def __init__(self):
self.opt = DemoOptions().parse()
self.opt.data_dir = './datasets/frontend_upload'
self.opt.checkpoints_dir = './checkpoints'
self.model = Pix2PixModel(self.opt)
self.model.opt.inpaint_mode = 'ref'
self.model.eval()
self.orient_scene.size = 14
def orient_increase(self):
if self.scene.size < 15:
self.scene.size += 1
def orient_decrease(self):
if self.scene.size > 1:
self.scene.size -= 1
def selectM(self):
if self.clickButtion1.isChecked():
print("select Reference Mask")
elif self.clickButtion2.isChecked():
print("select Edited Mask")
def selectO(self):
if self.clickButtion3.isChecked():
print("select Reference Orient")
elif self.clickButtion4.isChecked():
print("select Edited Orient")
if __name__ == "__main__":
opt = DemoOptions().parse()
model = Pix2PixModel(opt)
model.eval()
app = QApplication(sys.argv)
ex = Ex(model, opt)
sys.exit(app.exec_())
import time
import os
from options.demo_options import DemoOptions
opt = DemoOptions().parse() # set CUDA_VISIBLE_DEVICES before import torch
import torch
import pickle
import torchvision.transforms as transforms
from models.models import create_model
from util.visualizer import Visualizer
from pdb import set_trace as st
from util import html
from util.Vgg16 import Vgg16Part
from util.util import init_vgg16
from util.build_vocab import Vocabulary
import torch
from PIL import Image
import nltk
import json
from util import util
opt.nThreads = 1 # test code only supports nThreads=1
opt.batchSize = 1 #test code only supports batchSize=1
opt.serial_batches = True # no shuffle
opt.lambda_p = 0
# Load vocabulary wrapper.
with open(opt.vocab_path, 'rb') as f:
vocab = pickle.load(f)
opt.vocab = vocab
opt.vocab_size = len(vocab)