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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
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)
if __name__ == '__main__':
main() |