How to calculate eigenfaces in python?

Question

I'm trying to calculate eigenfaces for a set of images using python.

First I turn each image into a vector using:

list(map(lambda x:x.flatten(), x))

Then I calculate covariance matrix (after removing mean from all data):

# x is a numpy array

x = x - mean_image
cov_matrix = np.cov(x.T)

Then I calculate eigenvalues and eigenevtors:

eigen_values, eigen_vecotrs = np.linalg.eig(cov_matrix)

The results are vectors with complex numbers, so I only keep the real part to be able to show them:

eigen_vectors = np.real(eigen_vectors)

After trying to show eigenfaces ( eigenvectors ), the result is not even close to how an eigenface looks like:

I have managed to get a list of eigenfaces using np.linalg.svd() however I'm curious why my code does not work and how can I change it so it work as expected.

To fix the np.linalg.eig returning complex results I reduced the size of images, so it doesn't return complex numbers anymore however still my eigenvectors doesn't look like an eigenface:

Answer 1

proj_data = np.dot(x.transpose(),eigen_vector).T 
img = proj_data[i].reshape(height,width)

This will give you the expected result.

Answer 2

After calculating the eigenvectors you should transpose it. Or you will get mixed image.