def _crossover(self, population, selected_imgs, crossover_prob):
new_population = Population()
new_population.phenotype = population.phenotype
range_of_selected_images = range(len(selected_imgs))
for i in range_of_selected_images:
selected_img_index_range = list(range_of_selected_images)
random.shuffle(selected_img_index_range)
first_index = selected_img_index_range[0]
second_index = selected_img_index_range[1]
parent_1 = selected_imgs[first_index]
parent_2 = selected_imgs[second_index]
child = FakeImgCandidate(np.copy(self.img))
for x, y in population.phenotype:
r = random.random()
if r <= crossover_prob:
new_pixel_value = (parent_1.get_pixel_value(x, y) + parent_2.get_pixel_value(x, y)) / 2
child.set_pixel_value(x, y, new_pixel_value)
new_population.add_img(child)
for selected_img in selected_imgs:
new_population.add_img(selected_img)
return new_population
def _init_population(self, population_size, pixels_to_change_count):
population = Population()
width = self.img.shape[0] - 1
height = self.img.shape[1] - 1
all_indices = []
for i in range(0, width):
for j in range(0, height):
all_indices.append((i, j))
random.shuffle(all_indices)
population.phenotype = all_indices[0:pixels_to_change_count]
for i in range(population_size):
fake_img_candidate = FakeImgCandidate(np.copy(self.img))
for x, y in population.phenotype:
current_pixel_value = fake_img_candidate.get_pixel_value(x, y)
new_pixel_value = self._generate_new_pixel_value(current_pixel_value)
fake_img_candidate.set_pixel_value(x, y, new_pixel_value)
population.add_img(fake_img_candidate)
return population
