Author: Lara Lloret Iglesias (CSIC)

Project: This work is part of the DEEP Hybrid-DataCloud project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777435.

This is a plug-and-play tool for real-time pose estimation using deep neural networks. 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 one version that can detect multiple persons in an image/video. The module implemented here works on pictures (either uploaded or using an URL) and gives as output the different body keypoints with the corresponding coordinates and the associated key score. It also generates an image with the keypoints superimposed.

You can find more information about it in the DEEP Marketplace.

Table of contents

1. Installing this module
   1. Local installation
   2. Docker installation
2. Predict
3. Acknowledgements

Requirements

This project has been tested in Ubuntu 18.04 with Python 3.6.5. Further package requirements are described in the requirements.txt file.

• It is a requirement to have Tensorflow>=1.14.0 installed (either in gpu or cpu mode). This is not listed in the requirements.txt as it breaks GPU support.
• Run python -c 'import cv2' to check that you installed correctly the opencv-python package (sometimes dependencies are missed in pip installations).

To start using this framework clone the repo:

git clone https://github.com/deephdc/posenet-tf
cd posenet-tf
pip install -e .

now run DEEPaaS:

deepaas-run --listen-ip 0.0.0.0

and open https://0.0.0.0:5000/ui and look for the methods belonging to the posenetclas module.

We have also prepared a ready-to-use Docker container to run this module. To run it:

docker search deephdc
docker run -ti -p 5000:5000 -p 6006:6006 -p 8888:8888 deephdc/deep-oc-posenet-tf

Now open https://0.0.0.0:5000/ui and look for the methods belonging to the posenetclas module.

Go to https://0.0.0.0:5000/ui and look for the PREDICT POST method. Click on 'Try it out', change whatever test args you want and click 'Execute'. You can either supply a:

• a data argument with a path pointing to an image.

OR

• a url argument with an URL pointing to an image. Here is an example of such an url that you can use for testing purposes.

The original model, weights, code, etc. were created by Google and can be found here.

If you consider this project to be useful, please consider citing the DEEP Hybrid DataCloud project:

García, Álvaro López, et al. A Cloud-Based Framework for Machine Learning Workloads and Applications. IEEE Access 8 (2020): 18681-18692.