예제 #1
0
	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] = 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))
예제 #3
0
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, filename="python_fisherfaces_fisherfaces.png")

    from tinyfacerec.subspace import project, reconstruct

    E = []
    for i in xrange(min(W.shape[1], 16)):
예제 #4
0
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,