这个表情识别项目的第一步

下面是代码，作用是识别出人脸，并且把人脸标记为68个特征点。
然后把每次识别的一组（68个数据）保存在 .txt文件里，一秒识别n次，那么一秒就生成n个txt

本篇博客的实现方法参照了https://blog.csdn.net/zmdsjtu/article/details/53667929，这个很好用，感谢这位博主。

//#pragma comment(linker, "/subsystem:windows /entry:mainCRTStartup")//去除CMD窗口
#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>
#include<string>

using namespace dlib;
using namespace std;
void write_txt(string name1, string content1, bool over_write);
int main()
{

	try
	{

		cv::VideoCapture cap(0);

		//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;
		std::vector<rectangle> faces2;
		// Grab and process frames until the main window is closed by the user.
		int filename = 0;
		//	while (cv::waitKey(30) != 27)

		//	while (cv::waitKey(30) != 27){
		//	cv::VideoCapture cap("1.avi");
		if (!cap.isOpened())
		{
			cerr << "Unable to connect to camera" << endl;
			return 1;
		}

		int frame1 = cap.get(7);
		//	while (cv::waitKey(30) != 27 && --frame1 != 14)

		while (cv::waitKey(30) != 27)
		{
			// Grab a frame
			cv::Mat temp;
			cap >> temp;
			//	temp = cv::imread("1.bmp");
			cv_image<bgr_pixel> cimg(temp);
			// Detect faces 
			std::vector<rectangle> faces = detector(cimg);
			if (!faces.empty())faces2 = faces;
			// Find the pose of each face.
			std::vector<full_object_detection> shapes;
			if (faces.empty()) {

				//		for (unsigned long i = 0; i < faces2.size(); ++i)
				//	shapes.push_back(pose_model(cimg, faces2[i]));
			}
			else {
				for (unsigned long i = 0; i < faces.size(); ++i)
					shapes.push_back(pose_model(cimg, faces[i]));
			}
			if (!shapes.empty()) {
				cv::line(temp, cvPoint(faces[0].left(), faces[0].top()), cvPoint(faces[0].right(), faces[0].top()), cv::Scalar(255, 0, 0));
				cv::line(temp, cvPoint(faces[0].left(), faces[0].top()), cvPoint(faces[0].left(), faces[0].bottom()), cv::Scalar(255, 0, 0));


				filename++;
				float aaa2 = -(faces[0].top() - faces[0].bottom()) / 300.0;
				cout << aaa2 << endl;
				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);
					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);
					//--------------这部分是想用来采集训练数据的

					write_txt((to_string(filename) + ".txt"), to_string((shapes[0].part(i).x() - faces[0].left()) / aaa2), 0);
					write_txt((to_string(filename) + ".txt"), to_string((shapes[0].part(i).y() - faces[0].top()) / aaa2), 0);
					//----------------------------------------------------------------
				}
				//		Drawarrow(temp, shapes[0].part(36).x(), shapes[0].part(48).x(), shapes[0].part(30).x(), shapes[0].part(45).x(), shapes[0].part(54).x(), shapes[0].part(36).y(), shapes[0].part(48).y(), shapes[0].part(30).y(), shapes[0].part(45).y(), shapes[0].part(54).y());
			}
			//Display it all on the screen
			//		cv::namedWindow("Dlib特征点", CV_WINDOW_NORMAL);
			//		cv::setWindowProperty("Dlib特征点", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
			imshow("Dlib特征点", temp);

			//	cv::waitKey(-1);
		}
		//}
		cv::destroyAllWindows();
	}

	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;
	//}
}
void write_txt(string name1, string content1, bool over_write) {
	ofstream write_into(name1, over_write ? ios::trunc : ios::app);

	if (write_into.is_open()) {//如果成功的话       
		write_into << content1 << endl;
		write_into.close();
	}
}