vsc++姿态估计和人脸检测代码

以下是一个基于OpenCV和Dlib库的C++代码示例，用于姿态估计和人脸检测：

#include "opencv2/opencv.hpp"
#include "dlib/opencv.h"
#include "dlib/image_processing.h"
#include "dlib/image_processing/frontal_face_detector.h"
#include "dlib/image_processing/shape_predictor.h"
#include <iostream>
#include <fstream>

using namespace cv;
using namespace dlib;
using namespace std;

int main()
{
    try
    {
        frontal_face_detector detector = get_frontal_face_detector();
        shape_predictor predictor;
        deserialize("shape_predictor_68_face_landmarks.dat") >> predictor;

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

        namedWindow("Face Detection and Tracking", WINDOW_NORMAL);
        setWindowProperty("Face Detection and Tracking", WND_PROP_FULLSCREEN, WINDOW_FULLSCREEN);

        while (true)
        {
            Mat frame;
            cap >> frame;

            if (frame.empty())
            {
                cerr << "Unable to capture frame" << endl;
                break;
            }

            cv_image<bgr_pixel> cimg(frame);
            std::vector<rectangle> faces = detector(cimg);

            std::vector<full_object_detection> shapes;
            for (unsigned long i = 0; i < faces.size(); ++i)
            {
                full_object_detection shape = predictor(cimg, faces[i]);
                shapes.push_back(shape);
            }

            for (unsigned long i = 0; i < faces.size(); ++i)
            {
                rectangle r = faces[i];
                rectangle r_scaled(r.left() / 2, r.top() / 2, r.right() / 2, r.bottom() / 2);
                rectangle frame_rect(0, 0, frame.cols, frame.rows);

                if (frame_rect.contains(r_scaled.tl()) && frame_rect.contains(r_scaled.br()))
                {
                    draw_rectangle(frame, r, cv::Scalar(0, 255, 0), 2);
                    full_object_detection shape = shapes[i];
                    for (unsigned long j = 0; j < shape.num_parts(); ++j)
                    {
                        circle(frame, cv::Point(shape.part(j).x(), shape.part(j).y()), 2, cv::Scalar(0, 0, 255), -1);
                    }

                    // 人脸姿态估计
                    std::vector<cv::Point3d> model_points;
                    // 3D模型点
                    model_points.push_back(cv::Point3d(0.0f, 0.0f, 0.0f));               // 鼻尖
                    model_points.push_back(cv::Point3d(0.0f, -330.0f, -65.0f));          // 下巴
                    model_points.push_back(cv::Point3d(-225.0f, 170.0f, -135.0f));       // 左眼内角
                    model_points.push_back(cv::Point3d(225.0f, 170.0f, -135.0f));        // 右眼内角
                    model_points.push_back(cv::Point3d(-150.0f, -150.0f, -125.0f));      // 左嘴角
                    model_points.push_back(cv::Point3d(150.0f, -150.0f, -125.0f));       // 右嘴角

                    std::vector<cv::Point2d> image_points;
                    // 2D图像点
                    for (unsigned long j = 0; j < shape.num_parts(); ++j)
                    {
                        image_points.push_back(cv::Point2d(shape.part(j).x(), shape.part(j).y()));
                    }

                    cv::Mat camera_matrix = (cv::Mat_<double>(3, 3) <<
                        1.0, 0, frame.cols / 2,
                        0, 1.0, frame.rows / 2,
                        0, 0, 1.0);  // 内参矩阵

                    cv::Mat dist_coeffs = cv::Mat::zeros(4, 1, cv::DataType<double>::type); // 4个畸变系数：k1,k2,p1,p2

                    cv::Mat rotation_vector; // 旋转向量
                    cv::Mat translation_vector; // 平移向量
                    cv::solvePnP(model_points, image_points, camera_matrix, dist_coeffs, rotation_vector, translation_vector);

                    // 旋转向量转换为旋转矩阵
                    cv::Mat rotation_matrix;
                    cv::Rodrigues(rotation_vector, rotation_matrix);

                    // 投影矩阵
                    cv::Matx34d projection_matrix(
                        rotation_matrix.at<double>(0, 0), rotation_matrix.at<double>(0, 1), rotation_matrix.at<double>(0, 2), translation_vector.at<double>(0),
                        rotation_matrix.at<double>(1, 0), rotation_matrix.at<double>(1, 1), rotation_matrix.at<double>(1, 2), translation_vector.at<double>(1),
                        rotation_matrix.at<double>(2, 0), rotation_matrix.at<double>(2, 1), rotation_matrix.at<double>(2, 2), translation_vector.at<double>(2)
                    );

                    // 计算欧拉角
                    cv::Vec3d euler_angles;
                    cv::Matx33d rotation_matrix_ = rotation_matrix;
                    double sy = sqrt(rotation_matrix_(0, 0) * rotation_matrix_(0, 0) + rotation_matrix_(1, 0) * rotation_matrix_(1, 0));
                    bool singular = sy < 1e-6;
                    if (!singular)
                    {
                        euler_angles[0] = atan2(rotation_matrix_(2, 1), rotation_matrix_(2, 2));
                        euler_angles[1] = atan2(-rotation_matrix_(2, 0), sy);
                        euler_angles[2] = atan2(rotation_matrix_(1, 0), rotation_matrix_(0, 0));
                    }
                    else
                    {
                        euler_angles[0] = atan2(-rotation_matrix_(1, 2), rotation_matrix_(1, 1));
                        euler_angles[1] = atan2(-rotation_matrix_(2, 0), sy);
                        euler_angles[2] = 0;
                    }

                    // 显示欧拉角信息
                    stringstream ss;
                    ss << "Yaw: " << euler_angles[1] * 180 / CV_PI << " degrees" << endl;
                    ss << "Pitch: " << euler_angles[0] * 180 / CV_PI << " degrees" << endl;
                    ss << "Roll: " << euler_angles[2] * 180 / CV_PI << " degrees" << endl;
                    cv::putText(frame, ss.str(), cv::Point(10, frame.rows - 50), cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(0, 0, 255), 2);
                }
            }

            imshow("Face Detection and Tracking", frame);

            if (waitKey(1) == 27)
            {
                break;
            }
        }

        destroyAllWindows();
    }
    catch (exception& e)
    {
        cerr << e.what() << endl;
    }

    return 0;
}

这段代码实现了从摄像头捕捉视频流，实时检测人脸并标出关键点，同时根据人脸关键点进行姿态估计，并在视频中显示欧拉角信息。请注意，由于姿态估计需要3D模型点，因此需要预先定义这些点的坐标。在这个示例中，我们将这些点硬编码为固定值。这段代码还需要使用OpenCV和Dlib库，因此请确保在编译代码之前安装这些库。