| import cv2 | |
| import numpy as np | |
| import torch | |
| from basicsr.archs.rrdbnet_arch import RRDBNet | |
| def init_sr_model(model_path): | |
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32) | |
| model.load_state_dict(torch.load(model_path)['params'], strict=True) | |
| model.eval() | |
| model = model.cuda() | |
| return model | |
| def enhance(model, image): | |
| img = image.astype(np.float32) / 255. | |
| img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float() | |
| img = img.unsqueeze(0).cuda() | |
| with torch.no_grad(): | |
| output = model(img) | |
| output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy() | |
| output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) | |
| output = (output * 255.0).round().astype(np.uint8) | |
| return output | |