args.gpu = -1
args.win_size = int(args.win_size / 4.0) * 4 # make sure the width of the image can be divided by 4
if args.backend == 'caffe':
# initialize the colorization model
colorModel = CI.ColorizeImageCaffe(Xd=args.load_size)
colorModel.prep_net(args.gpu, args.color_prototxt, args.color_caffemodel)
distModel = CI.ColorizeImageCaffeDist(Xd=args.load_size)
distModel.prep_net(args.gpu, args.dist_prototxt, args.dist_caffemodel)
elif args.backend == 'pytorch':
colorModel = CI.ColorizeImageTorch(Xd=args.load_size,maskcent=args.pytorch_maskcent)
colorModel.prep_net(path=args.color_model)
distModel = CI.ColorizeImageTorchDist(Xd=args.load_size,maskcent=args.pytorch_maskcent)
distModel.prep_net(path=args.color_model, dist=True)
else:
print('backend type [%s] not found!' % args.backend)
# initialize application
app = QApplication(sys.argv)
window = gui_design.GUIDesign(color_model=colorModel, dist_model=distModel,
img_file=args.image_file, load_size=args.load_size, win_size=args.win_size)
app.setStyleSheet(qdarkstyle.load_stylesheet(pyside=False)) # comment this if you do not like dark stylesheet
app.setWindowIcon(QIcon('imgs/logo.png')) # load logo
window.setWindowTitle('iColor')
window.setWindowFlags(window.windowFlags() & ~Qt.WindowMaximizeButtonHint) # fix window siz
window.show()
app.exec_()
args = parse_args()
for arg in vars(args):
print('[%s] =' % arg, getattr(args, arg))
if(args.cpu_mode):
args.gpu = -1
args.win_size = int(args.win_size / 4.0) * 4 # make sure the width of the image can be divided by 4
# initialize the colorization model
colorModel = CI.ColorizeImageCaffe(Xd=args.load_size)
colorModel.prep_net(args.gpu,args.color_prototxt,args.color_caffemodel)
if (args.no_dist):
distModel = None
else:
distModel = CI.ColorizeImageCaffeDist(Xd=args.load_size)
distModel.prep_net(args.gpu,args.dist_prototxt,args.dist_caffemodel)
# initialize application
app = QApplication(sys.argv)
window = gui_design.GUIDesign(color_model=colorModel, dist_model=distModel,
img_file=args.image_file, load_size=args.load_size, win_size=args.win_size, user_study=args.user_study, ui_time=args.ui_time)
app.setStyleSheet(qdarkstyle.load_stylesheet(pyside=False)) # comment this if you do not like dark stylesheet
app.setWindowIcon(QIcon('imgs/logo.png')) # load logo
window.setWindowTitle('iColor')
window.setWindowFlags(window.windowFlags() & ~Qt.WindowMaximizeButtonHint) # fix window siz
window.show()
app.exec_()
if __name__ == '__main__':
args = parse_args()
if not args.model_file: # if the model_file is not specified
args.model_file = './models/%s.%s' % (args.model_name, args.model_type)
for arg in vars(args):
print('[%s] =' % arg, getattr(args, arg))
args.win_size = int(args.win_size / 4.0) * 4 # make sure the width of the image can be divided by 4
# initialize model and constrained optimization problem
model_class = locate('model_def.%s' % args.model_type)
model = model_class.Model(model_name=args.model_name, model_file=args.model_file)
opt_class = locate('constrained_opt_%s' % args.framework)
opt_solver = opt_class.OPT_Solver(model, batch_size=args.batch_size, d_weight=args.d_weight, divided_batch_size=args.mini_batch_size)
img_size = opt_solver.get_image_size()
opt_engine = constrained_opt.Constrained_OPT(opt_solver, batch_size=args.batch_size, n_iters=args.n_iters, topK=args.top_k,
morph_steps=args.morph_steps, interp=args.interp)
# initialize application
app = QApplication(sys.argv)
window = gui_design.GUIDesign(opt_engine, win_size=args.win_size, img_size=img_size, topK=args.top_k,
model_name=args.model_name, useAverage=args.average, shadow=args.shadow)
# app.setStyleSheet(qdarkstyle.load_stylesheet(pyside=False)) # comment this if you do not like dark stylesheet
app.setWindowIcon(QIcon('pics/logo.png')) # load logo
window.setWindowTitle('Interactive GAN')
window.setWindowFlags(window.windowFlags() & ~Qt.WindowMaximizeButtonHint) # fix window siz
window.show()
app.exec_()
for arg in vars(args):
print '[%s] =' % arg, getattr(args, arg)
args.win_size = int(
args.win_size /
4) * 4 # make sure the width of the image can be divided by 4
# initialize model and constrained optimization problem
model_class = locate('model_def.%s' % args.model_type)
model_G = model_class.Model(model_name=args.model_name,
model_file=args.model_file)
opt_class = locate('constrained_opt_%s' % args.framework)
opt_engine = opt_class.Constrained_OPT(model_G,
batch_size=args.batch_size,
n_iters=args.n_iters,
topK=args.top_k,
morph_steps=args.morph_steps,
interp=args.interp)
# initialize application
app = QApplication(sys.argv)
window = gui_design.GUIDesign(opt_engine,
batch_size=args.batch_size,
n_iters=args.n_iters,
win_size=args.win_size,
topK=args.top_k)
app.setStyleSheet(qdarkstyle.load_stylesheet(
pyside=False)) # comment this if you do not like dark stylesheet
app.setWindowIcon(QIcon('pics/logo.png'))
window.setWindowTitle('Interactive GAN')
window.setWindowFlags(window.windowFlags() & ~Qt.WindowMaximizeButtonHint)
window.show()
app.exec_()
dataset_name=args.model_name,
checkpoint_dir=args.checkpoint)
if not dcgan.load(args.checkpoint)[0]:
raise Exception("[!] Train a model first")
if not predict_z.load(args.checkpoint + "z")[0]:
raise Exception("[!] Train a model first")
opt_engine = constrained_opt.Constrained_OPT(dcgan,
predict_z,
image_size=args.image_size,
batch_size=args.batch_size,
dimz=args.dimz,
n_iters=args.n_iters,
topK=args.top_k,
morph_steps=args.morph_steps,
interp=args.interp)
# initialize application
app = QApplication(sys.argv)
window = gui_design.GUIDesign(opt_engine,
win_size=args.win_size,
img_size=args.image_size,
topK=args.top_k,
model_name=args.model_name)
#app.setStyleSheet(qdarkstyle.load_stylesheet(pyside=False)) # comment this if you do not like dark stylesheet
app.setWindowIcon(QIcon('logo.png')) # load logo
window.setWindowTitle('Interactive GAN')
window.setWindowFlags(window.windowFlags()
& ~Qt.WindowMaximizeButtonHint) # fix window siz
window.show()
app.exec_()
args.color_caffemodel)
distModel = CI.ColorizeImageCaffeDist(Xd=args.load_size)
distModel.prep_net(args.gpu, args.dist_prototxt, args.dist_caffemodel)
elif args.backend == 'pytorch':
colorModel = CI.ColorizeImageTorch(Xd=args.load_size)
colorModel.prep_net(path=args.color_model)
distModel = CI.ColorizeImageTorchDist(Xd=args.load_size)
distModel.prep_net(path=args.color_model, dist=True)
else:
print('backend type [%s] not found!' % args.backend)
# initialize application
app = QApplication(sys.argv)
window = gui_design.GUIDesign(
color_model=colorModel,
dist_model=distModel,
img_file=
'F:\\jetbrains\pycharm\\interactive-deep-colorization\\test_imgs\\mortar_pestle.jpg',
load_size=args.load_size,
win_size=args.win_size)
app.setStyleSheet(qdarkstyle.load_stylesheet(
pyside=False)) # comment this if you do not like dark stylesheet
app.setWindowIcon(QIcon('imgs/logo.png')) # load logo
window.setWindowTitle('iColor')
window.setWindowFlags(window.windowFlags()
& ~Qt.WindowMaximizeButtonHint) # fix window siz
window.show()
app.exec_()
