人脸检测库libfacedetection介绍

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https://github.com/ShiqiYu/libfacedetection，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 <iostream>

#include <string>

#include <vector>

#include <facedetect-dll.h>

#include <opencv2/opencv.hpp>  

int main()

{

    std::vector<std::string> 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<int> count_faces{, , , , , , , , , ,

        , , , , , , , , , };  

    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 -;

    }  

    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[] {"face frontal", "face multiview", "face multiview reinforce", "face surveillance"};  

    for (int method = ; method < ; method++) {

        detect_face detect = detect_methods[method];

        fprintf(stderr, "detect type: %s\n", detect_type[method].c_str());  

        for (int i = ; i < images.size(); i++) {

            cv::Mat src_ = cv::imread(path_images + images[i], );

            if (src_.empty()) {

                fprintf(stderr, "read image error: %s\n", images[i].c_str());

                return -;

            }  

            cv::Mat src;

            cv::cvtColor(src_, src, CV_BGR2GRAY);  

            int* results = nullptr;

            results = detect(src.data, src.cols, src.rows, src.step, 1.2f, , , );

            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 = ; faces < (results ? *results : ); faces++) {

                short* p = ((short*)(results + )) +  * faces;

                int x = p[];

                int y = p[];

                int w = p[];

                int h = p[];

                int neighbors = p[];

                int angle = p[];  

                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(, , ), );

            }  

            cv::imwrite(save_result, src_);

        }

    }  

    int width = ;

    int height = ;

    cv::Mat dst(height * , width * , CV_8UC3);

    for (int i = ; i < images.size(); i++) {

        std::string input_image = path_images + "2_" + images[i];

        cv::Mat src = cv::imread(input_image, );

        if (src.empty()) {

            fprintf(stderr, "read image error: %s\n", images[i].c_str());

            return -;

        }  

        cv::resize(src, src, cv::Size(width, height), , , );

        int x = (i * width) % (width * );

        int y = (i / ) * 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 ;

}

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：https://github.com/fengbingchun/Face_Test

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 <opencv.hpp>

#include <iostream>

#include <fstream>

using namespace cv;

using namespace std;

static void read_csv(const string& filename, cv::vector<Mat>& images, cv::vector<int>& 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, ));

            labels.push_back(atoi(classlabel.c_str()));

        }

    }

}

FaceRotate.h

#include <dlib/image_processing/frontal_face_detector.h>

#include <dlib/image_processing/render_face_detections.h>

#include <dlib/image_processing.h>

#include <dlib/gui_widgets.h>

#include <dlib/image_io.h>

#include<dlib/opencv/cv_image.h>

#include <dlib/opencv.h>

using namespace dlib;

frontal_face_detector detector = get_frontal_face_detector();

shape_predictor sp;//Already get

FaceRecognition.cpp

#include <FaceDetect.h>

#include <ReadCSV.h>

const int namenumber = ;//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<FaceRecognizer>  GetTrainModel(string fn_csv)//输入CSV文件的路径名

{

    vector<Mat> images;

    vector<int> 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();

    }

    // 如果没有读取到足够图片，我们也得退出.

    if (images.size() <= ) {

        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<FaceRecognizer> model = createEigenFaceRecognizer();//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<FaceRecognizer> 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(, ), FONT_HERSHEY_DUPLEX, , Scalar(, , ), );//model->predict(frame) = predictLabel 名字写在 1 1

            imshow("Face Recogniton", frame);

            waitKey();

        }

        else{ cout << "The Video's end." <<endl; break; }

    }

}

FaceDetect.cpp

用了掩码。

#include <FaceDetect.h>

#include <FaceRotate.h>

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(, ));//裁剪

    cv::Mat Mask = cv::imread("mask.jpg", );

    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()), gray.cols, gray.rows, gray.step, 1.2f, , );

    int peopleNUM = (pResults ? *pResults : );

    for (int i = ; i < peopleNUM; i++)//代表有几张人脸(pResults ? *pResults : 0)

    {

        short * p = ((short*)(pResults + )) +  * i;

        Rect opencvRect(p[], p[], p[], p[]);

        //gray = gray(opencvRect);

        cv::rectangle(frame, opencvRect, Scalar(, , ));

        dlib::rectangle dlibRect((long)opencvRect.tl().x, (long)opencvRect.tl().y, (long)opencvRect.br().x - , (long)opencvRect.br().y - );

        //人脸对齐技术提高了准确率

        dlib::full_object_detection shape = sp(dlib::cv_image<uchar>(gray), dlibRect);//标记点

        std::vector<full_object_detection> shapes;

        shapes.push_back(shape);//把点保存在了shape中

        dlib::array<array2d<rgb_pixel>>  face_chips;

        extract_image_chips(dlib::cv_image<uchar>(gray), get_face_chip_details(shapes), face_chips);

        Mat pic = toMat(face_chips[]);

        cvtColor(pic, pic, CV_BGR2GRAY);

        return FaceToOne(pic);

    }

    return error;

}

FaceDetect.h

#include <opencv.hpp>

#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 <dlib/image_processing/frontal_face_detector.h>

#include <dlib/image_processing/render_face_detections.h>

#include <dlib/image_processing.h>

#include <dlib/gui_widgets.h>

#include <dlib/image_io.h>

#include<dlib/opencv/cv_image.h>

#include <dlib/opencv.h>

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http://blog.csdn.net/mr_curry/article/details/51804072 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 <opencv.hpp>

#include <facedetect-dll.h>

using namespace cv;

using namespace std;

//define the buffer size. Do not change the size!

#define DETECT_BUFFER_SIZE 0x20000

int 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 -;

    }

    Mat src = imread("img.jpg");

    Mat gray;

    cvtColor(src, gray, CV_BGR2GRAY);

    int doLandmark = ;// do landmark detection

    pResults = facedetect_multiview_reinforce(pBuffer, (unsigned char*)(gray.ptr()), gray.cols, gray.rows, (int)gray.step,

        1.2f, , , , doLandmark);

    //print the detection results

    for (int i = ; i < (pResults ? *pResults : ); i++)

    {

        short * p = ((short*)(pResults + )) +  * i;

        rectangle(src, Rect(p[], p[], p[], p[]), Scalar(, , ), );

        if (doLandmark)

        {

            for (int j = ; j < ; j++)

                circle(src, Point((int)p[ +  * j], (int)p[ +  * j + ]), , Scalar(, , ),);

        }

    }

    imshow("Show", src);

    waitKey();

}

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 <opencv.hpp>

#include <facedetect-dll.h>

using namespace cv;

using namespace std;

//define the buffer size. Do not change the size!

#define DETECT_BUFFER_SIZE 0x20000

int 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 -;

    }

    int doLandmark = ;// do landmark detection

    VideoCapture cap();

    if (!cap.isOpened()){

        cout << "Please check your USB camera's interface num." << endl;

        return ;

    }

    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()), gray.cols, gray.rows, (int)gray.step,

                1.2f, , , , );

            for (int i = ; i < (pResults ? *pResults : ); i++)

            {

                short * p = ((short*)(pResults + )) +  * i;

                rectangle(src, Rect(p[], p[], p[], p[]), Scalar(, , ), );

                if (doLandmark)

                {

                    for (int j = ; j < ; j++)

                        circle(src, Point((int)p[ +  * j], (int)p[ +  * j + ]), , Scalar(, , ), );

                }

            }

            imshow("Show", src);

            waitKey();

        }

    }

}

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

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