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))
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 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 predict(self, X, minDist=0.1):
## minDist = np.finfo('float').max
## minDist = 0.1
## print "min dist: %f"%minDist
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
## print "min dist final: %f"%minDist
minClass = self.y[i]
## break
return minClass, minDist
def visualize(self):
# Using PCA (Principal Components Analysis) to recognize faces
# Face recognizer just in openCV version 2.4x, not in version 3.0 (alpha + beta)
# cv2.createEigenFaceRecognizer()
[X, y] = read_images(self._facePath)
# get k principal components
[D, W, mu] = pca(as_row_matrix(X), y)
# to check how many principal components (eigenfaces) - which has the same dimension with observed vector
# print(len(W[1, :]))
# print(len(W[:, 1]))
# Visualize eigenfaces
E = []
# take at most 16 people
for i in xrange(min(len(X), 16)):
e = W[:, i].reshape(X[0].shape)
# normalize value of eigenvectors in [0, 255] to display on image
E.append(normalize(e, 0, 255))
# display k principal components (k eigenvectors)
# eigenfaces encode facial features + illumination of image
subplot(title="Eigencefaces AT&T",
images=E,
rows=4,
cols=4,
sptitle="Eigenface",
colormap=cm.gray,
filename="pca_eigenface.png")
# Reconstructor image from eigenfaces
# array of vector number [10, 30, 50 ..., min(len(X), 320)]
steps = [i for i in xrange(10, min(len(X), 320), 20)]
E = []
# take at most 16 eigenvectors of first person
for i in xrange(min(len(steps), 16)):
num_eigenvectors = steps[i]
# project each vector x1, x2, ... xn to num_eigenvectors
P = project(W[:, 0:num_eigenvectors], X[0].reshape(1, -1), mu)
# reconstruct image from num_eigenvectors
R = reconstruct(W[:, 0:num_eigenvectors], P, mu)
R = R.reshape(X[0].shape)
E.append(normalize(R, 0, 255))
# reconstruct image from k (10, 30, ...) eigenvector
subplot(title="Reconstruction AT&T",
images=E,
rows=4,
cols=4,
sptitle="EigenVectors",
sptitles=steps,
colormap=cm.gray,
filename="pca_reconstruction.png")
def visualize(self):
# Using PCA (Principal Components Analysis) to recognize faces
# Face recognizer just in openCV version 2.4x, not in version 3.0 (alpha + beta)
# cv2.createEigenFaceRecognizer()
[X, y] = read_images(self._facePath)
# get k principal components
[D, W, mu] = pca(as_row_matrix(X), y)
# to check how many principal components (eigenfaces) - which has the same dimension with observed vector
# print(len(W[1, :]))
# print(len(W[:, 1]))
# Visualize eigenfaces
E = []
# take at most 16 people
for i in xrange(min(len(X), 16)):
e = W[:, i].reshape(X[0].shape)
# normalize value of eigenvectors in [0, 255] to display on image
E.append(normalize(e, 0, 255))
# display k principal components (k eigenvectors)
# eigenfaces encode facial features + illumination of image
subplot(title="Eigencefaces AT&T", images=E, rows=4, cols=4, sptitle="Eigenface", colormap=cm.gray,
filename="pca_eigenface.png")
# Reconstructor image from eigenfaces
# array of vector number [10, 30, 50 ..., min(len(X), 320)]
steps = [i for i in xrange(10, min(len(X), 320), 20)]
E = []
# take at most 16 eigenvectors of first person
for i in xrange(min(len(steps), 16)):
num_eigenvectors = steps[i]
# project each vector x1, x2, ... xn to num_eigenvectors
P = project(W[:, 0:num_eigenvectors], X[0].reshape(1, -1), mu)
# reconstruct image from num_eigenvectors
R = reconstruct(W[:, 0:num_eigenvectors], P, mu)
R = R.reshape(X[0].shape)
E.append(normalize(R, 0, 255))
# reconstruct image from k (10, 30, ...) eigenvector
subplot(title="Reconstruction AT&T", images=E, rows=4, cols=4, sptitle="EigenVectors", sptitles=steps,
colormap=cm.gray, filename="pca_reconstruction.png")
# 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.jpg")
EE = []
for i in xrange(min(W.shape[1], 20)):
e = W[:, i].reshape(-1, 1)
P = project(e, X[0].reshape(1, -1), mu)
R = reconstruct(e, P, mu)
R = R.reshape(X[0].shape)
EE.append(normalize(R, 0, 255))
subplot(title=" Fisherfaces Reconstruction Yale FDB ",
images=EE,
rows=4,
cols=5,
sptitle=" Fisherface",
colormap=cm.gray,
filename="python_fisherfaces_reconstruction.jpg")
def compute(self, X, y):
[D, self.W, self.mu] = pca(as_row_matrix(X), y, self.num_components)
self.y = y
for xi in X:
self.projections.append(project(self.W, xi.reshape(1, -1), self.mu))