ref = ref[:, :, :real.shape[2], :real.shape[3]]
N = len(reals) - 1
n = opt.paint_start_scale
in_s = imresize(ref, pow(opt.scale_factor, (N - n + 1)), opt)
in_s = in_s[:, :, :reals[n - 1].shape[2], :reals[n - 1].shape[3]]
in_s = imresize(in_s, 1 / opt.scale_factor, opt)
in_s = in_s[:, :, :reals[n].shape[2], :reals[n].shape[3]]
if opt.quantization_flag:
opt.mode = 'paint_train'
dir2trained_model = functions.generate_dir2save(opt)
# N = len(reals) - 1
# n = opt.paint_start_scale
real_s = imresize(real, pow(opt.scale_factor, (N - n)), opt)
real_s = real_s[:, :, :reals[n].shape[2], :reals[n].shape[3]]
real_quant, centers = functions.quant(real_s, opt.device)
plt.imsave('%s/real_quant.png' % dir2save, functions.convert_image_np(real_quant), vmin=0, vmax=1)
plt.imsave('%s/in_paint.png' % dir2save, functions.convert_image_np(in_s), vmin=0, vmax=1)
in_s = functions.quant2centers(ref, centers)
in_s = imresize(in_s, pow(opt.scale_factor, (N - n)), opt)
# in_s = in_s[:, :, :reals[n - 1].shape[2], :reals[n - 1].shape[3]]
# in_s = imresize(in_s, 1 / opt.scale_factor, opt)
in_s = in_s[:, :, :reals[n].shape[2], :reals[n].shape[3]]
plt.imsave('%s/in_paint_quant.png' % dir2save, functions.convert_image_np(in_s), vmin=0, vmax=1)
if (os.path.exists(dir2trained_model)):
# print('Trained model does not exist, training SinGAN for SR')
Gs, Zs, reals, NoiseAmp = functions.load_trained_pyramid(opt)
opt.mode = 'paint2image'
else:
train_paint(opt, Gs, Zs, reals, NoiseAmp, centers, opt.paint_start_scale)
opt.mode = 'paint2image'
ref = ref[:, :, :real.shape[2], :real.shape[3]]
N = len(reals) - 1
n = opt.paint_start_scale
in_s = imresize(ref, pow(opt.scale_factor, (N - n + 1)), opt)
in_s = in_s[:, :, :reals[n - 1].shape[2], :reals[n - 1].shape[3]]
in_s = imresize(in_s, 1 / opt.scale_factor, opt)
in_s = in_s[:, :, :reals[n].shape[2], :reals[n].shape[3]]
if opt.quantization_flag:
opt.mode = 'paint_train'
dir2trained_model = functions.generate_dir2save(opt)
# N = len(reals) - 1
# n = opt.paint_start_scale
real_s = imresize(real, pow(opt.scale_factor, (N - n)), opt)
real_s = real_s[:, :, :reals[n].shape[2], :reals[n].shape[3]]
real_quant, centers = functions.quant(real_s)
plt.imsave('%s/real_quant.png' % dir2save, functions.convert_image_np(real_quant), vmin=0, vmax=1)
plt.imsave('%s/in_paint.png' % dir2save, functions.convert_image_np(in_s), vmin=0, vmax=1)
in_s = functions.quant2centers(ref, centers)
in_s = imresize(in_s, pow(opt.scale_factor, (N - n)), opt)
# in_s = in_s[:, :, :reals[n - 1].shape[2], :reals[n - 1].shape[3]]
# in_s = imresize(in_s, 1 / opt.scale_factor, opt)
in_s = in_s[:, :, :reals[n].shape[2], :reals[n].shape[3]]
plt.imsave('%s/in_paint_quant.png' % dir2save, functions.convert_image_np(in_s), vmin=0, vmax=1)
if (os.path.exists(dir2trained_model)):
# print('Trained model does not exist, training SinGAN for SR')
Gs, Zs, reals, NoiseAmp = functions.load_trained_pyramid(opt)
opt.mode = 'paint2image'
else:
train_paint(opt, Gs, Zs, reals, NoiseAmp, centers, opt.paint_start_scale)
opt.mode = 'paint2image'