| """ |
| Copyright (c) 2022 Samsung Electronics Co., Ltd. |
| |
| Author: |
| Abhijith Punnappurath (abhijith.p@samsung.com) |
| |
| Licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License, (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/4.0 |
| 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. |
| For conditions of distribution and use, see the accompanying LICENSE.md file. |
| |
| """ |
|
|
| from shutil import copyfile |
| import os |
| from glob import glob |
| import numpy as np |
| import argparse |
| import pickle |
|
|
|
|
| def parse_args(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument('--base_path', default='data', type=str, |
| help='base address of real data') |
| parser.add_argument('--synth_path', |
| default='synthetic_datasets/night', |
| type=str, |
| help='synth data path') |
| parser.add_argument('--real_percent', |
| default='10', |
| type=int, |
| help='percentage of real images, rest will be synthetic') |
| parser.add_argument('--total_train', |
| default='60', |
| type=int, |
| help='percentage of real images, rest will be synthetic') |
| parser.add_argument( |
| '--seed', default=101, type=int, help='seed np random generator') |
| args = parser.parse_args() |
|
|
| print(args) |
|
|
| return args |
|
|
|
|
| def remove_data(remove_ind, args): |
| input_dir = os.path.join(args.base_path, 'train') |
| allfiles = [os.path.basename(x) for x in sorted(glob(os.path.join(input_dir, 'clean', '*.png')))] |
|
|
| for i in remove_ind: |
| for fol in ['clean_raw', 'noisy_raw', 'clean']: |
| os.remove(os.path.join(input_dir, fol, allfiles[i])) |
|
|
| for fol in ['metadata_raw']: |
| os.remove(os.path.join(input_dir, fol, allfiles[i][:-4] + '.p')) |
|
|
|
|
| def add_data(add_ind, args): |
| input_dir = os.path.join(args.base_path, 'train') |
| allfilessynth = [os.path.basename(x) for x in sorted(glob(os.path.join(args.synth_path, 'train', 'clean', '*.png')))] |
|
|
| for i in add_ind: |
| for fol in ['clean_raw', 'clean', 'noisy_raw']: |
| source = os.path.join(args.synth_path, 'train', fol, allfilessynth[i]) |
| destination = os.path.join(input_dir, fol, allfilessynth[i]) |
| copyfile(source, destination) |
|
|
| for fol in ['metadata_raw']: |
| source = os.path.join(args.synth_path, 'train', fol, allfilessynth[i][:-4] + '.p') |
| metadata = pickle.load(open(source, "rb")) |
| metadata['as_shot_neutral'] = metadata['avg_night_illuminant'] |
| destination = os.path.join(input_dir, fol, allfilessynth[i][:-4] + '.p') |
| pickle.dump(metadata, open(destination, "wb")) |
|
|
|
|
| if __name__ == "__main__": |
| args = parse_args() |
| np.random.seed(args.seed) |
| randperm = np.random.permutation(args.total_train) |
|
|
| |
| num_real = round(args.real_percent * args.total_train / 100) |
| |
| |
| remove_real = args.total_train - num_real |
|
|
| |
| ind = randperm[0:remove_real] |
| remove_data(ind, args) |
|
|
| |
| add_data(ind, args) |
|
|
| print('Done!') |
|
|
