def optimize(self, search='rigid', method='powell', start=None,
radius=BRAIN_RADIUS_MM, tol=1e-1, ftol=1e-2):
"""
radius: a parameter for the 'typical size' in mm of the object
being registered. This is used to reformat the parameter
vector (translation+rotation+scaling+shearing) so that each
element roughly represents a variation in mm.
"""
if start == None:
T = Affine(subtype=search, radius=radius)
else:
T = Affine(subtype=search, vec12=start.vec12, radius=radius)
tc0 = T.to_param()
# Loss function to minimize
def loss(tc):
T.from_param(tc)
return -self.eval(T)
def callback(tc):
T.from_param(tc)
print(T)
print(self.similarity + ' = %s' % self.eval(T))
print('')
# Switching to the appropriate optimizer
print('Initial guess...')
print(T)
if method=='simplex':
print ('Optimizing using the simplex method...')
tc = sp.optimize.fmin(loss, tc0, callback=callback, xtol=tol, ftol=ftol)
elif method=='powell':
print ('Optimizing using Powell method...')
tc = sp.optimize.fmin_powell(loss, tc0, callback=callback, xtol=tol, ftol=ftol)
elif method=='conjugate_gradient':
print ('Optimizing using conjugate gradient descent...')
tc = sp.optimize.fmin_cg(loss, tc0, callback=callback, gtol=ftol)
else:
raise ValueError('Unrecognized optimizer')
# Output
T.from_param(tc)
return T
def explore(self,
ux=[0], uy=[0], uz=[0],
rx=[0], ry=[0], rz=[0],
sx=[1], sy=[1], sz=[1],
qx=[0], qy=[0], qz=[0]):
grids = np.mgrid[0:len(ux), 0:len(uy), 0:len(uz),
0:len(rx), 0:len(ry), 0:len(rz),
0:len(sx), 0:len(sy), 0:len(sz),
0:len(qx), 0:len(qy), 0:len(qz)]
ntrials = np.prod(grids.shape[1:])
UX = np.asarray(ux)[grids[0,:]].ravel()
UY = np.asarray(uy)[grids[1,:]].ravel()
UZ = np.asarray(uz)[grids[2,:]].ravel()
RX = np.asarray(rx)[grids[3,:]].ravel()
RY = np.asarray(ry)[grids[4,:]].ravel()
RZ = np.asarray(rz)[grids[5,:]].ravel()
SX = np.asarray(sx)[grids[6,:]].ravel()
SY = np.asarray(sy)[grids[7,:]].ravel()
SZ = np.asarray(sz)[grids[8,:]].ravel()
QX = np.asarray(qx)[grids[9,:]].ravel()
QY = np.asarray(qy)[grids[10,:]].ravel()
QZ = np.asarray(qz)[grids[11,:]].ravel()
simis = np.zeros(ntrials)
vec12s = np.zeros([12, ntrials])
T = Affine()
for i in range(ntrials):
t = np.array([UX[i], UY[i], UZ[i],
RX[i], RY[i], RZ[i],
SX[i], SY[i], SZ[i],
QX[i], QY[i], QZ[i]])
T.set_vec12(t)
simis[i] = self.eval(T)
vec12s[:, i] = t
return simis, vec12s
