def solve(self, data: np.ndarray, maxiter: int = 150, tol: float = 5*10**(-4)):
if self.reg_mode is not None:
grad = Gradient(dims=self.domain_shape, edge = True, dtype='float64', kind='backward')
K = self.alpha * grad
if not self.tau:
norm = np.abs(np.asscalar(K.eigs(neigs=1, which='LM')))
sigma = 0.99 / norm
print("Calced tau: "+str(sigma) + ". "
"Next run with same alpha, set this tau value to decrease runtime.")
tau = sigma
else:
tau = self.tau
sigma = tau
if self.reg_mode == 'tv':
F_star = Projection(self.domain_shape, len(self.domain_shape))
else:
F_star = DatatermLinear()
F_star.set_proxdata(0)
else:
tau = 0.99
sigma = tau
F_star = DatatermLinear()
K = 0
G = DatatermRecBregman(self.O)
G.set_proxparam(tau)
G.set_proxdata(data.ravel())
F_star.set_proxparam(sigma)
pk = np.zeros(self.domain_shape)
pk = pk.T.ravel()
plt.Figure()
ulast = np.zeros(self.domain_shape)
u01 = ulast
i = 0
while np.linalg.norm(self.O * u01.ravel() - data.ravel()) > self.assessment:
print("norm error: " + str(np.linalg.norm(self.O * u01.ravel() - data.ravel())))
self.solver = PdHgm(K, F_star, G)
self.solver.maxiter = maxiter
self.solver.tol = tol
G.set_proxdata(data.ravel())
G.setP(pk)
self.solver.solve()
u01 = np.reshape(np.real(self.solver.var['x']), self.domain_shape)
pk = pk - (1 / self.alpha) * np.real(self.O.H*(self.O*u01.ravel() - data.ravel()))
i = i + 1
if self.plot_iteration:
plt.gray()
plt.imshow(u01, vmin=0, vmax=1)
plt.axis('off')
#plt.title('RRE =' + str(round(RRE_breg, 2)), y=-0.1, fontsize=20)
plt.savefig(self.data_output_path + 'Bregman_reconstruction_iter' + str(i) + '.png', bbox_inches='tight',
pad_inches=0)
plt.close()
return np.reshape(self.solver.var['x'], self.domain_shape)
def solve(self,
f: np.ndarray,
maxiter: int = 150,
tol: float = 5 * 10**(-4)):
if self.reg_mode is not None:
grad = Gradient(self.domain_shape,
edge=True,
dtype='float64',
kind='backward')
K = self.alpha * grad
if not self.tau:
norm = np.abs(np.asscalar(K.eigs(neigs=1, which='LM')))
sigma = 0.99 / norm
tau = sigma
print(
"Calced tau: " + str(sigma) + ". "
"Next run with same alpha, set this tau value to decrease runtime."
)
else:
tau = self.tau
sigma = tau
if self.reg_mode == 'tv':
F_star = Projection(self.domain_shape, len(self.domain_shape))
else:
F_star = DatatermLinear()
else:
tau = 0.99
sigma = tau
F_star = DatatermLinear()
K = 0
G = Dataterm(self.O)
G.set_proxparam(tau)
F_star.set_proxparam(sigma)
self.solver = PdHgm(K, F_star, G)
self.solver.maxiter = maxiter
self.solver.tol = tol
G.set_proxdata(f.ravel())
self.solver.solve()
return np.reshape(self.solver.var['x'], self.domain_shape)
def solve(self,
f: np.ndarray,
maxiter: int = 150,
tol: float = 5 * 10**(-4)):
if self.reg_mode is not None:
grad = Gradient(self.domain_shape, dtype='float64')
K = self.alpha * grad
if not self.tau:
norm = np.abs(np.asscalar(K.eigs(neigs=1, which='LM')))
sigma = 0.99 / norm
tau = sigma
else:
tau = self.tau
sigma = tau
if self.reg_mode == 'tv':
F_star = Projection(self.domain_shape, len(self.domain_shape))
else:
F_star = DatatermLinear()
else:
tau = 0.99
sigma = tau
F_star = DatatermLinear()
K = 0
G = Dataterm(self.O)
G.set_proxparam(tau)
F_star.set_proxparam(sigma)
self.solver = PdHgm(K, F_star, G)
self.solver.maxiter = maxiter
self.solver.tol = tol
G.set_proxdata(f.ravel())
self.solver.solve()
return np.reshape(self.solver.var['x'], self.domain_shape)
def multi_class_segmentation_bregman(img,
classes: list,
beta: float = 0.001,
delta: float = 1,
qk=None,
tau: float = None):
f = np.zeros(((img.shape[0], img.shape[1], len(classes))))
raveld_f = np.zeros(((img.shape[0] * img.shape[1], len(classes))))
for i in range(len(classes)):
#f[:, :, i] = (img.T - classes[i]) ** 2
raveld_f[:,
i] = delta * (img.ravel() - classes[i])**2 - beta * (qk[:, i])
#f = np.ravel(f, order='C')
f = raveld_f
shape = (img.shape[0], img.shape[1])
ex = np.ones((shape[1], 1))
ey = np.ones((1, shape[0]))
dx = sparse.diags([1, -1], [0, 1], shape=(shape[1], shape[1])).tocsr()
dx[shape[1] - 1, :] = 0
dy = sparse.diags([-1, 1], [0, 1], shape=(shape[0], shape[0])).tocsr()
dy[shape[0] - 1, :] = 0
grad = sparse.vstack((sparse.kron(dx,
sparse.eye(img.shape[0]).tocsr()),
sparse.kron(sparse.eye(img.shape[1]).tocsr(), dy)))
#grad = sparse.hstack([grad, sparse.csr_matrix( (grad.shape[0], grad.shape[1]), dtype=int)])
#gradT = sparse.block_diag([grad.T]*len(classes))
#grad = sparse.vstack([grad]*len(classes))
samp_values = f.shape[0] #np.prod(np.shape(f))
boundaries = 'neumann'
# grad = FirstDerivative(262144, boundaries=boundaries)
K = beta * grad
# vd1 = convex_segmentation(u0, beta0, classes)
G = DatatermLinear()
G.set_proxdata(f)
F_star = Projection(f.shape)
solver = PdHgm(K, F_star, G)
solver.var['x'] = np.zeros((K.shape[1], len(classes)))
solver.var['y'] = np.zeros((K.shape[0], len(classes)))
if tau:
tau0 = tau
else:
tau0 = 0.99 / normest(K)
print(tau0)
sigma0 = tau0
G.set_proxparam(tau0)
F_star.set_proxparam(sigma0)
solver.maxiter = 150
solver.tol = 10**(-6)
# G.set_proxdata(f)
solver.solve()
seg = np.reshape(solver.var['x'],
(img.shape[0], img.shape[1], len(classes)),
order='C')
# set(figure,'defaulttextinterpreter','latex');
a = seg
result = np.zeros(a.shape)
for i in range(a.shape[0]): # SLOW
for j in range(a.shape[1]):
idx = np.argmin((a[i, j, :]))
result[i, j, idx] = 1
result0 = sum([i * result[:, :, i] for i in range(len(classes))])
return result0, result
def solve(self,
data: np.ndarray,
maxiter: int = 150,
tol: float = 5 * 10**(-4)):
if self.reg_mode is not None:
if len(self.domain_shape) > 2:
grad = Gradient(dims=self.domain_shape,
edge=True,
dtype='float64')
else:
ex = np.ones((self.domain_shape[1], 1))
ey = np.ones((1, self.domain_shape[0]))
dx = sparse.diags([1, -1], [0, 1],
shape=(self.domain_shape[1],
self.domain_shape[1])).tocsr()
dx[self.domain_shape[1] - 1, :] = 0
dy = sparse.diags([-1, 1], [0, 1],
shape=(self.domain_shape[0],
self.domain_shape[0])).tocsr()
dy[self.domain_shape[0] - 1, :] = 0
grad = sparse.vstack(
(sparse.kron(dx,
sparse.eye(self.domain_shape[0]).tocsr()),
sparse.kron(sparse.eye(self.domain_shape[1]).tocsr(),
dy)))
K = self.alpha * grad
if not self.tau:
if np.prod(self.domain_shape) > 25000:
long = True
else:
long = False
if long:
print("Start evaluate tau. Long runtime.")
if len(self.domain_shape) > 2:
norm = np.abs(np.asscalar(K.eigs(neigs=1, which='LM')))
else:
norm = normest(K)
sigma = 0.99 / norm
if long:
print("Calc tau: " + str(sigma))
tau = sigma
else:
tau = self.tau
sigma = tau
if self.reg_mode == 'tv':
F_star = Projection(self.domain_shape, len(self.domain_shape))
else:
F_star = DatatermLinear()
F_star.set_proxdata(0)
else:
tau = 0.99
sigma = tau
F_star = DatatermLinear()
K = 0
G = DatatermLinear()
G.set_proxparam(tau)
G.set_proxdata(data.ravel())
F_star.set_proxparam(sigma)
self.solver = PdHgm(K, F_star, G)
self.solver.maxiter = maxiter
self.solver.tol = tol
self.solver.solve()
return np.reshape(self.solver.var['x'], self.domain_shape)
def solve(self,
data: np.ndarray,
maxiter: int = 150,
tol: float = 5 * 10**(-4)):
if self.reg_mode is not None:
if len(self.domain_shape) > 2:
grad = Gradient(dims=self.domain_shape,
edge=True,
dtype='float64')
else:
dx = sparse.diags([1, -1], [0, 1],
shape=(self.domain_shape[1],
self.domain_shape[1])).tocsr()
dx[self.domain_shape[1] - 1, :] = 0
dy = sparse.diags([-1, 1], [0, 1],
shape=(self.domain_shape[0],
self.domain_shape[0])).tocsr()
dy[self.domain_shape[0] - 1, :] = 0
grad = sparse.vstack(
(sparse.kron(dx,
sparse.eye(self.domain_shape[0]).tocsr()),
sparse.kron(sparse.eye(self.domain_shape[1]).tocsr(),
dy)))
K = self.alpha * grad
if not self.tau:
if np.prod(self.domain_shape) > 25000:
long = True
else:
long = False
if long:
print("Start evaluate tau. Long runtime.")
if len(self.domain_shape) > 2:
norm = np.abs(np.asscalar(K.eigs(neigs=1, which='LM')))
else:
norm = normest(K)
sigma = 0.99 / norm
if long:
print("Calc tau: " + str(sigma))
tau = sigma
else:
tau = self.tau
sigma = tau
if self.reg_mode == 'tv':
F_star = Projection(self.domain_shape, len(self.domain_shape))
else:
F_star = DatatermLinear()
F_star.set_proxdata(0)
else:
tau = 0.99
sigma = tau
F_star = DatatermLinear()
K = 0
G = DatatermLinearRecBregman()
G.set_proxparam(tau)
G.set_proxdata(data.ravel())
F_star.set_proxparam(sigma)
pk = np.zeros(self.domain_shape)
pk = pk.T.ravel()
plt.Figure()
ulast = np.zeros(self.domain_shape)
u01 = ulast
i = 0
while np.linalg.norm(u01.ravel() - data.ravel()) > self.assessment:
print(np.linalg.norm(u01.ravel() - data.ravel()))
print(self.assessment)
self.solver = PdHgm(K, F_star, G)
self.solver.maxiter = maxiter
self.solver.tol = tol
G.set_proxdata(data.ravel())
G.setQ(pk)
self.solver.solve()
u01 = np.reshape(np.real(self.solver.var['x']), self.domain_shape)
pk = pk - (1 / self.alpha) * (u01.ravel() - data.ravel())
i = i + 1
if self.plot_iteration:
plt.gray()
plt.imshow(u01, vmin=0, vmax=1)
plt.axis('off')
#plt.title('RRE =' + str(round(RRE_breg, 2)), y=-0.1, fontsize=20)
plt.savefig(self.data_output_path +
'Bregman_reconstruction_iter' + str(i) + '.png',
bbox_inches='tight',
pad_inches=0)
plt.close()
return np.reshape(self.solver.var['x'], self.domain_shape)
def multi_class_segmentation(img,
classes: list,
beta: float = 0.001,
tau: float = None):
raveld_f = np.zeros(((np.prod(img.shape), len(classes))))
for i in range(len(classes)):
raveld_f[:, i] = (img.ravel() - classes[i])**2
f = raveld_f
grad = pylops.Gradient(dims=img.shape,
edge=True,
dtype='float64',
kind="backward")
K = beta * grad
G = DatatermLinear()
G.set_proxdata(f)
F_star = Projection(f.shape, len(img.shape))
solver = PdHgm(K, F_star, G)
solver.var['x'] = np.zeros((K.shape[1], len(classes)))
solver.var['y'] = np.zeros((K.shape[0], len(classes)))
if tau:
tau0 = tau
else:
norm = np.abs(np.asscalar(K.eigs(neigs=1, which='LM')))
tau0 = 0.99 / norm
print(
"Calced tau: " + str(tau0) + ". "
"Next run with same beta, set this tau value to decrease runtime.")
sigma0 = tau0
G.set_proxparam(tau0)
F_star.set_proxparam(sigma0)
solver.maxiter = 200
solver.tol = 10**(-6)
solver.solve()
seg = np.reshape(solver.var['x'],
tuple(list(img.shape) + [len(classes)]),
order='C')
a = seg
result = [] #np.zeros(a.shape)
#for i in range(a.shape[0]): # SLOW
# for j in range(a.shape[1]):
# idx = np.argmin((a[i, j, :]))
# result[i, j, idx] = 1
tmp_result = np.argmin(a, axis=len(img.shape))
for i, c in enumerate(classes):
result.append((tmp_result == i).astype(int))
result0 = sum([i * result[i] for i in range(len(classes))])
result = np.array(result)
return result0, result
def multi_class_segmentation(img,
classes: list,
beta: float = 0.001,
tau: float = None):
#f = np.zeros( tuple( list(img.shape) + [len(classes)]))
raveld_f = np.zeros(((np.prod(img.shape), len(classes))))
for i in range(len(classes)):
#f[:, :, i] = (img.T - classes[i]) ** 2
raveld_f[:, i] = (img.ravel() - classes[i])**2
#f = np.ravel(f, order='C')
f = raveld_f
grad = pylops.Gradient(dims=img.shape, dtype='float64')
# grad = FirstDerivative(262144, boundaries=boundaries)
K = beta * grad
# vd1 = convex_segmentation(u0, beta0, classes)
G = DatatermLinear()
G.set_proxdata(f)
F_star = Projection(f.shape, len(img.shape))
solver = PdHgm(K, F_star, G)
solver.var['x'] = np.zeros((K.shape[1], len(classes)))
solver.var['y'] = np.zeros((K.shape[0], len(classes)))
if tau:
tau0 = tau
else:
tau0 = 0.99 / normest(K)
print(tau0)
sigma0 = tau0
G.set_proxparam(tau0)
F_star.set_proxparam(sigma0)
solver.maxiter = 200
solver.tol = 10**(-6)
# G.set_proxdata(f)
solver.solve()
seg = np.reshape(solver.var['x'],
tuple(list(img.shape) + [len(classes)]),
order='C')
a = seg
result = [] #np.zeros(a.shape)
#for i in range(a.shape[0]): # SLOW
# for j in range(a.shape[1]):
# idx = np.argmin((a[i, j, :]))
# result[i, j, idx] = 1
tmp_result = np.argmin(a, axis=len(img.shape))
for i, c in enumerate(classes):
result.append((tmp_result == i).astype(int))
result0 = sum([i * result[i] for i in range(len(classes))])
result = np.array(result)
return result0, result