def compute(self, X, y): [D, self.W, self.mu] = fisherfaces(asRowMatrix(X),y, self.num_components) # store labels self.y = y # store projections for xi in X: self.projections.append(project(self.W, xi.reshape(1,-1), self.mu))
def compute(self, X, y):
[D, self.W, self.mu] = pca(asRowMatrix(X), y, self.num_components)
# store labels
self.y = y
# store projections
for xi in X:
self.projections.append(project(self.W, xi.reshape(1, -1),
self.mu))
def fisherfaces(X,y,num_components=0): y = np.asarray(y) [n,d] = X.shape c = len(np.unique(y)) [eigenvalues_pca, eigenvectors_pca, mu_pca] = pca(X, y, (n-c)) [eigenvalues_lda, eigenvectors_lda] = lda(project(eigenvectors_pca, X, mu_pca), y, num_components) eigenvectors = np.dot(eigenvectors_pca,eigenvectors_lda) return [eigenvalues_lda, eigenvectors, mu_pca]
def predict(self, X):
minDist = np.finfo('float').max
minClass = -1
Q = project(self.W, X.reshape(1, -1), self.mu)
for i in range(len(self.projections)):
dist = self.dist_metric(self.projections[i], Q)
if dist < minDist:
minDist = dist
minClass = self.y[i]
return minClass
def predict(self, X):
minDist = np.finfo('float').max
minClass = -1
Q = project(self.W, X.reshape(1,-1), self.mu)
for i in xrange(len(self.projections)):
dist = self.dist_metric(self.projections[i], Q)
if dist < minDist:
minDist = dist
minClass = self.y[i]
return minClass
def fisherfaces(X, y, num_components=0):
y = np.asarray(y)
[n, d] = X.shape
c = len(np.unique(y))
[eigenvalues_pca, eigenvectors_pca, mu_pca] = pca(X, y, (n - c))
[eigenvalues_lda,
eigenvectors_lda] = lda(project(eigenvectors_pca, X, mu_pca), y,
num_components)
eigenvectors = np.dot(eigenvectors_pca, eigenvectors_lda)
return [eigenvalues_lda, eigenvectors, mu_pca]
# 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")
# 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")
########NEW FILE########
__FILENAME__ = example_fisherfaces
import sys
# append tinyfacerec to module search path
sys.path.append("..")
# import numpy and matplotlib colormaps
import numpy as np
# import tinyfacerec modules
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")
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")
