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Initial commit of AISuite Sample & Demo application source code
This commit has the Sample & Demo Application source code that will be available as part of AI Data Capture SDK GA release v3.0.2
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‎.gitignore‎

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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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# Log/OS Files
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*.log
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# Android Studio generated files and folders
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captures/
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.externalNativeBuild/
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.cxx/
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*.apk
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output-metadata.json
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# IntelliJ
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*.iml
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.idea/
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misc.xml
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deploymentTargetDropDown.xml
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render.experimental.xml
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# Keystore files
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*.jks
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*.keystore
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# Google Services (e.g. APIs or Firebase)
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google-services.json
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# Android Profiling
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*.hprof

‎AISuite_Demos/AIDataCaptureDemo/.gitignore‎

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*.iml
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.gradle
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/local.properties
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/.idea/caches
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/.idea/libraries
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/.idea/modules.xml
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/.idea/workspace.xml
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/.idea/navEditor.xml
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/.idea/assetWizardSettings.xml
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.DS_Store
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/build
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/captures
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.externalNativeBuild
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.cxx
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local.properties

‎AISuite_Demos/AIDataCaptureDemo/app/.gitignore‎

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/build

‎AISuite_Demos/AIDataCaptureDemo/app/build.gradle.kts‎

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plugins {
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alias(libs.plugins.android.application)
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alias(libs.plugins.kotlin.android)
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alias(libs.plugins.kotlin.compose)
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}
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android {
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namespace = "com.zebra.aidatacapturedemo"
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compileSdk = 36
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androidResources {
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noCompress.add("tar")
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noCompress.add("tar.crypt")
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}
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defaultConfig {
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applicationId = "com.zebra.aidatacapturedemo"
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minSdk = 34
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targetSdk = 36
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versionCode = 4
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val appVersion: String = libs.versions.appVersion.get().toString()
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versionName = appVersion
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buildConfigField("String", "AI_DataCaptureDemo_Version", "\"$appVersion\"")
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val zebraAIVisionSdk: String = libs.versions.zebraAIVisionSdk.get().toString()
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buildConfigField("String", "Zebra_AI_VisionSdk_Version", "\"$zebraAIVisionSdk\"")
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val barcodeLocalizer: String = libs.versions.barcodeLocalizer.get().toString()
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buildConfigField("String", "BarcodeLocalizer_Version", "\"$barcodeLocalizer\"")
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val textOcrRecognizer: String = libs.versions.textOcrRecognizer.get().toString()
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buildConfigField("String", "TextOcrRecognizer_Version", "\"$textOcrRecognizer\"")
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val productAndShelfRecognizer: String = libs.versions.productAndShelfRecognizer.get().toString()
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buildConfigField("String", "ProductAndShelfRecognizer_Version", "\"$productAndShelfRecognizer\"")
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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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isMinifyEnabled = false
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proguardFiles(
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getDefaultProguardFile("proguard-android-optimize.txt"),
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"proguard-rules.pro"
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)
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}
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}
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buildFeatures {
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viewBinding = true
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buildConfig = true
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compose = true
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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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isCoreLibraryDesugaringEnabled = true
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}
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kotlinOptions {
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jvmTarget = "1.8"
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}
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packaging {
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resources {
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excludes += "/META-INF/{AL2.0,LGPL2.1}"
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}
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jniLibs {
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useLegacyPackaging = true
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}
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}
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}
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dependencies {
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implementation(libs.androidx.core.ktx)
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implementation(libs.androidx.lifecycle.runtime.ktx)
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implementation(libs.androidx.activity.compose)
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implementation(platform(libs.androidx.compose.bom))
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implementation(libs.constraintlayout)
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implementation(libs.androidx.ui)
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implementation(libs.androidx.ui.graphics)
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implementation(libs.androidx.ui.tooling.preview)
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implementation(libs.androidx.material3)
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implementation(libs.play.services.tasks)
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implementation(libs.androidx.navigation.compose.android)
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implementation(libs.androidx.documentfile)
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coreLibraryDesugaring(libs.desugar.jdk.libs)
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implementation(libs.androidx.navigation.compose)
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implementation(libs.coil.compose)
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implementation(libs.jsoup)
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implementation(libs.camera.core)
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implementation(libs.camera.camera2)
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implementation(libs.camera.lifecycle)
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implementation(libs.camera.view)
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implementation(libs.androidx.camera.extensions)
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// JSON serialization
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implementation(libs.gson)
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//Below dependency is to get AI Suite SDK
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implementation(libs.zebra.ai.vision.sdk) { artifact { type = "aar" } }
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//Below dependency is to get Barcode Localizer model for AI Suite SDK
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implementation(libs.barcode.localizer) { artifact { type = "aar" } }
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//Below dependency is to get OCR model for AI Suite SDK
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implementation(libs.text.ocr.recognizer) { artifact { type = "aar" } }
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//Below dependency is to get product Recognition model for AI Suite SDK
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implementation(libs.product.and.shelf.recognizer) { artifact { type = "aar" } }
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androidTestImplementation(platform(libs.androidx.compose.bom))
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debugImplementation(libs.androidx.ui.tooling)
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debugImplementation(libs.androidx.ui.test.manifest)
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}

‎AISuite_Demos/AIDataCaptureDemo/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

‎AISuite_Demos/AIDataCaptureDemo/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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<uses-feature
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android:name="android.hardware.camera"
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android:required="false" />
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<uses-permission android:name="android.permission.INTERNET" />
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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:dataExtractionRules="@xml/data_extraction_rules"
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android:fullBackupContent="@xml/backup_rules"
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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.AIDataCaptureDemo">
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<activity
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android:name=".MainActivity"
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android:exported="true"
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android:label="@string/app_name"
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android:screenOrientation="portrait"
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android:theme="@style/Theme.AIDataCaptureDemo">
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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>

‎AISuite_Demos/AIDataCaptureDemo/app/src/main/assets/barcode_model_input_size.html‎

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<div id="summary">
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Model input size is the resolution your image is resized to before AI
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analysis.
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<strong
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>Smaller sizes are faster and use less memory, while larger sizes can help
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detect smaller or more distant barcodes &mdash;but also uses more
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processing power and memory.</strong
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>
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Choose the input size to balance speed and accuracy for your needs.
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<strong>Note:</strong> Model Input Size can be customized in increments of 32
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using the SDK. The options below represent some generic sizes.
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</div>
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<div id="description">
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Model input size is the resolution your image is resized to before AI
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analysis.
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<strong
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>Smaller sizes are faster, while larger sizes improve accuracy.</strong
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>
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Choose the input size to balance speed and accuracy for your needs.
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<strong>Note:</strong> Model Input Size can be customized in increments of 32
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using the SDK. The options below represent some generic sizes.
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</div>
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<div id="title">Model Input Size Options</div>
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<div id="details">
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<p><strong>Small &ndash; 640 x 640</strong></p>
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<ul>
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<li><strong>Fastest</strong> processing</li>
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<li>Best for large, clear, or close-up barcodes</li>
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<li>May miss small, damaged, or distant barcodes</li>
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</ul>
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<p><strong>Medium &ndash; 1280 x 1280</strong></p>
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<ul>
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<li>Balanced speed and accuracy</li>
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<li>Handles most barcode types and sizes in standard conditions</li>
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</ul>
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<p><strong>Large &ndash; 1600 x 1600</strong></p>
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<ul>
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<li><strong>Higher accuracy</strong></li>
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<li>Best for small, damaged, or distant barcodes, &nbsp;</li>
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<li><strong>Slower Processing</strong>, and <strong>highest memory/battery consumption</strong></li>
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<li><strong>Not recommended for CPU/GPU (inference type)</strong></li>
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</ul>
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</div>
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<div id="recommendation">
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<p>
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<strong>Recommendation:<br /></strong>
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</p>
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<ul>
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<li><strong>Start with 640x640</strong> for close/large barcode.</li>
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<li>Increase to the next size if you encounter issues reading barcodes.</li>
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<li>
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Use
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<strong
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>larger model input sizes only for the most demanding and challenging
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use cases</strong
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>, and only if your device has sufficient processing power and memory.
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</li>
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</ul>
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</div>
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<div id="tip">
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<p>
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<strong>Tip:</strong> Larger model input sizes improve accuracy but come at a
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significant cost to speed, memory, and battery life. If the app becomes slow
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or unstable, reduce the input size.
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<strong
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>Experiment to find the smallest size that works reliably for your use
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case.</strong
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>
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</p>
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</div>

‎AISuite_Demos/AIDataCaptureDemo/app/src/main/assets/barcode_resolution.html‎

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<div id="summary">
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Camera resolution is the number of pixels in your photo (e.g., 1MP =
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1280x720).
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<strong
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>Higher resolution captures more detail for small or distant barcodes but
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uses more power and memory.</strong
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>
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Benefits are limited if the model input size is low.
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</div>
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<div id="description">
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The camera resolution is the number of pixels in the image captured by your
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device (e.g., 1MP = 1280x720, 8MP = 3840x2160). This affects the detail in
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the original photo before it&rsquo;s resized for AI processing.
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<strong
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>Higher resolution means more detail, helping read small, or distant
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barcodes&mdash;but also uses more processing power and memory.</strong
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>
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If speed, battery life, or memory use are your top priorities, low resolution
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can be a good choice for large or close-up barcodes.
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<strong>Note:</strong> If the model input size is set low, using a high
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camera resolution won&rsquo;t improve results much, since the detailed image
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will be downscaled before processing.
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</div>
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<div id="title">Resolution Options</div>
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<div id="details">
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<p><strong>1MP (1280 x 720)</strong></p>
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<ul style="list-style-type: disc">
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<li>Fastest, power-efficient</li>
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<li>Best for simple, large, or close-up barcodes</li>
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<li>May miss small or damaged barcodes</li>
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</ul>
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<p><strong>2MP (1920 x 1080)</strong></p>
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<ul style="list-style-type: disc">
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<li>Good for general use, moderate detail</li>
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<li>Handles most standard barcode scanning scenarios</li>
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</ul>
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<p><strong>4MP (2688 x 1512)</strong></p>
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<ul style="list-style-type: disc">
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<li>
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Captures more detail, good for poor contrast or dense, smaller barcodes
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</li>
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<li>Higher memory and battery use</li>
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</ul>
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<p><strong>8MP (3840 x 2160)</strong></p>
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<ul style="list-style-type: disc">
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<li>Maximum detail and accuracy,</li>
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<li>Best for extremely small, dense, or challenging use cases.</li>
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<li>Slowest and highest memory/battery consumption</li>
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<li>
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Limited benefit if model input size is low; Not recommended for CPU/GPU
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(inference types)
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</li>
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</ul>
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</div>
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<div id="recommendation">
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<p><strong>Recommendation:</strong></p>
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<ul style="list-style-type: disc">
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<li>
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<strong>Start with 1 or 2MP.</strong> This offers a good balance between
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image detail, speed, and battery use.
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</li>
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<li>
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<strong>Increase</strong> to 4MP or 8MP&nbsp;<strong>only if</strong> you
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need to capture very small, faint, or distant barcodes, and your device
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can handle the extra processing.
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</li>
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<li>
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<strong>Match your camera resolution to your model input size:</strong> If
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your model input size is 640x640, higher resolutions provide little
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benefit. For larger model input sizes (like 1280x1280 or 1600x1600), a
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higher resolution can help.
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</li>
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</ul>
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</div>
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<div id="tip">
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<p>
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<strong>Tip:</strong>&nbsp;Make sure the barcode(s) you want to scan appears
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<strong>at least 8 pixels tall</strong> in your image for reliable
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recognition.
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<strong
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>Experiment to find the smallest resolution that works reliably for your
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use case</strong
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>
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</p>
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</div>

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