README.md

Preparing Jester

Introduction

@InProceedings{Materzynska_2019_ICCV,
  author = {Materzynska, Joanna and Berger, Guillaume and Bax, Ingo and Memisevic, Roland},
  title = {The Jester Dataset: A Large-Scale Video Dataset of Human Gestures},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
  month = {Oct},
  year = {2019}
}

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

Step 1. Prepare Annotations

First of all, you have to sign in and download annotations to $MMACTION2/data/jester/annotations on the official website.

Step 2. Prepare RGB Frames

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

You can download all RGB frame parts on jester website to $MMACTION2/data/jester/ and use the following command to extract.

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

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"), we add "filename_tmpl='{:05}.jpg'" to the dict of data.train, data.val and data.test in the config files related with jester 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/jester_extracted/
ln -s /mnt/SSD/jester_extracted/ ../../../data/jester/rawframes

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

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

Step 5. Check Directory Structure

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

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

mmaction2
├── mmaction
├── tools
├── configs
├── data
│   ├── jester
│   │   ├── jester_{train,val}_list_rawframes.txt
│   │   ├── jester_{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 Jester, please refer to getting_started.md.