This package is an implementation of the algorithm described in our paper (pdf) for estimating human pose in natural images.
- Python 2.7
- OpenCV python interface (python-opencv)
- Caffe
Currently, it is necessary to use caffe version at the link. A patch that you can apply to your own caffe repository will be provided shortly in the future.
This package has been tested on Ubuntu 14.04.
- Download IEF models and supporting code:
./setup.sh
In the main code directory launch ipython and run,
from src import test_demo as td
#Define pose-predictor class
ief = td.PoseIEF()
#Name of the image
imName = 'src/test_images/elvis.jpg'
#Point (x,y) on the torso of the person whose pose is to be estimated
bodyPt = (108, 98)
#Predict the pose
pose,_ = ief.predict(imName, bodyPt)
#Visualize the result
import scipy.misc as scm
im = scm.imread(imName)
td.vis.plot_pose_stickmodel(im, pose.squeeze().transpose((1,0)))Note: This code only runs 1 image in a single batch and is hence runs slower than what can be achieved with larger batch sizes.
See the wiki page.
The README will be shortly updated with more details.