G 也抱怨不确定的参考文献，即使包括库

我有一个类似的示例文件，让我们称其为 dnn_mmod_face_detection_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;
}

我有一个像

CC=g++
CFLAGS=-c -Wall -std=c++11 -v
LDFLAGS=-L/usr/local/cuda/lib64 -ldlib -lcudnn -lpthread -ldl -lrt -lX11 -lcublas -lcudnn -lcurand -lcusolver -lstdc++ -lm -lgcc_s -lc -lxcb -lXau -lXdmcp
SOURCES=dnn_mmod_face_detection_ex.cpp
OBJECTS=$(SOURCES:.cpp=.o)
EXECUTABLE=dnn_mmod_face_detection_ex
INCLUDE=
all: $(SOURCES) $(EXECUTABLE)
$(EXECUTABLE): $(OBJECTS)
    $(CC) $(LDFLAGS) $(OBJECTS) -o $@
.cpp.o:
    $(CC) $(CFLAGS) $(INCLUDE) $< -o $@

，但我几乎对每个DLIB库都有不确定的引用。即

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()'

我知道以下

1. 同一示例已编译，并在/dlib/build/test...目录(DLIB-129.9(内运行
2. 我通过发出命令ldd /dlib/build/test.../dnn_mmod_face_detection_ex
获得的LDFLAGS

弄清楚哪些库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(s(，但比粒子加速器更复杂。