from os import path as osp from torch.utils import data as data from torchvision.transforms.functional import normalize from basicsr.data.data_util import paths_from_lmdb from basicsr.utils import FileClient, imfrombytes, img2tensor, rgb2ycbcr, scandir from basicsr.utils.registry import DATASET_REGISTRY from pathlib import Path import random import cv2 import numpy as np import torch @DATASET_REGISTRY.register() class SingleImageDataset(data.Dataset): """Read only lq images in the test phase. Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc). There are two modes: 1. 'meta_info_file': Use meta information file to generate paths. 2. 'folder': Scan folders to generate paths. Args: opt (dict): Config for train datasets. It contains the following keys: dataroot_lq (str): Data root path for lq. meta_info_file (str): Path for meta information file. io_backend (dict): IO backend type and other kwarg. """ def __init__(self, opt): super(SingleImageDataset, self).__init__() self.opt = opt # file client (io backend) self.file_client = None self.io_backend_opt = opt['io_backend'] self.mean = opt['mean'] if 'mean' in opt else None self.std = opt['std'] if 'std' in opt else None self.lq_folder = opt['dataroot_lq'] if self.io_backend_opt['type'] == 'lmdb': self.io_backend_opt['db_paths'] = [self.lq_folder] self.io_backend_opt['client_keys'] = ['lq'] self.paths = paths_from_lmdb(self.lq_folder) elif 'meta_info_file' in self.opt: with open(self.opt['meta_info_file'], 'r') as fin: self.paths = [osp.join(self.lq_folder, line.rstrip().split(' ')[0]) for line in fin] else: self.paths = sorted(list(scandir(self.lq_folder, full_path=True))) 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 lq image lq_path = self.paths[index] img_bytes = self.file_client.get(lq_path, 'lq') img_lq = imfrombytes(img_bytes, float32=True) # color space transform if 'color' in self.opt and self.opt['color'] == 'y': img_lq = rgb2ycbcr(img_lq, y_only=True)[..., None] # BGR to RGB, HWC to CHW, numpy to tensor img_lq = img2tensor(img_lq, bgr2rgb=True, float32=True) # normalize if self.mean is not None or self.std is not None: normalize(img_lq, self.mean, self.std, inplace=True) return {'lq': img_lq, 'lq_path': lq_path} def __len__(self): return len(self.paths) @DATASET_REGISTRY.register() class SingleImageNPDataset(data.Dataset): """Read only lq images in the test phase. Read diffusion generated data for training CFW. Args: opt (dict): Config for train datasets. It contains the following keys: gt_path: Data root path for training data. The path needs to contain the following folders: gts: Ground-truth images. inputs: Input LQ images. latents: The corresponding HQ latent code generated by diffusion model given the input LQ image. samples: The corresponding HQ image given the HQ latent code, just for verification. io_backend (dict): IO backend type and other kwarg. """ def __init__(self, opt): super(SingleImageNPDataset, self).__init__() self.opt = opt # file client (io backend) self.file_client = None self.io_backend_opt = opt['io_backend'] self.mean = opt['mean'] if 'mean' in opt else None self.std = opt['std'] if 'std' in opt else None if 'image_type' not in opt: opt['image_type'] = 'png' if isinstance(opt['gt_path'], str): self.gt_paths = sorted([str(x) for x in Path(opt['gt_path']+'/gts').glob('*.'+opt['image_type'])]) self.lq_paths = sorted([str(x) for x in Path(opt['gt_path']+'/inputs').glob('*.'+opt['image_type'])]) self.np_paths = sorted([str(x) for x in Path(opt['gt_path']+'/latents').glob('*.npy')]) self.sample_paths = sorted([str(x) for x in Path(opt['gt_path']+'/samples').glob('*.'+opt['image_type'])]) else: self.gt_paths = sorted([str(x) for x in Path(opt['gt_path'][0]+'/gts').glob('*.'+opt['image_type'])]) self.lq_paths = sorted([str(x) for x in Path(opt['gt_path'][0]+'/inputs').glob('*.'+opt['image_type'])]) self.np_paths = sorted([str(x) for x in Path(opt['gt_path'][0]+'/latents').glob('*.npy')]) self.sample_paths = sorted([str(x) for x in Path(opt['gt_path'][0]+'/samples').glob('*.'+opt['image_type'])]) if len(opt['gt_path']) > 1: for i in range(len(opt['gt_path'])-1): self.gt_paths.extend(sorted([str(x) for x in Path(opt['gt_path'][i+1]+'/gts').glob('*.'+opt['image_type'])])) self.lq_paths.extend(sorted([str(x) for x in Path(opt['gt_path'][i+1]+'/inputs').glob('*.'+opt['image_type'])])) self.np_paths.extend(sorted([str(x) for x in Path(opt['gt_path'][i+1]+'/latents').glob('*.npy')])) self.sample_paths.extend(sorted([str(x) for x in Path(opt['gt_path'][i+1]+'/samples').glob('*.'+opt['image_type'])])) assert len(self.gt_paths) == len(self.lq_paths) assert len(self.gt_paths) == len(self.np_paths) assert len(self.gt_paths) == len(self.sample_paths) 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 lq image lq_path = self.lq_paths[index] gt_path = self.gt_paths[index] sample_path = self.sample_paths[index] np_path = self.np_paths[index] img_bytes = self.file_client.get(lq_path, 'lq') img_lq = imfrombytes(img_bytes, float32=True) img_bytes_gt = self.file_client.get(gt_path, 'gt') img_gt = imfrombytes(img_bytes_gt, float32=True) img_bytes_sample = self.file_client.get(sample_path, 'sample') img_sample = imfrombytes(img_bytes_sample, float32=True) latent_np = np.load(np_path) # color space transform if 'color' in self.opt and self.opt['color'] == 'y': img_lq = rgb2ycbcr(img_lq, y_only=True)[..., None] img_gt = rgb2ycbcr(img_gt, y_only=True)[..., None] img_sample = rgb2ycbcr(img_sample, y_only=True)[..., None] # BGR to RGB, HWC to CHW, numpy to tensor img_lq = img2tensor(img_lq, bgr2rgb=True, float32=True) img_gt = img2tensor(img_gt, bgr2rgb=True, float32=True) img_sample = img2tensor(img_sample, bgr2rgb=True, float32=True) latent_np = torch.from_numpy(latent_np).float() latent_np = latent_np.to(img_gt.device) # normalize if self.mean is not None or self.std is not None: normalize(img_lq, self.mean, self.std, inplace=True) normalize(img_gt, self.mean, self.std, inplace=True) normalize(img_sample, self.mean, self.std, inplace=True) return {'lq': img_lq, 'lq_path': lq_path, 'gt': img_gt, 'gt_path': gt_path, 'latent': latent_np[0], 'latent_path': np_path, 'sample': img_sample, 'sample_path': sample_path} def __len__(self): return len(self.gt_paths)