def generate(model_name,
anchor_image=None,
direction=None,
transfer=None,
noise_solutions=None,
factor=0.25,
base=None,
insert_limit=4):
#direction = 'L, R, T, B'
parser = get_arguments()
parser.add_argument('--input_dir',
help='input image dir',
default='Input/Images')
parser.add_argument('--mode',
help='random_samples | random_samples_arbitrary_sizes',
default='random_samples')
# for random_samples:
parser.add_argument('--gen_start_scale',
type=int,
help='generation start scale',
default=0)
opt = parser.parse_args("")
opt.input_name = model_name
if model_name == 'islands2_basis_2.jpg': #HARDCODED
opt.scale_factor = 0.6
opt = functions.post_config(opt)
Gs = []
Zs = []
reals = []
NoiseAmp = []
real = functions.read_image(opt)
#opt.input_name = anchor #CHANGE TO ANCHOR HERE
anchor = functions.read_image(opt)
functions.adjust_scales2image(real, opt)
Gs, Zs, reals, NoiseAmp = functions.load_trained_pyramid(opt)
in_s = functions.generate_in2coarsest(reals, 1, 1, opt)
array = SinGAN_anchor_generate(Gs,
Zs,
reals,
NoiseAmp,
opt,
gen_start_scale=opt.gen_start_scale,
anchor_image=anchor_image,
direction=direction,
transfer=transfer,
noise_solutions=noise_solutions,
factor=factor,
base=base,
insert_limit=insert_limit)
return array
elif opt.mode == 'random_samples_arbitrary_sizes':
print(
'random samples for image %s at size: scale_h=%f, scale_v=%f, already exist'
% (opt.input_name, opt.scale_h, opt.scale_v))
else:
try:
os.makedirs(dir2save)
except OSError:
pass
if opt.mode == 'random_samples':
real = functions.read_image(opt)
functions.adjust_scales2image(real, opt)
Gs, Zs, reals, NoiseAmp = functions.load_trained_pyramid(opt)
# 生成最粗糙的图像
in_s = functions.generate_in2coarsest(reals, 1, 1, opt)
SinGAN_generate(Gs,
Zs,
reals,
NoiseAmp,
opt,
gen_start_scale=opt.gen_start_scale)
# elif opt.mode == 'random_samples_arbitrary_sizes':
# real = functions.read_image(opt)
# functions.adjust_scales2image(real, opt)
# Gs, Zs, reals, NoiseAmp = functions.load_trained_pyramid(opt)
# # 传入scale_v, scale_h 生成任意尺寸的图像 scale 1:1.5
# in_s = functions.generate_in2coarsest(reals, opt.scale_v, opt.scale_h, opt)
# SinGAN_generate(Gs, Zs, reals, NoiseAmp, opt, in_s, scale_v=opt.scale_v, scale_h=opt.scale_h)
Zs = []
reals = []
NoiseAmp = []
dir2save = functions.generate_dir2save(opt)
if dir2save is None:
print('task does not exist')
elif (os.path.exists(dir2save)):
if opt.mode == 'random_samples':
print('random samples for image %s, start scale=%d, already exist' % (opt.input_name, opt.gen_start_scale))
elif opt.mode == 'random_samples_arbitrary_sizes':
print('random samples for image %s at size: scale_h=%f, scale_v=%f, already exist' % (
opt.input_name, opt.scale_h, opt.scale_v))
else:
try:
os.makedirs(dir2save)
except OSError:
pass
if opt.mode == 'random_samples':
real = functions.read_image(opt)
functions.adjust_scales2image(real, opt)
Gs, Zs, reals, NoiseAmp = functions.load_trained_pyramid(opt)
in_s = functions.generate_in2coarsest(reals, 1, 1, opt)
SinGAN_generate(Gs, Zs, reals, NoiseAmp, opt, gen_start_scale=opt.gen_start_scale)
elif opt.mode == 'random_samples_arbitrary_sizes':
real = functions.read_image(opt)
functions.adjust_scales2image(real, opt)
Gs, Zs, reals, NoiseAmp = functions.load_trained_pyramid(opt)
in_s = functions.generate_in2coarsest(reals, opt.scale_v, opt.scale_h, opt)
SinGAN_generate(Gs, Zs, reals, NoiseAmp, opt, in_s, scale_v=opt.scale_v, scale_h=opt.scale_h)
def test_generate(model_name,
anchor_image=None,
direction=None,
transfer=None,
noise_solutions=None,
factor=0.25,
base=None,
insert_limit=4):
#direction = 'L, R, T, B'
parser = get_arguments()
parser.add_argument('--input_dir',
help='input image dir',
default='Input/Images')
parser.add_argument('--mode',
help='random_samples | random_samples_arbitrary_sizes',
default='random_samples')
# for random_samples:
parser.add_argument('--gen_start_scale',
type=int,
help='generation start scale',
default=0)
opt = parser.parse_args("")
opt.input_name = model_name
opt = functions.post_config(opt)
Gs = []
Zs = []
reals = []
NoiseAmp = []
opt.input_name = 'island_basis_0.jpg' #grabbing image that exists...
real = functions.read_image(opt)
#opt.input_name = anchor #CHANGE TO ANCHOR HERE
#anchor = functions.read_image(opt)
functions.adjust_scales2image(real, opt)
opt.input_name = 'test1.jpg' #grabbing model that we want
Gs, Zs, reals, NoiseAmp = functions.load_trained_pyramid(opt)
#dummy stuff for dimensions
reals = []
real_ = real
real = imresize(real_, opt.scale1, opt)
reals = functions.creat_reals_pyramid(real, reals, opt)
in_s = functions.generate_in2coarsest(reals, 1, 1, opt)
array = SinGAN_anchor_generate(Gs,
Zs,
reals,
NoiseAmp,
opt,
gen_start_scale=opt.gen_start_scale,
anchor_image=anchor_image,
direction=direction,
transfer=transfer,
noise_solutions=noise_solutions,
factor=factor,
base=base,
insert_limit=insert_limit)
return array