hi
i am trying to reconstruct face images from eigenfaces derrived from
original set of face images.
i represented orig images by an ndarray with each row for each image
and each column for pixel intensity.I sorted the eigenvectors such
that each row of sortedeigenvectors is an eigenvector(first ro
least significant eigenimage
> is "deteriorated" is logical. Eigenimages are trying to represent in a
> linear way something that is not. The smallest variations are then
> represented by an artifact, and this is what you get.
>
>
thanks Matthieu ..if that is the logical behaviour then i believe my
hi
As discussed in the thread
http://groups.google.com/group/Numpy-discussion/browse_thread/thread/b9774ac757c3c
98e/a66aa2565d4e6a24
i tried to create an application to create eigenfaces from a set of
jpeg images.I followed these steps after obtaining an ndarray of 17
images (ie 17 rows where ea
> Longer solution:
>Scale your array:
> a_min = inputarray.min()
> a_max = inputarray.max()
> disp_array = ((inputarray-a_min)* 255/(a_max - a_min)).astype('uint8')\
> .
thanx Nadav..the scaling works..and makes clear images
but why .astype("uint8") ? can't i use the a
hi
while trying to make an eigenface image from a numpy array of floats i
tried this
from numpy import array
import Image
imagesize=(200,200)
def makeimage(inputarray,imagename):
inputarray.shape=(-1,)
newimg=Image.new('L', imagesize)
newimg.putdata(inputarray)
newimg.save
hi
can numpy.dot() be used instead of tensordot()? is there any
performance difference? I am talking about multipln btw numpy arrays
of dimensions 50 X 20,000 where elements are of float type.
RG
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I am learning eigenfaces using numpy . i use data from N images and
create eigenvectors to get a 'sorted eigenvectors' array of size N X
N. when i project the 'zero mean imagedata' i will get a facespace
array of N X numpixels. (where numpixels is total pixels in one image)
is eigenface t
