Prepare dataset

June 5, 2023 · View on GitHub

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

Preprocess

CASIA-B

  • Step1: Download the dataset
  • Step2: Unzip the dataset

OUMVLP

  • Step1: Download the dataset

  • Step2: Unzip the dataset, run

python misc/pretreamt_oumvlp_pose.py --datasetdir=<YOUR OUMVLP DATASET PATH> --dstdir=<YOUR TARGET PATH>
  • Step3: Transform 18 keypoints to 17 keypoints (To COCO keypoints format)
python misc/oumvlp17.py --datasetdir=<YOUR OUMVLP DATASET PATH> --dstdir=<YOUR TARGET PATH>
  • Processed 
    OUMVLP
       # keypoints = {
        #     0: "nose",  
        #     1: "neck"
        #     2: "Rshoulder"
        #     3: "Relbow"
        #     4: "Rwrist"
        #     5: "Lshoudler"
        #     6: "Lelbow"
        #     7: "Lwrist"
        #     8: "Rhip
        #     9: "Rknee"
        #     10: "Rankle"
        #     11: "Lhip"
        #     12: "Lknee"
        #     13: "Lankle"
        #     14: "Reye"
        #     15: "Leye"
        #     16: "Rear"
        #     17: "Lear"
        # }


COCO
           # keypoints = {
        #     0: "nose",
        #     1: "left_eye",
        #     2: "right_eye",
        #     3: "left_ear",
        #     4: "right_ear",
        #     5: "left_shoulder",
        #     6: "right_shoulder",
        #     7: "left_elbow",
        #     8: "right_elbow",
        #     9: "left_wrist",
        #     10: "right_wrist",
        #     11: "left_hip",
        #     12: "right_hip",
        #     13: "left_knee",
        #     14: "right_knee",
        #     15: "left_ankle",
        #     16: "right_ankle"
        # }

GREW

  • Step1: Download the data

  • Step2: Unzip the dataset, run :

python pretreatment_grew_pose.py --datasetdir=<YOUR OUMVLP DATASET PATH> --dstdir=<YOUR TARGET PATH>
  • Processed
          Original Dataset
            ├── make_pose(Silhouettes, Gait Energy Images (GEIs) , 2D and 3D poses)
                ├── 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
        Processed Dataset
            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
                    ├── 03
                        ├── etaGVnWf
                            ├── etaGVnWf.pkl
                        ├── eT1EXpgZ
                            ├── eT1EXpgZ.pkl

Gait3D

  • Step1: Download the data
  • Step2: Unzip the dataset, and transform json to pickle file, run
python pretreatment_gait3d_pose.py --datasetdir=<YOUR OUMVLP DATASET PATH> --dstdir=<YOUR TARGET PATH>
  • Processed
       Original Dataset
            input data format
            ├── 2D_Poses
            |  ├── 0000
            |      ├── camid0_videoid2
            |          ├── seq0
            |              ├── human_crop_f11627.txt
            |              ├── human_crop_f11628.txt
            |              ├── human_crop_f11629.txt
        Processed Dataset
            output data format
            ├── 2D_Poses-pkl
            │  ├── 0000
            │     ├── camid0_videoid2
            │        ├── seq0
            │           └──seq0.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.