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import cog
import tempfile
from pathlib import Path
import argparse
import shutil
import os
import glob
import torch
from skimage import img_as_ubyte
from PIL import Image
from model.SRMNet import SRMNet
from main_test_SRMNet import save_img, setup
import torchvision.transforms.functional as TF
import torch.nn.functional as F
class Predictor(cog.Predictor):
def setup(self):
model_dir = 'experiments/pretrained_models/AWGN_denoising_SRMNet.pth'
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='./checkpoints/SRMNet_real_denoise/models/model_bestPSNR.pth', type=str,
help='Path to weights')
self.args = parser.parse_args()
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@cog.input("image", type=Path, help="input image")
def predict(self, image):
# set input folder
input_dir = 'input_cog_temp'
os.makedirs(input_dir, exist_ok=True)
input_path = os.path.join(input_dir, os.path.basename(image))
shutil.copy(str(image), input_path)
# Load corresponding models architecture and weights
model = SRMNet()
model.eval()
model = model.to(self.device)
folder, save_dir = setup(self.args)
os.makedirs(save_dir, exist_ok=True)
out_path = Path(tempfile.mkdtemp()) / "out.png"
mul = 16
for file_ in sorted(glob.glob(os.path.join(folder, '*.PNG'))):
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 + 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])
save_img(str(out_path), restored)
clean_folder(input_dir)
return out_path
def clean_folder(folder):
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print('Failed to delete %s. Reason: %s' % (file_path, e)) |