Clone source code:
git clone https://github.com/MitchFuchs/asbar.git
Move into directory:
cd asbar
Create conda environment:
conda env create -f requirements.yaml
Activate conda environment:
conda activate asbar
Launch GUI:
./gui.sh
If the installation was successful, you should see this:
Follow the same structure as below:
├── ...
├── data # data folder
│ ├── <pose_dataset> # create a new folder and name it with the name of the pose dataset
| │ ├── train # create a new folder named 'train' containing training images
| │ | ├── train_0000.jpg # training image (name irrelevant)
| │ | ├── train_0001.jpg # training image (name irrelevant)
| │ | ├── train_0002.jpg # training image (name irrelevant)
│ | | └── ... # etc.
| │ ├── val # create a new folder named 'val' containing validation images
| │ | ├── val_0000.jpg # validation image (name irrelevant)
| │ | ├── val_0001.jpg # validation image (name irrelevant)
| │ | ├── val_0002.jpg # validation image (name irrelevant)
│ | | └── ... # etc.
| │ ├── train_annotations.json # add a json file with training annotations structured as below
| │ └── val_annotations.json # add a json file with validation annotations structured as below
│ └── ...
└── ...
{
"data": [
{
"file": "train_0000.jpg",
"species": "Rhesus_macaque",
"bbox": [x, y, w, h],
"landmarks": [x_1, y_1, x_2, y_2, ..., x_n, y_n],
"visibility": [1, 1, ..., 1]
},
{
"file": "train_0001.jpg",
"species": "Gorilla",
"bbox": [x, y, w, h],
"landmarks": [x_1, y_1, x_2, y_2, ..., x_n, y_n],
"visibility": [1, 0, ..., 0]
}
]
}
├── ...
├── data # data folder
│ ├── <behavior_dataset> # create a new folder and name it with the name of the behavior dataset
| │ ├── splits # create a new folder named 'splits' containing data split files
| │ | ├── trainingdata.txt # text file listing all video names (without file extension) of the training dataset
| │ | ├── validationdata.txt # text file listing all video names (without file extension) of the validation dataset
| │ | └── testdata.txt # text file listing all video names (without file extension) of the test dataset
| │ ├── videos # create a new folder named 'videos' containing all videos
| │ | ├── 0000.mp4 # video file (name irrelevant)
| │ | ├── 0001.mp4 # video file (name irrelevant)
| │ | ├── 0002.mp4 # video file (name irrelevant)
│ | | └── ... # etc.
| │ └── activities # add a pickle file with behavior annotations structured as below
│ └── ...
└── ...
{
"sitting": [ # behavior
[
0000, # video name (without file extension)
0, # animal_id (can be left as 0 or for tracking id)
126, # number of the first frame displaying the behavior
166, # number of the last frame displaying the behavior
[(x_1, y_1, x_2, y_2), ..., (x_1, y_1, x_2, y_2)] # list of bounding box coordinates for each frame, (x_top_left, y_top_left, x_bottom_right, y_bottom_right)
],
[
0000,
0,
180,
190,
[(x_1, y_1, x_2, y_2), ..., (x_1, y_1, x_2, y_2)]
],
],
"standing": [
[
0000,
0,
167,
179,
[(x_1, y_1, x_2, y_2), ..., (x_1, y_1, x_2, y_2)]
],
[
0001,
0,
23,
58,
[(x_1, y_1, x_2, y_2), ..., (x_1, y_1, x_2, y_2)]
]
]
}
If you use this material, please cite it as below.
Michael Fuchs, Emilie Genty, Klaus Zuberbühler, Paul Cotofrei. “ASBAR: an Animal Skeleton-Based Action Recognition framework. Recognizing great ape behaviors in the wild using pose estimation with domain adaptation”. In: bioRxiv (2023). doi: 10.1101/2023.09.24.559236. eprint: https://www.biorxiv.org/content/early/2023/09/25/2023.09.24.559236.full.pdf.
@article {asbar_Fuchs2023,
author = {Michael Fuchs and Emilie Genty and Klaus Zuberb{\"u}hler and Paul Cotofrei},
title = {ASBAR: an Animal Skeleton-Based Action Recognition framework. Recognizing great ape behaviors in the wild using pose estimation with domain adaptation},
elocation-id = {2023.09.24.559236},
year = {2023},
doi = {10.1101/2023.09.24.559236},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2023/09/25/2023.09.24.559236},
eprint = {https://www.biorxiv.org/content/early/2023/09/25/2023.09.24.559236.full.pdf},
journal = {bioRxiv}
}
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