# cut the window on x
tmp = image[begin_border_x:end_border_x]
# cut the window on y
for t in tmp:
t = t[begin_border_y:end_border_y]
# cut the pixels alpha
col = []
for pixel in t:
col.append(np.array([pixel[0], pixel[1], pixel[2]]))
window.append(col)
# log
print(f"Epochs: {i * y_size + j + 1}/{y_size*x_size}; window: [{begin_true_x}, {begin_true_y}] [{end_true_x}, {end_true_y}]; shape: {np.array(window).shape}")
# predict the new window
new_window = generator.predict(np.array([window,]))[0]
# pass throug all pixels of the new_window
# and push there in the new image
# the loop
for k in range(len(new_window)):
for l in range(len(new_window[0])):
# calculate the coords
# on the new image
x = k + (begin_border_x*4)
y = l + (begin_border_y*4)
# get the pixel value
p = new_window[k][l]
if e % 10 == 0:
generator.save_weights("./" + 'gen_model.h5')
discriminator.save_weights("./" + 'dis_model.h5')
# setup for matplotlib
fig = plt.figure(figsize=(10, 10))
sub_plots = []
x1, y1 = get_one_image()
x2, y2 = get_one_image()
x3, y3 = get_one_image()
x4, y4 = get_one_image()
X = np.concatenate((x1, x2, x3, x4))
for i in range(8):
sub_plots.append(plt.subplot(2, 4, i + 1))
ani = animation.FuncAnimation(fig, animate, interval=1)
plt.show()
x, y = get_one_image()
# show a upscalle image
new = generator.predict(x)
plt.imshow(x[0])
plt.show()
plt.imshow(new[0])
plt.show()
plt.imshow(y[0])
plt.show()