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Integrating TensorFlow Models with Unity
Oct 6, 2024
Importing TensorFlow Models into Unity
Introduction
Overview of the tutorial: Importing a Python model generated using TensorFlow into Unity.
Standard method: Using ONNX format with Barracuda library.
Alternative method: Running a Python server that communicates with Unity via WebSocket.
Reasons for Using the Alternative Method
Issues with converting TensorFlow models (h5, saved models) into ONNX format.
Challenges such as version mismatches and optimizers complicating the conversion.
Simplicity of the new method despite needing a Python server running.
How the Alternative Method Works
A Python server runs TensorFlow model.
Unity communicates through WebSocket for model input and output.
Example model trained on the EMNIST dataset (letters).
Ability to draw letters and get predictions.
User Interface Overview
UI features:
Two sliders controlling the radius of the input area and prediction strength.
Option for constant prediction (may stretch WebSocket).
Reset button to clear the drawing table.
Code Structure
Overview of Classes
Pixel Class
: Controls individual pixels (black, white, shades of gray).
Pixels Handler Class
: Generates a 21x21 pixel table as input and handles drawing.
Uses physics overlap circle to modify pixels based on mouse input.
Prediction Function
: Reads pixel data and sends it to the Python server for predictions.
Error Handling
: Placeholder for managing exceptions and connection issues.
Python Server Setup
Python code to load the model and set up a connection using ZeroMQ.
Continuously listens for incoming bytes of pixel data from Unity.
Converts incoming bytes to float array and makes predictions using TensorFlow.
Sends predictions back to Unity.
Important Notes
Ensure the Python server runs whenever Unity is launched.
Consider implementing scripts to manage server restarts.
Potential to run the Python server externally for better performance.
Conclusion
This method allows executing a model without full conversion to Unity.
Useful for testing models quickly.
Encouragement to leave feedback and suggestions for future tutorials.
Call to Action
Like and subscribe for more similar content.
Share experiences in the comments regarding TensorFlow models and Unity integration.
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