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import argparse
import base64
from io import BytesIO
from pathlib import Path
import os
import shutil
import sys
import time
import numpy as np
import torch.nn.functional as F
import torch
import streamlit as st
from st_click_detector import click_detector
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from torchvision.transforms import ToPILImage, Compose, ToTensor, Normalize
from PIL import Image
from huggingface_hub import hf_hub_download
PACKAGE_PARENT = '..'
WISE_DIR = '../wise/'
SCRIPT_DIR = os.path.dirname(os.path.realpath(os.path.join(os.getcwd(), os.path.expanduser(__file__))))
sys.path.append(os.path.normpath(os.path.join(SCRIPT_DIR, PACKAGE_PARENT)))
sys.path.append(os.path.normpath(os.path.join(SCRIPT_DIR, WISE_DIR)))
from local_ppn.options.test_options import TestOptions
from local_ppn.models import create_model
print(st.session_state["user"], " opened xDoG edits")
class CustomOpts(TestOptions):
def remove_options(self, parser, options):
for option in options:
for action in parser._actions:
print(action)
if vars(action)['option_strings'][0] == option:
parser._handle_conflict_resolve(None,[(option,action)])
break
def initialize(self, parser):
parser = super(CustomOpts, self).initialize(parser)
self.remove_options(parser, ["--dataroot"])
return parser
def print_options(self, opt):
pass
def add_predefined_images():
images = []
for f in os.listdir(os.path.join(SCRIPT_DIR, PACKAGE_PARENT, 'images','apdrawing')):
if not f.endswith('.jpg'):
continue
AB = Image.open(os.path.join(SCRIPT_DIR, PACKAGE_PARENT, 'images','apdrawing', f)).convert('RGB')
# split AB image into A and B
w, h = AB.size
w2 = int(w / 2)
A = AB.crop((0, 0, w2, h))
B = AB.crop((w2, 0, w, h))
images.append(A)
return images
@st.experimental_singleton
def make_model(_unused=None):
model_path = hf_hub_download(repo_id="MaxReimann/WISE-APDrawing-XDoG", filename="apdrawing_xdog_ppn_conv.pth")
os.makedirs(os.path.join(SCRIPT_DIR, PACKAGE_PARENT, "trained_models", "ours_apdrawing"), exist_ok=True)
shutil.copy2(model_path, os.path.join(SCRIPT_DIR, PACKAGE_PARENT, "trained_models", "ours_apdrawing", "latest_net_G.pth"))
opt = CustomOpts().parse() # get test options
# hard-code some parameters for test
opt.num_threads = 0 # test code only supports num_threads = 0
opt.batch_size = 1 # test code only supports batch_size = 1
# opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed.
opt.no_flip = True # no flip; comment this line if results on flipped images are needed.
opt.display_id = -1 # no visdom display; the test code saves the results to a HTML file.
opt.dataroot ="null"
opt.direction = "BtoA"
opt.model = "pix2pix"
opt.ppnG = "our_xdog"
opt.name = "ours_apdrawing"
opt.netG = "resnet_9blocks"
opt.no_dropout = True
opt.norm = "batch"
opt.load_size = 576
opt.crop_size = 512
opt.eval = False
model = create_model(opt) # create a model given opt.model and other options
model.setup(opt) # regular setup: load and print networks; create schedulers
if opt.eval:
model.eval()
return model, opt
def predict(image):
model, opt = make_model()
t = Compose([
ToTensor(),
Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
inp = image.resize((opt.crop_size, opt.crop_size), resample=Image.BICUBIC)
inp = t(inp).unsqueeze(0).cuda()
x = model.netG.module.ppn_part_forward(inp)
output = model.netG.module.conv_part_forward(x)
out_img = ToPILImage()(output.squeeze(0))
return out_img
st.title("xDoG+CNN Portrait Drawing ")
images = add_predefined_images()
html_code = '<div class="column" style="display: flex; flex-wrap: wrap; padding: 0 4px;">'
for i, image in enumerate(images):
buffered = BytesIO()
image.save(buffered, format="JPEG")
encoded = base64.b64encode(buffered.getvalue()).decode()
html_code += f"<a href='#' id='{i}' style='padding: 0px 5px'><img height='120px' style='margin-top: 8px;' src='data:image/jpeg;base64,{encoded}'></a>"
html_code += "</div>"
clicked = click_detector(html_code)
uploaded_im = st.file_uploader(f"OR: Load portrait:", type=["png", "jpg"], )
if uploaded_im is not None:
img = Image.open(uploaded_im)
img = img.convert('RGB')
buffered = BytesIO()
img.save(buffered, format="JPEG")
clicked_img = None
if clicked:
clicked_img = images[int(clicked)]
sel_img = img if uploaded_im is not None else clicked_img
if sel_img:
result_container = st.container()
coll1, coll2 = result_container.columns([3,2])
coll1.header("Result")
coll2.header("Global Edits")
model, opt = make_model()
t = Compose([
ToTensor(),
Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
inp = sel_img.resize((opt.crop_size, opt.crop_size), resample=Image.BICUBIC)
inp = t(inp).unsqueeze(0).cuda()
# vp = model.netG.module.ppn_part_forward(inp)
vp = model.netG.module.predict_parameters(inp)
inp = (inp * 0.5) + 0.5
effect = model.netG.module.apply_visual_effect.effect
with coll2:
# ("blackness", "contour", "strokeWidth", "details", "saturation", "contrast", "brightness")
show_params_names = ["strokeWidth", "blackness", "contours"]
display_means = []
params_mapping = {"strokeWidth": ['strokeWidth'], 'blackness': ["blackness"], "contours": [ "details", "contour"]}
def create_slider(name):
params = params_mapping[name] if name in params_mapping else [name]
means = [torch.mean(vp[:, effect.vpd.name2idx[n]]).item() for n in params]
display_mean = float(np.average(means) + 0.5)
display_means.append(display_mean)
slider = st.slider(f"Mean {name}: ", 0.0, 1.0, value=display_mean, step=0.05)
for i, param_name in enumerate(params):
vp[:, effect.vpd.name2idx[param_name]] += slider - (means[i]+ 0.5)
# vp.clamp_(-0.5, 0.5)
# pass
for name in show_params_names:
create_slider(name)
x = model.netG.module.apply_visual_effect(inp, vp)
x = (x - 0.5) / 0.5
only_x_dog = st.checkbox('only xdog', value=False, help='if checked, use only ppn+xdog, else use ppn+xdog+post-processing cnn')
if only_x_dog:
output = x[:,0].repeat(1,3,1,1)
print('shape output', output.shape)
else:
output = model.netG.module.conv_part_forward(x)
out_img = ToPILImage()(output.squeeze(0))
output = out_img.resize((320,320), resample=Image.BICUBIC)
with coll1:
st.image(output)
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