OpenCV学习(38) 人脸识别(3)

     

      前面我们学习了基于特征脸的人脸识别，现在我们学习一下基于Fisher脸的人脸识别，Fisher人脸识别基于LDA(线性判别算法)算法，算法的详细介绍可以参考下面两篇教程内容：

http://docs.opencv.org/modules/contrib/doc/facerec/facerec_tutorial.html

LDA算法细节参考：

http://www.cnblogs.com/mikewolf2002/p/3435750.html

 

程序代码:

#include "opencv2/core/core.hpp"
#include "opencv2/contrib/contrib.hpp"
#include "opencv2/highgui/highgui.hpp"

#include <iostream>
#include <fstream>
#include <sstream>

using namespace cv;
using namespace std;

static Mat norm_0_255(InputArray _src) 
    {
    Mat src = _src.getMat();
    Mat dst;
switch(src.channels()) 
        {
case 1:
        cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
break;
case 3:
        cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
break;
default:
        src.copyTo(dst);
break;
        }
return dst;
    }

static void read_csv(const string& filename, vector<Mat>& images, 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, 0));
            labels.push_back(atoi(classlabel.c_str()));
            }
        }
    }

int main(int argc, const char *argv[])
    {

    string fn_csv = string("facerec_at_t.txt");
    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;
        exit(1);
            }

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);
            }

int height = images[0].rows;

        Mat testSample = images[images.size() - 1];
int testLabel = labels[labels.size() - 1];

        images.pop_back();
        labels.pop_back();

        Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
        model->train(images, labels);
int predictedLabel = model->predict(testSample);

        string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
        cout << result_message << endl;

        Mat eigenvalues = model->getMat("eigenvalues");
        Mat W = model->getMat("eigenvectors");
        Mat mean = model->getMat("mean");
        imshow("mean", norm_0_255(mean.reshape(1, images[0].rows)));

for (int i = 0; i < min(16, W.cols); i++)
            {
            string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
            cout << msg << endl;
            Mat ev = W.col(i).clone();
            Mat grayscale = norm_0_255(ev.reshape(1, height));
            Mat cgrayscale;
            applyColorMap(grayscale, cgrayscale, COLORMAP_BONE);
            imshow(format("fisherface_%d", i), cgrayscale);

            }

for(int num_component = 0; num_component < min(16, W.cols); num_component++)
            {

            Mat ev = W.col(num_component);
            Mat projection = subspaceProject(ev, mean, images[0].reshape(1,1));
            Mat reconstruction = subspaceReconstruct(ev, mean, projection);
            reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
            imshow(format("fisherface_reconstruction_%d", num_component), reconstruction);

            }

while(1)
            waitKey(0);
return 0;
    }

      从代码中我们可以看到，最大的区别就是创建人脸识别模式类时候，调用的函数不一样，其它代码和特征脸识别的代码一样，对于train和predict函数来说，调用方式完全一样，只是底层的具体算法细节不一样。

    Ptr<FaceRecognizer> model = createFisherFaceRecognizer();

  

     下面是Fisher人脸识别类的train函数，从中可以看到，函数会先调用PCA算法进行降维，之后再执行LDA算法，求得Fisher特征值和特征向量。注意投影矩阵是PCA算法的特征向量和LDA算法特征向量的乘积。

    // 先用PCA算法降维perform a PCA and keep (N-C) components

    PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW, (N-C));

    // 把数据投影到 PCA空间，再对该数据执行LDA算法

    LDA lda(pca.project(data),labels, _num_components);

   // 保存总的均值向量

    _mean = pca.mean.reshape(1,1);

    _labels = labels.clone();

    lda.eigenvalues().convertTo(_eigenvalues, CV_64FC1);

   //计算投影矩阵=pca.eigenvectors * lda.eigenvectors.

    // Note: OpenCV stores the eigenvectors by row, so we need to transpose it!

    gemm(pca.eigenvectors, lda.eigenvectors(), 1.0, Mat(), 0.0, _eigenvectors, GEMM_1_T);

    //把原始矩阵投影到新的投影空间

    for(int sampleIdx = 0; sampleIdx < data.rows; sampleIdx++) {

        Mat p = subspaceProject(_eigenvectors, _mean, data.row(sampleIdx));

        _projections.push_back(p);

    }

 

    在程序中，我们仍然使用AT&T Facedatabase数据库的图片，原教程中推荐用Yale Facedatabase A，但是它的图像格式是gif，OpenCV不支持，只好放弃。

程序代码：FirstOpenCV34