Source code for mmedit.datasets.sr_folder_ref_dataset

# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp

from .base_sr_dataset import BaseSRDataset
from .registry import DATASETS

[docs]@DATASETS.register_module() class SRFolderRefDataset(BaseSRDataset): """ General paired image folder dataset for reference-based image restoration. The dataset loads ref (reference) image pairs Must contain: ref (reference) Optional: GT (Ground-Truth), LQ (Low Quality), or both Cannot only contain ref. Applies specified transforms and finally returns a dict containing paired data and other information. This is the "folder mode", which needs to specify the ref folder path and gt folder path, each folder containing the corresponding images. Image lists will be generated automatically. You can also specify the filename template to match the image pairs. For example, we have three folders with the following structures: :: data_root ├── ref │ ├── 0001.png │ ├── 0002.png ├── gt │ ├── 0001.png │ ├── 0002.png ├── lq │ ├── 0001_x4.png │ ├── 0002_x4.png then, you need to set: .. code-block:: python ref_folder = 'data_root/ref' gt_folder = 'data_root/gt' lq_folder = 'data_root/lq' filename_tmpl_gt='{}' filename_tmpl_lq='{}_x4' Args: pipeline (List[dict | callable]): A sequence of data transformations. scale (int): Upsampling scale ratio. ref_folder (str | :obj:`Path`): Path to a ref folder. gt_folder (str | :obj:`Path` | None): Path to a gt folder. Default: None. lq_folder (str | :obj:`Path` | None): Path to a gt folder. Default: None. test_mode (bool): Store `True` when building test dataset. Default: `False`. filename_tmpl_gt (str): Template for gt filename. Note that the template excludes the file extension. Default: '{}'. filename_tmpl_lq (str): Template for lq filename. Note that the template excludes the file extension. Default: '{}'. """ def __init__(self, pipeline, scale, ref_folder, gt_folder=None, lq_folder=None, test_mode=False, filename_tmpl_gt='{}', filename_tmpl_lq='{}'): super().__init__(pipeline, scale, test_mode) assert gt_folder or lq_folder, 'At least one of gt_folder and' \ 'lq_folder cannot be None.' self.scale = scale self.ref_folder = str(ref_folder) self.gt_folder = str(gt_folder) if gt_folder else None self.lq_folder = str(lq_folder) if lq_folder else None self.filename_tmpl_gt = filename_tmpl_gt self.filename_tmpl_lq = filename_tmpl_lq self.data_infos = self.load_annotations()
[docs] def load_annotations(self): """Load annoations for SR dataset. It loads the ref, LQ and GT image path from folders. Returns: list[dict]: A list of dicts for paired paths of ref, LQ and GT. """ data_infos = [] ref_paths = self.scan_folder(self.ref_folder) if self.gt_folder is not None: gt_paths = self.scan_folder(self.gt_folder) assert len(ref_paths) == len(gt_paths), ( f'ref and gt datasets have different number of images: ' f'{len(ref_paths)}, {len(gt_paths)}.') if self.lq_folder is not None: lq_paths = self.scan_folder(self.lq_folder) assert len(ref_paths) == len(lq_paths), ( f'ref and lq datasets have different number of images: ' f'{len(ref_paths)}, {len(lq_paths)}.') for ref_path in ref_paths: basename, ext = osp.splitext(osp.basename(ref_path)) data_dict = dict(ref_path=ref_path) if self.gt_folder is not None: gt_path = osp.join(self.gt_folder, (f'{self.filename_tmpl_gt.format(basename)}' f'{ext}')) assert gt_path in gt_paths, \ f'{gt_path} is not in gt_paths.' data_dict['gt_path'] = gt_path if self.lq_folder is not None: lq_path = osp.join(self.lq_folder, (f'{self.filename_tmpl_lq.format(basename)}' f'{ext}')) assert lq_path in lq_paths, \ f'{lq_path} is not in lq_paths.' data_dict['lq_path'] = lq_path data_infos.append(data_dict) return data_infos
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