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cleaned up some of the docs
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The goal of this project is to bring an easy to set up hardware and software wise, fast and accurate eye and/or mouth tracking solution for any headset that allows for mounting the eye tracking cameras and/or mouth tracking camera in or under the headset (in case of mouth tracking).
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It is also open source and open hardware meaning anyone can build it for them selfes and use it free of charge.
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It is also open source and open hardware meaning anyone can build it for themselves and use it free of charge.
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This project is highly inspired by the excelent Full Body Trakcing solution - [SlimeVR](https://docs.slimevr.dev)
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and is aimging to do the same - make the eye and mouth tracking technology independted of the headset so that anyone can use it with anything they want and also make it cheaper.
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This project is highly inspired by the excelent Full Body Tracking solution - [SlimeVR](https://docs.slimevr.dev)
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and is aimging to do the same - make the eye and mouth tracking technology independent of the headset so that anyone can use it with anything they want and also make it cheaper.
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Goals in phases:
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- Iris tracking - we can detect the iris and translate its location to an avatar though OSC / websockets
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- Eyelid blink tracking - we can detect blinking of the user and translate it to the character
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- Full eyelid tracking - we can detect and estimate the position of eyelids and translate it to the character
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- Mouth tracking - we can detect and estimate different mouth poses the user is making and translate them to the character
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- Mouth tracking - we can detect and estimate different mouth poses the user is making and translate them to the character
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@ -35,4 +35,4 @@ Shave off the IR filter off of any camera module:
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https://marksbench.com/electronics/removing-ir-filter-from-esp32-cam/
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#### Ommited because of safety
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I'm ommiting any IR emmiters here purposfully - I have no idea if any of them are safe to use for prolonged periods of time and thus I leave them out.
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I'm ommiting any IR emmiters here purposfully - I have no idea if any of them are safe to use for prolonged periods of time and thus I leave them out.
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@ -7,12 +7,12 @@ The goal is to:
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Now, having that out of the way, how should we process the data? It's being sent to us as an uncompressed stream from the ESP.
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We could use the OpenCV for image processing but what out detection?
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We could use the OpenCV for image processing, but what about detection?
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DLib has a very good eye / face detection pre-trained model
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There are some ready-to-go solutions for openCV but won't they be too heavy?
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How about training out own CNN model based on yey data sets?
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How about training our own CNN model based on eye datasets?
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They would require labeling but they were used with great success by others.
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Datasets - https://datagen.tech/blog/eye-datasets/
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