def main(args):
out_list = file_utils.image_files(args.gen_dir)
gt_list = file_utils.image_files(args.gt_dir)
for i, (out_path, gt_path) in enumerate(list(zip(out_list, gt_list))):
out = np.array(file_utils.pil_loader(out_path))
gt = np.array(file_utils.pil_loader(gt_path))
psnr = metrics.peak_signal_noise_ratio(gt, out)
ssim = metrics.structural_similarity(gt, out, multichannel=True)
print("Image {0}, PSNR: {1}, SSIM: {2}".format(i, psnr, ssim))
def main(args):
os.makedirs(args.outputs, exist_ok=True)
generator = get_derivable_generator(args.gan_model, args.inversion_type,
args)
loss = get_loss(args.loss_type, args)
generator.cuda()
loss.cuda()
inversion = get_inversion(args.optimization, args)
image_list = image_files(args.target_images)
frameSize = MODEL_POOL[args.gan_model]['resolution']
for i, images in enumerate(split_to_batches(image_list, 1)):
print('%d: Inverting %d images :' % (i + 1, 1), end='')
pt_image_str = '%s\n'
print(pt_image_str % tuple(images))
image_name_list = []
image_tensor_list = []
for image in images:
image_name_list.append(os.path.split(image)[1])
image_tensor_list.append(_add_batch_one(load_as_tensor(image)))
y_gt = _sigmoid_to_tanh(torch.cat(image_tensor_list, dim=0)).cuda()
# Invert
latent_estimates, history = inversion.invert(generator,
y_gt,
loss,
batch_size=1,
video=args.video)
# Get Images
y_estimate_list = torch.split(torch.clamp(_tanh_to_sigmoid(
generator(latent_estimates)),
min=0.,
max=1.).cpu(),
1,
dim=0)
# Save
for img_id, image in enumerate(images):
y_estimate_pil = Tensor2PIL(y_estimate_list[img_id])
y_estimate_pil.save(
os.path.join(args.outputs, image_name_list[img_id]))
# Create video
if args.video:
print('Create GAN-Inversion video.')
video = cv2.VideoWriter(filename=os.path.join(
args.outputs,
'%s_inversion.avi' % image_name_list[img_id]),
fourcc=cv2.VideoWriter_fourcc(*'MJPG'),
fps=args.fps,
frameSize=(frameSize, frameSize))
print('Save frames.')
for i, sample in enumerate(history):
image = generator(sample)
image_cv2 = convert_array_to_images(
image.detach().cpu().numpy())[0][:, :, ::-1]
video.write(image_cv2)
video.release()
def saveIMG():
image_list = image_files(default_load_img_pth)
for i, images in enumerate(split_to_batches(image_list, 1)):
image_name_list = []
image_tensor_list = []
for image in images:
image_name_list.append(os.path.split(image)[1])
image_tensor_list.append(_add_batch_one(load_as_tensor(image)))
# 保存结果
for img_id, image in enumerate(images):
down = convert_test(image_tensor_list[img_id],
factor=16,
mode='nearest')
y_nn_pil = Tensor2PIL(down)
y_nn_pil.save(
os.path.join(
os.path.join('img_test_output',
'%s-nn.png' % image_name_list[img_id][:-4])))
def main(args):
os.makedirs(args.outputs, exist_ok=True)
generator = get_derivable_generator(args.gan_model, args.inversion_type,
args) # 生成器
loss = get_loss(args.loss_type, args)
sr_loss = SR_loss(loss, args.down, args.factor) # SR计算loss的方式
# to cuda
generator.cuda()
loss.cuda()
inversion = get_inversion(args.optimization, args)
image_list = image_files(args.target_images)
frameSize = MODEL_POOL[args.gan_model]['resolution']
for i, images in enumerate(split_to_batches(image_list, 1)):
print('%d: Super-resolving %d images ' % (i + 1, 1), end='')
pt_image_str = '%s\n'
print(pt_image_str % tuple(images))
image_name_list = []
image_tensor_list = []
for image in images:
image_name_list.append(os.path.split(image)[1])
# image = _add_batch_one(load_as_tensor(image))
image = convert2target(_add_batch_one(load_as_tensor(image)),
'nearest') # 更改size使得适用于更多类型的分辨率图像
image_tensor_list.append(image)
# print("add..: ", _add_batch_one(load_as_tensor(image)).size()) # torch.Size([1, 3, 64, 64])
y_gt = _sigmoid_to_tanh(torch.cat(image_tensor_list, dim=0)).cuda()
# Invert
latent_estimates, history = inversion.invert(generator,
y_gt,
sr_loss,
batch_size=BATCH_SIZE,
video=args.video)
# Get Images
# 将optimizer优化好的latent_estimates再放入generator中生成图像
# 并且将batch_size那一列的数据去除
y_estimate_list = torch.split(torch.clamp(_tanh_to_sigmoid(
generator(latent_estimates)),
min=0.,
max=1.).cpu(),
1,
dim=0)
# 保存结果
for img_id, image in enumerate(images):
# up_nn, up_bic, down = downsample_images(image_tensor_list[img_id], factor=args.factor, mode=args.down)
# y_nn_pil = Tensor2PIL(up_nn) # 低分辨率化后的图像
y_estimate_pil = Tensor2PIL(y_estimate_list[img_id])
y_estimate_pil.save(
os.path.join(
os.path.join(args.outputs,
'%s.png' % image_name_list[img_id][:-4])))
#y_nn_pil.save(os.path.join(os.path.join(args.outputs, '%s-nn.png' % image_name_list[img_id][:-4])))
# Create video
if args.video:
print('Create GAN-Inversion video.')
video = cv2.VideoWriter(filename=os.path.join(
args.outputs, '%s_sr.avi' % image_name_list[img_id][:-4]),
fourcc=cv2.VideoWriter_fourcc(*'MJPG'),
fps=args.fps,
frameSize=(frameSize, frameSize))
print('Save frames.')
for i, sample in enumerate(history):
image = generator(
sample
) # 用generator从history（保存的训练中的estimate_latent的值）中生成图像
image_cv2 = convert_array_to_images(
image.detach().cpu().numpy())[0][:, :, ::-1]
video.write(image_cv2)
video.release()
def main(args):
os.makedirs(args.outputs, exist_ok=True)
out_dir, exp_name = create_experiments_directory(args, args.exp_id)
print(out_dir)
print(exp_name)
generator = get_derivable_generator(args.gan_model, args.inversion_type,
args)
generator.cuda()
if args.target_images.endswith('.png') or args.target_images.endswith(
'.jpg'):
image_list = os.path.abspath(args.target_images)
image_list = [image_list]
else:
image_list = image_files(args.target_images)
frameSize = MODEL_POOL[args.gan_model]['resolution']
n_blocks = generator.n_blocks
print('There are %d blocks in this generator.' % n_blocks) # 19 for pggan
latent_space = generator.PGGAN_LATENT[args.layer]
print('The latent space is ', latent_space)
with open(args.matrix_dir, 'rb') as file_in:
matrix = pkl.load(file_in)
print('Load matrix successfully.')
print('Matrix shape ', matrix.shape)
matrix2 = thrC(matrix, args.alpha)
predict, _ = post_proC(matrix2, args.n_subs, args.d_subs, args.power)
print(predict)
p_sum = [sum(predict == k) for k in range(1, args.cluster_numbers, 1)]
p_sum = np.array(p_sum)
p_sort = np.argsort(p_sum)[::-1]
print(p_sum)
predict_new = predict.copy()
for i in range(1, args.cluster_numbers, 1):
predict_new[predict == (p_sort[i - 1] + 1)] = i
predict = predict_new.copy()
p_sum = [sum(predict == k) for k in range(1, args.cluster_numbers, 1)]
print(predict)
print(p_sum)
# pre_see the images
gan_type, image_type = args.gan_model.split("_")
print('The gan type is %s, and the image type is %s' %
(gan_type, image_type))
test_image_dir = os.path.join('./bin/', gan_type, image_type)
print(test_image_dir)
files = os.listdir(test_image_dir)
test_zs = []
for i in range(len(files)):
if files[i].endswith('.pkl'):
with open(os.path.join(test_image_dir, files[i]), 'rb') as file_in:
test_zs.append(pkl.load(file_in))
test_zs = torch.from_numpy(
np.concatenate(test_zs, axis=0).astype(np.float32)).cuda()
print('Load all testing zs, shape is ', test_zs.size())
image_number = 3
sel_idx = np.random.choice(test_zs.shape[0],
size=[image_number],
replace=False)
F = generator([test_zs[sel_idx]], which_block=args.layer, pre_model=True)
features = F.detach().cpu().numpy()
predict_masks = []
for i in range(1, args.cluster_numbers + 1, 1):
mask = torch.from_numpy((predict == i).astype(np.float32)).cuda()
predict_masks += [mask.reshape((1, -1, 1, 1))]
for i, images in enumerate(split_to_batches(image_list, 1)):
# input_size = generator.PGGAN_LATENT[args.layer + 1]
# print("We are making ", input_size, ". ")
print('%d: Inverting %d images :' % (i + 1, 1), end='')
pt_image_str = '%s\n'
print(pt_image_str % tuple(images))
image_name_only = images[0].split(".")[0]
image_name_only = image_name_only.split("/")[-1]
print(image_name_only)
image_name_list = []
image_tensor_list = []
for image in images:
image_name_list.append(os.path.split(image)[1])
image_tensor_list.append(_add_batch_one(load_as_tensor(image)))
print("image_name_list", image_name_list)
print("image_tensor_list, [", image_tensor_list[0].size(), "]")
y_image = _sigmoid_to_tanh(torch.cat(image_tensor_list, dim=0)).cuda()
print('image size is ', y_image.size())
z_estimate = generator.init_value(batch_size=1,
which_layer=0,
init=args.init_type,
z_numbers=args.cluster_numbers *
args.code_per_cluster)
base_estimate = generator.init_value(batch_size=1,
which_layer=0,
init=args.init_type,
z_numbers=1)
if args.optimization == 'GD':
z_optimizer = torch.optim.SGD(z_estimate + base_estimate,
lr=args.lr)
elif args.optimization == 'Adam':
z_optimizer = torch.optim.Adam(z_estimate + base_estimate,
lr=args.lr)
else:
raise NotImplemented(
'We don\'t support this type of optimization.')
for iter in range(args.iterations):
for estimate in z_estimate:
estimate.requires_grad = True
for estimate in base_estimate:
estimate.requires_grad = True
features = generator([
z_estimate[0].reshape(
[args.cluster_numbers * args.code_per_cluster, 512, 1, 1])
],
which_block=args.layer,
pre_model=True)
base_feature = generator(
[base_estimate[0].reshape([1, 512, 1, 1])],
which_block=args.layer,
pre_model=True)
for t in range(args.cluster_numbers * args.code_per_cluster):
if t == 0:
f_mix = features[t].view(*(1, ) +
latent_space) * predict_masks[int(
t / args.code_per_cluster)]
else:
f_mix = f_mix + features[t].view(
*(1, ) + latent_space) * predict_masks[int(
t / args.code_per_cluster)]
f_mix = f_mix + base_feature
y_estimate = generator([f_mix],
which_block=args.layer,
post_model=True)
y_raw_estimate = generator([base_feature],
which_block=args.layer,
post_model=True)
z_optimizer.zero_grad()
loss = 0.01 * torch.mean(torch.pow(
y_estimate - y_image, 2.0)) + torch.mean(
torch.pow(y_raw_estimate - y_image, 2.0))
loss.backward()
z_optimizer.step()
if iter % args.report_value == 0:
print('Iter %d, layer %d, loss = %.4f.' %
(iter, args.layer, float(loss.item())))
if iter % args.report_image == 0:
print('Saving the images.')
y_estimate_pil = Tensor2PIL(
torch.clamp(_tanh_to_sigmoid(y_estimate.detach().cpu()),
min=0.0,
max=1.0))
y_estimate_pil.save(
os.path.join(
out_dir,
image_name_only + "_estimate_iter%d.png" % iter))
y_estimate_pil = Tensor2PIL(
torch.clamp(_tanh_to_sigmoid(
y_raw_estimate.detach().cpu()),
min=0.0,
max=1.0))
y_estimate_pil.save(
os.path.join(
out_dir,
image_name_only + "_raw_estimate_iter%d.png" % iter))
# add bias added output picture.
# save all the codes
codes = []
for code_idx in range(args.cluster_numbers *
args.code_per_cluster):
code_f = generator([z_estimate[0][code_idx]],
which_block=args.layer + 1,
pre_model=True)
code_y = generator([code_f],
which_block=args.layer + 1,
post_model=True).detach().cpu()
codes.append(
torch.clamp(_tanh_to_sigmoid(code_y), min=0, max=1))
codes = torch.cat(codes, dim=0).detach().cpu()
torchvision.utils.save_image(
codes,
os.path.join(
out_dir,
image_name_only + '_codes_iter%d.png' % (iter)),
nrow=(args.cluster_numbers * args.code_per_cluster) // 2)
if iter % args.report_model == 0:
print('Save the models')
save_dict = {
"z": z_estimate[0].detach().cpu().numpy(),
'matrix': matrix,
'layer': args.layer,
'predict': predict
}
with open(
os.path.join(
out_dir, 'save_dict_iter_%d_layer_%d.pkl' %
(iter, args.layer)), 'wb') as file_out:
pkl.dump(save_dict, file_out)
print('Save the models OK!')
def main(args):
os.makedirs(args.outputs + '/input', exist_ok=True)
os.makedirs(args.outputs + '/GT', exist_ok=True)
os.makedirs(args.outputs + '/mGANoutput', exist_ok=True)
with open(args.outputs + '/mGANargs.txt', 'w') as f:
json.dump(args.__dict__, f, indent=2)
generator = get_derivable_generator(args.gan_model, args.inversion_type,
args)
loss = get_loss(args.loss_type, args)
mask = parsing_mask('code/mganprior/masks/' + args.mask).cuda()
mask_cpu = parsing_mask('code/mganprior/masks/' + args.mask)
crop_loss = masked_loss(loss, mask)
generator.cuda()
loss.cuda()
inversion = get_inversion(args.optimization, args)
image_list = image_files(args.target_images)
if len(image_list) > 300:
print('Limiting the image set to 300.')
image_list = image_list[:300]
frameSize = MODEL_POOL[args.gan_model]['resolution']
start_time = time.time()
for i, images in enumerate(split_to_batches(image_list, 1)):
print('%d: Processing %d images :' % (i, 1), end='')
pt_image_str = '%s\n'
print(pt_image_str % tuple(images))
image_name_list = []
image_tensor_list = []
for image in images:
image_name_list.append(os.path.split(image)[1])
image_tensor_list.append(_add_batch_one(load_as_tensor(image)))
y_gt = _sigmoid_to_tanh(torch.cat(image_tensor_list, dim=0)).cuda()
# Invert
if args.varmask:
os.makedirs(args.outputs + '/mask', exist_ok=True)
mask_cpu = get_var_mask(y_gt.shape[-2:], args.min_p, args.max_p,
args.width_mean, args.width_var)
mask = mask_cpu.cuda()
save_image(mask,
os.path.join(args.outputs + '/mask/%d%s' % (i, '.png')))
crop_loss = masked_loss(loss, mask)
latent_estimates, history = inversion.invert(generator,
y_gt,
crop_loss,
batch_size=1,
video=args.video)
# Get Images
y_estimate_list = torch.split(torch.clamp(_tanh_to_sigmoid(
generator(latent_estimates)),
min=0.,
max=1.).cpu(),
1,
dim=0)
# Save
for img_id, image in enumerate(images):
y_RGB = Tensor2PIL(image_tensor_list[img_id])
y_RGB.save(args.outputs + '/GT/%d%s' %
(i, image_name_list[img_id][-4:]))
y_gt_pil = Tensor2PIL(
mask_images(image_tensor_list[img_id], mask_cpu))
y_estimate_pil = Tensor2PIL(y_estimate_list[img_id])
y_estimate_pil.save(
os.path.join(args.outputs + '/mGANoutput/%d%s' %
(i, image_name_list[img_id][-4:])))
y_gt_pil.save(
os.path.join(args.outputs + '/input/%d%s' %
(i, image_name_list[img_id][-4:])))
# Create video
if args.video:
print('Create GAN-Inversion video.')
video = cv2.VideoWriter(filename=os.path.join(
args.outputs, '%s_inpainting_%s.avi' %
(image_name_list[img_id][:-4], os.path.split(
args.mask[:-4])[1])),
fourcc=cv2.VideoWriter_fourcc(*'MJPG'),
fps=args.fps,
frameSize=(frameSize, frameSize))
print('Save frames.')
for i, sample in enumerate(history):
image = generator(sample)
image_cv2 = convert_array_to_images(
image.detach().cpu().numpy())[0][:, :, ::-1]
video.write(image_cv2)
video.release()
print(f'{(time.time()-start_time)/60:.2f}', 'minutes taken in total;',
f'{(time.time()-start_time)/60/len(image_list):.2f}', 'per image.')
def run(args):
os.makedirs(args.outputs, exist_ok=True) # 生成输出路径文件夹，存在则跳过
# 生成器
generator = get_derivable_generator(args.gan_model, args.inversion_type,
args)
loss = get_loss(args.loss_type, args) # 损失函数
generator.cuda() # pytorch需要手动放入GPU进行运算
loss.cuda()
inversion = get_inversion(args.optimization, args)
image_list = image_files(args.target_images) # 获取输入图片路径
frameSize = MODEL_POOL[args.gan_model]['resolution'] # 获取图像分辨率
# 按照batch大小分批处理图像
for i, images in enumerate(split_to_batches(image_list, 1)):
print('%d: Inverting %d images :' % (i + 1, 1), end='')
# pt_image_str = '%s\n'
print('%s\n' % tuple(images))
image_name_list = []
image_tensor_list = []
for image in images:
image_name_list.append(os.path.split(image)[1])
image_tensor_list.append(_add_batch_one(load_as_tensor(image)))
# torch.cat(tensors, dim=0, out=None) → Tensor
# tensors (sequence of Tensors) – any python sequence of tensors of the same type. Non-empty tensors provided must have the same shape, except in the cat dimension.
# dim (int, optional) – the dimension over which the tensors are concatenated
# out (Tensor, optional) – the output tensor.
y_gt = _sigmoid_to_tanh(
torch.cat(image_tensor_list,
dim=0)).cuda() # 在维度0上连接所有的tensor并且将值域映射到[-1, 1]
# 逆映射, 生成图像tensor
latent_estimates, history = inversion.invert(generator,
y_gt,
loss,
batch_size=BATCH_SIZE,
video=args.video)
# 将值域从[-1,1]映射到[0,1], 使用torch.clamp()进一步保证值域在[0,1]
y_estimate_list = torch.split(torch.clamp(_tanh_to_sigmoid(
generator(latent_estimates)),
min=0.,
max=1.).cpu(),
1,
dim=0)
# Save
for img_id, image in enumerate(images):
y_estimate_pil = Tensor2PIL(
y_estimate_list[img_id]) # 从tensor转化为PIL image并保存
y_estimate_pil.save(
os.path.join(args.outputs, image_name_list[img_id]))
# Create video
if args.video:
print('Create GAN-Inversion video.')
video = cv2.VideoWriter(filename=os.path.join(
args.outputs,
'%s_inversion.avi' % image_name_list[img_id]),
fourcc=cv2.VideoWriter_fourcc(*'MJPG'),
fps=args.fps,
frameSize=(frameSize, frameSize))
print('Save frames.')
for i, sample in enumerate(history):
image = generator(sample)
image_cv2 = convert_array_to_images(
image.detach().cpu().numpy())[0][:, :, ::-1]
video.write(image_cv2)
video.release()
def main(args):
os.makedirs(args.outputs, exist_ok=True)
generator = get_derivable_generator(args.gan_model, args.inversion_type,
args)
loss = get_loss(args.loss_type, args)
cor_loss = Color_loss(loss)
generator.cuda()
loss.cuda()
inversion = get_inversion(args.optimization, args)
image_list = image_files(args.target_images)
frameSize = MODEL_POOL[args.gan_model]['resolution']
for i, images in enumerate(split_to_batches(image_list, 1)):
print('%d: Processing %d images :' % (i + 1, 1), end='')
pt_image_str = '%s\n'
print(pt_image_str % tuple(images))
image_name_list = []
image_tensor_list = []
for image in images:
image_name_list.append(os.path.split(image)[1])
image_tensor_list.append(_add_batch_one(load_as_tensor(image)))
y_gt = _sigmoid_to_tanh(torch.cat(image_tensor_list, dim=0)).cuda()
# Invert
latent_estimates, history = inversion.invert(generator,
y_gt,
cor_loss,
batch_size=1,
video=args.video)
# Get Images
y_estimate_list = torch.split(torch.clamp(_tanh_to_sigmoid(
generator(latent_estimates)),
min=0.,
max=1.).cpu(),
1,
dim=0)
# Save
for img_id, image in enumerate(images):
up_gray = colorization_images(image_tensor_list[img_id])
y_gray_pil = Tensor2PIL(up_gray, mode='L')
y_gray_pil.save(
os.path.join(args.outputs,
'%s-%s.png' % (image_name_list[img_id], 'gray')))
Y_gt = Tensor2PIL(image_tensor_list[img_id],
mode='RGB').convert('YCbCr')
y_estimate_pil = Tensor2PIL(y_estimate_list[img_id],
mode='RGB').convert('YCbCr')
_, Cb, Cr = y_estimate_pil.split()
Y, _, _ = Y_gt.split()
y_colorization = Image.merge('YCbCr', (Y, Cb, Cr))
y_colorization.convert('RGB').save(
os.path.join(
args.outputs, '%s-%d.png' %
(image_name_list[img_id], math.floor(time.time()))))
# Create video
if args.video:
print('Create GAN-Inversion video.')
video = cv2.VideoWriter(filename=os.path.join(
args.outputs,
'%s_inversion.avi' % image_name_list[img_id]),
fourcc=cv2.VideoWriter_fourcc(*'MJPG'),
fps=args.fps,
frameSize=(frameSize, frameSize))
print('Save frames.')
for i, sample in enumerate(history):
image = torch.clamp(_tanh_to_sigmoid(generator(sample)),
min=0.,
max=1.).cpu()
image_pil = Tensor2PIL(image, mode='RGB').convert('YCbCr')
_, Cb, Cr = image_pil.split()
y_colorization = Image.merge('YCbCr',
(Y, Cb, Cr)).convert('RGB')
image_cv2 = cv2.cvtColor(np.asarray(y_colorization),
cv2.COLOR_RGB2BGR)
# image_cv2 = cv2.cvtColor(np.asarray(image_pil.convert('RGB')), cv2.COLOR_RGB2BGR)
video.write(image_cv2)
video.release()
def main(args):
generator = get_derivable_generator(args.gan_model, args.inversion_type,
args)
loss = get_loss(args.loss_type, args)
loss.cuda()
generator.cuda()
inversion = get_inversion(args.optimization, args)
os.makedirs(args.outputs, exist_ok=True)
save_manipulate_dir = os.path.join(args.outputs, args.attribute_name)
images_list = image_files(args.target_images)
for i, images in enumerate(images_list):
print('%d: Processing images ' % (i + 1), end='')
image_name = os.path.split(images)[1]
print(image_name)
image_name_list = []
image_tensor_list = []
image_name_list.append(os.path.split(images)[1])
image_tensor_list.append(_add_batch_one(load_as_tensor(images)))
y_gt = _sigmoid_to_tanh(torch.cat(image_tensor_list, dim=0)).cuda()
# Invert
latent_estimates, history = inversion.invert(generator,
y_gt,
loss,
batch_size=1)
image_manipulate_dir = os.path.join(save_manipulate_dir,
image_name[:-4])
os.makedirs(image_manipulate_dir, exist_ok=True)
wp = latent_estimates[0].cpu().detach().numpy()
mask = latent_estimates[1].cpu().detach().numpy()
# Visualize results with given w+ latent vector.
if args.original:
print('Save inversion.')
image = generator(latent_estimates)
image_cv2 = convert_array_to_images(image.detach().cpu().numpy())
cv2.imwrite(
os.path.join(image_manipulate_dir, 'original_inversion.png'),
image_cv2[0][:, :, ::-1])
boundary, bias = get_boundary(
os.path.join(
BOUNDARY_DIR,
'pggan_celebahq_%s_boundary.npy' % args.attribute_name))
wp_list = get_interpolated_wp(wp,
boundary,
max_step=args.max_step,
num_frames=args.fps * args.duration)
# Create video for attribute manipulation with given w+ latent_vector.
if args.video:
print('Create attribute manipulation video.')
video = cv2.VideoWriter(filename=os.path.join(
image_manipulate_dir,
'%s_manipulate.avi' % args.attribute_name),
fourcc=cv2.VideoWriter_fourcc(*'MJPG'),
fps=args.fps,
frameSize=(1024, 1024))
print('Save frames.')
for i, sample in enumerate(wp_list):
image = generator([
torch.from_numpy(sample).view((1, ) + sample.shape).cuda(),
torch.from_numpy(mask).cuda()
])
image_cv2 = convert_array_to_images(
image.detach().cpu().numpy())[0][:, :, ::-1]
cv2.imwrite(os.path.join(image_manipulate_dir, '%d.png' % i),
image_cv2)
video.write(image_cv2)
video.release()
def main(args):
os.makedirs(args.outputs+'/input', exist_ok=True)
os.makedirs(args.outputs+'/GT', exist_ok=True)
os.makedirs(args.outputs+'/mGANoutput', exist_ok=True)
with open(args.outputs+'/mGANargs.txt', 'w') as f:
json.dump(args.__dict__, f, indent=2)
generator = get_derivable_generator(args.gan_model, args.inversion_type, args)
loss = get_loss(args.loss_type, args)
cor_loss = Color_loss(loss)
generator.cuda()
loss.cuda()
inversion = get_inversion(args.optimization, args)
image_list = image_files(args.target_images)
if len(image_list)>300:
print('Limiting the image set to 300.')
image_list = image_list[:300]
frameSize = MODEL_POOL[args.gan_model]['resolution']
start_time = time.time()
for i, images in enumerate(split_to_batches(image_list, 1)):
print('%d: Processing %d images :' % (i, 1), end='')
pt_image_str = '%s\n'
print(pt_image_str % tuple(images))
image_name_list = []
image_tensor_list = []
for image in images:
image_name_list.append(os.path.split(image)[1])
image_tensor_list.append(_add_batch_one(load_as_tensor(image)))
y_gt = _sigmoid_to_tanh(torch.cat(image_tensor_list, dim=0)).cuda()
# Invert
latent_estimates, history = inversion.invert(generator, y_gt, cor_loss, batch_size=1, video=args.video)
# Get Images
y_estimate_list = torch.split(torch.clamp(_tanh_to_sigmoid(generator(latent_estimates)), min=0., max=1.).cpu(), 1, dim=0)
# Save
for img_id, image in enumerate(images):
up_gray = colorization_images(image_tensor_list[img_id])
y_gray_pil = Tensor2PIL(up_gray, mode='L')
y_gray_pil.save(args.outputs + '/input/%d%s' % (i, image_name_list[img_id][-4:]))
y_RGB = Tensor2PIL(image_tensor_list[img_id])
y_RGB.save(args.outputs + '/GT/%d%s' % (i, image_name_list[img_id][-4:]))
Y_gt = Tensor2PIL(image_tensor_list[img_id], mode='RGB').convert('YCbCr')
y_estimate_pil = Tensor2PIL(y_estimate_list[img_id], mode='RGB').convert('YCbCr')
_, Cb, Cr = y_estimate_pil.split()
Y, _, _ = Y_gt.split()
y_colorization = Image.merge('YCbCr', (Y, Cb, Cr))
y_colorization.convert('RGB').save(args.outputs + '/mGANoutput/%d%s' % (i, image_name_list[img_id][-4:]))
# Create video
if args.video:
print('Create GAN-Inversion video.')
video = cv2.VideoWriter(
filename=os.path.join(args.outputs, '%s_inversion.avi' % image_name_list[img_id]),
fourcc=cv2.VideoWriter_fourcc(*'MJPG'),
fps=args.fps,
frameSize=(frameSize, frameSize))
print('Save frames.')
for i, sample in enumerate(history):
image = torch.clamp(_tanh_to_sigmoid(generator(sample)), min=0., max=1.).cpu()
image_pil = Tensor2PIL(image, mode='RGB').convert('YCbCr')
_, Cb, Cr = image_pil.split()
y_colorization = Image.merge('YCbCr', (Y, Cb, Cr)).convert('RGB')
image_cv2 = cv2.cvtColor(np.asarray(y_colorization), cv2.COLOR_RGB2BGR)
# image_cv2 = cv2.cvtColor(np.asarray(image_pil.convert('RGB')), cv2.COLOR_RGB2BGR)
video.write(image_cv2)
video.release()
print(f'{(time.time()-start_time)/60:.2f}','minutes taken in total;', f'{(time.time()-start_time)/60/len(image_list):.2f}', 'per image.')
