def main():
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# 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()
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 = 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] = 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 = 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,
):
print(image)
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:
matplotlib.use('Agg')
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)))
import streamlit as st
from PIL import Image
import os
import style
st.title('Pytorch style transfer')
img = st.sidebar.selectbox('Select Image', ('amber.jpg', 'dog.jpg'))
style_name = st.sidebar.selectbox(
'Select Style', ('candy', 'mosaic', 'rain_princess', 'udnie'))
model = 'saved_models/' + style_name + '.pth'
input_image = 'images/content-images/' + img
print(input_image)
output_image = 'images/output-images/' + style_name + '-' + img
st.write('### Source image:')
image = Image.open(input_image)
st.image(image, width=400)
clicked = st.button('stylize')
if clicked:
model = style.load_model(model)
style.stylize(model, input_image, output_image)
st.write('### output image:')
image = Image.open(output_image)
st.image(image, width=400)
def mainapp():
temp = """
<link href='https://fonts.googleapis.com/css?family=Bigshot One' rel='stylesheet'>
<div style="background-color:#464e5f;padding:30px,margin:10px;border-radius:25px;">
<h3 style="color:white;text-align:center;padding:30px;font-family: 'Bigshot One';font-size:40px;">Computer Based Artistic Works</h3>
</div>
"""
st.markdown(temp, unsafe_allow_html=True)
st.sidebar.markdown(
"""<link href='https://fonts.googleapis.com/css?family=Bayon' rel='stylesheet'><br>
<div>
<p style="color:black;font-family: 'Bayon';font-size:20px;">Select Content Image</p>
</div>""",
unsafe_allow_html=True)
img = st.sidebar.selectbox('', ('amber.jpg', 'puppies.jpg', 'donuts.jpg',
'drinks.jpg', 'wall-of-china.jpg'))
st.sidebar.markdown(
"""<link href='https://fonts.googleapis.com/css?family=Bayon' rel='stylesheet'>
<div>
<p style="color:black;font-family: 'Bayon';font-size:20px;">Select Style</p>
</div>""",
unsafe_allow_html=True)
style_name = st.sidebar.selectbox(
'', ('candy', 'mosaic', 'rain-princess', 'udnie'))
model = "saved_models/" + style_name + ".pth"
style_image = "images/style-images/" + style_name + ".jpg"
input_image = "images/content-images/" + img
col1, col2 = st.columns(2)
col1.markdown(
"""<link href='https://fonts.googleapis.com/css?family=Bree Serif' rel='stylesheet'>
<div>
<h3 style="color:black;font-family: 'Bree Serif';font-size:30px;">Source Image:</h3>
</div>""",
unsafe_allow_html=True)
image = Image.open(input_image)
col1.image(image, width=330)
col2.markdown(
"""<link href='https://fonts.googleapis.com/css?family=Bree Serif' rel='stylesheet'>
<div>
<h3 style="color:black;font-family: 'Bree Serif';font-size:30px;">Style Image:</h3>
</div>""",
unsafe_allow_html=True)
image2 = Image.open(style_image)
col2.image(image2, width=330)
output_image = "images/output-images/" + style_name + "-" + img
session_state = SessionState.get(checkboxed=False)
def local_css(file_name):
with open(file_name) as f:
st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
local_css("style.css")
clicked = st.button("Stylize")
def get_image_download_link(img):
buffered = BytesIO()
img.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue()).decode()
href = f'<a href="data:file/jpg;base64,{img_str}" download="Image.jpg">Download result</a>'
return href
if clicked or session_state.checkboxed:
session_state.checkboxed = True
model = style.load_model(model)
style.stylize(model, input_image, output_image)
st.markdown(
"""<link href='https://fonts.googleapis.com/css?family=Bree Serif' rel='stylesheet'>
<div>
<h3 style="color:black;font-family: 'Bree Serif';font-size:30px;">Output Image:</h3>
</div>""",
unsafe_allow_html=True)
image = Image.open(output_image)
st.image(image, width=400)
st.info("Only Registered Users can Downoad the output Image!!")
t1 = """
<link href='https://fonts.googleapis.com/css?family=Bigshot One' rel='stylesheet'>
<div>
<h3 style="color:black;margin-bottom:-30px;font-family: 'Bigshot One';font-size:30px;">Have You Registered?<span style='font-size:30px;'>🤔</span></h3> </div><br><br>
"""
st.markdown(t1, unsafe_allow_html=True)
radio = st.radio("", ('Yes', 'No'))
if (radio == 'Yes'):
st.markdown(
"""<link href='https://fonts.googleapis.com/css?family=Bree Serif' rel='stylesheet'>
<div>
<h3 style="color:black;font-family: 'Bree Serif';font-size:20px;margin-bottom:-20px;">Enter Username..</h3>
</div>""",
unsafe_allow_html=True)
user = st.text_input("", "")
st.markdown(
"""<link href='https://fonts.googleapis.com/css?family=Bree Serif' rel='stylesheet'>
<div>
<h3 style="color:black;font-family: 'Bree Serif';font-size:20px;margin-bottom:-20px;">Enter Password..</h3>
</div>""",
unsafe_allow_html=True)
passw = st.text_input("", "", type="password")
if st.button("Go"):
result = login_user(user, passw)
if result:
st.success("Logged in as {}".format(user))
image1 = Image.open(output_image)
st.markdown(get_image_download_link(image1),
unsafe_allow_html=True)
else:
st.warning("Incorrect Username/Password")
if (radio == 'No'):
st.warning(
"Please Register Yourself Using the Signup Option in the Navigation bar"
)
# print(style_image_name)
image=Image.open(input_image_name)
style_image=Image.open(style_image_name)
col1, col2 = st.beta_columns(2)
col1.header("Source Image:")
col1.image(image, use_column_width=True)
col2.header("Style Image:")
col2.image(style_image, use_column_width=True)
clicked=st.button("Stylize")
if clicked:
model=style.load_model(model_name)
style.stylize(model,input_image_name,output_image_name)
st.write("### Output Image:")
image=Image.open(output_image_name)
st.image(image,width=400)
input_image_path = ''
if input_image is not None:
input_image_path = 'neural_style/images/content-images/' + input_image.name
st.write('### Source Image:')
if input_image is not None:
with open(
os.path.join("neural_style/images/content-images/",
input_image.name), "wb") as f:
f.write(input_image.getbuffer())
img = Image.open(input_image)
st.image(img, width=250)
if input_image:
st.write(input_image.name)
style_name = st.selectbox('Select Style',
('candy', 'mosaic', 'rain_princess', 'udnie'))
model = 'neural_style/saved_models/' + style_name + '.pth'
if input_image is not None:
output_image = 'neural_style/images/output-images/' + style_name + '-' + input_image.name
clicked = st.button('Stylize')
if clicked:
model = style.load_model(model)
style.stylize(model, input_image_path, output_image)
st.write('### Stylized Image:')
image = Image.open(output_image)
st.image(image, width=400)