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)