Metadata-Version: 2.1
Name: openpifpaf
Version: 0.12a6
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: [![Build Status](https://travis-ci.org/vita-epfl/openpifpaf.svg?branch=master)](https://travis-ci.org/vita-epfl/openpifpaf)<br />
        [CVPR 2019 paper](http://openaccess.thecvf.com/content_CVPR_2019/html/Kreiss_PifPaf_Composite_Fields_for_Human_Pose_Estimation_CVPR_2019_paper.html)
        <!-- [arxiv.org/abs/1903.06593](https://arxiv.org/abs/1903.06593) -->
        
        > PifPaf: Composite Fields for Human Pose Estimation
        >
        > We propose a new bottom-up method for multi-person 2D human pose
        > estimation that is particularly well suited for urban mobility such as self-driving cars
        > and delivery robots. The new method, PifPaf, uses a Part Intensity Field (PIF) to
        > localize body parts and a Part Association Field (PAF) to associate body parts with each other to form
        > full human poses.
        > Our method outperforms previous methods at low resolution and in crowded,
        > cluttered and occluded scenes
        > thanks to (i) our new composite field PAF encoding fine-grained information and (ii) the choice of Laplace loss for regressions which incorporates a notion of uncertainty.
        > Our architecture is based on a fully
        > convolutional, single-shot, box-free design.
        > We perform on par with the existing
        > state-of-the-art bottom-up method on the standard COCO keypoint task
        > and produce state-of-the-art results on a modified COCO keypoint task for
        > the transportation domain.
        
        
        # Example
        
        ![example image with overlaid pose predictions](https://github.com/vita-epfl/openpifpaf/raw/master/docs/coco/000000081988.jpg.predictions.png)
        
        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 coco/000000081988.jpg --image-output
        ```
        
        
        # [Guide](https://vita-epfl.github.io/openpifpaf/intro.html)
        
        Continue to our __[OpenPifPaf Guide](https://vita-epfl.github.io/openpifpaf/intro.html)__.
        
        
        [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
