This is a plug-and-play tool for real-time pose estimation using deep neural networks. The original model, weights, code, etc. was created by Google and can be found at https://github.com/tensorflow/tfjs-models/tree/master/posenet.
PoseNet can be used to estimate either a single pose or multiple poses, meaning there is a version of the algorithm that can detect only one person in an image/video and another version that can detect multiple persons in an image/video.
The PREDICT method expects an RGB image as input (or the url of an image) and returns as output the different body keypoints with the corresponding coordinates and the associated key score
Run locally on your computer
You can run this module directly on your computer, assuming that you have Docker installed, by following these steps:
$ docker pull deephdc/deep-oc-posenet-tf $ docker run -ti -p 5000:5000 deephdc/deep-oc-posenet-tf
If you do not have Docker available or you do not want to install it, you can use udocker within a Python virtualenv:
$ virtualenv udocker $ source udocker/bin/activate $ git clone https://github.com/indigo-dc/udocker $ cd udocker $ pip install . $ udocker pull deephdc/deep-oc-posenet-tf $ udocker create deephdc/deep-oc-posenet-tf $ udocker run -p 5000:5000 deephdc/deep-oc-posenet-tf
Once running, point your browser to
and you will see the API documentation, where you can test the module
functionality, as well as perform other actions (such as training).
For more information, refer to the user documentation.