mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
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364 lines
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364 lines
22 KiB
10 years ago
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Installation in Windows {#tutorial_windows_install}
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=======================
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The description here was tested on Windows 7 SP1. Nevertheless, it should also work on any other
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relatively modern version of Windows OS. If you encounter errors after following the steps described
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below, feel free to contact us via our [OpenCV Q&A forum](http://answers.opencv.org). We'll do our
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best to help you out.
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@note To use the OpenCV library you have two options: @ref Windows_Install_Prebuild or @ref
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CppTutWindowsMakeOwn. While the first one is easier to complete, it only works if you are coding
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with the latest Microsoft Visual Studio IDE and doesn't take advantage of the most advanced
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technologies we integrate into our library. .. _Windows_Install_Prebuild:
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Installation by Using the Pre-built Libraries
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---------------------------------------------
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1. Launch a web browser of choice and go to our [page on
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Sourceforge](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/).
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2. Choose a build you want to use and download it.
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3. Make sure you have admin rights. Unpack the self-extracting archive.
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4. You can check the installation at the chosen path as you can see below.
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5. To finalize the installation go to the @ref WindowsSetPathAndEnviromentVariable section.
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Installation by Making Your Own Libraries from the Source Files
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---------------------------------------------------------------
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You may find the content of this tutorial also inside the following videos: [Part
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1](https://www.youtube.com/watch?v=NnovZ1cTlMs) and [Part
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2](https://www.youtube.com/watch?v=qGNWMcfWwPU), hosted on YouTube.
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\htmlonly
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<div align="center">
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<iframe title="Install OpenCV by using its source files - Part 1" width="560" height="349" src="http://www.youtube.com/embed/NnovZ1cTlMs?rel=0&loop=1" frameborder="0" allowfullscreen align="middle"></iframe>
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<iframe title="Install OpenCV by using its source files - Part 2" width="560" height="349" src="http://www.youtube.com/embed/qGNWMcfWwPU?rel=0&loop=1" frameborder="0" allowfullscreen align="middle"></iframe>
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</div>
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\endhtmlonly
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**warning**
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These videos above are long-obsolete and contain inaccurate information. Be careful, since
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solutions described in those videos are no longer supported and may even break your install.
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If you are building your own libraries you can take the source files from our [Git
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repository](https://github.com/Itseez/opencv.git).
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Building the OpenCV library from scratch requires a couple of tools installed beforehand:
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- An IDE of choice (preferably), or just a CC++ compiler that will actually make the binary files.
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Here we will use the [Microsoft Visual Studio](https://www.microsoft.com/visualstudio/en-us).
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However, you can use any other IDE that has a valid CC++ compiler.
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- CMake_, which is a neat tool to make the project files (for your chosen IDE) from the OpenCV
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source files. It will also allow an easy configuration of the OpenCV build files, in order to
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make binary files that fits exactly to your needs.
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- Git to acquire the OpenCV source files. A good tool for this is TortoiseGit_. Alternatively,
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you can just download an archived version of the source files from our [page on
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Sourceforge](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/)
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OpenCV may come in multiple flavors. There is a "core" section that will work on its own.
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Nevertheless, there is a couple of tools, libraries made by 3rd parties that offer services of which
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the OpenCV may take advantage. These will improve its capabilities in many ways. In order to use any
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of them, you need to download and install them on your system.
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- The Python libraries_ are required to build the *Python interface* of OpenCV. For now use the
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version `2.7.{x}`. This is also a must if you want to build the *OpenCV documentation*.
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- Numpy_ is a scientific computing package for Python. Required for the *Python interface*.
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- Intel |copy| Threading Building Blocks (*TBB*)_ is used inside OpenCV for parallel code
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snippets. Using this will make sure that the OpenCV library will take advantage of all the cores
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you have in your systems CPU.
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- Intel |copy| Integrated Performance Primitives (*IPP*)_ may be used to improve the performance
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of color conversion, Haar training and DFT functions of the OpenCV library. Watch out, since
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this isn't a free service.
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- Intel |copy| IPP Asynchronous C/C++_ is currently focused delivering Intel |copy| Graphics
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support for advanced image processing and computer vision functions.
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- OpenCV offers a somewhat fancier and more useful graphical user interface, than the default one
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by using the Qt framework_. For a quick overview of what this has to offer look into the
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documentations *highgui* module, under the *Qt New Functions* section. Version 4.6 or later of
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the framework is required.
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- Eigen_ is a C++ template library for linear algebra.
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- The latest CUDA Toolkit_ will allow you to use the power lying inside your GPU. This will
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drastically improve performance for some algorithms (e.g the HOG descriptor). Getting more and
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more of our algorithms to work on the GPUs is a constant effort of the OpenCV team.
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- OpenEXR_ source files are required for the library to work with this high dynamic range (HDR)
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image file format.
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- The OpenNI Framework_ contains a set of open source APIs that provide support for natural
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interaction with devices via methods such as voice command recognition, hand gestures and body
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motion tracking.
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- Miktex_ is the best [TEX](https://secure.wikimedia.org/wikipedia/en/wiki/TeX) implementation on
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the Windows OS. It is required to build the *OpenCV documentation*.
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- Sphinx_ is a python documentation generator and is the tool that will actually create the
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*OpenCV documentation*. This on its own requires a couple of tools installed, We will cover this
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in depth at the @ref How to Install Sphinx \<HereInstallSphinx\> section.
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Now we will describe the steps to follow for a full build (using all the above frameworks, tools and
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libraries). If you do not need the support for some of these you can just freely skip this section.
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### Building the library
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1. Make sure you have a working IDE with a valid compiler. In case of the Microsoft Visual Studio
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just install it and make sure it starts up.
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2. Install CMake_. Simply follow the wizard, no need to add it to the path. The default install
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options are OK.
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3. Download and install an up-to-date version of msysgit from its [official
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site](http://code.google.com/p/msysgit/downloads/list). There is also the portable version,
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which you need only to unpack to get access to the console version of Git. Supposing that for
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some of us it could be quite enough.
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4. Install TortoiseGit_. Choose the 32 or 64 bit version according to the type of OS you work in.
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While installing, locate your msysgit (if it doesn't do that automatically). Follow the
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wizard -- the default options are OK for the most part.
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5. Choose a directory in your file system, where you will download the OpenCV libraries to. I
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recommend creating a new one that has short path and no special charachters in it, for example
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`D:/OpenCV`. For this tutorial I'll suggest you do so. If you use your own path and know, what
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you're doing -- it's OK.
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a) Clone the repository to the selected directory. After clicking *Clone* button, a window will
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appear where you can select from what repository you want to download source files
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(<https://github.com/Itseez/opencv.git>) and to what directory (`D:/OpenCV`).
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b) Push the OK button and be patient as the repository is quite a heavy download. It will take
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some time depending on your Internet connection.
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6. In this section I will cover installing the 3rd party libraries.
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a) Download the Python libraries_ and install it with the default options. You will need a
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couple other python extensions. Luckily installing all these may be automated by a nice tool
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called [Setuptools](http://pypi.python.org/pypi/setuptools#downloads). Download and install
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again.
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b) Installing Sphinx is easy once you have installed *Setuptools*. This contains a little
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application that will automatically connect to the python databases and download the latest
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version of many python scripts. Start up a command window (enter *cmd* into the windows
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start menu and press enter) and use the *CD* command to navigate to your Python folders
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Script sub-folder. Here just pass to the *easy_install.exe* as argument the name of the
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program you want to install. Add the *sphinx* argument.
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@note
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The *CD* navigation command works only inside a drive. For example if you are somewhere in the
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*C:* drive you cannot use it this to go to another drive (like for example *D:*). To do so you
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first need to change drives letters. For this simply enter the command *D:*. Then you can use
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the *CD* to navigate to specific folder inside the drive. Bonus tip: you can clear the screen by
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using the *CLS* command.
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This will also install its prerequisites [Jinja2](http://jinja.pocoo.org/docs/) and
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[Pygments](http://pygments.org/).
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1) The easiest way to install Numpy_ is to just download its binaries from the [sourceforga
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page](http://sourceforge.net/projects/numpy/files/NumPy/). Make sure your download and install
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exactly the binary for your python version (so for version `2.7`).
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2) Download the Miktex_ and install it. Again just follow the wizard. At the fourth step make
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sure you select for the *"Install missing packages on-the-fly"* the *Yes* option, as you can
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see on the image below. Again this will take quite some time so be patient.
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3) For the Intel |copy| Threading Building Blocks (*TBB*)_ download the source files and extract
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it inside a directory on your system. For example let there be `D:/OpenCV/dep`. For installing
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the Intel |copy| Integrated Performance Primitives (*IPP*)_ the story is the same. For
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exctracting the archives I recommend using the [7-Zip](http://www.7-zip.org/) application.
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4) For the Intel |copy| IPP Asynchronous C/C++_ download the source files and set environment
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variable **IPP_ASYNC_ROOT**. It should point to
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`<your Program Files(x86) directory>/Intel/IPP Preview */ipp directory`. Here \* denotes the
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particular preview name.
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5) In case of the Eigen_ library it is again a case of download and extract to the
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`D:/OpenCV/dep` directory.
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6) Same as above with OpenEXR_.
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7) For the OpenNI Framework_ you need to install both the [development
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build](http://www.openni.org/downloadfiles/opennimodules/openni-binaries/21-stable) and the
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[PrimeSensor
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Module](http://www.openni.org/downloadfiles/opennimodules/openni-compliant-hardware-binaries/32-stable).
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8) For the CUDA you need again two modules: the latest CUDA Toolkit_ and the *CUDA Tools SDK*.
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Download and install both of them with a *complete* option by using the 32 or 64 bit setups
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according to your OS.
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9) In case of the Qt framework_ you need to build yourself the binary files (unless you use the
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Microsoft Visual Studio 2008 with 32 bit compiler). To do this go to the [Qt
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Downloads](http://qt.nokia.com/downloads) page. Download the source files (not the
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installers!!!):
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Extract it into a nice and short named directory like `D:/OpenCV/dep/qt/` . Then you need to
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build it. Start up a *Visual* *Studio* *Command* *Prompt* (*2010*) by using the start menu
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search (or navigate through the start menu
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All Programs --\> Microsoft Visual Studio 2010 --\> Visual Studio Tools --\> Visual Studio Command Prompt (2010)).
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Now navigate to the extracted folder and enter inside it by using this console window. You
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should have a folder containing files like *Install*, *Make* and so on. Use the *dir* command
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to list files inside your current directory. Once arrived at this directory enter the
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following command:
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@code{.bash}
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configure.exe -release -no-webkit -no-phonon -no-phonon-backend -no-script -no-scripttools
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-no-qt3support -no-multimedia -no-ltcg
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@endcode
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Completing this will take around 10-20 minutes. Then enter the next command that will take a
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lot longer (can easily take even more than a full hour):
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@code{.bash}
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nmake
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@endcode
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After this set the Qt enviroment variables using the following command on Windows 7:
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@code{.bash}
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setx -m QTDIR D:/OpenCV/dep/qt/qt-everywhere-opensource-src-4.7.3
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@endcode
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Also, add the built binary files path to the system path by using the |PathEditor|_. In our
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case this is `D:/OpenCV/dep/qt/qt-everywhere-opensource-src-4.7.3/bin`.
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@note
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If you plan on doing Qt application development you can also install at this point the *Qt
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Visual Studio Add-in*. After this you can make and build Qt applications without using the *Qt
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Creator*. Everything is nicely integrated into Visual Studio.
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1. Now start the *CMake (cmake-gui)*. You may again enter it in the start menu search or get it
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from the All Programs --\> CMake 2.8 --\> CMake (cmake-gui). First, select the directory for the
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source files of the OpenCV library (1). Then, specify a directory where you will build the
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binary files for OpenCV (2).
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Press the Configure button to specify the compiler (and *IDE*) you want to use. Note that in
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case you can choose between different compilers for making either 64 bit or 32 bit libraries.
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Select the one you use in your application development.
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CMake will start out and based on your system variables will try to automatically locate as many
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packages as possible. You can modify the packages to use for the build in the WITH --\> WITH_X
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menu points (where *X* is the package abbreviation). Here are a list of current packages you can
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turn on or off:
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Select all the packages you want to use and press again the *Configure* button. For an easier
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overview of the build options make sure the *Grouped* option under the binary directory
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selection is turned on. For some of the packages CMake may not find all of the required files or
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directories. In case of these CMake will throw an error in its output window (located at the
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bottom of the GUI) and set its field values, to not found constants. For example:
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For these you need to manually set the queried directories or files path. After this press again
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the *Configure* button to see if the value entered by you was accepted or not. Do this until all
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entries are good and you cannot see errors in the field/value or the output part of the GUI. Now
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I want to emphasize an option that you will definitely love:
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ENABLE --\> ENABLE_SOLUTION_FOLDERS. OpenCV will create many-many projects and turning this
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option will make sure that they are categorized inside directories in the *Solution Explorer*.
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It is a must have feature, if you ask me.
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Furthermore, you need to select what part of OpenCV you want to build.
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- *BUILD_DOCS* -\> It creates two projects for building the documentation of OpenCV (there
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will be a separate project for building the HTML and the PDF files). Note that these aren't
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built together with the solution. You need to make an explicit build project command on
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these to do so.
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- *BUILD_EXAMPLES* -\> OpenCV comes with many example applications from which you may learn
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most of the libraries capabilities. This will also come handy to easily try out if OpenCV is
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fully functional on your computer.
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- *BUILD_PACKAGE* -\> Prior to version 2.3 with this you could build a project that will
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build an OpenCV installer. With this you can easily install your OpenCV flavor on other
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systems. For the latest source files of OpenCV it generates a new project that simply
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creates zip archive with OpenCV sources.
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- *BUILD_SHARED_LIBS* -\> With this you can control to build DLL files (when turned on) or
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static library files (\*.lib) otherwise.
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- *BUILD_TESTS* -\> Each module of OpenCV has a test project assigned to it. Building these
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test projects is also a good way to try out, that the modules work just as expected on your
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system too.
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- *BUILD_PERF_TESTS* -\> There are also performance tests for many OpenCV functions. If
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you're concerned about performance, build them and run.
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- *BUILD_opencv_python* -\> Self-explanatory. Create the binaries to use OpenCV from the
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Python language.
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Press again the *Configure* button and ensure no errors are reported. If this is the case you
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can tell CMake to create the project files by pushing the *Generate* button. Go to the build
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directory and open the created **OpenCV** solution. Depending on just how much of the above
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options you have selected the solution may contain quite a lot of projects so be tolerant on the
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IDE at the startup. Now you need to build both the *Release* and the *Debug* binaries. Use the
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drop-down menu on your IDE to change to another of these after building for one of them.
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In the end you can observe the built binary files inside the bin directory:
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For the documentation you need to explicitly issue the build commands on the *doc* project for
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the PDF files and on the *doc_html* for the HTML ones. Each of these will call *Sphinx* to do
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all the hard work. You can find the generated documentation inside the `Build/Doc/_html` for the
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HTML pages and within the `Build/Doc` the PDF manuals.
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To collect the header and the binary files, that you will use during your own projects, into a
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separate directory (simillary to how the pre-built binaries ship) you need to explicitely build
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the *Install* project.
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This will create an *Install* directory inside the *Build* one collecting all the built binaries
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into a single place. Use this only after you built both the *Release* and *Debug* versions.
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To test your build just go into the `Build/bin/Debug` or `Build/bin/Release` directory and start
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a couple of applications like the *contours.exe*. If they run, you are done. Otherwise,
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something definitely went awfully wrong. In this case you should contact us at our @ref cv::Q&A
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forum . If everything is okay the *contours.exe* output should resemble the following image (if
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built with Qt support):
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@note
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If you use the GPU module (CUDA libraries) make sure you also upgrade to the latest drivers of
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your GPU. Error messages containing invalid entries in (or cannot find) the nvcuda.dll are
|
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|
caused mostly by old video card drivers. For testing the GPU (if built) run the
|
||
|
|
*performance_gpu.exe* sample application.
|
||
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|
||
|
|
Set the OpenCV enviroment variable and add it to the systems path
|
||
|
|
-----------------------------------------------------------------
|
||
|
|
|
||
|
|
First we set an enviroment 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\vc10 (suggested for Visual Studio 2010 - 32 bit Windows)
|
||
|
|
setx -m OPENCV_DIR D:\OpenCV\Build\x64\vc10 (suggested for Visual Studio 2010 - 64 bit Windows)
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
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
|
||
|
|
unpleasent 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 applicaton 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 @ref Windows_Visual_Studio_How_To 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.
|
||
|
|
|