关于Ubuntu18.04/20.04安装后的一系列环境配置过程的总结
==Updating（更新博客链接：https://blog.csdn.net/M0rtzz/article/details/136060074）...==
ZZU-SR的同学配置环境前可以给我发邮件：[email protected]
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：
gedit ~/.bashrc# 在最后（WARNING：如果安装了Anaconda3，需要加在__conda_setup之前）加入如下代码段：# 显示git分支function customizePrompt(){ local none='\[\033[00m\]' # 重置所有属性到默认状态 local green='\[\033[0;32m\]' # 绿色，用于用户名和主机名 local user_at_host="${green}\[\033[1m\]\u@\h${none}" # 用户名和主机名显示为绿色并加粗 local blue='\[\033[0;34m\]' # 蓝色，用于当前工作目录 local git_branch_color='\[\033[1;43;37m\]' # 黄色背景，白色字体，用于git分支 local command_prompt='$' # 命令提示符 if [ $UID -eq 0 ]; then command_prompt='#' fi local working_directory="${blue}\[\033[1m\]\w${none}" # 当前工作目录显示为蓝色并加粗 # 使用__git_ps1函数来显示当前git分支，使用自定义的颜色 echo "${user_at_host}:${working_directory}\$(__git_ps1 \" ${git_branch_color}[%s]${none} \")${command_prompt} "} export PS1="$(customizePrompt)"# 复制上一个命令到系统剪切板，sudo apt install xselfunction copyLastCommand(){ # fc获取最后执行的命令，echo发送给xsel复制到剪切板 # -nl以列表形式显示命令历史，但不包括命令编号，-1只获取最近一条命令 echo -n $(fc -nl -1) | xsel --clipboard --input}# 创建一个别名，若与你的其他软件包内置命令冲突，请自行更换别名alias clc="copyLastCommand"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 multiversedeb 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 multiversedeb 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 multiversedeb 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 multiversedeb 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 multiversedeb 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 multiversedeb 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/
sudo apt updateanaconda镜像源（~/.condarc）:
channels: - defaultsshow_channel_urls: truedefault_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/msys2custom_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/envs3-设置/home文件夹下为英文
export LANG=en_USxdg-user-dirs-gtk-update编辑选择右边的Update Names
之后执行以下语句：
export LANG=zh_CNreboot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 nouveauoptions nouveau modeset=0保存后关闭，打开终端，输入：
sudo update-initramfs -ureboot5-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 install gcc g++ make zlib1gsudo ubuntu-drivers devices
寻找带有recommended的版本，输入
sudo apt install nvidia-driver-your_version nvidia-settings nvidia-prime（your_version是你的版本号）
sudo apt updatesudo apt upgradereboot验证版本
nvidia-smi
6-cuda安装：
https://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
gedit ~/.bashrc在最后输入
# cudaexport LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64export PATH=${PATH}:/usr/local/cuda/binexport 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接下来验证cuda版本：
nvcc --version
安装成功！
7-cudnn安装：
https://developer.nvidia.com/rdp/cudnn-archive
官方安装教程（选择合适版本的==NVIDIA cuDNN Installation Guide==）：
https://docs.nvidia.com/deeplearning/cudnn/archives/index.html
tar -xvf cudnn-linux-x86_64-8.x.x.x_cudaX.Y-archive.tar.xzsudo cp cudnn-*-archive/include/cudnn*.h /usr/local/cuda/include sudo cp -P cudnn-*-archive/lib/libcudnn* /usr/local/cuda/lib64 sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*验证是否安装成功
cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2
8-安装ROS（有些图忘记截了）
①ROS-melodic
导入Key
sudo gpg --keyserver 'hkp://keyserver.ubuntu.com:80' --recv-key C1CF6E31E6BADE8868B172B4F42ED6FBAB17C654sudo gpg --export C1CF6E31E6BADE8868B172B4F42ED6FBAB17C654 | sudo tee /usr/share/keyrings/ros.gpg > /dev/null设置中科大源
sudo sh -c 'echo "deb [signed-by=/usr/share/keyrings/ros.gpg] https://mirrors.ustc.edu.cn/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'sudo apt updatesudo apt install ros-melodic-desktop-fullecho "source /opt/ros/melodic/setup.bash" >> ~/.bashrcsource ~/.bashrcsudo apt install python-rosdep python-rosinstall python-rosinstall-generator python-wstool build-essentialsudo apt 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 initrosdepc updatesudo chmod 777 -R ~/.ros/roscore
②ROS-noetic
导入Key
sudo gpg --keyserver 'hkp://keyserver.ubuntu.com:80' --recv-key C1CF6E31E6BADE8868B172B4F42ED6FBAB17C654sudo gpg --export C1CF6E31E6BADE8868B172B4F42ED6FBAB17C654 | sudo tee /usr/share/keyrings/ros.gpg > /dev/null设置中科大源
sudo sh -c 'echo "deb [signed-by=/usr/share/keyrings/ros.gpg] https://mirrors.ustc.edu.cn/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'sudo apt update && sudo apt install ros-noetic-desktop-fullecho "source /opt/ros/noetic/setup.bash" >> ~/.bashrcsource ~/.bashrcsudo apt install python3-rosdep python3-rosinstall python3-rosinstall-generator python3-wstool build-essentialsudo apt 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 initrosdepc updatesudo chmod 777 -R ~/.ros/roscore
再新建两个终端，分别输入
rosrun turtlesim turtlesim_noderosrun 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 failed)等等）。下方OpenCV3的安装步骤仅供参考，OpenCV4.2.0的cmake命令及注意事项在本小节最后！==
①OpenCV3的安装步骤：
git clone -b 3.4.16 https://gitcode.com/mirrors/opencv/opencv.git opencv-3.4.16cd opencv-3.4.16git clone -b 3.4.16 https://gitcode.com/mirrors/opencv/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 updatesudo apt install libjasper1 libjasper-devsudo apt 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 checkinstallsudo apt install liblapacke-dev checkinstall进入opencv-3.4.16文件夹，打开终端，输入：
mkdir buildcd 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 ENABLE_PRECOMPILED_HEADERS=OFF \-D CMAKE_EXE_LINKER_FLAGS=-lcblas \-D WITH_LAPACK=OFF \-j$(nproc) ..过程中会出现IPPICV: Download: ippicv_2020_lnx_intel64_20191018_general.tgz
解决方法：
cd ../ && mkdir downloadscd 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://gitcode.com/mirrors/opencv/opencv_3rdparty
然后重新打开终端，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 ENABLE_PRECOMPILED_HEADERS=OFF \-D CMAKE_EXE_LINKER_FLAGS=-lcblas \-D WITH_LAPACK=OFF \-j$(nproc) ..
这些.i文件需要在国外下载，网上说下载好文件直接把他们放进相对应的目录下就行，实测不行（建议科学的上网，想试试网上说法的：
https://blog.csdn.net/curious_undergather/article/details/111639199
文件的话，开头百度云分享链接里都有)
cmake -D CMAKE_BUILD_TYPE=RELEASE \-D WITH_GTK_2_X=ON \-D OPENCV_ENABLE_NONFREE=ON \-D OPENCV_GENERATE_PKGCONFIG=YES \-D OPENCV_EXTRA_MODULES_PATH=/home/m0rtzz/Program_Files/opencv-3.4.16/opencv_contrib-3.4.16/modules \-D WITH_CUDA=ON \-D WITH_CUDNN=ON \-D WITH_FFMPEG=ON \-D WITH_OPENGL=ON \-D WITH_NVCUVID=ON \-D ENABLE_PRECOMPILED_HEADERS=OFF \-D CMAKE_EXE_LINKER_FLAGS=-lcblas \-D WITH_LAPACK=OFF \-j$(nproc) ..
sudo make -j$(nproc)
打开那个头文件，把报错所在行改为：
#include "lapacke.h"sudo make -j$(nproc)
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 ldconfigsudo gedit /etc/bash.bashrc加入
export PKG_CONFIG_PATH=${PKG_CONFIG_PATH}:/usr/local/lib/pkgconfig保存后关闭，打开终端，输入：
source /etc/bash.bashrc测试
cd ../samples/cpp/example_cmakecmake -j$(nproc) .sudo make -j$(nproc)./opencv_example
安装成功！
设置cv_bridge的版本（ROS-melodic，经实践发现毫无效果）：
sudo gedit /opt/ros/melodic/share/cv_bridge/cmake/cv_bridgeConfig.cmake# generated from catkin/cmake/template/pkgConfig.cmake.in # append elements to a list and remove existing duplicates from the list# copied from catkin/cmake/list_append_deduplicate.cmake to keep pkgConfig# self containedmacro(_list_append_deduplicate listname) if(NOT "${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 containedmacro(_list_append_unique listname) foreach(_item ${ARGN}) list(FIND ${listname} ${_item} _index) if(_index EQUAL -1) list(APPEND ${listname} ${_item}) endif() endforeach()endmacro() # pack a list of libraries with optional build configuration keywords# copied from catkin/cmake/catkin_libraries.cmake to keep pkgConfig# self containedmacro(_pack_libraries_with_build_configuration VAR) set(${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 "${_index} + 1") endwhile()endmacro() # unpack a list of libraries with optional build configuration keyword prefixes# copied from catkin/cmake/catkin_libraries.cmake to keep pkgConfig# self containedmacro(_unpack_libraries_with_build_configuration VAR) set(${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 prefixesif("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 ${cv_bridge_INSTALL_PREFIX})endif() # warn when using a deprecated packageif(NOT "" STREQUAL "") set(_msg "WARNING: package 'cv_bridge' is deprecated") # append custom deprecation text if available if(NOT "" STREQUAL "TRUE") set(_msg "${_msg} ()") endif() message("${_msg}")endif() # flag project as catkin-based to distinguish if a find_package()-ed project is a catkin projectset(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 <[email protected]>' 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 easierforeach(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(${extra})endforeach()opencv-3.4.4cmake命令：
cmake -D CMAKE_BUILD_TYPE=BUILD \-D CMAKE_INSTALL_PREFIX=/usr/local \-D WITH_GTK_2_X=ON \-D OPENCV_ENABLE_NONFREE=ON \-D OPENCV_GENERATE_PKGCONFIG=YES \-D OPENCV_EXTRA_MODULES_PATH=/home/m0rtzz/Program_Files/opencv-3.4.4/opencv_contrib-3.4.4/modules \-D WITH_CUDA=ON \-D WITH_CUDNN=ON \-D OPENCV_DNN_CUDA=ON \-D WITH_FFMPEG=ON \-D WITH_OPENGL=ON \-D WITH_NVCUVID=ON \-D ENABLE_PRECOMPILED_HEADERS=OFF \-D CMAKE_EXE_LINKER_FLAGS=-lcblas \-D WITH_LAPACK=OFF \-D WITH_OPENMP=ON \-D BUILD_TESTS=OFF \-D BUILD_opencv_xfeatures2d=ON \-D CUDA_ARCH_BIN=8.6 \-D CUDA_GENERATION=Auto \-D CUDA_HOST_COMPILER:FILEPATH=/usr/bin/gcc-7 \-j$(nproc) ..②OpenCV4.2.0的cmake命令及注意事项（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 OPENCV_DNN_CUDA=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/lib64/stubs/libcuda.so \-D CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda \-D CUDNN_LIBRARY=/usr/local/cuda/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 OPENCV_DNN_CUDA=ON \-D BUILD_TIFF=ON \-D ENABLE_PRECOMPILED_HEADERS=OFF \-D OPENCV_ENABLE_NONFREE=ON \-D CUDA_GENERATION=Auto \-D WITH_ADE=OFF \-D CUDA_CUDA_LIBRARY=true \-D CUDA_nppicom_LIBRARY=true \-j$(nproc) ..或：
cmake -D CMAKE_BUILD_TYPE=RELEASE \-D OPENCV_GENERATE_PKGCONFIG=ON \-D INSTALL_PYTHON_EXAMPLES=ON \-D INSTALL_C_EXAMPLES=ON \-D OPENCV_EXTRA_MODULES_PATH=/home/m0rtzz/Program_Files/opencv-4.2.0/opencv_contrib-4.2.0/modules \-D WITH_V4L=ON \-D WITH_QT=ON \-D WITH_GTK=ON \-D WITH_VTK=OFF \-D WITH_OPENGL=ON \-D WITH_OPENMP=ON \-D BUILD_EXAMPLES=ON \-D WITH_CUDA=ON \-D WITH_CUDNN=ON \-D OPENCV_DNN_CUDA=ON \-D BUILD_TIFF=ON \-D ENABLE_PRECOMPILED_HEADERS=OFF \-D OPENCV_ENABLE_NONFREE=ON \-D CUDA_GENERATION=Auto \-D WITH_ADE=OFF \-D CUDA_CUDA_LIBRARY=true \-D CUDNN_LIBRARY=/usr/local/cuda/lib64/libcudnn.so \-D CUDA_ARCH_BIN= 8.6\-D CUDA_nppicom_LIBRARY=stdc++ \-D CUDA_HOST_COMPILER:FILEPATH=/usr/bin/gcc \-j$(nproc) ..CUDA_ARCH_BIN查看命令：
sudo apt install mlocate sudo updatedb.mlocate $(mlocate deviceQuery | grep cuda | head -n 1)
==+（解决CUDNN8编译报错，需手动加入PR代码）==
https://github.com/opencv/opencv/pull/17685/files
.patch文件可用以下命令下载：
wget https://raw.gitcode.com/M0rtzz/opencv4-cudnn8-support-patch/assets/149 -O opencv_PR_17685.patch==+（如果不执行以下几步，编译darknet_ros会报错:error:‘IplImage’之类的）==
sudo cp /usr/local/lib/pkgconfig/opencv4.pc /usr/lib/pkgconfigcd /usr/lib/pkgconfigsudo cp opencv4.pc opencv.pc10-安装protobuf2.6.1
sudo apt install libtoolhttps://github.com/google/protobuf/releases/download/v2.6.1/protobuf-2.6.1.tar.gz
或镜像：
wget https://raw.gitcode.com/M0rtzz/protobuf-2.6.1/assets/199 -O protobuf-2.6.1.tar.gz解压压缩包后进入文件夹，打开终端，输入：
./autogen.sh
./configure --prefix=/usr/local/protobuf
sudo make -j$(nproc)
养成make check的好习惯
sudo make check -j$(nproc)
sudo make install
sudo gedit /etc/profile在最后加入：
#protobufexport PATH=${PATH}:/usr/local/protobuf/bin/export PKG_CONFIG_PATH=${PKG_CONFIG_PATH}:/usr/local/protobuf/lib/pkgconfig/保存后关闭，打开终端，输入：
source /etc/profilesudo gedit /etc/ld.so.conf在最后一行输入：
/usr/local/protobuf/lib保存后关闭，打开终端，输入：
sudo ldconfig
最后验证版本：
protoc --version11-配置OpenBLAS
sudo apt install gcc-arm-linux-gnueabihf libnewlib-arm-none-eabi libc6-dev-i386OpenBLAS源码（非最新）最上方百度网盘里有，或者使用公益加速源：
git clone https://gitclone.com/github.com/OpenMathLib/OpenBLAS.git OpenBLAScd OpenBLASsudo apt install gfortransudo make FC=gfortran TARGET=ARMV8 -j$(nproc)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
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：
gedit ~/.bashrc加入工作空间下lib文件夹的路径
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/home/m0rtzz/Workspaces/catkin_ws/lib保存后关闭，打开终端，输入：
source ~/.bashrc
解决！
报错：
Gtk-Message: 15:22:30.610: Failed to load module "canberra-gtk-module"解决方法：
sudo apt install libcanberra-gtk*13-百度智能云
sudo apt install curlinclude jsoncpp库的头文件改为
#include "jsoncpp/json/json.h"g++编译
g++ *.cpp -o * -lcurl -ljsoncpp运行
./*14-使在桌面上右键打开终端时进入Desktop目录（Ubuntu18.04）
https://packages.ubuntu.com/source/bionic/gnome-terminal
下载下图表格中的下边两个文件
下载好gnome-terminal_3.28.1.orig.tar.xz文件之后解压出一个文件夹gnome-terminal-3.28.1，将gnome-terminal_3.28.1-1ubuntu1.debian.tar.xz 里面debian目录下的文件解压到之前解压出的gnome-terminal-3.28.1目录下
在此目录下打开终端
git apply patches/*.patch安装依赖
sudo apt install intltool libvte-2.91-dev gsettings-desktop-schemas-dev uuid-dev libdconf-dev libpcre2-dev libgconf2-dev libxml2-utils gnome-shell libnautilus-extension-dev itstool yelp-tools pcre2-utils打开src/下的terminal-nautilus.c
找到
static inline gbooleandesktop_opens_home_dir (TerminalNautilus *nautilus){#if 0 return _client_get_bool (gconf_client, "/apps/nautilus-open-terminal/desktop_opens_home_dir", NULL);#endif return TRUE;}改为
static inline gbooleandesktop_opens_home_dir (TerminalNautilus *nautilus){#if 0 return _client_get_bool (gconf_client, "/apps/nautilus-open-terminal/desktop_opens_home_dir", NULL);#endif return FALSE;}src下打开终端
cd ..autoreconf --installautoconf./configure --prefix='/usr'sudo make -j$(nproc)sudo make check -j$(nproc)sudo make install
sudo cp /usr/lib/nautilus/extensions-3.0/libterminal-nautilus.so /usr/lib/x86_64-linux-gnu/nautilus/extensions-3.0/reboot问题解决！
15-同步双系统时间
sudo apt install ntpdatesudo ntpdate time.windows.comtimedatectl set-local-rtc 1 --adjust-system-clock
16-启动菜单的默认项
sudo gedit /etc/default/grub改一下GRUB_DEFAULT=后边的数字，默认是0，windows是第n个就设置为 n-1
保存后关闭，打开终端，输入：
sudo update-grub
reboot重启后问题解决~
17-安装darknet版yolov3及darknet-ros工作空间
git clone https://gitcode.com/mirrors/AlexeyAB/darknet.git darknetcd darknetsudo gedit Makefile修改以下前几行为：
GPU=1CUDNN=1CUDNN_HALF=1OPENCV=1AVX=0OPENMP=1LIBSO=1ZED_CAMERA=0ZED_CAMERA_v2_8=0然后修改NVCC=后边为nvcc路径：
NVCC=/usr/local/cuda/bin/nvcc
之后保存退出后，打开终端，输入：
sudo gedit /etc/ld.so.conf.d/cuda.conf加入以下内容后保存退出：
/usr/local/cuda/lib64打开终端输入：
sudo ldconfigsudo make -j$(nproc)./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 intltoolsudo apt upgrade intltoolsudo apt install libssl-dev之后：
git clone https://github.com/rayylee/mwget.git mwget或公益加速源：
git clone https://mirror.ghproxy.com/https://github.com/rayylee/mwget.git mwgetcd mwget./configuresudo make -j$(nproc)sudo make install之后mwget就安装成功了
我们用mwget多线程获取权重文件：
cd darknet/ && mkdir weights && cd weights/mwget https://pjreddie.com/media/files/yolov3.weights -n16上方命令是16线程获取 ，速度会快很多
到此为止darknet版yolov3就配置好了
下面我们测试一下：
./darknet detect cfg/yolov3.cfg weights/yolov3.weights data/dog.jpg输出以下就证明配置没有问题：
输出的最后一行报错：
Gtk-Message: 15:22:30.610: Failed to load module "canberra-gtk-module"解决方法：
sudo apt install libcanberra-gtk*安装之后重新运行就不会报错了。
配置 darknet-ros工作空间：
mkdir darknet-ros_test_ws && cd darknet-ros_test_ws/ && mkdir srccd src/ && catkin_init_workspacecd .. && catkin_make -j$(nproc)cd src/17.1. ①如果是OpenCV3：
git clone --recursive https://gitcode.com/mirrors/leggedrobotics/darknet_ros.git darknet_ros17.2. ①如果是OpenCV4：
git clone -b opencv4 --recursive https://github.com/kunaltyagi/darknet_ros.git darknet_roscd darknet_rosgit branch -agit checkout remotes/origin/opencv4git 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.cppvoid 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; }之后：
cd darknet_ros && sudo rm -rf darknetgit clone https://gitcode.com/mirrors/AlexeyAB/darknet.git darknetcatkin_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 -j$(nproc) darknet_ros --cmake-args -DCMAKE_CXX_FLAGS=-DCV__ENABLE_C_API_CTORS如果报错nvcc fatal : Unsupported gpu architecture 'compute_30'之类的，是因为CUDA11已经不支持compute_30了，我们将darknet_ros/darknet/Makefile和darknet_ros/darknet_ros/CMakeLists.txt中含有 'compute_30'的行进行注释后重新catkin_make：
18-Azure Kinect SDK-v1.4.0的安装（Ubuntu18.04源码编译安装，也可像本小节末尾Ubuntu20.04一样直接使用.deb包安装）
①Ubuntu18.04源码编译安装：
Reference：
https://blog.csdn.net/BlacKingZ/article/details/119115883
git clone -b v1.4.0 https://gitcode.com/microsoft/Azure-Kinect-Sensor-SDK.git Azure-Kinect-Sensor-SDK-v1.4.0sudo dpkg --add-architecture amd64sudo apt updatesudo apt install -y pkg-config ninja-build doxygen clang gcc-multilib g++-multilib python3 nasm cmake libgl1-mesa-dev libsoundio-dev libvulkan-dev libx11-dev libxcursor-dev libxinerama-dev libxrandr-dev libusb-1.0-0-dev libssl-dev libudev-dev mesa-common-dev uuid-devhttps://packages.microsoft.com/ubuntu/18.04/prod/pool/main/libk/
从上面的网站下载 libk4a1.2 中 libk4a1.2_1.2.0_amd64.deb文件
解压 .deb 文件，再解压内部的 data.tar.gz和control.tar.gz文件,并进入data文件夹，打开终端输入：
cd usr/lib/x86_64-linux-gnusudo cp libdepthengine.so.2.0 /usr/lib/x86_64-linux-gnu随后进入下载好的 Azure-Kinect-Sensor-SDK-v1.4.0文件夹下打开终端输入
mkdir build && cd buildcmake -j$(nproc) .. -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 -j$(nproc) .. -GNinja克隆完成后为如图所示：
之后输入：
sudo ninja -j$(nproc)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/testsudo apt updatesudo gedit /etc/apt/sources.list在最后一行加入：
## gcc-4.9deb http://dk.archive.ubuntu.com/ubuntu/ xenial maindeb http://dk.archive.ubuntu.com/ubuntu/ xenial universe##保存后关闭，打开终端，输入：
sudo apt updatesudo apt install gcc-4.9sudo apt upgrade libstdc++6sudo 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如果是.deb包安装，该udev规则文件（99-k4a.rules，将其保存在/etc/udev/rules.d下）内容如下：
# Bus 002 Device 116: ID 045e:097a Microsoft Corp. - Generic Superspeed USB Hub# Bus 001 Device 015: ID 045e:097b Microsoft Corp. - Generic USB Hub# Bus 002 Device 118: ID 045e:097c Microsoft Corp. - Azure Kinect Depth Camera# Bus 002 Device 117: ID 045e:097d Microsoft Corp. - Azure Kinect 4K Camera# Bus 001 Device 016: ID 045e:097e Microsoft Corp. - Azure Kinect Microphone ArrayBUS!="usb", ACTION!="add", SUBSYSTEM!=="usb_device", GOTO="k4a_logic_rules_end"ATTRS{idVendor}=="045e", ATTRS{idProduct}=="097a", MODE="0666", GROUP="plugdev"ATTRS{idVendor}=="045e", ATTRS{idProduct}=="097b", MODE="0666", GROUP="plugdev"ATTRS{idVendor}=="045e", ATTRS{idProduct}=="097c", MODE="0666", GROUP="plugdev"ATTRS{idVendor}=="045e", ATTRS{idProduct}=="097d", MODE="0666", GROUP="plugdev"ATTRS{idVendor}=="045e", ATTRS{idProduct}=="097e", MODE="0666", GROUP="plugdev"LABEL="k4a_logic_rules_end"②Ubuntu20.04，Reference：
https://blog.csdn.net/qq_42108414/article/details/129015474
19-配置科大讯飞
https://www.xfyun.cn/sdk/dispatcher
sudo apt install sox libsox-fmt-all pavucontrolsudo 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文件拷贝至工作空间的某个位置。
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 COMPONENTSroscpprospystd_msgscv_bridgemessage_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 ${catkin_LIBRARIES} ${OpenCV_LIBRARIES} ${PCL_LIBRARIES} -lcurl -ljsoncpp -lmsc -lrt -ldl -pthread -lasound /home/m0rtzz/Workspaces/tts_voice_test_ws/libs/x64/libmsc.so打开终端：
catkin_make若找不到asoundlib.h文件打开终端输入：
sudo apt install libasound2-dev编译通过~
20-配置realsense及realsense工作空间
sudo apt install ros-${ROS_DISTRO}-realsense2-camera ros-${ROS_DISTRO}-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 $(lsb_release -cs) main" -u
sudo apt update安装realsense lib
sudo apt install librealsense2-dkms librealsense2-utils
测试：
realsense-viewer
下载lib并指定版本为v2.50.0，否则接下来会与realsense-ros版本冲突导致无法打开摄像头:
git clone -b v2.50.0 https://gitcode.com/mirrors/IntelRealSense/librealsense.git librealsense-2.50.0
安装依赖：
sudo apt install libudev-dev pkg-config libgtk-3-dev libusb-1.0-0-dev pkg-config libglfw3-dev进入刚才克隆的librealsense文件夹内：
cd librealsense-2.50.0/./scripts/setup_udev_rules.sh./scripts/patch-realsense-ubuntu-lts.sh注意：上面的命令可能执行过慢，请耐心等待，或者科学的上网~
完成结果如下：
之后输入：
mkdir build && cd build# @file : CMakeLists.txt# NOTE: Modified by M0rtzzLINK_LIBRARIES(-lcurl -lcrypto)cmake -j$(nproc) ../ -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 -j$(nproc)sudo make install测试：
cd examples/capture./rs-capture
接下来我们配置realsense工作空间：
创建一个realsense_test_ws文件夹，进入文件夹下，打开终端：
mkdir src && cd src/下载功能包:
git clone -b ros1-legacy https://gitcode.com/mirrors/IntelRealSense/realsense-ros.git realsense-ros
cd ..catkin_make -j$(nproc) -DCATKIN_ENABLE_TESTING=False -DCMAKE_BUILD_TYPE=Releasecatkin_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/srccd kinova_test_ws/srccatkin_init_workspace
cd ..catkin_makeecho 'source /home/m0rtzz/Workspaces/kinova_test_ws/devel/setup.bash' >> ~/.bashrccd src/git clone https://gitcode.com/mirrors/Kinovarobotics/kinova-ros.git kinova-roscd ..安装缺少的moveit中相应的功能包 ：
sudo apt install ros-${ROS_DISTRO}-moveit-visual-tools ros-${ROS_DISTRO}-moveit-ros-planning-interfacecatkin_make -j$(nproc)
sudo cp src/kinova-ros/kinova_driver/udev/10-kinova-arm.rules /etc/udev/rules.d/安装Moveit和pr2：
sudo apt install $(apt-cache search ros-${ROS_DISTRO}-pr2- | grep -v "ros-${ROS_DISTRO}-pr2-apps" | cut -d' ' -f1)
完成~
22-配置机器人导航（实体）
安装 Arduino IDE:
https://www.arduino.cc/en/software
下载Linux 64bit安装包
tar -xvf arduino-1.8.19-linux64.tar.xzsudo mv arduino-1.8.19 /optcd /opt/arduino-1.8.19sudo chmod +x install.shsudo ./install.shsudo apt install ros-${ROS_DISTRO}-move-base* ros-${ROS_DISTRO}-turtlebot3-* ros-${ROS_DISTRO}-dwa-local-plannersudo apt install ros-${ROS_DISTRO}-joy ros-${ROS_DISTRO}-teleop-twist-joy ros-${ROS_DISTRO}-teleop-twist-keyboard ros-${ROS_DISTRO}-laser-proc ros-${ROS_DISTRO}-rgbd-launch ros-${ROS_DISTRO}-depthimage-to-laserscan ros-${ROS_DISTRO}-rosserial-arduino ros-${ROS_DISTRO}-rosserial-python ros-${ROS_DISTRO}-rosserial-server ros-${ROS_DISTRO}-rosserial-client ros-${ROS_DISTRO}-rosserial-msgs ros-${ROS_DISTRO}-amcl ros-${ROS_DISTRO}-map-server ros-${ROS_DISTRO}-move-base ros-${ROS_DISTRO}-urdf ros-${ROS_DISTRO}-xacro ros-${ROS_DISTRO}-compressed-image-transport ros-${ROS_DISTRO}-rqt-image-view ros-${ROS_DISTRO}-gmapping ros-${ROS_DISTRO}-navigation ros-${ROS_DISTRO}-interactive-markers安装 gmapping 包（用于构建地图）：
sudo apt install ros-${ROS_DISTRO}-gmapping安装地图服务包（用于保存与读取地图）:
sudo apt install ros-${ROS_DISTRO}-map-server安装 navigation 包（用于定位以及路径规划）:
sudo apt install ros-${ROS_DISTRO}-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/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/srccatkin_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_roscd 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="$(find rplidar_ros)/launch/rplidar.launch" /></launch>FIXME：Updating...
接下来创建机器人模型相关的功能包：
cd src/catkin_create_pkg robot_description_test urdf xacro
在功能包下新建 urdf 目录，编写具体的 urdf 文件（code命令是VSCode，没安装的小伙伴下边PS小节有下载网址~）:
cd robot_description_test/ && mkdir urdfcd urdf/ && touch {robot.urdf.xacro,robot_base.urdf.xacro,robot_camera.urdf.xacro,robot_laser.urdf.xacro} && code robot.urdf.xacro将下列代码粘贴进去：
<!-- File Name : robot.urdf.xacro --> <robot name="robot_test" xmlns:xacro="http://wiki.ros.org/xacro"> <xacro:include filename="robot_base.urdf.xacro" /> <xacro:include filename="robot_camera.urdf.xacro" /> <xacro:include filename="robot_laser.urdf.xacro" /> </robot>保存退出，打开终端输入：
code robot_base.urdf.xacro将下列代码粘贴进去：
<!-- File Name : robot_base.urdf.xacro --> <robot name="robot_test" xmlns:xacro="http://wiki.ros.org/xacro"> <xacro:property name="footprint_radius" value="0.001" /> <link name="base_footprint"> <visual> <geometry> <sphere radius="${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} ${joint_laser_z}" rpy="0 0 0" /> </joint></robot>保存退出，打开终端：
cd .. && mkdir launchtouch robot_test.launch && code robot_test.launch将下列代码粘贴进去：
<!-- File Name : robot_test.launch --> <launch> <param name="robot_description" command="$(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 mapcd 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 footprintrobot_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/120136500
首先安装依赖：
sudo apt install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler libatlas-base-dev libgflags-dev libgoogle-glog-dev liblmdb-dev && sudo apt install --no-install-recommends libboost-all-devgit clone https://gitcode.com/mirrors/BVLC/caffe.git caffecd caffe/ && sudo cp Makefile.config.example Makefile.configgedit 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 3OPENCV_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 OpenBlasBLAS := 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 $LIBRARY_DIRS.)# USE_PKG_CONFIG := 1 # N.B. both build and distribute dirs are cleared on `make clean`BUILD_DIR := buildDISTRIBUTE_DIR := distribute # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171# DEBUG := 1 # The ID of the GPU that 'make runtest' will use to run unit tests.TEST_GPUID := 0 # enable pretty build (comment to see full commands)Q ?= @gedit MakefilePROJECT := caffe CONFIG_FILE := Makefile.config# Explicitly check for the config file, otherwise make -k will proceed anyway.ifeq ($(wildcard $(CONFIG_FILE)),)$(error $(CONFIG_FILE) not found. See $(CONFIG_FILE).example.)endifinclude $(CONFIG_FILE) BUILD_DIR_LINK := $(BUILD_DIR)ifeq ($(RELEASE_BUILD_DIR),) RELEASE_BUILD_DIR := .$(BUILD_DIR)_releaseendififeq ($(DEBUG_BUILD_DIR),) DEBUG_BUILD_DIR := .$(BUILD_DIR)_debugendif DEBUG ?= 0ifeq ($(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 nameLIBRARY_NAME := $(PROJECT)LIB_BUILD_DIR := $(BUILD_DIR)/libSTATIC_NAME := $(LIB_BUILD_DIR)/lib$(LIBRARY_NAME).aDYNAMIC_VERSION_MAJOR := 1DYNAMIC_VERSION_MINOR := 0DYNAMIC_VERSION_REVISION := 0DYNAMIC_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 filesCU_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cu" -name "*.cu")# TEST_SRCS are the test source filesTEST_MAIN_SRC := src/$(PROJECT)/test/test_caffe_main.cppTEST_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 binariesTOOL_SRCS := $(shell find tools -name "*.cpp")# EXAMPLE_SRCS are the source files for the example binariesEXAMPLE_SRCS := $(shell find examples -name "*.cpp")# BUILD_INCLUDE_DIR contains any generated header files we want to include.BUILD_INCLUDE_DIR := $(BUILD_DIR)/src# PROTO_SRCS are the protocol buffer definitionsPROTO_SRC_DIR := src/$(PROJECT)/protoPROTO_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 filesPROTO_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.pyLINT_OUTPUT_DIR := $(BUILD_DIR)/.lintLINT_EXT := lint.txtLINT_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).cppPY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).soPY$(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)_.cppifneq ($(MATLAB_DIR),) MAT_SO_EXT := $(shell $(MATLAB_DIR)/bin/mexext)endifMAT$(PROJECT)_SO := matlab/+$(PROJECT)/private/$(PROJECT)_.$(MAT_SO_EXT) ############################### Derive generated files############################### The generated files for protocol buffersPROTO_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)/protoPY_PROTO_INIT := python/$(PROJECT)/proto/__init__.pyPROTO_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 objectsTOOL_OBJS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o})TOOL_BUILD_DIR := $(BUILD_DIR)/toolsTEST_CXX_BUILD_DIR := $(BUILD_DIR)/src/$(PROJECT)/testTEST_CU_BUILD_DIR := $(BUILD_DIR)/cuda/src/$(PROJECT)/testTEST_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 generationDEPS := ${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 binsTOOL_BINS := ${TOOL_OBJS:.o=.bin}EXAMPLE_BINS := ${EXAMPLE_OBJS:.o=.bin}# symlinks to tool bins without the ".bin" extensionTOOL_BIN_LINKS := ${TOOL_BINS:.bin=}# Put the test binaries in build/test for convenience.TEST_BIN_DIR := $(BUILD_DIR)/testTEST_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.txtCXX_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 := $(CUDA_DIR)/include CUDA_LIB_DIR :=# add <cuda>/lib64 only if it existsifneq ("$(wildcard $(CUDA_DIR)/lib64)","") CUDA_LIB_DIR += $(CUDA_DIR)/lib64endifCUDA_LIB_DIR += $(CUDA_DIR)/lib INCLUDE_DIRS += $(BUILD_INCLUDE_DIR) ./src ./includeifneq ($(CPU_ONLY), 1) INCLUDE_DIRS += $(CUDA_INCLUDE_DIR) LIBRARY_DIRS += $(CUDA_LIB_DIR) LIBRARIES := cudart cublas curandendif LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial # handle IO dependenciesUSE_LEVELDB ?= 1USE_LMDB ?= 1# This code is taken from https://github.com/sh1r0/caffe-android-libUSE_HDF5 ?= 1USE_OPENCV ?= 1 ifeq ($(USE_LEVELDB), 1) LIBRARIES += leveldb snappyendififeq ($(USE_LMDB), 1) LIBRARIES += lmdbendif# This code is taken from https://github.com/sh1r0/caffe-android-libifeq ($(USE_HDF5), 1) LIBRARIES += hdf5_hl hdf5endififeq ($(USE_OPENCV), 1) LIBRARIES += opencv_core opencv_highgui opencv_imgproc ifeq ($(OPENCV_VERSION), 3) LIBRARIES += opencv_imgcodecs endif endifPYTHON_LIBRARIES ?= boost_python python2.7WARNINGS := -Wall -Wno-sign-compare ############################### Set build directories############################## DISTRIBUTE_DIR ?= distributeDISTRIBUTE_SUBDIRS := $(DISTRIBUTE_DIR)/bin $(DISTRIBUTE_DIR)/libDIST_ALIASES := distifneq ($(strip $(DISTRIBUTE_DIR)),distribute) DIST_ALIASES += distributeendif 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 .DoxyfileDOXYGEN_OUTPUT_DIR ?= ./doxygenDOXYGEN_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 platformUNAME := $(shell uname -s)ifeq ($(UNAME), Linux) LINUX := 1else ifeq ($(UNAME), Darwin) OSX := 1 OSX_MAJOR_VERSION := $(shell sw_vers -productVersion | cut -f 1 -d .) OSX_MINOR_VERSION := $(shell sw_vers -productVersion | cut -f 2 -d .)endif # Linuxifeq ($(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,$(ORIGIN)/../libendif # OS X:# clang++ instead of g++# libstdc++ for NVCC compatibility on OS X >= 10.9 with CUDA < 7.0ifeq ($(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/libelse ORIGIN := \$$ORIGINendif # Custom compilerifdef CUSTOM_CXX CXX := $(CUSTOM_CXX)endif # Static linkingifneq (,$(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-archiveelse # The following line must not be indented with a tab, since we are not inside a target $(error Cannot static link with the $(CXX) compiler)endif # Debuggingifeq ($(DEBUG), 1) COMMON_FLAGS += -DDEBUG -g -O0 NVCCFLAGS += -Gelse COMMON_FLAGS += -DNDEBUG -O2endif # cuDNN acceleration configuration.ifeq ($(USE_CUDNN), 1) LIBRARIES += cudnn COMMON_FLAGS += -DUSE_CUDNNendif # NCCL acceleration configurationifeq ($(USE_NCCL), 1) LIBRARIES += nccl COMMON_FLAGS += -DUSE_NCCLendif # configure IO librariesifeq ($(USE_OPENCV), 1) COMMON_FLAGS += -DUSE_OPENCVendififeq ($(USE_LEVELDB), 1) COMMON_FLAGS += -DUSE_LEVELDBendififeq ($(USE_LMDB), 1) COMMON_FLAGS += -DUSE_LMDBifeq ($(ALLOW_LMDB_NOLOCK), 1) COMMON_FLAGS += -DALLOW_LMDB_NOLOCKendifendif# This code is taken from https://github.com/sh1r0/caffe-android-libifeq ($(USE_HDF5), 1) COMMON_FLAGS += -DUSE_HDF5endif # CPU-only configurationifeq ($(CPU_ONLY), 1) OBJS := $(PROTO_OBJS) $(CXX_OBJS) TEST_OBJS := $(TEST_CXX_OBJS) TEST_BINS := $(TEST_CXX_BINS) ALL_WARNS := $(ALL_CXX_WARNS) TEST_FILTER := --gtest_filter="-*GPU*" COMMON_FLAGS += -DCPU_ONLYendif # Python layer supportifeq ($(WITH_PYTHON_LAYER), 1) COMMON_FLAGS += -DWITH_PYTHON_LAYER LIBRARIES += $(PYTHON_LIBRARIES)endif # BLAS configuration (default = ATLAS)BLAS ?= atlasifeq ($(BLAS), mkl) # MKL LIBRARIES += mkl_rt COMMON_FLAGS += -DUSE_MKL MKLROOT ?= /opt/intel/mkl BLAS_INCLUDE ?= $(MKLROOT)/include BLAS_LIB ?= $(MKLROOT)/lib $(MKLROOT)/lib/intel64else ifeq ($(BLAS), open) # OpenBLAS LIBRARIES += openblaselse # 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 endifendifINCLUDE_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 -WuninitalizedMATLAB_CXXFLAGS := $(CXXFLAGS) -Wno-uninitializedLINKFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS) USE_PKG_CONFIG ?= 0ifeq ($(USE_PKG_CONFIG), 1) PKG_CONFIG := $(shell pkg-config opencv --libs)else PKG_CONFIG :=endifLDFLAGS += $(foreach librarydir,$(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 $req -i https://mirrors.aliyun.com/pypi/simple/; donecd .. && sudo make cleansudo make all -j$(nproc)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.hsudo gedit /usr/local/cuda/targets/x86_64-linux/include/cudnn_version.h
复制其中的非注释部分：
sudo gedit /usr/local/cuda/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 -j$(nproc)生成以下静态库和共享库文件：
测试，时间较慢，耐心等待~
sudo make test -j$(nproc)sudo make runtest -j$(nproc)sudo make pycaffe -j$(nproc)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://gitcode.com/mirrors/OpenKinect/libfreenect2.gitcd libfreenect2 && mkdir build && cd build/cmake -j$(nproc) .. -DENABLE_CXX11=ONsudo make -j$(nproc)
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
下载VTK-8.2.0.zip
解压之后，进入文件夹打开终端：
sudo apt install cmake-gui && mkdir build && cd build && cmake-gui
单击Configure后勾选以下两项后单击Configure和Generate
sudo make -j$(nproc)
sudo make install
接下来安装pcl：
git clone -b pcl-1.9.1 https://gitcode.com/mirrors/PointCloudLibrary/pcl.git pcl-1.9.1之后进入文件夹打开终端输入：
mkdir release && cd releasecmake -D CMAKE_BUILD_TYPE=None \-D CMAKE_INSTALL_PREFIX=/usr \-D BUILD_GPU=ON-DBUILD_apps=ON \-D BUILD_examples=ON \-D CMAKE_INSTALL_PREFIX=/usr \-j$(nproc) ..
sudo make -j$(nproc)
sudo make install
26-安装CarlaUE4（必须是Carla的UE仓库里的carla分支才可以通过安装Carla时的编译）
find . -name "*.sh" -exec dos2unix {} +find . -name "*.sh" -exec chmod +x {} +sudo chown -R m0rtzz: *若报错：
因Epic更新了gitdeps，但Github上却没有更新，所以需要进入Github官方仓库release界面寻找对应版本的Commit.gitdeps.xml替换原来的文件即可：
https://github.com/EpicGames/UnrealEngine/releases/tag
若报错：
个人认为是因执行Setup.sh脚本未赋予root权限导致依赖未安装完整，所以再次执行：
sudo ./Setup.sh// @file : CubemapUnwrapUtils.cpp// UseRHICmdList.GetBoundVertexShader() instead of GetVertexShader()RHICmdList.GetBoundPixelShader() instead of GetPixelShader()// Instead of the given macros, use code as below.GraphicsPSOInit.BoundShaderState.VertexShaderRHI = VertexShader.GetVertexShader();GraphicsPSOInit.BoundShaderState.PixelShaderRHI = PixelShader.GetPixelShader();http://cdn.unrealengine.com/Toolchain_Linux/native-linux-v17_clang-10.0.10centos.tar.gz
cd your-path/UnrealEngine_4.26/Engine/Extras/ThirdPartyNotUE/SDKs/HostLinux/Linux_x64/tar -zxvf native-linux-v17_clang-10.0.1-centos7.tar.gz27-安装Carla0.9.13（添加fisheye sensor模块）
修改Update.sh下载网址为南方科技大学镜像站的网址：
#CONTENT_LINK=http://carla-assets.s3.amazonaws.com/${CONTENT_ID}.tar.gzCONTENT_LINK=https://mirrors.sustech.edu.cn/carla/carla_content/${CONTENT_ID}.tar.gz// @file : test_streaming.cpp // Line 58carla::streaming::low_level::Server<tcp::Server> srv(io.service, TESTING_PORT); // Line 63carla::streaming::low_level::Client<tcp::Client> c; // Line 93carla::streaming::low_level::Server<tcp::Server> srv(io.service, TESTING_PORT); // Line 96carla::streaming::low_level::Client<tcp::Client> c;# @file : Package.sh(https://github.com/annaornatskaya/carla/tree/fisheye-sensor) # copy_if_changed "./Plugins/" "${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/" fi28-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）：
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 install terminator
trash命令：
sudo apt install trash-clitree命令：
sudo apt install tree
查看系统信息：
sudo apt install neofetch
rar文件解压工具：
sudo apt install unrar解决不能观看MP4文件：
sudo apt updatesudo apt install libdvdnav4 libdvdread4 gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly libdvd-pkgsudo apt install ubuntu-restricted-extrassudo dpkg-reconfigure libdvd-pkg系统优化：
sudo apt updatesudo apt 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.openintabbrowser.urlbar.openintabbrowser.tabs.loadBookmarksInTabs都设置为true~