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我試圖用Dlib運行Qt。會發生什麼是從Dlib需要CUDA的每個算法崩潰沒有錯誤，如果我在Visual Studio上運行相同的代碼，它完美的作品。 Qt和Dlib是使用Visual Studio 2015 x64和CUDA版本8.0構建的。Qt與Dlib和CUDA
的代碼是從DLIB一些示例，可以使用CUDA實現更好的性能：
#include <iostream>
#include <dlib/dnn.h>
#include <dlib/data_io.h>
#include <dlib/image_processing.h>
#include <dlib/gui_widgets.h>
using namespace std;
using namespace dlib;
// ----------------------------------------------------------------------------------------
template <long num_filters, typename SUBNET> using con5d = con<num_filters,5,5,2,2,SUBNET>;
template <long num_filters, typename SUBNET> using con5 = con<num_filters,5,5,1,1,SUBNET>;
template <typename SUBNET> using downsampler = relu<affine<con5d<32, relu<affine<con5d<32, relu<affine<con5d<16,SUBNET>>>>>>>>>;
template <typename SUBNET> using rcon5 = relu<affine<con5<45,SUBNET>>>;
using net_type = loss_mmod<con<1,9,9,1,1,rcon5<rcon5<rcon5<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>;
// ----------------------------------------------------------------------------------------
int main(int argc, char** argv) try
{
if (argc == 1)
{
cout << "Call this program like this:" << endl;
cout << "./dnn_mmod_face_detection_ex mmod_human_face_detector.dat faces/*.jpg" << endl;
cout << "\nYou can get the mmod_human_face_detector.dat file from:\n";
cout << "http://dlib.net/files/mmod_human_face_detector.dat.bz2" << endl;
return 0;
}
net_type net;
deserialize(argv[1]) >> net;
image_window win;
for (int i = 2; i < argc; ++i)
{
matrix<rgb_pixel> img;
load_image(img, argv[i]);
// Upsampling the image will allow us to detect smaller faces but will cause the
// program to use more RAM and run longer.
while(img.size() < 1800*1800)
pyramid_up(img);
// Note that you can process a bunch of images in a std::vector at once and it runs
// much faster, since this will form mini-batches of images and therefore get
// better parallelism out of your GPU hardware. However, all the images must be
// the same size. To avoid this requirement on images being the same size we
// process them individually in this example.
auto dets = net(img);
win.clear_overlay();
win.set_image(img);
for (auto&& d : dets)
win.add_overlay(d);
cout << "Hit enter to process the next image." << endl;
cin.get();
}
}
catch(std::exception& e)
{
cout << e.what() << endl;
}
在線路程序崩潰 「自動dets的淨=（IMG）;」
我.pro文件：
INCLUDEPATH += C:\dlib\dlib-19.4
LIBS += -LC:\dlib\dlib-19.4\mybuild\dlib_build\Release -ldlib
INCLUDEPATH += "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include"
LIBS +="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\curand.lib"
LIBS +="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\cublas.lib"
LIBS +="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\cublas_device.lib"
LIBS +="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\cudnn.lib"
LIBS +="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\cudart_static.lib"
感謝您的關注。
windows操作系統?? –
是的。 Windows 10 x64 – GDias
這是相同的[示例]（https://github.com/davisking/dlib/blob/master/examples/dnn_mmod_face_detection_ex.cpp）?? –