本文将从多个方面对Opencv CUDA编译进行详细的阐述和解读。通过以下小标题,我们将详细介绍如何进行编译。
一、环境搭建
在使用CUDA进行加速之前,需要进行CUDA的环境搭建。在这里以Ubuntu操作系统为例,具体操作如下:
sudo apt-get install linux-headers-`uname -r` -y sudo sh cuda_10.1.243_418.87.00_linux.run vim ~/.bashrc export PATH=$PATH:/usr/local/cuda-10.1/bin export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.1/lib64 source ~/.bashrc
以上操作创建了CUDA运行所需的依赖项、安装了CUDA并将其添加到环境变量中。
二、Opencv编译
接下来,我们需要下载Opencv源码并进行编译。具体操作如下:
sudo apt-get install build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev git clone https://github.com/opencv/opencv.git cd opencv git checkout 3.4.5 mkdir build cd build cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local .. make -j8 sudo make install
以上操作下载了Opencv源码、切换到3.4.5版本、创建了build文件夹并进行编译安装。
三、CUDA编译
接下来,我们需要编译CUDA。具体操作如下:
cd ~/NVIDIA_CUDA-10.1_Samples/ make -j8
以上操作进入CUDA Samples文件夹,并进行编译。如果编译成功,将生成许多样例可执行文件。
四、Opencv CUDA编译
在上述步骤顺利完成后,我们可以开始进行Opencv CUDA编译。具体操作如下:
cd ~/opencv/ mkdir build-cuda && cd build-cuda cmake -D CMAKE_BUILD_TYPE=RELEASE \ -D CMAKE_INSTALL_PREFIX=/usr/local \ -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \ -D WITH_CUDA=ON \ -D CUDA_ARCH_BIN=6.1 \ -D CUDA_ARCH_PTX=6.1 \ -D WITH_CUBLAS=ON \ -D BUILD_EXAMPLES=ON \ -D OPENCV_GENERATE_PKGCONFIG=ON \ .. make -j8 sudo make install
以上操作创建了build-cuda文件夹并进行了Opencv CUDA编译。其中CUDA_ARCH_BIN和CUDA_ARCH_PTX可根据自己的显卡和CUDA版本进行调整。
五、Opencv CUDA测试
最后,我们可以进行Opencv CUDA测试以验证CUDA是否正常使用。具体操作如下:
cd ~/opencv/samples/gpu ./gpu-template
以上操作进入Opencv GPU示例文件夹并运行gpu-template,如果输出如下内容,则可以证明CUDA正常使用:
Device 0: "GeForce GTX 1080 Ti" CUDA Driver Version / Runtime Version 10.1 / 10.1 CUDA Capability Major/Minor version number: 6.1 Total amount of global memory: 11175 MBytes (11720104960 bytes) (28) Multiprocessors, (128) CUDA Cores/MP: 3584 CUDA Cores GPU Max Clock rate: 1582 MHz (1.58 GHz) Memory Clock rate: 5505 Mhz Memory Bus Width: 352-bit L2 Cache Size: 2883584 bytes Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384) Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers 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 multiprocessor: 2048 Maximum number of threads per block: 1024 Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and kernel execution: Yes with 2 copy engine(s) Run time limit on kernels: Yes Integrated GPU sharing Host Memory: No Support host page-locked memory mapping: Yes Alignment requirement for Surfaces: Yes Device has ECC support: Disabled CUDA Device Driver Mode (TCC or WDDM): WDDM (Windows Display Driver Model) Device supports Unified Addressing (UVA): Yes Device supports Compute Preemption: No Supports Cooperative Kernel Launch: No Supports MultiDevice Co-op Kernel Launch: No Device PCI Domain ID / Bus ID / location ID: 0 / 131 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
至此,Opencv CUDA编译完成并且可以正常使用。
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