C++ g++；投诉未定义的引用，即使包含库

我有一个这样的示例文件，我们称它为

dnn\u mmod\u face\u detection\u ex.cpp

#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;
}

我知道以下几点

此示例已编译并在

/dlib/build/test…

目录（dlib-19.9）中运行

那些

LDFLAGS

I通过发出命令

ldd/dlib/build/test…/dnn\mmod\u face\u detection\u ex

---

找出缺少哪些库的正确方法是什么？我尝试跟踪dlib提供的Cmake文件，但它比粒子加速器更复杂。

将

LDFLAGS

放错位置是一个简单的错误。如果将其放置在末端，则它可以正常工作

$(EXECUTABLE): $(OBJECTS)
    $(CC) $(OBJECTS) -o $@ $(LDFLAGS)

---

我在编译器标志中没有看到任何include目录。尝试包含头目录和构建。我只是假设头文件被正确找到，否则链接器将不知道如何抱怨没有找到。此外，g++正在查找dlib文件夹所在的

/usr/local/include

is@Sitesh我可以确认，添加

dlib

以包含类似so

/usr/local/include/dlib

的路径会破坏编译。Dlib希望您调用这样的函数

#include

dnn_mmod_face_detection_ex.cpp:(.text+0x267): undefined reference to `dlib::image_window::image_window()'
dnn_mmod_face_detection_ex.cpp:(.text._ZNK4dlib8gpu_data4hostEv[_ZNK4dlib8gpu_data4hostEv]+0x14): undefined reference to `dlib::gpu_data::copy_to_host() const'
dnn_mmod_face_detection_ex.cpp:(.text._ZN4dlib16resizable_tensorC2Ev[_ZN4dlib16resizable_tensorC5Ev]+0x31): undefined reference to `dlib::cuda::tensor_descriptor::tensor_descriptor()'

$(EXECUTABLE): $(OBJECTS)
    $(CC) $(OBJECTS) -o $@ $(LDFLAGS)