【caffe】windows下vs2013+opencv3.2.0+opencv_contrib(包含dnn)+cmake3.8编译与配置

opencv目前已经支持caffe训练模型的读取，以及使用模型进行预测，这个功能是dnn模块实现的，而这个模块位于opencv_contrib中，此前编译的opencv3.2.0并没有将opencv_contrib中的模块加进来。因此，这里重新将opencv_contrib加入到opencv3.2.0进行编译。

这里假定已经安装了vs2013（或vs2015）和cmake等，没有安装的要先行安装好，再继续接下来的操作。

1、下载opencv和opencv_contrib源码

1.1 下载opencv3.2.0源码(https://github.com/opencv/opencv/releases/tag/3.2.0)。

1.2 下载opencv_contrib源码（https://github.com/opencv/opencv_contrib/releases）

注意：一定要下载和OpenCV源码版本一致的版本（这里均是3.2.0版本）。

2、Cmake配置与编译

2.1 将opencv源码和opencv_contrib源码均解压到编译文件目录下（这里是D:\Libraries\OpenCV320）。

2.2 在编译文件夹下新建opencv320-build和msvc2013_64文件夹，分别作为编译目录和安装目录。

打开Cmake，添加源码目录和编译目录，configure，选择Visual Studio 12 2013 Win64作为生成工具，finish,如下图。（如报错，请参考第5部分的常见问题与解决方案）

2.3 在OPENCV_EXTRA_MODULES_PATH选项中添加opencv_contribute中的modules路径。

同时，修改安装路径：

添加debug后缀，以避免安装时，release版本的将debug版本的覆盖掉。

继续configure，成功后，点generate，生成编译工程成功。（如报错，请参考第5部分的常见问题与解决方案）

3、vs2013编译与安装

generate成功以后，在opencv320-build文件夹下，会生成如下众多文件，打开OpenCV.sln。

分别在Debug和Release环境下，先BUILD->Build Solution，再将INSTALL设为启动项，BUILD->Project Only->Build Only Install。

编译安装成功，在msvc2013_64文件夹下，会看到如下文件夹：

4、配置opencv的环境。

4.1 设置环境变变量，将安装文件夹下的bin文件夹目录添加到环境变量路径中。


4.2 在编译文件夹下添加opencv320.props文件（具体位置和名称可以根据需要设定），并向该文件中添加如下内容（主要是头文件和静态库），保存。在vs2013中使用时opencv时，只需要将改文件添加到工程的property manager中即可。

<?xml version="1.0" encoding="utf-8"?>  <Project ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">    <ImportGroup Label="PropertySheets" />    <PropertyGroup Label="UserMacros" />    <PropertyGroup>      <IncludePath>D:\Libraries\OpenCV320\msvc2013-64\include;$(IncludePath)</IncludePath>      <LibraryPath Condition="'$(Platform)'=='X64'">D:\Libraries\OpenCV320\msvc2013-64\x64\vc12\lib;$(LibraryPath)</LibraryPath>    </PropertyGroup>    <ItemDefinitionGroup>      <Link Condition="'$(Configuration)'=='Debug'">        <AdditionalDependencies>opencv_calib3d320d.lib;opencv_core320d.lib;  opencv_cudaarithm320d.lib;opencv_cudabgsegm320d.lib;opencv_cudacodec320d.lib;  opencv_cudafeatures2d320d.lib;opencv_cudafilters320d.lib;opencv_cudaimgproc320d.lib;  opencv_cudalegacy320d.lib;opencv_cudaobjdetect320d.lib;opencv_cudaoptflow320d.lib;  opencv_cudastereo320d.lib;opencv_cudawarping320d.lib;opencv_cudev320d.lib;      opencv_features2d320d.lib;opencv_flann320d.lib;opencv_highgui320d.lib;      opencv_imgcodecs320d.lib;opencv_imgproc320d.lib;opencv_ml320d.lib;      opencv_objdetect320d.lib;opencv_photo320d.lib;opencv_shape320d.lib;      opencv_stitching320d.lib;opencv_superres320d.lib;opencv_video320d.lib;      opencv_videoio320d.lib;opencv_videostab320d.lib;  opencv_aruco320d.lib;opencv_bgsegm320d.lib;opencv_bioinspired320d.lib;  opencv_ccalib320d.lib;opencv_datasets320d.lib;opencv_dnn320d.lib;  opencv_dpm320d.lib;opencv_face320d.lib;opencv_fuzzy320d.lib;  opencv_line_descriptor320d.lib;opencv_optflow320d.lib;opencv_phase_unwrapping320d.lib;  opencv_plot320d.lib;opencv_reg320d.lib;opencv_rgbd320d.lib;opencv_saliency320d.lib;  opencv_stereo320d.lib;opencv_structured_light320d.lib;opencv_superres320d.lib;  opencv_surface_matching320d.lib;opencv_text320d.lib;opencv_tracking320d.lib;  opencv_xfeatures2d320d.lib;opencv_ximgproc320d.lib;opencv_xobjdetect320d.lib;  opencv_xphoto320d.lib;      %(AdditionalDependencies)</AdditionalDependencies>      </Link>      <Link Condition="'$(Configuration)'=='Release'">        <AdditionalDependencies>opencv_calib3d320.lib;opencv_core320.lib;  opencv_cudaarithm320.lib;opencv_cudabgsegm320.lib;opencv_cudacodec320.lib;  opencv_cudafeatures2d320.lib;opencv_cudafilters320.lib;opencv_cudaimgproc320.lib;  opencv_cudalegacy320.lib;opencv_cudaobjdetect320.lib;opencv_cudaoptflow320.lib;  opencv_cudastereo320.lib;opencv_cudawarping320.lib;opencv_cudev320.lib;      opencv_features2d320.lib;opencv_flann320.lib;opencv_highgui320.lib;      opencv_imgcodecs320.lib;opencv_imgproc320.lib;opencv_ml320.lib;      opencv_objdetect320.lib;opencv_photo320.lib;opencv_shape320.lib;      opencv_stitching320.lib;opencv_superres320.lib;opencv_video320.lib;      opencv_videoio320.lib;opencv_videostab320.lib;  opencv_aruco320.lib;opencv_bgsegm320.lib;opencv_bioinspired320.lib;  opencv_ccalib320.lib;opencv_datasets320.lib;opencv_dnn320.lib;  opencv_dpm320.lib;opencv_face320.lib;opencv_fuzzy320.lib;  opencv_line_descriptor320.lib;opencv_optflow320.lib;opencv_phase_unwrapping320.lib;  opencv_plot320.lib;opencv_reg320.lib;opencv_rgbd320.lib;opencv_saliency320.lib;  opencv_stereo320.lib;opencv_structured_light320.lib;opencv_superres320.lib;  opencv_surface_matching320.lib;opencv_text320.lib;opencv_tracking320.lib;  opencv_xfeatures2d320.lib;opencv_ximgproc320.lib;opencv_xobjdetect320.lib;  opencv_xphoto320.lib;      %(AdditionalDependencies)</AdditionalDependencies>      </Link>    </ItemDefinitionGroup>    <ItemGroup />  </Project>  

5、编译中常见的问题与解决方案：

a) Cmake编译，加入opencv_contrib中的modules后，进行configure，有些模块会报错，只需要将相应的模块勾选掉继续configure即可。

b) 如果编译的过程中出现反复，虽然configure成功，但是generate失败，或者generate成功，但是使用vs编译时出错。最好的办法是将此前的文件都删除，重新解压源码，进行Cmake配置和编译。

c) 如果此前系统中已经配置过opencv，建议将opencv的执行目录从环境变量里清除掉。

d) Cmake配置的过程中要保证网络的通畅，如果由于长时间没有下载第三方依赖库文件不成功而报错，可以直接在谷歌或度娘上搜索相关文件，下载下来手动放到相关文件夹下，再继配置即可。

e) opencv和opencv_contrib版本一定要一致，否则配置和编译会出错。

至此，编译成功，下一篇将介绍，如何在dnn中调用caffe的训练模型。

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参考：

[1] http://docs.opencv.org/3.2.0/de/d25/tutorial_dnn_build.html

[2] http://answers.opencv.org/question/147923/build-error-open-cv32-with-extra-libs/

2017.07.19