# openpifpaf
Continuously tested on Linux, MacOS and Windows:
[](https://github.com/openpifpaf/openpifpaf/actions?query=workflow%3ATests)
[](https://github.com/openpifpaf/openpifpaf/actions?query=workflow%3Adeploy-guide)
[](https://pepy.tech/project/openpifpaf)
<br />
[__New__ 2021 paper](https://arxiv.org/abs/2103.02440):
> __OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association__<br />
> _[Sven Kreiss](https://www.svenkreiss.com), [Lorenzo Bertoni](https://scholar.google.com/citations?user=f-4YHeMAAAAJ&hl=en), [Alexandre Alahi](https://scholar.google.com/citations?user=UIhXQ64AAAAJ&hl=en)_, 2021.
>
> Many image-based perception tasks can be formulated as detecting, associating
> and tracking semantic keypoints, e.g., human body pose estimation and tracking.
> In this work, we present a general framework that jointly detects and forms
> spatio-temporal keypoint associations in a single stage, making this the first
> real-time pose detection and tracking algorithm. We present a generic neural
> network architecture that uses Composite Fields to detect and construct a
> spatio-temporal pose which is a single, connected graph whose nodes are the
> semantic keypoints (e.g., a person's body joints) in multiple frames. For the
> temporal associations, we introduce the Temporal Composite Association Field
> (TCAF) which requires an extended network architecture and training method
> beyond previous Composite Fields. Our experiments show competitive accuracy
> while being an order of magnitude faster on multiple publicly available datasets
> such as COCO, CrowdPose and the PoseTrack 2017 and 2018 datasets. We also show
> that our method generalizes to any class of semantic keypoints such as car and
> animal parts to provide a holistic perception framework that is well suited for
> urban mobility such as self-driving cars and delivery robots.
Previous [CVPR 2019 paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Kreiss_PifPaf_Composite_Fields_for_Human_Pose_Estimation_CVPR_2019_paper.html).
# [Guide](https://openpifpaf.github.io/intro.html)
Detailed documentation is in our __[OpenPifPaf Guide](https://openpifpaf.github.io/intro.html)__.
For developers, there is also the
__[DEV Guide](https://openpifpaf.github.io/dev/intro.html)__
which is the same guide but based on the latest code in the `main` branch.
# Examples
Image credit: "[Learning to surf](https://www.flickr.com/photos/fotologic/6038911779/in/photostream/)" by fotologic which is licensed under [CC-BY-2.0].<br />
Created with:
```sh
pip3 install matplotlib openpifpaf
python3 -m openpifpaf.predict docs/coco/000000081988.jpg --image-output
```
---
Here is the [tutorial for body, foot, face and hand keypoints](https://openpifpaf.github.io/plugins_wholebody.html). Example:
Image credit: [Photo](https://de.wikipedia.org/wiki/Kamil_Vacek#/media/Datei:Kamil_Vacek_20200627.jpg) by [Lokomotive74](https://commons.wikimedia.org/wiki/User:Lokomotive74) which is licensed under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).<br />
Created with:
```sh
python -m openpifpaf.predict guide/wholebody/soccer.jpeg \
--checkpoint=shufflenetv2k30-wholebody --line-width=2 --image-output
```
---
Here is the [tutorial for car keypoints](https://openpifpaf.github.io/plugins_apollocar3d.html). Example:
Image credit: [Photo](https://commons.wikimedia.org/wiki/File:Streets_of_Saint_Petersburg,_Russia.jpg) by [Ninaras](https://commons.wikimedia.org/wiki/User:Ninaras) which is licensed under [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
Created with:
```sh
python -m openpifpaf.predict guide/images/peterbourg.jpg \
--checkpoint shufflenetv2k16-apollo-24 -o images \
--instance-threshold 0.05 --seed-threshold 0.05 \
--line-width 4 --font-size 0
```
---
Here is the [tutorial for animal keypoints (dogs, cats, sheep, horses and cows)](https://openpifpaf.github.io/plugins_animalpose.html). Example:
```sh
python -m openpifpaf.predict guide/images tappo_loomo.jpg \
--checkpoint=shufflenetv2k30-animalpose \
--line-width=6 --font-size=6 --white-overlay=0.3 \
--long-edge=500
```
# Commercial License
The open source license is in the [LICENSE](https://github.com/openpifpaf/openpifpaf/blob/main/LICENSE) file.
This software is also available for licensing via the EPFL Technology Transfer
Office (https://tto.epfl.ch/, info.tto@epfl.ch).
[CC-BY-2.0]: https://creativecommons.org/licenses/by/2.0/
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"description": "# openpifpaf\n\nContinuously tested on Linux, MacOS and Windows:\n[](https://github.com/openpifpaf/openpifpaf/actions?query=workflow%3ATests)\n[](https://github.com/openpifpaf/openpifpaf/actions?query=workflow%3Adeploy-guide)\n[](https://pepy.tech/project/openpifpaf)\n<br />\n[__New__ 2021 paper](https://arxiv.org/abs/2103.02440):\n\n> __OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association__<br />\n> _[Sven Kreiss](https://www.svenkreiss.com), [Lorenzo Bertoni](https://scholar.google.com/citations?user=f-4YHeMAAAAJ&hl=en), [Alexandre Alahi](https://scholar.google.com/citations?user=UIhXQ64AAAAJ&hl=en)_, 2021.\n>\n> Many image-based perception tasks can be formulated as detecting, associating\n> and tracking semantic keypoints, e.g., human body pose estimation and tracking.\n> In this work, we present a general framework that jointly detects and forms\n> spatio-temporal keypoint associations in a single stage, making this the first\n> real-time pose detection and tracking algorithm. We present a generic neural\n> network architecture that uses Composite Fields to detect and construct a\n> spatio-temporal pose which is a single, connected graph whose nodes are the\n> semantic keypoints (e.g., a person's body joints) in multiple frames. For the\n> temporal associations, we introduce the Temporal Composite Association Field\n> (TCAF) which requires an extended network architecture and training method\n> beyond previous Composite Fields. Our experiments show competitive accuracy\n> while being an order of magnitude faster on multiple publicly available datasets\n> such as COCO, CrowdPose and the PoseTrack 2017 and 2018 datasets. We also show\n> that our method generalizes to any class of semantic keypoints such as car and\n> animal parts to provide a holistic perception framework that is well suited for\n> urban mobility such as self-driving cars and delivery robots.\n\nPrevious [CVPR 2019 paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Kreiss_PifPaf_Composite_Fields_for_Human_Pose_Estimation_CVPR_2019_paper.html).\n\n\n# [Guide](https://openpifpaf.github.io/intro.html)\n\nDetailed documentation is in our __[OpenPifPaf Guide](https://openpifpaf.github.io/intro.html)__.\nFor developers, there is also the\n__[DEV Guide](https://openpifpaf.github.io/dev/intro.html)__\nwhich is the same guide but based on the latest code in the `main` branch.\n\n\n# Examples\n\n\n\nImage credit: \"[Learning to surf](https://www.flickr.com/photos/fotologic/6038911779/in/photostream/)\" by fotologic which is licensed under [CC-BY-2.0].<br />\nCreated with:\n```sh\npip3 install matplotlib openpifpaf\npython3 -m openpifpaf.predict docs/coco/000000081988.jpg --image-output\n```\n\n---\n\nHere is the [tutorial for body, foot, face and hand keypoints](https://openpifpaf.github.io/plugins_wholebody.html). Example:\n\n\nImage credit: [Photo](https://de.wikipedia.org/wiki/Kamil_Vacek#/media/Datei:Kamil_Vacek_20200627.jpg) by [Lokomotive74](https://commons.wikimedia.org/wiki/User:Lokomotive74) which is licensed under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).<br />\nCreated with:\n```sh\npython -m openpifpaf.predict guide/wholebody/soccer.jpeg \\\n --checkpoint=shufflenetv2k30-wholebody --line-width=2 --image-output\n```\n\n---\n\nHere is the [tutorial for car keypoints](https://openpifpaf.github.io/plugins_apollocar3d.html). Example:\n\n\nImage credit: [Photo](https://commons.wikimedia.org/wiki/File:Streets_of_Saint_Petersburg,_Russia.jpg) by [Ninaras](https://commons.wikimedia.org/wiki/User:Ninaras) which is licensed under [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).\n\nCreated with:\n```sh\npython -m openpifpaf.predict guide/images/peterbourg.jpg \\\n --checkpoint shufflenetv2k16-apollo-24 -o images \\\n --instance-threshold 0.05 --seed-threshold 0.05 \\\n --line-width 4 --font-size 0\n```\n\n---\n\nHere is the [tutorial for animal keypoints (dogs, cats, sheep, horses and cows)](https://openpifpaf.github.io/plugins_animalpose.html). Example:\n\n\n\n```sh\npython -m openpifpaf.predict guide/images tappo_loomo.jpg \\\n --checkpoint=shufflenetv2k30-animalpose \\\n --line-width=6 --font-size=6 --white-overlay=0.3 \\\n --long-edge=500\n```\n\n\n# Commercial License\n\nThe open source license is in the [LICENSE](https://github.com/openpifpaf/openpifpaf/blob/main/LICENSE) file.\nThis software is also available for licensing via the EPFL Technology Transfer\nOffice (https://tto.epfl.ch/, info.tto@epfl.ch).\n\n\n[CC-BY-2.0]: https://creativecommons.org/licenses/by/2.0/",
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