def plot(W):
"""
Plot basis vectors.
:param W: Basis matrix of the fitted factorization model.
:type W: `numpy.matrix`
"""
set_cmap('gray')
blank = new("L", (225 + 6, 280 + 6))
for i in xrange(5):
for j in xrange(5):
basis = np.array(W[:, 5 * i + j])[:, 0].reshape((56, 46))
basis = basis / np.max(basis) * 255
basis = 255 - basis
ima = fromarray(basis)
expand(ima, border = 1, fill = 'black')
blank.paste(ima.copy(), (j * 46 + j, i * 56 + i))
imshow(blank)
savefig("orl_faces.png")
def plot(W):
"""
Plot basis vectors.
:param W: Basis matrix of the fitted factorization model.
:type W: `numpy.matrix`
"""
set_cmap('gray')
blank = new("L", (225 + 6, 280 + 6))
for i in range(5):
for j in range(5):
basis = np.array(W[:, 5 * i + j])[:, 0].reshape((56, 46))
basis = basis / np.max(basis) * 255
basis = 255 - basis
ima = fromarray(basis)
expand(ima, border=1, fill='black')
blank.paste(ima.copy(), (j * 46 + j, i * 56 + i))
imshow(blank)
savefig("orl_faces.png")
def plot(W):
"""
Plot basis vectors.
:param W: Basis matrix of the fitted factorization model.
:type W: `numpy.matrix`
"""
set_cmap('gray')
blank = new("L", (133 + 6, 133 + 6))
for i in xrange(7):
for j in xrange(7):
basis = np.array(W[:, 7 * i + j])[:, 0].reshape((19, 19))
basis = basis / np.max(basis) * 255
basis = 255 - basis
ima = fromarray(basis)
ima = ima.rotate(180)
expand(ima, border=1, fill='black')
blank.paste(ima.copy(), (j * 19 + j, i * 19 + i))
imshow(blank)
savefig("cbcl_faces.png")
def plot(W):
"""
Plot basis vectors.
:param W: Basis matrix of the fitted factorization model.
:type W: `numpy.matrix`
"""
set_cmap('gray')
blank = new("L", (133 + 6, 133 + 6))
for i in range(7):
for j in range(7):
basis = np.array(W[:, 7 * i + j])[:, 0].reshape((19, 19))
basis = basis / np.max(basis) * 255
basis = 255 - basis
ima = fromarray(basis)
ima = ima.rotate(180)
expand(ima, border=1, fill='black')
blank.paste(ima.copy(), (j * 19 + j, i * 19 + i))
imshow(blank)
savefig("cbcl_faces.png")
def normalize(image):
cropped = image.crop(image.getbbox())
padded = Image.new('RGBA', (max(cropped.size), max(cropped.size)))
size = max(cropped.size)
offset = (int((size - cropped.width) / 2), int(
(size - cropped.height) / 2))
padded.paste(cropped, offset)
normalized = expand(padded, int(padded.height / 10))
normalized.convert('LA')
normalized.thumbnail((28, 28), Image.ANTIALIAS)
return normalized
def plot_basis(U, ncol, nrow, size=32):
"""
Plots all basis images
- U : the basis matrix
- ncol : # cols in the canvas
- nrow : # rows in the canvas
- size : the height/width of each basis image (assuming it's a square)
"""
# sns.heatmap(U)
# plt.show()
# return
plt.set_cmap("gray")
canvas = Image.new(
"L", (ncol * size + ncol + 1, nrow * size + nrow + 1)) # (w, h)
for i in range(nrow):
for j in range(ncol):
basis = U[:, i * ncol + j].reshape((size, size))
basis = basis / basis.max() * 255
img = expand(Image.fromarray(basis), border=1, fill=255)
canvas.paste(img.copy(), (j * size + j, i * size + i))
plt.imshow(canvas)
plt.show()
