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kinect-time

Gesture recognition using with an Xbox One Kinect on Linux.

Dataflow from Kinect to image classification:

Xbox One Kinect -> third_party/libfreenect2 (C++ driver) -> kinect/ (C++ kinect wrapper and de-resolution-er) -> pybind11 -> kinect-triton-client (python) -> nvidia py sdk (image classification with GestureNet)

The Kinect exports color images as 1920x1080 where each pixel is BGRX (4 bytes, last byte is empty space)

Gesture recognition with GestureNet

GestureNet is a gesture recognition model from nvidia.

Note: you will need the nvidia-container-toolkit package in order to run a docker container that uses your nvidia gpu. I use gentoo and made use of guru's overlay from this search: http://gpo.zugaina.org/app-emulation/nvidia-container-toolkit

This is the guide I'm following: https://developer.nvidia.com/blog/fast-tracking-hand-gesture-recognition-ai-applications-with-pretrained-models-from-ngc/

  • The GestureNet doc has fp16 in one place and FP_32 in another for the model input/output types
  • It also writes tlt-converter and trt-converter although only tao-converter was in the image, maybe it's older
  • tell the nvidia folk that their doc is outdated, make a PR if it's in github

GestureNet - pre-trained model: https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet

  • Input is RGB Images of 160 X 160 X 3 (Width X Height X Byte depth (R, G, B))
  • Limited to frontal views, bland backgrounds, and good lighting

Triton Inference Server - inference: https://ngc.nvidia.com/catalog/containers/nvidia:tritonserver

Usage

  • Ensure that you have an Xbox One Kinect with the extra Windows adapter that allows you to plug it in via USB.

  • If you build libfreenect2 with CUDA support, Clang won't work (I tried Clang 12.0.1) so just use GCC (I used GCC 11.2)

  • Build kinect-test-driver with:

    mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=RelWithDebInfo
cmake --build . --target test-driver

  • Download the pre-trained GestureNet model with get-gesture-net.sh

  • Convert model.etlt to model.plan with convert-model.sh

    • an .etlt file is platform agnostic while a .plan file isn't

  • Start the server with run-triton-server.sh

Tasklist

Kinect

  • Get running with print statement output
  • Kinect: Save frames to disk
    • started on impl
    • figure out format to save as??
      • Save relative to bytes per pixel
    • non-blocking Kinect::save_frames()
  • resize frames to resolution desired by inference engine (160x160x3)
    • started on this. Merging rows and then columns
    • Merge the width and just disregard the bottom half of the image
  • [ ] Use smart pointers instead of raw pointers around libfreenect2 objects
    • Nope, this isn't worth it. Just causes segfaults when the test-driver is done running
  • Create Python API with pybind11 so kinect-triton-client can use it
  • create frame stream
  • Actually scale down resolution from 1920x1080 to 160x160 properly
    • as of now, it just merges pixels width-wise and only grabs the top 160 pixels
  • Benchmark/Optimize frame conversion
  • Benchmark/Optimize frame saving

nvidia / GestureNet

  • convert GestureNet model file from .etlt to .plan
  • get inference server up and running

kinect-triton-client

kinect/

Wrapper around libfreenect2 to configure it as I need

third_party/libfreenect2

Using my fork https://github.com/mik90/libfreenect2 to grab images from the device. Allows me to build with CUDA due to some include issues.

This is kept as a git submodule since it is meant to be commited to an updated.

Note: May require GCC 10.x. So far I've been able to build with GCC 11 though.

kinect-triton-client/

gRPC-based python client to the triton inference server. Sends over images for classification.

Enter venv and download the packages with

cd kinect-triton-client
mkdir env
python3.10 -m venv env # This should match the version genereted by pybind11. Unsure what happens if it doesn't
source env/bin/activate
pip install -r requirements.txt

In the venv, the requirements.txt file is generated with pip freeze > requirements.txt

third_party/nvidia-ngc

Pre-trained GestureNet model from nvidia's model registry. Pulled via script.

third_party/grt

TODO: May not end up using this

Using my fork https://github.com/mik90/grt to try to get gestures from those images

  • Build artifacts are in /obj
  • Not added yet
  • I'd probably have to train my own model

trtis_model_repo/

Workspace for the pre-trained gesture classification model from nvidia