| 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.HWMNet import HWMNet |
|
|
| def main(): |
| parser = argparse.ArgumentParser(description='Demo Low-light Image enhancement') |
| 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/LOL_enhancement_HWMNet.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') |
|
|
| |
| model = HWMNet(in_chn=3, wf=96, depth=4) |
| 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) |
|
|
| |
| 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:] |
| new_state_dict[name] = v |
| model.load_state_dict(new_state_dict) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
