#include <dlib/image_processing/frontal_face_detector.h>
#include <dlib/image_processing/render_face_detections.h>
#include <dlib/image_processing.h>
#include <dlib/gui_widgets.h>
#include <dlib/image_io.h>
#include <dlib/opencv.h>  
#include <iostream>

#include <opencv2/opencv.hpp> 
using namespace dlib;
using namespace std;



int main(int argc, char** argv)
{  
    try
    {
        // This example takes in a shape model file and then a list of images to
        // process.  We will take these filenames in as command line arguments.
        // Dlib comes with example images in the examples/faces folder so give
        // those as arguments to this program.
        if (argc == 1)
        {
            cout << "Call this program like this:" << endl;
            cout << "./face_landmark_detection_ex shape_predictor_68_face_landmarks.dat faces/*.jpg" << endl;
            cout << "\nYou can get the shape_predictor_68_face_landmarks.dat file from:\n";
            cout << "http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" << endl;
            return 0;
        }
		std::cout <<"argc:"<< argc << std::endl;
        // We need a face detector.  We will use this to get bounding boxes for
        // each face in an image.
        frontal_face_detector detector = get_frontal_face_detector();
        // And we also need a shape_predictor.  This is the tool that will predict face
        // landmark positions given an image and face bounding box.  Here we are just
        // loading the model from the shape_predictor_68_face_landmarks.dat file you gave
        // as a command line argument.
        shape_predictor sp;
		std::cout << "argv[1]:"<< argv[1] << std::endl;
        deserialize(argv[1]) >> sp;


		image_window win;// win_faces;
        // Loop over all the images provided on the command line.
        for (int i = 2; i < argc; ++i)
        {
            cout << "processing image " << argv[i] << endl;
            array2d<rgb_pixel> img;
            load_image(img, argv[i]);
            // Make the image larger so we can detect small faces.
            pyramid_up(img);

            // Now tell the face detector to give us a list of bounding boxes
            // around all the faces in the image.
            std::vector<rectangle> dets = detector(img);
            cout << "Number of faces detected: " << dets.size() << endl;

            // Now we will go ask the shape_predictor to tell us the pose of
            // each face we detected.
            std::vector<full_object_detection> shapes;
            for (unsigned long j = 0; j < dets.size(); ++j)
            {
                full_object_detection shape = sp(img, dets[j]);
                cout << "number of parts: "<< shape.num_parts() << endl;
                cout << "pixel position of first part:  " << shape.part(0) << endl;
                cout << "pixel position of second part: " << shape.part(1) << endl;
                // You get the idea, you can get all the face part locations if
                // you want them.  Here we just store them in shapes so we can
                // put them on the screen.
                shapes.push_back(shape);
				cv::Mat temp = dlib::toMat(img);

				for (int k = 0; k < 68; ++k){
					circle(temp, cvPoint(shapes[j].part(k).x(), shapes[j].part(k).y()), 3, cv::Scalar(0, 0, 255), -1);
				}
            }

            // Now let's view our face poses on the screen.
            win.clear_overlay();
            win.set_image(img);
            //win.add_overlay(render_face_detections(shapes));

            //// We can also extract copies of each face that are cropped, rotated upright,
            //// and scaled to a standard size as shown here:
            //dlib::array<array2d<rgb_pixel> > face_chips;
            //extract_image_chips(img, get_face_chip_details(shapes), face_chips);
            //win_faces.set_image(tile_images(face_chips));

            cout << "Hit enter to process the next image..." << endl;
            cin.get();
        }
    }
    catch (exception& e)
    {
        cout << "\nexception thrown!" << endl;
        cout << e.what() << endl;
    }
	system("pause");
}
 

把shape_predictor_68_face_landmarks.dat檔案 和 faces資料夾（...\dlib-18.18\examples\faces）複製到該專案的Release資料夾下，使用命令列進入該專案的Release目錄下，執行該命令：

face_landmark_detection_ex.exe shape_predictor_68_face_landmarks.dat faces/2008_001322.jpg