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Merge pull request #1 from microsoft/yguo/sample-application
Add initial sample android application
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‎mobile/examples/image_classifications/android/.gitignore

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# Built application files
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*.apk
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*.aar
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*.ap_
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*.aab
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# Files for the ART/Dalvik VM
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*.dex
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# Java class files
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*.class
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# Generated files
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bin/
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gen/
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out/
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# Uncomment the following line in case you need and you don't have the release build type files in your app
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# release/
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# Gradle files
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.gradle/
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build/
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# Local configuration file (sdk path, etc)
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local.properties
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# Proguard folder generated by Eclipse
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proguard/
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# Log Files
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*.log
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# Android Studio Navigation editor temp files
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.navigation/
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# Android Studio captures folder
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captures/
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# IntelliJ
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*.iml
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.idea/workspace.xml
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.idea/tasks.xml
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.idea/gradle.xml
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.idea/assetWizardSettings.xml
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.idea/dictionaries
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.idea/libraries
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# Android Studio 3 in .gitignore file.
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.idea/caches
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.idea/modules.xml
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# Comment next line if keeping position of elements in Navigation Editor is relevant for you
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.idea/navEditor.xml
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# Keystore files
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# Uncomment the following lines if you do not want to check your keystore files in.
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#*.jks
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#*.keystore
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# External native build folder generated in Android Studio 2.2 and later
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.externalNativeBuild
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.cxx/
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# Google Services (e.g. APIs or Firebase)
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# google-services.json
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# Freeline
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freeline.py
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freeline/
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freeline_project_description.json
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# fastlane
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fastlane/report.xml
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fastlane/Preview.html
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fastlane/screenshots
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fastlane/test_output
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fastlane/readme.md
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# Version control
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vcs.xml
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# lint
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lint/intermediates/
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lint/generated/
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lint/outputs/
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lint/tmp/
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# lint/reports/
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.idea/
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.DS_Store
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‎mobile/examples/image_classifications/android/README.md

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# ONNX Runtime Mobile image classification Android sample application
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## Overview
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This is an example application for [ONNX Runtime](https://github.com/microsoft/onnxruntime) on Android. The demo app uses image classfication which is able to continuously classify the objects it sees from the device's camera in real-time and displays the most probable inference result on the screen.
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This example is loosely based on [Google CodeLabs - Getting Started with CameraX](https://codelabs.developers.google.com/codelabs/camerax-getting-started)
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### Model
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We use classic MobileNetV2(float) model and MobileNetV2 (uint8) in this sample app.
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## Requirements
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- Android Studio 4.1+ (installed on Mac/Windows/Linux)
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- Android SDK 29+
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- Android NDK r21+
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- Android device in developer mode and enable USB debugging
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## Build And Run
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### Prerequisites
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- MobileNetV2 ort format model
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- labels text file (used for image classification)
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- Prebuilt ONNX Runtime arm64 Android Archive(AAR) files, which can be directly imported in Android Studio
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The above three files are provided and can be downloaded [here](https://1drv.ms/u/s!Auaxv_56eyubgQX-S_kTP0AP66Km?e=e8YMX1).
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[Optional] You can also build your own ONNX Runtime arm64 AAR files for Android. (See [build instructions here](https://www.onnxruntime.ai/docs/how-to/build.html#android) and [Build Android Archive(AAR)](https://www.onnxruntime.ai/docs/how-to/build.html#build-android-archive-aar)).
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### Step 1. Clone the ONNX Runtime Mobile examples source code and download required model files
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Clone this ORT Mobile examples GitHub repository to your computer to get the sample application.
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Download the packages provided in `Prerequisites`.
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- Copy MobileNetV1 onnx model and the labels file to `example/image_classification/android/app/src/main/res/raw/`
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- Create `/libs` directory under `app/` and copy the AAR file `onnxruntime-release.aar` to `app/libs`
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Then open the sample application in Android Studio. To do this, open Android Studio and select `Open an existing project`, browse folders and open the folder `examples/image_classification/android/`.
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<img width=60% src="images/screenshot_1.png"/>
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### Step 2. Build the sample application in Android Studio
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Select `Build-Make Project` in the top toolbar in Android Studio and check the projects has built successfully.
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<img width=60% src="images/screenshot_3.png" alt="App Screenshot"/>
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<img width=60% src="images/screenshot_4.png" alt="App Screenshot"/>
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### Step 3. Connect your Android Device and run the app
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Connect your Android Device to the computer and select your device in the top-down device bar.
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<img width=60% src="images/screenshot_5.png" alt="App Screenshot"/>
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<img width=60% src="images/screenshot_6.png" alt="App Screenshot"/>
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Then Select `Run-Run app` and this will prompt the app to be installed on your device.
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Now you can test and try by opening the app `ort_image_classifier` on your device. The app may request your permission for using the camera.
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#
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Here's an example screenshot of the app.
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<img width=20% src="images/screenshot_2.jpg" alt="App Screenshot" />
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‎mobile/examples/image_classifications/android/app/.gitignore

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/build

‎mobile/examples/image_classifications/android/app/build.gradle

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plugins {
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id 'com.android.application'
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id 'kotlin-android'
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id 'kotlin-android-extensions'
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}
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android {
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compileSdkVersion 30
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buildToolsVersion "30.0.2"
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defaultConfig {
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applicationId "ai.onnxruntime.example.imageclassifier"
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minSdkVersion 24
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targetSdkVersion 30
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versionCode 1
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versionName "1.0"
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testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
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}
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buildTypes {
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release {
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minifyEnabled false
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proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
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}
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}
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compileOptions {
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sourceCompatibility JavaVersion.VERSION_1_8
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targetCompatibility JavaVersion.VERSION_1_8
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}
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kotlinOptions {
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jvmTarget = '1.8'
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}
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}
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dependencies {
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// CameraX core library using the camera2 implementation
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def camerax_version = "1.0.0-rc03"
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// The following line is optional, as the core library is included indirectly by camera-camera2
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implementation "androidx.camera:camera-camera2:${camerax_version}"
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// If you want to additionally use the CameraX Lifecycle library
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implementation "androidx.camera:camera-lifecycle:${camerax_version}"
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// If you want to additionally use the CameraX View class
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implementation "androidx.camera:camera-view:1.0.0-alpha22"
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implementation "org.jetbrains.kotlin:kotlin-stdlib-jdk8:$kotlin_version"
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implementation 'androidx.core:core-ktx:1.3.2'
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implementation 'androidx.appcompat:appcompat:1.2.0'
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implementation 'com.google.android.material:material:1.3.0'
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implementation 'androidx.constraintlayout:constraintlayout:2.0.4'
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testImplementation 'junit:junit:4.+'
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androidTestImplementation 'androidx.test.ext:junit:1.1.2'
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androidTestImplementation 'androidx.test.espresso:espresso-core:3.3.0'
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implementation(name: "onnxruntime-release", ext: "aar")
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}

‎mobile/examples/image_classifications/android/app/proguard-rules.pro

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# Add project specific ProGuard rules here.
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# You can control the set of applied configuration files using the
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# proguardFiles setting in build.gradle.
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#
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# For more details, see
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# http://developer.android.com/guide/developing/tools/proguard.html
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# If your project uses WebView with JS, uncomment the following
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# and specify the fully qualified class name to the JavaScript interface
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# class:
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#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
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# public *;
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#}
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# Uncomment this to preserve the line number information for
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# debugging stack traces.
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#-keepattributes SourceFile,LineNumberTable
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# If you keep the line number information, uncomment this to
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# hide the original source file name.
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#-renamesourcefileattribute SourceFile

‎mobile/examples/image_classifications/android/app/src/androidTest/java/ai/onnxruntime/example/imageclassifier/ExampleInstrumentedTest.kt

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package ai.onnxruntime.example.imageclassifier
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import androidx.test.ext.junit.runners.AndroidJUnit4
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import androidx.test.platform.app.InstrumentationRegistry
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import org.junit.Assert.assertEquals
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import org.junit.Test
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import org.junit.runner.RunWith
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/**
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* Instrumented test, which will execute on an Android device.
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*
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* See [testing documentation](http://d.android.com/tools/testing).
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*/
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@RunWith(AndroidJUnit4::class)
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class ExampleInstrumentedTest {
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@Test
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fun useAppContext() {
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// Context of the app under test.
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val appContext = InstrumentationRegistry.getInstrumentation().targetContext
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assertEquals("ai.onnxruntime.example.imageclassifier", appContext.packageName)
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}
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}

‎mobile/examples/image_classifications/android/app/src/main/AndroidManifest.xml

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<?xml version="1.0" encoding="utf-8"?>
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<manifest xmlns:android="http://schemas.android.com/apk/res/android"
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package="ai.onnxruntime.example.imageclassifier">
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<uses-feature android:name="android.hardware.camera.any" />
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<uses-permission android:name="android.permission.CAMERA" />
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<application
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android:allowBackup="true"
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android:icon="@mipmap/ic_launcher"
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android:label="@string/app_name"
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android:roundIcon="@mipmap/ic_launcher_round"
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android:supportsRtl="true"
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android:theme="@style/Theme.Ort_image_classifier">
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<activity android:name=".MainActivity">
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<intent-filter>
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<action android:name="android.intent.action.MAIN" />
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<category android:name="android.intent.category.LAUNCHER" />
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</intent-filter>
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</activity>
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</application>
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</manifest>

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