def style_transfer_high_resolution(patches,
sFs,
padding,
collection,
save_path,
save=True,
wct_mode="cpu"):
stylized_patches = []
init_thumbnail_instance_norm(wct, collection=collection)
for patch in tqdm(patches):
patch = patch.unsqueeze(0).to(device)
stylized_patch = styleTransfer(patch, sFs, wct_mode)
stylized_patch = F.interpolate(stylized_patch,
patch.shape[2:],
mode='bilinear',
align_corners=True)
stylized_patch = unpadding(stylized_patch, padding=padding)
stylized_patches.append(stylized_patch.cpu())
stylized_patches = torch.cat(stylized_patches, dim=0)
b, c, h, w = stylized_patches.shape
stylized_patches = stylized_patches.unsqueeze(dim=0)
stylized_patches = stylized_patches.view(1, b, c * h * w).permute(
0, 2, 1).contiguous()
output_size = (int(math.sqrt(b) * h), int(math.sqrt(b) * w))
stylized_image = F.fold(stylized_patches,
output_size=output_size,
kernel_size=(h, w),
stride=(h, w))
if save:
save_image(stylized_image, save_path)
def decompress_speck(path, dest_path):
if path[-1] != "/":
path += "/"
info = pickle.load(open(path + "info.dat", "r"))
if not os.path.exists(dest_path):
os.makedirs(dest_path)
codec = sk.speck()
for c in range(info.frames):
frame = pickle.load(open(path + str(c) + ".speck","rb"))
wavelet = codec.expand(frame["payload"], info.wavelet_cols,
info.wavelet_rows, info.wavelet_level,
frame["wise_bit"])
iframe = lwt.icdf97(wavelet)
iframe = tools.aquant(iframe, 100000)
iframe = tools.unpadding(iframe, (info.rows, info.cols))
if not cv2.imwrite(dest_path + str(c) + ".png",
iframe, [cv2.cv.CV_IMWRITE_PNG_COMPRESSION, 0]):
print "Failed to create: " + dest_path + str(c) + ".png"
pickle.dump(info, open(dest_path + "info.dat", "w"))
def style_transfer_high_resolution(patches, sF, padding, save_path, collection=False, save=True):
stylized_patches = []
init_thumbnail_instance_norm(matrix, collection=collection)
for patch in tqdm(patches):
patch = patch.unsqueeze(0).to(device)
cF = vgg(patch)
if (args.layer == 'r41'):
feature = matrix(cF[args.layer], sF[args.layer])
else:
feature = matrix(cF, sF)
stylized = dec(feature)
stylized = F.interpolate(stylized, patch.shape[2:], mode='bilinear', align_corners=True)
stylized = unpadding(stylized, padding=padding)
stylized_patches.append(stylized.cpu())
stylized_patches = torch.cat(stylized_patches, dim=0)
b, c, h, w = stylized_patches.shape
stylized_patches = stylized_patches.unsqueeze(dim=0)
stylized_patches = stylized_patches.view(1, b, c * h * w).permute(0, 2, 1).contiguous()
output_size = (int(math.sqrt(b) * h), int(math.sqrt(b) * w))
stylized_image = F.fold(stylized_patches, output_size=output_size, kernel_size=(h, w), stride=(h, w))
if save:
save_image(stylized_image, save_path)