OpenCV学习(38) 人脸识别(3)
2013-11-22 06:57
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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:/content/3762708.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
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