Since nobody has responded to this: I know nothing about PIL, but you can do this using numpy and scipy fairly easily, and you can transform PIL arrays into Numpy arrays pretty quickly as well.
-----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Sent: Thursday, February 21, 2008 2:41 AM To: python-list@python.org Subject: using PIL for PCA analysis hi guys i am trying out PCA analysis using python.I have a set of jpeg(rgbcolor) images whose pixel data i need to extract and make a matrix .( rows =num of images and cols=num of pixels) For this i need to represent an image as an array. i was able to do this using java's BufferedImage as below <javacode> int[] rgbdata = new int[width * height]; image.getRGB(0,0,width,height,rgbdata,0,width); doubles = new double[rgbdata.length]; int i; for ( i = 0; i < bytes.length; i++) { doubles[i] = (double)(rgbdata[i]); } </javacode> this doubles[] now represent a single image's pixels then i can get a matrix of say 4 images ..(each of 4X3 size) <sampledata> images[][] rows=4,cols=12 [ [-4413029.0, -1.0463919E7,... -5201255.0] [-5399916.0, -9411231.0, ... -6583163.0] [-3886937.0, -1.0202292E7,... -6648444.0] [-5597295.0, -7901339.0,... -5989995.0] ] </sampledata> i can normalise the above matrix to zeromean and then find covariance matrix by images * transpose(images) my problem is how i can use PIL to do the same thing..if i extract imagedata using im.getdata() i will get <sampledata> [ [(188, 169, 155), (96, 85, 81),.. (176, 162, 153)] [(173, 154, 148), (112, 101, 97),.. (155, 140, 133)] [(196, 176, 167), (100, 83, 76), ... (154, 141, 132)] [(170, 151, 145), (135, 111, 101), ... (164, 153, 149)] ] </sampledata> i donot know how to find covariance matrix from such a matrix..it would'v been ideal if they were single values instead of tuples..i can't use greyscale images since the unput images are all rgb jpeg can someone suggest a solution? thanks dn - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - This message is intended only for the personal and confidential use of the designated recipient(s) named above. If you are not the intended recipient of this message you are hereby notified that any review, dissemination, distribution or copying of this message is strictly prohibited. This communication is for information purposes only and should not be regarded as an offer to sell or as a solicitation of an offer to buy any financial product, an official confirmation of any transaction, or as an official statement of Lehman Brothers. Email transmission cannot be guaranteed to be secure or error-free. Therefore, we do not represent that this information is complete or accurate and it should not be relied upon as such. All information is subject to change without notice. -------- IRS Circular 230 Disclosure: Please be advised that any discussion of U.S. tax matters contained within this communication (including any attachments) is not intended or written to be used and cannot be used for the purpose of (i) avoiding U.S. tax related penalties or (ii) promoting, marketing or recommending to another party any transaction or matter addressed herein. -- http://mail.python.org/mailman/listinfo/python-list
