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import cv2
import math
import numpy as np
import os
import os.path as osp
import random
import time
import torch
from pathlib import Path
from torch.utils import data as data
from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
from basicsr.data.transforms import augment
from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
@DATASET_REGISTRY.register(suffix='basicsr')
class RealESRGANDataset(data.Dataset):
"""Dataset used for Real-ESRGAN model:
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
It loads gt (Ground-Truth) images, and augments them.
It also generates blur kernels and sinc kernels for generating low-quality images.
Note that the low-quality images are processed in tensors on GPUS for faster processing.
Args:
opt (dict): Config for train datasets. It contains the following keys:
dataroot_gt (str): Data root path for gt.
meta_info (str): Path for meta information file.
io_backend (dict): IO backend type and other kwarg.
use_hflip (bool): Use horizontal flips.
use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
Please see more options in the codes.
"""
def __init__(self, opt):
super(RealESRGANDataset, self).__init__()
self.opt = opt
self.file_client = None
self.io_backend_opt = opt['io_backend']
# file client (lmdb io backend)
self.paths = sorted([str(x) for x in Path(opt['df2k_path']).glob('*.png')])
self.paths.extend(sorted([str(x) for x in Path(opt['wed_path']).glob('*.bmp')]))
# blur settings for the first degradation
self.blur_kernel_size = opt['blur_kernel_size']
self.kernel_list = opt['kernel_list']
self.kernel_prob = opt['kernel_prob'] # a list for each kernel probability
self.blur_sigma = opt['blur_sigma']
self.betag_range = opt['betag_range'] # betag used in generalized Gaussian blur kernels
self.betap_range = opt['betap_range'] # betap used in plateau blur kernels
self.sinc_prob = opt['sinc_prob'] # the probability for sinc filters
# blur settings for the second degradation
self.blur_kernel_size2 = opt['blur_kernel_size2']
self.kernel_list2 = opt['kernel_list2']
self.kernel_prob2 = opt['kernel_prob2']
self.blur_sigma2 = opt['blur_sigma2']
self.betag_range2 = opt['betag_range2']
self.betap_range2 = opt['betap_range2']
self.sinc_prob2 = opt['sinc_prob2']
# a final sinc filter
self.final_sinc_prob = opt['final_sinc_prob']
self.kernel_range = [2 * v + 1 for v in range(3, 11)] # kernel size ranges from 7 to 21
# TODO: kernel range is now hard-coded, should be in the configure file
self.pulse_tensor = torch.zeros(21, 21).float() # convolving with pulse tensor brings no blurry effect
self.pulse_tensor[10, 10] = 1
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
# -------------------------------- Load gt images -------------------------------- #
# Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32.
gt_path = self.paths[index]
# avoid errors caused by high latency in reading files
retry = 3
while retry > 0:
try:
img_bytes = self.file_client.get(gt_path, 'gt')
except (IOError, OSError) as e:
# logger = get_root_logger()
# logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}')
# change another file to read
index = random.randint(0, self.__len__())
gt_path = self.paths[index]
time.sleep(1) # sleep 1s for occasional server congestion
else:
break
finally:
retry -= 1
img_gt = imfrombytes(img_bytes, float32=True)
# -------------------- Do augmentation for training: flip, rotation -------------------- #
img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot'])
# crop or pad to 400
# TODO: 400 is hard-coded. You may change it accordingly
h, w = img_gt.shape[0:2]
crop_pad_size = 400
# pad
if h < crop_pad_size or w < crop_pad_size:
pad_h = max(0, crop_pad_size - h)
pad_w = max(0, crop_pad_size - w)
img_gt = cv2.copyMakeBorder(img_gt, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT_101)
# crop
if img_gt.shape[0] > crop_pad_size or img_gt.shape[1] > crop_pad_size:
h, w = img_gt.shape[0:2]
# randomly choose top and left coordinates
top = random.randint(0, h - crop_pad_size)
left = random.randint(0, w - crop_pad_size)
img_gt = img_gt[top:top + crop_pad_size, left:left + crop_pad_size, ...]
# ------------------------ Generate kernels (used in the first degradation) ------------------------ #
kernel_size = random.choice(self.kernel_range)
if np.random.uniform() < self.opt['sinc_prob']:
# this sinc filter setting is for kernels ranging from [7, 21]
if kernel_size < 13:
omega_c = np.random.uniform(np.pi / 3, np.pi)
else:
omega_c = np.random.uniform(np.pi / 5, np.pi)
kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
else:
kernel = random_mixed_kernels(
self.kernel_list,
self.kernel_prob,
kernel_size,
self.blur_sigma,
self.blur_sigma, [-math.pi, math.pi],
self.betag_range,
self.betap_range,
noise_range=None)
# pad kernel
pad_size = (21 - kernel_size) // 2
kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))
# ------------------------ Generate kernels (used in the second degradation) ------------------------ #
kernel_size = random.choice(self.kernel_range)
if np.random.uniform() < self.opt['sinc_prob2']:
if kernel_size < 13:
omega_c = np.random.uniform(np.pi / 3, np.pi)
else:
omega_c = np.random.uniform(np.pi / 5, np.pi)
kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
else:
kernel2 = random_mixed_kernels(
self.kernel_list2,
self.kernel_prob2,
kernel_size,
self.blur_sigma2,
self.blur_sigma2, [-math.pi, math.pi],
self.betag_range2,
self.betap_range2,
noise_range=None)
# pad kernel
pad_size = (21 - kernel_size) // 2
kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size)))
# ------------------------------------- the final sinc kernel ------------------------------------- #
if np.random.uniform() < self.opt['final_sinc_prob']:
kernel_size = random.choice(self.kernel_range)
omega_c = np.random.uniform(np.pi / 3, np.pi)
sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21)
sinc_kernel = torch.FloatTensor(sinc_kernel)
else:
sinc_kernel = self.pulse_tensor
# BGR to RGB, HWC to CHW, numpy to tensor
img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0]
kernel = torch.FloatTensor(kernel)
kernel2 = torch.FloatTensor(kernel2)
return_d = {'gt': img_gt, 'kernel1': kernel, 'kernel2': kernel2, 'sinc_kernel': sinc_kernel, 'gt_path': gt_path}
return return_d
def __len__(self):
return len(self.paths)
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