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datasets.py
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datasets.py
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import glob
import random
import os
import numpy as np
import torch
import cv2
from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
import torchvision_x_functional as TF_x
class ImageDataset_sRGB(Dataset):
def __init__(self, root, mode="train", unpaird_data="fiveK", combined=True):
self.mode = mode
self.unpaird_data = unpaird_data
file = open(os.path.join(root,'train_input.txt'),'r')
set1_input_files = sorted(file.readlines())
self.set1_input_files = list()
self.set1_expert_files = list()
for i in range(len(set1_input_files)):
self.set1_input_files.append(os.path.join(root,"input","JPG/480p",set1_input_files[i][:-1] + ".jpg"))
self.set1_expert_files.append(os.path.join(root,"expertC","JPG/480p",set1_input_files[i][:-1] + ".jpg"))
file = open(os.path.join(root,'train_label.txt'),'r')
set2_input_files = sorted(file.readlines())
self.set2_input_files = list()
self.set2_expert_files = list()
for i in range(len(set2_input_files)):
self.set2_input_files.append(os.path.join(root,"input","JPG/480p",set2_input_files[i][:-1] + ".jpg"))
self.set2_expert_files.append(os.path.join(root,"expertC","JPG/480p",set2_input_files[i][:-1] + ".jpg"))
file = open(os.path.join(root,'test.txt'),'r')
test_input_files = sorted(file.readlines())
self.test_input_files = list()
self.test_expert_files = list()
for i in range(len(test_input_files)):
self.test_input_files.append(os.path.join(root,"input","JPG/480p",test_input_files[i][:-1] + ".jpg"))
self.test_expert_files.append(os.path.join(root,"expertC","JPG/480p",test_input_files[i][:-1] + ".jpg"))
if combined:
self.set1_input_files = self.set1_input_files + self.set2_input_files
self.set1_expert_files = self.set1_expert_files + self.set2_expert_files
def __getitem__(self, index):
if self.mode == "train":
img_name = os.path.split(self.set1_input_files[index % len(self.set1_input_files)])[-1]
img_input = Image.open(self.set1_input_files[index % len(self.set1_input_files)])
img_exptC = Image.open(self.set1_expert_files[index % len(self.set1_expert_files)])
elif self.mode == "test":
img_name = os.path.split(self.test_input_files[index % len(self.test_input_files)])[-1]
img_input = Image.open(self.test_input_files[index % len(self.test_input_files)])
img_exptC = Image.open(self.test_expert_files[index % len(self.test_expert_files)])
if self.mode == "train":
ratio_H = np.random.uniform(0.6,1.0)
ratio_W = np.random.uniform(0.6,1.0)
W,H = img_input._size
crop_h = round(H*ratio_H)
crop_w = round(W*ratio_W)
i, j, h, w = transforms.RandomCrop.get_params(img_input, output_size=(crop_h, crop_w))
img_input = TF.crop(img_input, i, j, h, w)
img_exptC = TF.crop(img_exptC, i, j, h, w)
#img_input = TF.resized_crop(img_input, i, j, h, w, (320,320))
#img_exptC = TF.resized_crop(img_exptC, i, j, h, w, (320,320))
if np.random.random() > 0.5:
img_input = TF.hflip(img_input)
img_exptC = TF.hflip(img_exptC)
a = np.random.uniform(0.8,1.2)
img_input = TF.adjust_brightness(img_input,a)
a = np.random.uniform(0.8,1.2)
img_input = TF.adjust_saturation(img_input,a)
img_input = TF.to_tensor(img_input)
img_exptC = TF.to_tensor(img_exptC)
return {"A_input": img_input, "A_exptC": img_exptC, "input_name": img_name}
def __len__(self):
if self.mode == "train":
return len(self.set1_input_files)
elif self.mode == "test":
return len(self.test_input_files)
class ImageDataset_XYZ(Dataset):
def __init__(self, root, mode="train", unpaird_data="fiveK", combined=True):
self.mode = mode
file = open(os.path.join(root,'train_input.txt'),'r')
set1_input_files = sorted(file.readlines())
self.set1_input_files = list()
self.set1_expert_files = list()
for i in range(len(set1_input_files)):
self.set1_input_files.append(os.path.join(root,"input","PNG/480p_16bits_XYZ_WB",set1_input_files[i][:-1] + ".png"))
self.set1_expert_files.append(os.path.join(root,"expertC","JPG/480p",set1_input_files[i][:-1] + ".jpg"))
file = open(os.path.join(root,'train_label.txt'),'r')
set2_input_files = sorted(file.readlines())
self.set2_input_files = list()
self.set2_expert_files = list()
for i in range(len(set2_input_files)):
self.set2_input_files.append(os.path.join(root,"input","PNG/480p_16bits_XYZ_WB",set2_input_files[i][:-1] + ".png"))
self.set2_expert_files.append(os.path.join(root,"expertC","JPG/480p",set2_input_files[i][:-1] + ".jpg"))
file = open(os.path.join(root,'test.txt'),'r')
test_input_files = sorted(file.readlines())
self.test_input_files = list()
self.test_expert_files = list()
for i in range(len(test_input_files)):
self.test_input_files.append(os.path.join(root,"input","PNG/480p_16bits_XYZ_WB",test_input_files[i][:-1] + ".png"))
self.test_expert_files.append(os.path.join(root,"expertC","JPG/480p",test_input_files[i][:-1] + ".jpg"))
if combined:
self.set1_input_files = self.set1_input_files + self.set2_input_files
self.set1_expert_files = self.set1_expert_files + self.set2_expert_files
def __getitem__(self, index):
if self.mode == "train":
img_name = os.path.split(self.set1_input_files[index % len(self.set1_input_files)])[-1]
img_input = cv2.imread(self.set1_input_files[index % len(self.set1_input_files)],-1)
img_exptC = Image.open(self.set1_expert_files[index % len(self.set1_expert_files)])
elif self.mode == "test":
img_name = os.path.split(self.test_input_files[index % len(self.test_input_files)])[-1]
img_input = cv2.imread(self.test_input_files[index % len(self.test_input_files)],-1)
img_exptC = Image.open(self.test_expert_files[index % len(self.test_expert_files)])
img_input = np.array(img_input)
#img_input = np.array(cv2.cvtColor(img_input,cv2.COLOR_BGR2RGB))
if self.mode == "train":
ratio_H = np.random.uniform(0.6,1.0)
ratio_W = np.random.uniform(0.6,1.0)
W,H = img_exptC._size
crop_h = round(H*ratio_H)
crop_w = round(W*ratio_W)
i, j, h, w = transforms.RandomCrop.get_params(img_exptC, output_size=(crop_h, crop_w))
img_input = TF_x.crop(img_input, i, j, h, w)
img_exptC = TF.crop(img_exptC, i, j, h, w)
if np.random.random() > 0.5:
img_input = TF_x.hflip(img_input)
img_exptC = TF.hflip(img_exptC)
a = np.random.uniform(0.6,1.4)
img_input = TF_x.adjust_brightness(img_input,a)
img_input = TF_x.to_tensor(img_input)
img_exptC = TF.to_tensor(img_exptC)
return {"A_input": img_input, "A_exptC": img_exptC, "input_name": img_name}
def __len__(self):
if self.mode == "train":
return len(self.set1_input_files)
elif self.mode == "test":
return len(self.test_input_files)
class ImageDataset_sRGB_unpaired(Dataset):
def __init__(self, root, mode="train", unpaird_data="fiveK"):
self.mode = mode
self.unpaird_data = unpaird_data
file = open(os.path.join(root,'train_input.txt'),'r')
set1_input_files = sorted(file.readlines())
self.set1_input_files = list()
self.set1_expert_files = list()
for i in range(len(set1_input_files)):
self.set1_input_files.append(os.path.join(root,"input","JPG/480p",set1_input_files[i][:-1] + ".jpg"))
self.set1_expert_files.append(os.path.join(root,"expertC","JPG/480p",set1_input_files[i][:-1] + ".jpg"))
file = open(os.path.join(root,'train_label.txt'),'r')
set2_input_files = sorted(file.readlines())
self.set2_input_files = list()
self.set2_expert_files = list()
for i in range(len(set2_input_files)):
self.set2_input_files.append(os.path.join(root,"input","JPG/480p",set2_input_files[i][:-1] + ".jpg"))
self.set2_expert_files.append(os.path.join(root,"expertC","JPG/480p",set2_input_files[i][:-1] + ".jpg"))
file = open(os.path.join(root,'test.txt'),'r')
test_input_files = sorted(file.readlines())
self.test_input_files = list()
self.test_expert_files = list()
for i in range(len(test_input_files)):
self.test_input_files.append(os.path.join(root,"input","JPG/480p",test_input_files[i][:-1] + ".jpg"))
self.test_expert_files.append(os.path.join(root,"expertC","JPG/480p",test_input_files[i][:-1] + ".jpg"))
def __getitem__(self, index):
if self.mode == "train":
img_name = os.path.split(self.set1_input_files[index % len(self.set1_input_files)])[-1]
img_input = Image.open(self.set1_input_files[index % len(self.set1_input_files)])
img_exptC = Image.open(self.set1_expert_files[index % len(self.set1_expert_files)])
seed = random.randint(1,len(self.set2_expert_files))
img2 = Image.open(self.set2_expert_files[(index + seed) % len(self.set2_expert_files)])
elif self.mode == "test":
img_name = os.path.split(self.test_input_files[index % len(self.test_input_files)])[-1]
img_input = Image.open(self.test_input_files[index % len(self.test_input_files)])
img_exptC = Image.open(self.test_expert_files[index % len(self.test_expert_files)])
img2 = img_exptC
if self.mode == "train":
ratio_H = np.random.uniform(0.6,1.0)
ratio_W = np.random.uniform(0.6,1.0)
W,H = img_input._size
crop_h = round(H*ratio_H)
crop_w = round(W*ratio_W)
W2,H2 = img2._size
crop_h = min(crop_h,H2)
crop_w = min(crop_w,W2)
i, j, h, w = transforms.RandomCrop.get_params(img_input, output_size=(crop_h, crop_w))
img_input = TF.crop(img_input, i, j, h, w)
img_exptC = TF.crop(img_exptC, i, j, h, w)
i, j, h, w = transforms.RandomCrop.get_params(img2, output_size=(crop_h, crop_w))
img2 = TF.crop(img2, i, j, h, w)
if np.random.random() > 0.5:
img_input = TF.hflip(img_input)
img_exptC = TF.hflip(img_exptC)
if np.random.random() > 0.5:
img2 = TF.hflip(img2)
#if np.random.random() > 0.5:
# img_input = TF.vflip(img_input)
# img_exptC = TF.vflip(img_exptC)
# img2 = TF.vflip(img2)
a = np.random.uniform(0.6,1.4)
img_input = TF.adjust_brightness(img_input,a)
a = np.random.uniform(0.8,1.2)
img_input = TF.adjust_saturation(img_input,a)
img_input = TF.to_tensor(img_input)
img_exptC = TF.to_tensor(img_exptC)
img2 = TF.to_tensor(img2)
return {"A_input": img_input, "A_exptC": img_exptC, "B_exptC": img2, "input_name": img_name}
def __len__(self):
if self.mode == "train":
return len(self.set1_input_files)
elif self.mode == "test":
return len(self.test_input_files)
class ImageDataset_XYZ_unpaired(Dataset):
def __init__(self, root, mode="train", unpaird_data="fiveK"):
self.mode = mode
self.unpaird_data = unpaird_data
file = open(os.path.join(root,'train_input.txt'),'r')
set1_input_files = sorted(file.readlines())
self.set1_input_files = list()
self.set1_expert_files = list()
for i in range(len(set1_input_files)):
self.set1_input_files.append(os.path.join(root,"input","PNG/480p_16bits_XYZ_WB",set1_input_files[i][:-1] + ".png"))
self.set1_expert_files.append(os.path.join(root,"expertC","JPG/480p",set1_input_files[i][:-1] + ".jpg"))
file = open(os.path.join(root,'train_label.txt'),'r')
set2_input_files = sorted(file.readlines())
self.set2_input_files = list()
self.set2_expert_files = list()
for i in range(len(set2_input_files)):
self.set2_input_files.append(os.path.join(root,"input","PNG/480p_16bits_XYZ_WB",set2_input_files[i][:-1] + ".png"))
self.set2_expert_files.append(os.path.join(root,"expertC","JPG/480p",set2_input_files[i][:-1] + ".jpg"))
file = open(os.path.join(root,'test.txt'),'r')
test_input_files = sorted(file.readlines())
self.test_input_files = list()
self.test_expert_files = list()
for i in range(len(test_input_files)):
self.test_input_files.append(os.path.join(root,"input","PNG/480p_16bits_XYZ_WB",test_input_files[i][:-1] + ".png"))
self.test_expert_files.append(os.path.join(root,"expertC","JPG/480p",test_input_files[i][:-1] + ".jpg"))
def __getitem__(self, index):
if self.mode == "train":
img_name = os.path.split(self.set1_input_files[index % len(self.set1_input_files)])[-1]
img_input = cv2.imread(self.set1_input_files[index % len(self.set1_input_files)],-1)
img_exptC = Image.open(self.set1_expert_files[index % len(self.set1_expert_files)])
seed = random.randint(1,len(self.set2_expert_files))
img2 = Image.open(self.set2_expert_files[(index + seed) % len(self.set2_expert_files)])
elif self.mode == "test":
img_name = os.path.split(self.test_input_files[index % len(self.test_input_files)])[-1]
img_input = cv2.imread(self.test_input_files[index % len(self.test_input_files)],-1)
img_exptC = Image.open(self.test_expert_files[index % len(self.test_expert_files)])
img2 = img_exptC
img_input = np.array(img_input)
#img_input = np.array(cv2.cvtColor(img_input,cv2.COLOR_BGR2RGB))
if self.mode == "train":
ratio_H = np.random.uniform(0.6,1.0)
ratio_W = np.random.uniform(0.6,1.0)
W,H = img_exptC._size
crop_h = round(H*ratio_H)
crop_w = round(W*ratio_W)
W2,H2 = img2._size
crop_h = min(crop_h,H2)
crop_w = min(crop_w,W2)
i, j, h, w = transforms.RandomCrop.get_params(img_exptC, output_size=(crop_h, crop_w))
img_input = TF_x.crop(img_input, i, j, h, w)
img_exptC = TF.crop(img_exptC, i, j, h, w)
i, j, h, w = transforms.RandomCrop.get_params(img2, output_size=(crop_h, crop_w))
img2 = TF.crop(img2, i, j, h, w)
if np.random.random() > 0.5:
img_input = TF_x.hflip(img_input)
img_exptC = TF.hflip(img_exptC)
if np.random.random() > 0.5:
img2 = TF.hflip(img2)
a = np.random.uniform(0.6,1.4)
img_input = TF_x.adjust_brightness(img_input,a)
img_input = TF_x.to_tensor(img_input)
img_exptC = TF.to_tensor(img_exptC)
img2 = TF.to_tensor(img2)
return {"A_input": img_input, "A_exptC": img_exptC, "B_exptC": img2, "input_name": img_name}
def __len__(self):
if self.mode == "train":
return len(self.set1_input_files)
elif self.mode == "test":
return len(self.test_input_files)
class ImageDataset_HDRplus(Dataset):
def __init__(self, root, mode="train", combined=True):
self.mode = mode
file = open(os.path.join(root,'train.txt'),'r')
set1_input_files = sorted(file.readlines())
self.set1_input_files = list()
self.set1_expert_files = list()
for i in range(len(set1_input_files)):
self.set1_input_files.append(os.path.join(root,"middle_480p",set1_input_files[i][:-1] + ".png"))
self.set1_expert_files.append(os.path.join(root,"output_480p",set1_input_files[i][:-1] + ".jpg"))
file = open(os.path.join(root,'test.txt'),'r')
test_input_files = sorted(file.readlines())
self.test_input_files = list()
self.test_expert_files = list()
for i in range(len(test_input_files)):
self.test_input_files.append(os.path.join(root,"middle_480p",test_input_files[i][:-1] + ".png"))
self.test_expert_files.append(os.path.join(root,"output_480p",test_input_files[i][:-1] + ".jpg"))
def __getitem__(self, index):
if self.mode == "train":
img_name = os.path.split(self.set1_input_files[index % len(self.set1_input_files)])[-1]
img_input = cv2.imread(self.set1_input_files[index % len(self.set1_input_files)],-1)
img_exptC = Image.open(self.set1_expert_files[index % len(self.set1_expert_files)])
elif self.mode == "test":
img_name = os.path.split(self.test_input_files[index % len(self.test_input_files)])[-1]
img_input = cv2.imread(self.test_input_files[index % len(self.test_input_files)],-1)
img_exptC = Image.open(self.test_expert_files[index % len(self.test_expert_files)])
img_input = np.array(img_input)
#img_input = np.array(cv2.cvtColor(img_input,cv2.COLOR_BGR2RGB))
if self.mode == "train":
ratio = np.random.uniform(0.6,1.0)
W,H = img_exptC._size
crop_h = round(H*ratio)
crop_w = round(W*ratio)
i, j, h, w = transforms.RandomCrop.get_params(img_exptC, output_size=(crop_h, crop_w))
try:
img_input = TF_x.crop(img_input, i, j, h, w)
except:
print(crop_h,crop_w,img_input.shape())
img_exptC = TF.crop(img_exptC, i, j, h, w)
if np.random.random() > 0.5:
img_input = TF_x.hflip(img_input)
img_exptC = TF.hflip(img_exptC)
a = np.random.uniform(0.6,1.4)
img_input = TF_x.adjust_brightness(img_input,a)
#a = np.random.uniform(0.8,1.2)
#img_input = TF_x.adjust_saturation(img_input,a)
img_input = TF_x.to_tensor(img_input)
img_exptC = TF.to_tensor(img_exptC)
return {"A_input": img_input, "A_exptC": img_exptC, "input_name": img_name}
def __len__(self):
if self.mode == "train":
return len(self.set1_input_files)
elif self.mode == "test":
return len(self.test_input_files)
class ImageDataset_HDRplus_unpaired(Dataset):
def __init__(self, root, mode="train"):
self.mode = mode
file = open(os.path.join(root,'train.txt'),'r')
set1_input_files = sorted(file.readlines())
self.set1_input_files = list()
self.set1_expert_files = list()
for i in range(len(set1_input_files)):
self.set1_input_files.append(os.path.join(root,"middle_480p",set1_input_files[i][:-1] + ".png"))
self.set1_expert_files.append(os.path.join(root,"output_480p",set1_input_files[i][:-1] + ".jpg"))
file = open(os.path.join(root,'train.txt'),'r')
set2_input_files = sorted(file.readlines())
self.set2_input_files = list()
self.set2_expert_files = list()
for i in range(len(set2_input_files)):
self.set2_input_files.append(os.path.join(root,"middle_480p",set2_input_files[i][:-1] + ".png"))
self.set2_expert_files.append(os.path.join(root,"output_480p",set2_input_files[i][:-1] + ".jpg"))
file = open(os.path.join(root,'test.txt'),'r')
test_input_files = sorted(file.readlines())
self.test_input_files = list()
self.test_expert_files = list()
for i in range(len(test_input_files)):
self.test_input_files.append(os.path.join(root,"middle_480p",test_input_files[i][:-1] + ".png"))
self.test_expert_files.append(os.path.join(root,"output_480p",test_input_files[i][:-1] + ".jpg"))
def __getitem__(self, index):
if self.mode == "train":
img_name = os.path.split(self.set1_input_files[index % len(self.set1_input_files)])[-1]
img_input = cv2.imread(self.set1_input_files[index % len(self.set1_input_files)],-1)
img_exptC = Image.open(self.set1_expert_files[index % len(self.set1_expert_files)])
seed = random.randint(1,len(self.set2_expert_files))
img2 = Image.open(self.set2_expert_files[(index + seed) % len(self.set2_expert_files)])
elif self.mode == "test":
img_name = os.path.split(self.test_input_files[index % len(self.test_input_files)])[-1]
img_input = cv2.imread(self.test_input_files[index % len(self.test_input_files)],-1)
img_exptC = Image.open(self.test_expert_files[index % len(self.test_expert_files)])
img2 = img_exptC
img_input = np.array(img_input)
#img_input = np.array(cv2.cvtColor(img_input,cv2.COLOR_BGR2RGB))
if self.mode == "train":
ratio = np.random.uniform(0.6,1.0)
W,H = img_exptC._size
crop_h = round(H*ratio)
crop_w = round(W*ratio)
W2,H2 = img2._size
crop_h = min(crop_h,H2)
crop_w = min(crop_w,W2)
i, j, h, w = transforms.RandomCrop.get_params(img_exptC, output_size=(crop_h, crop_w))
img_input = TF_x.crop(img_input, i, j, h, w)
img_exptC = TF.crop(img_exptC, i, j, h, w)
i, j, h, w = transforms.RandomCrop.get_params(img2, output_size=(crop_h, crop_w))
img2 = TF.crop(img2, i, j, h, w)
if np.random.random() > 0.5:
img_input = TF_x.hflip(img_input)
img_exptC = TF.hflip(img_exptC)
if np.random.random() > 0.5:
img2 = TF.hflip(img2)
a = np.random.uniform(0.8,1.2)
img_input = TF_x.adjust_brightness(img_input,a)
img_input = TF_x.to_tensor(img_input)
img_exptC = TF.to_tensor(img_exptC)
img2 = TF.to_tensor(img2)
return {"A_input": img_input, "A_exptC": img_exptC, "B_exptC": img2, "input_name": img_name}
def __len__(self):
if self.mode == "train":
return len(self.set1_input_files)
elif self.mode == "test":
return len(self.test_input_files)