hi all
I am new to python and learning PCA method by reading up Turk&Petland
papers etc
while trying out PCA on a set of greyscale images using python, and
numpy I tried to create eigenvectors and facespace.
i have
facesarray--- an NXP numpy.ndarray that contains data of images
N=numof images,P=pixels in an image
avgarray --1XP array containing avg value for each pixel
adjustedfaces=f acesarray-avgarray
adjustedmatrix= matrix(adjusted faces)
adjustedmatrix_ trans=adjustedm atrix.transpose ()
covariancematri x =adjustedmatrix *adjustedmatrix _trans
evalues,evect=e igh(covariancem atrix)
after sorting such that most significant eigenvectors are selected
evectmatrix is now my eigenvectors matrix
here is a sample using 4X3 greyscale images
evalues
[ -1.85852801e-13 6.31143639e+02 3.31182765e+03 5.29077871e+03]
evect
[[ 0.5 -0.06727772 0.6496399 -0.56871936]
[ 0.5 -0.77317718 -0.37697426 0.10043632]
[ 0.5 0.27108233 0.31014514 0.76179023]
[ 0.5 0.56937257 -0.58281078 -0.29350719]]
evectmatrix (sorted according to largest evalue first)
[[-0.56871936 0.6496399 -0.06727772 0.5 ]
[ 0.10043632 -0.37697426 -0.77317718 0.5 ]
[ 0.76179023 0.31014514 0.27108233 0.5 ]
[-0.29350719 -0.58281078 0.56937257 0.5 ]]
then i can create facespace by
facespace=evect mat*adjustedfac es
what i want to know is how i can create eigenface images .
when you mean an eigenvector ,is that a row of evectmatrix above? is
evectmatrix[0] the eigenvector for first image and so on? I would
appreciate if someone can make this clear.
also
when i tried to make an image by
im=im = Image.new('L', (width,height))
im.putdata(evec tmat[0])
im.save("eigenf ace0.jpg")
it created an image with all pixels dark with no details to see
the same occurrs if i use
im.putdata(face space[0])
can someone tell me if i am doing it wrong..
I am new to python and learning PCA method by reading up Turk&Petland
papers etc
while trying out PCA on a set of greyscale images using python, and
numpy I tried to create eigenvectors and facespace.
i have
facesarray--- an NXP numpy.ndarray that contains data of images
N=numof images,P=pixels in an image
avgarray --1XP array containing avg value for each pixel
adjustedfaces=f acesarray-avgarray
adjustedmatrix= matrix(adjusted faces)
adjustedmatrix_ trans=adjustedm atrix.transpose ()
covariancematri x =adjustedmatrix *adjustedmatrix _trans
evalues,evect=e igh(covariancem atrix)
after sorting such that most significant eigenvectors are selected
evectmatrix is now my eigenvectors matrix
here is a sample using 4X3 greyscale images
evalues
[ -1.85852801e-13 6.31143639e+02 3.31182765e+03 5.29077871e+03]
evect
[[ 0.5 -0.06727772 0.6496399 -0.56871936]
[ 0.5 -0.77317718 -0.37697426 0.10043632]
[ 0.5 0.27108233 0.31014514 0.76179023]
[ 0.5 0.56937257 -0.58281078 -0.29350719]]
evectmatrix (sorted according to largest evalue first)
[[-0.56871936 0.6496399 -0.06727772 0.5 ]
[ 0.10043632 -0.37697426 -0.77317718 0.5 ]
[ 0.76179023 0.31014514 0.27108233 0.5 ]
[-0.29350719 -0.58281078 0.56937257 0.5 ]]
then i can create facespace by
facespace=evect mat*adjustedfac es
what i want to know is how i can create eigenface images .
when you mean an eigenvector ,is that a row of evectmatrix above? is
evectmatrix[0] the eigenvector for first image and so on? I would
appreciate if someone can make this clear.
also
when i tried to make an image by
im=im = Image.new('L', (width,height))
im.putdata(evec tmat[0])
im.save("eigenf ace0.jpg")
it created an image with all pixels dark with no details to see
the same occurrs if i use
im.putdata(face space[0])
can someone tell me if i am doing it wrong..