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OpenCV学习(38) 人脸识别(3)

日期：2013-11-21点击：688收藏

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;
 }