Shortcuts

Source code for mmedit.datasets.sr_lmdb_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 SRLmdbDataset(BaseSRDataset): """General paired image lmdb dataset for image restoration. The dataset loads lq (Low Quality) and gt (Ground-Truth) image pairs, applies specified transforms and finally returns a dict containing paired data and other information. This is the "lmdb mode". In order to speed up IO, you are recommended to use lmdb. First, you need to make lmdb files. Suppose the lmdb files are path_to_lq/lq.lmdb and path_to_gt/gt.lmdb, then you can just set: .. code-block:: python lq_folder = path_to_lq/lq.lmdb gt_folder = path_to_gt/gt.lmdb Contents of lmdb. Taking the lq.lmdb for example, the file structure is: :: lq.lmdb ├── data.mdb ├── lock.mdb ├── meta_info.txt The data.mdb and lock.mdb are standard lmdb files and you can refer to https://lmdb.readthedocs.io/en/release/ for more details. The meta_info.txt is a specified txt file to record the meta information of our datasets. It will be automatically created when preparing datasets by our provided dataset tools. Each line in the txt file records 1. image name (with extension); 2. image shape; 3. compression level, separated by a white space. For example, the meta information of the lq.lmdb is: `baboon.png (120,125,3) 1`, which means: 1) image name (with extension): baboon.png; 2) image shape: (120,125,3); and 3) compression level: 1 We use the image name without extension as the lmdb key. Note that we use the same key for the corresponding lq and gt images. Args: lq_folder (str | :obj:`Path`): Path to a lq lmdb file. gt_folder (str | :obj:`Path`): Path to a gt lmdb file. pipeline (list[dict | callable]): A sequence of data transformations. scale (int): Upsampling scale ratio. test_mode (bool): Store `True` when building test dataset. Default: `False`. """ def __init__(self, lq_folder, gt_folder, pipeline, scale, test_mode=False): super().__init__(pipeline, scale, test_mode) self.lq_folder = str(lq_folder) self.gt_folder = str(gt_folder) self.scale = scale if not (self.gt_folder.endswith('.lmdb') and self.lq_folder.endswith('.lmdb')): raise ValueError( f'gt folder and lq folder should both in lmdb format. ' f'But received gt: {self.gt_folder}; lq: {self.lq_folder}') self.data_infos = self.load_annotations()
[docs] def load_annotations(self): """Load annoations for SR dataset. It loads the LQ and GT image path from the ``meta_info.txt`` in the LMDB files. Returns: dict: Returned dict for LQ and GT pairs. """ data_infos = [] # read keys from meta_info.txt in the gt folder # lq and gt keys are the same, ensured by the creation process # lq_path and gt_path are replaced by lmdb keys in lmdb mode. with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: for line in fin: key = line.split(' ')[0].split('.')[0] data_infos.append(dict(lq_path=key, gt_path=key)) return data_infos
Read the Docs v: v0.12.0
Versions
latest
stable
v0.12.0
Downloads
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.