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datasets.py
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import os
import os.path
import random
import numpy as np
from PIL import Image
import scipy.io as sio
import torch
import torch.utils.data as data
from torchvision import transforms
from torchvision.transforms import ToTensor
to_tensor = ToTensor()
def make_dataset(root):
return [(os.path.join(root, 'hazy', img_name),
os.path.join(root, 'trans', img_name),
os.path.join(root, 'gt', img_name))
for img_name in os.listdir(os.path.join(root, 'hazy'))]
def make_dataset_its(root):
items = []
for img_name in os.listdir(os.path.join(root, 'hazy')):
idx0, idx1, ato = os.path.splitext(img_name)[0].split('_')
gt = os.path.join(root, 'clear', idx0 + '.png')
trans = os.path.join(root, 'trans', idx0 + '_' + idx1 + '.png')
haze = os.path.join(root, 'hazy', img_name)
items.append([haze, trans, float(ato), gt])
return items
def make_dataset_ots(root):
items = []
for img_name in os.listdir(os.path.join(root, 'haze')):
idx, _, _ = os.path.splitext(img_name)[0].split('_')
gt = os.path.join(root, 'clear', idx + '.jpg')
haze = os.path.join(root, 'haze', img_name)
items.append([haze, gt])
return items
def make_dataset_ohaze(root: str, mode: str):
img_list = []
for img_name in os.listdir(os.path.join(root, mode, 'hazy')):
gt_name = img_name.replace('hazy', 'GT')
assert os.path.exists(os.path.join(root, mode, 'gt', gt_name))
img_list.append([os.path.join(root, mode, 'hazy', img_name),
os.path.join(root, mode, 'gt', gt_name)])
return img_list
def make_dataset_oihaze_train(root, suffix):
items = []
for img_name in os.listdir(os.path.join(root, 'haze' + suffix)):
gt = os.path.join(root, 'gt' + suffix, img_name)
haze = os.path.join(root, 'haze' + suffix, img_name)
items.append((haze, gt))
return items
def make_dataset_oihaze_train_triple(root, suffix):
items = []
for img_name in os.listdir(os.path.join(root, 'haze' + suffix)):
haze = os.path.join(root, 'haze' + suffix, img_name)
gt = os.path.join(root, 'gt' + suffix, img_name)
predict = os.path.join(root, 'predict' + suffix, img_name)
items.append((haze, gt, predict))
return items
def make_dataset_oihaze_test(root):
items = []
for img_name in os.listdir(os.path.join(root, 'haze')):
img_f_name, img_l_name = os.path.splitext(img_name)
gt_name = '%sGT%s' % (img_f_name[: -4], img_l_name)
gt = os.path.join(root, 'gt', gt_name)
haze = os.path.join(root, 'haze', img_name)
items.append((haze, gt))
return items
def random_crop(size, haze, gt, extra=None):
w, h = haze.size
assert haze.size == gt.size
if w < size or h < size:
haze = transforms.Resize(size)(haze)
gt = transforms.Resize(size)(gt)
w, h = haze.size
x1 = random.randint(0, w - size)
y1 = random.randint(0, h - size)
_haze = haze.crop((x1, y1, x1 + size, y1 + size))
_gt = gt.crop((x1, y1, x1 + size, y1 + size))
if extra is None:
return _haze, _gt
else:
# extra: trans or predict
assert haze.size == extra.size
_extra = extra.crop((x1, y1, x1 + size, y1 + size))
return _haze, _gt, _extra
class ImageFolder(data.Dataset):
def __init__(self, root, flip=False, crop=None):
self.root = root
self.imgs = make_dataset(root)
self.gt_ato_dict = sio.loadmat(os.path.join(root, 'ato.mat'))
self.flip = flip
self.crop = crop
def __getitem__(self, index):
haze_path, trans_path, gt_path = self.imgs[index]
name = os.path.splitext(os.path.split(haze_path)[1])[0]
haze = Image.open(haze_path).convert('RGB')
trans = Image.open(trans_path).convert('L')
gt = Image.open(gt_path).convert('RGB')
assert haze.size == trans.size
assert trans.size == gt.size
if self.crop:
haze, trans, gt = random_crop(self.crop, haze, trans, gt)
if self.flip and random.random() < 0.5:
haze = haze.transpose(Image.FLIP_LEFT_RIGHT)
trans = trans.transpose(Image.FLIP_LEFT_RIGHT)
gt = gt.transpose(Image.FLIP_LEFT_RIGHT)
haze = to_tensor(haze)
trans = to_tensor(trans)
gt = to_tensor(gt)
gt_ato = torch.Tensor([self.gt_ato_dict[name][0, 0]]).float()
return haze, trans, gt_ato, gt, name
def __len__(self):
return len(self.imgs)
class ItsDataset(data.Dataset):
"""
For RESIDE Indoor
"""
def __init__(self, root, flip=False, crop=None):
self.root = root
self.imgs = make_dataset_its(root)
self.flip = flip
self.crop = crop
def __getitem__(self, index):
haze_path, trans_path, ato, gt_path = self.imgs[index]
name = os.path.splitext(os.path.split(haze_path)[1])[0]
haze = Image.open(haze_path).convert('RGB')
trans = Image.open(trans_path).convert('L')
gt = Image.open(gt_path).convert('RGB')
assert haze.size == trans.size
assert trans.size == gt.size
if self.crop:
haze, gt, trans = random_crop(self.crop, haze, gt, trans)
if self.flip and random.random() < 0.5:
haze = haze.transpose(Image.FLIP_LEFT_RIGHT)
trans = trans.transpose(Image.FLIP_LEFT_RIGHT)
gt = gt.transpose(Image.FLIP_LEFT_RIGHT)
haze = to_tensor(haze)
trans = to_tensor(trans)
gt = to_tensor(gt)
gt_ato = torch.Tensor([ato]).float()
return haze, trans, gt_ato, gt, name
def __len__(self):
return len(self.imgs)
class OtsDataset(data.Dataset):
"""
For RESIDE Outdoor
"""
def __init__(self, root, flip=False, crop=None):
self.root = root
self.imgs = make_dataset_ots(root)
self.flip = flip
self.crop = crop
def __getitem__(self, index):
haze_path, gt_path = self.imgs[index]
name = os.path.splitext(os.path.split(haze_path)[1])[0]
haze = Image.open(haze_path).convert('RGB')
gt = Image.open(gt_path).convert('RGB')
assert haze.size == gt.size
if self.crop:
haze, gt = random_crop(self.crop, haze, gt)
if self.flip and random.random() < 0.5:
haze = haze.transpose(Image.FLIP_LEFT_RIGHT)
gt = gt.transpose(Image.FLIP_LEFT_RIGHT)
haze = to_tensor(haze)
gt = to_tensor(gt)
return haze, gt, name
def __len__(self):
return len(self.imgs)
class SotsDataset(data.Dataset):
def __init__(self, root, mode=None):
self.root = root
self.imgs = make_dataset(root)
self.mode = mode
def __getitem__(self, index):
haze_path, trans_path, gt_path = self.imgs[index]
name = os.path.splitext(os.path.split(haze_path)[1])[0]
haze = Image.open(haze_path).convert('RGB')
haze = to_tensor(haze)
idx0 = name.split('_')[0]
gt = Image.open(os.path.join(self.root, 'gt', idx0 + '.png')).convert('RGB')
gt = to_tensor(gt)
if gt.shape != haze.shape:
# crop the indoor images
gt = gt[:, 10: 470, 10: 630]
return haze, gt, name
def __len__(self):
return len(self.imgs)
class OHazeDataset(data.Dataset):
def __init__(self, root, mode):
self.root = root
self.mode = mode
self.imgs = make_dataset_ohaze(root, mode)
def __getitem__(self, index):
haze_path, gt_path = self.imgs[index]
name = os.path.splitext(os.path.split(haze_path)[1])[0]
img = Image.open(haze_path).convert('RGB')
gt = Image.open(gt_path).convert('RGB')
if 'train' in self.mode:
# img, gt = random_crop(416, img, gt)
if random.random() < 0.5:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
gt = gt.transpose(Image.FLIP_LEFT_RIGHT)
rotate_degree = np.random.choice([-90, 0, 90, 180])
img, gt = img.rotate(rotate_degree, Image.BILINEAR), gt.rotate(rotate_degree, Image.BILINEAR)
return to_tensor(img), to_tensor(gt), name
def __len__(self):
return len(self.imgs)
class OIHaze(data.Dataset):
def __init__(self, root, mode, suffix=None, flip=False, crop=None):
assert mode in ['train', 'test']
self.root = root
self.mode = mode
if mode == 'train':
self.img_name_list = make_dataset_oihaze_train(root, suffix)
else:
self.img_name_list = make_dataset_oihaze_test(root)
self.flip = flip
self.crop = crop
def __getitem__(self, index):
haze_path, gt_path = self.img_name_list[index]
name = os.path.splitext(os.path.split(haze_path)[1])[0]
haze = Image.open(haze_path).convert('RGB')
gt = Image.open(gt_path).convert('RGB')
if self.crop:
haze, gt = random_crop(self.crop, haze, gt)
if self.flip and random.random() < 0.5:
haze = haze.transpose(Image.FLIP_LEFT_RIGHT)
gt = gt.transpose(Image.FLIP_LEFT_RIGHT)
haze = to_tensor(haze)
gt = to_tensor(gt)
return haze, gt, name
def __len__(self):
return len(self.img_name_list)
class OIHaze5(data.Dataset):
def __init__(self, root, flip=False, rotate=None, resize=1024):
self.root = root
self.img_name_list = make_dataset_oihaze_test(root)
self.flip = flip
self.rotate = rotate
self.resize = transforms.Resize(resize)
def __getitem__(self, index):
haze_path, gt_path = self.img_name_list[index]
name = os.path.splitext(os.path.split(haze_path)[1])[0]
haze = Image.open(haze_path).convert('RGB')
gt = Image.open(gt_path).convert('RGB')
if self.flip and random.random() < 0.5:
haze = haze.transpose(Image.FLIP_LEFT_RIGHT)
gt = gt.transpose(Image.FLIP_LEFT_RIGHT)
if self.rotate:
rotate_degree = random.random() * 2 * self.rotate - self.rotate
haze = haze.rotate(rotate_degree, Image.BILINEAR)
gt = gt.rotate(rotate_degree, Image.BILINEAR)
haze_resize, gt_resize = self.resize(haze), self.resize(gt)
haze, gt = to_tensor(haze), to_tensor(gt)
haze_resize, gt_resize = to_tensor(haze_resize), to_tensor(gt_resize)
return haze, gt, haze_resize, gt_resize, name
def __len__(self):
return len(self.img_name_list)
class OIHaze_T(data.Dataset):
def __init__(self, root, mode, suffix=None, crop=None, flip=False, resize=1024):
self.root = root
assert mode in ['train', 'test']
if mode == 'train':
self.img_name_list = make_dataset_oihaze_train_triple(root, suffix)
else:
self.img_name_list = make_dataset_oihaze_test(root)
self.mode = mode
self.crop = crop
self.flip = flip
self.resize = transforms.Resize(resize)
def __getitem__(self, index):
if self.mode == 'train':
haze_path, gt_path, predict_path = self.img_name_list[index]
name = os.path.splitext(os.path.split(haze_path)[1])[0]
haze = Image.open(haze_path).convert('RGB')
gt = Image.open(gt_path).convert('RGB')
predict = Image.open(predict_path).convert('RGB')
if self.crop:
haze, gt, predict = random_crop(self.crop, haze, gt, predict)
if self.flip and random.random() < 0.5:
haze = haze.transpose(Image.FLIP_LEFT_RIGHT)
gt = gt.transpose(Image.FLIP_LEFT_RIGHT)
predict = predict.transpose(Image.FLIP_LEFT_RIGHT)
haze, gt, predict = to_tensor(haze), to_tensor(gt), to_tensor(predict)
return haze, gt, predict, name
else:
haze_path, gt_path = self.img_name_list[index]
name = os.path.splitext(os.path.split(haze_path)[1])[0]
haze = Image.open(haze_path).convert('RGB')
gt = Image.open(gt_path).convert('RGB')
haze_resize = self.resize(haze)
haze, gt, haze_resize = to_tensor(haze), to_tensor(gt), to_tensor(haze_resize)
return haze, gt, haze_resize, name
def __len__(self):
return len(self.img_name_list)
class OIHaze2(data.Dataset):
def __init__(self, root, mode, suffix=None, flip=False, crop=None, scale=None, rotate=None):
assert mode in ['train', 'test']
self.root = root
self.mode = mode
if mode == 'train':
self.img_name_list = make_dataset_oihaze_train(root, suffix)
else:
self.img_name_list = make_dataset_oihaze_test(root)
self.scale = transforms.Resize(scale)
self.flip = flip
self.crop = crop
self.rotate = rotate
def __getitem__(self, index):
haze_path, gt_path = self.img_name_list[index]
name = os.path.splitext(os.path.split(haze_path)[1])[0]
haze = Image.open(haze_path).convert('RGB')
gt = Image.open(gt_path).convert('RGB')
if self.mode == 'test':
haze_lr = self.scale(haze)
haze_lr = to_tensor(haze_lr)
else:
if self.rotate:
rotate_degree = random.random() * 2 * self.rotate - self.rotate
haze = haze.rotate(rotate_degree, Image.BILINEAR)
gt = gt.rotate(rotate_degree, Image.BILINEAR)
if self.crop:
haze, gt = random_crop(self.crop, haze, gt)
if self.flip and random.random() < 0.5:
haze = haze.transpose(Image.FLIP_LEFT_RIGHT)
gt = gt.transpose(Image.FLIP_LEFT_RIGHT)
haze = to_tensor(haze)
gt = to_tensor(gt)
if self.mode == 'test':
return haze, gt, haze_lr, name
else:
return haze, gt, name
def __len__(self):
return len(self.img_name_list)
class OIHaze2_2(data.Dataset):
def __init__(self, root, mode, flip=False, crop=None):
assert mode in ['train', 'test']
self.root = root
self.mode = mode
if mode == 'train':
self.img_name_list = make_dataset_oihaze_train(root)
else:
self.img_name_list = make_dataset_oihaze_test(root)
self.flip = flip
self.crop = crop
def __getitem__(self, index):
haze_path, gt_path = self.img_name_list[index]
name = os.path.splitext(os.path.split(haze_path)[1])[0]
haze = Image.open(haze_path).convert('RGB')
gt = Image.open(gt_path).convert('RGB')
if self.mode == 'test':
haze_lr = haze.resize((1024, 1024), resample=Image.BILINEAR)
haze_lr = to_tensor(haze_lr)
if self.crop:
haze, gt = random_crop(self.crop, haze, gt)
haze = to_tensor(haze)
gt = to_tensor(gt)
if self.mode == 'test':
return haze, gt, haze_lr, name
else:
return haze, gt, name
def __len__(self):
return len(self.img_name_list)
class OIHaze4(data.Dataset):
def __init__(self, root, mode, crop=None):
assert mode in ['train', 'test']
self.root = root
self.mode = mode
if mode == 'train':
self.img_name_list = make_dataset_oihaze_train(root)
else:
self.img_name_list = make_dataset_oihaze_test(root)
self.crop = crop
def __getitem__(self, index):
haze_path, gt_path = self.img_name_list[index]
name = os.path.splitext(os.path.split(haze_path)[1])[0]
haze = Image.open(haze_path).convert('RGB')
gt = Image.open(gt_path).convert('RGB')
if self.mode == 'train':
if self.crop:
haze, gt = random_crop(self.crop, haze, gt)
else:
haze_512 = to_tensor(transforms.Resize(512)(haze))
haze_1024 = to_tensor(transforms.Resize(1024)(haze))
haze_2048 = to_tensor(transforms.Resize(2048)(haze))
haze = to_tensor(haze)
gt = to_tensor(gt)
if self.mode == 'train':
return haze, gt, name
else:
return haze, gt, haze_512, haze_1024, haze_2048, name
def __len__(self):
return len(self.img_name_list)
class OIHaze3(data.Dataset):
def __init__(self, root):
self.root = root
self.img_name_list = make_dataset_oihaze_test(root)
def __getitem__(self, index):
haze_path, gt_path = self.img_name_list[index]
name = os.path.splitext(os.path.split(haze_path)[1])[0]
haze = Image.open(haze_path).convert('RGB')
gt = Image.open(gt_path).convert('RGB')
resize = transforms.Resize(512)
haze_lr = resize(haze)
haze_lr = to_tensor(haze_lr)
haze = to_tensor(haze)
gt = to_tensor(gt)
return haze, gt, haze_lr, name
def __len__(self):
return len(self.img_name_list)
class ImageFolder3(data.Dataset):
def __init__(self, root):
self.root = root
self.imgs = [os.path.join(root, img_name) for img_name in os.listdir(root)]
def __getitem__(self, index):
haze_path = self.imgs[index]
name = os.path.splitext(os.path.split(haze_path)[1])[0]
haze = Image.open(haze_path).convert('RGB')
haze = to_tensor(haze)
return haze, name
def __len__(self):
return len(self.imgs)