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import glob
import io
import numpy as np
import re
import os
import random
from io import BytesIO
from uuid import uuid4
import sqlite3
import h5py
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision.transforms import RandomCrop
from torchvision.transforms.functional import to_tensor
class ImageH5Data(Dataset):
def __init__(self, h5py_file, folder_name):
self.data = h5py.File(h5py_file, "r")[folder_name]
self.data_hr = self.data["train_hr"]
self.data_lr = self.data["train_lr"]
self.len_imgs = len(self.data_hr)
self.h5py_file = h5py_file
self.folder_name = folder_name
def __len__(self):
# with h5py.File(self.h5py_file, 'r') as f:
# return len(f[self.folder_name]['train_lr'])
return self.len_imgs
def __getitem__(self, index):
# with h5py.File(self.h5py_file, 'r') as f:
# data_lr = f[self.folder_name]['train_lr'][index]
# data_hr = f[self.folder_name]['train_lr'][index]
#
# return data_lr, data_hr
return self.data_lr[index], self.data_hr[index]
class ImageData(Dataset):
def __init__(
self,
img_folder,
patch_size=96,
shrink_size=2,
noise_level=1,
down_sample_method=None,
color_mod="RGB",
dummy_len=None,
):
self.img_folder = img_folder
all_img = glob.glob(self.img_folder + "/**", recursive=True)
self.img = list(
filter(
lambda x: x.endswith("png") or x.endswith("jpg") or x.endswith("jpeg"),
all_img,
)
)
self.total_img = len(self.img)
self.dummy_len = dummy_len if dummy_len is not None else self.total_img
self.random_cropper = RandomCrop(size=patch_size)
self.color_mod = color_mod
self.img_augmenter = ImageAugment(shrink_size, noise_level, down_sample_method)
def get_img_patches(self, img_file):
img_pil = Image.open(img_file).convert("RGB")
img_patch = self.random_cropper(img_pil)
lr_hr_patches = self.img_augmenter.process(img_patch)
return lr_hr_patches
def __len__(self):
return self.dummy_len # len(self.img)
def __getitem__(self, index):
idx = random.choice(range(0, self.total_img))
img = self.img[idx]
patch = self.get_img_patches(img)
if self.color_mod == "RGB":
lr_img = patch[0].convert("RGB")
hr_img = patch[1].convert("RGB")
elif self.color_mod == "YCbCr":
lr_img, _, _ = patch[0].convert("YCbCr").split()
hr_img, _, _ = patch[1].convert("YCbCr").split()
else:
raise KeyError("Either RGB or YCbCr")
return to_tensor(lr_img), to_tensor(hr_img)
class Image2Sqlite(ImageData):
def __getitem__(self, item):
img = self.img[item]
lr_hr_patch = self.get_img_patches(img)
if self.color_mod == "RGB":
lr_img = lr_hr_patch[0].convert("RGB")
hr_img = lr_hr_patch[1].convert("RGB")
elif self.color_mod == "YCbCr":
lr_img, _, _ = lr_hr_patch[0].convert("YCbCr").split()
hr_img, _, _ = lr_hr_patch[1].convert("YCbCr").split()
else:
raise KeyError("Either RGB or YCbCr")
lr_byte = self.convert_to_bytevalue(lr_img)
hr_byte = self.convert_to_bytevalue(hr_img)
return [lr_byte, hr_byte]
@staticmethod
def convert_to_bytevalue(pil_img):
img_byte = io.BytesIO()
pil_img.save(img_byte, format="png")
return img_byte.getvalue()
class ImageDBData(Dataset):
def __init__(
self,
db_file,
db_table="images",
lr_col="lr_img",
hr_col="hr_img",
max_images=None,
):
self.db_file = db_file
self.db_table = db_table
self.lr_col = lr_col
self.hr_col = hr_col
self.total_images = self.get_num_rows(max_images)
# self.lr_hr_images = self.get_all_images()
def __len__(self):
return self.total_images
# def get_all_images(self):
# with sqlite3.connect(self.db_file) as conn:
# cursor = conn.cursor()
# cursor.execute(f"SELECT * FROM {self.db_table} LIMIT {self.total_images}")
# return cursor.fetchall()
def get_num_rows(self, max_images):
with sqlite3.connect(self.db_file) as conn:
cursor = conn.cursor()
cursor.execute(f"SELECT MAX(ROWID) FROM {self.db_table}")
db_rows = cursor.fetchone()[0]
if max_images:
return min(max_images, db_rows)
else:
return db_rows
def __getitem__(self, item):
# lr, hr = self.lr_hr_images[item]
# lr = Image.open(io.BytesIO(lr))
# hr = Image.open(io.BytesIO(hr))
# return to_tensor(lr), to_tensor(hr)
# note sqlite rowid starts with 1
with sqlite3.connect(self.db_file) as conn:
cursor = conn.cursor()
cursor.execute(
f"SELECT {self.lr_col}, {self.hr_col} FROM {self.db_table} WHERE ROWID={item + 1}"
)
lr, hr = cursor.fetchone()
lr = Image.open(io.BytesIO(lr)).convert("RGB")
hr = Image.open(io.BytesIO(hr)).convert("RGB")
# lr = np.array(lr) # use scale [0, 255] instead of [0,1]
# hr = np.array(hr)
return to_tensor(lr), to_tensor(hr)
class ImagePatchData(Dataset):
def __init__(self, lr_folder, hr_folder):
self.lr_folder = lr_folder
self.hr_folder = hr_folder
self.lr_imgs = glob.glob(os.path.join(lr_folder, "**"))
self.total_imgs = len(self.lr_imgs)
def __len__(self):
return self.total_imgs
def __getitem__(self, item):
lr_file = self.lr_imgs[item]
hr_path = re.sub("lr", "hr", os.path.dirname(lr_file))
filename = os.path.basename(lr_file)
hr_file = os.path.join(hr_path, filename)
return to_tensor(Image.open(lr_file)), to_tensor(Image.open(hr_file))
class ImageAugment:
def __init__(self, shrink_size=2, noise_level=1, down_sample_method=None):
# noise_level (int): 0: no noise; 1: 75-95% quality; 2:50-75%
if noise_level == 0:
self.noise_level = [0, 0]
elif noise_level == 1:
self.noise_level = [5, 25]
elif noise_level == 2:
self.noise_level = [25, 50]
else:
raise KeyError("Noise level should be either 0, 1, 2")
self.shrink_size = shrink_size
self.down_sample_method = down_sample_method
def shrink_img(self, hr_img):
if self.down_sample_method is None:
resample_method = random.choice(
[Image.BILINEAR, Image.BICUBIC, Image.LANCZOS]
)
else:
resample_method = self.down_sample_method
img_w, img_h = tuple(map(lambda x: int(x / self.shrink_size), hr_img.size))
lr_img = hr_img.resize((img_w, img_h), resample_method)
return lr_img
def add_jpeg_noise(self, hr_img):
quality = 100 - round(random.uniform(*self.noise_level))
lr_img = BytesIO()
hr_img.save(lr_img, format="JPEG", quality=quality)
lr_img.seek(0)
lr_img = Image.open(lr_img)
return lr_img
def process(self, hr_patch_pil):
lr_patch_pil = self.shrink_img(hr_patch_pil)
if self.noise_level[1] > 0:
lr_patch_pil = self.add_jpeg_noise(lr_patch_pil)
return lr_patch_pil, hr_patch_pil
def up_sample(self, img, resample):
width, height = img.size
return img.resize(
(self.shrink_size * width, self.shrink_size * height), resample=resample
)
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