Shortcuts

Source code for mmedit.datasets.sr_test_multiple_gt_dataset

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

from .base_sr_dataset import BaseSRDataset
from .registry import DATASETS


[docs]@DATASETS.register_module() class SRTestMultipleGTDataset(BaseSRDataset): """Test dataset for video super resolution for recurrent networks. It assumes all video sequences under the root directory is used for test. The dataset loads several LQ (Low-Quality) frames and GT (Ground-Truth) frames. Then it applies specified transforms and finally returns a dict containing paired data and other information. Args: lq_folder (str | :obj:`Path`): Path to a lq folder. gt_folder (str | :obj:`Path`): Path to a gt folder. pipeline (list[dict | callable]): A sequence of data transformations. scale (int): Upsampling scale ratio. test_mode (bool): Store `True` when building test dataset. Default: `True`. """ def __init__(self, lq_folder, gt_folder, pipeline, scale, test_mode=True): super().__init__(pipeline, scale, test_mode) warnings.warn('"SRTestMultipleGTDataset" have been deprecated and ' 'will be removed in future release. Please use ' '"SRFolderMultipleGTDataset" instead. Details see ' 'https://github.com/open-mmlab/mmediting/pull/355') self.lq_folder = str(lq_folder) self.gt_folder = str(gt_folder) self.data_infos = self.load_annotations()
[docs] def load_annotations(self): """Load annoations for the test dataset. Returns: dict: Returned dict for LQ and GT pairs. """ sequences = sorted(glob.glob(osp.join(self.lq_folder, '*'))) data_infos = [] for sequence in sequences: sequence_length = len(glob.glob(osp.join(sequence, '*.png'))) data_infos.append( dict( lq_path=self.lq_folder, gt_path=self.gt_folder, key=sequence.replace(f'{self.lq_folder}/', ''), sequence_length=int(sequence_length))) 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.