def inpaint(self, picture: Picture) -> Picture:
picture = picture.copy()
# Initialisation de la barre de progression
number_of_pixels_to_process = len(picture.pixels[picture.pixels == self.value_missing_pixel]) // 3
progress_bar_widgets = ["Inpainting: processed ", Counter(), "/{} pixels [".format(number_of_pixels_to_process),
Percentage(), "], ", Timer(), ", ", ETA()]
progress_bar = ProgressBar(widgets=progress_bar_widgets,
minval=0,
maxval=number_of_pixels_to_process)
progress_bar.start()
# On récupère le dictionnaire
dictionary = picture.get_dictionnaire(self.patch_size, self.step, max_missing_pixel=0)
while self.value_missing_pixel in picture.pixels:
# On récupère le prochain patch à traiter
next_pixel = self._get_next_patch(picture, self.patch_size, self.value_out_of_bounds,
self.value_missing_pixel)
# On reconstruit le patch selectionné
next_patch = picture.get_patch(*next_pixel, self.patch_size)
self.fit(dictionary, next_patch)
for x, y in iter_patch_empty(picture.pixels, *next_pixel, self.patch_size):
next_pixel_value = self.predict(x - next_pixel[0] + (self.patch_size // 2),
y - next_pixel[1] + (self.patch_size // 2),
dictionary)
picture.pixels[x, y] = next_pixel_value
progress_bar.update(progress_bar.value + 1)
progress_bar.finish()
return picture
def main_outdoor():
# Load picture
original_picture = Picture(picture_path=OUTDOOR, color_model=COLOR_MODEL)
# Add a rectangle of noise
noisy_picture = original_picture.copy()
noisy_picture.add_rectangle(288, 497, 190, 80)
main_inpainting(original_picture, noisy_picture)
def main_castle():
# Load picture
original_picture = Picture(picture_path=CASTLE, color_model=COLOR_MODEL)
# Add a rectangle of noise
noisy_picture = original_picture.copy()
noisy_picture.add_rectangle(400, 380, 50, 20)
main_inpainting(original_picture, noisy_picture)
def main_outdoor():
# Chargement de l'image
original_picture = Picture(picture_path=OUTDOOR, codage=CODAGE)
# Ajout du bruit
noisy_picture = original_picture.copy()
noisy_picture.add_rectangle(288, 497, 190, 80)
main_inpainting(original_picture, noisy_picture)
def main_castle():
# Chargement de l'image
original_picture = Picture(picture_path=CASTLE, codage=CODAGE)
# Ajout du bruit
noisy_picture = original_picture.copy() # Ajout du bruit
noisy_picture.add_rectangle(400, 380, 50, 20)
main_inpainting(original_picture, noisy_picture)
def main_lena():
# Chargement de l'image
original_picture = Picture(picture_path=LENA_COLOR_512, codage=CODAGE)
# Ajout du bruit
noisy_picture = original_picture.copy()
noisy_picture.add_noise(0.005)
main_inpainting(original_picture, noisy_picture)
def main_lena():
# Load picture
original_picture = Picture(picture_path=LENA_COLOR_512,
color_model=COLOR_MODEL)
# Add noise
noisy_picture = original_picture.copy()
noisy_picture.add_noise(0.005)
main_inpainting(original_picture, noisy_picture)
def inpaint(self, picture: Picture) -> Picture:
picture = picture.copy()
# Init progressbar
number_of_pixels_to_process = len(
picture.pixels[picture.pixels == self.value_missing_pixel]) // 3
progress_bar_widgets = [
"Inpainting: processed ",
Counter(), "/{} pixels [".format(number_of_pixels_to_process),
Percentage(), "], ",
Timer(), ", ",
ETA()
]
# Retrieve the picture's dictionary
print("Computing picture's dictionary")
dictionary = picture.get_dictionary(self.patch_size,
self.step,
max_missing_pixel=0)
with ProgressBar(widgets=progress_bar_widgets,
minval=0,
maxval=number_of_pixels_to_process) as progress_bar:
progress_bar.start()
while self.value_missing_pixel in picture.pixels:
# Retrieve the next patch containing missing values
next_pixel = self._get_next_patch(picture, self.patch_size,
self.value_out_of_bounds,
self.value_missing_pixel)
# Inpaint the selected patch
next_patch = picture.get_patch(*next_pixel, self.patch_size)
self.fit(dictionary, next_patch)
for x, y in iter_patch_empty(picture.pixels, *next_pixel,
self.patch_size):
next_pixel_value = self.predict(
x - next_pixel[0] + (self.patch_size // 2),
y - next_pixel[1] + (self.patch_size // 2), dictionary)
picture.pixels[x, y] = next_pixel_value
progress_bar.update(progress_bar.value + 1)
return picture