帮我写一个用c++下用opencv4.6 dlib19.24 基于facenet实现的人脸比对项目

好的，以下是一个使用C++、OpenCV 4.6和Dlib 19.24实现的人脸比对项目。该项目基于Facenet算法，可实现两张人脸图片的相似度比对。

首先，需要安装OpenCV 4.6和Dlib 19.24，并在项目中引入它们的头文件和库文件。

然后，需要使用Dlib中的人脸检测器和Facenet模型进行人脸特征提取。具体步骤如下：

1. 加载人脸检测器和Facenet模型：

dlib::frontal_face_detector detector = dlib::get_frontal_face_detector();
dlib::shape_predictor sp;
dlib::deserialize("shape_predictor_68_face_landmarks.dat") >> sp;
dlib::anet_type net;
dlib::deserialize("dlib_face_recognition_resnet_model_v1.dat") >> net;

2. 对两张人脸图片进行人脸检测和特征提取：

cv::Mat img1 = cv::imread("image1.jpg");
cv::Mat img2 = cv::imread("image2.jpg");
dlib::matrix<dlib::rgb_pixel> dlib_img1, dlib_img2;
dlib::assign_image(dlib_img1, dlib::cv_image<dlib::bgr_pixel>(img1));
dlib::assign_image(dlib_img2, dlib::cv_image<dlib::bgr_pixel>(img2));

std::vector<dlib::rectangle> faces1 = detector(dlib_img1);
std::vector<dlib::rectangle> faces2 = detector(dlib_img2);
std::vector<dlib::matrix<float, 0, 1>> face1_descriptors, face2_descriptors;
for (auto face : faces1) {
    auto shape = sp(dlib_img1, face);
    dlib::matrix<dlib::rgb_pixel> face_chip;
    dlib::extract_image_chip(dlib_img1, dlib::get_face_chip_details(shape, 150, 0.25), face_chip);
    face1_descriptors.push_back(net(face_chip));
}
for (auto face : faces2) {
    auto shape = sp(dlib_img2, face);
    dlib::matrix<dlib::rgb_pixel> face_chip;
    dlib::extract_image_chip(dlib_img2, dlib::get_face_chip_details(shape, 150, 0.25), face_chip);
    face2_descriptors.push_back(net(face_chip));
}

3. 计算两张人脸图片的相似度：

float similarity = 0;
for (auto face1_descriptor : face1_descriptors) {
    for (auto face2_descriptor : face2_descriptors) {
        similarity = std::max(similarity, dlib::length(face1_descriptor - face2_descriptor));
    }
}

上述代码中，相似度的计算使用了欧几里得距离，即两个特征向量之间的欧几里得距离越小，相似度越高。

完整的代码如下：

#include <iostream>
#include <opencv2/opencv.hpp>
#include <dlib/opencv.h>
#include <dlib/image_processing/frontal_face_detector.h>
#include <dlib/image_processing/shape_predictor.h>
#include <dlib/dnn.h>
#include <dlib/image_io.h>

int main() {
    dlib::frontal_face_detector detector = dlib::get_frontal_face_detector();
    dlib::shape_predictor sp;
    dlib::deserialize("shape_predictor_68_face_landmarks.dat") >> sp;
    dlib::anet_type net;
    dlib::deserialize("dlib_face_recognition_resnet_model_v1.dat") >> net;

    cv::Mat img1 = cv::imread("image1.jpg");
    cv::Mat img2 = cv::imread("image2.jpg");
    dlib::matrix<dlib::rgb_pixel> dlib_img1, dlib_img2;
    dlib::assign_image(dlib_img1, dlib::cv_image<dlib::bgr_pixel>(img1));
    dlib::assign_image(dlib_img2, dlib::cv_image<dlib::bgr_pixel>(img2));

    std::vector<dlib::rectangle> faces1 = detector(dlib_img1);
    std::vector<dlib::rectangle> faces2 = detector(dlib_img2);
    std::vector<dlib::matrix<float, 0, 1>> face1_descriptors, face2_descriptors;
    for (auto face : faces1) {
        auto shape = sp(dlib_img1, face);
        dlib::matrix<dlib::rgb_pixel> face_chip;
        dlib::extract_image_chip(dlib_img1, dlib::get_face_chip_details(shape, 150, 0.25), face_chip);
        face1_descriptors.push_back(net(face_chip));
    }
    for (auto face : faces2) {
        auto shape = sp(dlib_img2, face);
        dlib::matrix<dlib::rgb_pixel> face_chip;
        dlib::extract_image_chip(dlib_img2, dlib::get_face_chip_details(shape, 150, 0.25), face_chip);
        face2_descriptors.push_back(net(face_chip));
    }

    float similarity = 0;
    for (auto face1_descriptor : face1_descriptors) {
        for (auto face2_descriptor : face2_descriptors) {
            similarity = std::max(similarity, dlib::length(face1_descriptor - face2_descriptor));
        }
    }

    std::cout << "Similarity: " << similarity << std::endl;

    return 0;
}