前面我們介紹了使用dlib進行人臉檢測，下面我們給出如何使用dlib進行人臉特征點檢測。我們直接貼出代碼。我們的代碼包括如下幾部分功能：

• 檢測單張圖片
• 檢測一個視頻
• 檢測一個camera
  先給出代碼：

#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>
#include <string> 
#include <dlib/opencv.h>
#include <opencv2/highgui/highgui.hpp>using namespace dlib;
using namespace std;// ----------------------------------------------------------------------------------------
void PrintHelp();
int FaceDetectionAndAlignment(const char* inputname);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){PrintHelp();return 0;}if (strcmp(argv[1],"Demo")==0){if (2==argc){return FaceDetectionAndAlignment("");}else if (3==argc){return FaceDetectionAndAlignment(argv[2]);}}else{PrintHelp();return 0;}}catch (exception& e){cout << "\nexception thrown!" << endl;cout << e.what() << endl;}
}// ----------------------------------------------------------------------------------------
void PrintHelp()
{cout << "Useage:" << endl;cout << "1. test model via a camera: face_alignment.exe  Demo " << endl;cout << "2. test model on a pic:     face_alignment.exe  Demo xx.jpg" << endl;  cout << "3. test model on a video:   face_alignment.exe  Demo xx.avi" << endl;cout << endl;
}
int FaceDetectionAndAlignment(const char* inputname)
{string inputName;CvCapture* capture = 0; cv::Mat frame, frameCopy, image;image_window win;frontal_face_detector detector = get_frontal_face_detector();shape_predictor pose_model;deserialize("D:/dlib/model/shape_predictor_68_face_landmarks.dat") >> pose_model;if (inputname != NULL){inputName.assign(inputname);}// name is empty or a numberif (inputName.empty()){capture = cvCaptureFromCAM(0);if (!capture){cout << "Capture from camera didn't work" << endl;return -1;}}// name is not emptyelse if (inputName.size()){if (inputName.find(".jpg") != string::npos || inputName.find(".png") != string::npos|| inputName.find(".bmp") != string::npos){image = cv::imread(inputName, 1);if (image.empty()){cout << "Read Image fail" << endl;return -1;}}else if (inputName.find(".mp4") != string::npos || inputName.find(".avi") != string::npos|| inputName.find(".wmv") != string::npos){capture = cvCaptureFromAVI(inputName.c_str());if (!capture){cout << "Capture from AVI didn't work" << endl;return -1;}}}// -- 2. Read the video streamif (capture!=nullptr){cout << "In capture ..." << endl;// Grab and process frames until the main window is closed by the user.while (!win.is_closed()){// Grab a frameIplImage* iplImg = cvQueryFrame(capture);iplImg = cvQueryFrame(capture);frame = cv::cvarrToMat(iplImg);if (frame.empty())break;if (iplImg->origin == IPL_ORIGIN_TL) //frame.copyTo(frameCopy);elsecv::flip(frame, frameCopy, 0);// Turn OpenCV's Mat into something dlib can deal with.  Note that this just// wraps the Mat object, it doesn't copy anything.  So cimg is only valid as// long as temp is valid.  Also don't do anything to temp that would cause it// to reallocate the memory which stores the image as that will make cimg// contain dangling pointers.  This basically means you shouldn't modify temp// while using cimg.cv_image<bgr_pixel> cimg(frameCopy);// Detect faces std::vector<rectangle> faces = detector(cimg);// Find the pose of each face.std::vector<full_object_detection> shapes;for (unsigned long i = 0; i < faces.size(); ++i)shapes.push_back(pose_model(cimg, faces[i]));// Display it all on the screenwin.clear_overlay();win.set_image(cimg);win.add_overlay(render_face_detections(shapes));if (cv::waitKey(10) >= 0)goto _cleanup_;}cv::waitKey(0);_cleanup_:cvReleaseCapture(&capture);}else{if (!image.empty()){cout << "In image read" << endl;cv_image<bgr_pixel> cimg(image);// Detect faces std::vector<rectangle> faces = detector(cimg);// Find the pose of each face.std::vector<full_object_detection> shapes;for (unsigned long i = 0; i < faces.size(); ++i)shapes.push_back(pose_model(cimg, faces[i]));// Display it all on the screenwin.clear_overlay();win.set_image(cimg);win.add_overlay(render_face_detections(shapes));cout << "Hit enter to exit..." << endl;cin.get();}}return 0;}

再逐一介紹。

檢測單張圖片

右擊項目，選擇屬性，調試填寫：

Demo  E:\\datasets\\helen\\testset\\30427236_1.jpg

如下圖：

實驗結果：

相當不錯的效果了。

檢測視頻

右擊項目，選擇屬性，調試填寫：

Demo  E:\\datasets\\vid.wmv

效果視頻地址：
http://7xtmgn.com1.z0.glb.clouddn.com/dlibvideo.mp4

檢測camera

右擊項目，選擇屬性，調試填寫：

Demo 

效果視頻地址：
http://7xtmgn.com1.z0.glb.clouddn.com/dlibcamera.mp4

完整源代碼：dlib face_alignment