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Preparing Something-Something V1

Introduction

@misc{goyal2017something,
      title={The "something something" video database for learning and evaluating visual common sense},
      author={Raghav Goyal and Samira Ebrahimi Kahou and Vincent Michalski and Joanna Materzyńska and Susanne Westphal and Heuna Kim and Valentin Haenel and Ingo Fruend and Peter Yianilos and Moritz Mueller-Freitag and Florian Hoppe and Christian Thurau and Ingo Bax and Roland Memisevic},
      year={2017},
      eprint={1706.04261},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

For basic dataset information, you can refer to the dataset paper. Before we start, please make sure that the directory is located at $MMACTION2/tools/data/sthv1/.

Step 1. Prepare Annotations

Since the official website of Something-Something V1 is currently unavailable, you can download the annotations from third-part source to $MMACTION2/data/sthv1/ .

Step 2. Prepare RGB Frames

Since the official dataset doesn't provide the original video data and only extracted RGB frames are available, you have to directly download RGB frames.

You can download all compressed file parts from third-part source to $MMACTION2/data/sthv1/ and use the following command to uncompress.

cd $MMACTION2/data/sthv1/
cat 20bn-something-something-v1-?? | tar zx
cd $MMACTION2/tools/data/sthv1/

For users who only want to use RGB frames, you can skip to step 5 to generate file lists in the format of rawframes. Since the prefix of official JPGs is "%05d.jpg" (e.g., "00001.jpg"), users need to add "filename_tmpl='{:05}.jpg'" to the dict of data.train, data.val and data.test in the config files related with sthv1 like this:

data = dict(
    videos_per_gpu=16,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=ann_file_train,
        data_prefix=data_root,
        filename_tmpl='{:05}.jpg',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=ann_file_val,
        data_prefix=data_root_val,
        filename_tmpl='{:05}.jpg',
        pipeline=val_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=ann_file_test,
        data_prefix=data_root_val,
        filename_tmpl='{:05}.jpg',
        pipeline=test_pipeline))

Step 3. Extract Flow

This part is optional if you only want to use RGB frames.

Before extracting, please refer to install.md for installing denseflow.

If you have plenty of SSD space, then we recommend extracting frames there for better I/O performance.

You can run the following script to soft link SSD.

# execute these two line (Assume the SSD is mounted at "/mnt/SSD/")
mkdir /mnt/SSD/sthv1_extracted/
ln -s /mnt/SSD/sthv1_extracted/ ../../../data/sthv1/rawframes

Then, you can run the following script to extract optical flow based on RGB frames.

cd $MMACTION2/tools/data/sthv1/
bash extract_flow.sh

Step 4. Encode Videos

This part is optional if you only want to use RGB frames.

You can run the following script to encode videos.

cd $MMACTION2/tools/data/sthv1/
bash encode_videos.sh

Step 5. Generate File List

You can run the follow script to generate file list in the format of rawframes and videos.

cd $MMACTION2/tools/data/sthv1/
bash generate_{rawframes, videos}_filelist.sh

Step 6. Check Directory Structure

After the whole data process for Something-Something V1 preparation, you will get the rawframes (RGB + Flow), and annotation files for Something-Something V1.

In the context of the whole project (for Something-Something V1 only), the folder structure will look like:

mmaction2
├── mmaction
├── tools
├── configs
├── data
│   ├── sthv1
│   │   ├── sthv1_{train,val}_list_rawframes.txt
│   │   ├── sthv1_{train,val}_list_videos.txt
│   │   ├── annotations
│   |   ├── videos
│   |   |   ├── 1.mp4
│   |   |   ├── 2.mp4
│   |   |   ├──...
│   |   ├── rawframes
│   |   |   ├── 1
│   |   |   |   ├── 00001.jpg
│   |   |   |   ├── 00002.jpg
│   |   |   |   ├── ...
│   |   |   |   ├── flow_x_00001.jpg
│   |   |   |   ├── flow_x_00002.jpg
│   |   |   |   ├── ...
│   |   |   |   ├── flow_y_00001.jpg
│   |   |   |   ├── flow_y_00002.jpg
│   |   |   |   ├── ...
│   |   |   ├── 2
│   |   |   ├── ...

For training and evaluating on Something-Something V1, please refer to Training and Test Tutorial.