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import cv2
import math
import numpy as np
import random
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 img2tensor, DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
from basicsr.data.transforms import paired_random_crop
AUGMENT_OPT = {
'use_hflip': False,
'use_rot': False
}
KERNEL_OPT = {
'blur_kernel_size': 21,
'kernel_list': ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'],
'kernel_prob': [0.45, 0.25, 0.12, 0.03, 0.12, 0.03],
'sinc_prob': 0.1,
'blur_sigma': [0.2, 3],
'betag_range': [0.5, 4],
'betap_range': [1, 2],
'blur_kernel_size2': 21,
'kernel_list2': ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'],
'kernel_prob2': [0.45, 0.25, 0.12, 0.03, 0.12, 0.03],
'sinc_prob2': 0.1,
'blur_sigma2': [0.2, 1.5],
'betag_range2': [0.5, 4],
'betap_range2': [1, 2],
'final_sinc_prob': 0.8,
}
DEGRADE_OPT = {
'resize_prob': [0.2, 0.7, 0.1], # up, down, keep
'resize_range': [0.15, 1.5],
'gaussian_noise_prob': 0.5,
'noise_range': [1, 30],
'poisson_scale_range': [0.05, 3],
'gray_noise_prob': 0.4,
'jpeg_range': [30, 95],
# the second degradation process
'second_blur_prob': 0.8,
'resize_prob2': [0.3, 0.4, 0.3], # up, down, keep
'resize_range2': [0.3, 1.2],
'gaussian_noise_prob2': 0.5,
'noise_range2': [1, 25],
'poisson_scale_range2': [0.05, 2.5],
'gray_noise_prob2': 0.4,
'jpeg_range2': [30, 95],
'gt_size': 512,
'no_degradation_prob': 0.01,
'use_usm': True,
'sf': 8,
'random_size': False,
'resize_lq': True
}
class RealESRGANDegradation:
def __init__(self, augment_opt=None, kernel_opt=None, degrade_opt=None, device='cuda', resolution=None):
if augment_opt is None:
augment_opt = AUGMENT_OPT
self.augment_opt = augment_opt
if kernel_opt is None:
kernel_opt = KERNEL_OPT
self.kernel_opt = kernel_opt
if degrade_opt is None:
degrade_opt = DEGRADE_OPT
self.degrade_opt = degrade_opt
if resolution is not None:
self.degrade_opt['gt_size'] = resolution
self.device = device
self.jpeger = DiffJPEG(differentiable=False).to(self.device)
self.usm_sharpener = USMSharp().to(self.device)
# blur settings for the first degradation
self.blur_kernel_size = kernel_opt['blur_kernel_size']
self.kernel_list = kernel_opt['kernel_list']
self.kernel_prob = kernel_opt['kernel_prob'] # a list for each kernel probability
self.blur_sigma = kernel_opt['blur_sigma']
self.betag_range = kernel_opt['betag_range'] # betag used in generalized Gaussian blur kernels
self.betap_range = kernel_opt['betap_range'] # betap used in plateau blur kernels
self.sinc_prob = kernel_opt['sinc_prob'] # the probability for sinc filters
# blur settings for the second degradation
self.blur_kernel_size2 = kernel_opt['blur_kernel_size2']
self.kernel_list2 = kernel_opt['kernel_list2']
self.kernel_prob2 = kernel_opt['kernel_prob2']
self.blur_sigma2 = kernel_opt['blur_sigma2']
self.betag_range2 = kernel_opt['betag_range2']
self.betap_range2 = kernel_opt['betap_range2']
self.sinc_prob2 = kernel_opt['sinc_prob2']
# a final sinc filter
self.final_sinc_prob = kernel_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 get_kernel(self):
# ------------------------ Generate kernels (used in the first degradation) ------------------------ #
kernel_size = random.choice(self.kernel_range)
if np.random.uniform() < self.kernel_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.kernel_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.kernel_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
kernel = torch.FloatTensor(kernel)
kernel2 = torch.FloatTensor(kernel2)
return (kernel, kernel2, sinc_kernel)
@torch.no_grad()
def __call__(self, img_gt, kernels=None):
'''
:param: img_gt: BCHW, RGB, [0, 1] float32 tensor
'''
if kernels is None:
kernel = []
kernel2 = []
sinc_kernel = []
for _ in range(img_gt.shape[0]):
k, k2, sk = self.get_kernel()
kernel.append(k)
kernel2.append(k2)
sinc_kernel.append(sk)
kernel = torch.stack(kernel)
kernel2 = torch.stack(kernel2)
sinc_kernel = torch.stack(sinc_kernel)
else:
# kernels created in dataset.
kernel, kernel2, sinc_kernel = kernels
# ----------------------- Pre-process ----------------------- #
im_gt = img_gt.to(self.device)
if self.degrade_opt['sf'] == 8:
resized_gt = torch.nn.functional.interpolate(im_gt, scale_factor=0.5, mode='area')
else:
resized_gt = im_gt
if self.degrade_opt['use_usm']:
resized_gt = self.usm_sharpener(resized_gt)
resized_gt = resized_gt.to(memory_format=torch.contiguous_format).float()
kernel = kernel.to(self.device)
kernel2 = kernel2.to(self.device)
sinc_kernel = sinc_kernel.to(self.device)
ori_h, ori_w = im_gt.size()[2:4]
# ----------------------- The first degradation process ----------------------- #
# blur
out = filter2D(resized_gt, kernel)
# random resize
updown_type = random.choices(
['up', 'down', 'keep'],
self.degrade_opt['resize_prob'],
)[0]
if updown_type == 'up':
scale = random.uniform(1, self.degrade_opt['resize_range'][1])
elif updown_type == 'down':
scale = random.uniform(self.degrade_opt['resize_range'][0], 1)
else:
scale = 1
mode = random.choice(['area', 'bilinear', 'bicubic'])
out = torch.nn.functional.interpolate(out, scale_factor=scale, mode=mode)
# add noise
gray_noise_prob = self.degrade_opt['gray_noise_prob']
if random.random() < self.degrade_opt['gaussian_noise_prob']:
out = random_add_gaussian_noise_pt(
out,
sigma_range=self.degrade_opt['noise_range'],
clip=True,
rounds=False,
gray_prob=gray_noise_prob,
)
else:
out = random_add_poisson_noise_pt(
out,
scale_range=self.degrade_opt['poisson_scale_range'],
gray_prob=gray_noise_prob,
clip=True,
rounds=False)
# JPEG compression
jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.degrade_opt['jpeg_range'])
out = torch.clamp(out, 0, 1) # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
out = self.jpeger(out, quality=jpeg_p)
# ----------------------- The second degradation process ----------------------- #
# blur
if random.random() < self.degrade_opt['second_blur_prob']:
out = out.contiguous()
out = filter2D(out, kernel2)
# random resize
updown_type = random.choices(
['up', 'down', 'keep'],
self.degrade_opt['resize_prob2'],
)[0]
if updown_type == 'up':
scale = random.uniform(1, self.degrade_opt['resize_range2'][1])
elif updown_type == 'down':
scale = random.uniform(self.degrade_opt['resize_range2'][0], 1)
else:
scale = 1
mode = random.choice(['area', 'bilinear', 'bicubic'])
out = torch.nn.functional.interpolate(
out,
size=(int(ori_h / self.degrade_opt['sf'] * scale),
int(ori_w / self.degrade_opt['sf'] * scale)),
mode=mode,
)
# add noise
gray_noise_prob = self.degrade_opt['gray_noise_prob2']
if random.random() < self.degrade_opt['gaussian_noise_prob2']:
out = random_add_gaussian_noise_pt(
out,
sigma_range=self.degrade_opt['noise_range2'],
clip=True,
rounds=False,
gray_prob=gray_noise_prob,
)
else:
out = random_add_poisson_noise_pt(
out,
scale_range=self.degrade_opt['poisson_scale_range2'],
gray_prob=gray_noise_prob,
clip=True,
rounds=False,
)
# JPEG compression + the final sinc filter
# We also need to resize images to desired sizes. We group [resize back + sinc filter] together
# as one operation.
# We consider two orders:
# 1. [resize back + sinc filter] + JPEG compression
# 2. JPEG compression + [resize back + sinc filter]
# Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
if random.random() < 0.5:
# resize back + the final sinc filter
mode = random.choice(['area', 'bilinear', 'bicubic'])
out = torch.nn.functional.interpolate(
out,
size=(ori_h // self.degrade_opt['sf'],
ori_w // self.degrade_opt['sf']),
mode=mode,
)
out = out.contiguous()
out = filter2D(out, sinc_kernel)
# JPEG compression
jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.degrade_opt['jpeg_range2'])
out = torch.clamp(out, 0, 1)
out = self.jpeger(out, quality=jpeg_p)
else:
# JPEG compression
jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.degrade_opt['jpeg_range2'])
out = torch.clamp(out, 0, 1)
out = self.jpeger(out, quality=jpeg_p)
# resize back + the final sinc filter
mode = random.choice(['area', 'bilinear', 'bicubic'])
out = torch.nn.functional.interpolate(
out,
size=(ori_h // self.degrade_opt['sf'],
ori_w // self.degrade_opt['sf']),
mode=mode,
)
out = out.contiguous()
out = filter2D(out, sinc_kernel)
# clamp and round
im_lq = torch.clamp(out, 0, 1.0)
# random crop
gt_size = self.degrade_opt['gt_size']
patch_gt, patch_lq, gt_crop_param = paired_random_crop(im_gt, im_lq, gt_size, self.degrade_opt['sf'])
if self.degrade_opt['resize_lq']:
im_lq = torch.nn.functional.interpolate(
im_lq,
size=(im_gt.size(-2),
im_gt.size(-1)),
mode='bicubic',
)
patch_lq = torch.nn.functional.interpolate(
patch_lq,
size=(patch_gt.size(-2),
patch_gt.size(-1)),
mode='bicubic',
)
# if random.random() < self.degrade_opt['no_degradation_prob'] or torch.isnan(im_lq).any():
# im_lq = im_gt
# sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue
im_lq = im_lq.contiguous() # for the warning: grad and param do not obey the gradient layout contract
im_lq = im_lq*2 - 1.0
im_gt = im_gt*2 - 1.0
patch_lq = patch_lq*2 - 1.0
patch_gt = patch_gt*2 - 1.0
if self.degrade_opt['random_size']:
raise NotImplementedError
im_lq, im_gt = self.randn_cropinput(im_lq, im_gt)
im_lq = torch.clamp(im_lq, -1.0, 1.0)
im_gt = torch.clamp(im_gt, -1.0, 1.0)
patch_lq = torch.clamp(patch_lq, -1.0, 1.0)
patch_gt = torch.clamp(patch_gt, -1.0, 1.0)
return (im_lq, im_gt, patch_lq, patch_gt, gt_crop_param)
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