Metadata-Version: 2.1
Name: openpifpaf
Version: 0.12.7
Summary: PifPaf: Composite Fields for Human Pose Estimation
Home-page: https://github.com/vita-epfl/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/vita-epfl/openpifpaf/workflows/Tests/badge.svg?branch=main)](https://github.com/vita-epfl/openpifpaf/actions?query=workflow%3ATests)
        [![deploy-guide](https://github.com/vita-epfl/openpifpaf/workflows/deploy-guide/badge.svg)](https://github.com/vita-epfl/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).
        
        
        # Example
        
        ![example image with overlaid pose predictions](https://github.com/vita-epfl/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 openpifpaf matplotlib
        python3 -m openpifpaf.predict docs/coco/000000081988.jpg --image-min-dpi=200 --show-file-extension=jpeg --image-output
        ```
        
        
        # [Guide](https://vita-epfl.github.io/openpifpaf/intro.html)
        
        Continue to our __[OpenPifPaf Guide](https://vita-epfl.github.io/openpifpaf/intro.html)__.
        
        For developers, there is also the
        __[DEV Guide](https://vita-epfl.github.io/openpifpaf/dev/intro.html)__
        which is the same guide but based on the latest code in the `main` branch.
        
        
        [CC-BY-2.0]: https://creativecommons.org/licenses/by/2.0/
        
        
        # Commercial License
        
        This software is available for licensing via the EPFL Technology Transfer
        Office (https://tto.epfl.ch/, info.tto@epfl.ch).
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: onnx
Provides-Extra: coreml
Provides-Extra: test
Provides-Extra: train
