Spaces:
Running
Running
| # Copyright (c) 2020 Huawei Technologies Co., Ltd. | |
| # Licensed under CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike 4.0 International) (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode | |
| # | |
| # The code is released for academic research use only. For commercial use, please contact Huawei Technologies Co., Ltd. | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # | |
| # This file contains content licensed by https://github.com/xinntao/BasicSR/blob/master/LICENSE/LICENSE | |
| import glob | |
| import sys | |
| sys.path.append('../..') | |
| from natsort import natsort | |
| import SRFlow.code.options.options as option | |
| from SRFlow.code.models import create_model | |
| import torch | |
| from SRFlow.code.utils.util import opt_get | |
| from SRFlow.code.models.SRFlow_model import SRFlowModel | |
| import numpy as np | |
| import os | |
| import cv2 | |
| def fiFindByWildcard(wildcard): | |
| return natsort.natsorted(glob.glob(wildcard, recursive=True)) | |
| def load_model(conf_path): | |
| opt = option.parse(conf_path, is_train=False) | |
| opt['gpu_ids'] = None | |
| opt = option.dict_to_nonedict(opt) | |
| model = SRFlowModel(opt, 0) | |
| model_path = opt_get(opt, ['model_path'], None) | |
| model.load_network(load_path='models/SRFlow/35000_G.pth', network=model.netG) | |
| return model, opt | |
| def predict(model, lr): | |
| model.feed_data({"LQ": t(lr)}, need_GT=False) | |
| model.test() | |
| visuals = model.get_current_visuals(need_GT=False) | |
| return visuals.get('rlt', visuals.get("SR")) | |
| def t(array): return torch.Tensor(np.expand_dims(array.transpose([2, 0, 1]), axis=0).astype(np.float32)) / 255 | |
| def rgb(t): return ( | |
| np.clip((t[0] if len(t.shape) == 4 else t).detach().cpu().numpy().transpose([1, 2, 0]), 0, 1) * 255).astype( | |
| np.uint8) | |
| def imread(path): | |
| return cv2.imread(path)[:, :, [2, 1, 0]] | |
| def imwrite(path, img): | |
| os.makedirs(os.path.dirname(path), exist_ok=True) | |
| cv2.imwrite(path, img[:, :, [2, 1, 0]]) | |
| def imCropCenter(img, size): | |
| h, w, c = img.shape | |
| h_start = max(h // 2 - size // 2, 0) | |
| h_end = min(h_start + size, h) | |
| w_start = max(w // 2 - size // 2, 0) | |
| w_end = min(w_start + size, w) | |
| return img[h_start:h_end, w_start:w_end] | |
| def impad(img, top=0, bottom=0, left=0, right=0, color=255): | |
| return np.pad(img, [(top, bottom), (left, right), (0, 0)], 'reflect') | |