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Here we provide the scripts for reference to automatically process the foreground mask and generate the human skeleton.

The target dataset which need to be processed should be like:

Target Dataset
└── 001/
    ├── 0000.png
    └── 0001.png
    ...
└── 002/
    ├── 0000.png
    └── 0001.png 
    ...

Grounded-SAM

Installation

# 1. Follow the official repo (https://github.com/IDEA-Research/Grounded-Segment-Anything) for package installation

# 2. download the pre-trained weights
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
wget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth

Run

python ./annotator/grounded-sam/run.py --dataset_root /path/to/target/dataset

OpenPose

Installation

# 1. Follow the pytorch-openpose repo (https://github.com/Hzzone/pytorch-openpose) for package installation

# 2. download the pre-trained weights
wget -O body_pose_model.pth https://www.dropbox.com/sh/7xbup2qsn7vvjxo/AABaYNMvvNVFRWqyDXl7KQUxa/body_pose_model.pth?dl=1

Run

python ./annotator/openpose/run.py --dataset_root /path/to/target/dataset

TSV preparation

After running Grounded SAM and OpenPose, prepare the data structured as below:

Target Dataset
└── 001/
    ├── 0000.png
    ├── 0001.png
    ├── ...
    ├────── groundsam
    |       ├── 000001.png.mask.jpg
    |       ├── 000002.png.mask.jpg
    |       └── ...     
    └────── openpose_json
            ├── 000001.png.json
            ├── 000002.png.json
            └── ...
└── 002/
    ├── 0000.png
    ├── 0001.png
    ├── ...
    ├────── groundsam
    |       ├── 000001.png.mask.jpg
    |       ├── 000002.png.mask.jpg
    |       └── ...     
    └────── openpose_json
            ├── 000001.png.json
            ├── 000002.png.json
            └── ...

We also provide a preprocessed toy dataset as example. You may find the example dataset in folder ./tsv_example/toy_dataset

Run the following script to convert your dataset to tsv format

python ./tsv_example/create_custom_dataset_tsvs.py --split train --root_folder PATH_TO_YOUR_DATASET_FOLDER --output_folder PATH_TO_DESIRED_FOLDER

For instance, PATH_TO_YOUR_DATASET_FOLDER=./tsv_example/toy_dataset and PATH_TO_DESIRED_FOLDER=./tsv_example/toy_dataset/tsv

TSV visualization

Once the tsv files are generated, we provide a jupyter notebook for data visualization. Please refer to visualize_tsv.ipynb for details.