# Preparing AVA

## Introduction

<!-- [DATASET] -->

```BibTeX

@inproceedings{gu2018ava,

  title={Ava: A video dataset of spatio-temporally localized atomic visual actions},

  author={Gu, Chunhui and Sun, Chen and Ross, David A and Vondrick, Carl and Pantofaru, Caroline and Li, Yeqing and Vijayanarasimhan, Sudheendra and Toderici, George and Ricco, Susanna and Sukthankar, Rahul and others},

  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},

  pages={6047--6056},

  year={2018}

}

```

For basic dataset information, please refer to the official [website](https://research.google.com/ava/index.html).

Before we start, please make sure that the directory is located at `$MMACTION2/tools/data/ava/`.

## Step 1. Prepare Annotations

First of all, you can run the following script to prepare annotations.

```shell

bash download_annotations.sh

```

This command will download `ava_v2.1.zip` for AVA `v2.1` annotation. If you need the AVA `v2.2` annotation, you can try the following script.

```shell

VERSION=2.2 bash download_annotations.sh

```

## Step 2. Prepare Videos

Then, use the following script to prepare videos. The codes are adapted from the [official crawler](https://github.com/cvdfoundation/ava-dataset).

Note that this might take a long time.

```shell

bash download_videos.sh

```

Or you can use the following command to downloading AVA videos in parallel using a python script.

```shell

bash download_videos_parallel.sh

```

Note that if you happen to have sudoer or have [GNU parallel](https://www.gnu.org/software/parallel/) on your machine,

you can speed up the procedure by downloading in parallel.

```shell

# sudo apt-get install parallel

bash download_videos_gnu_parallel.sh

```

## Step 3. Cut Videos

Cut each video from its 15th to 30th minute and make them at 30 fps.

```shell

bash cut_videos.sh

```

## Step 4. Extract RGB and Flow

Before extracting, please refer to [install.md](/docs/install.md) for installing [denseflow](https://github.com/open-mmlab/denseflow).

If you have plenty of SSD space, then we recommend extracting frames there for better I/O performance. And you can run the following script to soft link the extracted frames.

```shell

# execute these two line (Assume the SSD is mounted at "/mnt/SSD/")

mkdir /mnt/SSD/ava_extracted/

ln -s /mnt/SSD/ava_extracted/ ../data/ava/rawframes/

```

If you only want to play with RGB frames (since extracting optical flow can be time-consuming), consider running the following script to extract **RGB-only** frames using denseflow.

```shell

bash extract_rgb_frames.sh

```

If you didn't install denseflow, you can still extract RGB frames using ffmpeg by the following script.

```shell

bash extract_rgb_frames_ffmpeg.sh

```

If both are required, run the following script to extract frames.

```shell

bash extract_frames.sh

```

## Step 5. Fetch Proposal Files

The scripts are adapted from FAIR's [Long-Term Feature Banks](https://github.com/facebookresearch/video-long-term-feature-banks).

Run the following scripts to fetch the pre-computed proposal list.

```shell

bash fetch_ava_proposals.sh

```

## Step 6. Folder Structure

After the whole data pipeline for AVA preparation.

you can get the rawframes (RGB + Flow), videos and annotation files for AVA.

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

(*minimal* means that some data are not necessary: for example, you may want to evaluate AVA using the original video format.)

```

mmaction2

├── mmaction

├── tools

├── configs

├── data

│   ├── ava

│   │   ├── annotations

│   │   |   ├── ava_dense_proposals_train.FAIR.recall_93.9.pkl

│   │   |   ├── ava_dense_proposals_val.FAIR.recall_93.9.pkl

│   │   |   ├── ava_dense_proposals_test.FAIR.recall_93.9.pkl

│   │   |   ├── ava_train_v2.1.csv

│   │   |   ├── ava_val_v2.1.csv

│   │   |   ├── ava_train_excluded_timestamps_v2.1.csv

│   │   |   ├── ava_val_excluded_timestamps_v2.1.csv

│   │   |   ├── ava_action_list_v2.1_for_activitynet_2018.pbtxt

│   │   ├── videos

│   │   │   ├── 053oq2xB3oU.mkv

│   │   │   ├── 0f39OWEqJ24.mp4

│   │   │   ├── ...

│   │   ├── videos_15min

│   │   │   ├── 053oq2xB3oU.mkv

│   │   │   ├── 0f39OWEqJ24.mp4

│   │   │   ├── ...

│   │   ├── rawframes

│   │   │   ├── 053oq2xB3oU

|   │   │   │   ├── img_00001.jpg

|   │   │   │   ├── img_00002.jpg

|   │   │   │   ├── ...

```

For training and evaluating on AVA, please refer to [getting_started](/docs/getting_started.md).

## Reference

1. O. Tange (2018): GNU Parallel 2018, March 2018, https://doi.org/10.5281/zenodo.1146014