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| # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # 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. | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import numpy as np | |
| import os | |
| import sys | |
| import json | |
| from PIL import Image | |
| import cv2 | |
| __dir__ = os.path.dirname(os.path.abspath(__file__)) | |
| sys.path.insert(0, __dir__) | |
| sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..'))) | |
| os.environ["FLAGS_allocator_strategy"] = 'auto_growth' | |
| import paddle | |
| from ppocr.data import create_operators, transform | |
| from ppocr.modeling.architectures import build_model | |
| from ppocr.postprocess import build_post_process | |
| from ppocr.utils.save_load import load_model | |
| from ppocr.utils.utility import get_image_file_list | |
| import tools.program as program | |
| def main(): | |
| global_config = config['Global'] | |
| # build post process | |
| post_process_class = build_post_process(config['PostProcess'], | |
| global_config) | |
| # sr transform | |
| config['Architecture']["Transform"]['infer_mode'] = True | |
| model = build_model(config['Architecture']) | |
| load_model(config, model) | |
| # create data ops | |
| transforms = [] | |
| for op in config['Eval']['dataset']['transforms']: | |
| op_name = list(op)[0] | |
| if 'Label' in op_name: | |
| continue | |
| elif op_name in ['SRResize']: | |
| op[op_name]['infer_mode'] = True | |
| elif op_name == 'KeepKeys': | |
| op[op_name]['keep_keys'] = ['img_lr'] | |
| transforms.append(op) | |
| global_config['infer_mode'] = True | |
| ops = create_operators(transforms, global_config) | |
| save_visual_path = config['Global'].get('save_visual', "infer_result/") | |
| if not os.path.exists(os.path.dirname(save_visual_path)): | |
| os.makedirs(os.path.dirname(save_visual_path)) | |
| model.eval() | |
| for file in get_image_file_list(config['Global']['infer_img']): | |
| logger.info("infer_img: {}".format(file)) | |
| img = Image.open(file).convert("RGB") | |
| data = {'image_lr': img} | |
| batch = transform(data, ops) | |
| images = np.expand_dims(batch[0], axis=0) | |
| images = paddle.to_tensor(images) | |
| preds = model(images) | |
| sr_img = preds["sr_img"][0] | |
| lr_img = preds["lr_img"][0] | |
| fm_sr = (sr_img.numpy() * 255).transpose(1, 2, 0).astype(np.uint8) | |
| fm_lr = (lr_img.numpy() * 255).transpose(1, 2, 0).astype(np.uint8) | |
| img_name_pure = os.path.split(file)[-1] | |
| cv2.imwrite("{}/sr_{}".format(save_visual_path, img_name_pure), | |
| fm_sr[:, :, ::-1]) | |
| logger.info("The visualized image saved in infer_result/sr_{}".format( | |
| img_name_pure)) | |
| logger.info("success!") | |
| if __name__ == '__main__': | |
| config, device, logger, vdl_writer = program.preprocess() | |
| main() | |