[Numpy-discussion] confusion about eigenvector

devnew@gmai... devnew@gmai...
Thu Mar 6 08:39:56 CST 2008

ok..I coded everything again from scratch..looks like i was having a
problem with matrix class
when i used a matrix for facespace
 facespace=sortedeigenvectorsmatrix *  adjustedfacematrix
and trying to convert the row to an image (eigenface).
.i was getting black images instead of eigenface images.

def make_simple_image(v, filename,imsize):
	v.shape=(-1,) #change to 1 dim array
	im = Image.new('L', imsize)

i made it an array instead of matrix
this produces eigenface images

another observation,
the eigenface images obtained are too dark,unlike the eigenface images
generated by Arnar's code.so i examined the elements of the facespace

sample rows:
[ -82.35294118,  -82.88235294,  -91.58823529 ,...,  -66.47058824,
   -68.23529412,  -60.76470588]
[  89.64705882   82.11764706   79.41176471 ...,  172.52941176
   170.76470588  165.23529412]

looks like these are signed ints..

i used another make_image() function that converts the elements
def make_image(v, filename,imsize):
	v.shape = (-1,)	#change to 1 dim array
	a, b = v.min(), v.max()
	span = max(abs(b), abs(a))
	im = Image.new('L', imsize)
	im.putdata((v * 127. / span) + 128)

This function makes clearer images..i think the calculations convert
the elements to unsigned 8-bit values (as pointed out by Robin in
another posting..) ,i am wondering if there is a more direct way to
get clearer pics out of the facespace row elements

More information about the Numpy-discussion mailing list