Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,943 Bytes
0324143 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 |
import cv2
import math
import numpy as np
import os
import os.path as osp
import random
import time
import torch
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']
self.gt_folder = opt['dataroot_gt']
# file client (lmdb io backend)
if self.io_backend_opt['type'] == 'lmdb':
self.io_backend_opt['db_paths'] = [self.gt_folder]
self.io_backend_opt['client_keys'] = ['gt']
if not self.gt_folder.endswith('.lmdb'):
raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
self.paths = [line.split('.')[0] for line in fin]
else:
# disk backend with meta_info
# Each line in the meta_info describes the relative path to an image
with open(self.opt['meta_info']) as fin:
paths = [line.strip().split(' ')[0] for line in fin]
self.paths = [os.path.join(self.gt_folder, v) for v in paths]
# 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)
|