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