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| ## Restormer: Efficient Transformer for High-Resolution Image Restoration | |
| ## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang | |
| ## https://arxiv.org/abs/2111.09881 | |
| import torch | |
| import torch.nn.functional as F | |
| import os | |
| from skimage import img_as_ubyte | |
| import cv2 | |
| import argparse | |
| parser = argparse.ArgumentParser(description='Test Restormer on your own images') | |
| parser.add_argument('--input_path', default='./temp/image.jpg', type=str, help='Directory of input images or path of single image') | |
| parser.add_argument('--result_dir', default='./temp/', type=str, help='Directory for restored results') | |
| parser.add_argument('--task', required=True, type=str, help='Task to run', choices=['Motion_Deblurring', | |
| 'Single_Image_Defocus_Deblurring', | |
| 'Deraining', | |
| 'Real_Denoising', | |
| 'Gaussian_Gray_Denoising', | |
| 'Gaussian_Color_Denoising']) | |
| args = parser.parse_args() | |
| task = args.task | |
| out_dir = os.path.join(args.result_dir, task) | |
| os.makedirs(out_dir, exist_ok=True) | |
| if task == 'Motion_Deblurring': | |
| model = torch.jit.load('motion_deblurring.pt') | |
| elif task == 'Single_Image_Defocus_Deblurring': | |
| model = torch.jit.load('single_image_defocus_deblurring.pt') | |
| elif task == 'Deraining': | |
| model = torch.jit.load('deraining.pt') | |
| elif task == 'Real_Denoising': | |
| model = torch.jit.load('real_denoising.pt') | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # device = torch.device('cpu') | |
| # stx() | |
| model = model.to(device) | |
| model.eval() | |
| img_multiple_of = 8 | |
| with torch.inference_mode(): | |
| if torch.cuda.is_available(): | |
| torch.cuda.ipc_collect() | |
| torch.cuda.empty_cache() | |
| img = cv2.cvtColor(cv2.imread(args.input_path), cv2.COLOR_BGR2RGB) | |
| input_ = torch.from_numpy(img).float().div(255.).permute(2,0,1).unsqueeze(0).to(device) | |
| # Pad the input if not_multiple_of 8 | |
| h,w = input_.shape[2], input_.shape[3] | |
| H,W = ((h+img_multiple_of)//img_multiple_of)*img_multiple_of, ((w+img_multiple_of)//img_multiple_of)*img_multiple_of | |
| padh = H-h if h%img_multiple_of!=0 else 0 | |
| padw = W-w if w%img_multiple_of!=0 else 0 | |
| input_ = F.pad(input_, (0,padw,0,padh), 'reflect') | |
| # print(h,w) | |
| restored = torch.clamp(model(input_),0,1) | |
| # Unpad the output | |
| restored = img_as_ubyte(restored[:,:,:h,:w].permute(0, 2, 3, 1).cpu().detach().numpy()[0]) | |
| out_path = os.path.join(out_dir, os.path.split(args.input_path)[-1]) | |
| cv2.imwrite(out_path,cv2.cvtColor(restored, cv2.COLOR_RGB2BGR)) | |
| # print(f"\nRestored images are saved at {out_dir}") |