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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 !') |