52Hz/HWMNet_lowlight_enhancement

1import argparse2import cv23import glob4import numpy as np5from collections import OrderedDict6from skimage import img_as_ubyte7import os8import torch9import requests10from PIL import Image11import torchvision.transforms.functional as TF12import torch.nn.functional as F13from natsort import natsorted14from model.HWMNet import HWMNet 15 16def main():17    parser = argparse.ArgumentParser(description='Demo Low-light Image enhancement')18    parser.add_argument('--input_dir', default='test/', type=str, help='Input images')19    parser.add_argument('--result_dir', default='result/', type=str, help='Directory for results')20    parser.add_argument('--weights',21                        default='experiments/pretrained_models/LOL_enhancement_HWMNet.pth', type=str,22                        help='Path to weights')23 24    args = parser.parse_args()25 26    inp_dir = args.input_dir27    out_dir = args.result_dir28 29    os.makedirs(out_dir, exist_ok=True)30 31    files = natsorted(glob.glob(os.path.join(inp_dir, '*')))32 33    if len(files) == 0:34        raise Exception(f"No files found at {inp_dir}")35 36    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')37 38    # Load corresponding models architecture and weights39    model = HWMNet(in_chn=3, wf=96, depth=4)40    model = model.to(device)41    model.eval()42    load_checkpoint(model, args.weights)43    44 45    mul = 1646    for file_ in files:47        img = Image.open(file_).convert('RGB')48        input_ = TF.to_tensor(img).unsqueeze(0).to(device)49 50        # Pad the input if not_multiple_of 851        h, w = input_.shape[2], input_.shape[3]52        H, W = ((h + mul) // mul) * mul, ((w + mul) // mul) * mul53        padh = H - h if h % mul != 0 else 054        padw = W - w if w % mul != 0 else 055        input_ = F.pad(input_, (0, padw, 0, padh), 'reflect')56        with torch.no_grad():57            restored = model(input_)58 59        restored = torch.clamp(restored, 0, 1)60        restored = restored[:, :, :h, :w]61        restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()62        restored = img_as_ubyte(restored[0])63 64        f = os.path.splitext(os.path.split(file_)[-1])[0]65        save_img((os.path.join(out_dir, f + '.png')), restored)66 67 68def save_img(filepath, img):69    cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))70 71 72def load_checkpoint(model, weights):73    checkpoint = torch.load(weights, map_location=torch.device('cpu'))74    try:75        model.load_state_dict(checkpoint["state_dict"])76    except:77        state_dict = checkpoint["state_dict"]78        new_state_dict = OrderedDict()79        for k, v in state_dict.items():80            name = k[7:]  # remove `module.`81            new_state_dict[name] = v82        model.load_state_dict(new_state_dict)83 84 85if __name__ == '__main__':86    main()