def main():
parser = build_parser()
options = parser.parse_args()
width = options.width
style_scale = options.style_scale
content_image = imread(options.content)
style_image = imread(options.style)
if width is not None:
new_shape = (int(
math.floor(
float(content_image.shape[0]) / content_image.shape[1] *
width)), width)
content_image = sm.imresize(content_image, new_shape)
if style_scale is not None:
style_image = sm.imresize(style_image, style_scale)
initial = options.initial
if initial is not None:
initial = sm.imresize(imread(initial), content_image.shape[:2])
image = stylize(options.network,
initial,
content_image,
style_image,
options.iterations,
options.content_weight,
options.style_weight,
options.tv_weight,
options.learning_rate,
print_iter=options.print_iter)
imsave(options.output, image)
def main():
parser = build_parser()
options = parser.parse_args()
content_image = imread(options.content)
style_image = imread(options.style)
width = options.width
if width is not None:
new_shape = (int(math.floor(float(content_image.shape[0]) /
content_image.shape[1] * width)), width)
content_image = scipy.misc.imresize(content_image, new_shape)
target_shape = content_image.shape
style_image = scipy.misc.imresize(style_image, options.style_scale *
target_shape[1] / style_image.shape[1])
initial = options.initial
if initial is not None:
initial = scipy.misc.imresize(imread(initial), content_image.shape[:2])
image = stylize(options.network, initial, content_image, style_image,
options.iterations, options.content_weight, options.style_weight,
options.tv_weight, options.learning_rate,
print_iterations=options.print_iterations,
checkpoint_iterations=options.checkpoint_iterations)
imsave(options.output, image)
def main():
parser = build_parser()
options = parser.parse_args()
content_image = imread(options.content)
style_images = []
for style in options.styles:
style_images.append(imread(style))
width = options.width
if width is not None:
new_shape = (int(
math.floor(
float(content_image.shape[0]) / content_image.shape[1] *
width)), width)
content_image = scipy.misc.imresize(content_image, new_shape)
target_shape = content_image.shape
for i in range(len(style_images)):
style_scale = STYLE_SCALE
if options.style_scales != None:
style_scale = options.style_scales[i]
style_images[i] = scipy.misc.imresize(
style_images[i],
style_scale * target_shape[1] / style_images[i].shape[1])
style_blend_weights = options.style_blend_weights
if style_blend_weights == None:
# default is equal weights
style_blend_weights = [1.0 / len(style_images) for _ in style_images]
else:
total_blend_weight = sum(style_blend_weights)
style_blend_weights = [
weight / total_blend_weight for weight in style_blend_weights
]
initial = options.initial
if initial is not None:
initial = scipy.misc.imresize(imread(initial), content_image.shape[:2])
image = stylize(options.network,
initial,
content_image,
style_images,
options.iterations,
options.content_weight,
options.style_weight,
style_blend_weights,
options.tv_weight,
options.learning_rate,
print_iterations=options.print_iterations,
checkpoint_iterations=options.checkpoint_iterations)
imsave(options.output, image)
def main():
parser = build_parser()
options = parser.parse_args()
content_image = imread(options.content)
style_images = [imread(style) for style in options.styles]
width = options.width
if width is not None:
new_shape = (int(math.floor(float(content_image.shape[0]) / content_image.shape[1] * width)), width)
content_image = scipy.misc.imresize(content_image, new_shape)
target_shape = content_image.shape
for i in range(len(style_images)):
style_scale = STYLE_SCALE
if options.style_scales is not None:
style_scale = options.style_scales[i]
style_images[i] = scipy.misc.imresize(style_images[i], style_scale * target_shape[1] / style_images[i].shape[1])
style_blend_weights = options.style_blend_weights
if style_blend_weights is None:
# default is equal weights
style_blend_weights = [1.0 / len(style_images) for _ in style_images]
else:
total_blend_weight = sum(style_blend_weights)
style_blend_weights = [weight / total_blend_weight for weight in style_blend_weights]
initial = options.initial
if initial is not None:
initial = scipy.misc.imresize(imread(initial), content_image.shape[:2])
image = stylize(
options.network,
initial,
content_image,
style_images,
options.iterations,
options.content_weight,
options.style_weight,
style_blend_weights,
options.tv_weight,
options.learning_rate,
print_iterations=options.print_iterations,
checkpoint_iterations=options.checkpoint_iterations,
)
imsave(options.output, image)
def generate(content_file, style_file, output_file):
# https://stackoverflow.com/a/42121886
key = 'TF_CPP_MIN_LOG_LEVEL'
if key not in os.environ:
os.environ[key] = '2'
parser = build_parser()
options = parser.parse_args(["--content",content_file,"--styles", style_file,"--output",output_file])
if not os.path.isfile(options.network):
parser.error("Network %s does not exist. (Did you forget to "
"download it?)" % options.network)
if [options.checkpoint_iterations,
options.checkpoint_output].count(None) == 1:
parser.error("use either both of checkpoint_output and "
"checkpoint_iterations or neither")
if options.checkpoint_output is not None:
if re.match(r'^.*(\{.*\}|%.*).*$', options.checkpoint_output) is None:
parser.error("To save intermediate images, the checkpoint_output "
"parameter must contain placeholders (e.g. "
"`foo_{}.jpg` or `foo_%d.jpg`")
content_image = imread(options.content)
style_images = [imread(style) for style in options.styles]
width = options.width
if width is not None:
new_shape = (int(math.floor(float(content_image.shape[0]) /
content_image.shape[1] * width)), width)
content_image = scipy.misc.imresize(content_image, new_shape)
target_shape = content_image.shape
for i in range(len(style_images)):
style_scale = STYLE_SCALE
if options.style_scales is not None:
style_scale = options.style_scales[i]
style_images[i] = scipy.misc.imresize(style_images[i], style_scale *
target_shape[1] / style_images[i].shape[1])
style_blend_weights = options.style_blend_weights
if style_blend_weights is None:
# default is equal weights
style_blend_weights = [1.0/len(style_images) for _ in style_images]
else:
total_blend_weight = sum(style_blend_weights)
style_blend_weights = [weight/total_blend_weight
for weight in style_blend_weights]
initial = options.initial
if initial is not None:
initial = scipy.misc.imresize(imread(initial), content_image.shape[:2])
# Initial guess is specified, but not noiseblend - no noise should be blended
if options.initial_noiseblend is None:
options.initial_noiseblend = 0.0
else:
# Neither inital, nor noiseblend is provided, falling back to random
# generated initial guess
if options.initial_noiseblend is None:
options.initial_noiseblend = 1.0
if options.initial_noiseblend < 1.0:
initial = content_image
# try saving a dummy image to the output path to make sure that it's writable
if os.path.isfile(options.output) and not options.overwrite:
raise IOError("%s already exists, will not replace it without "
"the '--overwrite' flag" % options.output)
try:
imsave(options.output, np.zeros((500, 500, 3)))
except:
raise IOError('%s is not writable or does not have a valid file '
'extension for an image file' % options.output)
loss_arrs = None
for iteration, image, loss_vals in stylize(
network=options.network,
initial=initial,
initial_noiseblend=options.initial_noiseblend,
content=content_image,
styles=style_images,
preserve_colors=options.preserve_colors,
iterations=options.iterations,
content_weight=options.content_weight,
content_weight_blend=options.content_weight_blend,
style_weight=options.style_weight,
style_layer_weight_exp=options.style_layer_weight_exp,
style_blend_weights=style_blend_weights,
tv_weight=options.tv_weight,
learning_rate=options.learning_rate,
beta1=options.beta1,
beta2=options.beta2,
epsilon=options.epsilon,
pooling=options.pooling,
print_iterations=options.print_iterations,
checkpoint_iterations=options.checkpoint_iterations,
):
if (image is not None) and (options.checkpoint_output is not None):
imsave(fmt_imsave(options.checkpoint_output, iteration), image)
if (loss_vals is not None) \
and (options.progress_plot or options.progress_write):
if loss_arrs is None:
itr = []
loss_arrs = OrderedDict((key, []) for key in loss_vals.keys())
for key,val in loss_vals.items():
loss_arrs[key].append(val)
itr.append(iteration)
imsave(options.output, image)
if options.progress_write:
fn = "{}/progress.txt".format(os.path.dirname(options.output))
tmp = np.empty((len(itr), len(loss_arrs)+1), dtype=float)
tmp[:,0] = np.array(itr)
for ii,val in enumerate(loss_arrs.values()):
tmp[:,ii+1] = np.array(val)
np.savetxt(fn, tmp, header=' '.join(['itr'] + list(loss_arrs.keys())))
if options.progress_plot:
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
fig,ax = plt.subplots()
for key, val in loss_arrs.items():
ax.semilogy(itr, val, label=key)
ax.legend()
ax.set_xlabel("iterations")
ax.set_ylabel("loss")
fig.savefig("{}/progress.png".format(os.path.dirname(options.output)))
