def displayData(X):
print "Visualizing"
m, n = X.shape
width = round(sqrt(n))
height = width
display_rows = int(floor(sqrt(m)))
display_cols = int(ceil(m/display_rows))
print "Cell width:", width
print "Cell height:", height
print "Display rows:", display_rows
print "Display columns:", display_cols
display = zeros((display_rows*height,display_cols*width))
# Iterate through the training sets, reshape each one and populate
# the display matrix with the letter matrixes.
for xrow in range(0, m):
rowindex = divide(xrow, display_cols)
columnindex = remainder(xrow, display_cols)
rowstart = int(rowindex*height)
rowend = int((rowindex+1)*height)
colstart = int(columnindex*width)
colend = int((columnindex+1)*width)
display[rowstart:rowend, colstart:colend] = X[xrow,:].reshape(height,width).transpose()
imshow(display, cmap=get_cmap('binary'), interpolation='none')
# Show plot without blocking
draw()
def _ichroma(self, V, **kwargs):
"""
::
Inverse chromagram transform. Make a signal from a folded constant-Q transform.
"""
if not (self._have_hcqft or self._have_cqft):
return None
a,b = self.HCQFT.shape if self._have_hcqft else self.CQFT.shape
complete_octaves = a/self.nbpo # integer division, number of complete octaves
if P.remainder(a,self.nbpo):
complete_octaves += 1
X = P.repeat(V, complete_octaves, 0)[:a,:] # truncate if necessary
X /= X.max()
X *= P.atleast_2d(P.linspace(1,0,X.shape[0])).T # weight the spectrum
self.x_hat = self._icqft(X, **kwargs)
return self.x_hat
def _ichroma(self, V, **kwargs):
"""
::
Inverse chromagram transform. Make a signal from a folded constant-Q transform.
"""
if not (self._have_hcqft or self._have_cqft):
return None
a,b = self.HCQFT.shape if self._have_hcqft else self.CQFT.shape
complete_octaves = a/self.nbpo # integer division, number of complete octaves
if P.remainder(a,self.nbpo):
complete_octaves += 1
X = P.repeat(V, complete_octaves, 0)[:a,:] # truncate if necessary
X /= X.max()
X *= P.atleast_2d(P.linspace(1,0,X.shape[0])).T # weight the spectrum
self.x_hat = self._icqft(X, **kwargs)
return self.x_hat
def correct(mat):
S = remainder(H * mat, 2)
pos = squeeze(asarray(matrix([4, 2, 1]) * S))
cor = fromfunction(lambda i, j:i == pos - 1, mat.shape) * 1
return mat + cor
# Affiche une image
def showimage(image):
if image.ndim == 2:
gray()
imshow(image)
'''
Application du code de Hamming aux chaînes de caractères
'''
s = 'Ceci est un code de Hamming'
print ("Chaîne initiale :", s)
C = stringToBits(s.encode('latin-1')) # Conversion chaîne->bits
E = addError(G * C, p) # Codage et ajout d'une erreur
S = remainder(R * E, 2) # Décodage sans correction
print ("Sans correction :", bitsToString(S).decode('latin-1'))
Z = remainder(R * correct(E), 2) # Décodage avec correction
print ("Avec correction :", bitsToString(Z).decode('latin-1'))
'''
Application du code de Hamming aux images
'''
# Chargement de l'image
image = imread("lena.jpg")
figure()
# Affichage de l'image initiale
