52Hz/SRMNet_AWGN_denoising

1import argparse2import cv23import glob4import numpy as np5from collections import OrderedDict6from skimage import img_as_ubyte7import os8import torch9import requests10from PIL import Image11import torchvision.transforms.functional as TF12import torch.nn.functional as F13from natsort import natsorted14from model.SRMNet import SRMNet15 16def clean_folder(folder):17    for filename in os.listdir(folder):18        file_path = os.path.join(folder, filename)19        try:20            if os.path.isfile(file_path) or os.path.islink(file_path):21                os.unlink(file_path)22            elif os.path.isdir(file_path):23                shutil.rmtree(file_path)24        except Exception as e:25            print('Failed to delete %s. Reason: %s' % (file_path, e))26 27def save_img(filepath, img):28    cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))29 30 31def load_checkpoint(model, weights):32    checkpoint = torch.load(weights, map_location=torch.device('cpu'))33    try:34        model.load_state_dict(checkpoint["state_dict"])35    except:36        state_dict = checkpoint["state_dict"]37        new_state_dict = OrderedDict()38        for k, v in state_dict.items():39            name = k[7:]  # remove `module.`40            new_state_dict[name] = v41        model.load_state_dict(new_state_dict)42 43        44def main():45    parser = argparse.ArgumentParser(description='Demo Image Denoising')46    parser.add_argument('--input_dir', default='test', type=str, help='Input images')47    parser.add_argument('--result_dir', default='result', type=str, help='Directory for results')48    parser.add_argument('--weights',49                        default='experiments/pretrained_models/AWGN_denoising_SRMNet.pth', type=str,50                        help='Path to weights')51 52    args = parser.parse_args()53 54    inp_dir = args.input_dir55    out_dir = args.result_dir56 57    os.makedirs(out_dir, exist_ok=True)58 59    files = natsorted(glob.glob(os.path.join(inp_dir, '*')))60 61    if len(files) == 0:62        raise Exception(f"No files found at {inp_dir}")63 64    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')65 66    # Load corresponding models architecture and weights67    model = SRMNet()68    model = model.to(device)69    model.eval()70    load_checkpoint(model, args.weights)71    72 73    mul = 1674    for file_ in files:75        img = Image.open(file_).convert('RGB')76        input_ = TF.to_tensor(img).unsqueeze(0).to(device)77 78        # Pad the input if not_multiple_of 879        h, w = input_.shape[2], input_.shape[3]80        H, W = ((h + mul) // mul) * mul, ((w + mul) // mul) * mul81        padh = H - h if h % mul != 0 else 082        padw = W - w if w % mul != 0 else 083        input_ = F.pad(input_, (0, padw, 0, padh), 'reflect')84        with torch.no_grad():85            restored = model(input_)86 87        restored = torch.clamp(restored, 0, 1)88        restored = restored[:, :, :h, :w]89        restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()90        restored = img_as_ubyte(restored[0])91 92        f = os.path.splitext(os.path.split(file_)[-1])[0]93        save_img((os.path.join(out_dir, f + '.png')), restored)94    clean_folder(inp_dir)95    96 97if __name__ == '__main__':98    main()