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| 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) | |