Opencv 4 Linux application in visual studio 2017- Ubuntu Linux subsystem on windows 10

This tutorial is a step-by-step guide for the development of the Opencv 4 Linux application in Visual Studio 2017 under Windows. The motivation is simple. I would like to work in Visual Studio 2017, which is simply great. I personally love it. It is not the only motivation. I am a Windows user, but the market is full of Linux-based cloud machines and docker. So, I need to target Linux. The OpenCV running on Linux is faster than my 5-year experience(consider any possible optimization options). Another motivating point is the Windows file system. Write an application for Linux that accesses the Windows file system.
linux development in visual studio

Again, I am a Windows person with lots of Linux experience, and lots of datasets, images, and video stored on Windows machines. I would like to use this data without any transfer or copying of my datasets. Just directly open the c:/myPositiveImages image directory in the Linux app as /mnt/c/myPositiveImages. Yes, it is possible.  I am doing this to prepare my datasets stored in the Windows file system and prepare the proper inputs for the Yolo darknet. Yolo is not so great on Windows and is hard to compile. Data, and images prepared in Windows are not compatible with most of the Linux apps and mainly Yolo input. You can say some argument like. Prepare your dataset on Windows and transfer it to Linux and read it by Yolo, Learn your detector and enjoy deep neural networks. No this doesn't work. Yolo has a problem reading datasets prepared on Windows. This is why Visual Studio 2017 and Linux subsystem, where data from Windows are processed by a Linux app. This is only one motivation from the many possible. I simply like Visual Studio, but my target is Linux cloud, Linux containers,s or IoT devices like raspberry pi.

What will I learn in this OpenCV tutorial? (win-Linux tutorial)

  1. Prepare an environment, where Visual Studio communicates with remote or local Linux
  2. Install OpenCV from the GIT
  3. Build Linux Opencv application from Windows, linker, and compiler settings
  4. Use Windows file system in Linux Opencv app

Skills needed for Visual Studio connected to Linux

Hopefully, you can follow this tutorial without too many problems. Anyway, this is advanced staff and some knowledge is beneficial for the smooth process of configuration of the development environment.

-Do not be afraid of Linux and the command line
-Linux basics
-Do not be afraid to read step by step this text
-Try to figure out what went wrong if something fails
-Certificates, ssh
-Visu Studio

Environment on windows

1)Windows 10, just 10. This is not working in Windows 7. Linux subsystem can be turned on and Linux easily installed from the Windows store, but just on W10. Open PowerShell as Administrator and run the following command

Enable-WindowsOptionalFeature -Online -FeatureName Microsoft-Windows-Subsystem-Linux

The Windows subsystem Linux can be turned on as well in control panels (It is harder and harder to find control panels in W10). Just google it if you do not have PowerShell.

2)The second part of preparation installs Linux from the Windows store. Just find and install Ubuntu 18.04.
Linux under windows

3)Install Visual Studio 2017 from scratch or modify it. Include all C++ packages and make sure to install Linux development with C++. Mark the option on the following menu in the red circle.
Cross platform development in Visual Studio

Summary, 1) The windows subsystem Linux is enabled. 2) Ubuntu is installed from the Windows store. 3) Visual Studio is installed with Linux development C++. 

Prepare Linux Environment to communicate with Visual Studio

Now, it is the time to start your Linux. Find the Ubuntu icon and start. This will open the Ubuntu command line for you.  Let's start with the upgrade of the package database and install updates by following two commands.

sudo apt-get update                  update your package database
sudo apt-get upgrade                upgrade packages in your linux

The system is prepared for the installation of necessary additional packages. The process is visible on the following image and all commands listed below the image. The image is attached to see the correct response to your commands.

Visual studio linux development

sudo apt install -y build-essential             Install essential packages to build Linux apps.
sudo apt install -y gdb                               Install GDB debugger program
sudo apt install -y gdbserver                     Install GDB server

The build-essential package contains the following programs. make, libc6, gcc, g++, dpkg-dev. Basic package to build(C++ compiler g++, C compiler gcc, c library and header files, etc) and link your program. The gdb is a debugger and gdb server is a way to communicate with this debugger and see the information directly in Visual Studio. 
The visual studio communicates with Linux through SSH and a shared file system. In the same way, Putty communicates with your Linux instance in a cloud machine. We need an OpenSSH server and configured ssh for PasswordAuthentification. 

sudo apt install -y openssh-server             Install openssh server
sudo nano /etc/ssh/sshd_config                 sshd configuration file and modify as follows:
Set PasswordAuthentication to Yes   “PasswordAuthentication” setting and make sure it’s set to “yes”:

sudo ssh-keygen -A                                      Generate new host keys for SSH
You can expect this as the response "ssh-keygen: generating new host keys: DSA"

sudo service ssh start                                    Start ssh service
You can expect this as the response "Starting OpenBSD Secure Shell server sshd".
The SSH configuration and key generation is the most problematic part. The problem's solution is not trivial for many people. Hopefully, now everything is prepared to communicate with Linux from Visual Studio.

Install and build opencv 4 in Linux from GIT

This part is easy for a little bit experienced person. The following text describes the process of downloading latest GIT repository with Opencv 4. Build the OpenCV library by CMAKE and install the OpenCV shared library into the system.
Install opencv on ubuntu linux

I have one /home/opencv directory. Here just issue following commands:
git clone
git clone

This will create two folders opencv and opencv_contrib with download source code from the GitHub repository.  By following commands, everything that you need to build OpenCV. Even the packages are already installed. 

[we have this]             sudo apt-get install build-essential
[additional required]   sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
[optional]                    sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev

Next to opencv and opencv_contrib create the new directory build by mkdir and enter the directory.

mkdir build
cd build

The next step runs the configuration by cmake. Cmake creates your make file, all the parameters for compiling the opencv project, and links with installed packages. 

cmake -D CMAKE_BUILD_TYPE=Release -D -D CMAKE_INSTALL_PREFIX=/usr/local ..

Do not forget the .. double dots. to compile with opencv_contrib specify the path to contrib modules by parameter -D OPENCV_EXTRA_MODULES_PATH="PATH/opencv_contrib/modules/"
The cmake finishes with all the configuration you need to build opencv by make. This takes a bit of time. The final command that installs your library under the system directory /usr/local/lib as on the picture below commands. 

make -j7
sudo make install

Connect Visual Studio with Linux by cross-platform connection manager

To perform this configuration successfully the Prepare Linux Environment to communicate with the Visual Studio chapter needs to be set up correctly. The first is to create a new project in Visual Studio 2017. I picked up Console Application (Linux).

Visual Studio console linux application

Now is the time to connect my project with a remote system. This means with my localhost Linux subsystem under Windows 10. In main Visual Studio, menu go to Tools-Options-Cross Platform. You will perform the operation under Add- and fille Connect to Remote System menu. 
Host name                   - probably localhost or ip
Port                              - You are running OpenSSH with most probably port 22
user name                    - Your user name to Linux
Authentification type  -Password
Password                     -Your password for the specified user (This option is enabled by sshd_config “PasswordAuthentication” setting and make sure it’s set to “yes” described in the prepare Linux environment section ).

Visual studio connect to linux subsystem

The Remote IntelliSense is a very useful feature. Update button for selected already defined remote machine download header file into the local cache. When you are writing the code for Linux and using Linux or opencv headers Visual Studio knows the input parameters, and format, and marks them as known. This is a huge feature. Otherwise, most of your code will be marked as unknown. 

Headers from remote machine visual studio
Now you can add some source code and just verify your connection by printing something to the console. Maybe some headers like #include <cstdio> or #incude <iostream> are required.

#include <cstdio>
#include <iostream>
int main()
    printf("hello from OpencvLinux!\n");
    return 0;

Configure Linux Opencv 4 projects in Visual Studio

This is not an easy step. The project configuration is complex and usually done by CMAKE. Cmake prepares all the compiler options, C++ language standards and linking dependencies options for you. Visual Studio is used  g++compiler and linker. I configure a simple project in pure Linux, run cmake configuration, and transfer all the compiler and linker options into Visual Studio. First, Look at the full compiler and linker Configuration in the following sections. The explanation follows under compiler and linker full options. 

Opencv visual studio g++ compiler  configuration

This is the full compiler options 

"g++" -W"switch" -W"no-deprecated-declarations" -W"empty-body" -W"conversion" -W"return-type" -W"parentheses" -W"no-pointer-sign" -W"no-format" -W"uninitialized" -W"unreachable-code" -W"unused-function" -W"unused-value" -W"unused-variable" -std=c++11 -w -fno-strict-aliasing -I "/usr/local/include/opencv4/opnecv2" -g0 "g++" -O2 "3600000" -fno-threadsafe-statics -D "NDEBUG" -W"switch" -W"no-deprecated-declarations" -W"empty-body" -W"conversion" -W"return-type" -W"parentheses" -W"no-format" -W"uninitialized" -W"unreachable-code" -W"unused-function" -W"unused-value" -W"unused-variable" -fno-rtti -fno-omit-frame-pointer -std=c11 -fno-exceptions "1" -o  "C:\Users\Vlada\source\repos\OpencvLinux\OpencvLinux\obj\x64\Release\%(filename).o" 

Opencv visual studio g++ linker configuration 

This  is the full linker options

-o"C:\Users\Vlada\source\repos\OpencvLinux\OpencvLinux\bin\x64\Release\OpencvLinux.out" -Wl,-rpath-link="/lib/x86_64-linux-gnu/" -Wl,-rpath-link="/usr/local/lib" -Wl,-rpath-link="/usr/lib/x86_64-linux-gnu/" -Wl,-rpath-link="/usr/local/lib/" -Wl,-rpath-link="/usr/lib/gcc/x86_64-linux-gnu/7" "3600000" -Wl,-z,norelro -Wl,--no-undefined "g++" -l"opencv_core" -l"opencv_imgcodecs" -l"opencv_imgproc" -l"opencv_video" -l"opencv_videoio" -l"pthread" -l"jpeg" -l"gcc_s" -l"stdc++" -l"tbb" -l"rt" -l"dl" -l"m" -l"z" -l"png16" -l"c" 

Opencv 4 compiler and linker options

The first thing is to add all in the property page of the C/C++ project all g++, include the directory, C++11 standard in case of OpenCV 4, and other things from the picture below. The windows below include all the options and you can mimic all configurations in the same window rather than in separate tabs.

The Linker OpenCV 4 configuration part is more tricky

Library dependencies -l"opencv_core" -l"opencv_imgcodecs" -l"opencv_imgproc" -l"opencv_video" -l"opencv_videoio" -l"pthread" -l"jpeg" -l"gcc_s" -l"stdc++" -l"tbb" -l"rt" -l"dl" -l"m" -l"z" -l"png16" -l"c"  

You are just filling the following values into the Library dependencies option without -l 
opencv 4  linker

The shared library search path option is important to locate the library specified in the previous step. My shared libraries like c,z,tbb,stdc++ .... are located in these shared lib locations:



(you can have different hard to say), You can compile your C++ project directly in Linux by Cmake and look what shared libraries are used by your program. By command, LDD OpencvLinux is possible list your shared dependencies library with version and location. Build a simple opencv 4 project by cmake and include all in Visual Studio.

Opencv 4 shared library

All linker configuration can be mimic by follow all the setting in this table.

Opencv Yolo darknet program loading image from windows

The following program is stupid, not parametric externally, but reads images from the Windows file system and performs some actions. I will rewrite this program and explain in more detail in the following tutorials how to prepare data for Yolo darknet. The scope of this article is a little bit different.

Yolo darknet dataset preparation program description 

The program reads images from the positive image library /mnt/c/npos" (C:/npos) and the negative image library /mnt/c/neg (C:/neg). The positive image is inserted into a negative one on a random location. You can see an inserted person in the center of the image. This result is stored as input of Yolo darknet. 1.jpg sample with 1.txt file 1 0.490885 0.811523 0.031684 0.0651042 with class, and person normalized location in the image.

One last output is txt file of all the input images for train.txt and test.txt.

Sample code in progress

#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgcodecs/imgcodecs.hpp"
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/videoio/videoio.hpp"
#include "opencv2/video/video.hpp"
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/videoio/videoio.hpp>
#include <opencv2/imgcodecs/imgcodecs.hpp>
#include <vector>
#include <cstdio>
#include <iostream>
#include <fstream>
#include <sstream>
#include <vector>
#include <string>

#include <time.h>
#include <ctime>
#include <random>
#include <iostream>
#include <sys/types.h>
#include <dirent.h>
                        using namespace std;
    using namespace cv;

    void read_directory(const std::string &name, vector<String> &v)
        cout << "read dir" << endl;
        DIR *dirp = opendir(name.c_str());
        struct dirent *dp;
        while ((dp = readdir(dirp)) != NULL)
            cout << dp->d_name << endl;
        cout << "closing" << endl;

    int main()

        vector<String> neg;
        read_directory("/mnt/c/neg", neg);
        vector<String> pos;
        read_directory("/mnt/c/npos", pos);
        cout << "dir read ends" << endl;
        long lowest = 0;
        if (pos.size() > neg.size())
            lowest = neg.size();
            lowest = pos.size();
        cout << lowest << endl;
        std::random_device rd;
        std::mt19937 gen(rd());
        std::ofstream outtest("/mnt/c/test.txt");
        std::ofstream outtrain("/mnt/c/train.txt");
        cout << "step 2" << endl;
        for (int i = 2; i < lowest; i++)
            cout << << endl;
            cout << << endl;
        long iter = 2;
        for (int i = 3; i < lowest; i++)
            cout << "xxxxxxxxxxxxxxxxx" << endl;
            cout << << endl;
            string pom = "/mnt/c/npos/" +;
            Mat po = imread(pom, IMREAD_COLOR);
            string nem = "/mnt/c/neg/" +;
            cout << nem << endl;
            Mat ne = imread(nem, IMREAD_COLOR);
            cout << "xxxxxxxxxxxxxxxxx" << endl;
            cout << "after reading" << endl;
            std::uniform_int_distribution<> dis(1ne.cols - po.cols + 1);
            std::uniform_int_distribution<> dis2(1ne.rows - po.rows + 1);
            int a = dis(gen);
            int b = dis2(gen);
            cout << a << endl;
            cout << b << endl;
            cout << po.cols << endl;
            cout << po.rows << endl;
            po.copyTo(ne(Rect(a, b, po.colspo.rows)));
            cout << "writing result" << endl;
            string base = "/mnt/c/result/";
            stringstream builder;
            builder << base << iter << ".jpg";
            imwrite(builder.str(), ne);
            stringstream builder2;
            string base2 = "/mnt/c/result/";
            builder2 << base << iter << ".txt";
            std::ofstream outfile(builder2.str());
            stringstream builder4;
            builder4 << base2 << iter << ".jpg" << endl;
            outtest << builder4.str();
            outtrain << builder4.str();
            //outfile << base2 << " " << a << " " << b << " " << a+po.cols << " "<< b+po.rows  << std::endl;
            outfile << 1 << " " << (float)a / ne.cols << " " << (float)b
             / ne.rows << " " << (float)po.cols / ne.cols << " " <<
              (float)po.rows / ne.rows << std::endl;
            iter = iter + 1;
        cout << "all is done" << endl;
        return 0;

Common problems with opencv like an undefined reference to cv::imwrite

1) Restart SSH if Visual Studio stop communicates with Linux subsystem:sudo service ssh restart

2)Problems with a linker, by cmake all is configured ok, by making file and own g++ setting like in the case of Visual Studio the problem can be C++11 standard in opnecv 4 (std::__cxx11), including files but most probably the settings of the shared libraries. 

undefined reference to cv::imwrite(std::basic_string

undefined reference to `cv::imread(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, int)' undefined reference to 

cv::imwrite(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, cv::_InputArray const&, std::vector<int, std::allocator<int> > const&)
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