Metadata-Version: 2.1
Name: openpifpaf
Version: 0.13.4
Summary: PifPaf: Composite Fields for Human Pose Estimation
Home-page: https://github.com/openpifpaf/openpifpaf
Author: Sven Kreiss
Author-email: research@svenkreiss.com
License: GNU AGPLv3
Description: # openpifpaf
        
        Continuously tested on Linux, MacOS and Windows:
        [![Tests](https://github.com/openpifpaf/openpifpaf/workflows/Tests/badge.svg?branch=main)](https://github.com/openpifpaf/openpifpaf/actions?query=workflow%3ATests)
        [![deploy-guide](https://github.com/openpifpaf/openpifpaf/workflows/deploy-guide/badge.svg)](https://github.com/openpifpaf/openpifpaf/actions?query=workflow%3Adeploy-guide)
        [![Downloads](https://pepy.tech/badge/openpifpaf)](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
        
        ![example image with overlaid pose predictions](https://github.com/openpifpaf/openpifpaf/raw/main/docs/coco/000000081988.jpg.predictions.jpeg)
        
        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:
        ![example image with overlaid wholebody pose predictions](https://raw.githubusercontent.com/openpifpaf/openpifpaf/main/docs/soccer.jpeg.predictions.jpeg)
        
        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:
        ![example image cars](https://raw.githubusercontent.com/openpifpaf/openpifpaf/main/docs/peterbourg.jpg.predictions.jpeg)
        
        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:
        ![example image cars](https://raw.githubusercontent.com/openpifpaf/openpifpaf/main/docs/tappo_loomo.jpg.predictions.jpeg)
        
        
        ```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/
        
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: backbones
Provides-Extra: dev
Provides-Extra: onnx
Provides-Extra: coreml
Provides-Extra: test
Provides-Extra: train
