Prepare Sintel dataset¶

@inproceedings{Butler:ECCV:2012,
title = {A naturalistic open source movie for optical flow evaluation},
author = {Butler, D. J. and Wulff, J. and Stanley, G. B. and Black, M. J.},
booktitle = {European Conf. on Computer Vision (ECCV)},
editor = {{A. Fitzgibbon et al. (Eds.)}},
publisher = {Springer-Verlag},
series = {Part IV, LNCS 7577},
month = oct,
pages = {611--625},
year = {2012}
}

@inproceedings{Wulff:ECCVws:2012,
 title = {Lessons and insights from creating a synthetic optical flow benchmark},
 author = {Wulff, J. and Butler, D. J. and Stanley, G. B. and Black, M. J.},
 booktitle = {ECCV Workshop on Unsolved Problems in Optical Flow and Stereo Estimation},
 editor = {{A. Fusiello et al. (Eds.)}},
 publisher = {Springer-Verlag},
 series = {Part II, LNCS 7584},
 month = oct,
 pages = {168--177},
 year = {2012}
}

Download and Unpack dataset¶

Please download the datasets from the official websites.

wget http://files.is.tue.mpg.de/sintel/MPI-Sintel-complete.zip
# or use US mirror wget http://sintel.cs.washington.edu/MPI-Sintel-complete.zip
unzip MPI-Sintel-complete.zip

If your dataset folder structure is different from the following, you may need to change the corresponding paths.

├── Sintel
|   ├── training
|   |   ├── clean
|   |   |   ├── xxxx_x
|   |   |   |    ├── frame_xxxx.png
|   |   ├── final
|   |   |   ├── xxxx_x
|   |   |   |    ├── frame_xxxx.png
|   |   ├── flow
|   |   |   |    ├── frame_xxxx.flo
|   |   ├── invalid
|   |   |   ├── xxxx_x
|   |   |   |    ├── frame_xxxx.png

Generate annotation file¶

We provide a convenient script to generate annotation file, which list all of data samples in the dataset. You can use the following command to generate annotation file.

python tools/dataset_converters/prepare_sintel.py [optional arguments]

This scrip accepts these arguments:

--data-root ${DATASET_DIR}: The dataset directory of Sintel, default to 'data/Sintel'.
--save-dir ${SAVE_DIR}: The directory for saving the annotation file, default to'data/Sintel/', and annotation files for train and test dataset will be save as ${SAVE_DIR}/train.json and ${SAVE_DIR}/test.json

Note:

Annotation file is not required for local file storage, and it will be used in dataset config file when using cloud object storage like s3 storage. There is an example for using object storage:

file_client_args= dict(
    backend='s3',
    path_mapping=dict(
        {'data/': 's3://dataset_path'}))
train_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=file_client_args),
    dict(type='LoadAnnotations', file_client_args=file_client_args)]
sintel_clean_train = dict(
    type='Sintel',
    ann_file='train.json', # train.json is in data_root i.e. data/Sintel/
    pipeline=train_pipeline,
    data_root='data/Sintel',
    test_mode=False,
    pass_style='clean')