def fg(x, gx):
legradient = grad_dt(x)
gx[0:size] = legradient[0:size]
gx[size:2 * size] = legradient[size:2 * size]
return phi(x)
###################### FONCTION DE DEFLOUTAGE D'IMAGE ########################
# On definit notre premier point x (ici l'image floue et un kernel quelconque)
x = np.concatenate((image, psf), axis=0)
g = np.zeros(2*size)
f = np.array([fg(x,g)], dtype="double")
print "f initial = ", f
# On execute la fonction de minimisation
x_out = opkpy_v3.opk_minimize(x, fg, g, algorithm="vmlmb",
linesearch="cubic", vmlmb="lbfgs",
limited=0, maxeval=200, maxiter = 200)
objet_defloute = x_out[0:size].reshape((NbLigne,NbColonne))
kernel_defloute = x_out[size:2*size].reshape((NbLigne,NbColonne))
image_out = np.real(np.fft.ifft(np.fft.fft(x_out[0:size]) * np.fft.fft(np.roll(x_out[size:2*size]/sum(x_out[size:2*size]),np.round((size+1)/2))))).reshape((NbLigne,NbColonne))
# objets
matshow(image_mat,cmap=cm.gray) # point de depart
matshow(objet_mat,cmap=cm.gray) # objectif
matshow(objet_defloute,cmap=cm.gray) # resultat
# psf
matshow(psf_mat,cmap=cm.gray)
matshow(kernel_mat,cmap=cm.gray)
matshow(kernel_defloute,cmap=cm.gray)
# nlcg="PolakRibierePolyak", limited=1)
#print"PolakRibierePolyak :" + str(x_out) + "\n \n"
#x_out = opkpy_v3.opk_minimize(x, fg_Rosen, g, algorithm="nlcg", linesearch="nonmonotone",
# nlcg="Fletcher", limited=1)
#print"Fletcher :" + str(x_out) + "\n \n"
#x_out = opkpy_v3.opk_minimize(x, fg_Rosen, g, algorithm="nlcg", linesearch="nonmonotone",
# nlcg="LiuStorey", limited=1)
#print"LiuStorey :" + str(x_out) + "\n \n"
#x_out = opkpy_v3.opk_minimize(x, fg_Rosen, g, algorithm="nlcg", linesearch="nonmonotone",
# nlcg="DaiYuan", limited=1)
#print"DaiYuan :" + str(x_out) + "\n \n"
#x_out = opkpy_v3.opk_minimize(x, fg_Rosen, g, algorithm="nlcg", linesearch="nonmonotone",
# nlcg="PerryShanno", limited=1)
#print"PerryShanno :" + str(x_out) + "\n \n"
x_out = opkpy_v3.opk_minimize(x, fg_Rosen, g, algorithm="vmlmb", linesearch="nonmonotone",
nlcg="HagerZhang", limited=0)
print("HagerZhang :" + str(x_out) + "\n \n")
##############################################################################
################################# PARAMETRES #################################
# x, f, g
# bl, bu ---> only for vmlmb
###### algorithm
###### linesearch
# autostep
###### nlcg ---> only for nlcg
###### vmlmb ---> only for vmlmb
# delta, epsilon