def ksvd_image(filename, rank): # image compression
path, ext = os.path.splitext(filename)
image = Image.open(filename)
w = image.width
h = image.height
gray_image = image.convert('L')
a = np.asarray(gray_image)
ksvd = KSVD(rank=rank)
A, X = ksvd.fit(a)
image = Image.fromarray(np.uint8(A.dot(X)))
print(np.sum(A > 0))
file = path + '_r' + str(rank) + '_ksvd' + ext
image.save(file)
print('Saved as ' + file)
def ksvd_random_signal(): # random signal test
A = np.random.randn(30, 60)
for j in range(A.shape[1]):
A[:, j] /= np.linalg.norm(A[:, j])
X = np.zeros((A.shape[1], 4000))
candidate = np.array(list(range(X.shape[1])))
for j in range(X.shape[1]):
marker = np.random.choice(candidate, 4, replace=False)
X[:, marker] = np.random.normal(0.0, 1.0, 4)
Y_a = X + np.random.normal(0.0, 0.1, X.shape)
ksvd = KSVD(rank=np.linalg.matrix_rank(Y_a.T), num_of_NZ=4)
A, X = ksvd.fit(Y_a)
return A, X