Opencv3.4与contrib模块的下载以及编译

Opencv3.4与contrib的下载以及编译

• Opencv的下载：

opencv以及contrib的下载请到GitHub，下载zip压缩的release版本（正式发布发版本）。Tar.gz也不是不行，只不过这个压缩格式是Linux用得多。

• Cmake下载

百度第一个就是cmake的官网，点进去下载最新版，不需要多解释了。

• Cmake编译

选好opencv的源码，就是那个source文件夹的位置，再选好你想把这个库编译到哪里，我直接把它放到了D:/opencv_contrib3.4，这个随个人习惯。选好之后点configure，如果这个文件夹不存在它会提示你新建，确定。

选你的VS版本，这里一定要选对是哪一版，2015还是2017，32位还是64位。本人是64位的VS2017

坐等读条——

然后添加OPENCV_EXTRA_MODULES_PATH的信息，

确认好之后再次configure，等一会读条完了会看到configuring done，之后再点generate，都完了之后会有Configuring done和Generating done两行。如下图：

Cmake编译完成后，接下去的步骤请看官方英文教程。（官方文档大法好，我看别的没看明白看官方就看明白了）

 

如有cuda的需求请看：https://blog.csdn.net/Goerge_L/article/details/79777989

（其实就是多几步安装好cuda，主要流程没有变化）

但是！！！！！！！！！！！Cuda的安装不少，自行谷歌或者百度吧。包括安装的时候一些报错，以及cuda对VS的支持慢一拍等。用到再说（心累.jpg）

 

 

以下是opencv官方的编译以及安装教程：

Installation in Windows

原网页：https://docs.opencv.org/3.4.2/d3/d52/tutorial_windows_install.html

 

The description here was tested on Windows 7 SP1. Nevertheless, it should also work on any other relatively modern version of Windows OS. If you encounter errors after following the steps described below, feel free to contact us via our OpenCV Q&A forum. We'll do our best to help you out.

Note

To use the OpenCV library you have two options: Installation by Using the Pre-built Libraries or Installation by Making Your Own Libraries from the Source Files . While the first one is easier to complete, it only works if you are coding with the latest Microsoft Visual Studio IDE and do not take advantage of the most advanced technologies we integrate into our library. .. _Windows_Install_Prebuild:

Installation by Using the Pre-built Libraries

1. Launch a web browser of choice and go to our page on Sourceforge.
2. Choose a build you want to use and download it.
3. Make sure you have admin rights. Unpack the self-extracting archive.
4. You can check the installation at the chosen path as you can see below.

  5.To finalize the installation go to the Set the OpenCV environment variable and add it to the systems path section.

Installation by Using git-bash (version>=2.14.1) and cmake (version >=3.9.1)

1. You must download cmake (version >=3.9.1) and install it. You must add cmake to PATH variable during installation
2. You must install git-bash (version>=2.14.1). Don't add git to PATH variable during installation
3. Run git-bash. You observe a command line window. Suppose you want to build opencv and opencv_contrib in c:/lib
4. In git command line enter following command (if folder does not exist) :

5. mkdir /c/lib
   cd /c/lib

6. save this script with name installOCV.sh in c:/lib

7. #!/bin/bash -e
   myRepo=$(pwd)
   CMAKE_CONFIG_GENERATOR="Visual Studio 14 2015 Win64"
   if [  ! -d "$myRepo/opencv"  ]; then
       echo "clonning opencv"
       git clone https://github.com/opencv/opencv.git
       mkdir Build
       mkdir Build/opencv
       mkdir Install
       mkdir Install/opencv
   else
       cd opencv
       git pull --rebase
       cd ..
   fi
   if [  ! -d "$myRepo/opencv_contrib"  ]; then
       echo "clonning opencv_contrib"
       git clone https://github.com/opencv/opencv_contrib.git
       mkdir Build
       mkdir Build/opencv_contrib
   else
       cd opencv_contrib
       git pull --rebase
       cd ..
   fi
   RepoSource=opencv
   pushd Build/$RepoSource
   CMAKE_OPTIONS='-DBUILD_PERF_TESTS:BOOL=OFF -DBUILD_TESTS:BOOL=OFF -DBUILD_DOCS:BOOL=OFF  -DWITH_CUDA:BOOL=OFF -DBUILD_EXAMPLES:BOOL=OFF -DINSTALL_CREATE_DISTRIB=ON'
   cmake -G"$CMAKE_CONFIG_GENERATOR" $CMAKE_OPTIONS -DOPENCV_EXTRA_MODULES_PATH="$myRepo"/opencv_contrib/modules -DCMAKE_INSTALL_PREFIX="$myRepo"/install/"$RepoSource" "$myRepo/$RepoSource"
   echo "************************* $Source_DIR -->debug"
   cmake --build .  --config debug
   echo "************************* $Source_DIR -->release"
   cmake --build .  --config release
   cmake --build .  --target install --config release
   cmake --build .  --target install --config debug
   popd

8. In git command line enter following command :

9. ./installOCV.sh

10. Drink a coffee or two... opencv is ready : That's all!
11. Next time you run this script, opencv and opencv_contrib will be updated and rebuild

Installation by Making Your Own Libraries from the Source Files

You may find the content of this tutorial also inside the following videos: Part 1 and Part 2, hosted on YouTube.

warning

These videos above are long-obsolete and contain inaccurate information. Be careful, since solutions described in those videos are no longer supported and may even break your install.

If you are building your own libraries you can take the source files from our Git repository.

Building the OpenCV library from scratch requires a couple of tools installed beforehand:

• An IDE of choice (preferably), or just a CC++ compiler that will actually make the binary files. Here we will use the Microsoft Visual Studio. However, you can use any other IDE that has a valid CC++ compiler.
• CMake, which is a neat tool to make the project files (for your chosen IDE) from the OpenCV source files. It will also allow an easy configuration of the OpenCV build files, in order to make binary files that fits exactly to your needs.
• Git to acquire the OpenCV source files. A good tool for this is TortoiseGit. Alternatively, you can just download an archived version of the source files from our page on Sourceforge

OpenCV may come in multiple flavors. There is a "core" section that will work on its own. Nevertheless, there is a couple of tools, libraries made by 3rd parties that offer services of which the OpenCV may take advantage. These will improve its capabilities in many ways. In order to use any of them, you need to download and install them on your system.

• The Python libraries are required to build the Python interface of OpenCV. For now use the version 2.7.{x}. This is also a must if you want to build the OpenCV documentation.
• Numpy is a scientific computing package for Python. Required for the Python interface.
• Intel Threading Building Blocks (TBB) is used inside OpenCV for parallel code snippets. Using this will make sure that the OpenCV library will take advantage of all the cores you have in your system's CPU.
• Intel Integrated Performance Primitives (IPP) may be used to improve the performance of color conversion, Haar training and DFT functions of the OpenCV library. Watch out, since this is not a free service.
• Intel IPP Asynchronous C/C++ is currently focused delivering Intel Graphics support for advanced image processing and computer vision functions.
• OpenCV offers a somewhat fancier and more useful graphical user interface, than the default one by using the Qt framework. For a quick overview of what this has to offer, look into the documentations highgui module, under the Qt New Functions section. Version 4.6 or later of the framework is required.
• Eigen is a C++ template library for linear algebra.
• The latest CUDA Toolkit will allow you to use the power lying inside your GPU. This will drastically improve performance for some algorithms (e.g the HOG descriptor). Getting more and more of our algorithms to work on the GPUs is a constant effort of the OpenCV team.
• OpenEXR source files are required for the library to work with this high dynamic range (HDR) image file format.
• The OpenNI Framework contains a set of open source APIs that provide support for natural interaction with devices via methods such as voice command recognition, hand gestures, and body motion tracking. Prebuilt binaries can be found here. The source code of OpenNI and OpenNI2 are also available on Github.
• Doxygen is a documentation generator and is the tool that will actually create the OpenCV documentation.

Now we will describe the steps to follow for a full build (using all the above frameworks, tools and libraries). If you do not need the support for some of these, you can just freely skip this section.

Building the library

1. Make sure you have a working IDE with a valid compiler. In case of the Microsoft Visual Studio just install it and make sure it starts up.
2. Install CMake. Simply follow the wizard, no need to add it to the path. The default install options are OK.
3. Download and install an up-to-date version of msysgit from its official site. There is also the portable version, which you need only to unpack to get access to the console version of Git. Supposing that for some of us it could be quite enough.
4. Install TortoiseGit. Choose the 32 or 64 bit version according to the type of OS you work in. While installing, locate your msysgit (if it does not do that automatically). Follow the wizard – the default options are OK for the most part.
5. Choose a directory in your file system, where you will download the OpenCV libraries to. I recommend creating a new one that has short path and no special characters in it, for example D:/OpenCV. For this tutorial, I will suggest you do so. If you use your own path and know, what you are doing – it is OK.
   1. Clone the repository to the selected directory. After clicking Clone button, a window will appear where you can select from what repository you want to download source files (https://github.com/opencv/opencv.git) and to what directory (D:/OpenCV).
   2. Push the OK button and be patient as the repository is quite a heavy download. It will take some time depending on your Internet connection.
6. In this section, I will cover installing the 3rd party libraries.
   1. Download the Python libraries and install it with the default options. You will need a couple other python extensions. Luckily installing all these may be automated by a nice tool called Setuptools. Download and install again.
   2. The easiest way to install Numpy is to just download its binaries from the sourceforge page. Make sure your download and install exactly the binary for your python version (so for version 2.7).
   3. For the Intel Threading Building Blocks (TBB) download the source files and extract it inside a directory on your system. For example let there be D:/OpenCV/dep. For installing the Intel Integrated Performance Primitives (IPP) the story is the same. For extracting the archives, I recommend using the 7-Zip application.

1. 1. For the Intel IPP Asynchronous C/C++ download the source files and set environment variable IPP_ASYNC_ROOT. It should point to /Intel/IPP Preview */ipp directory. Here * denotes the particular preview name.
   2. In case of the Eigen library it is again a case of download and extract to the D:/OpenCV/dep directory.
   3. Same as above with OpenEXR.
   4. For the OpenNI Framework you need to install both the development build and the PrimeSensor Module.
   5. For the CUDA you need again two modules: the latest CUDA Toolkit and the CUDA Tools SDK. Download and install both of them with a complete option by using the 32 or 64 bit setups according to your OS.
   6. In case of the Qt framework you need to build yourself the binary files (unless you use the Microsoft Visual Studio 2008 with 32 bit compiler). To do this go to the Qt Downloads page. Download the source files (not the installers!!!):

Extract it into a nice and short named directory like D:/OpenCV/dep/qt/ . Then you need to build it. Start up a Visual Studio Command Prompt (2010) by using the start menu search (or navigate through the start menu All Programs –> Microsoft Visual Studio 2010 –> Visual Studio Tools –> Visual Studio Command Prompt (2010)).

Now navigate to the extracted folder and enter inside it by using this console window. You should have a folder containing files like Install, Make and so on. Use the dir command to list files inside your current directory. Once arrived at this directory enter the following command:

configure.exe -release -no-webkit -no-phonon -no-phonon-backend -no-script -no-scripttools

-no-qt3support -no-multimedia -no-ltcg

Completing this will take around 10-20 minutes. Then enter the next command that will take a lot longer (can easily take even more than a full hour):

nmake

After this set the Qt environment variables using the following command on Windows 7:

setx -m QTDIR D:/OpenCV/dep/qt/qt-everywhere-opensource-src-4.7.3

Also, add the built binary files path to the system path by using the PathEditor. In our case this is D:/OpenCV/dep/qt/qt-everywhere-opensource-src-4.7.3/bin.

Note

If you plan on doing Qt application development you can also install at this point the Qt Visual Studio Add-in. After this you can make and build Qt applications without using the Qt Creator. Everything is nicely integrated into Visual Studio.

1. Now start the CMake (cmake-gui). You may again enter it in the start menu search or get it from the All Programs –> CMake 2.8 –> CMake (cmake-gui). First, select the directory for the source files of the OpenCV library (1). Then, specify a directory where you will build the binary files for OpenCV (2).

Press the Configure button to specify the compiler (and IDE) you want to use. Note that in case you can choose between different compilers for making either 64 bit or 32 bit libraries. Select the one you use in your application development.

CMake will start out and based on your system variables will try to automatically locate as many packages as possible. You can modify the packages to use for the build in the WITH –> WITH_X menu points (where X is the package abbreviation). Here are a list of current packages you can turn on or off:

Select all the packages you want to use and press again the Configure button. For an easier overview of the build options make sure the Grouped option under the binary directory selection is turned on. For some of the packages CMake may not find all of the required files or directories. In case of these, CMake will throw an error in its output window (located at the bottom of the GUI) and set its field values to not found constants. For example:

For these you need to manually set the queried directories or files path. After this press again the Configure button to see if the value entered by you was accepted or not. Do this until all entries are good and you cannot see errors in the field/value or the output part of the GUI. Now I want to emphasize an option that you will definitely love: ENABLE –> ENABLE_SOLUTION_FOLDERS. OpenCV will create many-many projects and turning this option will make sure that they are categorized inside directories in the Solution Explorer. It is a must have feature, if you ask me.

Furthermore, you need to select what part of OpenCV you want to build.

• • BUILD_DOCS -> It creates two projects for building the documentation of OpenCV (there will be a separate project for building the HTML and the PDF files). Note that these are not built together with the solution. You need to make an explicit build project command on these to do so.
  • BUILD_EXAMPLES -> OpenCV comes with many example applications from which you may learn most of the libraries capabilities. This will also come handy to easily try out if OpenCV is fully functional on your computer.
  • BUILD_PACKAGE -> Prior to version 2.3 with this you could build a project that will build an OpenCV installer. With this, you can easily install your OpenCV flavor on other systems. For the latest source files of OpenCV, it generates a new project that simply creates a zip archive with OpenCV sources.
  • BUILD_SHARED_LIBS -> With this you can control to build DLL files (when turned on) or static library files (*.lib) otherwise.
  • BUILD_TESTS -> Each module of OpenCV has a test project assigned to it. Building these test projects is also a good way to try out, that the modules work just as expected on your system too.
  • BUILD_PERF_TESTS -> There are also performance tests for many OpenCV functions. If you are concerned about performance, build them and run.
  • BUILD_opencv_python -> Self-explanatory. Create the binaries to use OpenCV from the Python language.

VS的编译从这里看起（我记得是从这里）

Press again the Configure button and ensure no errors are reported. If this is the case, you can tell CMake to create the project files by pushing the Generate button. Go to the build directory and open the created OpenCV solution. Depending on just how much of the above options you have selected the solution may contain quite a lot of projects so be tolerant on the IDE at the startup. Now you need to build both the Release and the Debug binaries. Use the drop-down menu on your IDE to change to another of these after building for one of them.

In the end, you can observe the built binary files inside the bin directory:

For the documentation, you need to explicitly issue the build commands on the doxygen project for the HTML documentation. It will call Doxygen to do all the hard work. You can find the generated documentation inside the build/doc/doxygen/html.

To collect the header and the binary files, that you will use during your own projects, into a separate directory (similarly to how the pre-built binaries ship) you need to explicitly build the Install project.

This will create an Install directory inside the Build one collecting all the built binaries into a single place. Use this only after you built both the Release and Debug versions.

To test your build just go into the Build/bin/Debug or Build/bin/Release directory and start a couple of applications like the contours.exe. If they run, you are done. Otherwise, something definitely went awfully wrong. In this case you should contact us at our Q&A forum. If everything is okay, the contours.exe output should resemble the following image (if built with Qt support):

Note

If you use the GPU module (CUDA libraries), make sure you also upgrade to the latest drivers of your GPU. Error messages containing invalid entries in (or cannot find) the nvcuda.dll are caused mostly by old video card drivers. For testing the GPU (if built) run the performance_gpu.exe sample application.

Set the OpenCV environment variable and add it to the systems path

First we set an environment variable to make easier our work. This will hold the build directory of our OpenCV library that we use in our projects. Start up a command window and enter:

setx -m OPENCV_DIR D:\OpenCV\Build\x86\vc11 (suggested for Visual Studio 2012 - 32 bit Windows)
setx -m OPENCV_DIR D:\OpenCV\Build\x64\vc11 (suggested for Visual Studio 2012 - 64 bit Windows)
setx -m OPENCV_DIR D:\OpenCV\Build\x86\vc12 (suggested for Visual Studio 2013 - 32 bit Windows)
setx -m OPENCV_DIR D:\OpenCV\Build\x64\vc12 (suggested for Visual Studio 2013 - 64 bit Windows)
setx -m OPENCV_DIR D:\OpenCV\Build\x64\vc14 (suggested for Visual Studio 2015 - 64 bit Windows)

Here the directory is where you have your OpenCV binaries (extracted or built). You can have different platform (e.g. x64 instead of x86) or compiler type, so substitute appropriate value. Inside this, you should have two folders called lib and bin. The -m should be added if you wish to make the settings computer wise, instead of user wise.

If you built static libraries then you are done. Otherwise, you need to add the bin folders path to the systems path. This is because you will use the OpenCV library in form of *"Dynamic-link libraries"* (also known as DLL). Inside these are stored all the algorithms and information the OpenCV library contains. The operating system will load them only on demand, during runtime. However, to do this the operating system needs to know where they are. The systems PATH contains a list of folders where DLLs can be found. Add the OpenCV library path to this and the OS will know where to look if he ever needs the OpenCV binaries. Otherwise, you will need to copy the used DLLs right beside the applications executable file (exe) for the OS to find it, which is highly unpleasant if you work on many projects. To do this start up again the PathEditor and add the following new entry (right click in the application to bring up the menu):

%OPENCV_DIR%\bin

Save it to the registry and you are done. If you ever change the location of your build directories or want to try out your application with a different build, all you will need to do is to update the OPENCV_DIR variable via the setx command inside a command window.

Now you can continue reading the tutorials with the How to build applications with OpenCV inside the "Microsoft Visual Studio" section. There you will find out how to use the OpenCV library in your own projects with the help of the Microsoft Visual Studio IDE.

 

 

 

How to build applications with OpenCV inside the "Microsoft Visual Studio"

原网页https://docs.opencv.org/3.4.2/dd/d6e/tutorial_windows_visual_studio_opencv.html

Everything I describe here will apply to the C\C++ interface of OpenCV. I start out from the assumption that you have read and completed with success the Installation in Windows tutorial. Therefore, before you go any further make sure you have an OpenCV directory that contains the OpenCV header files plus binaries and you have set the environment variables as described here Set the OpenCV environment variable and add it to the systems path .

The OpenCV libraries, distributed by us, on the Microsoft Windows operating system are in a Dynamic Linked Libraries (DLL). These have the advantage that all the content of the library is loaded only at runtime, on demand, and that countless programs may use the same library file. This means that if you have ten applications using the OpenCV library, no need to have around a version for each one of them. Of course you need to have the dll of the OpenCV on all systems where you want to run your application.

Another approach is to use static libraries that have lib extensions. You may build these by using our source files as described in the Installation in Windows tutorial. When you use this the library will be built-in inside your exe file. So there is no chance that the user deletes them, for some reason. As a drawback your application will be larger one and as, it will take more time to load it during its startup.

To build an application with OpenCV you need to do two things:

• Tell to the compiler how the OpenCV library looks. You do this by showing it the header files.
• Tell to the linker from where to get the functions or data structures of OpenCV, when they are needed.

If you use the lib system you must set the path where the library files are and specify in which one of them to look. During the build the linker will look into these libraries and add the definitions and implementation of all used functions and data structures to the executable file.

If you use the DLL system you must again specify all this, however now for a different reason. This is a Microsoft OS specific stuff. It seems that the linker needs to know that where in the DLL to search for the data structure or function at the runtime. This information is stored inside lib files. Nevertheless, they aren't static libraries. They are so called import libraries. This is why when you make some DLLs in Windows you will also end up with some lib extension libraries. The good part is that at runtime only the DLL is required.

To pass on all this information to the Visual Studio IDE you can either do it globally (so all your future projects will get this information) or locally (so only for you current project). The advantage of the global one is that you only need to do it once; however, it may be undesirable to clump all your projects all the time with all this information. In case of the global one how you do it depends on the Microsoft Visual Studio you use. There is a 2008 and previous versions and a 2010 way of doing it. Inside the global section of this tutorial I'll show what the main differences are.

The base item of a project in Visual Studio is a solution. A solution may contain multiple projects. Projects are the building blocks of an application. Every project will realize something and you will have a main project in which you can put together this project puzzle. In case of the many simple applications (like many of the tutorials will be) you do not need to break down the application into modules. In these cases, your main project will be the only existing one. Now go create a new solution inside Visual studio by going through the File –> New –> Project menu selection. Choose Win32 Console Application as type. Enter its name and select the path where to create it. Then in the upcoming dialog make sure you create an empty project.

The local method

这里的设置在VS2017里面这样打开（右上的大框框）。没有见到属性管理器的话在这里输入并搜索，以及打开）

 

属性管理器：

该属性配置是64位debug模式，用到release模式就选择release模式的配置。打开如下：

之后的操作按照官方教程进行。

Every project is built separately from the others. Due to this every project has its own rule package. Inside this rule packages are stored all the information the IDE needs to know to build your project. For any application there are at least two build modes: a Release and a Debug one. The Debug has many features that exist so you can find and resolve easier bugs inside your application. In contrast the Release is an optimized version, where the goal is to make the application run as fast as possible or to be as small as possible. You may figure that these modes also require different rules to use during build. Therefore, there exist different rule packages for each of your build modes. These rule packages are called inside the IDE as project properties and you can view and modify them by using the Property Manager. You can bring this up with View –> Property Pages (For Visual Studio 2013 onwards, go to View –> Other Windows –> Property Manager). Expand it and you can see the existing rule packages (called Property Sheets).

The really useful stuff of these is that you may create a rule package once and you can later just add it to your new projects. Create it once and reuse it later. We want to create a new Property Sheet that will contain all the rules that the compiler and linker needs to know. Of course we will need a separate one for the Debug and the Release Builds. Start up with the Debug one as shown in the image below:

Use for example the OpenCV_Debug name. Then by selecting the sheet Right Click –> Properties. In the following I will show to set the OpenCV rules locally, as I find unnecessary to pollute projects with custom rules that I do not use it. Go the C++ groups General entry and under the *"Additional Include Directories"* add the path to your OpenCV include. If you don't have *"C/C++"* group, you should add any .c/.cpp file to the project.

$(OPENCV_DIR)\..\..\include

When adding third party libraries settings it is generally a good idea to use the power behind the environment variables. The full location of the OpenCV library may change on each system. Moreover, you may even end up yourself with moving the install directory for some reason. If you would give explicit paths inside your property sheet your project will end up not working when you pass it further to someone else who has a different OpenCV install path. Moreover, fixing this would require to manually modifying every explicit path. A more elegant solution is to use the environment variables. Anything that you put inside a parenthesis started with a dollar sign will be replaced at runtime with the current environment variables value. Here comes in play the environment variable setting we already made in our previous tutorial Set the OpenCV environment variable and add it to the systems path .

Next go to the Linker –> General and under the *"Additional Library Directories"* add the libs directory:

$(OPENCV_DIR)\lib

Then you need to specify the libraries in which the linker should look into. To do this go to the Linker –> Input and under the *"Additional Dependencies"* entry add the name of all modules which you want to use:

The names of the libraries are as follow:

opencv_(The Name of the module)(The version Number of the library you use)d.lib

A full list, for the latest version would contain:

opencv_calib3d300d.lib
opencv_core300d.lib
opencv_features2d300d.lib
opencv_flann300d.lib
opencv_highgui300d.lib
opencv_imgcodecs300d.lib
opencv_imgproc300d.lib
opencv_ml300d.lib
opencv_objdetect300d.lib
opencv_photo300d.lib
opencv_shape300d.lib
opencv_stitching300d.lib
opencv_superres300d.lib
opencv_ts300d.lib
opencv_video300d.lib
opencv_videoio300d.lib
opencv_videostab300d.lib

Alternatively, your OpenCV download may have been built into one large .lib file. Check by looking in OpenCV\build\architecture\vc14\lib. In this case all you would add is, for the version 3.3.0:

opencv_world330.lib

The letter d at the end just indicates that these are the libraries required for the debug. Now click ok to save and do the same with a new property inside the Release rule section. Make sure to omit the d letters from the library names and to save the property sheets with the save icon above them.

You can find your property sheets inside your projects directory. At this point, it is a wise decision to back them up into some special directory, to always have them at hand in the future, whenever you create an OpenCV project. Note that for Visual Studio 2010 the file extension is props, while for 2008 this is vsprops.

Next time when you make a new OpenCV project just use the "Add Existing Property Sheet..." menu entry inside the Property Manager to easily add the OpenCV build rules.

The global method

In case you find it too troublesome to add the property pages to each and every one of your projects you can also add this rules to a *"global property page"*. However, this applies only to the additional include and library directories. The name of the libraries to use you still need to specify manually by using for instance: a Property page.

In Visual Studio 2008 you can find this under the: Tools –> Options –> Projects and Solutions –> VC++ Directories.

In Visual Studio 2010 this has been moved to a global property sheet which is automatically added to every project you create:

The process is the same as described in case of the local approach. Just add the include directories by using the environment variable OPENCV_DIR.

Test it!

Now to try this out download our little test source code or get it from the sample code folder of the OpenCV sources. Add this to your project and build it. Here's its content:

#include 
#include 
#include 
#include 

using namespace cv;
using namespace std;

int main( int argc, char** argv )
{
    if( argc != 2)
    {
     cout <<" Usage: display_image ImageToLoadAndDisplay" << endl;
     return -1;
    }

    Mat image;
    image = imread(argv[1], IMREAD_COLOR); // Read the file

    if( image.empty() ) // Check for invalid input
    {
        cout << "Could not open or find the image" << std::endl ;
        return -1;
    }

    namedWindow( "Display window", WINDOW_AUTOSIZE ); // Create a window for display.
    imshow( "Display window", image ); // Show our image inside it.

    waitKey(0); // Wait for a keystroke in the window
    return 0;
}

You can start a Visual Studio build from two places. Either inside from the IDE (keyboard combination: Control-F5) or by navigating to your build directory and start the application with a double click. The catch is that these two aren't the same. When you start it from the IDE its current working directory is the projects directory, while otherwise it is the folder where the application file currently is (so usually your build directory). Moreover, in case of starting from the IDE the console window will not close once finished. It will wait for a keystroke of yours.

This is important to remember when you code inside the code open and save commands. Your resources will be saved ( and queried for at opening!!!) relatively to your working directory. This is unless you give a full, explicit path as a parameter for the I/O functions. In the code above we open this OpenCV logo. Before starting up the application, make sure you place the image file in your current working directory. Modify the image file name inside the code to try it out on other images too. Run it and voil á:

Command line arguments with Visual Studio

Throughout some of our future tutorials, you'll see that the programs main input method will be by giving a runtime argument. To do this you can just start up a command windows (cmd + Enter in the start menu), navigate to your executable file and start it with an argument. So for example in case of my upper project this would look like:

D:
CD OpenCV\MySolutionName\Release
MySolutionName.exe exampleImage.jpg

Here I first changed my drive (if your project isn't on the OS local drive), navigated to my project and start it with an example image argument. While under Linux system it is common to fiddle around with the console window on the Microsoft Windows many people come to use it almost never. Besides, adding the same argument again and again while you are testing your application is, somewhat, a cumbersome task. Luckily, in the Visual Studio there is a menu to automate all this:

Specify here the name of the inputs and while you start your application from the Visual Studio environment you have automatic argument passing. In the next introductory tutorial you'll see an in-depth explanation of the upper source code: Load and Display an Image.

 

 

噢？听说你看到这里了？那我给你介绍一些有用的小插件吧（逃：

Edge浏览器：

Chrome浏览器：

以上插件翻译不保证准确性，重点部分应当自行阅读英文（逃~