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TensorFlow Lite Gesture Classification Android Example

Overview

This app performs gesture classification on live camera feed and displays the results in real-time on the screen.

Application can run either on device or emulator.

Build the demo using Android Studio

Prerequisites

  • If you don't have already, install Android Studio, following the instructions on their website.

  • You need an Android device and Android development environment with minimum API 21.

  • Android Studio 3.2 or later.

  • This is the guide to generate TensorFlow.js model.

  • Name the .tflite file as model.tflite and the subset of labels should be named as labels.txt.

Building

  • Open Android Studio, and from the Welcome screen, select Open an existing Android Studio project.

  • From the Open File or Project window that appears, navigate to and select the tensorflow-lite/examples/gesture_classification/android directory from wherever you cloned the TensorFlow Lite sample GitHub repo. Click OK.

  • If it asks you to do a Gradle Sync, click OK.

  • You may also need to install various platforms and tools, if you get errors like "Failed to find target with hash string 'android-21'" and similar. Click the Run button (the green arrow) or select Run > Run 'android' from the top menu. You may need to rebuild the project using Build > Rebuild Project.

  • If it asks you to use Instant Run, click Proceed Without Instant Run.

  • Also, you need to have an Android device plugged in with developer options enabled at this point. See here for more details on setting up developer devices.

  • Read the following doc to generate TFLite model file.

  • Copy the labels.txt and put into assets folder.

  • Now once you get the TFLite and label file, put that into assets folder.

Additional Note

Ensure that labels.txt and model.tflite files are added into the project which are downloaded from web app. Also ensure gesture_labels.txt file is not deleted or modified from assets folder.

See Also

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