人脸识别-<转>

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发布时间：2019-06-29

本文共 12112 字，大约阅读时间需要 40 分钟。

libfacedetection是于仕琪老师放到GitHub上的二进制库，没有源码，它的License是MIT，可以商用。目前只提供了windows 32和64位的release动态库，主页为，采用的算法好像是Multi-BlockLBP，提供了四套接口，分别为frontal、frontal_surveillance、multiview、multiview_reinforce，其中multiview_reinforce效果最好，速度比其它稍慢，四套接口的参数类型完全一致，可以根据需要对参数min_neighbors和min_object_width进行调整。

新建一个控制台工程，用来测试libfacedetection，测试代码如下：

#include 
     
        #include 
      
         #include 
       
          #include 
        
           #include 
         
              int main()  {      std::vector
          
            images{ "1.jpg", "2.jpg", "3.jpg", "4.jpeg", "5.jpeg", "6.jpg", "7.jpg", "8.jpg", "9.jpg", "10.jpg", "11.jpeg", "12.jpg", "13.jpeg", "14.jpg", "15.jpeg", "16.jpg", "17.jpg", "18.jpg", "19.jpg", "20.jpg" }; std::vector
           
             count_faces{ 1, 2, 6, 0, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 8, 2}; std::string path_images{ "E:/GitCode/Face_Test/testdata/" }; if (images.size() != count_faces.size()) { fprintf(stderr, "their size that images and count_faces are mismatch\n"); return -1; } typedef int* (*detect_face)(unsigned char * gray_image_data, int width, int height, int step, float scale, int min_neighbors, int min_object_width, int max_object_width); detect_face detect_methods[]{ &facedetect_frontal, &facedetect_multiview, &facedetect_multiview_reinforce, &facedetect_frontal_surveillance }; std::string detect_type[4] { "face frontal", "face multiview", "face multiview reinforce", "face surveillance"}; for (int method = 0; method < 4; method++) { detect_face detect = detect_methods[method]; fprintf(stderr, "detect type: %s\n", detect_type[method].c_str()); for (int i = 0; i < images.size(); i++) { cv::Mat src_ = cv::imread(path_images + images[i], 1); if (src_.empty()) { fprintf(stderr, "read image error: %s\n", images[i].c_str()); return -1; } cv::Mat src; cv::cvtColor(src_, src, CV_BGR2GRAY); int* results = nullptr; results = detect(src.data, src.cols, src.rows, src.step, 1.2f, 2, 10, 0); std::string save_result = path_images + std::to_string(method) + "_" + images[i]; //fprintf(stderr, "save result: %s\n", save_result.c_str()); for (int faces = 0; faces < (results ? *results : 0); faces++) { short* p = ((short*)(results + 1)) + 6 * faces; int x = p[0]; int y = p[1]; int w = p[2]; int h = p[3]; int neighbors = p[4]; int angle = p[5]; fprintf(stderr, "image_name: %s, faces_num: %d, face_rect=[%d, %d, %d, %d], neighbors=%d, angle=%d\n", images[i].c_str(), *results, x, y, w, h, neighbors, angle); cv::rectangle(src_, cv::Rect(x, y, w, h), cv::Scalar(0, 255, 0), 2); } cv::imwrite(save_result, src_); } } int width = 200; int height = 200; cv::Mat dst(height * 5, width * 4, CV_8UC3); for (int i = 0; i < images.size(); i++) { std::string input_image = path_images + "2_" + images[i]; cv::Mat src = cv::imread(input_image, 1); if (src.empty()) { fprintf(stderr, "read image error: %s\n", images[i].c_str()); return -1; } cv::resize(src, src, cv::Size(width, height), 0, 0, 4); int x = (i * width) % (width * 4); int y = (i / 4) * height; cv::Mat part = dst(cv::Rect(x, y, width, height)); src.copyTo(part); } std::string output_image = path_images + "result.png"; cv::imwrite(output_image, dst); fprintf(stderr, "ok\n"); return 0; }

从网上找了20张图像，验证此库的检测率，下图是采用multiview_reinforce接口的检测结果：

GitHub：

原文地址：http://blog.csdn.net/fengbingchun/article/details/52964163

==========================================================================

Opencv与dlib联合进行人脸关键点检测与识别

前言

依赖库：opencv 2.4.9 /dlib 19.0/libfacedetection

本篇不记录如何配置，重点在算法实现上。使用libfacedetection实现人脸区域检测，联合dlib标记人脸特征点，最后使用opencv的FaceRecognizer实现人脸识别。

准备工作

1、配置好Opencv2.4.9。（Opencv3.1需要另外下载一个包才有FaceRecognizer）

2、配置好dlib 19.0(版本其实没有多大关系)

3、配置好ShiQi.Yu的人脸检测库

思想

训练模块：人脸检测——>获取人脸区域的点坐标——>人脸关键点标记——>人脸对正——>归一化处理——>保存图片——>手动筛选图片——>训练样本——>得到train.xml

识别模块：读取train.xml——>循环（人脸检测——>获取人脸区域的点坐标——>人脸关键点标记——>人脸对正——>归一化处理——>送入model->predict——>预测出结果——>putText在方框上写出名字）

结果

识别速度：0.15~0.25秒，Release平台。

识别精度：还可以，基本不会识别错，样本没有选择需要识别的东西哦。

使用了一段中国好声音的视频做识别。

当然，这里用的是Fisherface算法，主要还是样本不多，已经可以搞定了。

代码

ReadCSV.h

#include 
     
      #include 
      
       #include 
       
        using namespace cv;using namespace std;static void read_csv(const string& filename, cv::vector
        
         & images, cv::vector
         
          & labels, char separator = ';') {    std::ifstream file(filename.c_str(), ifstream::in);    if (!file) {        string error_message = "No valid input file was given, please check the given filename.";        CV_Error(CV_StsBadArg, error_message);    }    string line, path, classlabel;    while (getline(file, line)) {        stringstream liness(line);        getline(liness, path, separator);        getline(liness, classlabel);        if (!path.empty() && !classlabel.empty()) {            images.push_back(imread(path, 0));            labels.push_back(atoi(classlabel.c_str()));        }    }}

FaceRotate.h

#include 
     
      #include 
      
       #include 
       
        #include 
        
         #include 
         
          #include
          
           #include 
           
            using namespace dlib;frontal_face_detector detector = get_frontal_face_detector();shape_predictor sp;//Already get

FaceRecognition.cpp

#include 
     
      #include 
      
       const int namenumber = 4;//测试的人脸数量const string textname[namenumber] = { "Hariem", "Miss.Na", "Mr.Wang", "Jay.Chou" };//做一个储存人脸名字的数组Ptr
       
          GetTrainModel(string fn_csv)//输入CSV文件的路径名{    vector
        
          images;     vector
         
           labels;    try {        read_csv(fn_csv, images, labels);    }    catch (cv::Exception& e) {        cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;        // 文件有问题，我们啥也做不了了，退出了        exit(1);    }    // 如果没有读取到足够图片，我们也得退出.    if (images.size() <= 1) {        string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";        CV_Error(CV_StsError, error_message);    }    Ptr
          
            model = createEigenFaceRecognizer(80);//创建人脸识别类 可修改 LBPHFace、EigenFace、FisherFace model->train(images, labels); return model;}int main(){ Dlib_Predefine();//加载dlib的文件 Ptr
           
             model = GetTrainModel("face.csv");//获得模型 VideoCapture cap("好声音.mp4"); Mat frame,gray; while (true) { cap >> frame; if (!frame.empty()) { gray = FaceDetect(frame); if (!gray.empty()) putText(frame, textname[model->predict(gray)], Point(50, 50), FONT_HERSHEY_DUPLEX, 3, Scalar(230, 255, 0), 2);//model->predict(frame) = predictLabel 名字写在 1 1 imshow("Face Recogniton", frame); waitKey(1); } else{ cout << "The Video's end." <

FaceDetect.cpp

用了掩码。

#include 
     
      #include 
      
       void Dlib_Predefine(){    deserialize("shape_predictor_68_face_landmarks.dat") >> sp;//读入标记点文件}cv::Mat FaceToOne(cv::Mat source)//归一化处理函数{    cv::equalizeHist(source, source);//直方图均衡    cv::resize(source, source, cv::Size(92, 112));//裁剪    cv::Mat Mask = cv::imread("mask.jpg", 0);    cv::Mat changedMask;    source.copyTo(changedMask, Mask);    return changedMask;}Mat FaceDetect(Mat frame)//脸是否存在{    Mat gray, error;    cvtColor(frame, gray, CV_BGR2GRAY);    int * pResults = NULL;    pResults = facedetect_frontal_tmp((unsigned char*)(gray.ptr(0)), gray.cols, gray.rows, gray.step, 1.2f, 5, 24);    int peopleNUM = (pResults ? *pResults : 0);    for (int i = 0; i < peopleNUM; i++)//代表有几张人脸(pResults ? *pResults : 0)    {        short * p = ((short*)(pResults + 1)) + 6 * i;        Rect opencvRect(p[0], p[1], p[2], p[3]);        //gray = gray(opencvRect);        cv::rectangle(frame, opencvRect, Scalar(230, 255, 0));        dlib::rectangle dlibRect((long)opencvRect.tl().x, (long)opencvRect.tl().y, (long)opencvRect.br().x - 1, (long)opencvRect.br().y - 1);        //人脸对齐技术提高了准确率        dlib::full_object_detection shape = sp(dlib::cv_image
       
        (gray), dlibRect);//标记点        std::vector
        
          shapes;        shapes.push_back(shape);//把点保存在了shape中        dlib::array
         
          
           > face_chips; extract_image_chips(dlib::cv_image
           
            (gray), get_face_chip_details(shapes), face_chips); Mat pic = toMat(face_chips[0]); cvtColor(pic, pic, CV_BGR2GRAY); return FaceToOne(pic); } return error;}

FaceDetect.h

#include 
     
      #include "facedetect-dll.h"using namespace cv;using namespace std;Mat FaceDetect(Mat frame);void Dlib_Predefine();//dlib 预定义的函数

FaceRotate.h

#include 
     
      #include 
      
       #include 
       
        #include 
        
         #include 
         
          #include
          
           #include 
           
            using namespace dlib;frontal_face_detector detector = get_frontal_face_detector();shape_predictor sp;//Already get

Mask图片：

有几个说明

已经全部更正。

原文地址：http://blog.csdn.net/mr_curry/article/details/51994497

==========================================================

如何快糙好猛的使用libfacedetection库【最新版】

前言

最近已经很少看CSDN了。这一年多准备考研，基本上怕是不会再怎么上了。以前有一个如何快糙好猛的使用Shiqi.Yu老师的公开人脸检测库（附源码）的BLOG，因为于老师的库已经更新了，所以重新写一下吧。

PS：这个库越来越强了，已经可以做人脸关键点检测了。关键点检测可以用于矫正人脸，再也不要用慢的要死的dlib啦~~

配置

五张图带你解决问题：（X64，Debug）

然后你需要把opencv的属性表也引进来：

两个方法，加系统变量或者放到和exe同一个文件夹下。加了系统变量后重启一次才生效，所以这里就直接放咯

代码

我们直接用FDDB上评测效果最好的函数：facedetect_multiview_reinforce

#include 
     
      #include 
      
       using namespace cv;using namespace std;//define the buffer size. Do not change the size!#define DETECT_BUFFER_SIZE 0x20000int main(){    int * pResults = NULL;    //pBuffer is used in the detection functions.    //If you call functions in multiple threads, please create one buffer for each thread!    unsigned char * pBuffer = (unsigned char *)malloc(DETECT_BUFFER_SIZE);    if (!pBuffer)    {        fprintf(stderr, "Can not alloc buffer.\n");        return -1;    }    Mat src = imread("img.jpg");    Mat gray;    cvtColor(src, gray, CV_BGR2GRAY);    int doLandmark = 1;// do landmark detection    pResults = facedetect_multiview_reinforce(pBuffer, (unsigned char*)(gray.ptr(0)), gray.cols, gray.rows, (int)gray.step,        1.2f, 2, 48, 0, doLandmark);    //print the detection results    for (int i = 0; i < (pResults ? *pResults : 0); i++)    {        short * p = ((short*)(pResults + 1)) + 142 * i;        rectangle(src, Rect(p[0], p[1], p[2], p[3]), Scalar(0, 255, 0), 2);        if (doLandmark)        {            for (int j = 0; j < 68; j++)                circle(src, Point((int)p[6 + 2 * j], (int)p[6 + 2 * j + 1]), 1, Scalar(0, 0, 255),2);        }    }    imshow("Show", src);    waitKey(0);}

效果还是很赞：

视频流中的人脸检测代码就是用VideoCapture解析为Mat然后循环检测啊：

#include 
     
      #include 
      
       using namespace cv;using namespace std;//define the buffer size. Do not change the size!#define DETECT_BUFFER_SIZE 0x20000int main(){    int * pResults = NULL;    //pBuffer is used in the detection functions.    //If you call functions in multiple threads, please create one buffer for each thread!    unsigned char * pBuffer = (unsigned char *)malloc(DETECT_BUFFER_SIZE);    if (!pBuffer)    {        fprintf(stderr, "Can not alloc buffer.\n");        return -1;    }    int doLandmark = 1;// do landmark detection    VideoCapture cap(0);    if (!cap.isOpened()){        cout << "Please check your USB camera's interface num." << endl;        return 0;    }    Mat src;    while (true)    {        cap >> src;        if (!src.empty()){            Mat gray;            cvtColor(src, gray, CV_BGR2GRAY);            pResults = facedetect_multiview_reinforce(pBuffer, (unsigned char*)(gray.ptr(0)), gray.cols, gray.rows, (int)gray.step,                1.2f, 2, 48, 0, 1);            for (int i = 0; i < (pResults ? *pResults : 0); i++)            {                short * p = ((short*)(pResults + 1)) + 142 * i;                rectangle(src, Rect(p[0], p[1], p[2], p[3]), Scalar(0, 255, 0), 2);                if (doLandmark)                {                    for (int j = 0; j < 68; j++)                        circle(src, Point((int)p[6 + 2 * j], (int)p[6 + 2 * j + 1]), 1, Scalar(0, 0, 255), 2);                }            }            imshow("Show", src);            waitKey(1);        }    }}

原文地址：http://blog.csdn.net/Mr_Curry/article/details/65945071

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