def get_gradient_vector_flow(self, potential):
""" calculate the gradient vector flow from the given potential.
This function is not very well tested, yet """
# use Eq. 12 from Paper `Gradient Vector Flow: A New External Force for Snakes`
u, v = self.get_buffers([0, 1], potential.shape)
fx = cv2.Sobel(potential, cv2.CV_64F, 1, 0, ksize=5)
fy = cv2.Sobel(potential, cv2.CV_64F, 0, 1, ksize=5)
fxy = fx**2 + fy**2
# print fx.max(), fy.max(), fxy.max()
# debug.show_image(potential, fx, fy, fxy, u, v)
mu = 10
def dudt(u):
return mu*cv2.Laplacian(u, cv2.CV_64F) - (u - fx)*fxy
def dvdt(v):
return mu*cv2.Laplacian(v, cv2.CV_64F) - (v - fy)*fxy
N = 10000 #< maximum number of steps that the integrator is allowed
dt = 1e-4 #< time step
for n in xrange(N):
rhs = dudt(u)
residual = np.abs(rhs).sum()
if n % 1000 == 0:
print n*dt, '%e' % residual
u += dt*rhs
for n in xrange(N):
rhs = dvdt(v)
# residual = np.abs(rhs).sum()
# if n % 100 == 0:
# print n*dt, '%e' % residual
v += dt*rhs
debug.show_image(potential, (u, v))
return (u, v)
def show_debug_image(self):
""" shows the debug image on screen """
self.add_debug_output()
debug.show_image(self.image)