openpifpaf
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.
@article{kreiss2019pifpaf,
title={PifPaf: Composite Fields for Human Pose Estimation},
author={Kreiss, Sven and Bertoni, Lorenzo and Alahi, Alexandre},
journal={CVPR, arXiv preprint arXiv:1903.06593},
year={2019}
}
Demo
Image credit: "Learning to surf" by fotologic which is licensed under CC-BY-2.0.
Created with:
python3 -m openpifpaf.predict --show docs/coco/000000081988.jpg
For more demos, see the
openpifpafwebdemo project and
the openpifpaf.webcam command.
There is also a Google Colab demo.
Install
Python 3 is required. Python 2 is not supported.
Do not clone this repository
and make sure there is no folder named openpifpaf in your current directory.
pip3 install openpifpaf
For a live demo, we recommend to try the
openpifpafwebdemo project.
Alternatively, openpifpaf.webcam provides a live demo as well.
It requires OpenCV. To use a globally installed
OpenCV from inside a virtual environment, create the virtualenv with the
--system-site-packages option and verify that you can do import cv2.
For development of the openpifpaf source code itself, you need to clone this repository and then:
pip3 install numpy cython
pip3 install --editable '.[train,test]'
The last command installs the Python package in the current directory (signified by the dot) with the optional dependencies needed for training and testing.
Interfaces
python3 -m openpifpaf.predict --helppython3 -m openpifpaf.webcam --helppython3 -m openpifpaf.train --helppython3 -m openpifpaf.eval_coco --helppython3 -m openpifpaf.logs --help
Example commands to try:
# live demo
MPLBACKEND=macosx python3 -m openpifpaf.webcam --scale 0.1 --source=0
# single image
python3 -m openpifpaf.predict my_image.jpg --show
Pre-trained Models
Put the files from this
Google Drive
into your outputs folder. The three standard, pretrained models are also available when
using the command line option --checkpoint resnet50, --checkpoint resnet101
and --checkpoint resnet152.
To visualize logs:
python3 -m openpifpaf.logs \
outputs/resnet50block5-pif-paf-edge401-190424-122009.pkl.log \
outputs/resnet101block5-pif-paf-edge401-190412-151013.pkl.log \
outputs/resnet152block5-pif-paf-edge401-190412-121848.pkl.log
Train
See datasets for setup instructions. See studies.ipynb for previous studies.
Train a model:
python3 -m openpifpaf.train \
--lr=1e-3 \
--momentum=0.95 \
--epochs=75 \
--lr-decay 60 70 \
--batch-size=8 \
--basenet=resnet50block5 \
--head-quad=1 \
--headnets pif paf \
--square-edge=401 \
--regression-loss=laplace \
--lambdas 30 2 2 50 3 3 \
--freeze-base=1
You can refine an existing model with the --checkpoint option.
To produce evaluations at every epoch, check the directory for new snapshots every 5 minutes:
while true; do \
CUDA_VISIBLE_DEVICES=0 find outputs/ -name "resnet101block5-pif-paf-l1-190109-113346.pkl.epoch???" -exec \
python3 -m openpifpaf.eval_coco --checkpoint {} -n 500 --long-edge=641 --skip-existing \; \
; \
sleep 300; \
done
Person Skeletons
COCO / kinematic tree / dense:
Created with python3 -m openpifpaf.data.
Video
Processing a video frame by frame from video.avi to video.pose.mp4 using ffmpeg:
export VIDEO=video.avi # change to your video file
mkdir ${VIDEO}.images
ffmpeg -i ${VIDEO} -qscale:v 2 -vf scale=641:-1 -f image2 ${VIDEO}.images/%05d.jpg
python3 -m openpifpaf.predict --checkpoint resnet152 ${VIDEO}.images/*.jpg
ffmpeg -framerate 24 -pattern_type glob -i ${VIDEO}.images/'*.jpg.skeleton.png' -vf scale=640:-1 -c:v libx264 -pix_fmt yuv420p ${VIDEO}.pose.mp4
In this process, ffmpeg scales the video to 641px which can be adjusted.
Documentation Pages
Related Projects
1 maintainer
Release files for openpifpaf 0.8.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| openpifpaf-0.8.0.tar.gz | 177.3 kB | Details |
Release files / openpifpaf-0.8.0.tar.gz
| Download URL | openpifpaf-0.8.0.tar.gz |
|---|---|
| Size | 177.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
82087cc9d02d2a9deaab63500137696463aeeb08291bb03db302891cc3472cd0
|
|
BLAKE2b-256 checksum How to use checksums |
c76dfd6390233b214de19f6745be730978d6737a669c369af8309a31d57773ed
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.3
|
