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Preparing JHMDB

Introduction

[DATASET]

@inproceedings{Jhuang:ICCV:2013,
    title = {Towards understanding action recognition},
    author = {H. Jhuang and J. Gall and S. Zuffi and C. Schmid and M. J. Black},
    booktitle = {International Conf. on Computer Vision (ICCV)},
    month = Dec,
    pages = {3192-3199},
    year = {2013}
}

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/jhmdb/.

Download and Extract

You can download the RGB frames, optical flow and ground truth annotations from google drive. The data are provided from MOC, which is adapted from act-detector.

After downloading the JHMDB.tar.gz file and put it in $MMACTION2/tools/data/jhmdb/, you can run the following command to extract.

tar -zxvf JHMDB.tar.gz

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/JHMDB/
ln -s /mnt/SSD/JHMDB/ ../../../data/jhmdb

Check Directory Structure

After extracting, you will get the FlowBrox04 directory, Frames directory and JHMDB-GT.pkl for JHMDB.

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

mmaction2
├── mmaction
├── tools
├── configs
├── data
│   ├── jhmdb
│   |   ├── FlowBrox04
│   |   |   ├── brush_hair
│   |   |   |   ├── April_09_brush_hair_u_nm_np1_ba_goo_0
│   |   |   |   |   ├── 00001.jpg
│   |   |   |   |   ├── 00002.jpg
│   |   |   |   |   ├── ...
│   |   |   |   |   ├── 00039.jpg
│   |   |   |   |   ├── 00040.jpg
│   |   |   |   ├── ...
│   |   |   |   ├── Trannydude___Brushing_SyntheticHair___OhNOES!__those_fukin_knots!_brush_hair_u_nm_np1_fr_goo_2
│   |   |   ├── ...
│   |   |   ├── wave
│   |   |   |   ├── 21_wave_u_nm_np1_fr_goo_5
│   |   |   |   ├── ...
│   |   |   |   ├── Wie_man_winkt!!_wave_u_cm_np1_fr_med_0
│   |   ├── Frames
│   |   |   ├── brush_hair
│   |   |   |   ├── April_09_brush_hair_u_nm_np1_ba_goo_0
│   |   |   |   |   ├── 00001.png
│   |   |   |   |   ├── 00002.png
│   |   |   |   |   ├── ...
│   |   |   |   |   ├── 00039.png
│   |   |   |   |   ├── 00040.png
│   |   |   |   ├── ...
│   |   |   |   ├── Trannydude___Brushing_SyntheticHair___OhNOES!__those_fukin_knots!_brush_hair_u_nm_np1_fr_goo_2
│   |   |   ├── ...
│   |   |   ├── wave
│   |   |   |   ├── 21_wave_u_nm_np1_fr_goo_5
│   |   |   |   ├── ...
│   |   |   |   ├── Wie_man_winkt!!_wave_u_cm_np1_fr_med_0
│   |   ├── JHMDB-GT.pkl

Note: The JHMDB-GT.pkl exists as a cache, it contains 6 items as follows:

  1. labels (list): List of the 21 labels.
  2. gttubes (dict): Dictionary that contains the ground truth tubes for each video. A gttube is dictionary that associates with each index of label and a list of tubes. A tube is a numpy array with nframes rows and 5 columns, each col is in format like <frame index> <x1> <y1> <x2> <y2>.
  3. nframes (dict): Dictionary that contains the number of frames for each video, like 'walk/Panic_in_the_Streets_walk_u_cm_np1_ba_med_5': 16.
  4. train_videos (list): A list with nsplits=1 elements, each one containing the list of training videos.
  5. test_videos (list): A list with nsplits=1 elements, each one containing the list of testing videos.
  6. resolution (dict): Dictionary that outputs a tuple (h,w) of the resolution for each video, like 'pour/Bartender_School_Students_Practice_pour_u_cm_np1_fr_med_1': (240, 320).