未能在OpenCV的GPU（CUDA）

的OpenCV => 3.2
操作系統/平臺=>視窗10 64位創建過濾器
編譯=>視覺工作室2015社區
CUDA Toolkit版本=> 8.0

d詳細描述

我正在使用基於GPU的功能和操作。我自己構建了帶有CUDA支持的OpenCV，並且大多數GPU功能和操作都可以正常工作。但是，當涉及到過濾相關的功能等createGaussianFilter或createSobelFilter異常下面被捕獲：

C：\的OpenCV \的OpenCV-3.2.0 \模塊\ cudafilters \ SRC \ filtering.cpp：414：錯誤：（ -215）rowFilter_在功能`匿名命名空間「:: SeparableLinearFilter :: SeparableLinearFilter！= 0

代碼重現

// 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; 
}

來源

2017-08-01 Summer Sun

你可以嘗試用：'CUDA :: createSobelFilter（CV_32F，CV_32F，1，0，3 ，1，BORDER_DEFAULT）;'？ – Catree

@Catree它沒關係！謝謝！但我不明白爲什麼？你想寫一個答案，以便我可以接受它嗎？ –

你可以找到here測試Sober過濾器的CUDA版本的代碼。

在我看來，這是OpenCV開發者的選擇（CUDA API允許雙精度計算，因爲我認爲時間很長）。或者雙精度浮點不被接受，因爲效率不高，精度越高，不值得性能下降。計算機圖形不需要這種精度，因此GPU架構具有更多單精度單位（更多信息，請參閱here,2010）。

另請參閱CUDA faq。

注：這是專爲遊戲GPU VS專業GPU的情況下（見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.

來源

2017-08-01 15:33:11 Catree

這很有幫助，謝謝！ –

未能在OpenCV的GPU（CUDA）

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