sys.exit()
# read images
[X, y] = read_images(sys.argv[1])
# perform a full pca
[D, W, mu] = pca(asRowMatrix(X), y)
import matplotlib.cm as cm
# turn the first (at most) 16 eigenvectors into grayscale
# images (note: eigenvectors are stored by column!)
E = []
for i in xrange(min(len(X), 16)):
e = W[:, i].reshape(X[0].shape)
E.append(normalize(e, 0, 255))
# plot them and store the plot to "python_eigenfaces.pdf"
subplot(title="Eigenfaces AT&T Facedatabase",
images=E,
rows=4,
cols=4,
sptitle="Eigenface",
colormap=cm.jet,
filename="python_pca_eigenfaces.png")
from tinyfacerec.subspace import project, reconstruct
# reconstruction steps
steps = [i for i in xrange(10, min(len(X), 320), 20)]
E = []
for i in xrange(min(len(steps), 16)):
sys.exit()
# read images
[X,y] = read_images(sys.argv[1])
# perform a full pca
[D, W, mu] = pca(asRowMatrix(X), y)
import matplotlib.cm as cm
# turn the first (at most) 16 eigenvectors into grayscale
# images (note: eigenvectors are stored by column!)
E = []
for i in xrange(min(len(X), 16)):
e = W[:,i].reshape(X[0].shape)
E.append(normalize(e,0,255))
# plot them and store the plot to "python_eigenfaces.pdf"
subplot(title="Eigenfaces AT&T Facedatabase", images=E, rows=4, cols=4, sptitle="Eigenface", colormap=cm.jet, filename="python_pca_eigenfaces.png")
from tinyfacerec.subspace import project, reconstruct
# reconstruction steps
steps=[i for i in xrange(10, min(len(X), 320), 20)]
E = []
for i in xrange(min(len(steps), 16)):
numEvs = steps[i]
P = project(W[:,0:numEvs], X[0].reshape(1,-1), mu)
R = reconstruct(W[:,0:numEvs], P, mu)
# reshape and append to plots
R = R.reshape(X[0].shape)
E.append(normalize(R,0,255))