opencv cuda Linux编译,windows 10 上源码编译OpenCV并支持CUDA | compile opencv with CUDA

compile opencv with CUDA support on windows 10

Series

Guide

requirements:

windows: 10

opencv: 3.1.0

nvidia driver: gtx 1060 382.05 (gtx 970m)

GPU arch(s): sm_61 (sm_52)

cuda: 8.0

cudnn: 5.0.5

cmake: 3.10.0

vs: vs2015 64

nvidia cuda CC

笔记本版本的显卡和台式机的计算能力是有差距的。

cpu vs gpu

for opencv functions

get source

Get opencv 3.1.0 for git and fix some bugs

git clone https://github.com/opencv/opencv.git

cd opencv

git checkout -b v3.1.0 3.1.0

# fix bugs for 3.1.0

git cherry-pick 10896

git cherry-pick cdb9c

git cherry-pick 24dbb

git branch

master

* v3.1.0

compile

mkdir build && cd build && cmake-gui ..

config

configure with VS 2015 win64 with options

BUILD_SHARED_LIBS ON

CMAKE_CONFIGURATION_TYPES Release # Release

CMAKE_CXX_FLAGS_RELEASE /MD /O2 /Ob2 /DNDEBUG /MP # for multiple processor

WITH_VTK OFF

BUILD_PERF_TESTS OFF # if ON, build errors occur

WITH_CUDA ON

CUDA_TOOLKIT_ROOT_DIR C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v8.0

#CUDA_ARCH_BIN 3.0 3.5 5.0 5.2 6.0 6.1 # very time-consuming

CUDA_ARCH_PTX 3.0

for opencv

CUDA_ARCH_BIN 3.0 3.5 5.0 5.2 6.0 6.1 relate with

-gencode;arch=compute_30,code=sm_30;-gencode;arch=compute_35,code=sm_35;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_52,code=sm_52;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;

CUDA_ARCH_PTX 3.0 relate with

-gencode;arch=compute_30,code=compute_30;

for caffe

the CUDA_ARCH_BIN parameter specifies multiple architectures so as to support a variety of GPU boards. otherwise, cuda programs will not run with other type of GPU boards.

为了支持在多个不同计算能力的GPU上运行可执行程序，opencv/caffe编译过程中需要支持多个不同架构，eg. CUDA_ARCH_BIN 3.0 3.5 5.0 5.2 6.0 6.1, 因此编译过程非常耗时。在编译的而过程中尽可能选择需要发布release版本的GPU架构进行配置编译。

configure and output:

Selecting Windows SDK version 10.0.14393.0 to target Windows 10.0.17134.

found IPP (ICV version): 9.0.1 [9.0.1]

at: C:/compile/opencv/3rdparty/ippicv/unpack/ippicv_win

CUDA detected: 8.0

CUDA NVCC target flags: -gencode;arch=compute_30,code=sm_30;-gencode;arch=compute_30,code=compute_30

Could NOT find Doxygen (missing: DOXYGEN_EXECUTABLE)

To enable PlantUML support, set PLANTUML_JAR environment variable or pass -DPLANTUML_JAR= option to cmake

Could NOT find PythonInterp: Found unsuitable version "1.4", but required is at least "3.4" (found C:/Users/zunli/.babun/cygwin/bin/python)

Could NOT find PythonInterp: Found unsuitable version "1.4", but required is at least "3.2" (found C:/Users/zunli/.babun/cygwin/bin/python)

Could NOT find Matlab (missing: MATLAB_MEX_SCRIPT MATLAB_INCLUDE_DIRS MATLAB_ROOT_DIR MATLAB_LIBRARIES MATLAB_LIBRARY_DIRS MATLAB_MEXEXT MATLAB_ARCH MATLAB_BIN)

General configuration for OpenCV 3.1.0 =====================================

Version control: 3.1.0-3-g5e9beb8

Platform:

Host: Windows 10.0.17134 AMD64

CMake: 3.10.0

CMake generator: Visual Studio 14 2015 Win64

CMake build tool: C:/Program Files (x86)/MSBuild/14.0/bin/MSBuild.exe

MSVC: 1900

C/C++:

Built as dynamic libs?: YES

C++ Compiler: C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/cl.exe (ver 19.0.24215.1)

C++ flags (Release): /DWIN32 /D_WINDOWS /W4 /GR /EHa /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi /wd4251 /wd4324 /wd4275 /wd4589 /MP8 /MD /O2 /Ob2 /DNDEBUG /MP /Zi

C++ flags (Debug): /DWIN32 /D_WINDOWS /W4 /GR /EHa /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi /wd4251 /wd4324 /wd4275 /wd4589 /MP8 /MDd /Zi /Ob0 /Od /RTC1

C Compiler: C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/cl.exe

C flags (Release): /DWIN32 /D_WINDOWS /W3 /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi /MP8 /MD /O2 /Ob2 /DNDEBUG /Zi

C flags (Debug): /DWIN32 /D_WINDOWS /W3 /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi /MP8 /MDd /Zi /Ob0 /Od /RTC1

Linker flags (Release): /machine:x64 /INCREMENTAL:NO /debug

Linker flags (Debug): /machine:x64 /debug /INCREMENTAL

Precompiled headers: YES

Extra dependencies: comctl32 gdi32 ole32 setupapi ws2_32 vfw32 cudart nppc nppi npps cufft -LC:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v8.0/lib/x64

3rdparty dependencies: zlib libjpeg libwebp libpng libtiff libjasper IlmImf

OpenCV modules:

To be built: cudev core cudaarithm flann imgproc ml video cudabgsegm cudafilters cudaimgproc cudawarping imgcodecs photo shape videoio cudacodec highgui objdetect ts features2d calib3d cudafeatures2d cudalegacy cudaobjdetect cudaoptflow cudastereo stitching superres videostab python2

Disabled: world

Disabled by dependency: -

Unavailable: java python3 viz

Windows RT support: NO

GUI:

QT: NO

Win32 UI: YES

OpenGL support: NO

VTK support: NO

Media I/O:

ZLib: build (ver 1.2.8)

JPEG: build (ver 90)

WEBP: build (ver 0.3.1)

PNG: build (ver 1.6.19)

TIFF: build (ver 42 - 4.0.2)

JPEG 2000: build (ver 1.900.1)

OpenEXR: build (ver 1.7.1)

GDAL: NO

Video I/O:

Video for Windows: YES

DC1394 1.x: NO

DC1394 2.x: NO

FFMPEG: YES (prebuilt binaries)

codec: YES (ver 56.41.100)

format: YES (ver 56.36.101)

util: YES (ver 54.27.100)

swscale: YES (ver 3.1.101)

resample: NO

gentoo-style: YES

GStreamer: NO

OpenNI: NO

OpenNI PrimeSensor Modules: NO

OpenNI2: NO

PvAPI: NO

GigEVisionSDK: NO

DirectShow: YES

Media Foundation: NO

XIMEA: NO

Intel PerC: NO

Parallel framework: Concurrency

Other third-party libraries:

Use IPP: 9.0.1 [9.0.1]

at: C:/compile/opencv/3rdparty/ippicv/unpack/ippicv_win

Use IPP Async: NO

Use Eigen: NO

Use Cuda: YES (ver 8.0)

Use OpenCL: YES

Use custom HAL: NO

NVIDIA CUDA

Use CUFFT: YES

Use CUBLAS: NO

USE NVCUVID: NO

NVIDIA GPU arch: 30 35 50 52 60 61

NVIDIA PTX archs: 30

Use fast math: NO

OpenCL:

Version: dynamic

Include path: C:/compile/opencv/3rdparty/include/opencl/1.2

Use AMDFFT: NO

Use AMDBLAS: NO

Python 2:

Interpreter: C:/Python27/python.exe (ver 2.7.13)

Libraries: C:/Python27/libs/python27.lib (ver 2.7.13)

numpy: C:/Python27/lib/site-packages/numpy/core/include (ver 1.11.3)

packages path: C:/Python27/Lib/site-packages

Python 3:

Interpreter: NO

Python (for build): C:/Python27/python.exe

Java:

ant: NO

JNI: C:/Program Files/Java/jdk1.8.0_161/include C:/Program Files/Java/jdk1.8.0_161/include/win32 C:/Program Files/Java/jdk1.8.0_161/include

Java wrappers: NO

Java tests: NO

Matlab: Matlab not found or implicitly disabled

Documentation:

Doxygen: NO

PlantUML: NO

Tests and samples:

Tests: YES

Performance tests: NO

C/C++ Examples: NO

Install path: C:/compile/opencv/build/install

cvconfig.h is in: C:/compile/opencv/build

-----------------------------------------------------------------

Configuring done

Generating done

Notice for gencode

CUDA NVCC target flags: -gencode;arch=compute_30,code=sm_30;-gencode;arch=compute_35,code=sm_35;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_52,code=sm_52;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_30,code=compute_30

build

Open OpenCV.sln with VS 2015 and build release version.

this may take hours to finish.

errors

possible solutions

With BUILD_PERF_TESTS and BUILD_TESTS disabled, I managed to build OpenCV 3.1 with CUDA 8.0 on Windows 10 with VS2015 x64 arch target. Without building test/performance modules, the build process costs less time as well : )

I actually got it to work both on my laptop and my desktop (GTX960M and GTX970 respectively) running with OpenCV 3.2 and the latest version of CUDA 8.0 for Win10 in Visual Studio 15 Community! What I did was to enable WITH_CUBLAS aswell as WITH_CUDA. I also turned off BUILD_PERF_TESTS and BUILD_TESTS. The configuration was built using the Visual Studio 14 2015 C++ compiler.

my solution:

disable `BUILD_PERF_TESTS`

configure and build again. this time cost only about 1 minutes.

after error fixed,build results

demo

cuda-module

OpenCV GPU module is written using CUDA, therefore it benefits from the CUDA ecosystem.

GPU modules includes class cv::cuda::GpuMat which is a primary container for data kept in GPU memory. It’s interface is very similar with cv::Mat, its CPU counterpart. All GPU functions receive GpuMat as input and output arguments. This allows to invoke several GPU algorithms without downloading data. GPU module API interface is also kept similar with CPU interface where possible. So developers who are familiar with Opencv on CPU could start using GPU straightaway.

The GPU module is designed as a host-level API. This means that if you have pre-compiled OpenCV GPU binaries, you are not required to have the CUDA Toolkit installed or write any extra code to make use of the GPU.

CMakeLists.txt

find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc features2d calib3d

cudaarithm cudabgsegm cudafilters cudaimgproc cudawarping cudafeatures2d # for cuda-enabled

) #

MESSAGE( [Main] " OpenCV_INCLUDE_DIRS = ${OpenCV_INCLUDE_DIRS}")

MESSAGE( [Main] " OpenCV_LIBS = ${OpenCV_LIBS}")

demo.cpp

In the sample below an image is loaded from local file, next it is uploaded to GPU, thresholded, downloaded and displayed.

#include

#include

#include

#include

#include

#include

int test_opencv_gpu()

{

try

{

cv::Mat src_host = cv::imread("file.png", CV_LOAD_IMAGE_GRAYSCALE);

cv::cuda::GpuMat dst, src;

src.upload(src_host);

cv::cuda::threshold(src, dst, 128.0, 255.0, CV_THRESH_BINARY);

cv::Mat result_host;

dst.download(result_host);

cv::imshow("Result", result_host);

cv::waitKey();

}

catch (const cv::Exception& ex)

{

std::cout << "Error: " << ex.what() << std::endl;

}

return 0;

}

cpu vs gpu time cost

(1)对于分辨率不特别大的图片间的ORB特征匹配，CPU运算得比GPU版的快(由于图像上传到GPU消耗了时间)

(2)但对于分辨率较大的图片，或者GPU比CPU好的机器(比如Nvidia Jetson系列)，GPU版的ORB算法比CPU版的程序更高效。

problems

(1) 使用cuda版本的opencv caffe网络的第一次创建非常耗时，后面的网络创建则非常快。

(2) opencv的gpu代码比cpu代码慢，初次启动多耗费20s左右。(事实是由于编译的caffe和GPU计算力不匹配导致的)

reasons

Your problem is that CUDA needs to initialize! And it will generally takes between serveral seconds

Why first function call is slow?

That is because of initialization overheads. On first GPU function call Cuda Runtime API is initialized implicitly.

The first gpu function call is always takes more time, because CUDA initialize context for device.

The following calls will be faster.

Not Reasons:

(1) CPU clockspeed is 10x faster than GPU clockspeed.

(2) memory transfer times between host (CPU) and device (GPU) (upload,downloa data)

deploy

runtime errors

gtx 1060 编译的opencv caffe在gtx 970m上运行出现错误

im2col.cu Check failed: error == cudaSuccess (8 vs. 0) invalid device function

gtx 1060 sm_61

gtx 970m sm_52

im2col 是caffe的源文件，表明gtx 970m的计算能力不支持可执行文件的运行。

reasons

Roughly speaking, the code compilation flow goes like this:

CUDA C/C++ device code source --> PTX --> SASS

The virtual architecture (e.g. compute_20, whatever is specified by -arch compute...) determines what type of PTX code will be generated. The additional switches (e.g. -code sm_21) determine what type of SASS code will be generated. SASS is actually executable object code for a GPU (machine language). An executable can contain multiple versions of SASS and/or PTX, and there is a runtime loader mechanism that will pick appropriate versions based on the GPU actually being used.

win7/win10 deploy

compile opencv caffe on windows 10 for GTX 1060

deoply on windows 7 for GTX 1080 Ti successfully

for win7, if we install 398.82-desktop-win8-win7-64bit-international-whql.exe,errors may occur:

> nvidia-smi.exe

Failed to initialize NVML: Unknown error

Solutions: use older drivers 385.69

linux/window performance

(1) api在linux平均耗时3ms;同样的代码在windows平均耗时14ms

(2) vs编译开启代码优化前后性能相差接近5倍，125ms vs 25ms

(3) cmake编译RELEASE选项默认已经开启了代码优化 -O3

Reference

History

20180713: created.

Copyright

Post author: kezunlin

Copyright Notice: All articles in this blog are licensed under CC BY-NC-SA 3.0 unless stating additionally.