add random degradation image dataset, remain psf degradation addition
huangcaohui
parent
a8d01a7e3b
commit
ccffddb550
|
|
@ -287,6 +287,33 @@ def paths_from_lmdb(folder):
|
|||
return paths
|
||||
|
||||
|
||||
def paths_from_meta_info_file(folder, meta_info_file, filename_tmpl): # note: add new
|
||||
"""Generate paths from folder.
|
||||
|
||||
Args:
|
||||
folder (str): Folder path.
|
||||
meta_info_file (str): Path to the meta information file.
|
||||
filename_tmpl (str): Template for each filename. Note that the
|
||||
template excludes the file extension. Usually the filename_tmpl is
|
||||
for files in the input folder.
|
||||
|
||||
Returns:
|
||||
list[str]: Returned path list.
|
||||
"""
|
||||
|
||||
with open(meta_info_file, 'r') as fin:
|
||||
input_names = [line.split(' ')[0] for line in fin]
|
||||
|
||||
paths = []
|
||||
for input in input_names:
|
||||
basename, ext = osp.splitext(osp.basename(input))
|
||||
name = f'{filename_tmpl.format(basename)}{ext}'
|
||||
path = osp.join(folder, name)
|
||||
paths.append(path)
|
||||
|
||||
return paths
|
||||
|
||||
|
||||
def generate_gaussian_kernel(kernel_size=13, sigma=1.6):
|
||||
"""Generate Gaussian kernel used in `duf_downsample`.
|
||||
|
||||
|
|
|
|||
|
|
@ -89,7 +89,7 @@ class PairedImageDataset(data.Dataset):
|
|||
img_bytes = self.file_client.get(gt_path, 'gt')
|
||||
try:
|
||||
img_gt = imfrombytes(img_bytes, float32=True)
|
||||
except:
|
||||
except Exception:
|
||||
raise Exception("gt path {} not working".format(gt_path))
|
||||
|
||||
lq_path = self.paths[index]['lq_path']
|
||||
|
|
@ -97,7 +97,7 @@ class PairedImageDataset(data.Dataset):
|
|||
img_bytes = self.file_client.get(lq_path, 'lq')
|
||||
try:
|
||||
img_lq = imfrombytes(img_bytes, float32=True)
|
||||
except:
|
||||
except Exception:
|
||||
raise Exception("lq path {} not working".format(lq_path))
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,136 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Copyright (c) 2022 megvii-model. All Rights Reserved.
|
||||
# ------------------------------------------------------------------------
|
||||
# Modified from BasicSR (https://github.com/xinntao/BasicSR)
|
||||
# Copyright 2018-2020 BasicSR Authors
|
||||
# ------------------------------------------------------------------------
|
||||
from torch.utils import data as data
|
||||
from torchvision.transforms.functional import normalize
|
||||
|
||||
from basicsr.data.data_util import (paths_from_folder,
|
||||
paths_from_meta_info_file,
|
||||
paths_from_lmdb)
|
||||
from basicsr.data.transforms import augment, paired_random_crop
|
||||
from basicsr.utils import FileClient, imfrombytes, img2tensor, padding
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
sys.path.append(str(Path(__file__).resolve().parents[3]))
|
||||
from psf import PSF
|
||||
|
||||
|
||||
class RandomDegradationImageDataset(data.Dataset):
|
||||
"""Single image dataset for image restoration. Using random degradation for
|
||||
HQ image to obtain LQ images.
|
||||
|
||||
Read HQ (High Quality, e.g. HR (High Resolution), blurry, noisy, etc) only.
|
||||
|
||||
There are three modes:
|
||||
1. 'lmdb': Use lmdb files.
|
||||
If opt['io_backend'] == lmdb.
|
||||
2. 'meta_info_file': Use meta information file to generate paths.
|
||||
If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
|
||||
3. 'folder': Scan folders to generate paths.
|
||||
The rest.
|
||||
|
||||
Args:
|
||||
opt (dict): Config for train datasets. It contains the following keys:
|
||||
dataroot_gt (str): Data root path for gt.
|
||||
dataroot_lq (str): Data root path for lq, but not using.
|
||||
meta_info_file (str): Path for meta information file.
|
||||
io_backend (dict): IO backend type and other kwarg.
|
||||
filename_tmpl (str): Template for each filename. Note that the
|
||||
template excludes the file extension. Default: '{}'.
|
||||
gt_size (int): Cropped patched size for gt patches.
|
||||
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.
|
||||
phase (str): 'train' or 'val'.
|
||||
"""
|
||||
|
||||
def __init__(self, opt):
|
||||
super(RandomDegradationImageDataset, 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.gt_folder = opt['dataroot_gt']
|
||||
if 'filename_tmpl' in opt:
|
||||
self.filename_tmpl = opt['filename_tmpl']
|
||||
else:
|
||||
self.filename_tmpl = '{}'
|
||||
|
||||
if self.io_backend_opt['type'] == 'lmdb':
|
||||
self.io_backend_opt['db_paths'] = [self.gt_folder]
|
||||
self.io_backend_opt['client_keys'] = ['gt']
|
||||
self.paths = paths_from_lmdb(self.gt_folder)
|
||||
elif 'meta_info_file' in self.opt and self.opt[
|
||||
'meta_info_file'] is not None:
|
||||
self.paths = paths_from_meta_info_file(
|
||||
self.gt_folder, self.opt['meta_info_file'], self.filename_tmpl)
|
||||
else:
|
||||
self.paths = paths_from_folder(self.gt_folder)
|
||||
|
||||
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']
|
||||
|
||||
# Load gt images. Dimension order: HWC; channel order: BGR;
|
||||
# image range: [0, 1], float32.
|
||||
gt_path = self.paths[index]
|
||||
# print('gt path,', gt_path)
|
||||
img_bytes = self.file_client.get(gt_path, 'gt')
|
||||
try:
|
||||
img_gt = imfrombytes(img_bytes, float32=True)
|
||||
except Exception:
|
||||
raise Exception("gt path {} not working".format(gt_path))
|
||||
|
||||
# lq_path = self.paths[index]['lq_path']
|
||||
# # print(', lq path', lq_path)
|
||||
# img_bytes = self.file_client.get(lq_path, 'lq')
|
||||
# try:
|
||||
# img_lq = imfrombytes(img_bytes, float32=True)
|
||||
# except:
|
||||
# raise Exception("lq path {} not working".format(lq_path))
|
||||
|
||||
img_lq = None
|
||||
|
||||
# augmentation for training
|
||||
if self.opt['phase'] == 'train':
|
||||
gt_size = self.opt['gt_size']
|
||||
# padding
|
||||
img_gt, img_lq = padding(img_gt, img_lq, gt_size)
|
||||
|
||||
# random crop
|
||||
img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale,
|
||||
gt_path)
|
||||
# flip, rotation
|
||||
img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_flip'],
|
||||
self.opt['use_rot'])
|
||||
|
||||
# TODO: color space transform
|
||||
# BGR to RGB, HWC to CHW, numpy to tensor
|
||||
img_gt, img_lq = img2tensor([img_gt, 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)
|
||||
normalize(img_gt, self.mean, self.std, inplace=True)
|
||||
|
||||
return {
|
||||
'lq': img_lq,
|
||||
'gt': img_gt,
|
||||
'gt_path': gt_path
|
||||
}
|
||||
|
||||
def __len__(self):
|
||||
return len(self.paths)
|
||||
|
|
@ -386,7 +386,7 @@ class ImageRestorationModel(BaseModel):
|
|||
# self.dist_validation(*args, **kwargs)
|
||||
|
||||
def nondist_validation(self, dataloader, current_iter, tb_logger,
|
||||
save_img, rgb2bgr, use_image):
|
||||
save_img, rgb2bgr, use_image): # note: add new here
|
||||
dataset_name = dataloader.dataset.opt['name']
|
||||
with_metrics = self.opt['val'].get('metrics') is not None
|
||||
if with_metrics:
|
||||
|
|
|
|||
Loading…
Reference in New Issue