//女票被學妹約出去看電影了，所以有點無聊的我來寫部落格了。

主要在官網給的Demo基礎之上用Opencv把特徵點描繪出來了。

很早之前寫過一篇配置Dlib環境的部落格，現在來稍微梳理下提取特徵點的使用方法。

慣例先放效果圖吧：

動圖如下：

接著就是簡單粗暴的程式碼：

//@[email protected]
//2016-12-4
//http://blog.csdn.net/zmdsjtu/article/details/53454071
#include <dlib/opencv.h>
#include <opencv2/opencv.hpp>
#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>

using namespace dlib;
using namespace std;

int main()
{
	try
	{
		cv::VideoCapture cap(0);
		if (!cap.isOpened())
		{
			cerr << "Unable to connect to camera" << endl;
			return 1;
		}

		//image_window win;

		// Load face detection and pose estimation models.
		frontal_face_detector detector = get_frontal_face_detector();
		shape_predictor pose_model;
		deserialize("shape_predictor_68_face_landmarks.dat") >> pose_model;

		// Grab and process frames until the main window is closed by the user.
		while (cv::waitKey(30) != 27)
		{
			// Grab a frame
			cv::Mat temp;
			cap >> temp;

			cv_image<bgr_pixel> cimg(temp);
			// 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]));
	
			if (!shapes.empty()) {
				for (int i = 0; i < 68; i++) {
					circle(temp, cvPoint(shapes[0].part(i).x(), shapes[0].part(i).y()), 3, cv::Scalar(0, 0, 255), -1);
					//	shapes[0].part(i).x();//68個
				}
			}
			//Display it all on the screen
			imshow("Dlib特徵點", temp);

		}
	}
	catch (serialization_error& e)
	{
		cout << "You need dlib's default face landmarking model file to run this example." << endl;
		cout << "You can get it from the following URL: " << endl;
		cout << "   http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" << endl;
		cout << endl << e.what() << endl;
	}
	catch (exception& e)
	{
		cout << e.what() << endl;
	}
}
 

circle(temp, cvPoint(shapes[0].part(i).x(), shapes[0].part(i).y()), 3, cv::Scalar(0, 0, 255), -1);

每個特徵點的編號如下：

在上述畫圖的基礎上加了如下一行程式碼：

putText(temp, to_string(i), cvPoint(shapes[0].part(i).x(), shapes[0].part(i).y()), CV_FONT_HERSHEY_PLAIN, 1, cv::Scalar(255, 0, 0),1,4);
 

對照著上圖，比如說想獲取鼻尖的座標，那麼橫座標就是shapes[0].part[30].x()，其餘的類似。

在這個的基礎上就可以做很多有意思的事情啦，2333

最後祝大家開發愉快：）

//順便祝女票大人和學妹看電影愉快（攤手）