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 import torch.nn.functional as F from natsort import natsorted from model.SRMNet import SRMNet from utils import util_calculate_psnr_ssim as util def main(): 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', default='experiments/pretrained_models/real_denoising_SRMNet.pth', type=str, help='Path to weights') args = parser.parse_args() inp_dir = args.input_dir 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') # Load corresponding models architecture and weights 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) # Pad the input if not_multiple_of 8 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] save_img((os.path.join(out_dir, f + '.png')), restored) def save_img(filepath, img): 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:] # remove `module.` new_state_dict[name] = v model.load_state_dict(new_state_dict) def setup(args): save_dir = 'result/' folder = 'test/' return folder, save_dir if __name__ == '__main__': main()