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dataset.py
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dataset.py
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import numpy as np
import os
from torch.utils.data import Dataset
import torch
from utils import is_png_file, load_img, Augment_RGB_torch
import torch.nn.functional as F
import random
augment = Augment_RGB_torch()
transforms_aug = [method for method in dir(augment) if callable(getattr(augment, method)) if not method.startswith('_')]
##################################################################################################
class DataLoaderTrain(Dataset):
def __init__(self, rgb_dir, img_options=None, target_transform=None):
super(DataLoaderTrain, self).__init__()
self.target_transform = target_transform
gt_dir = 'target'
input_dir = 'input'
clean_files = sorted(os.listdir(os.path.join(rgb_dir, gt_dir)))
noisy_files = sorted(os.listdir(os.path.join(rgb_dir, input_dir)))
self.clean_filenames = [os.path.join(rgb_dir, gt_dir, x) for x in clean_files if is_png_file(x)]
self.noisy_filenames = [os.path.join(rgb_dir, input_dir, x) for x in noisy_files if is_png_file(x)]
self.img_options=img_options
self.tar_size = len(self.clean_filenames) # get the size of target
def __len__(self):
return self.tar_size
def __getitem__(self, index):
tar_index = index % self.tar_size
clean = torch.from_numpy(np.float32(load_img(self.clean_filenames[tar_index])))
noisy = torch.from_numpy(np.float32(load_img(self.noisy_filenames[tar_index])))
clean = clean.permute(2,0,1)
noisy = noisy.permute(2,0,1)
clean_filename = os.path.split(self.clean_filenames[tar_index])[-1]
noisy_filename = os.path.split(self.noisy_filenames[tar_index])[-1]
#Crop Input and Target
ps = self.img_options['patch_size']
H = clean.shape[1]
W = clean.shape[2]
# r = np.random.randint(0, H - ps) if not H-ps else 0
# c = np.random.randint(0, W - ps) if not H-ps else 0
if H-ps==0:
r=0
c=0
else:
r = np.random.randint(0, H - ps)
c = np.random.randint(0, W - ps)
clean = clean[:, r:r + ps, c:c + ps]
noisy = noisy[:, r:r + ps, c:c + ps]
apply_trans = transforms_aug[random.getrandbits(3)]
clean = getattr(augment, apply_trans)(clean)
noisy = getattr(augment, apply_trans)(noisy)
return clean, noisy, clean_filename, noisy_filename
##################################################################################################
class DataLoaderTrain_Gaussian(Dataset):
def __init__(self, rgb_dir, noiselevel=5, img_options=None, target_transform=None):
super(DataLoaderTrain_Gaussian, self).__init__()
self.target_transform = target_transform
clean_files = sorted(os.listdir(rgb_dir))
self.clean_filenames = [os.path.join(rgb_dir, x) for x in clean_files if is_png_file(x)]
self.noiselevel = noiselevel
self.img_options=img_options
self.tar_size = len(self.clean_filenames) # get the size of target
print(self.tar_size)
def __len__(self):
return self.tar_size
def __getitem__(self, index):
tar_index = index % self.tar_size
clean = np.float32(load_img(self.clean_filenames[tar_index]))
noisy = clean + np.float32(np.random.normal(0, self.noiselevel, np.array(clean).shape)/255.)
noisy = np.clip(noisy,0.,1.)
clean = torch.from_numpy(clean)
noisy = torch.from_numpy(noisy)
clean = clean.permute(2,0,1)
noisy = noisy.permute(2,0,1)
clean_filename = os.path.split(self.clean_filenames[tar_index])[-1]
noisy_filename = os.path.split(self.clean_filenames[tar_index])[-1]
#Crop Input and Target
ps = self.img_options['patch_size']
H = clean.shape[1]
W = clean.shape[2]
r = np.random.randint(0, H - ps)
c = np.random.randint(0, W - ps)
clean = clean[:, r:r + ps, c:c + ps]
noisy = noisy[:, r:r + ps, c:c + ps]
apply_trans = transforms_aug[random.getrandbits(3)]
clean = getattr(augment, apply_trans)(clean)
noisy = getattr(augment, apply_trans)(noisy)
return clean, noisy, clean_filename, noisy_filename
##################################################################################################
class DataLoaderVal(Dataset):
def __init__(self, rgb_dir, target_transform=None):
super(DataLoaderVal, self).__init__()
self.target_transform = target_transform
gt_dir = 'target'
input_dir = 'input'
clean_files = sorted(os.listdir(os.path.join(rgb_dir, gt_dir)))
noisy_files = sorted(os.listdir(os.path.join(rgb_dir, input_dir)))
self.clean_filenames = [os.path.join(rgb_dir, gt_dir, x) for x in clean_files if is_png_file(x)]
self.noisy_filenames = [os.path.join(rgb_dir, input_dir, x) for x in noisy_files if is_png_file(x)]
self.tar_size = len(self.clean_filenames)
def __len__(self):
return self.tar_size
def __getitem__(self, index):
tar_index = index % self.tar_size
clean = torch.from_numpy(np.float32(load_img(self.clean_filenames[tar_index])))
noisy = torch.from_numpy(np.float32(load_img(self.noisy_filenames[tar_index])))
clean_filename = os.path.split(self.clean_filenames[tar_index])[-1]
noisy_filename = os.path.split(self.noisy_filenames[tar_index])[-1]
clean = clean.permute(2,0,1)
noisy = noisy.permute(2,0,1)
return clean, noisy, clean_filename, noisy_filename
##################################################################################################
class DataLoaderTest(Dataset):
def __init__(self, rgb_dir, target_transform=None):
super(DataLoaderTest, self).__init__()
self.target_transform = target_transform
noisy_files = sorted(os.listdir(os.path.join(rgb_dir, 'input')))
self.noisy_filenames = [os.path.join(rgb_dir, 'input', x) for x in noisy_files if is_png_file(x)]
self.tar_size = len(self.noisy_filenames)
def __len__(self):
return self.tar_size
def __getitem__(self, index):
tar_index = index % self.tar_size
noisy = torch.from_numpy(np.float32(load_img(self.noisy_filenames[tar_index])))
noisy_filename = os.path.split(self.noisy_filenames[tar_index])[-1]
noisy = noisy.permute(2,0,1)
return noisy, noisy_filename
##################################################################################################