def cost(ctrl):
ctrl = np.array(ctrl)
ctrl = np.reshape(ctrl, ((len(ctrl) / 2), 2))
spline.set_ctrl_pts(ctrl, only_free=True)
if img is None:
ret = compute_energy_internal_total(spline=spline, nbr_dct=1000)
else:
ret = compute_energy_internal_total(spline=spline,
nbr_dct=1000) + \
gamma * compute_energy_external_total(spline=spline,
img=img,
nbr_dct=1000)
return ret
def cost(ctrl_pts):
'''
Compute the total energy given the new control points. It acts as the
cost function of the optimisation.
Parameters
----------
ctrl_pts : numpy.ndarray
The flattened vector of control points
Returns
-------
ret: float
The total energy corresponding to the given control points.
'''
ctrl_pts = np.array(ctrl_pts)
ctrl_pts = np.reshape(ctrl_pts, ((len(ctrl_pts) / 2), 2))
spline.set_ctrl_pts(ctrl_pts, only_free=True)
ret = compute_energy_internal_total(spline=spline,
ba_ratio=ba_ratio,
nbr_dct=nbr) + \
gamma * compute_energy_external_total(spline=spline,
img=img,
nbr_dct=nbr,
normalized=True)
return ret
def cost(ctrl):
ctrl = np.array(ctrl)
ctrl = np.reshape(ctrl, ((len(ctrl) / 2), 2))
spline.set_ctrl_pts(ctrl, only_free=True)
return compute_energy_external_total(spline=spline, img=img,
nbr_dct=10000,
normalized=True) + \
compute_energy_internal_total(spline=spline, nbr_dct=10000)
def cost(ctrl_pts):
'''
Compute the total energy given the new control points. It is the cost
function of the optimisation. It also helps to compute the traditional
jacobian.
Parameters
----------
ctrl_pts : numpy.ndarray
The flattened vector of control points
Returns
-------
ret: float
The total energy corresponding to the given control points.If
img=None, it returns the partial internal energy. If not, it
returns the partial total (internal + external) energy.
'''
ctrl_pts = np.array(ctrl_pts)
ctrl_pts = np.reshape(ctrl_pts, ((len(ctrl_pts) / 2), 2))
spline.set_ctrl_pts(ctrl_pts, only_free=True)
if img is None:
ret = compute_energy_internal_total(spline=spline,
ba_ratio=ba_ratio,
nbr_dct=nbr)
else:
ret = compute_energy_internal_total(spline=spline,
ba_ratio=ba_ratio,
nbr_dct=nbr) + \
gamma * compute_energy_external_total(spline=spline,
img=img,
nbr_dct=nbr,
normalized=True)
return ret
def changement_rayon_total():
# How the total energy changes with respect to the radius given that we
# know the optimal radius
n_dct = 1000
img, _ = create_image(30)
R = np.linspace(20, 40, 1000)
e_ext = np.zeros(1000)
e_int = np.zeros(1000)
e_total = np.zeros(1000)
dict = {}
gamma = 50
for i, data in enumerate(R):
spl = spline_initialize(data)
e_int[i] = compute_energy_internal_total(spline=spl, ba_ratio=400,
nbr_dct=n_dct)
e_ext[i] = compute_energy_external_total(spl, img, n_dct)
e_total[i] = e_int[i] + gamma * e_ext[i]
dict[data] = e_total[i]
print min(dict.iteritems(), key=operator.itemgetter(1))[0]
fig = plt.figure()
fig.canvas.set_window_title("Rayon")
ax = fig.add_subplot(111)
ax1 = fig.add_subplot(311)
ax2 = fig.add_subplot(312)
ax3 = fig.add_subplot(313)
ax1.title.set_text('Le changement avec gamma = ' + str(gamma))
ax1.plot(R, e_int, 'r', label='Energie interne')
ax1.legend(fontsize='large')
ax2.plot(R, e_ext, 'g', label='Energie externe')
ax2.legend(fontsize='large')
ax3.plot(R, e_total, 'b', label='Energie totale')
ax3.legend(fontsize='large')
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['right'].set_color('none')
ax.tick_params(labelcolor='w', top='off', bottom='off', left='off',
right='off')
ax.set_xlabel('Rayon')
ax.set_ylabel("Energie")
plt.show()
def compute_internal():
st = timeit.default_timer()
for i in range(50):
compute_energy_internal_total(initialize(50))
ed = timeit.default_timer()
print ed - st