Visual Studio 2017 + CUDA 9.2 + OpenCV 3.4.5 安裝配置教程（轉）

1.安裝 Visual Studio 2017

勾選適用於桌面的 VC++ 2015.3 v14.00(v140) 工具集

 

2.安裝 CUDA 9.2

先安裝 Base Installer

 

 

再安裝 Patch 1 (Released Aug 16, 2018)

 

設置環境變量

CUDA_PATH = C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2
CUDA_PATH_V9_2 = C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2
CUDA_LIB_PATH = %CUDA_PATH%\lib\x64 
CUDA_BIN_PATH = %CUDA_PATH%\bin 
CUDA_SDK_PATH = C:\ProgramData\NVIDIA Corporation\CUDA Samples\v9.2 
CUDA_SDK_BIN_PATH = %CUDA_SDK_PATH%\bin\win64 
CUDA_SDK_LIB_PATH = %CUDA_SDK_PATH%\common\lib\x64 

檢查環境變量 set cuda

驗證安裝，進入 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2\extras\demo_suite 目錄，執行 deviceQuery.exe

 

 

執行 bandwidthTest.exe

 

 

Result = PASS 說明安裝成功

 

3.安裝 cuDNN 7.5, for CUDA 9.2

解壓 cudnn-9.2-windows10-x64-v7.5.0.56.zip，將 cudnn-9.2-windows10-x64-v7.5.0.56\cuda\bin\cudnn64_7.dll 複製到 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2\bin
將 cudnn-9.2-windows10-x64-v7.5.0.56\cuda\include\cudnn.h 複製到 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2\include
將 cudnn-9.2-windows10-x64-v7.5.0.56\cuda\lib\x64\cudnn.lib 複製到 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2\lib\x64
檢查環境變量

 

Variable Name: CUDA_PATH 
Variable Value: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2

4.安裝 OpenCV 3.4.5

下載完成後解壓

5.使用 CMake 編譯 OpenCV

 

勾選 WITH_CUDA，否則使用 GPU 模塊時會出現問題

重新生成解決方案

選擇僅生成 INSTALL
添加環境變量 D:\Programming\OpenCV\opencv\build\install\x64\vc15\bin

 

6.測試 CUDA

進入 C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Tools\MSVC\14.16.27023\bin\Hostx64\x64
執行 cl.exe：

 

看到 cl.exe 的版本爲 19.16.27027.1
進入 C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Tools\MSVC\14.16.27023\bin\Hostx64\x64>cd C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2\include\crt 查看 host_config.h，發現第 131 行爲：

 

 

將 1913 修改爲 1916

 

7.配置 Visual Studio

新建空項目，配置項目屬性：
在屬性 - 配置屬性 - VC++目錄 - 包含目錄中添加路徑：
D:\Programming\OpenCV\opencv\build\install\include
D:\Programming\OpenCV\opencv\build\install\include\opencv
D:\Programming\OpenCV\opencv\build\install\include\opencv2
在屬性 - 配置屬性 - VC++目錄 - 庫目錄中添加路徑：
D:\Programming\OpenCV\opencv\build\install\x64\vc15\lib
在屬性 - 配置屬性 - 鏈接器 - 輸入 - 附加依賴項中添加：
opencv_calib3d345d.lib opencv_core345d.lib opencv_cudaarithm345d.lib opencv_cudabgsegm345d.lib opencv_cudacodec345d.lib opencv_cudafeatures2d345d.lib opencv_cudafilters345d.lib opencv_cudaimgproc345d.lib opencv_cudalegacy345d.lib opencv_cudaobjdetect345d.lib opencv_cudaoptflow345d.lib opencv_cudastereo345d.lib opencv_cudawarping345d.lib opencv_cudev345d.lib opencv_dnn345d.lib opencv_features2d345d.lib opencv_flann345d.lib opencv_highgui345d.lib opencv_imgcodecs345d.lib opencv_imgproc345d.lib opencv_ml345d.lib opencv_objdetect345d.lib opencv_photo345d.lib opencv_shape345d.lib opencv_stitching345d.lib opencv_superres345d.lib opencv_video345d.lib opencv_videoio345d.lib opencv_videostab345d.lib
右鍵項目 - 生成依賴項 - 生成自定義 - CUDA 9.2(.targets, .props)

在屬性 - 配置屬性 - VC++目錄 - 包含目錄中添加路徑：
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2\include
在屬性 - 配置屬性 - 鏈接器 - 常規 - 附加庫目錄中添加路徑：
$(CUDA_PATH_V9_2)\lib\$(Platform)
在屬性 - 配置屬性 - 鏈接器 - 輸入 - 附加依賴項中添加庫：
cublas.lib;cublas_device.lib;cuda.lib;cudadevrt.lib;cudart.lib;cudart_static.lib;cufft.lib;cufftw.lib;curand.lib;cusolver.lib;cusparse.lib;nppc.lib;nppial.lib;nppicc.lib;nppicom.lib;nppidei.lib;nppif.lib;nppig.lib;nppim.lib;nppist.lib;nppisu.lib;nppitc.lib;npps.lib;nvblas.lib;nvcuvid.lib;nvgraph.lib;nvml.lib;nvrtc.lib;OpenCL.lib;
設置完成後右鍵源文件 - 添加 - 新建項 - CUDA C/C++ File

 

7.導出模板

8.執行程序

 

#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/core/cuda.hpp>

using namespace std;
using namespace cv;
using namespace cuda;


int main() {
    DeviceInfo deviceInfo;
    bool isDeviceOK = deviceInfo.isCompatible();

    cout << "Is GPU OK:" << isDeviceOK << endl;

    system("pause");
    return 0;
}

作者：_酒釀芋圓
鏈接：https://www.jianshu.com/p/b3da8508f301