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| import glob | |
| import torch | |
| from os import path as osp | |
| from torch.utils import data as data | |
| from basicsr.data.data_util import duf_downsample, generate_frame_indices, read_img_seq | |
| from basicsr.utils import get_root_logger, scandir | |
| from basicsr.utils.registry import DATASET_REGISTRY | |
| class VideoTestDataset(data.Dataset): | |
| """Video test dataset. | |
| Supported datasets: Vid4, REDS4, REDSofficial. | |
| More generally, it supports testing dataset with following structures: | |
| :: | |
| dataroot | |
| ├── subfolder1 | |
| ├── frame000 | |
| ├── frame001 | |
| ├── ... | |
| ├── subfolder2 | |
| ├── frame000 | |
| ├── frame001 | |
| ├── ... | |
| ├── ... | |
| For testing datasets, there is no need to prepare LMDB files. | |
| 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. | |
| io_backend (dict): IO backend type and other kwarg. | |
| cache_data (bool): Whether to cache testing datasets. | |
| name (str): Dataset name. | |
| meta_info_file (str): The path to the file storing the list of test folders. If not provided, all the folders | |
| in the dataroot will be used. | |
| num_frame (int): Window size for input frames. | |
| padding (str): Padding mode. | |
| """ | |
| def __init__(self, opt): | |
| super(VideoTestDataset, self).__init__() | |
| self.opt = opt | |
| self.cache_data = opt['cache_data'] | |
| self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq'] | |
| self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []} | |
| # file client (io backend) | |
| self.file_client = None | |
| self.io_backend_opt = opt['io_backend'] | |
| assert self.io_backend_opt['type'] != 'lmdb', 'No need to use lmdb during validation/test.' | |
| logger = get_root_logger() | |
| logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}') | |
| self.imgs_lq, self.imgs_gt = {}, {} | |
| if 'meta_info_file' in opt: | |
| with open(opt['meta_info_file'], 'r') as fin: | |
| subfolders = [line.split(' ')[0] for line in fin] | |
| subfolders_lq = [osp.join(self.lq_root, key) for key in subfolders] | |
| subfolders_gt = [osp.join(self.gt_root, key) for key in subfolders] | |
| else: | |
| subfolders_lq = sorted(glob.glob(osp.join(self.lq_root, '*'))) | |
| subfolders_gt = sorted(glob.glob(osp.join(self.gt_root, '*'))) | |
| if opt['name'].lower() in ['vid4', 'reds4', 'redsofficial']: | |
| for subfolder_lq, subfolder_gt in zip(subfolders_lq, subfolders_gt): | |
| # get frame list for lq and gt | |
| subfolder_name = osp.basename(subfolder_lq) | |
| img_paths_lq = sorted(list(scandir(subfolder_lq, full_path=True))) | |
| img_paths_gt = sorted(list(scandir(subfolder_gt, full_path=True))) | |
| max_idx = len(img_paths_lq) | |
| assert max_idx == len(img_paths_gt), (f'Different number of images in lq ({max_idx})' | |
| f' and gt folders ({len(img_paths_gt)})') | |
| self.data_info['lq_path'].extend(img_paths_lq) | |
| self.data_info['gt_path'].extend(img_paths_gt) | |
| self.data_info['folder'].extend([subfolder_name] * max_idx) | |
| for i in range(max_idx): | |
| self.data_info['idx'].append(f'{i}/{max_idx}') | |
| border_l = [0] * max_idx | |
| for i in range(self.opt['num_frame'] // 2): | |
| border_l[i] = 1 | |
| border_l[max_idx - i - 1] = 1 | |
| self.data_info['border'].extend(border_l) | |
| # cache data or save the frame list | |
| if self.cache_data: | |
| logger.info(f'Cache {subfolder_name} for VideoTestDataset...') | |
| self.imgs_lq[subfolder_name] = read_img_seq(img_paths_lq) | |
| self.imgs_gt[subfolder_name] = read_img_seq(img_paths_gt) | |
| else: | |
| self.imgs_lq[subfolder_name] = img_paths_lq | |
| self.imgs_gt[subfolder_name] = img_paths_gt | |
| else: | |
| raise ValueError(f'Non-supported video test dataset: {type(opt["name"])}') | |
| def __getitem__(self, index): | |
| folder = self.data_info['folder'][index] | |
| idx, max_idx = self.data_info['idx'][index].split('/') | |
| idx, max_idx = int(idx), int(max_idx) | |
| border = self.data_info['border'][index] | |
| lq_path = self.data_info['lq_path'][index] | |
| select_idx = generate_frame_indices(idx, max_idx, self.opt['num_frame'], padding=self.opt['padding']) | |
| if self.cache_data: | |
| imgs_lq = self.imgs_lq[folder].index_select(0, torch.LongTensor(select_idx)) | |
| img_gt = self.imgs_gt[folder][idx] | |
| else: | |
| img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx] | |
| imgs_lq = read_img_seq(img_paths_lq) | |
| img_gt = read_img_seq([self.imgs_gt[folder][idx]]) | |
| img_gt.squeeze_(0) | |
| return { | |
| 'lq': imgs_lq, # (t, c, h, w) | |
| 'gt': img_gt, # (c, h, w) | |
| 'folder': folder, # folder name | |
| 'idx': self.data_info['idx'][index], # e.g., 0/99 | |
| 'border': border, # 1 for border, 0 for non-border | |
| 'lq_path': lq_path # center frame | |
| } | |
| def __len__(self): | |
| return len(self.data_info['gt_path']) | |
| class VideoTestVimeo90KDataset(data.Dataset): | |
| """Video test dataset for Vimeo90k-Test dataset. | |
| It only keeps the center frame for testing. | |
| For testing datasets, there is no need to prepare LMDB files. | |
| 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. | |
| io_backend (dict): IO backend type and other kwarg. | |
| cache_data (bool): Whether to cache testing datasets. | |
| name (str): Dataset name. | |
| meta_info_file (str): The path to the file storing the list of test folders. If not provided, all the folders | |
| in the dataroot will be used. | |
| num_frame (int): Window size for input frames. | |
| padding (str): Padding mode. | |
| """ | |
| def __init__(self, opt): | |
| super(VideoTestVimeo90KDataset, self).__init__() | |
| self.opt = opt | |
| self.cache_data = opt['cache_data'] | |
| if self.cache_data: | |
| raise NotImplementedError('cache_data in Vimeo90K-Test dataset is not implemented.') | |
| self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq'] | |
| self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []} | |
| neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])] | |
| # file client (io backend) | |
| self.file_client = None | |
| self.io_backend_opt = opt['io_backend'] | |
| assert self.io_backend_opt['type'] != 'lmdb', 'No need to use lmdb during validation/test.' | |
| logger = get_root_logger() | |
| logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}') | |
| with open(opt['meta_info_file'], 'r') as fin: | |
| subfolders = [line.split(' ')[0] for line in fin] | |
| for idx, subfolder in enumerate(subfolders): | |
| gt_path = osp.join(self.gt_root, subfolder, 'im4.png') | |
| self.data_info['gt_path'].append(gt_path) | |
| lq_paths = [osp.join(self.lq_root, subfolder, f'im{i}.png') for i in neighbor_list] | |
| self.data_info['lq_path'].append(lq_paths) | |
| self.data_info['folder'].append('vimeo90k') | |
| self.data_info['idx'].append(f'{idx}/{len(subfolders)}') | |
| self.data_info['border'].append(0) | |
| def __getitem__(self, index): | |
| lq_path = self.data_info['lq_path'][index] | |
| gt_path = self.data_info['gt_path'][index] | |
| imgs_lq = read_img_seq(lq_path) | |
| img_gt = read_img_seq([gt_path]) | |
| img_gt.squeeze_(0) | |
| return { | |
| 'lq': imgs_lq, # (t, c, h, w) | |
| 'gt': img_gt, # (c, h, w) | |
| 'folder': self.data_info['folder'][index], # folder name | |
| 'idx': self.data_info['idx'][index], # e.g., 0/843 | |
| 'border': self.data_info['border'][index], # 0 for non-border | |
| 'lq_path': lq_path[self.opt['num_frame'] // 2] # center frame | |
| } | |
| def __len__(self): | |
| return len(self.data_info['gt_path']) | |
| class VideoTestDUFDataset(VideoTestDataset): | |
| """ Video test dataset for DUF dataset. | |
| Args: | |
| opt (dict): Config for train dataset. Most of keys are the same as VideoTestDataset. | |
| It has the following extra keys: | |
| use_duf_downsampling (bool): Whether to use duf downsampling to generate low-resolution frames. | |
| scale (bool): Scale, which will be added automatically. | |
| """ | |
| def __getitem__(self, index): | |
| folder = self.data_info['folder'][index] | |
| idx, max_idx = self.data_info['idx'][index].split('/') | |
| idx, max_idx = int(idx), int(max_idx) | |
| border = self.data_info['border'][index] | |
| lq_path = self.data_info['lq_path'][index] | |
| select_idx = generate_frame_indices(idx, max_idx, self.opt['num_frame'], padding=self.opt['padding']) | |
| if self.cache_data: | |
| if self.opt['use_duf_downsampling']: | |
| # read imgs_gt to generate low-resolution frames | |
| imgs_lq = self.imgs_gt[folder].index_select(0, torch.LongTensor(select_idx)) | |
| imgs_lq = duf_downsample(imgs_lq, kernel_size=13, scale=self.opt['scale']) | |
| else: | |
| imgs_lq = self.imgs_lq[folder].index_select(0, torch.LongTensor(select_idx)) | |
| img_gt = self.imgs_gt[folder][idx] | |
| else: | |
| if self.opt['use_duf_downsampling']: | |
| img_paths_lq = [self.imgs_gt[folder][i] for i in select_idx] | |
| # read imgs_gt to generate low-resolution frames | |
| imgs_lq = read_img_seq(img_paths_lq, require_mod_crop=True, scale=self.opt['scale']) | |
| imgs_lq = duf_downsample(imgs_lq, kernel_size=13, scale=self.opt['scale']) | |
| else: | |
| img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx] | |
| imgs_lq = read_img_seq(img_paths_lq) | |
| img_gt = read_img_seq([self.imgs_gt[folder][idx]], require_mod_crop=True, scale=self.opt['scale']) | |
| img_gt.squeeze_(0) | |
| return { | |
| 'lq': imgs_lq, # (t, c, h, w) | |
| 'gt': img_gt, # (c, h, w) | |
| 'folder': folder, # folder name | |
| 'idx': self.data_info['idx'][index], # e.g., 0/99 | |
| 'border': border, # 1 for border, 0 for non-border | |
| 'lq_path': lq_path # center frame | |
| } | |
| class VideoRecurrentTestDataset(VideoTestDataset): | |
| """Video test dataset for recurrent architectures, which takes LR video | |
| frames as input and output corresponding HR video frames. | |
| Args: | |
| opt (dict): Same as VideoTestDataset. Unused opt: | |
| padding (str): Padding mode. | |
| """ | |
| def __init__(self, opt): | |
| super(VideoRecurrentTestDataset, self).__init__(opt) | |
| # Find unique folder strings | |
| self.folders = sorted(list(set(self.data_info['folder']))) | |
| def __getitem__(self, index): | |
| folder = self.folders[index] | |
| if self.cache_data: | |
| imgs_lq = self.imgs_lq[folder] | |
| imgs_gt = self.imgs_gt[folder] | |
| else: | |
| raise NotImplementedError('Without cache_data is not implemented.') | |
| return { | |
| 'lq': imgs_lq, | |
| 'gt': imgs_gt, | |
| 'folder': folder, | |
| } | |
| def __len__(self): | |
| return len(self.folders) | |