test_DND_real_denoising.py

## 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 numpy as np
import os
import argparse
from tqdm import tqdm

import torch
import torch.nn as nn
import torch.nn.functional as F
import utils

from SRMNet import SRMNet
from skimage import img_as_ubyte
import h5py
import scipy.io as sio
from pdb import set_trace as stx

parser = argparse.ArgumentParser(description='Real Image Denoising using Restormer')

parser.add_argument('--input_dir', default='D:/NCHU/Dataset/Denoise/Real-world noise/DND/', type=str, help='Directory of validation images')
parser.add_argument('--result_dir', default='./test_results/DND/', type=str, help='Directory for results')
parser.add_argument('--weights', default='./checkpoints/SRMNet_real_denoise/models/model_bestPSNR.pth', type=str, help='Path to weights')
parser.add_argument('--save_images', default=True, help='Save denoised images in result directory')

args = parser.parse_args()

result_dir_mat = os.path.join(args.result_dir, 'mat')
os.makedirs(result_dir_mat, exist_ok=True)

if args.save_images:
    result_dir_png = os.path.join(args.result_dir, 'png')
    os.makedirs(result_dir_png, exist_ok=True)

model_restoration = SRMNet()

checkpoint = torch.load(args.weights)
model_restoration.load_state_dict(checkpoint['state_dict'])
print("===>Testing using weights: ",args.weights)
model_restoration.cuda()
# model_restoration = nn.DataParallel(model_restoration)
model_restoration.eval()

israw = False
eval_version="1.0"

# Load info
infos = h5py.File(os.path.join(args.input_dir, 'info.mat'), 'r')
info = infos['info']
bb = info['boundingboxes']

# Process data
with torch.no_grad():
    for i in tqdm(range(50)):
        Idenoised = np.zeros((20,), dtype=np.object)
        filename = '%04d.mat'%(i+1)
        filepath = os.path.join(args.input_dir, 'images_srgb', filename)
        img = h5py.File(filepath, 'r')
        Inoisy = np.float32(np.array(img['InoisySRGB']).T)

        # bounding box
        ref = bb[0][i]
        boxes = np.array(info[ref]).T

        for k in range(20):
            idx = [int(boxes[k,0]-1),int(boxes[k,2]),int(boxes[k,1]-1),int(boxes[k,3])]
            noisy_patch = torch.from_numpy(Inoisy[idx[0]:idx[1],idx[2]:idx[3],:]).unsqueeze(0).permute(0,3,1,2).cuda()
            restored_patch = model_restoration(noisy_patch)
            restored_patch = torch.clamp(restored_patch,0,1).cpu().detach().permute(0, 2, 3, 1).squeeze(0).numpy()
            Idenoised[k] = restored_patch

            if args.save_images:
                save_file = os.path.join(result_dir_png, '%04d_%02d.png'%(i+1,k+1))
                denoised_img = img_as_ubyte(restored_patch)
                utils.save_img(save_file, denoised_img)

        # save denoised data
        sio.savemat(os.path.join(result_dir_mat, filename),
                    {"Idenoised": Idenoised,
                     "israw": israw,
                     "eval_version": eval_version},
                    )