# Preparing UCF-101

## Introduction

<!-- [DATASET] -->

```BibTeX

@article{Soomro2012UCF101AD,

  title={UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild},

  author={K. Soomro and A. Zamir and M. Shah},

  journal={ArXiv},

  year={2012},

  volume={abs/1212.0402}

}

```

For basic dataset information, you can refer to the dataset [website](https://www.crcv.ucf.edu/research/data-sets/ucf101/).

Before we start, please make sure that the directory is located at `$MMACTION2/tools/data/ucf101/`.

## Step 1. Prepare Annotations

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

```shell

bash download_annotations.sh

```

## Step 2. Prepare Videos

Then, you can run the following script to prepare videos.

```shell

bash download_videos.sh

```

For better decoding speed, you can resize the original videos into smaller sized, densely encoded version by:

```

python ../resize_videos.py ../../../data/ucf101/videos/ ../../../data/ucf101/videos_256p_dense_cache --dense --level 2 --ext avi

```

## Step 3. Extract RGB and Flow

This part is **optional** if you only want to use the video loader.

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. The extracted frames (RGB + Flow) will take up about 100GB.

You can run the following script to soft link SSD.

```shell

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

mkdir /mnt/SSD/ucf101_extracted/

ln -s /mnt/SSD/ucf101_extracted/ ../../../data/ucf101/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 OpenCV by the following script, but it will keep the original size of the images.

```shell

bash extract_rgb_frames_opencv.sh

```

If both are required, run the following script to extract frames using "tvl1" algorithm.

```shell

bash extract_frames.sh

```

## Step 4. Generate File List

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

```shell

bash generate_videos_filelist.sh

bash generate_rawframes_filelist.sh

```

## Step 5. Check Directory Structure

After the whole data process for UCF-101 preparation,

you will get the rawframes (RGB + Flow), videos and annotation files for UCF-101.

In the context of the whole project (for UCF-101 only), the folder structure will look like:

```

mmaction2

├── mmaction

├── tools

├── configs

├── data

│   ├── ucf101

│   │   ├── ucf101_{train,val}_split_{1,2,3}_rawframes.txt

│   │   ├── ucf101_{train,val}_split_{1,2,3}_videos.txt

│   │   ├── annotations

│   │   ├── videos

│   │   │   ├── ApplyEyeMakeup

│   │   │   │   ├── v_ApplyEyeMakeup_g01_c01.avi

│   │   │   ├── YoYo

│   │   │   │   ├── v_YoYo_g25_c05.avi

│   │   ├── rawframes

│   │   │   ├── ApplyEyeMakeup

│   │   │   │   ├── v_ApplyEyeMakeup_g01_c01

│   │   │   │   │   ├── img_00001.jpg

│   │   │   │   │   ├── img_00002.jpg

│   │   │   │   │   ├── ...

│   │   │   │   │   ├── flow_x_00001.jpg

│   │   │   │   │   ├── flow_x_00002.jpg

│   │   │   │   │   ├── ...

│   │   │   │   │   ├── flow_y_00001.jpg

│   │   │   │   │   ├── flow_y_00002.jpg

│   │   │   ├── ...

│   │   │   ├── YoYo

│   │   │   │   ├── v_YoYo_g01_c01

│   │   │   │   ├── ...

│   │   │   │   ├── v_YoYo_g25_c05

```

For training and evaluating on UCF-101, please refer to [getting_started.md](/docs/getting_started.md).