vscode c++ 环境配置(终极版)

1. window系统 c++ 环境配置

1.1 配置MinGw编译器

（1）下载mingw64

mingw64 的按照包，我已经放在百度网盘上了，搭建可自行下载:

链接: https://pan.baidu.com/s/1NoPGAYFuP5ysXTf8wtvbEA?pwd=wd6w 提取码: wd6w

（2）配置环境变量
将下载好的

mingw64.zip

解压，找到解压后

bin

文件所在路径

然后将bin所在路径，如我这里的

D:\install\mingw64\bin

添加到系统环境变量中。

（3）验证是否安装成功
命令提示符中cmd窗口输入

gcc -v

或

gcc --version

，若显示版本号则说明安装成功

1.2 配置C/C++环境

C++环境是通过

c_pp_propertoes.json

,

launch.json

和

tasks.json

三个文件配置的。

方法1 利用工具自动配置

本文介绍一种非常简单的环境配置方法，借助网上提供的

vscode c++配置器

来实现。

• 在https://v4.vscch.tk/ 下载安装包
• 解压安装包，并点击vscch.exe 进行一步步按照；安装非常简单，按照默认安装方式即可。 该工具可以自动识别MinGW编译器的安装位置，如果没有添加到环境变量中，它会自动帮忙配置到环境变量。
• 如果未下载MinGW，它会提示你下载，然后帮忙自动配置环境变量。
• 完成安装后，会自动在你选择的工作路径下，生成配置好的c_pp_propertoes.json,launch.json和tasks.json， 以及helloworld.cpp测试代码。可以运行，测试c++环境是否配置成功:注意： 根据自动生成的json配置，只能在安装时指定的工作目录下，编写c++ code才有效，因此做了如下修改，这样将3个json拷贝到其他目录，也可以运行和调试c++程序。 将single file build改为build

launch.json

的修改如下：

tasks.json

的修改如下：

方法2 直接创建3个json文件

在项目目录上创建

.vscode

目录，并创建

c_cpp_properties.json

,

launch.json

,

tasks.json

,三个json文件的按照我提供的配置。

(1)

c_cpp_properties.json 

{"configurations":[{"compilerPath":"D:\\install\\mingw64\\bin\\g++.exe","cppStandard":"c++17","includePath":["${{workspaceFolder}}/**"],"intelliSenseMode":"windows-gcc-x64","name":"Win32"}],"version":4}

(2)

launch.json

{"configurations":[{"MIMode":"gdb","args":[],"cwd":"${fileDirname}","env":{"PATH":"D:\\install\\mingw64\\bin;${env:PATH}"},"environment":[],"externalConsole":true,"internalConsosleOptions":"neverOpen","miDebuggerPath":"D:\\install\\mingw64\\bin\\gdb.exe","name":"build","preLaunchTask":"build","program":"${fileDirname}\\${fileBasenameNoExtension}.exe","request":"launch","stopAtEntry":false,"type":"cppdbg"}],"version":"0.2.0"}

(3)

tasks.json

{"options":{"env":{"Path":"D:\\install\\mingw64\\bin;${env:Path}"}},"tasks":[{"args":["-g","${file}","-o","${fileDirname}\\${fileBasenameNoExtension}.exe","-std=c++17"],"command":"D:\\install\\mingw64\\bin\\g++.exe","group":{"isDefault":true,"kind":"build"},"label":"build","presentation":{"clear":true,"echo":false,"focus":false,"panel":"shared","reveal":"silent","showReuseMessage":false},"problemMatcher":"$gcc","type":"process"},{"args":[],"command":"${fileDirname}\\${fileBasenameNoExtension}.exe","dependsOn":"single file build","label":"run and pause","options":{"env":{"Path":"D:\\install\\mingw64\\bin;${env:Path}"}},"presentation":{"clear":true,"echo":false,"focus":false,"panel":"shared","reveal":"never","showReuseMessage":false},"problemMatcher":[],"type":"pause-console"}],"version":"2.0.0"}

注意： 将这3个

json

文件中的

D:\\install\\mingw64

路径设置为你本机自己的路径

1.3 C/C++环境测试

编写一个测试的

helloworld.cpp

,代码如下:

// 按下 F6 编译运行。// 按下 F5 编译调试。// 按下 Ctrl + Shift + B 编译。#include<iostream>intmain(){// 在标准输出中打印 "Hello, world!"
    std::cout <<"Hello, world!"<< std::endl;}// 此文件编译运行将输出 "Hello, world!"。// 按下 F6 后，你将在弹出的终端窗口中看到这一行字。

配置和代码见我已上传百度网盘：
链接：https://pan.baidu.com/s/13R79Wxn91Z4G7RCcXpr8fQ?pwd=xe2x
提取码：xe2x

参考： https://zhuanlan.zhihu.com/p/545908287?utm_id=0

2. linux系统 c++ 环境配置

2.1 配置详解

linux系统vsocde的配置和window系统配置基本上是一样的，通过

c_pp_propertoes.json

,

launch.json

和

tasks.json

，

settings.json

4个文件配置的

c++环境

。

**(1)

settings.json

**

{"files.associations":{"*.cpp":"cpp","*.cu":"cuda-cpp","deque":"cpp","string":"cpp","vector":"cpp","*.tcc":"cpp","__hash_table":"cpp","__split_buffer":"cpp","__tree":"cpp","array":"cpp","bitset":"cpp","initializer_list":"cpp","iterator":"cpp","map":"cpp","queue":"cpp","random":"cpp","set":"cpp","stack":"cpp","string_view":"cpp","unordered_map":"cpp","utility":"cpp","__atomic":"cpp","__functional_base":"cpp","__functional_base_03":"cpp","__tuple":"cpp","algorithm":"cpp","chrono":"cpp","type_traits":"cpp","filesystem":"cpp","functional":"cpp","limits":"cpp","memory":"cpp","ratio":"cpp","tuple":"cpp","istream":"cpp","ostream":"cpp","future":"cpp","cctype":"cpp","clocale":"cpp","cmath":"cpp","cstdarg":"cpp","cstddef":"cpp","cstdio":"cpp","cstdlib":"cpp","cstring":"cpp","ctime":"cpp","cwchar":"cpp","cwctype":"cpp","atomic":"cpp","hash_map":"cpp","hash_set":"cpp","bit":"cpp","codecvt":"cpp","complex":"cpp","condition_variable":"cpp","cstdint":"cpp","list":"cpp","unordered_set":"cpp","exception":"cpp","memory_resource":"cpp","numeric":"cpp","optional":"cpp","system_error":"cpp","fstream":"cpp","iomanip":"cpp","iosfwd":"cpp","iostream":"cpp","mutex":"cpp","new":"cpp","sstream":"cpp","stdexcept":"cpp","streambuf":"cpp","thread":"cpp","cfenv":"cpp","cinttypes":"cpp","typeindex":"cpp","typeinfo":"cpp","ios":"cpp","__nullptr":"cpp","__bit_reference":"cpp","__node_handle":"cpp","__locale":"cpp","variant":"cpp"}}

settings.json

可以上面提供的配置，不需要修改。

**(2)

c_cpp_propertoes.json

**

c_cpp_propertoes.json：

配置c++ 编译时的选项，包括编译器的路径、C/C++标注, 指定头文件的搜索路径（如opencv等）

{"configurations":[{"name":"Linux","includePath":["${workspaceFolder}/**","/home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/trt8cuda112cudnn8/include/**","/home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/cpp-packages/opencv4.2/include/**"],"compilerPath":"/usr/bin/gcc","cStandard":"gnu11","cppStandard":"gnu++11","intelliSenseMode":"linux-gcc-x64"}],"version":4}

配置includePath

"includePath":["${workspaceFolder}/**",
                "/home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/trt8cuda112cudnn8/include/**",
                "/home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/cpp-packages/opencv4.2/include/**"],

includePath

: 设置头文件的搜索路径，让编译器可以找打相应的头文件。

• 第一项： "${workspaceFolder}/**"添加项目的工作路径作为头文件的搜索路径，此项默认添加
• 第二项： 配置TensorRT部署时，需要依赖的头文件，包括tensorrt自身的、cuda、cudnn、protobuf下的头文件。（项目中如果不依赖TensorRT、cuda则不需要配置）

ls  /home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/trt8cuda112cudnn8/include/

可以看到依赖的一些头文件：

cd 到其中一个比如cuda,可以详细看到cuda包含的

.h文件

• 第三项：配置了依赖的opencv 头文件。（项目中如果不依赖与opencv，则不需要配置）

配置gcc编译器路径

"compilerPath":"/usr/bin/gcc"# 设置gcc编译器即可，不需要设置g++

通过

ls /usr/bin

，可以看到

gcc, g++

等编译器都在该目录下

指定C/C++语言标注版本

"cStandard":"gnu11",
"cppStandard":"gnu++11",

智能感知方式

"intelliSenseMode":"linux-gcc-x64"

**(3)

tasks.json

**
tasks.json：

指定 编译时需要执行cmake命令

, 并且在每次

launch(debug)

时，都会先运行运行

tasks

(这里指的是都会编译一遍)。

因此

tasks

下面

label

名需要和

launch.json

中的

"preLaunchTask"

参数设置的一样，比如都是

build

。

{"version":"2.0.0","tasks":[{"label":"build","type":"shell","command":"make pro -j6"}]}

• command: 执行cmake的命令，其中pro为可执行文件名(makefile中指定的生成可执行文件的名称)，-j6表示6个进程同时执行，如果想编译快点，可以将数字设置的大一点。

**(4)

launch.json

**

{"version":"0.2.0","configurations":[{"name":"program-debug","type":"cppdbg","request":"launch","program":"${workspaceFolder}/workspace/pro","args":[],"stopAtEntry":false,"cwd":"${workspaceFolder}/workspace","externalConsole":false,"MIMode":"gdb","miDebuggerPath":"/usr/bin/gdb","environment":[{"name":"LD_LIBRARY_PATH","value":"/home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/trt8cuda112cudnn8/lib64:/home/yuanwushui/anaconda3/lib:/home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/trt8cuda112cudnn8/py39:/home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/cpp-packages/opencv4.2/lib:/home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/lib:$LD_LIBRARY_PATH"}],"setupCommands":[{"text":"-enable-pretty-printing","ignoreFailures":true}],"preLaunchTask":"build"}]}

launch.json

设置c++ debug的选项。

"configurations"

下面的配置参数说明如下：

• program：指定编译好的可执行文件pro的路径, 其中pro是在makefile中指定输出可执行文件名。

"program":"${workspaceFolder}/workspace/pro",

• cwd: 为可执行文件所在目录
• "externalConsole": 运行时，是否需要运行在外部的控制台。如果设为True的话，会再CMD控制台运行(windows)，如果false，会运行在编译器所在控制台。默认设为false即可
• miDebuggerPath: 指定调试器(gdb)的路径
• environment: 配置环境变量。环境变量名为LD_LIBRARY_PATH，环境变量值通过value来指定。LD_LIBRARY_PATH主要用来指定需要一开的外部库文件的搜索路径。在vlaue中指定库文件的搜索路径，以:隔开。如项目需要依赖tensorrt,cuda,anaconda,opencv, 则需要添加这些依赖的库文件。注意value需要以:不同库的搜索路径隔开，最后需要以:$LD_LIBRARY_PATH结尾。

"environment":[{"name":"LD_LIBRARY_PATH","value":"/home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/trt8cuda112cudnn8/lib64:/home/yuanwushui/anaconda3/lib:/home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/trt8cuda112cudnn8/py39:/home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/cpp-packages/opencv4.2/lib:/home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/lib:$LD_LIBRARY_PATH"}],

• setupCommands: 设置打印选项，如对print输出进行美化，保持默认即可，不需要修改

"setupCommands":[{"text":"-enable-pretty-printing","ignoreFailures":true}],

• preLaunchTask: Launch(debug)前需要依赖的task任务，注意需要与tasks.json中任务的label设置的名称一致，比如都为build,不然无法调试和编译。通过设置该选项，在调试时不需要手动去编译可执行文件，系统通过preLaunchTask自动帮忙编译。

"preLaunchTask":"build"

2.2 makefile详解

cc        := g++
name      := pro
workdir   := workspace
srcdir    := src
objdir    := objs
stdcpp    := c++11
cuda_home := /home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/trt8cuda112cudnn8
syslib    := /home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/lib
cpp_pkg   := /home/yuanwushui/anaconda3/lib/python3.9/site-packages/trtpy/cpp-packages  #opencv4.2
cuda_arch := 
nvcc      :=$(cuda_home)/bin/nvcc -ccbin=$(cc)# 定义cpp的路径查找和依赖项mk文件
cpp_srcs :=$(shell find$(srcdir) -name "*.cpp")
cpp_objs :=$(cpp_srcs:.cpp=.cpp.o)
cpp_objs :=$(cpp_objs:$(srcdir)/%=$(objdir)/%)
cpp_mk   :=$(cpp_objs:.cpp.o=.cpp.mk)# 定义cu文件的路径查找和依赖项mk文件
cu_srcs :=$(shell find$(srcdir) -name "*.cu")
cu_objs :=$(cu_srcs:.cu=.cu.o)
cu_objs :=$(cu_objs:$(srcdir)/%=$(objdir)/%)
cu_mk   :=$(cu_objs:.cu.o=.cu.mk)# 定义opencv和cuda需要用到的库文件
link_cuda      := cudart cudnn
link_trtpro    := 
link_tensorRT  := nvinfer nvinfer_plugin
link_opencv    := opencv_core opencv_imgproc opencv_imgcodecs
link_sys       := stdc++ dl protobuf
link_librarys  :=$(link_cuda)$(link_tensorRT)$(link_sys)$(link_opencv)# 定义头文件路径，请注意斜杠后边不能有空格# 只需要写路径，不需要写-I
include_paths := src              \$(cuda_home)/include/cuda     \$(cuda_home)/include/tensorRT \$(cpp_pkg)/opencv4.2/include  \$(cuda_home)/include/protobuf

# 定义库文件路径，只需要写路径，不需要写-L
library_paths :=$(cuda_home)/lib64 $(syslib)$(cpp_pkg)/opencv4.2/lib

# 把library path给拼接为一个字符串，例如a b c => a:b:c# 然后使得LD_LIBRARY_PATH=a:b:c
empty := 
library_path_export :=$(subst $(empty)$(empty),:,$(library_paths))# 把库路径和头文件路径拼接起来成一个，批量自动加-I、-L、-l
run_paths     :=$(foreach item,$(library_paths),-Wl,-rpath=$(item))
include_paths :=$(foreach item,$(include_paths),-I$(item))
library_paths :=$(foreach item,$(library_paths),-L$(item))
link_librarys :=$(foreach item,$(link_librarys),-l$(item))# 如果是其他显卡，请修改-gencode=arch=compute_75,code=sm_75为对应显卡的能力# 显卡对应的号码参考这里：https://developer.nvidia.com/zh-cn/cuda-gpus#compute# 如果是 jetson nano，提示找不到-m64指令，请删掉 -m64选项。不影响结果
cpp_compile_flags := -std=$(stdcpp) -w -g -O0 -m64 -fPIC -fopenmp -pthread
cu_compile_flags  := -std=$(stdcpp) -w -g -O0 -m64 $(cuda_arch) -Xcompiler "$(cpp_compile_flags)"
link_flags        := -pthread -fopenmp -Wl,-rpath='$$ORIGIN'

cpp_compile_flags +=$(include_paths)
cu_compile_flags  +=$(include_paths)
link_flags        +=$(library_paths)$(link_librarys)$(run_paths)# 如果头文件修改了，这里的指令可以让他自动编译依赖的cpp或者cu文件
ifneq ($(MAKECMDGOALS), clean)
-include $(cpp_mk)$(cu_mk)
endif

$(name):$(workdir)/$(name)

all       :$(name)
run       :$(name)
    @cd $(workdir)&& ./$(name)$(run_args)$(workdir)/$(name):$(cpp_objs)$(cu_objs)
    @echo Link $@
    @mkdir -p $(dir $@)
    @$(cc) $^ -o $@$(link_flags)$(objdir)/%.cpp.o :$(srcdir)/%.cpp
    @echo Compile CXX $<
    @mkdir -p $(dir $@)
    @$(cc) -c $< -o $@$(cpp_compile_flags)$(objdir)/%.cu.o :$(srcdir)/%.cu
    @echo Compile CUDA $<
    @mkdir -p $(dir $@)
    @$(nvcc) -c $< -o $@$(cu_compile_flags)# 编译cpp依赖项，生成mk文件$(objdir)/%.cpp.mk :$(srcdir)/%.cpp
    @echo Compile depends C++ $<
    @mkdir -p $(dir $@)
    @$(cc) -M $< -MF $@ -MT $(@:.cpp.mk=.cpp.o)$(cpp_compile_flags)# 编译cu文件的依赖项，生成cumk文件$(objdir)/%.cu.mk :$(srcdir)/%.cu
    @echo Compile depends CUDA $<
    @mkdir -p $(dir $@)
    @$(nvcc) -M $< -MF $@ -MT $(@:.cu.mk=.cu.o)$(cu_compile_flags)# 定义清理指令
clean :
    @rm -rf $(objdir)$(workdir)/$(name)$(workdir)/*.trtmodel $(workdir)/*.onnx 
    @rm -rf $(workdir)/image-draw.jpg $(workdir)/input-image.jpg $(workdir)/pytorch.jpg

# 防止符号被当做文件
.PHONY : clean run $(name)# 导出依赖库路径，使得能够运行起来export LD_LIBRARY_PATH:=$(library_path_export)

2.3 案例说明

未完待续