windows10 安装wordpress
VS2015+OpenCV3.0.0+CUDA10.0
- 环境：
- 第一步：安装显卡驱动：461.40-desktop-win10-64bit-international-nsd-dch-whql.exe
- 第二步：安装Visual Studio：2015
- 第三步：安装CUDA： cuda_10.0.130_411.31_win10
- 下载Cudnn: cudnn-10.0-windows10-x64-v7.6.1.34.zip
- 第四步：安装TBB：tbb2018_20171205oss
- 第五步：安装cmake: cmake-3.4.3-win32-x86.exe
- 第六步：下载OpenCV：opencv-3.0.0.exe
- OpenCV-contrib：opencv_contrib-3.0.0.tar.gz
- 第七步：cmake编译设置：
- 第八步： VS编译生成库文件（==进行编译前先阅读相关问题，少走弯路，减少编译时间==）
- 相关问题：
- 第九步：配置OpenCV环境
- 第十步：环境测试
环境：
操作系统：Windows 10 显卡：GeForce GTX1660 Ti
第一步：安装显卡驱动：461.40-desktop-win10-64bit-international-nsd-dch-whql.exe下载地址： https://cn.download.nvidia.com/Windows/461.40/461.40-notebook-win10-64bit-international-nsd-dch-whql.exe
第二步：安装Visual Studio：2015下载地址：https://jingyan.baidu.com/article/c45ad29c223421051753e23a.html
第三步：安装CUDA： cuda_10.0.130_411.31_win10下载地址：https://developer.nvidia.com/cuda-10.0-download-archive 添加环境变量：
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\libnvvp C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\lib\x64 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\extras\CUPTI\libx64在cmd命令窗口中输入：nvcc -V，查看cuda是否安装成功：
下载Cudnn: cudnn-10.0-windows10-x64-v7.6.1.34.zip下载地址：https://developer.nvidia.com/rdp/cudnn-download
解压cudnn压缩包，将Cudnn包中所带的文件放到Cuda目录(上面的CUDA_PATH的路径)下对应的文件夹即可 （cuda默认安装路径为C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0）
cuda测试： cmd到C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\extras\demo_suite目录下
执行bandwidthTest.exe
执行deviceQuery.exe
两次结果均为pass即为安装成功，记住显卡算力为7.5。
第四步：安装TBB：tbb2018_20171205oss下载地址：https:///oneapi-src/oneTBB/releases
解压好以后，添加环境变量：
D:\Program Files\tbb2018_20171205oss\bin\intel64\vc14第五步：安装cmake: cmake-3.4.3-win32-x86.exe下载地址：https://cmake.org/download/
添加环境变量：D:\Program Files (x86)\CMake\bin
改完之后重启一次
第六步：下载OpenCV：opencv-3.0.0.exe下载地址：https://opencv.org/releases.html 下载后解压到D:\opencv300cuda
OpenCV-contrib：opencv_contrib-3.0.0.tar.gz下载地址：https:///opencv/opencv_contrib/releases 下载后解压到D:\opencv300cuda
第七步：cmake编译设置：Configure后选择Visual Studio 14 2015 Win64
Cmake中红色的部分说明还没有更新，需要点击Configure更新，每次更改完一些配置或者参数之后都要点击Configure更新一下。最后Configure后没有输出红色信息后，确定配置无误再点Generate，生成项目文件。
第八步： VS编译生成库文件（进行编译前先阅读相关问题，少走弯路，减少编译时间）打开D:\opencv300cuda\opencv\cudabuild100下的OpenCV.sln 编译，找到“ALL_BUILD”，右键->“生成”，然后开始漫长的等待…… 编译输出没有错误，失败0个后，找到“CmakeTargets”下的“INSTALL”，右键->"仅用于项目“->“仅生成INSTALL”。
相关问题：取消勾选BUILD_EXAMPLES、BUILD_PERF_TESTS、BUILD_TBB、WITH_MATLAB、BUILD_opencv_world、BUILD_opencv_face，减少编译时间 勾选WITH_CUDA、WITH_TBB
特别注意，Cmake的版本一定要用3.4.3，不然会出现很多问题，比如报错：
无法打开输入文件“…\lib\Release\opencv_bioinspired300.lib”
无法打开文件\lib\Debug\opencv_bioinspired300d.lib
参考链接：
解决方法：在VS中打开位于路径“……\ opencv_contrib-3.1.0\ modules\bioinspired\ src\ opencl ” 下的文件retina_kernel.cl： 把所有的注释的==//*****************************==删掉。然后重新使用cmake 配置生成。
error C2061: 语法错误: 标识符“NppiGraphcutState”
参考链接：
解决方案：找到…\sources\modules\cudalegacy\src目录下的graphcuts.cpp文件，将
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
改为
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) || (CUDART_VERSION>= 8000)error C2382: “std::tuplecv::Size,perf:`anonymous-namespace’::MatDepth,perf::`anonymous-namespace’::MatCn::operator =”: 重定义
解决办法：在cmake编译时取消勾选BUILD_PERF_TESTS。
CMake Deprecation Warning at CMakeLists.txt:69 (cmake_policy): The OLD behavior for policy CMP0022 will be removed from a future version of CMake.
The cmake-policies(7) manual explains that the OLD behaviors of all policies are deprecated and that a policy should be set to OLD only under specific short-term circumstances. Projects should be ported to the NEW behavior and not rely on setting a policy to OLD.
CMake Deprecation Warning at CMakeLists.txt:74 (cmake_policy): The OLD behavior for policy CMP0026 will be removed from a future version of CMake.
The cmake-policies(7) manual explains that the OLD behaviors of all policies are deprecated and that a policy should be set to OLD only under specific short-term circumstances. Projects should be ported to the NEW behavior and not rely on setting a policy to OLD.
cmake版本太高，将cmake版本换为3.4.3。
CMake Error: The following variables are used in this project, but they are set to NOTFOUND. Please set them or make sure they are set and tested correctly in the CMake files: CUDA_nppi_LIBRARY (ADVANCED)
参考链接：
解决方案如下：
1）.找到FindCUDA.cmake文件，找到行
find_cuda_helper_libs(nppi)
改为
find_cuda_helper_libs(nppial) find_cuda_helper_libs(nppicc) find_cuda_helper_libs(nppicom) find_cuda_helper_libs(nppidei) find_cuda_helper_libs(nppif) find_cuda_helper_libs(nppig) find_cuda_helper_libs(nppim) find_cuda_helper_libs(nppist) find_cuda_helper_libs(nppisu) find_cuda_helper_libs(nppitc)2）.找到行
set(CUDA_npp_LIBRARY "${CUDA_nppc_LIBRARY};${CUDA_nppi_LIBRARY};${CUDA_npps_LIBRARY}")改为
set(CUDA_npp_LIBRARY "${CUDA_nppc_LIBRARY};${CUDA_nppial_LIBRARY};${CUDA_nppicc_LIBRARY};${CUDA_nppicom_LIBRARY};${CUDA_nppidei_LIBRARY};${CUDA_nppif_LIBRARY};${CUDA_nppig_LIBRARY};${CUDA_nppim_LIBRARY};${CUDA_nppist_LIBRARY};${CUDA_nppisu_LIBRARY};${CUDA_nppitc_LIBRARY};${CUDA_npps_LIBRARY}")3）.找到行
unset(CUDA_nppi_LIBRARY CACHE)
改为
unset(CUDA_nppial_LIBRARY CACHE) unset(CUDA_nppicc_LIBRARY CACHE) unset(CUDA_nppicom_LIBRARY CACHE) unset(CUDA_nppidei_LIBRARY CACHE) unset(CUDA_nppif_LIBRARY CACHE) unset(CUDA_nppig_LIBRARY CACHE) unset(CUDA_nppim_LIBRARY CACHE) unset(CUDA_nppist_LIBRARY CACHE) unset(CUDA_nppisu_LIBRARY CACHE) unset(CUDA_nppitc_LIBRARY CACHE)4）.找到文件 OpenCVDetectCUDA.cmake，修改以下几行
set(__cuda_arch_ptx "") if(CUDA_GENERATION STREQUAL "Fermi") set(__cuda_arch_bin "2.0") elseif(CUDA_GENERATION STREQUAL "Kepler") set(__cuda_arch_bin "3.0 3.5 3.7")改为
set(__cuda_arch_ptx "") if(CUDA_GENERATION STREQUAL "Kepler") set(__cuda_arch_bin "3.0 3.5 3.7") elseif(CUDA_GENERATION STREQUAL "Maxwell") set(__cuda_arch_bin "5.0 5.2")5）.cuda9中有一个单独的halffloat(cuda_fp16.h)头文件,也应该被包括在opencv的目录里，将头文件cuda_fp16.h添加至 …\opencv\modules\cudev\include\opencv2\cudev\common.hpp 即在common.hpp中添加
#include <cuda_fp16.h>重新生成即可
error MSB6006: “cmd.exe”已退出,代码为 1。
参考链接：
解决办法：将cmake中CUDA_HOST_COMPILER设置为
D:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin\cl.exeunsupported gpu architecture‘compute_20’
参考链接：
解决办法：cmake中将CUDA_ARCH_BIN 和CUDA_ARCH_PTX对应的小于3的值全部删掉。
第九步：配置OpenCV环境
1.编译完成之后，在目标文件夹中多了一个install文件夹，里边就有配置OpenCV的Debug和Release版本需要的各种文件，可以将其拷贝至自定义目录下，我的拷贝路径为D:\opencv300cuda。 添加环境变量：D:\opencv300cuda\x64\vc14\bin，之后重启电脑。
2.VS新建项目，配置属性：
1）VC++目录->包含目录 添加：
D:\opencv300cuda\include D:\opencv300cuda\include\opencv D:\opencv300cuda\include\opencv22)VC++目录->库目录 添加：
D:\opencv300cuda\x64\vc14\lib链接器->输入->附加依赖项 添加:
debug版本：
opencv_bgsegm300d.lib opencv_bioinspired300d.lib opencv_calib3d300d.lib opencv_ccalib300d.lib opencv_core300d.lib opencv_cudaarithm300d.lib opencv_cudabgsegm300d.lib opencv_cudacodec300d.lib opencv_cudafeatures2d300d.lib opencv_cudafilters300d.lib opencv_cudaimgproc300d.lib opencv_cudalegacy300d.lib opencv_cudaobjdetect300d.lib opencv_cudaoptflow300d.lib opencv_cudastereo300d.lib opencv_cudawarping300d.lib opencv_cudev300d.lib opencv_features2d300d.lib opencv_flann300d.lib opencv_hal300d.lib opencv_highgui300d.lib opencv_imgcodecs300d.lib opencv_imgproc300d.lib opencv_latentsvm300d.lib opencv_line_descriptor300d.lib opencv_ml300d.lib opencv_objdetect300d.lib opencv_optflow300d.lib opencv_photo300d.lib opencv_reg300d.lib opencv_rgbd300d.lib opencv_saliency300d.lib opencv_shape300d.lib opencv_superres300d.lib opencv_surface_matching300d.lib opencv_text300d.lib opencv_tracking300d.lib opencv_ts300d.lib opencv_video300d.lib opencv_videoio300d.lib opencv_videostab300d.lib opencv_xfeatures2d300d.lib opencv_ximgproc300d.lib opencv_xobjdetect300d.lib opencv_xphoto300d.librelease版本：
opencv_bgsegm300.lib opencv_bioinspired300.lib opencv_calib3d300.lib opencv_ccalib300.lib opencv_core300.lib opencv_cudaarithm300.lib opencv_cudabgsegm300.lib opencv_cudacodec300.lib opencv_cudafeatures2d300.lib opencv_cudafilters300.lib opencv_cudaimgproc300.lib opencv_cudalegacy300.lib opencv_cudaobjdetect300.lib opencv_cudaoptflow300.lib opencv_cudastereo300.lib opencv_cudawarping300.lib opencv_cudev300.lib opencv_features2d300.lib opencv_flann300.lib opencv_hal300.lib opencv_highgui300.lib opencv_imgcodecs300.lib opencv_imgproc300.lib opencv_latentsvm300.lib opencv_line_descriptor300.lib opencv_ml300.lib opencv_objdetect300.lib opencv_optflow300.lib opencv_photo300.lib opencv_reg300.lib opencv_rgbd300.lib opencv_saliency300.lib opencv_shape300.lib opencv_stitching300.lib opencv_superres300.lib opencv_surface_matching300.lib opencv_text300.lib opencv_tracking300.lib opencv_ts300.lib opencv_video300.lib opencv_videoio300.lib opencv_videostab300.lib opencv_xfeatures2d300.lib opencv_ximgproc300.lib opencv_xobjdetect300.lib opencv_xphoto300.lib3.将D:\opencv300cuda\x64\vc14\bin下所有的.dll文件复制到C:\Windows\System32和C:\Windows\SysWOW64下。
第十步：环境测试
程序1（m1.cpp）：
#include "opencv2/opencv.hpp" //#include "opencv2/core.hpp" //#include "opencv2/highgui.hpp" //#include "opencv2/videoio.hpp" //#include "opencv2/core/cuda.hpp" #include<opencv2/cudaarithm.hpp> #include<opencv2/cudaoptflow.hpp> #include<opencv2/cudaobjdetect.hpp> #include<opencv2/cudawarping.hpp> #include<opencv2/cudafilters.hpp> #include<opencv2/cudaimgproc.hpp> #include "opencv2/cudabgsegm.hpp" #include<iostream> using namespace std; using namespace cv; using namespace cv::cuda; int main() { /*int num_devices = cv::cuda::getCudaEnabledDeviceCount(); if (num_devices <= 0) { cerr << "There is no device." << endl; return -1; } int enable_device_id = -1; for (int i = 0; i < num_devices; i++) { cv::cuda::DeviceInfo dev_info(i); if (dev_info.isCompatible()) { enable_device_id = i; } } if (enable_device_id < 0) { cerr << "GPU module isn't built for GPU" << endl; } cv::cuda::setDevice(enable_device_id);*/ cuda::printCudaDeviceInfo(cuda::getDevice()); int count = cuda::getCudaEnabledDeviceCount(); printf("GPU Device Number:%d\n", count); printf("**************************************************\n"); /*-------------------------以下四种验证方式任意选取一种即可-------------------------*/ //获取显卡简单信息 cuda::printShortCudaDeviceInfo(cuda::getDevice()); //有显卡信息表示GPU模块配置成功 cuda::printCudaDeviceInfo(cuda::getDevice()); //有显卡信息表示GPU模块配置成功 int Device_Num = cuda::getCudaEnabledDeviceCount();//获取显卡设备数量 cout << Device_Num << endl; //返回值大于0表示GPU模块配置成功 cuda::DeviceInfo Device_State;//获取显卡设备状态 bool Device_OK = Device_State.isCompatible(); cout << "Device_State: " << Device_OK << endl; //返回值大于0表示GPU模块配置成功 waitKey(); return 0; }***** VIDEOINPUT LIBRARY - 0.1995 - TFW07 ***** *** CUDA Device Query (Runtime API) version (CUDART static linking) *** Device count: 1 Device 0: "GeForce GTX 1660 Ti" CUDA Driver Version / Runtime Version 11.20 / 10.0 CUDA Capability Major/Minor version number: 7.5 Total amount of global memory: 6144 MBytes (6442450944 bytes) GPU Clock Speed: 1.59 GHz Max Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072,65536), 3D=(16384,16384,16384) Max Layered Texture Size (dim) x layers 1D=(32768) x 2048, 2D=(32768,32768) x 2048 Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total number of registers available per block: 65536 Warp size: 32 Maximum number of threads per block: 1024 Maximum sizes of each dimension of a block: 1024 x 1024 x 64 Maximum sizes of each dimension of a grid: 2147483647 x 65535 x 65535 Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and execution: Yes with 6 copy engine(s) Run time limit on kernels: Yes Integrated GPU sharing Host Memory: No Support host page-locked memory mapping: Yes Concurrent kernel execution: Yes Alignment requirement for Surfaces: Yes Device has ECC support enabled: No Device is using TCC driver mode: No Device supports Unified Addressing (UVA): Yes Device PCI Bus ID / PCI location ID: 1 / 0 Compute Mode: Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.20, CUDA Runtime Version = 10.0, NumDevs = 1 GPU Device Number:1 ************************************************** Device 0: "GeForce GTX 1660 Ti" 6144Mb, sm_75, Driver/Runtime ver.11.20/10.0 *** CUDA Device Query (Runtime API) version (CUDART static linking) *** Device count: 1 Device 0: "GeForce GTX 1660 Ti" CUDA Driver Version / Runtime Version 11.20 / 10.0 CUDA Capability Major/Minor version number: 7.5 Total amount of global memory: 6144 MBytes (6442450944 bytes) GPU Clock Speed: 1.59 GHz Max Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072,65536), 3D=(16384,16384,16384) Max Layered Texture Size (dim) x layers 1D=(32768) x 2048, 2D=(32768,32768) x 2048 Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total number of registers available per block: 65536 Warp size: 32 Maximum number of threads per block: 1024 Maximum sizes of each dimension of a block: 1024 x 1024 x 64 Maximum sizes of each dimension of a grid: 2147483647 x 65535 x 65535 Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and execution: Yes with 6 copy engine(s) Run time limit on kernels: Yes Integrated GPU sharing Host Memory: No Support host page-locked memory mapping: Yes Concurrent kernel execution: Yes Alignment requirement for Surfaces: Yes Device has ECC support enabled: No Device is using TCC driver mode: No Device supports Unified Addressing (UVA): Yes Device PCI Bus ID / PCI location ID: 1 / 0 Compute Mode: Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.20, CUDA Runtime Version = 10.0, NumDevs = 1 1 Device_State: 1程序2（m2.cpp）:
#include "opencv2/opencv.hpp" //#include "opencv2/core.hpp" //#include "opencv2/highgui.hpp" //#include "opencv2/videoio.hpp" //#include "opencv2/core/cuda.hpp" #include<opencv2/cudaarithm.hpp> #include<opencv2/cudaoptflow.hpp> #include<opencv2/cudaobjdetect.hpp> #include<opencv2/cudawarping.hpp> #include<opencv2/cudafilters.hpp> #include<opencv2/cudaimgproc.hpp> #include "opencv2/cudabgsegm.hpp" #include<iostream> using namespace std; using namespace cv; using namespace cv::cuda; int main() { Mat src_image = imread("E:/finished202102/picture/test.png"); imshow("image", src_image); Mat dst_image, dst_imge1; vector<Mat> channels;//定义存储的容器 split(src_image, channels); Mat src_image0 = channels[0];//b通道的图像 cuda::GpuMat d_src_img(src_image);//upload src image to gpu cuda::GpuMat d_dst_img, d_src_img00; cuda::cvtColor(d_src_img, d_dst_img, CV_BGR2GRAY); d_dst_img.download(dst_image); cuda::GpuMat d_src_img0(src_image0); cuda::equalizeHist(d_src_img0, d_src_img00); Mat hist_image; d_src_img00.download(hist_image); imshow("gray", dst_image); imshow("hist", hist_image); waitKey(); return 0; }程序3（m3.cpp）:
#include <iostream> #include "opencv2/opencv_modules.hpp" #include "opencv2/core.hpp" #include "opencv2/features2d.hpp" #include "opencv2/highgui.hpp" #include "opencv2/cudafeatures2d.hpp" #include "opencv2/xfeatures2d/cuda.hpp" #include "opencv2/opencv.hpp" #include "opencv2/core.hpp" #include "opencv2/highgui.hpp" #include "opencv2/videoio.hpp" #include "opencv2/core/cuda.hpp" #include<opencv2/cudaarithm.hpp> #include<opencv2/cudaoptflow.hpp> #include<cudaobjdetect.hpp> #include<opencv2/cudawarping.hpp> #include<opencv2/cudafilters.hpp> #include<opencv2/cudaimgproc.hpp> #include "opencv2/cudabgsegm.hpp" using namespace std; using namespace cv; using namespace cv::cuda; int main() { cuda::printShortCudaDeviceInfo(cuda::getDevice()); GpuMat img1, img2; img1.upload(imread("1.jpg", IMREAD_GRAYSCALE)); img2.upload(imread("2.jpg", IMREAD_GRAYSCALE)); SURF_CUDA surf; // detecting keypoints & computing descriptors GpuMat keypoints1GPU, keypoints2GPU; GpuMat descriptors1GPU, descriptors2GPU; surf(img1, GpuMat(), keypoints1GPU, descriptors1GPU); surf(img2, GpuMat(), keypoints2GPU, descriptors2GPU); cout << "FOUND " << keypoints1GPU.cols << " keypoints on first image" << endl; cout << "FOUND " << keypoints2GPU.cols << " keypoints on second image" << endl; // matching descriptors Ptr<cuda::DescriptorMatcher> matcher = cuda::DescriptorMatcher::createBFMatcher(surf.defaultNorm()); vector<DMatch> matches; matcher->match(descriptors1GPU, descriptors2GPU, matches); // downloading results vector<KeyPoint> keypoints1, keypoints2; vector<float> descriptors1, descriptors2; surf.downloadKeypoints(keypoints1GPU, keypoints1); surf.downloadKeypoints(keypoints2GPU, keypoints2); surf.downloadDescriptors(descriptors1GPU, descriptors1); surf.downloadDescriptors(descriptors2GPU, descriptors2); // drawing the results Mat img_matches; drawMatches(Mat(img1), keypoints1, Mat(img2), keypoints2, matches, img_matches); namedWindow("matches", 0); imshow("matches", img_matches); waitKey(0); return 0; }***** VIDEOINPUT LIBRARY - 0.1995 - TFW07 ***** Device 0: "GeForce GTX 1660 Ti" 6144Mb, sm_75, Driver/Runtime ver.11.20/10.0 FOUND 643 keypoints on first image FOUND 1161 keypoints on second image