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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') | |
| 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)) | |