使用Dlib库进行68个人脸特征点检测

dlib人脸检测共可检测出68个检测点
官网上的例子：http://dlib.net/face_landmark_detection_ex.cpp.html
进行适当的改写。
其中：D:\OpenCV\shape_predictor_68_face_landmarks.dat
是从 http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 下载的

#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 <iostream>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.        // 这个例子需要一个形状模型文件和一系列的图片.//        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;//从这个地址下载模型标记点数据//            return 0;//        }        // We need a face detector.  We will use this to get bounding boxes for        // each face in an image.        //****需要一个人脸检测器，获得一个边界框        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.        //****也需要一个形状预测器，这是一个工具用来预测给定的图片和脸边界框的标记点的位置。        //****这里我们仅仅从shape_predictor_68_face_landmarks.dat文件加载模型        shape_predictor sp;//定义个shape_predictor类的实例        deserialize("D:\\OpenCV\\shape_predictor_68_face_landmarks.dat") >> sp;        image_window win, win_faces;        // Loop over all the images provided on the command line.        // ****循环所有图片//        for (int i = 2; i < argc; ++i)        {//            cout << "processing image " << argv[i] << endl;            array2d<rgb_pixel> img;//注意变量类型 rgb_pixel 三通道彩色图像            load_image(img, "D:\\img.jpg");            // 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);//检测人脸，获得边界框            cout << "Number of faces detected: " << dets.size() << endl;//检测到人脸的数量            // Now we will go ask the shape_predictor to tell us the pose of            // each face we detected.            //****调用shape_predictor类函数，返回每张人脸的姿势            std::vector<full_object_detection> shapes;//注意形状变量的类型，full_object_detection            for (unsigned long j = 0; j < dets.size(); ++j)            {                full_object_detection shape = sp(img, dets[j]);//预测姿势，注意输入是两个，一个是图片，另一个是从该图片检测到的边界框                cout << "number of parts: " << shape.num_parts() << endl;                //cout << "pixel position of first part:  " << shape.part(0) << endl;//获得第一个点的坐标,注意第一个点是从0开始的                //cout << "pixel position of second part: " << shape.part(1) << endl;//获得第二个点的坐标                //打印出全部68个点                for (int i = 0; i < 68; i++)                {                    cout << "第 " << i+1 << " 个点的坐标： " << shape.part(i) << 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);            }            // Now let's view our face poses on the screen.            //**** 显示结果            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:            //****我们也能提取每张剪裁后的人脸的副本，旋转和缩放到一个标准尺寸            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;    }}

效果如下：