image = image.reshape([HEIGHT, WIDTH]) # print(np.max(image),np.min(image)) # image = np.clip(image+0.5,0,1) images += [image] return images ### GENERATE SECTION print('GENERATE SECTION') for current_iteration in range(N): print('CURRENT_ITERATION:', current_iteration) if START_SERVER is True: viz.start_image_server(generate_image, COLOR_STRING, SEED + current_iteration) break else: if TRAIN_NNET is True: print('\tTRAINING NNET') training_image = train_nnet(SEED + current_iteration) print('\tGENERATING IMAGE') # images = generate_neuron_images(COLOR_STRING, SEED + current_iteration) images = generate_image(COLOR_STRING, SEED + current_iteration) if SAVE_IMAGES is True: print('\tSAVING IMAGE') file.export_image('%d_%d_%d' % (current_iteration, SEED + current_iteration,
gridpatterny = m.RMM(values=valuesy, self_length=20) parenty = m.SProg(values=gridpatterny) gridx = m.generate_grid_lines(parentx, WIDTH) # gridy = m.generate_grid_lines(parenty,HEIGHT) gridy = [HEIGHT // 2] # print(gridx) # print(gridy) color_repository = color.build_color_repository(color_string) final_img = r.call_and_bind(generate_image, gridx, gridy, color_repository) return final_img # imgs = generate_full_image(COLOR_STRING) # file.export_image( # '%d_%d' % (current_iteration,int(round(time.time() * 1000))), # imgs.astype('uint8'),format='png') # if N == 1: viz.start_image_server(COLOR_STRING, generate_full_image, SEED + current_iteration) else: imgs = generate_full_image(COLOR_STRING, SEED) file.export_image('%d_%d_%d' % (current_iteration, SEED + current_iteration, int(round(time.time() * 1000))), imgs.astype('uint8'), format='png')