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 reconstruct (W , Y , mu = None ) :
if mu is None :
return np . dot (Y ,W.T)
return np . dot (Y , W .T) + mu
# read images
[X , y] = read_images ('D:\homework and assignments\computer vision\face and gender recognition\images')
# perform a full pca
[D , W , mu ] = pca ( asRowMatrix (X ) , y)
# import tinyfacerec modules
from tinyfacerec.subspace import pca
from tinyfacerec.util import normalize, asRowMatrix, read_images
from tinyfacerec.visual import subplot
if __name__ == '__main__':
if len(sys.argv) != 2:
print "USAGE: example_eigenfaces.py </path/to/images>"
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",
import sys
# append tinyfacerec to module search path
sys.path.append("..")
# import numpy and matplotlib colormaps
import numpy as np
# import tinyfacerec modules
from tinyfacerec.subspace import pca
from tinyfacerec.util import normalize, asRowMatrix, read_images
from tinyfacerec.visual import subplot
# read images
[X,y] = read_images('att_faces')
# perform a full pca
[D, W, mu] = pca(asRowMatrix(X[1:]), 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.gray, filename="python_pca_eigenfaces.pdf")
from tinyfacerec.subspace import project, reconstruct
# reconstruction steps
steps=[i for i in range(10, min(len(X), 400), 20)]
E = []
# import tinyfacerec modules
from tinyfacerec.subspace import pca
from tinyfacerec.util import normalize, asRowMatrix, read_images
from tinyfacerec.visual import subplot
if __name__ == '__main__':
if len(sys.argv) != 2:
print "USAGE: example_eigenfaces.py </path/to/images>"
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
# import tinyfacerec modules
from tinyfacerec.subspace import pca
from tinyfacerec.util import normalize, asRowMatrix, read_images
from tinyfacerec.visual import subplot
if __name__ == '__main__':
if len(sys.argv) != 2:
print "USAGE: example_eigenfaces.py </path/to/images>"
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
import numpy as np
# import tinyfacerec modules
from tinyfacerec.subspace import fisherfaces
from tinyfacerec.util import normalize, asRowMatrix, read_images
from tinyfacerec.visual import subplot
if __name__ == '__main__':
if len(sys.argv) != 2:
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,