301 lines
11 KiB
Python
301 lines
11 KiB
Python
# ------------------------------------------------------------------------
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# Copyright (c) 2022 megvii-model. All Rights Reserved.
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# ------------------------------------------------------------------------
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# Modified from BasicSR (https://github.com/xinntao/BasicSR)
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# Copyright 2018-2020 BasicSR Authors
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# ------------------------------------------------------------------------
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from torch.utils import data as data
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from torchvision.transforms.functional import normalize, resize
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from basicsr.data.data_util import (paired_paths_from_folder,
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paired_paths_from_lmdb,
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paired_paths_from_meta_info_file)
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from basicsr.data.transforms import augment, paired_random_crop, paired_random_crop_hw
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from basicsr.utils import FileClient, imfrombytes, img2tensor, padding
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import os
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import numpy as np
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class PairedImageSRLRDataset(data.Dataset):
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"""Paired image dataset for image restoration.
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Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
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GT image pairs.
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There are three modes:
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1. 'lmdb': Use lmdb files.
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If opt['io_backend'] == lmdb.
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2. 'meta_info_file': Use meta information file to generate paths.
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If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
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3. 'folder': Scan folders to generate paths.
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The rest.
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Args:
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opt (dict): Config for train datasets. It contains the following keys:
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dataroot_gt (str): Data root path for gt.
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dataroot_lq (str): Data root path for lq.
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meta_info_file (str): Path for meta information file.
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io_backend (dict): IO backend type and other kwarg.
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filename_tmpl (str): Template for each filename. Note that the
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template excludes the file extension. Default: '{}'.
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gt_size (int): Cropped patched size for gt patches.
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use_flip (bool): Use horizontal flips.
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use_rot (bool): Use rotation (use vertical flip and transposing h
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and w for implementation).
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scale (bool): Scale, which will be added automatically.
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phase (str): 'train' or 'val'.
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"""
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def __init__(self, opt):
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super(PairedImageSRLRDataset, self).__init__()
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self.opt = opt
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# file client (io backend)
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self.file_client = None
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self.io_backend_opt = opt['io_backend']
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self.mean = opt['mean'] if 'mean' in opt else None
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self.std = opt['std'] if 'std' in opt else None
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self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
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if 'filename_tmpl' in opt:
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self.filename_tmpl = opt['filename_tmpl']
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else:
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self.filename_tmpl = '{}'
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if self.io_backend_opt['type'] == 'lmdb':
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self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
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self.io_backend_opt['client_keys'] = ['lq', 'gt']
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self.paths = paired_paths_from_lmdb(
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[self.lq_folder, self.gt_folder], ['lq', 'gt'])
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elif 'meta_info_file' in self.opt and self.opt[
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'meta_info_file'] is not None:
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self.paths = paired_paths_from_meta_info_file(
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[self.lq_folder, self.gt_folder], ['lq', 'gt'],
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self.opt['meta_info_file'], self.filename_tmpl)
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else:
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import os
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nums_lq = len(os.listdir(self.lq_folder))
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nums_gt = len(os.listdir(self.gt_folder))
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# nums_lq = sorted(nums_lq)
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# nums_gt = sorted(nums_gt)
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# print('lq gt ... opt')
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# print(nums_lq, nums_gt, opt)
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assert nums_gt == nums_lq
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self.nums = nums_lq
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# {:04}_L {:04}_R
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# self.paths = paired_paths_from_folder(
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# [self.lq_folder, self.gt_folder], ['lq', 'gt'],
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# self.filename_tmpl)
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def __getitem__(self, index):
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if self.file_client is None:
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self.file_client = FileClient(
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self.io_backend_opt.pop('type'), **self.io_backend_opt)
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scale = self.opt['scale']
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# Load gt and lq images. Dimension order: HWC; channel order: BGR;
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# image range: [0, 1], float32.
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# gt_path = self.paths[index]['gt_path']
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gt_path_L = os.path.join(self.gt_folder, '{:04}_L.png'.format(index + 1))
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gt_path_R = os.path.join(self.gt_folder, '{:04}_R.png'.format(index + 1))
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# print('gt path,', gt_path)
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img_bytes = self.file_client.get(gt_path_L, 'gt')
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try:
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img_gt_L = imfrombytes(img_bytes, float32=True)
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except:
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raise Exception("gt path {} not working".format(gt_path_L))
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img_bytes = self.file_client.get(gt_path_R, 'gt')
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try:
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img_gt_R = imfrombytes(img_bytes, float32=True)
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except:
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raise Exception("gt path {} not working".format(gt_path_R))
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lq_path_L = os.path.join(self.lq_folder, '{:04}_L.png'.format(index + 1))
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lq_path_R = os.path.join(self.lq_folder, '{:04}_R.png'.format(index + 1))
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# lq_path = self.paths[index]['lq_path']
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# print(', lq path', lq_path)
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img_bytes = self.file_client.get(lq_path_L, 'lq')
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try:
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img_lq_L = imfrombytes(img_bytes, float32=True)
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except:
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raise Exception("lq path {} not working".format(lq_path_L))
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img_bytes = self.file_client.get(lq_path_R, 'lq')
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try:
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img_lq_R = imfrombytes(img_bytes, float32=True)
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except:
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raise Exception("lq path {} not working".format(lq_path_R))
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img_gt = np.concatenate([img_gt_L, img_gt_R], axis=-1)
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img_lq = np.concatenate([img_lq_L, img_lq_R], axis=-1)
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# augmentation for training
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if self.opt['phase'] == 'train':
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gt_size = self.opt['gt_size']
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# padding
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img_gt, img_lq = padding(img_gt, img_lq, gt_size)
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# random crop
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img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale,
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gt_path_L)
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# flip, rotation
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img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_flip'],
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self.opt['use_rot'])
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# TODO: color space transform
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# BGR to RGB, HWC to CHW, numpy to tensor
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img_gt, img_lq = img2tensor([img_gt, img_lq],
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bgr2rgb=True,
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float32=True)
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# normalize
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if self.mean is not None or self.std is not None:
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normalize(img_lq, self.mean, self.std, inplace=True)
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normalize(img_gt, self.mean, self.std, inplace=True)
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# if scale != 1:
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# c, h, w = img_lq.shape
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# img_lq = resize(img_lq, [h*scale, w*scale])
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# print('img_lq .. ', img_lq.shape, img_gt.shape)
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return {
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'lq': img_lq,
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'gt': img_gt,
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'lq_path': f'{index+1:04}',
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'gt_path': f'{index+1:04}',
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}
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def __len__(self):
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return self.nums // 2
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class PairedStereoImageDataset(data.Dataset):
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'''
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Paired dataset for stereo SR (Flickr1024, KITTI, Middlebury)
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'''
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def __init__(self, opt):
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super(PairedStereoImageDataset, self).__init__()
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self.opt = opt
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# file client (io backend)
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self.file_client = None
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self.io_backend_opt = opt['io_backend']
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self.mean = opt['mean'] if 'mean' in opt else None
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self.std = opt['std'] if 'std' in opt else None
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self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
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if 'filename_tmpl' in opt:
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self.filename_tmpl = opt['filename_tmpl']
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else:
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self.filename_tmpl = '{}'
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assert self.io_backend_opt['type'] == 'disk'
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import os
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self.lq_files = os.listdir(self.lq_folder)
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self.gt_files = os.listdir(self.gt_folder)
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self.nums = len(self.gt_files)
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def __getitem__(self, index):
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if self.file_client is None:
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self.file_client = FileClient(
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self.io_backend_opt.pop('type'), **self.io_backend_opt)
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gt_path_L = os.path.join(self.gt_folder, self.gt_files[index], 'hr0.png')
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gt_path_R = os.path.join(self.gt_folder, self.gt_files[index], 'hr1.png')
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img_bytes = self.file_client.get(gt_path_L, 'gt')
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try:
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img_gt_L = imfrombytes(img_bytes, float32=True)
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except:
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raise Exception("gt path {} not working".format(gt_path_L))
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img_bytes = self.file_client.get(gt_path_R, 'gt')
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try:
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img_gt_R = imfrombytes(img_bytes, float32=True)
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except:
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raise Exception("gt path {} not working".format(gt_path_R))
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lq_path_L = os.path.join(self.lq_folder, self.lq_files[index], 'lr0.png')
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lq_path_R = os.path.join(self.lq_folder, self.lq_files[index], 'lr1.png')
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# lq_path = self.paths[index]['lq_path']
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# print(', lq path', lq_path)
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img_bytes = self.file_client.get(lq_path_L, 'lq')
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try:
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img_lq_L = imfrombytes(img_bytes, float32=True)
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except:
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raise Exception("lq path {} not working".format(lq_path_L))
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img_bytes = self.file_client.get(lq_path_R, 'lq')
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try:
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img_lq_R = imfrombytes(img_bytes, float32=True)
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except:
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raise Exception("lq path {} not working".format(lq_path_R))
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img_gt = np.concatenate([img_gt_L, img_gt_R], axis=-1)
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img_lq = np.concatenate([img_lq_L, img_lq_R], axis=-1)
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scale = self.opt['scale']
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# augmentation for training
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if self.opt['phase'] == 'train':
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if 'gt_size_h' in self.opt and 'gt_size_w' in self.opt:
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gt_size_h = int(self.opt['gt_size_h'])
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gt_size_w = int(self.opt['gt_size_w'])
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else:
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gt_size = int(self.opt['gt_size'])
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gt_size_h, gt_size_w = gt_size, gt_size
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if 'flip_RGB' in self.opt and self.opt['flip_RGB']:
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idx = [
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[0, 1, 2, 3, 4, 5],
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[0, 2, 1, 3, 5, 4],
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[1, 0, 2, 4, 3, 5],
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[1, 2, 0, 4, 5, 3],
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[2, 0, 1, 5, 3, 4],
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[2, 1, 0, 5, 4, 3],
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][int(np.random.rand() * 6)]
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img_gt = img_gt[:, :, idx]
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img_lq = img_lq[:, :, idx]
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# random crop
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img_gt, img_lq = img_gt.copy(), img_lq.copy()
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img_gt, img_lq = paired_random_crop_hw(img_gt, img_lq, gt_size_h, gt_size_w, scale,
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'gt_path_L_and_R')
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# flip, rotation
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imgs, status = augment([img_gt, img_lq], self.opt['use_hflip'],
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self.opt['use_rot'], vflip=self.opt['use_vflip'], return_status=True)
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img_gt, img_lq = imgs
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img_gt, img_lq = img2tensor([img_gt, img_lq],
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bgr2rgb=True,
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float32=True)
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# normalize
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if self.mean is not None or self.std is not None:
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normalize(img_lq, self.mean, self.std, inplace=True)
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normalize(img_gt, self.mean, self.std, inplace=True)
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return {
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'lq': img_lq,
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'gt': img_gt,
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'lq_path': os.path.join(self.lq_folder, self.lq_files[index]),
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'gt_path': os.path.join(self.gt_folder, self.gt_files[index]),
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}
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def __len__(self):
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return self.nums |