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_()