# -*- coding: utf-8 -*-
# BSD 3-Clause License
#
# Copyright (c) 2017
# All rights reserved.
# Copyright 2022 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ==========================================================================
# -*- coding: utf-8 -*-
# BSD 3-Clause License
#
# Copyright (c) 2017
# All rights reserved.
# Copyright 2022 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ==========================================================================
# -*- coding: utf-8 -*-
# BSD 3-Clause License
#
# Copyright (c) 2017
# All rights reserved.
# Copyright 2022 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ==========================================================================
# -*- coding: utf-8 -*-
# BSD 3-Clause License
#
# Copyright (c) 2017
# All rights reserved.
# Copyright 2022 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ==========================================================================
import os.path
import random
import numpy as np
import torch.utils.data as data
import utils.utils_image as util
class DatasetPlainPatch(data.Dataset):
'''
# -----------------------------------------
# Get L/H for image-to-image mapping.
# Both "paths_L" and "paths_H" are needed.
# -----------------------------------------
# e.g., train denoiser with L and H patches
# create a large patch dataset first
# -----------------------------------------
'''
def __init__(self, opt):
super(DatasetPlainPatch, self).__init__()
print('Get L/H for image-to-image mapping. Both "paths_L" and "paths_H" are needed.')
self.opt = opt
self.n_channels = opt['n_channels'] if opt['n_channels'] else 3
self.patch_size = self.opt['H_size'] if self.opt['H_size'] else 64
self.num_patches_per_image = opt['num_patches_per_image'] if opt['num_patches_per_image'] else 40
self.num_sampled = opt['num_sampled'] if opt['num_sampled'] else 3000
# -------------------
# get the path of L/H
# -------------------
self.paths_H = util.get_image_paths(opt['dataroot_H'])
self.paths_L = util.get_image_paths(opt['dataroot_L'])
assert self.paths_H, 'Error: H path is empty.'
assert self.paths_L, 'Error: L path is empty. This dataset uses L path, you can use dataset_dnpatchh'
if self.paths_L and self.paths_H:
assert len(self.paths_L) == len(self.paths_H), 'H and L datasets have different number of images - {}, {}.'.format(len(self.paths_L), len(self.paths_H))
# ------------------------------------
# number of sampled images
# ------------------------------------
self.num_sampled = min(self.num_sampled, len(self.paths_H))
# ------------------------------------
# reserve space with zeros
# ------------------------------------
self.total_patches = self.num_sampled * self.num_patches_per_image
self.H_data = np.zeros([self.total_patches, self.path_size, self.path_size, self.n_channels], dtype=np.uint8)
self.L_data = np.zeros([self.total_patches, self.path_size, self.path_size, self.n_channels], dtype=np.uint8)
# ------------------------------------
# update H patches
# ------------------------------------
self.update_data()
def update_data(self):
"""
# ------------------------------------
# update whole L/H patches
# ------------------------------------
"""
self.index_sampled = random.sample(range(0, len(self.paths_H), 1), self.num_sampled)
n_count = 0
for i in range(len(self.index_sampled)):
L_patches, H_patches = self.get_patches(self.index_sampled[i])
for (L_patch, H_patch) in zip(L_patches, H_patches):
self.L_data[n_count,:,:,:] = L_patch
self.H_data[n_count,:,:,:] = H_patch
n_count += 1
print('Training data updated! Total number of patches is: %5.2f X %5.2f = %5.2f\n' % (len(self.H_data)//128, 128, len(self.H_data)))
def get_patches(self, index):
"""
# ------------------------------------
# get L/H patches from L/H images
# ------------------------------------
"""
L_path = self.paths_L[index]
H_path = self.paths_H[index]
img_L = util.imread_uint(L_path, self.n_channels) # uint format
img_H = util.imread_uint(H_path, self.n_channels) # uint format
H, W = img_H.shape[:2]
L_patches, H_patches = [], []
num = self.num_patches_per_image
for _ in range(num):
rnd_h = random.randint(0, max(0, H - self.path_size))
rnd_w = random.randint(0, max(0, W - self.path_size))
L_patch = img_L[rnd_h:rnd_h + self.path_size, rnd_w:rnd_w + self.path_size, :]
H_patch = img_H[rnd_h:rnd_h + self.path_size, rnd_w:rnd_w + self.path_size, :]
L_patches.append(L_patch)
H_patches.append(H_patch)
return L_patches, H_patches
def __getitem__(self, index):
if self.opt['phase'] == 'train':
patch_L, patch_H = self.L_data[index], self.H_data[index]
# --------------------------------
# augmentation - flip and/or rotate
# --------------------------------
mode = random.randint(0, 7)
patch_L = util.augment_img(patch_L, mode=mode)
patch_H = util.augment_img(patch_H, mode=mode)
patch_L, patch_H = util.uint2tensor3(patch_L), util.uint2tensor3(patch_H)
else:
L_path, H_path = self.paths_L[index], self.paths_H[index]
patch_L = util.imread_uint(L_path, self.n_channels)
patch_H = util.imread_uint(H_path, self.n_channels)
patch_L, patch_H = util.uint2tensor3(patch_L), util.uint2tensor3(patch_H)
return {'L': patch_L, 'H': patch_H}
def __len__(self):
return self.total_patches