opencv -dnn人脸识别（2016-10-27）

来源：互联网发布：数显温度控制器单片机编辑：程序博客网时间：2024/06/05 22:34

转自：http://blog.csdn.net/shakevincent/article/details/52946499

版权声明：本文为博主原创文章，未经博主允许不得转载。

随着深度学习的发展，opencv3.1也可以直接调用caffe或者torch。下面是使用OpenCV的dnn模块来进行人脸识别：
1：编译opencv3.1
首先下载opencv源码https://github.com/opencv/opencv
下载Cmake https://cmake.org/download/
下载opencv的
具体的camke过程可以参考这篇博客：
http://www.cnblogs.com/jliangqiu2016/p/5597501.html
编译完成后可以把不需要的文件删除仅保留include bin lib 文件即可。
这里写图片描述

编译好的opencv3.1和普通opencv的配置过程一样：

opencv_aruco310.libopencv_bgsegm310.libopencv_bioinspired310.libopencv_calib3d310.libopencv_ccalib310.libopencv_core310.libopencv_cudaarithm310.libopencv_cudabgsegm310.libopencv_cudacodec310.libopencv_cudafeatures2d310.libopencv_cudafilters310.libopencv_cudaimgproc310.libopencv_cudalegacy310.libopencv_cudaobjdetect310.libopencv_cudaoptflow310.libopencv_cudastereo310.libopencv_cudawarping310.libopencv_cudev310.libopencv_datasets310.libopencv_dnn310.libopencv_dpm310.libopencv_face310.libopencv_features2d310.libopencv_flann310.libopencv_fuzzy310.libopencv_highgui310.libopencv_imgcodecs310.libopencv_imgproc310.libopencv_line_descriptor310.libopencv_ml310.libopencv_objdetect310.libopencv_optflow310.libopencv_photo310.libopencv_plot310.libopencv_reg310.libopencv_rgbd310.libopencv_saliency310.libopencv_shape310.libopencv_stereo310.libopencv_stitching310.libopencv_structured_light310.libopencv_superres310.libopencv_surface_matching310.libopencv_text310.libopencv_tracking310.libopencv_ts310.libopencv_video310.libopencv_videoio310.libopencv_videostab310.libopencv_viz310.libopencv_xfeatures2d310.libopencv_ximgproc310.libopencv_xobjdetect310.libopencv_xphoto310.lib1
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在opencv的源码中提供了dnn的test.cpp
下面具体分析代码：
/* Find best class for the blob (i. e. class with maximal probability) */
获取prob层的输出：实际意义为测试图片所对应与标签的概率值。resize成一个列向量，然后排序，输出最大值和最大值所对应的位置。
这里写图片描述

void getMaxClass(dnn::Blob &probBlob, int *classId, double *classProb){    Mat probMat = probBlob.matRefConst().reshape(1, 1); //reshape the blob to 1x1000 matrix    Point classNumber;    minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);    *classId = classNumber.x;}1
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相关系数函数：一种相似性度量用于判断两个人的相似性距离。

float mean(const std::vector<float>& v){    assert(v.size() != 0);    float ret = 0.0;    for (std::vector<float>::size_type i = 0; i != v.size(); ++i)    {        ret += v[i];    }    return ret / v.size();}float cov(const std::vector<float>& v1, const std::vector<float>& v2){    assert(v1.size() == v2.size() && v1.size() > 1);    float ret = 0.0;    float v1a = mean(v1), v2a = mean(v2);    for (std::vector<float>::size_type i = 0; i != v1.size(); ++i)    {        ret += (v1[i] - v1a) * (v2[i] - v2a);    }    return ret / (v1.size() - 1);}// 相关系数float coefficient(const std::vector<float>& v1, const std::vector<float>& v2){    assert(v1.size() == v2.size());    return cov(v1, v2) / sqrt(cov(v1, v1) * cov(v2, v2));}1
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cos相似性距离函数：

//cos 相似性度量float cos_distance(const std::vector<float>& vecfeature1, vector<float>& vecfeature2){    float cos_dis=0;    float dotmal=0, norm1=0, norm2=0;    for (int i = 0; i < vecfeature1.size(); i++)    {        dotmal += vecfeature1[i] * vecfeature2[i];        norm1 += vecfeature1[i] * vecfeature1[i];        norm2 += vecfeature2[i] * vecfeature2[i];    }    norm1 = sqrt(norm1);    norm2 = sqrt(norm2);    cos_dis = dotmal / (norm1*norm2);    return cos_dis;}1
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下面是主函数：

/**/////#include <opencv2/dnn.hpp>#include <opencv2/imgproc.hpp>#include <opencv2/highgui.hpp>using namespace cv;using namespace cv::dnn;#include <fstream>#include <iostream>#include <cstdlib>#include <time.h>#include<math.h>using namespace std;/* Find best class for the blob (i. e. class with maximal probability) */void getMaxClass(dnn::Blob &probBlob, int *classId, double *classProb){    Mat probMat = probBlob.matRefConst().reshape(1, 1); //reshape the blob to 1x1000 matrix    Point classNumber;    minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);    *classId = classNumber.x;}std::vector<String> readClassNames(const char *filename = "synset_words.txt"){    std::vector<String> classNames;    std::ifstream fp(filename);    if (!fp.is_open())    {        std::cerr << "File with classes labels not found: " << filename << std::endl;        exit(-1);    }    std::string name;    while (!fp.eof())    {        std::getline(fp, name);        if (name.length())            classNames.push_back(name.substr(name.find(' ') + 1));    }    fp.close();    return classNames;}string Int_String(int a){    std::stringstream ss;    std::string str;    ss << a;    ss >> str;    return str;}float mean(const std::vector<float>& v){    assert(v.size() != 0);    float ret = 0.0;    for (std::vector<float>::size_type i = 0; i != v.size(); ++i)    {        ret += v[i];    }    return ret / v.size();}float cov(const std::vector<float>& v1, const std::vector<float>& v2){    assert(v1.size() == v2.size() && v1.size() > 1);    float ret = 0.0;    float v1a = mean(v1), v2a = mean(v2);    for (std::vector<float>::size_type i = 0; i != v1.size(); ++i)    {        ret += (v1[i] - v1a) * (v2[i] - v2a);    }    return ret / (v1.size() - 1);}// 相关系数float coefficient(const std::vector<float>& v1, const std::vector<float>& v2){    assert(v1.size() == v2.size());    return cov(v1, v2) / sqrt(cov(v1, v1) * cov(v2, v2));}//cos 相似性度量float cos_distance(const std::vector<float>& vecfeature1, vector<float>& vecfeature2){    float cos_dis=0;    float dotmal=0, norm1=0, norm2=0;    for (int i = 0; i < vecfeature1.size(); i++)    {        dotmal += vecfeature1[i] * vecfeature2[i];        norm1 += vecfeature1[i] * vecfeature1[i];        norm2 += vecfeature2[i] * vecfeature2[i];    }    norm1 = sqrt(norm1);    norm2 = sqrt(norm2);    cos_dis = dotmal / (norm1*norm2);    return cos_dis;}int main(){    String modelTxt = "VGG_FACE_deploy.prototxt";    String modelBin = "VGG_FACE.caffemodel";    //String imageFile = (argc > 1) ? argv[1] : "ak.png";    /*String modelTxt = "bvlc_googlenet.prototxt";    String modelBin = "bvlc_googlenet.caffemodel";    String imageFile = (argc > 1) ? argv[1] : "1.jpg";*/    //! [Create the importer of Caffe model]    Ptr<dnn::Importer> importer;    try                                     //Try to import Caffe GoogleNet model    {        importer = dnn::createCaffeImporter(modelTxt, modelBin);    }    catch (const cv::Exception &err)        //Importer can throw errors, we will catch them    {        std::cerr << err.msg << std::endl;    }    //! [Create the importer of Caffe model]    if (!importer)    {        std::cerr << "Can't load network by using the following files: " << std::endl;        std::cerr << "prototxt:   " << modelTxt << std::endl;        std::cerr << "caffemodel: " << modelBin << std::endl;        std::cerr << "bvlc_googlenet.caffemodel can be downloaded here:" << std::endl;        std::cerr << "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel" << std::endl;        exit(-1);    }    //! [Initialize network]    dnn::Net net;    importer->populateNet(net);    importer.release();                     //We don't need importer anymore    //! [Initialize network]    //! [Prepare blob]    //===============进行训练样本提取=======================可修改====================    //========================五个人，每人一张照片====================================    std::vector<Mat> train;    std::vector<int> train_label;    int train_man = 1, train_num = 1;//训练的人的种类、人的个数    for (train_man = 1; train_man <= 4; train_man++)    {        for (train_num = 1; train_num <= 1; train_num++)        {            string train_road = "VGG_train/" + Int_String(train_man) + "-" + Int_String(train_num) + ".jpg";            cv::Mat train_Sample = imread(train_road);        //  cv::imshow("train_1",train_Sample);        //  waitKey(1);            if (!train_Sample.empty())            {                train.push_back(train_Sample);                train_label.push_back(train_man);                cout << "There is train pic!!" << train_man << "" << train_num << endl;            }            else            {                cout << "There is no pic!!" << train_man << "" << train_num;                getchar();                exit(-1);            }        }    }    clock_t start, finish;    double totaltime;    start = clock();    dnn::Blob train_blob = dnn::Blob(train);    net.setBlob(".data", train_blob);    cout << "Please wait..." << endl;    net.forward();    dnn::Blob prob = net.getBlob("fc7");//提取哪一层    Mat probMat = prob.matRefConst().reshape(1, 1); //reshape the blob to 1x4096 matrix    finish = clock();    totaltime = (double)(finish - start) / CLOCKS_PER_SEC;    totaltime = totaltime / 4;    std::cout << "extract feature the train image is :" << totaltime << "sec" << std::endl;    vector <   vector <float>   >   feature_vector;    feature_vector.clear();    int train_man_num = 0;//第几个人    clock_t start2, finish2;    double totaltime2;    start2 = clock();    for (train_man_num = 0; train_man_num <= 3; train_man_num++)    {        vector<float> feature_one;//单个人的feature        int channel = 0;        while (channel < 4096)//看网络相应层的output        {            feature_one.push_back(*prob.ptrf(train_man_num, channel, 1, 1));            channel++;            string train_txt = Int_String(train_man_num) + ".txt";            ofstream myfile(train_txt, ios::app);  //example.txt是你要输出的文件的名字,这里把向量都分开保存为txt，以便于后面可以直接读取            myfile << *prob.ptrf(train_man_num, channel, 1, 1) << endl;        }        feature_vector.push_back(feature_one);//把它赋给二维数组        feature_one.clear();    }    finish2 = clock();    totaltime2 = (double)(finish2 - start2) / CLOCKS_PER_SEC;    totaltime2 = totaltime2 / 4;    std::cout << "save the train image feature is :" << totaltime2 << "sec" << std::endl;    cout << "Successful extract!!!" << endl;    train_blob.offset();    //===============================================================================//    //                                                                               //    //                                 Test                                          //    //                                                                               //    //===============================================================================//    //string test_fileroad = "C://wamp//www//pic//" + Int_String(x) + ".jpg";//图片的地方，改成摄像头也可以。    Mat testSample = imread("C:\\Users\\naslab\\Desktop\\opencv_dnn _face_train\\opencv_dnn\\VGG_test\\1.jpg");    if (testSample.empty())        cout << "There is no testSample ..." << endl;    else    {        //testSample = Facedetect(testSample);        vector<Mat> test;        vector<int> test_label;        test.push_back(testSample);        test_label.push_back(0);        //then        dnn::Blob test_blob = dnn::Blob(test);//如果用原来的似乎会报错。。。        net.setBlob(".data", test_blob);        cout << "extracting features..." << endl;        clock_t start1, finish1;        double totaltime1;        start1 = clock();        net.forward();        dnn::Blob prob_test = net.getBlob("fc7");        vector<float> test_feature;//第8层的特征        int channel = 0;        while (channel < 4096)        {            test_feature.push_back(*prob_test.ptrf(0, channel, 1, 1));            channel++;        }        finish1 = clock();        totaltime1 = (double)(finish1 - start1) / CLOCKS_PER_SEC;        std::cout << "extract feature the train image is :" << totaltime1 << "sec" << std::endl;        cout << "we got it.." << endl;        float higher_score = 0;//相似度        int T_number = 0;        for (int test_num_vector = 0; test_num_vector <= 3; test_num_vector++)        {            float score1 = coefficient(feature_vector[test_num_vector], test_feature);            float score = cos_distance(feature_vector[test_num_vector], test_feature);            cout << "The coefficient" << test_num_vector << "----------to--------" << score1 << endl;            cout << "The cos_distance" << test_num_vector << "----------to--------" << score << endl;            if (score > higher_score)            {                higher_score = score;                T_number = test_num_vector;            }        }        cv::imshow("trainSample", train[T_number]);//可以直接把和测试样本最相近的一张图亮出来        cv::waitKey(1);    }    cv::imshow("testSample", testSample);    cv::waitKey(0);} //main1
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里面我有所修改，本来提取的是fc8层的，后来改成fc7层4096维特征。
这里写图片描述

这速度真喜人！！！！！！！！提取个特征就要8秒！！！！！！！
1：程序的改进方向：
1：保存提取的特征为dat文件，这样可以预先训练，直接测试即可
2：程序输出的是Bolb格式的数据，保存数据占用的时间比较长，可以修改一下。
3：还是使用caffe for windows吧！
下面是一些参考链接：
http://blog.csdn.net/mr_curry/article/details/52183263
http://docs.opencv.org/trunk/d5/de7/tutorial_dnn_googlenet.html
http://docs.opencv.org/trunk/de/d25/tutorial_dnn_build.html

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