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:

（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>int main() {// 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.json4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

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