![]() ![]() Personally, I am currently fine with only one DLLĮNABLE_AVX -> ON // This and the following entries enable the use of special processor instructions which should speed up performance. This is easier for deploying since only one file needs to be located alongside the executable, but also increases the size of the application directory due to the (unnecessary) code of unused modules. I changed only the following entries and left the rest on defaultīUILD_DOCS -> OFF // I don't need the documentation since I look it up online anyway (or look directly in the header files)īUILD_EXAMPLES -> ON // Examples are quite useful to get a first impression of an algorithmīUILD_opencv_world -> ON // This builds a unified DLL containing the machine code for all modules. In my case, I build with Visual Studio 2015 for the 圆4 architecture, therefore, selecting Visual StuWin64Īfter this step, you see all the options (which are all read) you can configure your own build process. the compiler toolset of the Visual Studio version). A window opens where you need to specify the target toolset (i.e. The folder sources/opencv-master contains the source code from the git repository (which is also the folder referenced by the PDB files) cmake_build contains all the files created and needed for building. The build folder is the installation directory where the created binaries will be stored. I would recommend to store also the latest commit ID and save it in an appropriate text file ( version.txt) for future referenceĬreate the following folder structure (I downloaded the sources as zip file) First, get a copy of the latest master branch.Again, this is also something you can change during debugging in Visual Studio itself but I would rather prefer an out of the box solution. Therefore, is important to make sure the DLL files are stored directly in the installation directory. But it does look in the folder of the original location of the DLL (e.g. Even though it should look in the directory of the DLL itself, this did not work for me. Another caveat is that Visual Studio has a predefined mechanism where to search for PDB files which belong to a loaded DLL. Even though it seems that this can be changed later in Visual Studio (I have never tested it myself), it would be advisable to use the same paths as in the following build instruction in order to get the source mapping working out of the box. Unfortunately, PDB files contain only the absolute path to the original source code by default. That being said, I want now give an instruction of how I create my builds including debug information. The assertion code is much more useful when you see the surrounding context (including stack trace, variables, etc.) which produced it. ![]() This is useful for all libraries, but especially with OpenCV since they like to use assertions (which is great) and so it might happen that your application crashes with some cryptic message which shows the original failing code. With this information, it is possible to step through the original source code during debugging in Visual Studio. They contain (among others) the mapping between the machine code and the original source line which produced the machine code. This is great for performance but not so great if errors occur and you want to look up in the original source code the identify the problem. ![]() dll during runtime on Windows) you link only against the compiled machine code. Usually, when you link your code against some library (e.g. These are especially useful because of the mapping between the binaries of the library and the original source code. Especially, they do not contain the PDB files created during the build process. So, why the effort of creating an own build? Well, while the official builds are great to begin, they lack unfortunately of the debug information from the build process. Nevertheless, in this article, I want to describe how to get your own build of the OpenCV library. Getting started is pretty easy since pre-built binaries are available. Good introductions to the computer vision field with OpenCV are offered in the official documentation (look out for the tutorials) or in the corresponding book written by the authors of the library itself. In its native form, it is a C/C++ library, although, ports to other languages (e.g. OpenCV is a free library for computer vision containing an extensive number of algorithms related to computer vision problems.
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