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Prepare dataset

Suppose you have downloaded the original dataset, we need to preprocess the data and save it as pickle file. Remember to set your path to the root of processed dataset in configs/*.yaml.

Preprocess

CASIA-B

Download URL: http://www.cbsr.ia.ac.cn/GaitDatasetB-silh.zip

  • Original 
    CASIA-B
    001 (subject)
        bg-01 (type)
            000 (view)
                001-bg-01-000-001.png (frame)
                001-bg-01-000-002.png (frame)
                ......
            ......
        ......
    ......
  • Run python datasets/pretreatment.py --input_path CASIA-B --output_path CASIA-B-pkl
  • Processed 
    CASIA-B-pkl
    001 (subject)
        bg-01 (type)
                000 (view)
                    000.pkl (contains all frames)
            ......
        ......
    ......

OUMVLP

Step1: Download URL: http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html

Step2: Unzip the dataset, you will get a structure directory like:

python datasets/OUMVLP/extractor.py --input_path Path_of_OUMVLP-base --output_path Path_of_OUMVLP-raw --password Given_Password
  • Original 
    OUMVLP-raw
    Silhouette_000-00 (view-sequence)
        00001 (subject)
            0001.png (frame)
            0002.png (frame)
            ......
        00002
            0001.png (frame)
            0002.png (frame)
            ......
        ......
    Silhouette_000-01
        00001
            0001.png (frame)
            0002.png (frame)
            ......
        00002
            0001.png (frame)
            0002.png (frame)
            ......
        ......
    Silhouette_015-00
        ......
    Silhouette_015-01
        ......
    ......

Step3 : To rearrange directory of OUMVLP dataset, turning to id-type-view structure, Run

python datasets/OUMVLP/rearrange_OUMVLP.py --input_path Path_of_OUMVLP-raw --output_path Path_of_OUMVLP-rearranged

Step4: Transforming images to pickle file, run

python datasets/pretreatment.py --input_path Path_of_OUMVLP-rearranged --output_path Path_of_OUMVLP-pkl
  • Processed 
    OUMVLP-pkl
    00001 (subject)
        00 (sequence)
            000 (view)
                000.pkl (contains all frames)
            015 (view)
                015.pkl (contains all frames)
            ...
        01 (sequence)
            000 (view)
                000.pkl (contains all frames)
            015 (view)
                015.pkl (contains all frames)
            ......
    00002 (subject)
        ......
    ......

GREW

Step1: Download the data

Step2: Unzip the dataset, you will get a structure directory like:

  • Original 
    GREW-raw
├── train
    ├── 00001
        ├── 4XPn5Z28
            ├── 00001.png
            ├── 00001_2d_pose.txt
            ├── 00001_3d_pose.txt
        ├── 4XPn5Z28_gei.png
├── test
    ├── gallery
        ├── 00001
            ├── 79XJefi8
                ├── 00001.png
                ├── 00001_2d_pose.txt
                ├── 00001_3d_pose.txt
            ├── 79XJefi8_gei.png
    ├── probe
        ├── 01DdvEHX
            ├── 00001.png
            ├── 00001_2d_pose.txt
            ├── 00001_3d_pose.txt
        ├── 01DdvEHX_gei.png
    ...
...

Step3 : To rearrange directory of GREW dataset, turning to id-type-view structure, Run

python datasets/GREW/rearrange_GREW.py --input_path Path_of_GREW-raw --output_path Path_of_GREW-rearranged

Step4: Transforming images to pickle file, run

python datasets/pretreatment.py --input_path Path_of_GREW-rearranged --output_path Path_of_GREW-pkl
  • Processed 
    GREW-pkl
├── 00001train (subject in training set)
    ├── 00
        ├── 4XPn5Z28
            ├── 4XPn5Z28.pkl
        ├──5TXe8svE
            ├── 5TXe8svE.pkl
            ......
├── 00001 (subject in testing set)
    ├── 01
        ├── 79XJefi8
            ├── 79XJefi8.pkl
    ├── 02
        ├── t16VLaQf
            ├── t16VLaQf.pkl
├── probe
    ├── etaGVnWf
        ├── etaGVnWf.pkl
    ├── eT1EXpgZ
        ├── eT1EXpgZ.pkl
    ...
...

Split dataset

You can use the partition file in dataset folder directly, or you can create yours. Remember to set your path to the partition file in configs/*.yaml.