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import argparse
import numpy as np
import torch
from PIL import Image
from torchvision.transforms.functional import to_tensor
# Training settings
parser = argparse.ArgumentParser(description="PyTorch Super Res Example")
parser.add_argument("--input_image", type=str, required=True, help="input image to use")
parser.add_argument("--model", type=str, required=True, help="model file to use")
parser.add_argument("--output_filename", type=str, help="where to save the output image")
parser.add_argument("--cuda", action="store_true", help="use cuda")
opt = parser.parse_args()
print(opt)
img = Image.open(opt.input_image).convert("YCbCr")
y, cb, cr = img.split()
model = torch.load(opt.model)
input = to_tensor(y).view(1, -1, y.size[1], y.size[0])
if opt.cuda:
model = model.cuda()
input = input.cuda()
model.eval()
with torch.no_grad():
out = model(input)
out = out.cpu()
out_img_y = out[0].detach().numpy()
out_img_y *= 255.0
out_img_y = out_img_y.clip(0, 255)
out_img_y = Image.fromarray(np.uint8(out_img_y[0]), mode="L")
out_img_cb = cb.resize(out_img_y.size, Image.BICUBIC)
out_img_cr = cr.resize(out_img_y.size, Image.BICUBIC)
out_img = Image.merge("YCbCr", [out_img_y, out_img_cb, out_img_cr]).convert("RGB")
out_img.save(opt.output_filename)
print("output image saved to ", opt.output_filename)
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