def run():
parser = argparse.ArgumentParser(
description='Neural artistic style. Generates an image by combining '
'the subject from one image and the style from another.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
# Added by Phil
parser.add_argument('--src1', required=True, type=str,
help='First image to be compared.')
parser.add_argument('--mat-path', required=True, type=str,
help='Path where the grams should be stored.')
# Optionals
parser.add_argument('--vgg19', default='imagenet-vgg-verydeep-19.mat',
type=str, help='VGG-19 .mat file.')
parser.add_argument('--pool-method', default='avg', type=str,
choices=['max', 'avg'], help='Subsampling scheme.')
parser.add_argument('--style-weights', nargs='*', type=weight_tuple,
default=[(0, 1), (2, 1), (4, 1), (8, 1), (12, 1)],
help='List of style weights (conv_idx,weight).')
args = parser.parse_args()
layers, img_mean = vgg19_net(args.vgg19, pool_method=args.pool_method)
# Inputs
pixel_mean = np.mean(img_mean, axis=(0, 1))
style_img = imread(args.src1) - pixel_mean
# Setup network
style_weights = weight_array(args.style_weights)
net = GramNet(layers, style_weights)
# Compute style grams
grams = net.compute_grams(to_bc01(style_img))
# Store style grams
filename = get_filename_without_extension(args.src1)
for i, gram in enumerate(grams):
if gram is not None:
print(gram.__class__.__name__)
print("Layer {}: Shape: {}".format(i, gram.shape))
path = os.path.join(args.mat_path, "{}_{}x{}".format(filename, gram.shape[0], gram.shape[1]))
scipy.io.savemat(path, dict(data=gram.__array__()))
def run():
parser = argparse.ArgumentParser(
description="Neural artistic style. Generates an image by combining "
"the subject from one image and the style from another.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("--subject", required=True, type=str, help="Subject image.")
parser.add_argument("--style", required=True, type=str, help="Style image.")
parser.add_argument("--output", default="out.png", type=str, help="Output image.")
parser.add_argument("--init", default=None, type=str, help="Initial image. Subject is chosen as default.")
parser.add_argument(
"--init-noise",
default=0.0,
type=float_range,
help="Weight between [0, 1] to adjust the noise level " "in the initial image.",
)
parser.add_argument("--random-seed", default=None, type=int, help="Random state.")
parser.add_argument("--animation", default="animation", type=str, help="Output animation directory.")
parser.add_argument("--iterations", default=500, type=int, help="Number of iterations to run.")
parser.add_argument("--learn-rate", default=2.0, type=float, help="Learning rate.")
parser.add_argument("--smoothness", type=float, default=2e-7, help="Weight of smoothing scheme.")
parser.add_argument(
"--subject-weights",
nargs="*",
type=weight_tuple,
default=[(9, 1)],
help="List of subject weights (conv_idx,weight).",
)
parser.add_argument(
"--style-weights",
nargs="*",
type=weight_tuple,
default=[(0, 1), (2, 1), (4, 1), (8, 1), (12, 1)],
help="List of style weights (conv_idx,weight).",
)
parser.add_argument("--subject-ratio", type=float, default=2e-2, help="Weight of subject relative to style.")
parser.add_argument("--pool-method", default="avg", type=str, choices=["max", "avg"], help="Subsampling scheme.")
parser.add_argument("--vgg19", default="imagenet-vgg-verydeep-19.mat", type=str, help="VGG-19 .mat file.")
args = parser.parse_args()
if args.random_seed is not None:
np.random.seed(args.random_seed)
layers, img_mean = vgg19_net(args.vgg19, pool_method=args.pool_method)
# Inputs
pixel_mean = np.mean(img_mean, axis=(0, 1))
style_img = imread(args.style) - pixel_mean
subject_img = imread(args.subject) - pixel_mean
if args.init is None:
init_img = subject_img
else:
init_img = imread(args.init) - pixel_mean
noise = np.random.normal(size=init_img.shape, scale=np.std(init_img))
init_img = init_img * (1 - args.init_noise) + noise * args.init_noise
# Setup network
subject_weights = weight_array(args.subject_weights) * args.subject_ratio
style_weights = weight_array(args.style_weights)
net = StyleNetwork(
layers,
to_bc01(init_img),
to_bc01(subject_img),
to_bc01(style_img),
subject_weights,
style_weights,
args.smoothness,
)
# Repaint image
def net_img():
return to_rgb(net.image) + pixel_mean
if not os.path.exists(args.animation):
os.mkdir(args.animation)
params = net._params
learn_rule = dp.Adam(learn_rate=args.learn_rate)
learn_rule_states = [learn_rule.init_state(p) for p in params]
for i in range(args.iterations):
imsave(os.path.join(args.animation, "%.4d.png" % i), net_img())
cost = np.mean(net._update())
for param, state in zip(params, learn_rule_states):
learn_rule.step(param, state)
print("Iteration: %i, cost: %.4f" % (i, cost))
imsave(args.output, net_img())
def run():
parser = argparse.ArgumentParser(
description='Neural artistic style. Generates an image by combining '
'the subject from one image and the style from another.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('--subject', required=True, type=str,
help='Subject image.')
parser.add_argument('--style', required=True, type=str,
help='Style image.')
parser.add_argument('--output', default='out.png', type=str,
help='Output image.')
parser.add_argument('--init', default=None, type=str,
help='Initial image. Subject is chosen as default.')
parser.add_argument('--init-noise', default=0.0, type=float_range,
help='Weight between [0, 1] to adjust the noise level '
'in the initial image.')
parser.add_argument('--random-seed', default=None, type=int,
help='Random state.')
parser.add_argument('--animation', default='animation', type=str,
help='Output animation directory.')
parser.add_argument('--iterations', default=500, type=int,
help='Number of iterations to run.')
parser.add_argument('--learn-rate', default=2.0, type=float,
help='Learning rate.')
parser.add_argument('--smoothness', type=float, default=5e-8,
help='Weight of smoothing scheme.')
parser.add_argument('--subject-weights', nargs='*', type=weight_tuple,
default=[(9, 1)],
help='List of subject weights (conv_idx,weight).')
parser.add_argument('--style-weights', nargs='*', type=weight_tuple,
default=[(0, 1), (2, 1), (4, 1), (8, 1), (12, 1)],
help='List of style weights (conv_idx,weight).')
parser.add_argument('--subject-ratio', type=float, default=2e-2,
help='Weight of subject relative to style.')
parser.add_argument('--pool-method', default='avg', type=str,
choices=['max', 'avg'], help='Subsampling scheme.')
parser.add_argument('--vgg19', default='imagenet-vgg-verydeep-19.mat',
type=str, help='VGG-19 .mat file.')
args = parser.parse_args()
if args.random_seed is not None:
np.random.seed(args.random_seed)
layers, img_mean = vgg19_net(args.vgg19, pool_method=args.pool_method)
# Inputs
pixel_mean = np.mean(img_mean, axis=(0, 1))
style_img = imread(args.style) - pixel_mean
subject_img = imread(args.subject) - pixel_mean
if args.init is None:
init_img = subject_img
else:
init_img = imread(args.init) - pixel_mean
noise = np.random.normal(size=init_img.shape, scale=np.std(init_img)*1e-1)
init_img = init_img * (1 - args.init_noise) + noise * args.init_noise
# Setup network
subject_weights = weight_array(args.subject_weights) * args.subject_ratio
style_weights = weight_array(args.style_weights)
net = StyleNetwork(layers, to_bc01(init_img), to_bc01(subject_img),
to_bc01(style_img), subject_weights, style_weights,
args.smoothness)
# Repaint image
def net_img():
return to_rgb(net.image) + pixel_mean
if not os.path.exists(args.animation):
os.mkdir(args.animation)
params = net._params
learn_rule = dp.Adam(learn_rate=args.learn_rate)
learn_rule_states = [learn_rule.init_state(p) for p in params]
for i in range(args.iterations):
imsave(os.path.join(args.animation, '%.4d.png' % i), net_img())
cost = np.mean(net._update())
for param, state in zip(params, learn_rule_states):
learn_rule.step(param, state)
print('Iteration: %i, cost: %.4f' % (i, cost))
imsave(args.output, net_img())
play_done_sound()
def run():
parser = argparse.ArgumentParser(
description='Neural artistic style. Generates an image by combining '
'the subject from one image and the style from another.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--subject',
required=True,
type=str,
help='Subject image.')
parser.add_argument('--style',
required=True,
type=str,
help='Style image.')
parser.add_argument('--output',
default='out.png',
type=str,
help='Output image.')
parser.add_argument('--init',
default=None,
type=str,
help='Initial image. Subject is chosen as default.')
parser.add_argument('--init-noise',
default=0.0,
type=float_range,
help='Weight between [0, 1] to adjust the noise level '
'in the initial image.')
parser.add_argument('--random-seed',
default=None,
type=int,
help='Random state.')
parser.add_argument('--animation',
default='animation',
type=str,
help='Output animation directory.')
parser.add_argument('--iterations',
default=500,
type=int,
help='Number of iterations to run.')
parser.add_argument('--learn-rate',
default=2.0,
type=float,
help='Learning rate.')
parser.add_argument('--smoothness',
type=float,
default=2e-7,
help='Weight of smoothing scheme.')
parser.add_argument('--subject-weights',
nargs='*',
type=weight_tuple,
default=[(9, 1)],
help='List of subject weights (conv_idx,weight).')
parser.add_argument('--style-weights',
nargs='*',
type=weight_tuple,
default=[(0, 1), (2, 1), (4, 1), (8, 1), (12, 1)],
help='List of style weights (conv_idx,weight).')
parser.add_argument('--subject-ratio',
type=float,
default=2e-2,
help='Weight of subject relative to style.')
parser.add_argument('--pool-method',
default='avg',
type=str,
choices=['max', 'avg'],
help='Subsampling scheme.')
parser.add_argument('--vgg19',
default='imagenet-vgg-verydeep-19.mat',
type=str,
help='VGG-19 .mat file.')
args = parser.parse_args()
if args.random_seed is not None:
np.random.seed(args.random_seed)
layers, img_mean = vgg19_net(args.vgg19, pool_method=args.pool_method)
# Inputs
pixel_mean = np.mean(img_mean, axis=(0, 1))
style_img = imread(args.style) - pixel_mean
subject_img = imread(args.subject) - pixel_mean
if args.init is None:
init_img = subject_img
else:
init_img = imread(args.init) - pixel_mean
noise = np.random.normal(size=init_img.shape, scale=np.std(init_img))
init_img = init_img * (1 - args.init_noise) + noise * args.init_noise
# Setup network
subject_weights = weight_array(args.subject_weights) * args.subject_ratio
style_weights = weight_array(args.style_weights)
net = StyleNetwork(layers, to_bc01(init_img), to_bc01(subject_img),
to_bc01(style_img), subject_weights, style_weights,
args.smoothness)
# Repaint image
def net_img():
return to_rgb(net.image) + pixel_mean
if not os.path.exists(args.animation):
os.mkdir(args.animation)
params = net._params
learn_rule = dp.Adam(learn_rate=args.learn_rate)
learn_rule_states = [learn_rule.init_state(p) for p in params]
for i in range(args.iterations):
imsave(os.path.join(args.animation, '%.4d.png' % i), net_img())
cost = np.mean(net._update())
for param, state in zip(params, learn_rule_states):
learn_rule.step(param, state)
print('Iteration: %i, cost: %.4f' % (i, cost))
imsave(args.output, net_img())
def process_image_pair(**kwargs):
start = time.time()
if kwargs['random_seed'] is not None:
np.random.seed(kwargs['random_seed'])
print('loading mat net...')
layers, img_mean = vgg19_net(kwargs['vgg19'],
pool_method='avg') #kwargs['pool_method'])
# Inputs
pixel_mean = np.mean(img_mean, axis=(0, 1))
style_img = imread(kwargs['style_path']) - pixel_mean
subject_img = imread(kwargs['content_path']) - pixel_mean
# if kwargs['init'] is None:
# init_img = subject_img
# else:
# init_img = imread(kwargs['init']) - pixel_mean
init_img = subject_img
noise = np.random.normal(size=init_img.shape,
scale=np.std(init_img) * 1e-1)
init_img = init_img * (1 -
kwargs['init_noise']) + noise * kwargs['init_noise']
# Setup network
print('building network...')
#FIXME: Set defaults
subject_weights = weight_array(
kwargs['subject_weights']) * kwargs['subject_ratio']
style_weights = weight_array(kwargs['style_weights'])
net = StyleNetwork(layers, to_bc01(init_img), to_bc01(subject_img),
to_bc01(style_img), subject_weights, style_weights,
kwargs['smoothness'])
# Repaint image
def net_img():
return to_rgb(net.image) + pixel_mean
if kwargs['animation']:
os.mkdir(kwargs['animation_dir'])
params = net.params
learn_rule = dp.Adam(learn_rate=kwargs['learn_rate'])
learn_rule_states = [learn_rule.init_state(p) for p in params]
for i in range(kwargs['iterations']):
print('iteration %d', i)
if kwargs['animation']:
imsave(os.path.join(kwargs['animation_dir'], '%.4d.png' % i),
net_img())
cost = np.mean(net.update())
for param, state in zip(params, learn_rule_states):
learn_rule.step(param, state)
print('Iteration: %i, cost: %.4f' % (i, cost))
imsave(kwargs['output_path'], net_img())
print("Time Elapsed: %0.2f", time.time() - start)
def run():
parser = argparse.ArgumentParser(
description='Neural artistic style.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('--subject', required=True, type=str,
help='subject image')
parser.add_argument('--style', required=True, type=str,
help='style image')
parser.add_argument('--output', default='out.png', type=str,
help='output image')
parser.add_argument('--animation', default='animation', type=str,
help='output animation directory')
parser.add_argument('--iterations', default=500, type=int,
help='Number of iterations')
parser.add_argument('--learn-rate', default=5.0, type=float,
help='Learning rate')
parser.add_argument('--subject-weights', nargs='*', type=weight_tuple,
default=[(9, 1)],
help='list of subject weights (conv_idx,weight)')
parser.add_argument('--style-weights', nargs='*', type=weight_tuple,
default=[(0, 1), (2, 1), (4, 1), (8, 1), (12, 1)],
help='list of style weights (conv_idx,weight)')
parser.add_argument('--subject-ratio', type=float, default=2e-2,
help='weight of subject relative to style')
parser.add_argument('--vgg19', default='imagenet-vgg-verydeep-19.mat',
type=str, help='VGG-19 .mat file')
args = parser.parse_args()
layers, img_mean = vgg19_net(args.vgg19, pool_method='avg')
# Inputs
pixel_mean = np.mean(img_mean, axis=(0, 1))
style_img = imread(args.style)
subject_img = imread(args.subject)
style_img -= pixel_mean
subject_img -= pixel_mean
# Setup network
subject_weights = weight_array(args.subject_weights) * args.subject_ratio
style_weights = weight_array(args.style_weights)
net = StyleNetwork(layers, to_bc01(subject_img), to_bc01(style_img),
subject_weights, style_weights)
# Repaint image
def net_img():
return to_rgb(net.image) + pixel_mean
if not os.path.exists(args.animation):
os.mkdir(args.animation)
params = net._params
learn_rule = dp.Adam(learn_rate=args.learn_rate)
learn_rule_states = [learn_rule.init_state(p) for p in params]
for i in range(args.iterations):
imsave(os.path.join(args.animation, '%.4d.png' % i), net_img())
cost = np.mean(net._update())
for param, state in zip(params, learn_rule_states):
learn_rule.step(param, state)
print('Iteration: %i, cost: %.4f' % (i, cost))
imsave(args.output, net_img())
def run():
parser = argparse.ArgumentParser(
description='Neural artistic style.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--subject',
required=True,
type=str,
help='subject image')
parser.add_argument('--style', required=True, type=str, help='style image')
parser.add_argument('--output',
default='out.png',
type=str,
help='output image')
parser.add_argument('--animation',
default='animation',
type=str,
help='output animation directory')
parser.add_argument('--iterations',
default=500,
type=int,
help='Number of iterations')
parser.add_argument('--learn-rate',
default=5.0,
type=float,
help='Learning rate')
parser.add_argument('--subject-weights',
nargs='*',
type=weight_tuple,
default=[(9, 1)],
help='list of subject weights (conv_idx,weight)')
parser.add_argument('--style-weights',
nargs='*',
type=weight_tuple,
default=[(0, 1), (2, 1), (4, 1), (8, 1), (12, 1)],
help='list of style weights (conv_idx,weight)')
parser.add_argument('--subject-ratio',
type=float,
default=2e-2,
help='weight of subject relative to style')
parser.add_argument('--vgg19',
default='imagenet-vgg-verydeep-19.mat',
type=str,
help='VGG-19 .mat file')
args = parser.parse_args()
layers, img_mean = vgg19_net(args.vgg19, pool_method='avg')
# Inputs
pixel_mean = np.mean(img_mean, axis=(0, 1))
style_img = imread(args.style)
subject_img = imread(args.subject)
style_img -= pixel_mean
subject_img -= pixel_mean
# Setup network
subject_weights = weight_array(args.subject_weights) * args.subject_ratio
style_weights = weight_array(args.style_weights)
net = StyleNetwork(layers, to_bc01(subject_img), to_bc01(style_img),
subject_weights, style_weights)
# Repaint image
def net_img():
return to_rgb(net.image) + pixel_mean
if not os.path.exists(args.animation):
os.mkdir(args.animation)
params = net._params
learn_rule = dp.Adam(learn_rate=args.learn_rate)
learn_rule_states = [learn_rule.init_state(p) for p in params]
for i in range(args.iterations):
imsave(os.path.join(args.animation, '%.4d.png' % i), net_img())
cost = np.mean(net._update())
for param, state in zip(params, learn_rule_states):
learn_rule.step(param, state)
print('Iteration: %i, cost: %.4f' % (i, cost))
imsave(args.output, net_img())