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load_data.py
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load_data.py
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import torch
import torch.utils.data as D
import cv2
import numpy as np
import torchvision.transforms.functional as TF
from torchvision import transforms
import random
import os
from PIL import Image
from config import Configs
class Read_data(D.Dataset):
"""
The data loader class for 1 set (for example train)
Args:
base_dir (str): the data path
file_label (list of str): the names of the data instances
set (str): the set (train, valid or test)
split_size (int): the image (patch) size
augmentation (bool): whwther to apply augmentation
flipped (bool): whether the data is flipped
"""
def __init__(self, base_dir, file_label,set, split_size, augmentation=True , flipped = False):
self.base_dir = base_dir
self.file_label = file_label
self.set = set
self.split_size = split_size
self.augmentation = augmentation
self.flipped = flipped
def __getitem__(self, index):
img_name = self.file_label[index]
idx, deg_img, gt_img = self.readImages(img_name)
return idx, deg_img, gt_img
def __len__(self):
return len(self.file_label)
def readImages(self, file_name):
"""
Read a pair of images (degraded + clean gt)
Args:
file_name (str): the index (name) of the image pair
Returns:
file_name (str): the index (name) of the image pair
out_deg_img (np.array): the degraded image
out_gt_img (np.array): the clean image
"""
url_deg = self.base_dir +'/'+ self.set+'/' + file_name
url_gt = self.base_dir +'/'+ self.set+'_gt/'+file_name
deg_img = cv2.imread(url_deg)
gt_img = cv2.imread(url_gt)
if self.flipped:
deg_img = cv2.rotate(deg_img, cv2.ROTATE_180)
gt_img = cv2.rotate(gt_img, cv2.ROTATE_180)
try:
deg_img.any()
except:
print('###!Cannot find image: ' + url_deg)
try:
gt_img.any()
except:
print('###!Cannot find image: ' + url_gt)
deg_img = Image.fromarray(np.uint8(deg_img))
gt_img = Image.fromarray(np.uint8(gt_img))
# apply data augmentation
if self.augmentation:
# random crop
i, j, h, w = transforms.RandomCrop.get_params(deg_img, output_size=(self.split_size, self.split_size))
deg_img = TF.crop(deg_img, i, j, h, w)
gt_img = TF.crop(gt_img, i, j, h, w)
# random horizontal flipping
if random.random() > 0.5:
deg_img = TF.hflip(deg_img)
gt_img = TF.hflip(gt_img)
# random vertical flipping
if random.random() > 0.5:
deg_img = TF.vflip(deg_img)
gt_img = TF.vflip(gt_img)
deg_img = (np.array(deg_img) /255).astype('float32')
gt_img = (np.array(gt_img) / 255).astype('float32')
# normalize data
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
out_deg_img = np.zeros([3, *deg_img.shape[:-1]])
out_gt_img = np.zeros([3, *gt_img.shape[:-1]])
for i in range(3):
out_deg_img[i] = (deg_img[:,:,i] - mean[i]) / std[i]
out_gt_img[i] = (gt_img[:,:,i] - mean[i]) / std[i]
return file_name, out_deg_img, out_gt_img
def load_datasets(flipped=False):
"""
Create the 3 datasets (train/valid/test) to be used by the dataloaders.
Args:
flipped (bool): whwther to flip the images of the val dataset (was used
in 1 experiment to check the effect of flipping)
Returns:
data_train (Dateset): train data
data_valid (Dateset): valid data
data_test (Dateset): test data
"""
cfg = Configs().parse()
base_dir = cfg.data_path
split_size = cfg.split_size
data_tr = os.listdir(cfg.data_path+'train')
np.random.shuffle(data_tr)
data_va = os.listdir(cfg.data_path+'valid')
np.random.shuffle(data_va)
data_te = os.listdir(cfg.data_path+'test')
np.random.shuffle(data_te)
data_train = Read_data(base_dir, data_tr, 'train', split_size, augmentation=True)
data_valid = Read_data(base_dir, data_va, 'valid', split_size, augmentation=False, flipped = flipped)
data_test = Read_data(base_dir, data_te, 'test', split_size, augmentation=False)
return data_train, data_valid, data_test
def sort_batch(batch):
"""
Transform a batch of data to pytorch tensor
Args:
batch [str, np.array, np.array]: a batch of data
Returns:
data_index (tensor): the indexes of the source/target pair
data_in (tensor): the source images (degraded)
data_out (tensor): the target images (clean gt)
"""
n_batch = len(batch)
data_index = []
data_in = []
data_out = []
for i in range(n_batch):
idx, img, gt_img = batch[i]
data_index.append(idx)
data_in.append(img)
data_out.append(gt_img)
data_index = np.array(data_index)
data_in = np.array(data_in, dtype='float32')
data_out = np.array(data_out, dtype='float32')
data_in = torch.from_numpy(data_in)
data_out = torch.from_numpy(data_out)
return data_index, data_in, data_out
def all_data_loader(batch_size):
"""
Create the 3 data loaders
Args:
batch_size (int): the batch_size
Returns:
train_loader (dataloader): train data loader
valid_loader (dataloader): valid data loader
test_loader (dataloader): test data loader
"""
data_train, data_valid, data_test = load_datasets()
train_loader = torch.utils.data.DataLoader(data_train, collate_fn=sort_batch, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(data_valid, collate_fn=sort_batch, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
test_loader = torch.utils.data.DataLoader(data_test, collate_fn=sort_batch, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
return train_loader, valid_loader, test_loader