未能在OpenCV的GPU（CUDA）

的OpenCV => 3.2
操作系统/平台=>视窗10 64位创建过滤器
编译=>视觉工作室2015社区
CUDA Toolkit版本=> 8.0

d详细描述

我正在使用基于GPU的功能和操作。我自己构建了带有CUDA支持的OpenCV，并且大多数GPU功能和操作都可以正常工作。但是，当涉及到过滤相关的功能等createGaussianFilter或createSobelFilter异常下面被捕获：

C：\的OpenCV \的OpenCV-3.2.0 \模块\ cudafilters \ SRC \ filtering.cpp：414：错误：（ -215）rowFilter_在功能`匿名命名空间“:: SeparableLinearFilter :: SeparableLinearFilter！= 0

代码重现

// C++ code example 
// A very simple snnipet 
#include <opencv2/core/core.hpp> 
#include <opencv2/highgui/highgui.hpp> 
#include <opencv2/core/cuda.hpp> 
#include <opencv2/cudaimgproc.hpp> 
#include <opencv2/cudafilters.hpp> 
#include <iostream> 

using namespace cv; 
using namespace std; 

int main(int argc, char** argv) 
{ 
    try 
    { 
     Ptr<cuda::Filter> filterX = cuda::createSobelFilter(CV_64F, CV_64F, 1, 0, 3, 1, BORDER_DEFAULT); // x direction 
    } 
    catch (cv::Exception& e) 
    { 
     const char* err_msg = e.what(); 
     std::cout << "exception caught: " << err_msg << std::endl; 
    } 

    return 0; 
}

来源

2017-08-01 Summer Sun

你可以尝试用：'CUDA :: createSobelFilter（CV_32F，CV_32F，1，0，3 ，1，BORDER_DEFAULT）;'？ – Catree

@Catree它没关系！谢谢！但我不明白为什么？你想写一个答案，以便我可以接受它吗？ –

你可以找到here测试Sober过滤器的CUDA版本的代码。

在我看来，这是OpenCV开发者的选择（CUDA API允许双精度计算，因为我认为时间很长）。或者双精度浮点不被接受，因为效率不高，精度越高，不值得性能下降。计算机图形不需要这种精度，因此GPU架构具有更多单精度单位（更多信息，请参阅here,2010）。

另请参阅CUDA faq。

注：这是专为游戏GPU VS专业GPU的情况下（见here，2015年）：

Summary of NVIDIA GPUs

NVIDIA's GTX series are known for their great FP32 performance but are very poor in their FP64 performance. The performance generally ranges between 1:24 (Kepler) and 1:32 (Maxwell). The exceptions to this are the GTX Titan cards which blur the lines between the consumer GTX series and the professional Tesla/Quadro cards.

The Kepler architecture Quadro and Tesla series card provide full double precision performance with 1:3 FP32. However, with the Quadro M6000, NVIDIA has decided to provide only minimal FP64 performance by giving it only 1:32 of FP32 capability and touting the M6000 as the best graphics card rather than the best graphics+compute card like the Quadro K6000.

AMD GPUs

AMD GPUs perform fairly well for FP64 compared to FP32. Most AMD cards (including consumer/gaming series) will give between 1:3 and 1:8 FP32 performance for FP64. The AMD Tahiti architectures tested in these benchmarks here do not suffer from the same problems FP64 problems as NVIDIA's GTX series and give a 1:4 performance. Newer Hawaii architecture consumer grade GPUs are expected to provide 1:8 performance.

The FirePro W9100, W8100 and S9150 will give you an incredible FP64 1:2 FP32 performance.

Overall, AMD GPUs hold a reputation for good double precision performance ratios compared to their NVIDIA counterparts.

来源

2017-08-01 15:33:11 Catree

这很有帮助，谢谢！ –

未能在OpenCV的GPU（CUDA）

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