Package: openpifpaf-vita
openpifpafContinuously tested on Linux, MacOS and Windows:New2021 paper:OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal AssociationSven Kreiss,Lorenzo Bertoni,Alexandre Alahi, 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.PreviousCVPR 2019 paper.Have fun withour latest real-time interactive demo!GuideDetailed documentation is in ourOpenPifPaf Guide.
For developers, there is also theDEV Guidewhich is the same guide but based on the latest code in themainbranch.InstallationThis version of OpenPifPaf (openpifpaf-vita) cannot co-exist with the original one (openpifpaf) in the same environment.
If you have previously installed the packageopenpifpaf, remove it before installation to avoid conflicts.This project was forked fromOpenPifPaf v0.13.1and developed separately from version v0.14.0 on.Do not clone this repository.
Make sure there is no folder namedopenpifpaf-vitain your current directory, and run:pip3installopenpifpaf-vitaYou need to installmatplotlibto produce visual outputs:pip3installmatplotlibTo modify OpenPifPaf itself, please followModify Code.ExamplesImage credit: "Learning to surf" by fotologic which is licensed underCC-BY-2.0.Created with:python3-mopenpifpaf.predictdocs/coco/000000081988.jpg--image-outputHere is thetutorial for body, foot, face and hand keypoints. Example:Image credit:PhotobyLokomotive74which is licensed underCC-BY-4.0.Created with:python-mopenpifpaf.predictguide/wholebody/soccer.jpeg\--checkpoint=shufflenetv2k30-wholebody--line-width=2--image-outputHere is thetutorial for car keypoints. Example:Image credit:PhotobyNinaraswhich is licensed underCC-BY-SA 4.0.Created with:python-mopenpifpaf.predictguide/images/peterbourg.jpg\--checkpointshufflenetv2k16-apollo-24-oimages\--instance-threshold0.05--seed-threshold0.05\--line-width4--font-size0Here is thetutorial for animal keypoints (dogs, cats, sheep, horses and cows). Example:python-mopenpifpaf.predictguide/imagestappo_loomo.jpg\--checkpoint=shufflenetv2k30-animalpose\--line-width=6--font-size=6--white-overlay=0.3\--long-edge=500Commercial LicenseThe open source license is in theLICENSEfile.
This software is also available for licensing via the EPFL Technology Transfer
Office (https://tto.epfl.ch/,info.tto@epfl.ch).