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dataset_RGB.py
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dataset_RGB.py
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import os
import numpy as np
from torch.utils.data import Dataset
import torch
from PIL import Image
import torchvision.transforms.functional as TF
from pdb import set_trace as stx
import random
def is_image_file(filename):
return any(filename.endswith(extension) for extension in ['jpeg', 'JPEG', 'jpg', 'png', 'JPG', 'PNG', 'gif'])
class DataLoaderTrain(Dataset):
def __init__(self, rgb_dir, img_options=None):
super(DataLoaderTrain, self).__init__()
inp_files = sorted(os.listdir(os.path.join(rgb_dir, 'input')))
tar_files = sorted(os.listdir(os.path.join(rgb_dir, 'target')))
self.inp_filenames = [os.path.join(rgb_dir, 'input', x) for x in inp_files if is_image_file(x)]
self.tar_filenames = [os.path.join(rgb_dir, 'target', x) for x in tar_files if is_image_file(x)]
self.img_options = img_options
self.sizex = len(self.tar_filenames) # get the size of target
self.ps = self.img_options['patch_size']
def __len__(self):
return self.sizex
def __getitem__(self, index):
index_ = index % self.sizex
ps = self.ps
inp_path = self.inp_filenames[index_]
tar_path = self.tar_filenames[index_]
inp_img = Image.open(inp_path)
tar_img = Image.open(tar_path)
w,h = tar_img.size
padw = ps-w if w<ps else 0
padh = ps-h if h<ps else 0
# Reflect Pad in case image is smaller than patch_size
if padw!=0 or padh!=0:
inp_img = TF.pad(inp_img, (0,0,padw,padh), padding_mode='reflect')
tar_img = TF.pad(tar_img, (0,0,padw,padh), padding_mode='reflect')
aug = random.randint(0, 2)
if aug == 1:
inp_img = TF.adjust_gamma(inp_img, 1)
tar_img = TF.adjust_gamma(tar_img, 1)
aug = random.randint(0, 2)
if aug == 1:
sat_factor = 1 + (0.2 - 0.4*np.random.rand())
inp_img = TF.adjust_saturation(inp_img, sat_factor)
tar_img = TF.adjust_saturation(tar_img, sat_factor)
inp_img = TF.to_tensor(inp_img)
tar_img = TF.to_tensor(tar_img)
hh, ww = tar_img.shape[1], tar_img.shape[2]
rr = random.randint(0, hh-ps)
cc = random.randint(0, ww-ps)
aug = random.randint(0, 8)
# Crop patch
inp_img = inp_img[:, rr:rr+ps, cc:cc+ps]
tar_img = tar_img[:, rr:rr+ps, cc:cc+ps]
# Data Augmentations
if aug==1:
inp_img = inp_img.flip(1)
tar_img = tar_img.flip(1)
elif aug==2:
inp_img = inp_img.flip(2)
tar_img = tar_img.flip(2)
elif aug==3:
inp_img = torch.rot90(inp_img,dims=(1,2))
tar_img = torch.rot90(tar_img,dims=(1,2))
elif aug==4:
inp_img = torch.rot90(inp_img,dims=(1,2), k=2)
tar_img = torch.rot90(tar_img,dims=(1,2), k=2)
elif aug==5:
inp_img = torch.rot90(inp_img,dims=(1,2), k=3)
tar_img = torch.rot90(tar_img,dims=(1,2), k=3)
elif aug==6:
inp_img = torch.rot90(inp_img.flip(1),dims=(1,2))
tar_img = torch.rot90(tar_img.flip(1),dims=(1,2))
elif aug==7:
inp_img = torch.rot90(inp_img.flip(2),dims=(1,2))
tar_img = torch.rot90(tar_img.flip(2),dims=(1,2))
filename = os.path.splitext(os.path.split(tar_path)[-1])[0]
return tar_img, inp_img, filename
class DataLoaderVal(Dataset):
def __init__(self, rgb_dir, img_options=None, rgb_dir2=None):
super(DataLoaderVal, self).__init__()
inp_files = sorted(os.listdir(os.path.join(rgb_dir, 'input')))
tar_files = sorted(os.listdir(os.path.join(rgb_dir, 'target')))
self.inp_filenames = [os.path.join(rgb_dir, 'input', x) for x in inp_files if is_image_file(x)]
self.tar_filenames = [os.path.join(rgb_dir, 'target', x) for x in tar_files if is_image_file(x)]
self.img_options = img_options
self.sizex = len(self.tar_filenames) # get the size of target
self.ps = self.img_options['patch_size']
def __len__(self):
return self.sizex
def __getitem__(self, index):
index_ = index % self.sizex
ps = self.ps
inp_path = self.inp_filenames[index_]
print("8888888888888888888888888888888888888888888888888888888888888")
print(inp_path)
print("8888888888888888888888888888888888888888888888888888888888888")
print("8888888888888888888888888888888888888888888888888888888888888")
print("8888888888888888888888888888888888888888888888888888888888888")
tar_path = self.tar_filenames[index_]
inp_img = Image.open(inp_path)
tar_img = Image.open(tar_path)
# Validate on center crop
if self.ps is not None:
inp_img = TF.center_crop(inp_img, (ps,ps))
tar_img = TF.center_crop(tar_img, (ps,ps))
inp_img = TF.to_tensor(inp_img)
tar_img = TF.to_tensor(tar_img)
filename = os.path.splitext(os.path.split(inp_path)[-1])[0]
return tar_img, inp_img, filename
class DataLoaderTest(Dataset):
def __init__(self, inp_dir, img_options):
super(DataLoaderTest, self).__init__()
inp_files = sorted(os.listdir(inp_dir))
self.inp_filenames = [os.path.join(inp_dir, x) for x in inp_files if is_image_file(x)]
self.inp_size = len(self.inp_filenames)
self.img_options = img_options
def __len__(self):
return self.inp_size
def __getitem__(self, index):
path_inp = self.inp_filenames[index]
filename = os.path.splitext(os.path.split(path_inp)[-1])[0]
inp = Image.open(path_inp)
inp = TF.to_tensor(inp)
return inp, filename