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Preparing UCF-101

Introduction

[DATASET]

@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. 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.

bash download_annotations.sh

Step 2. Prepare Videos

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

bash download_videos.sh

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

python ../resize_video.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 for installing 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.

# 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.

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.

bash extract_rgb_frames_opencv.sh

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

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.

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.