import math
import numpy as np
import random
import torch
import torch.utils.data as data
import utils.utils_image as util
from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
from basicsr.utils import DiffJPEG, USMSharp
from numpy.typing import NDArray
from PIL import Image
from utils.utils_video import img2tensor
from torch import Tensor
from data.degradations import apply_real_esrgan_degradations
class DatasetSR(data.Dataset):
'''
# -----------------------------------------
# Get L/H for SISR.
# If only "paths_H" is provided, sythesize bicubicly downsampled L on-the-fly.
# -----------------------------------------
# e.g., SRResNet
# -----------------------------------------
'''
def __init__(self, opt):
super(DatasetSR, self).__init__()
self.opt = opt
self.n_channels = opt['n_channels'] if opt['n_channels'] else 3
self.sf = opt['scale'] if opt['scale'] else 4
self.patch_size = self.opt['H_size'] if self.opt['H_size'] else 96
self.L_size = self.patch_size // self.sf
# ------------------------------------
# get paths of L/H
# ------------------------------------
self.paths_H = util.get_image_paths(opt['dataroot_H'])
self.paths_L = util.get_image_paths(opt['dataroot_L'])
assert self.paths_H, 'Error: H path is empty.'
if self.paths_L and self.paths_H:
assert len(self.paths_L) == len(self.paths_H), 'L/H mismatch - {}, {}.'.format(len(self.paths_L), len(self.paths_H))
self.jpeg_simulator = DiffJPEG()
self.usm_sharpener = USMSharp()
blur_kernel_list1 = ['iso', 'aniso', 'generalized_iso',
'generalized_aniso', 'plateau_iso', 'plateau_aniso']
blur_kernel_list2 = ['iso', 'aniso', 'generalized_iso',
'generalized_aniso', 'plateau_iso', 'plateau_aniso']
blur_kernel_prob1 = [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
blur_kernel_prob2 = [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
kernel_size = 21
blur_sigma1 = [0.05, 0.2]
blur_sigma2 = [0.05, 0.1]
betag_range1 = [0.7, 1.3]
betag_range2 = [0.7, 1.3]
betap_range1 = [0.7, 1.3]
betap_range2 = [0.7, 1.3]
def _decide_kernels(self) -> NDArray:
blur_kernel1 = random_mixed_kernels(
self.blur_kernel_list1,
self.blur_kernel_prob1,
self.kernel_size,
self.blur_sigma1,
self.blur_sigma1, [-math.pi, math.pi],
self.betag_range1,
self.betap_range1,
noise_range=None
)
blur_kernel2 = random_mixed_kernels(
self.blur_kernel_list2,
self.blur_kernel_prob2,
self.kernel_size,
self.blur_sigma2,
self.blur_sigma2, [-math.pi, math.pi],
self.betag_range2,
self.betap_range2,
noise_range=None
)
if self.kernel_size < 13:
omega_c = np.random.uniform(np.pi / 3, np.pi)
else:
omega_c = np.random.uniform(np.pi / 5, np.pi)
sinc_kernel = circular_lowpass_kernel(omega_c, self.kernel_size, pad_to=21)
return (blur_kernel1, blur_kernel2, sinc_kernel)
def __getitem__(self, index):
L_path = None
# ------------------------------------
# get H image
# ------------------------------------
H_path = self.paths_H[index]
img_H = util.imread_uint(H_path, self.n_channels)
img_H = util.uint2single(img_H)
# ------------------------------------
# modcrop
# ------------------------------------
img_H = util.modcrop(img_H, self.sf)
# ------------------------------------
# get L image
# ------------------------------------
if self.paths_L:
# --------------------------------
# directly load L image
# --------------------------------
L_path = self.paths_L[index]
img_L = util.imread_uint(L_path, self.n_channels)
img_L = util.uint2single(img_L)
else:
# --------------------------------
# sythesize L image via matlab's bicubic
# --------------------------------
H, W = img_H.shape[:2]
img_L = util.imresize_np(img_H, 1 / self.sf, True)
src_tensor = img2tensor(img_L.copy(), bgr2rgb=False,
float32=True).unsqueeze(0)
blur_kernel1, blur_kernel2, sinc_kernel = self._decide_kernels()
(img_L_2, sharp_img_L, degraded_img_L) = apply_real_esrgan_degradations(
src_tensor,
blur_kernel1=Tensor(blur_kernel1).unsqueeze(0),
blur_kernel2=Tensor(blur_kernel2).unsqueeze(0),
second_blur_prob=0.2,
sinc_kernel=Tensor(sinc_kernel).unsqueeze(0),
resize_prob1=[0.2, 0.7, 0.1],
resize_prob2=[0.3, 0.4, 0.3],
resize_range1=[0.9, 1.1],
resize_range2=[0.9, 1.1],
gray_noise_prob1=0.2,
gray_noise_prob2=0.2,
gaussian_noise_prob1=0.2,
gaussian_noise_prob2=0.2,
noise_range=[0.01, 0.2],
poisson_scale_range=[0.05, 0.45],
jpeg_compression_range1=[85, 100],
jpeg_compression_range2=[85, 100],
jpeg_simulator=self.jpeg_simulator,
random_crop_gt_size=256,
sr_upsample_scale=1,
usm_sharpener=self.usm_sharpener
)
# Image.fromarray((degraded_img_L[0] * 255).permute(
# 1, 2, 0).cpu().numpy().astype(np.uint8)).save(
# "/home/cll/Desktop/degraded_L.png")
# Image.fromarray((img_L * 255).astype(np.uint8)).save(
# "/home/cll/Desktop/img_L.png")
# Image.fromarray((img_L_2[0] * 255).permute(
# 1, 2, 0).cpu().numpy().astype(np.uint8)).save(
# "/home/cll/Desktop/img_L_2.png")
# exit()
# ------------------------------------
# if train, get L/H patch pair
# ------------------------------------
if self.opt['phase'] == 'train':
H, W, C = img_L.shape
# --------------------------------
# randomly crop the L patch
# --------------------------------
rnd_h = random.randint(0, max(0, H - self.L_size))
rnd_w = random.randint(0, max(0, W - self.L_size))
img_L = img_L[rnd_h:rnd_h + self.L_size, rnd_w:rnd_w + self.L_size, :]
# --------------------------------
# crop corresponding H patch
# --------------------------------
rnd_h_H, rnd_w_H = int(rnd_h * self.sf), int(rnd_w * self.sf)
img_H = img_H[rnd_h_H:rnd_h_H + self.patch_size, rnd_w_H:rnd_w_H + self.patch_size, :]
# --------------------------------
# augmentation - flip and/or rotate + RealESRGAN modified degradations
# --------------------------------
mode = random.randint(0, 7)
img_L, img_H = util.augment_img(img_L, mode=mode), util.augment_img(img_H, mode=mode)
# ------------------------------------
# L/H pairs, HWC to CHW, numpy to tensor
# ------------------------------------
img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(img_L)
if L_path is None:
L_path = H_path
return {'L': img_L, 'H': img_H, 'L_path': L_path, 'H_path': H_path}
def __len__(self):
return len(self.paths_H)