content_segmentation_masks = extract_segmentation_masks(content_segmentation_image) style_segmentation_masks = extract_segmentation_masks(style_segmentation_image) # otherwise compute it else: print("Create segmentation.") content_segmentation, style_segmentation = compute_segmentation(args.content_image, args.style_image) cv2.imwrite(change_filename(args.seg_dir, args.content_image, '_seg_raw', '.png'), content_segmentation) cv2.imwrite(change_filename(args.seg_dir, args.style_image, '_seg_raw', '.png'), style_segmentation) content_segmentation_masks, style_segmentation_masks = merge_segments(content_segmentation, style_segmentation, args.semantic_thresh, args.similarity_metric) cv2.imwrite(change_filename(args.seg_dir, args.content_image, '_seg', '.png'), reduce_dict(content_segmentation_masks, content_image)) cv2.imwrite(change_filename(args.seg_dir, args.style_image, '_seg', '.png'), reduce_dict(style_segmentation_masks, style_image)) if args.init == "noise": random_noise_scaling_factor = 0.0001 random_noise = np.random.randn(*content_image.shape).astype(np.float32) init_image = vgg.postprocess(random_noise * random_noise_scaling_factor).astype(np.float32) elif args.init == "content": init_image = content_image elif args.init == "style": init_image = style_image else: print("Init image parameter {} unknown.".format(args.init)) exit(1)
content_segmentation_masks = extract_segmentation_masks( content_segmentation_image) else: print("Create segmentation.") content_segmentation = compute_segmentation(args.content_image) cv2.imwrite( change_filename(result_dir, args.content_image, '_seg_raw', '.png'), content_segmentation) content_segmentation_masks, color_to_gram_dict, anp_results = merge_anps( content_segmentation, style_text, args.adjective_threshold, args.noun_threshold, result_dir) cv2.imwrite( change_filename(result_dir, args.content_image, '_seg', '.png'), reduce_dict(content_segmentation_masks, content_image)) if args.init == "noise": random_noise_scaling_factor = 0.0001 random_noise = np.random.randn(*content_image.shape).astype( np.float32) init_image = vgg.postprocess( random_noise * random_noise_scaling_factor).astype(np.float32) elif args.init == "content": init_image = content_image elif args.init == "style": init_image = style_image else: print("Init image parameter {} unknown.".format(args.init)) exit(0)