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import argparse
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import cv2
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import glob
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import numpy as np
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from collections import OrderedDict
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from skimage import img_as_ubyte
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import os
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import torch
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import requests
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from PIL import Image
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import torchvision.transforms.functional as TF
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import torch.nn.functional as F
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from model.SRMNet import SRMNet
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from utils import util_calculate_psnr_ssim as util
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def save_img(filepath, img):
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cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
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def load_checkpoint(model, weights):
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checkpoint = torch.load(weights)
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try:
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model.load_state_dict(checkpoint["state_dict"])
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except:
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state_dict = checkpoint["state_dict"]
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new_state_dict = OrderedDict()
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for k, v in state_dict.items():
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name = k[7:]
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new_state_dict[name] = v
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model.load_state_dict(new_state_dict)
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def main():
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parser = argparse.ArgumentParser(description='Demo Image Denoising')
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parser.add_argument('--input_dir', default='test/', type=str, help='Input images')
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parser.add_argument('--result_dir', default='result/', type=str, help='Directory for results')
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parser.add_argument('--weights',
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default='experiments/pretrained_models/AWGN_denoising_SRMNet.pth', type=str,
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help='Path to weights')
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args = parser.parse_args()
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inp_dir = args.input_dir
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out_dir = args.result_dir
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os.makedirs(out_dir, exist_ok=True)
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files = sorted(glob.glob(os.path.join(inp_dir, '*.PNG')))
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if len(files) == 0:
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raise Exception(f"No files found at {inp_dir}")
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model = SRMNet()
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model.cuda()
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load_checkpoint(model, args.weights)
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model.eval()
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mul = 16
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for file_ in files:
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img = Image.open(file_).convert('RGB')
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input_ = TF.to_tensor(img).unsqueeze(0).cuda()
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h, w = input_.shape[2], input_.shape[3]
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H, W = ((h + mul) // mul) * mul, ((w + mul) // mul) * mul
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padh = H - h if h % mul != 0 else 0
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padw = W - w if w % mul != 0 else 0
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input_ = F.pad(input_, (0, padw, 0, padh), 'reflect')
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with torch.no_grad():
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restored = model(input_)
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restored = torch.clamp(restored, 0, 1)
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restored = restored[:, :, :h, :w]
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restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()
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restored = img_as_ubyte(restored[0])
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f = os.path.splitext(os.path.split(file_)[-1])[0]
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save_img((os.path.join(out_dir, f + '.png')), restored)
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def setup(args):
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save_dir = 'result/'
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folder = 'test/'
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return folder, save_dir
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if __name__ == '__main__':
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main() |
