import numpy as np import random import torch from pathlib import Path from torch.utils import data as data from basicsr.data.transforms import augment, paired_random_crop from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor from basicsr.utils.flow_util import dequantize_flow from basicsr.utils.registry import DATASET_REGISTRY @DATASET_REGISTRY.register() class REDSDataset(data.Dataset): """REDS dataset for training. The keys are generated from a meta info txt file. basicsr/data/meta_info/meta_info_REDS_GT.txt Each line contains: 1. subfolder (clip) name; 2. frame number; 3. image shape, seperated by a white space. Examples: 000 100 (720,1280,3) 001 100 (720,1280,3) ... Key examples: "000/00000000" GT (gt): Ground-Truth; LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames. Args: opt (dict): Config for train dataset. It contains the following keys: dataroot_gt (str): Data root path for gt. dataroot_lq (str): Data root path for lq. dataroot_flow (str, optional): Data root path for flow. meta_info_file (str): Path for meta information file. val_partition (str): Validation partition types. 'REDS4' or 'official'. io_backend (dict): IO backend type and other kwarg. num_frame (int): Window size for input frames. gt_size (int): Cropped patched size for gt patches. interval_list (list): Interval list for temporal augmentation. random_reverse (bool): Random reverse input frames. use_flip (bool): Use horizontal flips. use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). scale (bool): Scale, which will be added automatically. """ def __init__(self, opt): super(REDSDataset, self).__init__() self.opt = opt self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq']) self.flow_root = Path(opt['dataroot_flow']) if opt['dataroot_flow'] is not None else None assert opt['num_frame'] % 2 == 1, (f'num_frame should be odd number, but got {opt["num_frame"]}') self.num_frame = opt['num_frame'] self.num_half_frames = opt['num_frame'] // 2 self.keys = [] with open(opt['meta_info_file'], 'r') as fin: for line in fin: folder, frame_num, _ = line.split(' ') self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))]) # remove the video clips used in validation if opt['val_partition'] == 'REDS4': val_partition = ['000', '011', '015', '020'] elif opt['val_partition'] == 'official': val_partition = [f'{v:03d}' for v in range(240, 270)] else: raise ValueError(f'Wrong validation partition {opt["val_partition"]}.' f"Supported ones are ['official', 'REDS4'].") self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition] # file client (io backend) self.file_client = None self.io_backend_opt = opt['io_backend'] self.is_lmdb = False if self.io_backend_opt['type'] == 'lmdb': self.is_lmdb = True if self.flow_root is not None: self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root, self.flow_root] self.io_backend_opt['client_keys'] = ['lq', 'gt', 'flow'] else: self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root] self.io_backend_opt['client_keys'] = ['lq', 'gt'] # temporal augmentation configs self.interval_list = opt['interval_list'] self.random_reverse = opt['random_reverse'] interval_str = ','.join(str(x) for x in opt['interval_list']) logger = get_root_logger() logger.info(f'Temporal augmentation interval list: [{interval_str}]; ' f'random reverse is {self.random_reverse}.') def __getitem__(self, index): if self.file_client is None: self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) scale = self.opt['scale'] gt_size = self.opt['gt_size'] key = self.keys[index] clip_name, frame_name = key.split('/') # key example: 000/00000000 center_frame_idx = int(frame_name) # determine the neighboring frames interval = random.choice(self.interval_list) # ensure not exceeding the borders start_frame_idx = center_frame_idx - self.num_half_frames * interval end_frame_idx = center_frame_idx + self.num_half_frames * interval # each clip has 100 frames starting from 0 to 99 while (start_frame_idx < 0) or (end_frame_idx > 99): center_frame_idx = random.randint(0, 99) start_frame_idx = (center_frame_idx - self.num_half_frames * interval) end_frame_idx = center_frame_idx + self.num_half_frames * interval frame_name = f'{center_frame_idx:08d}' neighbor_list = list(range(start_frame_idx, end_frame_idx + 1, interval)) # random reverse if self.random_reverse and random.random() < 0.5: neighbor_list.reverse() assert len(neighbor_list) == self.num_frame, (f'Wrong length of neighbor list: {len(neighbor_list)}') # get the GT frame (as the center frame) if self.is_lmdb: img_gt_path = f'{clip_name}/{frame_name}' else: img_gt_path = self.gt_root / clip_name / f'{frame_name}.png' img_bytes = self.file_client.get(img_gt_path, 'gt') img_gt = imfrombytes(img_bytes, float32=True) # get the neighboring LQ frames img_lqs = [] for neighbor in neighbor_list: if self.is_lmdb: img_lq_path = f'{clip_name}/{neighbor:08d}' else: img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png' img_bytes = self.file_client.get(img_lq_path, 'lq') img_lq = imfrombytes(img_bytes, float32=True) img_lqs.append(img_lq) # get flows if self.flow_root is not None: img_flows = [] # read previous flows for i in range(self.num_half_frames, 0, -1): if self.is_lmdb: flow_path = f'{clip_name}/{frame_name}_p{i}' else: flow_path = (self.flow_root / clip_name / f'{frame_name}_p{i}.png') img_bytes = self.file_client.get(flow_path, 'flow') cat_flow = imfrombytes(img_bytes, flag='grayscale', float32=False) # uint8, [0, 255] dx, dy = np.split(cat_flow, 2, axis=0) flow = dequantize_flow(dx, dy, max_val=20, denorm=False) # we use max_val 20 here. img_flows.append(flow) # read next flows for i in range(1, self.num_half_frames + 1): if self.is_lmdb: flow_path = f'{clip_name}/{frame_name}_n{i}' else: flow_path = (self.flow_root / clip_name / f'{frame_name}_n{i}.png') img_bytes = self.file_client.get(flow_path, 'flow') cat_flow = imfrombytes(img_bytes, flag='grayscale', float32=False) # uint8, [0, 255] dx, dy = np.split(cat_flow, 2, axis=0) flow = dequantize_flow(dx, dy, max_val=20, denorm=False) # we use max_val 20 here. img_flows.append(flow) # for random crop, here, img_flows and img_lqs have the same # spatial size img_lqs.extend(img_flows) # randomly crop img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale, img_gt_path) if self.flow_root is not None: img_lqs, img_flows = img_lqs[:self.num_frame], img_lqs[self.num_frame:] # augmentation - flip, rotate img_lqs.append(img_gt) if self.flow_root is not None: img_results, img_flows = augment(img_lqs, self.opt['use_flip'], self.opt['use_rot'], img_flows) else: img_results = augment(img_lqs, self.opt['use_flip'], self.opt['use_rot']) img_results = img2tensor(img_results) img_lqs = torch.stack(img_results[0:-1], dim=0) img_gt = img_results[-1] if self.flow_root is not None: img_flows = img2tensor(img_flows) # add the zero center flow img_flows.insert(self.num_half_frames, torch.zeros_like(img_flows[0])) img_flows = torch.stack(img_flows, dim=0) # img_lqs: (t, c, h, w) # img_flows: (t, 2, h, w) # img_gt: (c, h, w) # key: str if self.flow_root is not None: return {'lq': img_lqs, 'flow': img_flows, 'gt': img_gt, 'key': key} else: return {'lq': img_lqs, 'gt': img_gt, 'key': key} def __len__(self): return len(self.keys)