MarkerRecognitionAI

AI Marker recognition with Tensorflow Lite and CameraX

Key Steps:

1. Downolad Landmark tflite model file from https://www.kaggle.com/models/google/landmarks/frameworks/tfLite in my case i have landmarks_classifier_africa

2. In Android studio, create asserts directory and add the tflite file there

3. Add gradle dependencies for CameraX and TenserFlow:

     // CameraX core library using the camera2 implementation
     val cameraxVersion = "1.4.0-alpha03"
     implementation("androidx.camera:camera-core:${cameraxVersion}")
     implementation("androidx.camera:camera-camera2:${cameraxVersion}")
     implementation("androidx.camera:camera-lifecycle:${cameraxVersion}")
     implementation("androidx.camera:camera-video:${cameraxVersion}")
     implementation("androidx.camera:camera-view:${cameraxVersion}")
     implementation("androidx.camera:camera-extensions:${cameraxVersion}")
   

    // Tensorflow Lite dependencies
     implementation("org.tensorflow:tensorflow-lite-task-vision:0.4.2")
     // Import the GPU delegate plugin Library for GPU inference
     implementation("org.tensorflow:tensorflow-lite-gpu-delegate-plugin:0.4.0")
     implementation("org.tensorflow:tensorflow-lite-gpu:2.9.0")
   

   => you could also use the tenserFlow dependencie integrated recently in play-service

4. In AndroidManifest, add camera permission:

     <uses-feature
         android:name="android.hardware.camera"
         android:required="false" />
     <uses-permission android:name="android.permission.CAMERA"/>
   

5. For UI, since PreviewView from CameraX is not available as composable yet, i wrapped in AndroidView to create the CameraPreview