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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms.functional as TF
from PIL import Image
import os
import utils
from skimage import img_as_ubyte
from collections import OrderedDict
from natsort import natsorted
from glob import glob
import cv2
import argparse
from model.CMFNet import CMFNet
model = CMFNet()
parser = argparse.ArgumentParser(description='Demo Image Restoration')
parser.add_argument('--input_dir', default='./demo_samples/deraindrop', type=str, help='Input images folder')
parser.add_argument('--result_dir', default='./demo_results', type=str, help='Directory for results')
parser.add_argument('--weights', default='./pretrained_model/deraindrop_DeRainDrop_CMFNet.pth', type=str, help='Path to weights')
args = parser.parse_args()
def save_img(filepath, img):
cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
def load_checkpoint(model, weights):
checkpoint = torch.load(weights)
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)
inp_dir = args.input_dir
out_dir = args.result_dir
os.makedirs(out_dir, exist_ok=True)
files = natsorted(glob(os.path.join(inp_dir, '*.jpg'))
+ glob(os.path.join(inp_dir, '*.JPG'))
+ glob(os.path.join(inp_dir, '*.png'))
+ glob(os.path.join(inp_dir, '*.PNG')))
if len(files) == 0:
raise Exception(f"No files found at {inp_dir}")
# Load corresponding model architecture and weights
model = CMFNet()
model.cuda()
load_checkpoint(model, args.weights)
model.eval()
img_multiple_of = 8
print('restoring images......')
for file_ in files:
img = Image.open(file_).convert('RGB')
input_ = TF.to_tensor(img).unsqueeze(0).cuda()
# 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')
with torch.no_grad():
restored = model(input_)
restored = restored[4]
restored = torch.clamp(restored, 0, 1)
# Un-pad the output
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)
print(f"Files saved at {out_dir}")
print('finish !') |