openCV学习笔记（十五） —— 人脸识别 —— 读取摄像头进行人脸图像采集和人脸识别
实现以下功能:
交互式对话框：请选择要执行的动作
- 人脸采集：打开摄像头，采集照片，保存训练模型
人脸识别：
- 打开摄像头，采集照片，预识别；
- 输入图像的路径，图像识别
输出结果：
- 找到匹配的对象，输出名字；
- 未找到匹配的对象，提示：人脸采集并保存训练模型
程序
/*交互式对话框：请选择要执行的动作1.人脸采集：打开摄像头，采集照片，保存训练模型2.人脸识别：<1>打开摄像头，采集照片，预识别；<2>输入图像的路径，图像识别输出结果：找到匹配的对象，输出名字；未找到匹配的对象，提示：人脸采集并保存训练模型*/#include <iostream>#include <fstream>#include <string>#include <vector>#include <direct.h>#include <io.h>#include <opencv2/opencv.hpp>#include <opencv2/highgui.hpp>#include <opencv2/objdetect.hpp>#include <opencv2/face.hpp>using namespace std;using namespace cv;using namespace cv::face;int face_image_save(string dir_name, string cascade_path);int face_model_train_save(string cvs_path);int face_recg(Mat image, string cascade_path);//void read_cvs(const string& filename, vector<Mat>& img, vector<int>& lable, char separator = ';');//void write_cvs(string& filename, string line, int label_number, char separator = ';');string cascade_xml_path = "F:\\project\\vs2017\\openCV_test\\openCV_test\\cascade\\haarcascades\\haarcascade_frontalface_default.xml";string face_image_path = "F:\\project\\vs2017\\openCV_test\\openCV_test\\face_recg\\face\\";string cvs_path = "F:\\project\\vs2017\\openCV_test\\openCV_test\\face_recg\\face\\at.txt";int label_number = 100;#define MY_SIZE Size(92, 112)int main(){while (true){printf("\n<------------------ 欢迎使用人脸识别系统，请输入你的选择 ------------------>\n1 -- 人脸图像采集\n2 -- 人脸识别\n3 -- 退出系统\n");int action = 0;cin >> action;if (action == 1){printf("请输入你的名字：");string name;cin >> name;string dir_name = face_image_path + name + "\\";int ret = face_image_save(dir_name, cascade_xml_path);if (ret == 0){face_model_train_save(cvs_path);}}else if (action == 2){printf("\n\n请选择图片来源：\n1 -- 从指定目录选择图片\n2 -- 打开摄像头读取图片\n");cin >> action;Mat test_img;switch (action){case 1:{printf("请输入图片的完整路径\n");string img_path;while (true){cin >> img_path;if (_access(img_path.c_str(), 0) == -1){printf("没有找到图片，请重新选择图片！！！\n");}else{test_img = imread(img_path.c_str());break;}}}break;case 2:default:{printf("请对准摄像头，我们将读取照片...\n");//打开摄像头VideoCapture capture;capture.open(0);if (!capture.isOpened()){printf("Can't open camera...\n");return -1;}capture.read(test_img);capture.release();}break;}//人脸识别face_recg(test_img, cascade_xml_path);}else{break;}}return 0;}//使用CVS文件读取图片和标签void read_cvs(const string& filename, vector<Mat>& img, vector<int>& lable, char separator = ';'){Mat tmp_img;ifstream cvs_file(filename.c_str());assert(cvs_file);string line, path, tag;while (getline(cvs_file, line)){stringstream lines(line);getline(lines, path, separator);getline(lines, tag);if (!path.empty() && !tag.empty()){tmp_img = imread(path, IMREAD_GRAYSCALE); //读入图像时转成灰度图assert(!tmp_img.empty());img.push_back(tmp_img);lable.push_back(atoi(tag.c_str()));}}tmp_img.release();}void write_cvs(string& filename, string line, int label_number, char separator = ';'){ofstream cvs_file;string str = line + separator + format("%d\n", label_number);//printf("%s\n", str.c_str());cvs_file.open(filename.c_str(), ios::app);if (cvs_file.is_open()){cvs_file << str;cvs_file.close();}}int face_image_save(string dir_name, string cascade_path){//printf("我们将保存你的图片到以下路径：%s\n", dir_name.c_str());if (_access(dir_name.c_str(), 0) == -1){//printf("不存在文件夹，我们将创建...\n");_mkdir(dir_name.c_str());}else{printf("人脸图片已经保存在数据库，不需要重复读取\n");return -1;}VideoCapture capture;//打开笔记本摄像头capture.open(0);if (!capture.isOpened()){printf("can't open camera...\n");return -1;}//加载分类器CascadeClassifier cascade;cascade.load(cascade_path);if (cascade.empty()){printf("can't load classifier\n");return -1;}Mat img, img_gray, faceROI;vector<Rect> faces;int count = 0;string my_path, tmp_path;while (true){capture.read(img);cvtColor(img, img_gray, CV_RGB2GRAY); //转化为灰度图equalizeHist(img_gray, img_gray); //直方图均衡化cascade.detectMultiScale(img_gray, faces, 1.1, 6, 0, Size(30, 30)); //人脸识别，获取faces矢量数据集if (faces.size() > 0){for (int i = 0; i < faces.size(); ++i){faceROI = img_gray(faces[i]);//ROI部分为将要保存的图片，用img_gray获取灰度图部分，保存成灰度图；如果将img_gray改为img，就变成彩色图片了resize(faceROI, faceROI, MY_SIZE); //调整ROI图片大小到指定大小rectangle(img, faces[i], Scalar(0, 255, 0), 2, 8, 0);imshow("view", img); //显示视频图像imshow("ROI", faceROI); //显示ROI区域tmp_path = format("%d.jpg", count++);my_path = dir_name + tmp_path;imwrite(my_path, faceROI); //保存采集到的图片到指定目录write_cvs(cvs_path, my_path, label_number);}}if (waitKey(30) == 27 || count == 20){break;}}//保存的标签值label_number++;capture.release();destroyAllWindows();return 0;}int face_model_train_save(string cvs_path){printf("创建人脸识别的模型并进行训练，保存训练结果中（需要一些时间）...\n");vector<Mat> faces;vector<int> labels;try{read_cvs(cvs_path, faces, labels);}catch (Exception &e){cerr << "Error opening file, reason :" << e.msg << endl;return -1;}if (faces.size() <= 1){printf("Too few face images\n");return -1;}/* 创建人脸识别的模型，并进行训练，之后保存训练结果*/double start1, start2, start3;double end1, end2, end3;//PCA特征脸算法start1 = (double)getTickCount();Ptr<EigenFaceRecognizer> eigen_model = EigenFaceRecognizer::create();eigen_model->train(faces, labels);eigen_model->save("my_eigen_face_model.xml");end1 = (getTickCount() - start1) / getTickFrequency();//LDA线性判别分析start2 = (double)getTickCount();Ptr<FisherFaceRecognizer> fisher_model = FisherFaceRecognizer::create();fisher_model->train(faces, labels);fisher_model->save("my_fisher_face_model.xml");end2 = (getTickCount() - start2) / getTickFrequency();//LBP局部二值模式直方图start3 = (double)getTickCount();Ptr<LBPHFaceRecognizer> lbph_model = LBPHFaceRecognizer::create();lbph_model->train(faces, labels);lbph_model->save("my_lbph_face_model.xml");end3 = (getTickCount() - start3) / getTickFrequency();printf("PAC算法训练所用时间： %f s\n", end1);printf("LDA算法训练所用时间： %f s\n", end2);printf("LBP算法训练所用时间： %f s\n", end3);printf("训练结束，保存成功！\n");return 0;}int face_recg(Mat image, string cascade_path){printf("人脸识别中...\n");//显示读入的图片imshow("origin", image);waitKey(30);//加载分类器CascadeClassifier cascade;cascade.load(cascade_path);if (cascade.empty()){printf("can't load classifier\n");return -1;}//读取人脸识别模型double start1, start2, start3;double end1, end2, end3;start1 = (double)getTickCount();Ptr<EigenFaceRecognizer> eigen_model = Algorithm::load<EigenFaceRecognizer>("my_eigen_face_model.xml"); //Eigen Faces算法end1 = (getTickCount() - start1) / getTickFrequency();start2 = (double)getTickCount();Ptr<FisherFaceRecognizer> fisher_model = Algorithm::load<FisherFaceRecognizer>("my_fisher_face_model.xml");//Fisher Faces算法end2 = (getTickCount() - start2) / getTickFrequency();start3 = (double)getTickCount();Ptr<LBPHFaceRecognizer> lbph_model = Algorithm::load<LBPHFaceRecognizer>("my_lbph_face_model.xml"); //LBP 算法end3 = (getTickCount() - start3) / getTickFrequency();printf("加载PAC算法训练模型所用时间： %f s\n", end1);printf("加载LDA算法训练模型所用时间： %f s\n", end2);printf("加载LBP算法训练模型所用时间： %f s\n", end3);Mat faceROI, img_gray;int p_label;vector<Rect> faces;cvtColor(image, img_gray, CV_RGB2GRAY); //转化为灰度图equalizeHist(img_gray, img_gray); //直方图均衡化cascade.detectMultiScale(img_gray, faces, 1.1, 6, 0, Size(30, 30)); //人脸识别，获取faces矢量数据集//printf("size = %zd\n", faces.size());if (faces.size() > 0){for (int i = 0; i < faces.size(); ++i){faceROI = img_gray(faces[i]);resize(faceROI, faceROI, MY_SIZE); //调整成训练集一样的大小rectangle(image, faces[i], Scalar(0, 255, 0), 2, 8, 0);imshow("Origin", image);p_label = eigen_model->predict(faceROI);cout << "Eigen算法识别到的结果：" << p_label << endl;p_label = fisher_model->predict(faceROI);cout << "Fisher算法识别到的结果：" << p_label << endl;p_label = lbph_model->predict(faceROI);cout << "LBPH算法识别到的结果：" << p_label << endl;imshow("result", faceROI);}}destroyAllWindows();return 0;}
程序运行结果：
人脸图片采集
人脸识别-摄像头读取图片
从目录读取图片
从运行结果来看，LBPH算法最准确！
港控/mmm°
小灰灰
ゝ一纸荒年。
还没有评论，来说两句吧...