Here is the page I referenced for the octave version ... it includes examples very similar to what you want. I will be posting a very similar example in Python later this month.
I don't have any Python code on hand for the Petland paper, but I think matlab example should be easy to translate to scipy/matplotlib using the montage function: load faces.mat %Form covariance matrix C=cov(faces'); %build eigenvectors and eigenvalues [E,D] = eig(C); %sort based on eigenvalue [B,index] = sortrows(D'); E2=E'; E2(index,:)'; eigensorted=E2(index,:)'; %show eigenfaces clear Z; for i=1:length(eigensorted) Z(:,:,1,i)=reshape(eigensorted(:,i)-1.5*min(min(min(eigensorted))), 19,19); end montage(Z) %show top 16 eigenfaces clear Z; for i=1:16 Z(:,:,1,i)=reshape(eigensorted(:,i)-min(min(min(eigensorted))), 19,19); end montage(Z) On Fri, Feb 29, 2008 at 2:50 PM, Peter Skomoroch <[EMAIL PROTECTED]> wrote: > RoyG, > > The timing of your question couldn't be better, I just did an blog post on > this (I also plugged scipy and the EPD): > > > http://www.datawrangling.com/python-montage-code-for-displaying-arrays.html > > The code basically replicates the matlab montage() function and approach > to handling grayscale images using matplotlib. > > -Pete > > > On Fri, Feb 29, 2008 at 2:15 PM, [EMAIL PROTECTED] <[EMAIL PROTECTED]> > wrote: > > > hi guys > > I have a set of face images with which i want to do face recognition > > using Petland's PCA method.I gathered these steps from their docs > > > > 1.represent matrix of face images data > > 2.find the adjusted matrix by substracting the mean face > > 3.calculate covariance matrix (cov=A* A_transpose) where A is from > > step2 > > 4.find eigenvectors and select those with highest eigenvalues > > 5.calculate facespace=eigenvectors*A > > > > > > when it comes to implementation i have doubts as to how i should > > represent the matrix of face images? > > using PIL image.getdata() i can make an array of each greyscale image. > > Should the matrix be like each row contains an array representing an > > image? That will make a matrix with rows=numimages and > > columns=numpixels > > > > cavariancematrix =A *A_transpose will create a square matrix of > > shape(numimages,numimages) > > Using numpy.linalg.eigh(covariancematrix) will give eigenvectors of > > same shape as the covariance matrix. > > > > I would like to know if this is the correct way to do this..I have no > > big expertise in linear algebra so i would be grateful if someone can > > confirm the right way of doing this > > > > RoyG > > _______________________________________________ > > Numpy-discussion mailing list > > Numpy-discussion@scipy.org > > http://projects.scipy.org/mailman/listinfo/numpy-discussion > > > > > > -- > Peter N. Skomoroch > [EMAIL PROTECTED] > http://www.datawrangling.com -- Peter N. Skomoroch [EMAIL PROTECTED] http://www.datawrangling.com
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