def charger_image(self):
path = filedialog.askopenfilename(parent=self.view,
initialdir=self.config.datadir,
title="Please select a file:",
filetypes=[('all files', '.*'),
('text files', '.txt')])
img = Image.open(path)
for bu in self.view.buttons:
bu.enable()
self.view.afficher_valeur.config(state=tk.NORMAL)
self.view.histogramme.config(state=tk.NORMAL)
self.view.spectre.config(state=tk.NORMAL)
self.original = img.copy()
self.old = img.copy()
self.resultat = img.copy()
afficher_image(self.original,
self.config.figure,
1,
3,
1,
title="Original",
clear=True)
plt.show()
def annuler(self):
self.resultat = self.old.copy()
afficher_image(self.resultat,
self.config.figure,
1,
3,
3,
title="Résultat précédent")
plt.show()
def reinitialiser(self):
self.old = self.original.copy()
self.resultat = self.original.copy()
afficher_image(self.original,
self.config.figure,
1,
3,
1,
title="Original",
clear=True)
plt.show()
def afficher_resultat(self, name):
afficher_image(self.old,
self.config.figure,
1,
3,
2,
title="Résultat précédent")
afficher_image(self.resultat,
self.config.figure,
1,
3,
3,
title="Résultat " + str(name))
plt.show()
def spectre(self):
spectre = filtre._to_freq(self.resultat)
afficher_image(normalize(np.log10(abs(spectre))), "Spectre", 1, 1, 1,
"Spectre")
plt.show()
def main():
# l = [1,5,3,2,11,7,8,4]
image = np.array(Image.open(IMAGE[sys.argv[1]]))
I = np.array(image)
M = len(I)
N = len(I[0])
# P = ddp_cumule(image)
# J = normalize(opt(image, egalisation, P=P, excluded=["P"]), 255, dtype=np.uint8)
# afficher_image(image, "Figure", 1, 2, 1, "Original")
# afficher_image(J, "Figure", 1, 2, 2, "Resultat")
# plot_fig(P, "Info", 1, 2, 1, "Plot")
# plot_fig(ddp_cumule(J), "Info", 1, 2, 2, "Resultat")
# plt.show()
# # img = filtre_median(image, N8)
# bruite = bruit_s_p(image, prob=0.05)
# bruite = bruit_gauss(bruite, s=10)
# # print(LAPLACIEN)
# # print(masque_moyenneur(3))
# img = filtre(bruite, masque_moyenneur(5))
# med = filtre(img, masque_moyenneur(5))
# r = laplacien(med, LAPLACIEN)
# sobel = op_sobel(r)
# afficher_image(image, "Figure", 2, 3, 1, "Original")
# afficher_image(bruite, "Figure", 2, 3, 2, "Bruité")
# afficher_image(img, "Figure", 2, 3, 3, "Moyenne")
# afficher_image(med, "Figure", 2, 3, 4, "Median")
# afficher_image(r, "Figure", 2, 3, 5, "Réhaussé")
# afficher_image(sobel, "Figure", 2, 3, 6, "Réhaussé")
# plt.show()
# afficher_image(image, "Figure", 1, 3, 1, "Original")
# # H = 1-passe_bas_gaussien(len(image), len(image[0]), 10)
# H = filtre_pum(passe_bas_butterworth, 0.5, 2, M, N, 20)
# afficher_image(H, "Figure", 1, 3, 2, "Filtre")
# rehausse, spectre, j_h = filtre_freq(image, H)
# # afficher_image(normalize(np.log10(abs(spectre)), 255, np.uint8), "Figure", 1, 4, 3, "Spectre")
# afficher_image(normalize(abs(rehausse), 255, np.uint8), "Figure", 1, 3, 3, "Resultat")
# plt.show()
# i_sp = bruit(bruit(I, poivre, 0.05), sel, 0.05)
# i_gauss = bruit_gauss(image, u=0, s=50)
i_uniforme = bruit_uniform(image, 2, 80)
# i_rayleigh = normalize(bruit(I, rayleigh, a=1, b=10000))
# i_exp = normalize(bruit(I, exponentiel, a=0.01))
# i_periodique = bruit_periodique(image, 0.2, 0.2, A=30)
# afficher_image(image, "Bruit", 2, 3, 1, "Original")
# afficher_image(i_periodique, "Bruit", 2, 3, 2, "Periodique")
# afficher_image(i_gauss, "Bruit", 2, 3, 3, "Gauss")
afficher_image(i_uniforme, "Bruit", 2, 3, 4, "Uniforme")
# afficher_image(i_sp, "Bruit", 2, 3, 5, "Rayleigh")
# afficher_image(i_exp, "Bruit", 2, 3, 6, "Erland")
# afficher_image(i_exp, "Bruit2", 2, 3, 1, "Original")
# afficher_image(image, "Bruit2", 2, 3, 2, "Exponentiel")
# afficher_image(image, "Bruit2", 2, 3, 3, "PS+Gauss+Uniforme")
# afficher_image(image, "Bruit2", 2, 3, 4, "")
# afficher_image(image, "Bruit2", 2, 3, 5, "")
# afficher_image(image, "Bruit2", 2, 3, 6, "")
plt.show()