|
|
人臉識(shí)別程序，歡迎大家參考
2dpca的matlab源程序：
- function [eigvectors, eigvalues, meanData, newTrainData, newTestData] = TDPCA(trainData, testData, height, width, numvecs)
- %2DPCA Two Dimensional Principal component analysis
- % Usage:
- % [eigvectors, eigvalues, meanData, newTrainData, newTestData] = TDPCA(trainData, testData, height, width, numvecs)
- %
- % trainData: Rows of vectors of training data points
- % testData: Rows of vectors of testing data points
- % height: height of the image matrix
- % width: width of the image matrix
- % numvecs: the needed number of eigenvectors
- %
- % meanData: Mean of all the data.
- % newTrainData: The data after projection (mean removed)
- % newTestData: The data after projection (mean removed)
- % eigvectors: Each column of this matrix is a eigenvector of the convariance
- % matrix defined in 2DPCA
- % eigvalues: Eigenvalues of the convariance matrix
- %
- %
- % Reference paper: J.Yang,D.Zhang,A.F.Frangi,and J.Yang.Two-dimensional
- % pca:A new approach to a appearance-based face
- % represenation and recognition. IEEE Trans.on
- % PAMI,2004
- % Written by Zhonghua Shen (cnjsnt_s@yahoo.com.cn), 2006.07
- % Check arguments
- if nargin ~= 5
- error('usage: [eigvectors, eigvalues, meanData, newTrainData, newTestData] = TDPCA(trainData, testData, height, width, numvecs)');
- end;
- [nSam,nFea] = size(trainData);
- fprintf(1,'Computing average matrix...\n');
- meanDataVector = mean(trainData);
- meanData = reshape(meanDataVector,height,width);
- fprintf(1,'Calculating matrix differences from avg and 2DPCA covariance matrix L...\n');
- L = zeros(width,width);
- ddata = zeros(nSam,nFea);
- for i = 1:nSam
- ddata(i,:) = trainData(i,:)-meanDataVector;
- dummyMat = reshape(ddata(i,:),height,width);
- L = L + dummyMat'*dummyMat;
- end;
- L = L/nSam;
- L = (L + L')/2;
- fprintf(1,'Calculating eigenvectors of L...\n');
- [eigvectors,eigvalues] = eig(L);
- fprintf(1,'Sorting eigenvectors according to eigenvalues...\n');
- [eigvectors,eigvalues] = sortem(eigvectors,eigvalues);
- eigvalues = diag(eigvalues);
- fprintf(1,'Normalize Vectors to unit length, kill vectors corr. to tiny evalues...\n');
- num_good = 0;
- for i = 1:size(eigvectors,2)
- eigvectors(:,i) = eigvectors(:,i)/norm(eigvectors(:,i));
- if eigvalues(i) < 0.00001
- % Set the vector to the 0 vector; set the value to 0.
- eigvalues(i) = 0;
- eigvectors(:,i) = zeros(size(eigvectors,1),1);
- else
- num_good = num_good + 1;
- end;
- end;
- if (numvecs > num_good)
- fprintf(1,'Warning: numvecs is %d; only %d exist.\n',numvecs,num_good);
- numvecs = num_good;
- end;
- eigvectors = eigvectors(:,1:numvecs);
- if nargout == 5
- fprintf(1,'Feature extraction and calculating new training and testing data...\n');
- newTrainData = zeros(nSam,height*numvecs);
- for i = 1:nSam
- dummyMat = reshape(ddata(i,:),height,width);
- newTrainData(i,:) = reshape(dummyMat*eigvectors,1,height*numvecs);
- end
- %testData
- ……………………
- …………限于本文篇幅 余下代碼請(qǐng)從51黑下載附件…………
全部資料51hei下載地址：
2dpca的matlab源代碼.rar
(1.22 KB, 下載次數(shù): 53)
2018-7-18 20:58 上傳
點(diǎn)擊文件名下載附件
下載積分: 黑幣 -5
|
|
QQ好友和群
QQ空間
騰訊微博
騰訊朋友
收藏
淘帖
頂
踩
