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Preparing Moments in Time

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/mit/.

Step 1. Prepare Annotations and Videos

First of all, you can run the following script to download the videos along with the annotations.

bash download_data.sh

Step 2. 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. And you can run the following script to soft link the extracted frames.

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

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_{rawframes, videos}_filelist.sh

Step 5. Check Directory Structure

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

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

mmaction2
├── data
│   └── mit
│       ├── annotations
│       │   ├── license.txt
│       │   ├── moments_categories.txt
│       │   ├── README.txt
│       │   ├── trainingSet.csv
│       │   └── validationSet.csv
│       ├── mit_train_rawframe_anno.txt
│       ├── mit_train_video_anno.txt
│       ├── mit_val_rawframe_anno.txt
│       ├── mit_val_video_anno.txt
│       ├── rawframes
│       │   ├── training
│       │   │   ├── adult+female+singing
│       │   │   │   ├── 0P3XG_vf91c_35
│       │   │   │   │   ├── flow_x_00001.jpg
│       │   │   │   │   ├── flow_x_00002.jpg
│       │   │   │   │   ├── ...
│       │   │   │   │   ├── flow_y_00001.jpg
│       │   │   │   │   ├── flow_y_00002.jpg
│       │   │   │   │   ├── ...
│       │   │   │   │   ├── img_00001.jpg
│       │   │   │   │   └── img_00002.jpg
│       │   │   │   └── yt-zxQfALnTdfc_56
│       │   │   │   │   ├── ...
│       │   │   └── yawning
│       │   │       ├── _8zmP1e-EjU_2
│       │   │       │   ├── ...
│       │   └── validation
│       │   │       ├── ...
│       └── videos
│           ├── training
│           │   ├── adult+female+singing
│           │   │   ├── 0P3XG_vf91c_35.mp4
│           │   │   ├── ...
│           │   │   └── yt-zxQfALnTdfc_56.mp4
│           │   └── yawning
│           │       ├── ...
│           └── validation
│           │   ├── ...
└── mmaction
└── ...

For training and evaluating on Moments in Time, please refer to getting_started.md.