| """
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| Copyright (c) 2022 Samsung Electronics Co., Ltd.
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|
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| Author:
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| Abhijith Punnappurath (abhijith.p@samsung.com)
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|
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| Licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License, (the "License");
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| you may not use this file except in compliance with the License.
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| You may obtain a copy of the License at https://creativecommons.org/licenses/by-nc/4.0
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| Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an
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| "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| See the License for the specific language governing permissions and limitations under the License.
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| For conditions of distribution and use, see the accompanying LICENSE.md file.
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|
|
| """
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|
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|
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| import glob
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| import cv2
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| import numpy as np
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| import pickle
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|
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| from pipeline.pipeline import run_pipeline
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|
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| aug_times = 1
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|
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| params = {
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| 'save_as': 'png',
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| 'white_balancer': 'default',
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| 'demosaicer': '',
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|
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| 'tone_curve': 'simple-s-curve',
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| 'output_stage': 'demosaic',
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| }
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| stages = ['raw', 'active_area_cropping', 'linearization', 'normal', 'white_balance',
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| 'demosaic']
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| def data_aug(img, mode=0):
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| if mode == 0:
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| return img
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| elif mode == 1:
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| return np.flipud(img)
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| elif mode == 2:
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| return np.rot90(img)
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| elif mode == 3:
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| return np.flipud(np.rot90(img))
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| elif mode == 4:
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| return np.rot90(img, k=2)
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| elif mode == 5:
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| return np.flipud(np.rot90(img, k=2))
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| elif mode == 6:
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| return np.rot90(img, k=3)
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| elif mode == 7:
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| return np.flipud(np.rot90(img, k=3))
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|
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| def get_all_patches(img, patch_size=48, stride=48):
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| h, w, cc = img.shape
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| patches = []
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|
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| for i in range(0, h - patch_size + 1, stride):
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| for j in range(0, w - patch_size + 1, stride):
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| x = img[i:i + patch_size, j:j + patch_size, :]
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|
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| for k in range(0, aug_times):
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| x_aug = data_aug(x, mode=0)
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| patches.append(x_aug)
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| return patches
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| def gen_patches_sRGB(file_name, patch_size=48, stride=48):
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|
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| img = cv2.imread(file_name, cv2.IMREAD_UNCHANGED)
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| img = np.array(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), dtype=np.float32)
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| patches = get_all_patches(img, patch_size, stride)
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| return patches
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| def gen_patches_raw(file_name, meta_name, wb_illum='avg', patch_size=48, stride=48):
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|
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| meta_data_org = pickle.load(open(meta_name, "rb"))
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|
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| if wb_illum == 'avg':
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| meta_data_org['as_shot_neutral'] = meta_data_org['avg_night_illuminant']
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|
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| img = cv2.imread(file_name, cv2.IMREAD_UNCHANGED)
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| img = run_pipeline(img, params=params, metadata=meta_data_org, stages=stages)
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| patches = get_all_patches(img, patch_size, stride)
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| return patches
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| def datagenerator_sRGB(data_dir='dummy_dataset/train/clean', batch_size=128, patch_size=48, stride=48, verbose=False):
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| file_list = sorted(glob.glob(data_dir + '/*.png'))
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| data = []
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| for i in range(len(file_list)):
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| patch = gen_patches_sRGB(file_list[i], patch_size=patch_size, stride=stride)
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| data.append(patch)
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| if verbose:
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| print(str(i + 1) + '/' + str(len(file_list)) + ' is done')
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| data = np.concatenate(data)
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| discard_n = len(data) - len(data) // batch_size * batch_size
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| data = np.delete(data, range(discard_n), axis=0)
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| print('^_^-sRGB training data finished-^_^')
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| return data
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| def datagenerator_raw(data_dir='dummy_dataset/train/clean_raw', meta_dir='dummy_dataset/train/metadata_raw', wb_illum='avg',
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| batch_size=128, patch_size=48, stride=48, verbose=False):
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|
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| file_list = sorted(glob.glob(data_dir + '/*.png'))
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| meta_list = sorted(glob.glob(meta_dir + '/*.p'))
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| data = []
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|
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| for i in range(len(file_list)):
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| patch = gen_patches_raw(file_list[i], meta_list[i], wb_illum=wb_illum, patch_size=patch_size, stride=stride)
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| data.append(patch)
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| if verbose:
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| print(str(i + 1) + '/' + str(len(file_list)) + ' is done')
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| data = np.concatenate(data)
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| discard_n = len(data) - len(data) // batch_size * batch_size
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| data = np.delete(data, range(discard_n), axis=0)
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|
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| print('^_^-raw training data finished-^_^')
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| return data
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|
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| if __name__ == '__main__':
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| batch_size, patch_size, stride = 128, 48, 48
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| data_sRGB = datagenerator_sRGB(data_dir='real_night/train/clean', batch_size=batch_size, patch_size=patch_size,
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| stride=stride)
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| data_raw = datagenerator_raw(data_dir='real_night/train/clean_raw', meta_dir='dummy_dataset/train/metadata_raw',
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| wb_illum='avg', batch_size=batch_size, patch_size=patch_size, stride=stride)
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| print('Done!')
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|
|
