OpenPose is a machine learning model that estimates body and hand pose in an image and returns location and confidence for each of 19 joints.
This is based on the implementation of OpenPose found here. This repository contains scripts for optimized on-device export suitable to run on Qualcomm® devices. More details on model performance accross various devices, can be found here.
Sign up to start using Qualcomm AI Hub and run these models on a hosted Qualcomm® device.
Install the package via pip:
pip install "qai_hub_models[openpose]"Once installed, run the following simple CLI demo:
python -m qai_hub_models.models.openpose.demoMore details on the CLI tool can be found with the --help option. See
demo.py for sample usage of the model including pre/post processing
scripts. Please refer to our general instructions on using
models for more usage instructions.
This repository contains export scripts that produce a model optimized for on-device deployment. This can be run as follows:
python -m qai_hub_models.models.openpose.exportAdditional options are documented with the --help option. Note that the above
script requires access to Deployment instructions for Qualcomm® AI Hub.
- The license for the original implementation of OpenPose can be found here.
- The license for the compiled assets for on-device deployment can be found here
- OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
- Source Model Implementation
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us.