Preparing REDS Dataset¶

@InProceedings{Nah_2019_CVPR_Workshops_REDS,
  author = {Nah, Seungjun and Baik, Sungyong and Hong, Seokil and Moon, Gyeongsik and Son, Sanghyun and Timofte, Radu and Lee, Kyoung Mu},
  title = {NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {June},
  year = {2019}
}

Training dataset: REDS dataset.
Validation dataset: REDS dataset and Vid4.

Note that we merge train and val datasets in REDS for easy switching between REDS4 partition (used in EDVR) and the official validation partition. The original val dataset (clip names from 000 to 029) are modified to avoid conflicts with training dataset (total 240 clips). Specifically, the clip names are changed to 240, 241, … 269.

You can prepare the REDS dataset by running:

python tools/dataset_converters/reds/preprocess_reds_dataset.py --root-path ./data/REDS

mmagic
├── mmagic
├── tools
├── configs
├── data
│   ├── REDS
│   │   ├── train_sharp
│   │   │   ├── 000
│   │   │   ├── 001
│   │   │   ├── ...
│   │   ├── train_sharp_bicubic
│   │   │   ├── X4
│   │   │   |   ├── 000
│   │   │   |   ├── 001
│   │   │   |   ├── ...
│   │   ├── meta_info_reds4_train.txt
│   │   ├── meta_info_reds4_val.txt
│   │   ├── meta_info_official_train.txt
│   │   ├── meta_info_official_val.txt
│   │   ├── meta_info_REDS_GT.txt
│   ├── REDS4
│   │   ├── GT
│   │   ├── sharp_bicubic

Prepare LMDB dataset for REDS¶

If you want to use LMDB datasets for faster IO speed, you can make LMDB files by:

python tools/dataset_converters/reds/preprocess_reds_dataset.py --root-path ./data/REDS --make-lmdb

Crop to sub-images¶

MMagic also supports cropping REDS images to sub-images for faster IO. We provide such a script:

python tools/dataset_converters/reds/crop_sub_images.py --data-root ./data/REDS  -scales 4

The generated data is stored under REDS and the data structure is as follows, where _sub indicates the sub-images.

mmagic
├── mmagic
├── tools
├── configs
├── data
│   ├── REDS
│   │   ├── train_sharp
│   │   │   ├── 000
│   │   │   ├── 001
│   │   │   ├── ...
│   │   ├── train_sharp_sub
│   │   │   ├── 000_s001
│   │   │   ├── 000_s002
│   │   │   ├── ...
│   │   │   ├── 001_s001
│   │   │   ├── ...
│   │   ├── train_sharp_bicubic
│   │   │   ├── X4
│   │   │   │   ├── 000
│   │   │   │   ├── 001
│   │   │   │   ├── ...
│   │   │   ├── X4_sub
│   │   │   ├── 000_s001
│   │   │   ├── 000_s002
│   │   │   ├── ...
│   │   │   ├── 001_s001
│   │   │   ├── ...

Note that by default preprocess_reds_dataset.py does not make lmdb and annotation file for the cropped dataset. You may need to modify the scripts a little bit for such operations.