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A preliminary hand post recognition library using Tensorflow, for use with the Noitom Hi5 gloves

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# tensorglove
A hand pose recognition library using Tensorflow, for use with the Noitom Hi5 gloves. 
This project was the result of 2 hackathons, so is fairly preliminary.

![Alt Text](https://raw.githubusercontent.com/mikeoconnor0308/tensorglove/master/images/tensorglove_v0.1.gif)

## Getting Started

The repository consists of two components: a Unity project for performing the hand tracking in VR, and a set of python scripts for performing
the machine learning.

To train the neural network and run the server, the following packages are required:

* Tensorflow 1.7
* python-osc (for running the server). 
* pandas

Simply run the script `training.py` to train the neural network. After training, you can run `predict_server.py` to start
serving predictions over OSC.

To run the Unity project, the following plugins will need to be added to the project (in the glovetest/Assets folder):

* [Noitom Unity package](https://hi5vrglove.com/downloads)
* [SteamVR Unity plugin](https://assetstore.unity.com/packages/templates/systems/steamvr-plugin-32647) 
* [Mixamo Melee Axe Pack](https://assetstore.unity.com/packages/3d/animations/melee-axe-pack-35320) 
* [Lightning Bolt Effect for Unity](https://assetstore.unity.com/packages/tools/particles-effects/lightning-bolt-effect-for-unity-59471)

The Standard Character Assets must also be imported, which can be done in Unity from Assets/Import Package/Characters

Open the Unity project (root at the folder `glovetest`) and open the GloveGestureTest scene.

The `Hi5CsvRecorder` script in the hierarchy can be used to record poses for use in training the neural network. Select which gesture you 
are training for (currently from None, Click, Fist and Point) and press L or R to record data for the left or right hand respectively.

The `TensorflowOscGesture` script can be used to communicate with the tensorglove server. The basic demo only works for the right hand and 
changes the colour of the hand based on the gesture detected.

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