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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import cv2
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import glob
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import random
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import logging
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import numpy as np
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from paddle.io import Dataset
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from .base_sr_dataset import BaseDataset
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from .builder import DATASETS
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logger = logging.getLogger(__name__)
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@DATASETS.register()
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class VSRFolderDataset(BaseDataset):
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"""Video super-resolution for folder format.
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Args:
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lq_folder (str): Path to a low quality image folder.
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gt_folder (str): Path to a ground truth image folder.
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ann_file (str): Path to the annotation file.
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preprocess (list[dict|callable]): A list functions of data transformations.
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num_frames (int): Number of frames of each input clip.
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times (int): Repeat times of datset length.
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"""
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def __init__(self,
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lq_folder,
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gt_folder,
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preprocess,
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num_frames=None,
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times=1):
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super().__init__(preprocess)
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self.lq_folder = str(lq_folder)
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self.gt_folder = str(gt_folder)
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self.num_frames = num_frames
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self.times = times
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self.data_infos = self.prepare_data_infos()
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def prepare_data_infos(self):
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sequences = sorted(glob.glob(os.path.join(self.lq_folder, '*')))
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data_infos = []
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for sequence in sequences:
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sequence_length = len(glob.glob(os.path.join(sequence, '*.png')))
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if self.num_frames is None:
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num_frames = sequence_length
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else:
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num_frames = self.num_frames
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data_infos.append(
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dict(
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lq_path=self.lq_folder,
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gt_path=self.gt_folder,
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key=sequence.replace(f'{self.lq_folder}/', ''),
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num_frames=num_frames,
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sequence_length=sequence_length))
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return data_infos
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