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
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