那么你能帮用c++实现吗
时间: 2024-01-02 17:03:28 浏览: 26
当然可以,以下是一个使用OpenCV和Dlib库实现人脸识别和训练的C++代码示例,供您参考:
```cpp
#include <iostream>
#include <opencv2/opencv.hpp>
#include <dlib/opencv.h>
#include <dlib/image_processing/frontal_face_detector.h>
#include <dlib/image_processing.h>
#include <dlib/svm_threaded.h>
using namespace std;
using namespace cv;
using namespace dlib;
// 读取数据集
void load_dataset(vector<Mat> &dataset, vector<int> &labels) {
for (int i = 1; i <= 10; i++) {
for (int j = 1; j <= 9; j++) {
string filename = "dataset/" + to_string(i) + "/" + to_string(j) + ".jpg";
Mat img = imread(filename, IMREAD_GRAYSCALE);
dataset.push_back(img);
labels.push_back(i);
}
}
}
// 提取人脸特征
matrix<float,0,1> extract_features(Mat img, frontal_face_detector detector, shape_predictor predictor) {
std::vector<rectangle> dets = detector(img);
if (dets.size() > 0) {
full_object_detection shape = predictor(img, dets[0]);
matrix<rgb_pixel> face_chip;
extract_image_chip(img, get_face_chip_details(shape,150,0.25), face_chip);
matrix<float,0,1> descriptor = mean(mat(svm_image_to_vector(face_chip)));
return descriptor;
} else {
return matrix<float,0,1>();
}
}
// 训练分类器
void train(vector<Mat> dataset, vector<int> labels, svm_c_linear_trainer<linear_kernel< matrix<float,0,1> > > &trainer, frontal_face_detector detector, shape_predictor predictor) {
std::vector<matrix<float,0,1>> descriptors;
for (int i = 0; i < dataset.size(); i++) {
matrix<float,0,1> descriptor = extract_features(dataset[i], detector, predictor);
if (descriptor.size() > 0) {
descriptors.push_back(descriptor);
}
}
std::vector<int> new_labels;
for (int i = 0; i < descriptors.size(); i++) {
new_labels.push_back(labels[i]);
}
trainer.set_c(10);
trainer.be_verbose();
decision_function<linear_kernel< matrix<float,0,1> > > df = trainer.train(descriptors, new_labels);
df_function<linear_kernel< matrix<float,0,1> > > df2 = df;
std::vector<pending_sample<linear_kernel< matrix<float,0,1> > >> ps;
for (int i = 0; i < descriptors.size(); i++) {
ps.push_back(pending_sample<linear_kernel< matrix<float,0,1> > >(descriptors[i], labels[i]));
}
svm_c_linear_decision_function<linear_kernel< matrix<float,0,1> > > df3 = df2.get_decision_function();
df3.normalizer = df2.get_normalizer();
serialize("face_detection.dat") << detector;
serialize("shape_predictor.dat") << predictor;
serialize("face_descriptor.dat") << df3;
}
// 测试分类器
int test(Mat img, frontal_face_detector detector, shape_predictor predictor, decision_function<linear_kernel< matrix<float,0,1> > > &df) {
matrix<float,0,1> descriptor = extract_features(img, detector, predictor);
if (descriptor.size() > 0) {
int label = df(descriptor);
return label;
} else {
return -1;
}
}
int main(int argc, char** argv) {
// 加载数据集
vector<Mat> dataset;
vector<int> labels;
load_dataset(dataset, labels);
// 训练分类器
svm_c_linear_trainer<linear_kernel< matrix<float,0,1> > > trainer;
frontal_face_detector detector = get_frontal_face_detector();
shape_predictor predictor;
deserialize("shape_predictor_68_face_landmarks.dat") >> predictor;
train(dataset, labels, trainer, detector, predictor);
// 加载测试图片
Mat img = imread("test.jpg", IMREAD_GRAYSCALE);
// 测试分类器
decision_function<linear_kernel< matrix<float,0,1> > > df;
deserialize("face_descriptor.dat") >> df;
int label = test(img, detector, predictor, df);
// 输出识别结果
cout << "Label: " << label << endl;
return 0;
}
```
需要注意的是,以上代码是C++语言的示例代码,使用的是OpenCV和Dlib库的C++接口。为了使用Dlib库中的人脸检测器和人脸特征提取器,还需要下载相关的模型文件,代码中使用的是`shape_predictor_68_face_landmarks.dat`文件。此外,代码中还需要使用相关数据集和模型文件,您可以根据自己的需求进行调整和修改。