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dataset.py
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dataset.py
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import torch.utils.data as data
import torch
import numpy as np
import os
from os import listdir
from os.path import join
from PIL import Image
import random
from random import randrange
from scipy.io import loadmat
import lmdb
import h5py
from torchvision.transforms import Compose, ToTensor
def input_transform():
return Compose([
#CenterCrop(crop_size),
#Resize(crop_size // upscale_factor),
ToTensor(), #(C,H, W)Tensor��ʽ��normalization ( /255) to [0,1.0]
])
def target_transform():
return Compose([
#CenterCrop(crop_size),
ToTensor(),
])
def is_image_file(filename):
return any(filename.endswith(extension) for extension in [".png", ".jpg", ".jpeg"])
def is_mat_file(filename):
return any(filename.endswith(extension) for extension in [".mat"])
def is_h5_file(filename):
return any(filename.endswith(extension) for extension in [".h5"])
def is_lmdb_file(filename):
return any(filename.endswith(extension) for extension in [".mdb"])
def load_img(filepath):
img = Image.open(filepath).convert('RGB')
#y, _, _ = img.split()
return img
def load_HRmatdata(filepath):
data = loadmat(filepath)['HR']
return data
def load_LRmatdata(filepath):
data = loadmat(filepath)['LR']
return data
def toTensor(input):
data = torch.from_numpy(input.copy()).float()
return data
def turn_to_4D(data):
data_new = data.reshape([1, data.shape[0], data.shape[1], data.shape[2]])
return data_new
def get_patch(img_ins,target, patch_size, ix=-1, iy=-1):
(ih, iw) = img_ins[0].shape
ip = patch_size
if ix == -1:
ix = random.randrange(0, iw - patch_size + 1)
if iy == -1:
iy = random.randrange(0, ih - patch_size + 1)
img_ins_out = []
for i in range(len(img_ins)):
img_in = img_ins[i]
img_in = img_in[ iy:iy + ip, ix:ix + ip]
img_in = np.expand_dims(img_in, axis=2)
img_ins_out.append(img_in)
target = target[ iy:iy + ip, ix:ix + ip]
target = np.expand_dims(target, axis=2)
return img_ins_out, target
def get_patch_test(img_ins,target):
img_ins_out = []
for i in range(len(img_ins)):
img_in = img_ins[i]
img_in = img_in[:,:]
img_in = np.expand_dims(img_in, axis=2)
img_ins_out.append(img_in)
target = target[:,:]
target = np.expand_dims(target, axis=2)
return img_ins_out, target
def get_patch_test_LF(img_ins, targets):
img_ins_out = []
targets_out = []
for i in range(len(img_ins)):
img_in = img_ins[i]
img_in = img_in[:,:]
img_in = np.expand_dims(img_in, axis=2)
img_ins_out.append(img_in)
for i in range(len(targets)):
target = targets[i]
target = target[:,:]
target = np.expand_dims(target, axis=2)
targets_out.append(target)
return img_ins_out, targets_out
def get_patch_lr_test(img_in0,img_in1,img_in2,img_in3):
img_in0 = img_in0[:, :]
img_in0 = np.expand_dims(img_in0, axis=2)
img_in1 = img_in1[:, :]
img_in1 = np.expand_dims(img_in1, axis=2)
img_in2 = img_in2[:, :]
img_in2 = np.expand_dims(img_in2, axis=2)
img_in3 = img_in3[:, :]
img_in3 = np.expand_dims(img_in3, axis=2)
return img_in0, img_in1, img_in2, img_in3
def augment(img_in, img_tar, flip_h=True, rot=True):
info_aug = {'flip_h': False, 'flip_v': False, 'trans': False}
if random.random() < 0.5 and flip_h:
img_in = torch.from_numpy(np.flip(img_in,axis=2).copy())
img_tar = torch.from_numpy(np.flip(img_tar,axis=2).copy())
info_aug['flip_h'] = True
if rot:
if random.random() < 0.5:
img_in = torch.from_numpy(np.flip(img_in,axis=1).copy())
img_tar = torch.from_numpy(np.flip(img_in,axis=1).copy())
info_aug['flip_v'] = True
if random.random() < 0.5:
img_in = torch.from_numpy(np.ascontiguousarray(np.transpose(img_in,(0,2,1))[np.newaxis,:]))
img_tar = torch.from_numpy(np.ascontiguousarray(np.transpose(img_tar,(0,2,1))[np.newaxis,:]))
info_aug['trans'] = True
return img_in, img_tar, info_aug
class DatasetFromFolder(data.Dataset):
def __init__(self, HR_dir, LR_dir, patch_size, upscale_factor, data_augmentation, Thrconv = False, input_transform=None,target_transform=None):
super(DatasetFromFolder, self).__init__()
self.image_filenames = [join(HR_dir, x) for x in listdir(HR_dir) if is_mat_file(x)]
self.lr_dir = LR_dir
self.patch_size = patch_size
self.upscale_factor = upscale_factor
#self.dataset = dataset
self.data_augmentation = data_augmentation
self.Thrconv = Thrconv
self.input_transform = input_transform
self.target_transform = target_transform
def __getitem__(self, index):
target = load_HRmatdata(self.image_filenames[index])
_, file = os.path.split(self.image_filenames[index])
target_name = os.path.splitext(file)[0]
input_name=target_name.replace('HR','LR')
input = load_LRmatdata(os.path.join(self.lr_dir,input_name+'.mat'))
input, target = get_patch(input, target, self.patch_size, self.upscale_factor)
if self.data_augmentation:
input, target, _ = augment(input, target)
if self.Thrconv:
input, target= turn_to_4D(input,target)
if self.input_transform:
input = self.input_transform(input)
if self.target_transform:
target = self.target_transform(target)
return input, target
def __len__(self):
return len(self.image_filenames)
class DatasetFromLMDB(data.Dataset):
def __init__(self, file_path, patch_size, data_augmentation=False,input_transform=None,target_transform=None):
super(DatasetFromLMDB, self).__init__()
#self.opt = opt
self.filenames = file_path
self.patch_size = patch_size
self.data_augmentation = data_augmentation
self.input_transform = input_transform
self.target_transform = target_transform
self.env = lmdb.open(self.filenames)
self.txn = self.env.begin(write=False)
def __getitem__(self, index):
# print(index)
data0_bin = self.txn.get((str(index)+'LR0').encode())
# print(type(data0_bin))
data0_buf = np.frombuffer(data0_bin, dtype="uint8")
data0 = data0_buf.reshape(self.patch_size, self.patch_size)
data1_bin = self.txn.get((str(index)+'LR1').encode())
data1_buf = np.frombuffer(data1_bin, dtype="uint8")
data1 = data1_buf.reshape(self.patch_size, self.patch_size)
data2_bin = self.txn.get((str(index)+'LR2').encode())
data2_buf = np.frombuffer(data2_bin, dtype="uint8")
data2 = data2_buf.reshape(self.patch_size, self.patch_size)
data3_bin = self.txn.get((str(index)+'LR3').encode())
data3_buf = np.frombuffer(data3_bin, dtype="uint8")
data3 = data3_buf.reshape(self.patch_size, self.patch_size)
data4_bin = self.txn.get((str(index)+'LR4').encode())
data4_buf = np.frombuffer(data4_bin, dtype="uint8")
data4 = data4_buf.reshape(self.patch_size, self.patch_size)
data5_bin = self.txn.get((str(index)+'LR5').encode())
data5_buf = np.frombuffer(data5_bin, dtype="uint8")
data5 = data5_buf.reshape(self.patch_size, self.patch_size)
data6_bin = self.txn.get((str(index)+'LR6').encode())
data6_buf = np.frombuffer(data6_bin, dtype="uint8")
data6 = data6_buf.reshape(self.patch_size, self.patch_size)
data7_bin = self.txn.get((str(index)+'LR7').encode())
data7_buf = np.frombuffer(data7_bin, dtype="uint8")
data7 = data7_buf.reshape(self.patch_size, self.patch_size)
data8_bin = self.txn.get((str(index)+'LR8').encode())
data8_buf = np.frombuffer(data8_bin, dtype="uint8")
data8 = data8_buf.reshape(self.patch_size, self.patch_size)
ref_ind = 4
if(ref_ind==0):
target_bin = self.txn.get((str(index)+'HR0').encode())
target_buf = np.frombuffer(target_bin, dtype="uint8")
target = target_buf.reshape(self.patch_size, self.patch_size)
if(ref_ind==1):
target_bin = self.txn.get((str(index)+'HR1').encode())
target_buf = np.frombuffer(target_bin, dtype="uint8")
target = target_buf.reshape(self.patch_size, self.patch_size)
if(ref_ind==2):
target_bin = self.txn.get((str(index)+'HR2').encode())
target_buf = np.frombuffer(target_bin, dtype="uint8")
target = target_buf.reshape(self.patch_size, self.patch_size)
if(ref_ind==3):
target_bin = self.txn.get((str(index)+'HR3').encode())
target_buf = np.frombuffer(target_bin, dtype="uint8")
target = target_buf.reshape(self.patch_size, self.patch_size)
if(ref_ind==4):
target_bin = self.txn.get((str(index)+'HR4').encode())
target_buf = np.frombuffer(target_bin, dtype="uint8")
target = target_buf.reshape(self.patch_size, self.patch_size)
if(ref_ind==5):
target_bin = self.txn.get((str(index)+'HR5').encode())
target_buf = np.frombuffer(target_bin, dtype="uint8")
target = target_buf.reshape(self.patch_size, self.patch_size)
if(ref_ind==6):
target_bin = self.txn.get((str(index)+'HR6').encode())
target_buf = np.frombuffer(target_bin, dtype="uint8")
target = target_buf.reshape(self.patch_size, self.patch_size)
if(ref_ind==7):
target_bin = self.txn.get((str(index)+'HR7').encode())
target_buf = np.frombuffer(target_bin, dtype="uint8")
target = target_buf.reshape(self.patch_size, self.patch_size)
if(ref_ind==8):
target_bin = self.txn.get((str(index)+'HR8').encode())
target_buf = np.frombuffer(target_bin, dtype="uint8")
target = target_buf.reshape(self.patch_size, self.patch_size)
inputs = [data0,data1,data2,data3,data4,data5,data6,data7,data8] #list
inputs,target = get_patch_test(inputs,target) #list[numpy]
# print(input0.shape)
if self.data_augmentation:
input, target, _ = augment(input, target)
inputs_tensor = []
if self.input_transform:
for i in range(len(inputs)):
input = self.input_transform(inputs[i])
inputs_tensor.append(input) ##list[tensor]
inputs_last = torch.cat(inputs_tensor,0).unsqueeze(1) #[an2,c,h,w]
# print(input0.shape)
if self.target_transform:
target = self.target_transform(target)
return inputs_last, target
def __len__(self):
num = self.txn.stat()['entries'] // 18
# num = 146944
return num
class DatasetFromLMDB_LF(data.Dataset):
def __init__(self, file_path, patch_size, data_augmentation=False,input_transform=None,target_transform=None):
super(DatasetFromLMDB_LF, self).__init__()
#self.opt = opt
self.filenames = file_path
self.patch_size = patch_size
self.data_augmentation = data_augmentation
self.input_transform = input_transform
self.target_transform = target_transform
self.env = lmdb.open(self.filenames)
self.txn = self.env.begin(write=False)
def __getitem__(self, index):
data0_bin = self.txn.get((str(index)+'LR0').encode())
data0_buf = np.frombuffer(data0_bin, dtype="uint8")
data0 = data0_buf.reshape(self.patch_size, self.patch_size)
data1_bin = self.txn.get((str(index)+'LR1').encode())
data1_buf = np.frombuffer(data1_bin, dtype="uint8")
data1 = data1_buf.reshape(self.patch_size, self.patch_size)
data2_bin = self.txn.get((str(index)+'LR2').encode())
data2_buf = np.frombuffer(data2_bin, dtype="uint8")
data2 = data2_buf.reshape(self.patch_size, self.patch_size)
data3_bin = self.txn.get((str(index)+'LR3').encode())
data3_buf = np.frombuffer(data3_bin, dtype="uint8")
data3 = data3_buf.reshape(self.patch_size, self.patch_size)
data4_bin = self.txn.get((str(index)+'LR4').encode())
data4_buf = np.frombuffer(data4_bin, dtype="uint8")
data4 = data4_buf.reshape(self.patch_size, self.patch_size)
data5_bin = self.txn.get((str(index)+'LR5').encode())
data5_buf = np.frombuffer(data5_bin, dtype="uint8")
data5 = data5_buf.reshape(self.patch_size, self.patch_size)
data6_bin = self.txn.get((str(index)+'LR6').encode())
data6_buf = np.frombuffer(data6_bin, dtype="uint8")
data6 = data6_buf.reshape(self.patch_size, self.patch_size)
data7_bin = self.txn.get((str(index)+'LR7').encode())
data7_buf = np.frombuffer(data7_bin, dtype="uint8")
data7 = data7_buf.reshape(self.patch_size, self.patch_size)
data8_bin = self.txn.get((str(index)+'LR8').encode())
data8_buf = np.frombuffer(data8_bin, dtype="uint8")
data8 = data8_buf.reshape(self.patch_size, self.patch_size)
target0_bin = self.txn.get((str(index)+'HR0').encode())
target0_buf = np.frombuffer(target0_bin, dtype="uint8")
target0 = target0_buf.reshape(self.patch_size, self.patch_size)
target1_bin = self.txn.get((str(index)+'HR1').encode())
target1_buf = np.frombuffer(target1_bin, dtype="uint8")
target1 = target1_buf.reshape(self.patch_size, self.patch_size)
target2_bin = self.txn.get((str(index)+'HR2').encode())
target2_buf = np.frombuffer(target2_bin, dtype="uint8")
target2 = target2_buf.reshape(self.patch_size, self.patch_size)
target3_bin = self.txn.get((str(index)+'HR3').encode())
target3_buf = np.frombuffer(target3_bin, dtype="uint8")
target3 = target3_buf.reshape(self.patch_size, self.patch_size)
target4_bin = self.txn.get((str(index)+'HR4').encode())
target4_buf = np.frombuffer(target4_bin, dtype="uint8")
target4 = target4_buf.reshape(self.patch_size, self.patch_size)
target5_bin = self.txn.get((str(index)+'HR5').encode())
target5_buf = np.frombuffer(target5_bin, dtype="uint8")
target5 = target5_buf.reshape(self.patch_size, self.patch_size)
target6_bin = self.txn.get((str(index)+'HR6').encode())
target6_buf = np.frombuffer(target6_bin, dtype="uint8")
target6 = target6_buf.reshape(self.patch_size, self.patch_size)
target7_bin = self.txn.get((str(index)+'HR7').encode())
target7_buf = np.frombuffer(target7_bin, dtype="uint8")
target7 = target7_buf.reshape(self.patch_size, self.patch_size)
target8_bin = self.txn.get((str(index)+'HR8').encode())
target8_buf = np.frombuffer(target8_bin, dtype="uint8")
target8 = target8_buf.reshape(self.patch_size, self.patch_size)
inputs = [data0,data1,data2,data3,data4,data5,data6,data7,data8] #list
targets = [target0,target1,target2,target3,target4,target5,target6,target7,target8] #list
inputs,targets = get_patch_test_LF(inputs,targets) #list[numpy]
# print(input0.shape)
if self.data_augmentation:
input, target, _ = augment(inputs, target)
inputs_tensor = []
if self.input_transform:
for i in range(len(inputs)):
input = self.input_transform(inputs[i])
inputs_tensor.append(input) ##list[tensor]
inputs_last = torch.cat(inputs_tensor,0).unsqueeze(1) #[an2,c,h,w]
targets_tensor = []
if self.target_transform:
for i in range(len(targets)):
target = self.target_transform(targets[i])
targets_tensor.append(target)
targets_last = torch.cat(targets_tensor,0).unsqueeze(1)
return inputs_last, targets_last
def __len__(self):
num = self.txn.stat()['entries'] //8
# num = 146944
return num
class DatasetFromHdf5(data.Dataset):
def __init__(self, file_path, patch_size, data_augmentation=False,input_transform=None,target_transform=None):
super(DatasetFromHdf5, self).__init__()
self.filenames = [join(file_path, x) for x in listdir(file_path) if is_h5_file(x)]
self.patch_size = patch_size
self.data_augmentation = data_augmentation
self.input_transform = input_transform
self.target_transform = target_transform
def __getitem__(self, index):
hf = h5py.File(self.filenames[index])
data0 = hf.get('data0')
data1 = hf.get('data1')
data2 = hf.get('data2')
data3 = hf.get('data3')
data4 = hf.get('data4')
data5 = hf.get('data5')
data6 = hf.get('data6')
data7 = hf.get('data7')
data8 = hf.get('data8')
target = hf.get('label')
inputs = [data0,data1,data2,data3,data4,data5,data6,data7,data8] #list
inputs,target = get_patch(inputs,target,self.patch_size) #list[numpy]
# print(input0.shape)
if self.data_augmentation:
input, target, _ = augment(input, target)
inputs_tensor = []
if self.input_transform:
for i in range(len(inputs)):
input = self.input_transform(inputs[i])
inputs_tensor.append(input) ##list[tensor]
inputs_last = torch.cat(inputs_tensor,0).unsqueeze(1)
if self.target_transform:
target = self.target_transform(target)
return inputs_last, target
def __len__(self):
return len(self.filenames)
class DatasetTestFromHdf5(data.Dataset):
def __init__(self, file_path, input_transform=None, target_transform=None):
super(DatasetTestFromHdf5, self).__init__()
self.filenames = sorted([join(file_path, x) for x in listdir(file_path) if is_h5_file(x)])
self.input_transform = input_transform
self.target_transform = target_transform
def __getitem__(self, index):
hf = h5py.File(self.filenames[index])
data0 = hf.get('data0')
data1 = hf.get('data1')
data2 = hf.get('data2')
data3 = hf.get('data3')
data4 = hf.get('data4')
data5 = hf.get('data5')
data6 = hf.get('data6')
data7 = hf.get('data7')
data8 = hf.get('data8')
target = hf.get('label')
input_dir = self.filenames[index]
_, file = os.path.split(input_dir)
inputs = [data0,data1,data2,data3,data4,data5,data6,data7,data8] #list
inputs,target = get_patch_test(inputs,target) #list[numpy]
inputs_tensor = []
if self.input_transform:
for i in range(len(inputs)):
input = self.input_transform(inputs[i])
inputs_tensor.append(input) ##list[tensor]
inputs_last = torch.cat(inputs_tensor,0).unsqueeze(1) #[an2,c,h,w]
# print(inputs_last.size())
if self.target_transform:
target = self.target_transform(target)
return inputs_last, target, file
def __len__(self):
return len(self.filenames)
class DatasetLRTestFromHdf5(data.Dataset):
def __init__(self, file_path, input_transform=None):
super(DatasetLRTestFromHdf5, self).__init__()
self.filenames = [join(file_path, x) for x in listdir(file_path) if is_h5_file(x)]
self.input_transform = input_transform
self.target_transform = target_transform
def __getitem__(self, index):
hf = h5py.File(self.filenames[index])
data0 = hf.get('data0')
data1 = hf.get('data1')
data2 = hf.get('data2')
data3 = hf.get('data3')
input_dir = self.filenames[index]
_, file = os.path.split(input_dir)
input0,input1,input2,input3= get_patch_lr_test(data0,data1,data2,data3)
if self.input_transform:
input0 = self.input_transform(input0)
input1 = self.input_transform(input1)
input2 = self.input_transform(input2)
input3 = self.input_transform(input3)
return input0, input1, input2, input3, file
def __len__(self):
return len(self.filenames)
class DatasetTest(data.Dataset):
def __init__(self, file_path, upscale_factor, data_augmentation=False,input_transform=None):
super(DatasetTest, self).__init__()
self.filenames = [join(file_path, x) for x in listdir(file_path) if is_mat_file(x)]
self.upscale_factor = upscale_factor
self.data_augmentation = data_augmentation
self.input_transform = input_transform
def __getitem__(self, index):
input = load_LRmatdata(self.filenames[index])
input_dir = self.filenames[index]
_, file = os.path.split(input_dir)
# if self.data_augmentation:
# input, target, _ = augment(input, target)
if self.input_transform:
input = self.input_transform(input)
return input, file
def __len__(self):
return len(self.filenames)