def transform(args): style = args.style #img_width = img_height = args.image_size output_file = args.output input_file = args.input original_color = args.original_color blend_alpha = args.blend media_filter = args.media_filter aspect_ratio, x = img_util.preprocess_reflect_image(input_file, size_multiple=4) img_width = img_height = x.shape[1] net = nets.image_transform_net(img_width, img_height) model = nets.loss_net(net, img_width, img_height, "", 0, 0) model.compile( "adam", loss.dummy_loss) # Dummy loss since we are learning from regularizes model.load_weights("pretrained/" + style + '_weights.h5', by_name=False) t1 = time.time() y = net.predict(x)[0] y = crop_image(y, aspect_ratio) print("process: %s" % (time.time() - t1)) ox = crop_image(x[0], aspect_ratio) y = median_filter_all_colours(y, media_filter) if blend_alpha > 0: y = blend(ox, y, blend_alpha) if original_color > 0: y = original_colors(ox, y, original_color) imsave('%s_output.png' % output_file, y)
def main(args): texture = args.texture style = args.style #img_width = img_height = args.image_size output_file = args.output input_file = args.input original_color = args.original_color blend_alpha = args.blend media_filter = args.media_filter #processing for texture model aspect_ratio, x = preprocess_reflect_image(input_file, size_multiple=4) img_width = img_height = x.shape[1] net = nets.image_transform_net(img_width, img_height) model = nets.loss_net(net.output, net.input, img_width, img_height, "", 0, 0) model.compile( Adam(), dummy_loss) # Dummy loss since we are learning from regularizes #load texture model model.load_weights(texture, by_name=False) t1 = time.time() y = net.predict(x)[0] y = crop_image(y, aspect_ratio) print("process: %s" % (time.time() - t1)) ox = crop_image(x[0], aspect_ratio) y = median_filter_all_colours(y, media_filter) if blend_alpha > 0: y = blend(ox, y, blend_alpha) if original_color > 0: y = original_colors(ox, y, original_color) imsave('%s_texture.png' % output_file, y) imshow(y) #processing for second style transform aspect_ratio2, x2 = preprocess_reflect_layer2(y, size_multiple=4) img_width2 = img_height2 = x2.shape[1] net2 = nets.image_transform_net(img_width2, img_height2) model2 = nets.loss_net(net2.output, net2.input, img_width2, img_height2, "", 0, 0) model2.compile(Adam(), dummy_loss) #load style model model2.load_weights(style, by_name=False) y2 = net2.predict(x2)[0] y2 = crop_image(y2, aspect_ratio) print("process: %s" % (time.time() - t1)) ox2 = crop_image(x2[0], aspect_ratio2) y2 = median_filter_all_colours(y2, media_filter) if blend_alpha > 0: y2 = blend(ox2, y2, blend_alpha) if original_color > 0: y2 = original_colors(ox2, y2, original_color) #save and display the transformed image imsave('%s_output.png' % output_file, y2) imshow(y2)