# 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)
# 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))
# plot them and store the plot to "python_reconstruction.pdf"
subplot(title="Reconstruction AT&T Facedatabase", images=E, rows=4, cols=4, sptitle="Eigenvectors", sptitles=steps, colormap=cm.gray, filename="python_pca_reconstruction.png")
[X,y] = read_images("/home/priyanka/Desktop/CV/scripts/att_faces")
[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 range(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.pdf")
from tinyfacerec.subspace import project, reconstruct
# reconstruction steps
steps=[i for i in range(10, min(len(X), 320), 20)]
E = []
for i in range(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)
# print(R)
# print(E)
# reshape and append to plots
R = R.reshape(X[0].shape)
print "USAGE: example_fisherfaces.py </path/to/images>"
sys.exit()
# read images
[X,y] = read_images(sys.argv[1])
# perform a full pca
[D, W, mu] = fisherfaces(asRowMatrix(X), y)
#import colormaps
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(W.shape[1], 16)):
e = W[:,i].reshape(X[0].shape)
E.append(normalize(e,0,255))
# plot them and store the plot to "python_fisherfaces_fisherfaces.pdf"
subplot(title="Fisherfaces AT&T Facedatabase", images=E, rows=4, cols=4, sptitle="Fisherface", colormap=cm.jet, filename="python_fisherfaces_fisherfaces.png")
from tinyfacerec.subspace import project, reconstruct
E = []
for i in xrange(min(W.shape[1], 16)):
e = W[:,i].reshape(-1,1)
P = project(e, X[0].reshape(1,-1), mu)
R = reconstruct(e, P, mu)
# reshape and append to plots
R = R.reshape(X[0].shape)
E.append(normalize(R,0,255))
# plot them and store the plot to "python_reconstruction.pdf"
subplot(title="Fisherfaces Reconstruction Yale FDB", images=E, rows=4, cols=4, sptitle="Fisherface", colormap=cm.gray, filename="python_fisherfaces_reconstruction.png")
[X, y] = read_images(sys.argv[1])
# perform a full pca
[D, W, mu] = fisherfaces(asRowMatrix(X), y)
#import colormaps
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(W.shape[1], 16)):
e = W[:, i].reshape(X[0].shape)
E.append(normalize(e, 0, 255))
# plot them and store the plot to "python_fisherfaces_fisherfaces.pdf"
subplot(title="Fisherfaces AT&T Facedatabase",
images=E,
rows=4,
cols=4,
sptitle="Fisherface",
colormap=cm.jet,
filename="python_fisherfaces_fisherfaces.png")
from tinyfacerec.subspace import project, reconstruct
E = []
for i in xrange(min(W.shape[1], 16)):
e = W[:, i].reshape(-1, 1)
P = project(e, X[0].reshape(1, -1), mu)
R = reconstruct(e, P, mu)
# reshape and append to plots
R = R.reshape(X[0].shape)
E.append(normalize(R, 0, 255))
# plot them and store the plot to "python_reconstruction.pdf"