB_BUILD_DIR) $(TEST_BIN_DIR) $(PY_PROTO_BUILD_DIR) $(LINT_OUTPUT_DIR) \
	$(DISTRIBUTE_SUBDIRS) $(PROTO_BUILD_INCLUDE_DIR))
 
##############################
# Set directory for Doxygen-generated documentation
##############################
DOXYGEN_CONFIG_FILE ?= ./.Doxyfile
# should be the same as OUTPUT_DIRECTORY in the .Doxyfile
DOXYGEN_OUTPUT_DIR ?= ./doxygen
DOXYGEN_COMMAND ?= doxygen
# All the files that might have Doxygen documentation.
DOXYGEN_SOURCES := $(shell find \
	src/$(PROJECT) \
	include/$(PROJECT) \
	python/ \
	matlab/ \
	examples \
	tools \
	-name "*.cpp" -or -name "*.hpp" -or -name "*.cu" -or -name "*.cuh" -or \
        -name "*.py" -or -name "*.m")
DOXYGEN_SOURCES += $(DOXYGEN_CONFIG_FILE)
 
 
##############################
# Configure build
##############################
 
# Determine platform
UNAME := $(shell uname -s)
ifeq ($(UNAME), Linux)
	LINUX := 1
else ifeq ($(UNAME), Darwin)
	OSX := 1
	OSX_MAJOR_VERSION := $(shell sw_vers -productVersion | cut -f 1 -d .)
	OSX_MINOR_VERSION := $(shell sw_vers -productVersion | cut -f 2 -d .)
endif
 
# Linux
ifeq ($(LINUX), 1)
	CXX ?= /usr/bin/g++
	GCCVERSION := $(shell $(CXX) -dumpversion | cut -f1,2 -d.)
	# older versions of gcc are too dumb to build boost with -Wuninitalized
	ifeq ($(shell echo | awk '{exit $(GCCVERSION) < 4.6;}'), 1)
		WARNINGS += -Wno-uninitialized
	endif
	# boost::thread is reasonably called boost_thread (compare OS X)
	# We will also explicitly add stdc++ to the link target.
	LIBRARIES += boost_thread stdc++
	VERSIONFLAGS += -Wl,-soname,$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../lib
endif
 
# OS X:
# clang++ instead of g++
# libstdc++ for NVCC compatibility on OS X >= 10.9 with CUDA < 7.0
ifeq ($(OSX), 1)
	CXX := /usr/bin/clang++
	ifneq ($(CPU_ONLY), 1)
		CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release [0-9.]*' | tr -d '[a-z ]')
		ifeq ($(shell echo | awk '{exit $(CUDA_VERSION) < 7.0;}'), 1)
			CXXFLAGS += -stdlib=libstdc++
			LINKFLAGS += -stdlib=libstdc++
		endif
		# clang throws this warning for cuda headers
		WARNINGS += -Wno-unneeded-internal-declaration
		# 10.11 strips DYLD_* env vars so link CUDA (rpath is available on 10.5+)
		OSX_10_OR_LATER   := $(shell [ $(OSX_MAJOR_VERSION) -ge 10 ] && echo true)
		OSX_10_5_OR_LATER := $(shell [ $(OSX_MINOR_VERSION) -ge 5 ] && echo true)
		ifeq ($(OSX_10_OR_LATER),true)
			ifeq ($(OSX_10_5_OR_LATER),true)
				LDFLAGS += -Wl,-rpath,$(CUDA_LIB_DIR)
			endif
		endif
	endif
	# gtest needs to use its own tuple to not conflict with clang
	COMMON_FLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1
	# boost::thread is called boost_thread-mt to mark multithreading on OS X
	LIBRARIES += boost_thread-mt
	# we need to explicitly ask for the rpath to be obeyed
	ORIGIN := @loader_path
	VERSIONFLAGS += -Wl,-install_name,@rpath/$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../../build/lib
else
	ORIGIN := \$$ORIGIN
endif
 
# Custom compiler
ifdef CUSTOM_CXX
	CXX := $(CUSTOM_CXX)
endif
 
# Static linking
ifneq (,$(findstring clang++,$(CXX)))
	STATIC_LINK_COMMAND := -Wl,-force_load $(STATIC_NAME)
else ifneq (,$(findstring g++,$(CXX)))
	STATIC_LINK_COMMAND := -Wl,--whole-archive $(STATIC_NAME) -Wl,--no-whole-archive
else
  # The following line must not be indented with a tab, since we are not inside a target
  $(error Cannot static link with the $(CXX) compiler)
endif
 
# Debugging
ifeq ($(DEBUG), 1)
	COMMON_FLAGS += -DDEBUG -g -O0
	NVCCFLAGS += -G
else
	COMMON_FLAGS += -DNDEBUG -O2
endif
 
# cuDNN acceleration configuration.
ifeq ($(USE_CUDNN), 1)
	LIBRARIES += cudnn
	COMMON_FLAGS += -DUSE_CUDNN
endif
 
# NCCL acceleration configuration
ifeq ($(USE_NCCL), 1)
	LIBRARIES += nccl
	COMMON_FLAGS += -DUSE_NCCL
endif
 
# configure IO libraries
ifeq ($(USE_OPENCV), 1)
	COMMON_FLAGS += -DUSE_OPENCV
endif
ifeq ($(USE_LEVELDB), 1)
	COMMON_FLAGS += -DUSE_LEVELDB
endif
ifeq ($(USE_LMDB), 1)
	COMMON_FLAGS += -DUSE_LMDB
ifeq ($(ALLOW_LMDB_NOLOCK), 1)
	COMMON_FLAGS += -DALLOW_LMDB_NOLOCK
endif
endif
# This code is taken from https://github.com/sh1r0/caffe-android-lib
ifeq ($(USE_HDF5), 1)
	COMMON_FLAGS += -DUSE_HDF5
endif
 
# CPU-only configuration
ifeq ($(CPU_ONLY), 1)
	OBJS := $(PROTO_OBJS) $(CXX_OBJS)
	TEST_OBJS := $(TEST_CXX_OBJS)
	TEST_BINS := $(TEST_CXX_BINS)
	ALL_WARNS := $(ALL_CXX_WARNS)
	TEST_FILTER := --gtest_filter="-*GPU*"
	COMMON_FLAGS += -DCPU_ONLY
endif
 
# Python layer support
ifeq ($(WITH_PYTHON_LAYER), 1)
	COMMON_FLAGS += -DWITH_PYTHON_LAYER
	LIBRARIES += $(PYTHON_LIBRARIES)
endif
 
# BLAS configuration (default = ATLAS)
BLAS ?= atlas
ifeq ($(BLAS), mkl)
	# MKL
	LIBRARIES += mkl_rt
	COMMON_FLAGS += -DUSE_MKL
	MKLROOT ?= /opt/intel/mkl
	BLAS_INCLUDE ?= $(MKLROOT)/include
	BLAS_LIB ?= $(MKLROOT)/lib $(MKLROOT)/lib/intel64
else ifeq ($(BLAS), open)
	# OpenBLAS
	LIBRARIES += openblas
else
	# ATLAS
	ifeq ($(LINUX), 1)
		ifeq ($(BLAS), atlas)
			# Linux simply has cblas and atlas
			LIBRARIES += cblas atlas
		endif
	else ifeq ($(OSX), 1)
		# OS X packages atlas as the vecLib framework
		LIBRARIES += cblas
		# 10.10 has accelerate while 10.9 has veclib
		XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep 'version' | sed 's/[^0-9]*\([0-9]\).*/\1/')
		XCODE_CLT_GEQ_7 := $(shell [ $(XCODE_CLT_VER) -gt 6 ] && echo 1)
		XCODE_CLT_GEQ_6 := $(shell [ $(XCODE_CLT_VER) -gt 5 ] && echo 1)
		ifeq ($(XCODE_CLT_GEQ_7), 1)
			BLAS_INCLUDE ?= /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/$(shell ls /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/ | sort | tail -1)/System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/Headers
		else ifeq ($(XCODE_CLT_GEQ_6), 1)
			BLAS_INCLUDE ?= /System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
			LDFLAGS += -framework Accelerate
		else
			BLAS_INCLUDE ?= /System/Library/Frameworks/vecLib.framework/Versions/Current/Headers/
			LDFLAGS += -framework vecLib
		endif
	endif
endif
INCLUDE_DIRS += $(BLAS_INCLUDE)
LIBRARY_DIRS += $(BLAS_LIB)
 
LIBRARY_DIRS += $(LIB_BUILD_DIR)
 
# Automatic dependency generation (nvcc is handled separately)
CXXFLAGS += -MMD -MP
 
# Complete build flags.
COMMON_FLAGS += $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
NVCCFLAGS += -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
NVCCFLAGS += -D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
# mex may invoke an older gcc that is too liberal with -Wuninitalized
MATLAB_CXXFLAGS := $(CXXFLAGS) -Wno-uninitialized
LINKFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
 
USE_PKG_CONFIG ?= 0
ifeq ($(USE_PKG_CONFIG), 1)
	PKG_CONFIG := $(shell pkg-config opencv --libs)
else
	PKG_CONFIG :=
endif
LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) $(PKG_CONFIG) \
		$(foreach library,$(LIBRARIES),-l$(library))
PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
 
# 'superclean' target recursively* deletes all files ending with an extension
# in $(SUPERCLEAN_EXTS) below.  This may be useful if you've built older
# versions of Caffe that do not place all generated files in a location known
# to the 'clean' target.
#
# 'supercleanlist' will list the files to be deleted by make superclean.
#
# * Recursive with the exception that symbolic links are never followed, per the
# default behavior of 'find'.
SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 
# Set the sub-targets of the 'everything' target.
EVERYTHING_TARGETS := all py$(PROJECT) test warn lint
# Only build matcaffe as part of "everything" if MATLAB_DIR is specified.
ifneq ($(MATLAB_DIR),)
	EVERYTHING_TARGETS += mat$(PROJECT)
endif
 
##############################
# Define build targets
##############################
.PHONY: all lib test clean docs linecount lint lintclean tools examples $(DIST_ALIASES) \
	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
	superclean supercleanlist supercleanfiles warn everything
 
all: lib tools examples
 
lib: $(STATIC_NAME) $(DYNAMIC_NAME)
 
everything: $(EVERYTHING_TARGETS)
 
linecount:
	cloc --read-lang-def=$(PROJECT).cloc \
		src/$(PROJECT) include/$(PROJECT) tools examples \
		python matlab
 
lint: $(EMPTY_LINT_REPORT)
 
lintclean:
	@ $(RM) -r $(LINT_OUTPUT_DIR) $(EMPTY_LINT_REPORT) $(NONEMPTY_LINT_REPORT)
 
docs: $(DOXYGEN_OUTPUT_DIR)
	@ cd ./docs ; ln -sfn ../$(DOXYGEN_OUTPUT_DIR)/html doxygen
 
$(DOXYGEN_OUTPUT_DIR): $(DOXYGEN_CONFIG_FILE) $(DOXYGEN_SOURCES)
	$(DOXYGEN_COMMAND) $(DOXYGEN_CONFIG_FILE)
 
$(EMPTY_LINT_REPORT): $(LINT_OUTPUTS) | $(BUILD_DIR)
	@ cat $(LINT_OUTPUTS) > $@
	@ if [ -s "$@" ]; then \
		cat $@; \
		mv $@ $(NONEMPTY_LINT_REPORT); \
		echo "Found one or more lint errors."; \
		exit 1; \
	  fi; \
	  $(RM) $(NONEMPTY_LINT_REPORT); \
	  echo "No lint errors!";
 
$(LINT_OUTPUTS): $(LINT_OUTPUT_DIR)/%.lint.txt : % $(LINT_SCRIPT) | $(LINT_OUTPUT_DIR)
	@ mkdir -p $(dir $@)
	@ python $(LINT_SCRIPT) $< 2>&1 \
		| grep -v "^Done processing " \
		| grep -v "^Total errors found: 0" \
		> $@ \
		|| true
 
test: $(TEST_ALL_BIN) $(TEST_ALL_DYNLINK_BIN) $(TEST_BINS)
 
tools: $(TOOL_BINS) $(TOOL_BIN_LINKS)
 
examples: $(EXAMPLE_BINS)
 
py$(PROJECT): py
 
py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 
$(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX) | $(DYNAMIC_NAME)
	@ echo CXX/LD -o $@ $<
	$(Q)$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
		-o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(PYTHON_LDFLAGS) \
		-Wl,-rpath,$(ORIGIN)/../../build/lib
 
mat$(PROJECT): mat
 
mat: $(MAT$(PROJECT)_SO)
 
$(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
	@ if [ -z "$(MATLAB_DIR)" ]; then \
		echo "MATLAB_DIR must be specified in $(CONFIG_FILE)" \
			"to build mat$(PROJECT)."; \
		exit 1; \
	fi
	@ echo MEX $<
	$(Q)$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) \
			CXX="$(CXX)" \
			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
			CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
	@ if [ -f "$(PROJECT)_.d" ]; then \
		mv -f $(PROJECT)_.d $(BUILD_DIR)/${MAT$(PROJECT)_SO:.$(MAT_SO_EXT)=.d}; \
	fi
 
runtest: $(TEST_ALL_BIN)
	$(TOOL_BUILD_DIR)/caffe
	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER)
 
pytest: py
	cd python; python -m unittest discover -s caffe/test
 
mattest: mat
	cd matlab; $(MATLAB_DIR)/bin/matlab -nodisplay -r 'caffe.run_tests(), exit()'
 
warn: $(EMPTY_WARN_REPORT)
 
$(EMPTY_WARN_REPORT): $(ALL_WARNS) | $(BUILD_DIR)
	@ cat $(ALL_WARNS) > $@
	@ if [ -s "$@" ]; then \
		cat $@; \
		mv $@ $(NONEMPTY_WARN_REPORT); \
		echo "Compiler produced one or more warnings."; \
		exit 1; \
	  fi; \
	  $(RM) $(NONEMPTY_WARN_REPORT); \
	  echo "No compiler warnings!";
 
$(ALL_WARNS): %.o.$(WARNS_EXT) : %.o
 
$(BUILD_DIR_LINK): $(BUILD_DIR)/.linked
 
# Create a target ".linked" in this BUILD_DIR to tell Make that the "build" link
# is currently correct, then delete the one in the OTHER_BUILD_DIR in case it
# exists and $(DEBUG) is toggled later.
$(BUILD_DIR)/.linked:
	@ mkdir -p $(BUILD_DIR)
	@ $(RM) $(OTHER_BUILD_DIR)/.linked
	@ $(RM) -r $(BUILD_DIR_LINK)
	@ ln -s $(BUILD_DIR) $(BUILD_DIR_LINK)
	@ touch $@
 
$(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
	@ mkdir -p $@
 
$(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
	@ echo LD -o $@
	$(Q)$(CXX) -shared -o $@ $(OBJS) $(VERSIONFLAGS) $(LINKFLAGS) $(LDFLAGS)
	@ cd $(BUILD_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT);   ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)
 
$(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
	@ echo AR -o $@
	$(Q)ar rcs $@ $(OBJS)
 
$(BUILD_DIR)/%.o: %.cpp $(PROTO_GEN_HEADER) | $(ALL_BUILD_DIRS)
	@ echo CXX $<
	$(Q)$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
		|| (cat $@.$(WARNS_EXT); exit 1)
	@ cat $@.$(WARNS_EXT)
 
$(PROTO_BUILD_DIR)/%.pb.o: $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_GEN_HEADER) \
		| $(PROTO_BUILD_DIR)
	@ echo CXX $<
	$(Q)$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
		|| (cat $@.$(WARNS_EXT); exit 1)
	@ cat $@.$(WARNS_EXT)
 
$(BUILD_DIR)/cuda/%.o: %.cu | $(ALL_BUILD_DIRS)
	@ echo NVCC $<
	$(Q)$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -M $< -o ${@:.o=.d} \
		-odir $(@D)
	$(Q)$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
		|| (cat $@.$(WARNS_EXT); exit 1)
	@ cat $@.$(WARNS_EXT)
 
$(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
		| $(DYNAMIC_NAME) $(TEST_BIN_DIR)
	@ echo CXX/LD -o $@ $<
	$(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib
 
$(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CU_BUILD_DIR)/%.o \
	$(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
	@ echo LD $<
	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib
 
$(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CXX_BUILD_DIR)/%.o \
	$(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
	@ echo LD $<
	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib
 
# Target for extension-less symlinks to tool binaries with extension '*.bin'.
$(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
	@ $(RM) $@
	@ ln -s $(notdir $<) $@
 
$(TOOL_BINS): %.bin : %.o | $(DYNAMIC_NAME)
	@ echo CXX/LD -o $@
	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(LDFLAGS) \
		-Wl,-rpath,$(ORIGIN)/../lib
 
$(EXAMPLE_BINS): %.bin : %.o | $(DYNAMIC_NAME)
	@ echo CXX/LD -o $@
	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(LDFLAGS) \
		-Wl,-rpath,$(ORIGIN)/../../lib
 
proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)
 
$(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_BUILD_DIR)/%.pb.h : \
		$(PROTO_SRC_DIR)/%.proto | $(PROTO_BUILD_DIR)
	@ echo PROTOC $<
	$(Q)protoc --proto_path=$(PROTO_SRC_DIR) --cpp_out=$(PROTO_BUILD_DIR) $<
 
$(PY_PROTO_BUILD_DIR)/%_pb2.py : $(PROTO_SRC_DIR)/%.proto \
		$(PY_PROTO_INIT) | $(PY_PROTO_BUILD_DIR)
	@ echo PROTOC \(python\) $<
	$(Q)protoc --proto_path=src --python_out=python $<
 
$(PY_PROTO_INIT): | $(PY_PROTO_BUILD_DIR)
	touch $(PY_PROTO_INIT)
 
clean:
	@- $(RM) -rf $(ALL_BUILD_DIRS)
	@- $(RM) -rf $(OTHER_BUILD_DIR)
	@- $(RM) -rf $(BUILD_DIR_LINK)
	@- $(RM) -rf $(DISTRIBUTE_DIR)
	@- $(RM) $(PY$(PROJECT)_SO)
	@- $(RM) $(MAT$(PROJECT)_SO)
 
supercleanfiles:
	$(eval SUPERCLEAN_FILES := $(strip \
			$(foreach ext,$(SUPERCLEAN_EXTS), $(shell find . -name '*$(ext)' \
			-not -path './data/*'))))
 
supercleanlist: supercleanfiles
	@ \
	if [ -z "$(SUPERCLEAN_FILES)" ]; then \
		echo "No generated files found."; \
	else \
		echo $(SUPERCLEAN_FILES) | tr ' ' '\n'; \
	fi
 
superclean: clean supercleanfiles
	@ \
	if [ -z "$(SUPERCLEAN_FILES)" ]; then \
		echo "No generated files found."; \
	else \
		echo "Deleting the following generated files:"; \
		echo $(SUPERCLEAN_FILES) | tr ' ' '\n'; \
		$(RM) $(SUPERCLEAN_FILES); \
	fi
 
$(DIST_ALIASES): $(DISTRIBUTE_DIR)
 
$(DISTRIBUTE_DIR): all py | $(DISTRIBUTE_SUBDIRS)
	# add proto
	cp -r src/caffe/proto $(DISTRIBUTE_DIR)/
	# add include
	cp -r include $(DISTRIBUTE_DIR)/
	mkdir -p $(DISTRIBUTE_DIR)/include/caffe/proto
	cp $(PROTO_GEN_HEADER_SRCS) $(DISTRIBUTE_DIR)/include/caffe/proto
	# add tool and example binaries
	cp $(TOOL_BINS) $(DISTRIBUTE_DIR)/bin
	cp $(EXAMPLE_BINS) $(DISTRIBUTE_DIR)/bin
	# add libraries
	cp $(STATIC_NAME) $(DISTRIBUTE_DIR)/lib
	install -m 644 $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib
	cd $(DISTRIBUTE_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT);   ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)
	# add python - it's not the standard way, indeed...
	cp -r python $(DISTRIBUTE_DIR)/
 
-include $(DEPS)

cd python/

使用阿里云镜像安装依赖库：

for req in $(cat requirements.txt); do pip3 install $req -i https://mirrors.aliyun.com/pypi/simple/; done 

cd .. && sudo make clean

sudo make all -j16

 由于caffe最后支持的版本是cuDNN7.6.5，为了能在cuDNN8的环境下编译通过，需要修改两个cpp文件，路径为/caffe/src/caffe/layers下的cudnn_conv_layer.cpp和cudnn_deconv_layer.cpp两个文件，分别将他们内容替换为：

/**
 * @File Name : cudnn_conv_layer.cpp
 */
 
#ifdef USE_CUDNN
#include <algorithm>
#include <vector>
 
#include "caffe/layers/cudnn_conv_layer.hpp"
 
namespace caffe
{
 
// Set to three for the benefit of the backward pass, which
// can use separate streams for calculating the gradient w.r.t.
// bias, filter weights, and bottom data for each group independently
#define CUDNN_STREAMS_PER_GROUP 3
 
  /**
   * TODO(dox) explain cuDNN interface
   */
  template <typename Dtype>
  void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
      const vector<Blob<Dtype> *> &bottom, const vector<Blob<Dtype> *> &top)
  {
    ConvolutionLayer<Dtype>::LayerSetUp(bottom, top);
    // Initialize CUDA streams and cuDNN.
    stream_ = new cudaStream_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
    handle_ = new cudnnHandle_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
 
    // Initialize algorithm arrays
    fwd_algo_ = new cudnnConvolutionFwdAlgo_t[bottom.size()];
    bwd_filter_algo_ = new cudnnConvolutionBwdFilterAlgo_t[bottom.size()];
    bwd_data_algo_ = new cudnnConvolutionBwdDataAlgo_t[bottom.size()];
 
    // initialize size arrays
    workspace_fwd_sizes_ = new size_t[bottom.size()];
    workspace_bwd_filter_sizes_ = new size_t[bottom.size()];
    workspace_bwd_data_sizes_ = new size_t[bottom.size()];
 
    // workspace data
    workspaceSizeInBytes = 0;
    workspaceData = NULL;
    workspace = new void *[this->group_ * CUDNN_STREAMS_PER_GROUP];
 
    for (size_t i = 0; i < bottom.size(); ++i)
    {
      // initialize all to default algorithms
      fwd_algo_[i] = (cudnnConvolutionFwdAlgo_t)0;
      bwd_filter_algo_[i] = (cudnnConvolutionBwdFilterAlgo_t)0;
      bwd_data_algo_[i] = (cudnnConvolutionBwdDataAlgo_t)0;
      // default algorithms don't require workspace
      workspace_fwd_sizes_[i] = 0;
      workspace_bwd_data_sizes_[i] = 0;
      workspace_bwd_filter_sizes_[i] = 0;
    }
 
    for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++)
    {
      CUDA_CHECK(cudaStreamCreate(&stream_[g]));
      CUDNN_CHECK(cudnnCreate(&handle_[g]));
      CUDNN_CHECK(cudnnSetStream(handle_[g], stream_[g]));
      workspace[g] = NULL;
    }
 
    // Set the indexing parameters.
    bias_offset_ = (this->num_output_ / this->group_);
 
    // Create filter descriptor.
    const int *kernel_shape_data = this->kernel_shape_.cpu_data();
    const int kernel_h = kernel_shape_data[0];
    const int kernel_w = kernel_shape_data[1];
    cudnn::createFilterDesc<Dtype>(&filter_desc_,
                                   this->num_output_ / this->group_, this->channels_ / this->group_,
                                   kernel_h, kernel_w);
 
    // Create tensor descriptor(s) for data and corresponding convolution(s).
    for (int i = 0; i < bottom.size(); i++)
    {
      cudnnTensorDescriptor_t bottom_desc;
      cudnn::createTensor4dDesc<Dtype>(&bottom_desc);
      bottom_descs_.push_back(bottom_desc);
      cudnnTensorDescriptor_t top_desc;
      cudnn::createTensor4dDesc<Dtype>(&top_desc);
      top_descs_.push_back(top_desc);
      cudnnConvolutionDescriptor_t conv_desc;
      cudnn::createConvolutionDesc<Dtype>(&conv_desc);
      conv_descs_.push_back(conv_desc);
    }
 
    // Tensor descriptor for bias.
    if (this->bias_term_)
    {
      cudnn::createTensor4dDesc<Dtype>(&bias_desc_);
    }
 
    handles_setup_ = true;
  }
 
  template <typename Dtype>
  void CuDNNConvolutionLayer<Dtype>::Reshape(
      const vector<Blob<Dtype> *> &bottom, const vector<Blob<Dtype> *> &top)
  {
    ConvolutionLayer<Dtype>::Reshape(bottom, top);
    CHECK_EQ(2, this->num_spatial_axes_)
        << "CuDNNConvolution input must have 2 spatial axes "
        << "(e.g., height and width). "
        << "Use 'engine: CAFFE' for general ND convolution.";
    bottom_offset_ = this->bottom_dim_ / this->group_;
    top_offset_ = this->top_dim_ / this->group_;
    const int height = bottom[0]->shape(this->channel_axis_ + 1);
    const int width = bottom[0]->shape(this->channel_axis_ + 2);
    const int height_out = top[0]->shape(this->channel_axis_ + 1);
    const int width_out = top[0]->shape(this->channel_axis_ + 2);
    const int *pad_data = this->pad_.cpu_data();
    const int pad_h = pad_data[0];
    const int pad_w = pad_data[1];
    const int *stride_data = this->stride_.cpu_data();
    const int stride_h = stride_data[0];
    const int stride_w = stride_data[1];
#if CUDNN_VERSION_MIN(8, 0, 0)
    int RetCnt;
    bool found_conv_algorithm;
    size_t free_memory, total_memory;
    cudnnConvolutionFwdAlgoPerf_t fwd_algo_pref_[4];
    cudnnConvolutionBwdDataAlgoPerf_t bwd_data_algo_pref_[4];
 
    // get memory sizes
    cudaMemGetInfo(&free_memory, &total_memory);
#else
    // Specify workspace limit for kernels directly until we have a
    // planning strategy and a rewrite of Caffe's GPU memory mangagement
    size_t workspace_limit_bytes = 8 * 1024 * 1024;
#endif
    for (int i = 0; i < bottom.size(); i++)
    {
      cudnn::setTensor4dDesc<Dtype>(&bottom_descs_[i],
                                    this->num_,
                                    this->channels_ / this->group_, height, width,
                                    this->channels_ * height * width,
                                    height * width, width, 1);
      cudnn::setTensor4dDesc<Dtype>(&top_descs_[i],
                                    this->num_,
                                    this->num_output_ / this->group_, height_out, width_out,
                                    this->num_output_ * this->out_spatial_dim_,
                                    this->out_spatial_dim_, width_out, 1);
      cudnn::setConvolutionDesc<Dtype>(&conv_descs_[i], bottom_descs_[i],
                                       filter_desc_, pad_h, pad_w,
                                       stride_h, stride_w);
 
#if CUDNN_VERSION_MIN(8, 0, 0)
      // choose forward algorithm for filter
      // in forward filter the CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED is not implemented in cuDNN 8
      CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm_v7(handle_[0],
                                                         bottom_descs_[i],
                                                         filter_desc_,
                                                         conv_descs_[i],
                                                         top_descs_[i],
                                                         4,
                                                         &RetCnt,
                                                         fwd_algo_pref_));
 
      found_conv_algorithm = false;
      for (int n = 0; n < RetCnt; n++)
      {
        if (fwd_algo_pref_[n].status == CUDNN_STATUS_SUCCESS &&
            fwd_algo_pref_[n].algo != CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED &&
            fwd_algo_pref_[n].memory < free_memory)
        {
          found_conv_algorithm = true;
          fwd_algo_[i] = fwd_algo_pref_[n].algo;
          workspace_fwd_sizes_[i] = fwd_algo_pref_[n].memory;
          break;
        }
      }
      if (!found_conv_algorithm)
        LOG(ERROR) << "cuDNN did not return a suitable algorithm for convolution.";
      else
      {
        // choose backward algorithm for filter
        // for better or worse, just a fixed constant due to the missing
        // cudnnGetConvolutionBackwardFilterAlgorithm in cuDNN version 8.0
        bwd_filter_algo_[i] = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0;
        // twice the amount of the forward search to be save
        workspace_bwd_filter_sizes_[i] = 2 * workspace_fwd_sizes_[i];
      }
 
      // choose backward algo for data
      CUDNN_CHECK(cudnnGetConvolutionBackwardDataAlgorithm_v7(handle_[0],
                                                              filter_desc_,
                                                              top_descs_[i],
                                                              conv_descs_[i],
                                                              bottom_descs_[i],
                                                              4,
                                                              &RetCnt,
                                                              bwd_data_algo_pref_));
 
      found_conv_algorithm = false;
      for (int n = 0; n < RetCnt; n++)
      {
        if (bwd_data_algo_pref_[n].status == CUDNN_STATUS_SUCCESS &&
            bwd_data_algo_pref_[n].algo != CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD &&
            bwd_data_algo_pref_[n].algo != CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED &&
            bwd_data_algo_pref_[n].memory < free_memory)
        {
          found_conv_algorithm = true;
          bwd_data_algo_[i] = bwd_data_algo_pref_[n].algo;
          workspace_bwd_data_sizes_[i] = bwd_data_algo_pref_[n].memory;
          break;
        }
      }
      if (!found_conv_algorithm)
        LOG(ERROR) << "cuDNN did not return a suitable algorithm for convolution.";
#else
      // choose forward and backward algorithms + workspace(s)
      CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(handle_[0],
                                                      bottom_descs_[i],
                                                      filter_desc_,
                                                      conv_descs_[i],
                                                      top_descs_[i],
                                                      CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
                                                      workspace_limit_bytes,
                                                      &fwd_algo_[i]));
 
      CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(handle_[0],
                                                          bottom_descs_[i],
                                                          filter_desc_,
                                                          conv_descs_[i],
                                                          top_descs_[i],
                                                          fwd_algo_[i],
                                                          &(workspace_fwd_sizes_[i])));
 
      // choose backward algorithm for filter
      CUDNN_CHECK(cudnnGetConvolutionBackwardFilterAlgorithm(handle_[0],
                                                             bottom_descs_[i], top_descs_[i], conv_descs_[i], filter_desc_,
                                                             CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
                                                             workspace_limit_bytes, &bwd_filter_algo_[i]));
 
      // get workspace for backwards filter algorithm
      CUDNN_CHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(handle_[0],
                                                                 bottom_descs_[i], top_descs_[i], conv_descs_[i], filter_desc_,
                                                                 bwd_filter_algo_[i], &workspace_bwd_filter_sizes_[i]));
 
      // choose backward algo for data
      CUDNN_CHECK(cudnnGetConvolutionBackwardDataAlgorithm(handle_[0],
                                                           filter_desc_, top_descs_[i], conv_descs_[i], bottom_descs_[i],
                                                           CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
                                                           workspace_limit_bytes, &bwd_data_algo_[i]));
 
      // get workspace size
      CUDNN_CHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(handle_[0],
                                                               filter_desc_, top_descs_[i], conv_descs_[i], bottom_descs_[i],
                                                               bwd_data_algo_[i], &workspace_bwd_data_sizes_[i]));
#endif
    }
    // reduce over all workspace sizes to get a maximum to allocate / reallocate
    size_t total_workspace_fwd = 0;
    size_t total_workspace_bwd_data = 0;
    size_t total_workspace_bwd_filter = 0;
 
    for (size_t i = 0; i < bottom.size(); i++)
    {
      total_workspace_fwd = std::max(total_workspace_fwd,
                                     workspace_fwd_sizes_[i]);
      total_workspace_bwd_data = std::max(total_workspace_bwd_data,
                                          workspace_bwd_data_sizes_[i]);
      total_workspace_bwd_filter = std::max(total_workspace_bwd_filter,
                                            workspace_bwd_filter_sizes_[i]);
    }
    // get max over all operations
    size_t max_workspace = std::max(total_workspace_fwd,
                                    total_workspace_bwd_data);
    max_workspace = std::max(max_workspace, total_workspace_bwd_filter);
    // ensure all groups have enough workspace
    size_t total_max_workspace = max_workspace *
                                 (this->group_ * CUDNN_STREAMS_PER_GROUP);
 
    // this is the total amount of storage needed over all groups + streams
    if (total_max_workspace > workspaceSizeInBytes)
    {
      DLOG(INFO) << "Reallocating workspace storage: " << total_max_workspace;
      workspaceSizeInBytes = total_max_workspace;
 
      // free the existing workspace and allocate a new (larger) one
      cudaFree(this->workspaceData);
 
      cudaError_t err = cudaMalloc(&(this->workspaceData), workspaceSizeInBytes);
      if (err != cudaSuccess)
      {
        // force zero memory path
        for (int i = 0; i < bottom.size(); i++)
        {
          workspace_fwd_sizes_[i] = 0;
          workspace_bwd_filter_sizes_[i] = 0;
          workspace_bwd_data_sizes_[i] = 0;
          fwd_algo_[i] = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
          bwd_filter_algo_[i] = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0;
          bwd_data_algo_[i] = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;
        }
 
        // NULL out all workspace pointers
        for (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++)
        {
          workspace[g] = NULL;
        }
        // NULL out underlying data
        workspaceData = NULL;
        workspaceSizeInBytes = 0;
      }
 
      // if we succeed in the allocation, set pointer aliases for workspaces
      for (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++)
      {
        workspace[g] = reinterpret_cast<char *>(workspaceData) + g * max_workspace;
      }
    }
 
    // Tensor descriptor for bias.
    if (this->bias_term_)
    {
      cudnn::setTensor4dDesc<Dtype>(&bias_desc_,
                                    1, this->num_output_ / this->group_, 1, 1);
    }
  }
 
  template <typename Dtype>
  CuDNNConvolutionLayer<Dtype>::~CuDNNConvolutionLayer()
  {
    // Check that handles have been setup before destroying.
    if (!handles_setup_)
    {
      return;
    }
 
    for (int i = 0; i < bottom_descs_.size(); i++)
    {
      cudnnDestroyTensorDescriptor(bottom_descs_[i]);
      cudnnDestroyTensorDescriptor(top_descs_[i]);
      cudnnDestroyConvolutionDescriptor(conv_descs_[i]);
    }
    if (this->bias_term_)
    {
      cudnnDestroyTensorDescriptor(bias_desc_);
    }
    cudnnDestroyFilterDescriptor(filter_desc_);
 
    for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++)
    {
      cudaStreamDestroy(stream_[g]);
      cudnnDestroy(handle_[g]);
    }
 
    cudaFree(workspaceData);
    delete[] stream_;
    delete[] handle_;
    delete[] fwd_algo_;
    delete[] bwd_filter_algo_;
    delete[] bwd_data_algo_;
    delete[] workspace_fwd_sizes_;
    delete[] workspace_bwd_data_sizes_;
    delete[] workspace_bwd_filter_sizes_;
  }
 
  INSTANTIATE_CLASS(CuDNNConvolutionLayer);
 
} // namespace caffe
#endif

/**
 * @File Name : cudnn_deconv_layer.cpp
 */
 
#ifdef USE_CUDNN
#include <algorithm>
#include <vector>
 
#include "caffe/layers/cudnn_deconv_layer.hpp"
 
namespace caffe
{
 
// Set to three for the benefit of the backward pass, which
// can use separate streams for calculating the gradient w.r.t.
// bias, filter weights, and bottom data for each group independently
#define CUDNN_STREAMS_PER_GROUP 3
 
  /**
   * TODO(dox) explain cuDNN interface
   */
  template <typename Dtype>
  void CuDNNDeconvolutionLayer<Dtype>::LayerSetUp(
      const vector<Blob<Dtype> *> &bottom, const vector<Blob<Dtype> *> &top)
  {
    DeconvolutionLayer<Dtype>::LayerSetUp(bottom, top);
    // Initialize CUDA streams and cuDNN.
    stream_ = new cudaStream_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
    handle_ = new cudnnHandle_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
 
    // Initialize algorithm arrays
    fwd_algo_ = new cudnnConvolutionFwdAlgo_t[bottom.size()];
    bwd_filter_algo_ = new cudnnConvolutionBwdFilterAlgo_t[bottom.size()];
    bwd_data_algo_ = new cudnnConvolutionBwdDataAlgo_t[bottom.size()];
 
    // initialize size arrays
    workspace_fwd_sizes_ = new size_t[bottom.size()];
    workspace_bwd_filter_sizes_ = new size_t[bottom.size()];
    workspace_bwd_data_sizes_ = new size_t[bottom.size()];
 
    // workspace data
    workspaceSizeInBytes = 0;
    workspaceData = NULL;
    workspace = new void *[this->group_ * CUDNN_STREAMS_PER_GROUP];
 
    for (size_t i = 0; i < bottom.size(); ++i)
    {
      // initialize all to default algorithms
      fwd_algo_[i] = (cudnnConvolutionFwdAlgo_t)0;
      bwd_filter_algo_[i] = (cudnnConvolutionBwdFilterAlgo_t)0;
      bwd_data_algo_[i] = (cudnnConvolutionBwdDataAlgo_t)0;
      // default algorithms don't require workspace
      workspace_fwd_sizes_[i] = 0;
      workspace_bwd_data_sizes_[i] = 0;
      workspace_bwd_filter_sizes_[i] = 0;
    }
 
    for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++)
    {
      CUDA_CHECK(cudaStreamCreate(&stream_[g]));
      CUDNN_CHECK(cudnnCreate(&handle_[g]));
      CUDNN_CHECK(cudnnSetStream(handle_[g], stream_[g]));
      workspace[g] = NULL;
    }
 
    // Set the indexing parameters.
    bias_offset_ = (this->num_output_ / this->group_);
 
    // Create filter descriptor.
    const int *kernel_shape_data = this->kernel_shape_.cpu_data();
    const int kernel_h = kernel_shape_data[0];
    const int kernel_w = kernel_shape_data[1];
    cudnn::createFilterDesc<Dtype>(&filter_desc_,
                                   this->channels_ / this->group_,
                                   this->num_output_ / this->group_,
                                   kernel_h,
                                   kernel_w);
 
    // Create tensor descriptor(s) for data and corresponding convolution(s).
    for (int i = 0; i < bottom.size(); i++)
    {
      cudnnTensorDescriptor_t bottom_desc;
      cudnn::createTensor4dDesc<Dtype>(&bottom_desc);
      bottom_descs_.push_back(bottom_desc);
      cudnnTensorDescriptor_t top_desc;
      cudnn::createTensor4dDesc<Dtype>(&top_desc);
      top_descs_.push_back(top_desc);
      cudnnConvolutionDescriptor_t conv_desc;
      cudnn::createConvolutionDesc<Dtype>(&conv_desc);
      conv_descs_.push_back(conv_desc);
    }
 
    // Tensor descriptor for bias.
    if (this->bias_term_)
    {
      cudnn::createTensor4dDesc<Dtype>(&bias_desc_);
    }
 
    handles_setup_ = true;
  }
 
  template <typename Dtype>
  void CuDNNDeconvolutionLayer<Dtype>::Reshape(
      const vector<Blob<Dtype> *> &bottom, const vector<Blob<Dtype> *> &top)
  {
    DeconvolutionLayer<Dtype>::Reshape(bottom, top);
    CHECK_EQ(2, this->num_spatial_axes_)
        << "CuDNNDeconvolutionLayer input must have 2 spatial axes "
        << "(e.g., height and width). "
        << "Use 'engine: CAFFE' for general ND convolution.";
    bottom_offset_ = this->bottom_dim_ / this->group_;
    top_offset_ = this->top_dim_ / this->group_;
    const int height = bottom[0]->shape(this->channel_axis_ + 1);
    const int width = bottom[0]->shape(this->channel_axis_ + 2);
    const int height_out = top[0]->shape(this->channel_axis_ + 1);
    const int width_out = top[0]->shape(this->channel_axis_ + 2);
    const int *pad_data = this->pad_.cpu_data();
    const int pad_h = pad_data[0];
    const int pad_w = pad_data[1];
    const int *stride_data = this->stride_.cpu_data();
    const int stride_h = stride_data[0];
    const int stride_w = stride_data[1];
#if CUDNN_VERSION_MIN(8, 0, 0)
    int RetCnt;
    bool found_conv_algorithm;
    size_t free_memory, total_memory;
    cudnnConvolutionFwdAlgoPerf_t fwd_algo_pref_[4];
    cudnnConvolutionBwdDataAlgoPerf_t bwd_data_algo_pref_[4];
 
    // get memory sizes
    cudaMemGetInfo(&free_memory, &total_memory);
#else
    // Specify workspace limit for kernels directly until we have a
    // planning strategy and a rewrite of Caffe's GPU memory mangagement
    size_t workspace_limit_bytes = 8 * 1024 * 1024;
#endif
    for (int i = 0; i < bottom.size(); i++)
    {
      cudnn::setTensor4dDesc<Dtype>(&bottom_descs_[i],
                                    this->num_,
                                    this->channels_ / this->group_,
                                    height,
                                    width,
                                    this->channels_ * height * width,
                                    height * width,
                                    width,
                                    1);
      cudnn::setTensor4dDesc<Dtype>(&top_descs_[i],
                                    this->num_,
                                    this->num_output_ / this->group_,
                                    height_out,
                                    width_out,
                                    this->num_output_ * height_out * width_out,
                                    height_out * width_out,
                                    width_out,
                                    1);
      cudnn::setConvolutionDesc<Dtype>(&conv_descs_[i],
                                       top_descs_[i],
                                       filter_desc_,
                                       pad_h,
                                       pad_w,
                                       stride_h,
                                       stride_w);
 
#if CUDNN_VERSION_MIN(8, 0, 0)
      // choose forward algorithm for filter
      // in forward filter the CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED is not implemented in cuDNN 8
      CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm_v7(handle_[0],
                                                         top_descs_[i],
                                                         filter_desc_,
                                                         conv_descs_[i],
                                                         bottom_descs_[i],
                                                         4,
                                                         &RetCnt,
                                                         fwd_algo_pref_));
 
      found_conv_algorithm = false;
      for (int n = 0; n < RetCnt; n++)
      {
        if (fwd_algo_pref_[n].status == CUDNN_STATUS_SUCCESS &&
            fwd_algo_pref_[n].algo != CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED &&
            fwd_algo_pref_[n].memory < free_memory)
        {
          found_conv_algorithm = true;
          fwd_algo_[i] = fwd_algo_pref_[n].algo;
          workspace_fwd_sizes_[i] = fwd_algo_pref_[n].memory;
          break;
        }
      }
      if (!found_conv_algorithm)
        LOG(ERROR) << "cuDNN did not return a suitable algorithm for convolution.";
      else
      {
        // choose backward algorithm for filter
        // for better or worse, just a fixed constant due to the missing
        // cudnnGetConvolutionBackwardFilterAlgorithm in cuDNN version 8.0
        bwd_filter_algo_[i] = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0;
        // twice the amount of the forward search to be save
        workspace_bwd_filter_sizes_[i] = 2 * workspace_fwd_sizes_[i];
      }
 
      // choose backward algo for data
      CUDNN_CHECK(cudnnGetConvolutionBackwardDataAlgorithm_v7(handle_[0],
                                                              filter_desc_,
                                                              bottom_descs_[i],
                                                              conv_descs_[i],
                                                              top_descs_[i],
                                                              4,
                                                              &RetCnt,
                                                              bwd_data_algo_pref_));
 
      found_conv_algorithm = false;
      for (int n = 0; n < RetCnt; n++)
      {
        if (bwd_data_algo_pref_[n].status == CUDNN_STATUS_SUCCESS &&
            bwd_data_algo_pref_[n].algo != CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD &&
            bwd_data_algo_pref_[n].algo != CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED &&
            bwd_data_algo_pref_[n].memory < free_memory)
        {
          found_conv_algorithm = true;
          bwd_data_algo_[i] = bwd_data_algo_pref_[n].algo;
          workspace_bwd_data_sizes_[i] = bwd_data_algo_pref_[n].memory;
          break;
        }
      }
      if (!found_conv_algorithm)
        LOG(ERROR) << "cuDNN did not return a suitable algorithm for convolution.";
#else
      // choose forward and backward algorithms + workspace(s)
      CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(
          handle_[0],
          top_descs_[i],
          filter_desc_,
          conv_descs_[i],
          bottom_descs_[i],
          CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
          workspace_limit_bytes,
          &fwd_algo_[i]));
 
      // We have found that CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM is
      // buggy. Thus, if this algo was chosen, choose winograd instead. If
      // winograd is not supported or workspace is larger than threshold, choose
      // implicit_gemm instead.
      if (fwd_algo_[i] == CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM)
      {
        size_t winograd_workspace_size;
        cudnnStatus_t status = cudnnGetConvolutionForwardWorkspaceSize(
            handle_[0],
            top_descs_[i],
            filter_desc_,
            conv_descs_[i],
            bottom_descs_[i],
            CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD,
            &winograd_workspace_size);
        if (status != CUDNN_STATUS_SUCCESS ||
            winograd_workspace_size >= workspace_limit_bytes)
        {
          fwd_algo_[i] = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
        }
        else
        {
          fwd_algo_[i] = CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD;
        }
      }
 
      CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(
          handle_[0],
          top_descs_[i],
          filter_desc_,
          conv_descs_[i],
          bottom_descs_[i],
          fwd_algo_[i],
          &(workspace_fwd_sizes_[i])));
 
      // choose backward algorithm for filter
      CUDNN_CHECK(cudnnGetConvolutionBackwardFilterAlgorithm(
          handle_[0],
          top_descs_[i],
          bottom_descs_[i],
          conv_descs_[i],
          filter_desc_,
          CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
          workspace_limit_bytes,
          &bwd_filter_algo_[i]));
 
      // get workspace for backwards filter algorithm
      CUDNN_CHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(
          handle_[0],
          top_descs_[i],
          bottom_descs_[i],
          conv_descs_[i],
          filter_desc_,
          bwd_filter_algo_[i],
          &workspace_bwd_filter_sizes_[i]));
 
      // choose backward algo for data
      CUDNN_CHECK(cudnnGetConvolutionBackwardDataAlgorithm(
          handle_[0],
          filter_desc_,
          bottom_descs_[i],
          conv_descs_[i],
          top_descs_[i],
          CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
          workspace_limit_bytes,
          &bwd_data_algo_[i]));
 
      // get workspace size
      CUDNN_CHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(
          handle_[0],
          filter_desc_,
          bottom_descs_[i],
          conv_descs_[i],
          top_descs_[i],
          bwd_data_algo_[i],
          &workspace_bwd_data_sizes_[i]));
#endif
    }
 
    // reduce over all workspace sizes to get a maximum to allocate / reallocate
    size_t total_workspace_fwd = 0;
    size_t total_workspace_bwd_data = 0;
    size_t total_workspace_bwd_filter = 0;
 
    for (size_t i = 0; i < bottom.size(); i++)
    {
      total_workspace_fwd = std::max(total_workspace_fwd,
                                     workspace_fwd_sizes_[i]);
      total_workspace_bwd_data = std::max(total_workspace_bwd_data,
                                          workspace_bwd_data_sizes_[i]);
      total_workspace_bwd_filter = std::max(total_workspace_bwd_filter,
                                            workspace_bwd_filter_sizes_[i]);
    }
    // get max over all operations
    size_t max_workspace = std::max(total_workspace_fwd,
                                    total_workspace_bwd_data);
    max_workspace = std::max(max_workspace, total_workspace_bwd_filter);
    // ensure all groups have enough workspace
    size_t total_max_workspace = max_workspace *
                                 (this->group_ * CUDNN_STREAMS_PER_GROUP);
 
    // this is the total amount of storage needed over all groups + streams
    if (total_max_workspace > workspaceSizeInBytes)
    {
      DLOG(INFO) << "Reallocating workspace storage: " << total_max_workspace;
      workspaceSizeInBytes = total_max_workspace;
 
      // free the existing workspace and allocate a new (larger) one
      cudaFree(this->workspaceData);
 
      cudaError_t err = cudaMalloc(&(this->workspaceData), workspaceSizeInBytes);
      if (err != cudaSuccess)
      {
        // force zero memory path
        for (int i = 0; i < bottom.size(); i++)
        {
          workspace_fwd_sizes_[i] = 0;
          workspace_bwd_filter_sizes_[i] = 0;
          workspace_bwd_data_sizes_[i] = 0;
          fwd_algo_[i] = CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING;
          bwd_filter_algo_[i] = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0;
          bwd_data_algo_[i] = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;
        }
 
        // NULL out all workspace pointers
        for (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++)
        {
          workspace[g] = NULL;
        }
        // NULL out underlying data
        workspaceData = NULL;
        workspaceSizeInBytes = 0;
      }
 
      // if we succeed in the allocation, set pointer aliases for workspaces
      for (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++)
      {
        workspace[g] = reinterpret_cast<char *>(workspaceData) + g * max_workspace;
      }
    }
 
    // Tensor descriptor for bias.
    if (this->bias_term_)
    {
      cudnn::setTensor4dDesc<Dtype>(
          &bias_desc_, 1, this->num_output_ / this->group_, 1, 1);
    }
  }
 
  template <typename Dtype>
  CuDNNDeconvolutionLayer<Dtype>::~CuDNNDeconvolutionLayer()
  {
    // Check that handles have been setup before destroying.
    if (!handles_setup_)
    {
      return;
    }
 
    for (int i = 0; i < bottom_descs_.size(); i++)
    {
      cudnnDestroyTensorDescriptor(bottom_descs_[i]);
      cudnnDestroyTensorDescriptor(top_descs_[i]);
      cudnnDestroyConvolutionDescriptor(conv_descs_[i]);
    }
    if (this->bias_term_)
    {
      cudnnDestroyTensorDescriptor(bias_desc_);
    }
    cudnnDestroyFilterDescriptor(filter_desc_);
 
    for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++)
    {
      cudaStreamDestroy(stream_[g]);
      cudnnDestroy(handle_[g]);
    }
 
    cudaFree(workspaceData);
    delete[] workspace;
    delete[] stream_;
    delete[] handle_;
    delete[] fwd_algo_;
    delete[] bwd_filter_algo_;
    delete[] bwd_data_algo_;
    delete[] workspace_fwd_sizes_;
    delete[] workspace_bwd_data_sizes_;
    delete[] workspace_bwd_filter_sizes_;
  }
 
  INSTANTIATE_CLASS(CuDNNDeconvolutionLayer);
 
} // namespace caffe
#endif
 
 

 由于cuDNN对代码进行了改版，在cudnn.h文件中不再指出cudnn的版本号，而是放在了cudnn_version.h文件中，所以，将cudnn_version.h中对于版本段的代码复制到cudnn.h文件中，代码如下：

locate cudnn_version.h

sudo gedit /usr/local/cuda-11.4/targets/x86_64-linux/include/cudnn_version.h

 

 复制其中的非注释部分：

sudo gedit /usr/local/cuda-11.4/targets/x86_64-linux/include/cudnn.h

粘贴到最开头：

 然后打开caffe包下的cudnn.hpp文件并指定cudnn.h路径：

 之后重新执行编译：

sudo make clean && make all -j16

生成以下静态库和共享库文件：

 测试，时间较慢，耐心等待~

sudo make test -j16

sudo make runtest -j16

sudo make pycaffe -j16

 可能会有报错，但问题不大，我们只是需要那些库文件~

24.安装libfreenect2

git clone https://github.com/OpenKinect/libfreenect2.git

cd libfreenect2 && mkdir build && cd build/

cmake -j16 .. -DENABLE_CXX11=ON 

sudo make -j16

sudo make install

sudo cp ../platform/linux/udev/90-kinect2.rules /etc/udev/rules.d/

25.安装vtk8.2.0及PCL1.9.1

https://vtk.org/download/https://vtk.org/download/下载VTK-8.2.0.zip

 解压之后，进入文件夹打开终端：

mkdir build && cd  build && cmake-gui

 

 单击Configure后勾选以下两项后单击Configure和Generate

 

 

sudo make -j16

sudo make install

接下来安装pcl：

git clone -b pcl-1.9.1 https://gitee.com/yubaoliu/pcl.git pcl-1.9.1

之后进入文件夹打开终端输入：

mkdir release && cd release

cmake -DCMAKE_BUILD_TYPE=None -DCMAKE_INSTALL_PREFIX=/usr \ -DBUILD_GPU=ON-DBUILD_apps=ON -DBUILD_examples=ON \ -DCMAKE_INSTALL_PREFIX=/usr ..

sudo make -j16

sudo make install

26.安装CarlaUE4（必须是Carla的UE仓库里的carla分支才可以通过安装Carla时的编译）

find . -name "*.sh" -exec dos2unix {} +

find . -name "*.sh" -exec chmod +x {} +

sudo chown -R m0rtzz: *

 若报错：

 因Epic更新了gitdeps，但Github上却没有更新，所以需要进入Github官方仓库release界面寻找对应版本的Commit.gitdeps.xml替换原来的文件即可：

https://github.com/EpicGames/UnrealEngine/releases/taghttps://github.com/EpicGames/UnrealEngine/releases/tag

若报错：

 个人认为是因执行Setup.sh脚本未赋予root权限导致依赖未安装完整，所以再次执行：

sudo ./Setup.sh

// @file : CubemapUnwrapUtils.cpp
 
Use
RHICmdList.GetBoundVertexShader() instead of GetVertexShader()
RHICmdList.GetBoundPixelShader() instead of GetPixelShader()
Instead of the given macros, use code as below.
GraphicsPSOInit.BoundShaderState.VertexShaderRHI = VertexShader.GetVertexShader();
GraphicsPSOInit.BoundShaderState.PixelShaderRHI = PixelShader.GetPixelShader();

http://cdn.unrealengine.com/Toolchain_Linux/native-linux-v17_clang-10.0.10centos.tar.gzhttp://cdn.unrealengine.com/Toolchain_Linux/native-linux-v17_clang-10.0.10centos.tar.gz

cd your-path/UnrealEngine_4.26/Engine/Extras/ThirdPartyNotUE/SDKs/HostLinux/Linux_x64/

tar -zxvf native-linux-v17_clang-10.0.1-centos7.tar.gz

27.安装Carla0.9.13（添加fisheye sensor模块）

修改Update.sh下载网址为南方科技大学镜像站的网址：

#CONTENT_LINK=http://carla-assets.s3.amazonaws.com/${CONTENT_ID}.tar.gz
CONTENT_LINK=https://mirrors.sustech.edu.cn/carla/carla_content/${CONTENT_ID}.tar.gz

// @file : test_streaming.cpp
 
// Line 58
carla::streaming::low_level::Server<tcp::Server> srv(io.service, TESTING_PORT);
 
// Line 63
carla::streaming::low_level::Client<tcp::Client> c;
 
// Line 93
carla::streaming::low_level::Server<tcp::Server> srv(io.service, TESTING_PORT);
 
// Line 96
carla::streaming::low_level::Client<tcp::Client> c;

# @file : Package.sh(https://github.com/annaornatskaya/carla/tree/fisheye-sensor)
 
  # copy_if_changed "./Plugins/" "${DESTINATION}/Plugins/"
 
  copy_if_changed "./Unreal/CarlaUE4/Content/Carla/HDMaps/*.pcd" "${DESTINATION}/HDMaps/"
  copy_if_changed "./Unreal/CarlaUE4/Content/Carla/HDMaps/Readme.md" "${DESTINATION}/HDMaps/README"
 
  # NOTE: Modified by M0rtzz
  if [ -d "./Plugins/" ] ; then
    copy_if_changed "./Plugins/" "${DESTINATION}/Plugins/"
  fi

  P.S：

推荐一些linux办公常用的软件（linux版，不包括wine环境下，全部下载deb格式的安装包，系统架构可通过命令uname -a查看）：

百度网盘 客户端下载

向日葵远程控制app官方下载 - 贝锐向日葵官网

QQ Linux版-新不止步·乐不设限

下载中心-腾讯会议

WPS Office 2019 for Linux-支持多版本下载_WPS官方网站

搜狗输入法-首页（下载安装包后，官方会跳转至安装教程，严格按照步骤执行）

Documentation for Visual Studio Code（推荐打开Settings Sync，换电脑时设置可以同步）

 可以水平和垂直分割的bash终端：

sudo apt-get install terminator

trash命令：

sudo apt-get install trash-cli

tree命令：

sudo apt-get install tree

查看系统信息：

sudo apt-get install neofetch

rar文件解压工具：

sudo apt-get install unrar

 解决不能观看MP4文件：

sudo apt-get update

sudo apt-get install libdvdnav4 libdvdread4 gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly libdvd-pkg

sudo apt-get install ubuntu-restricted-extras

sudo dpkg-reconfigure libdvd-pkg

 系统优化：

sudo apt-get update

sudo apt-get install gnome-tweak-tool

火狐浏览器优化：

地址栏输入：

about:config

full-screen-api.warning.timeout 

 设置为0~

full-screen-api.transition-duration.enter

 和

full-screen-api.transition-duration.leave

  都设置为0 0~

browser.search.openintab

browser.urlbar.openintab

browser.tabs.loadBookmarksInTabs

都设置为true~