Example #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))
Example #2
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))
Example #3
0
 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
Example #5
0
 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
Example #6
0
    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")
Example #7
0
    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")
Example #8
0
# 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")
Example #9
0
 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))